From 47aecc65e70b1c645653a88a028e4d3e70adf301 Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 6 Mar 2025 09:52:16 +0000 Subject: [PATCH] Update documentation --- sed/latest | 1 + sed/latest/searchindex.js | 1 - ..._hextof_workflow_trXPS_bam_correction.html | 1389 -------- ...low_trXPS_energy_calibration_using_SB.html | 1318 -------- sed/latest/tutorial/1_binning_fake_data.html | 921 ------ ..._for_example_time-resolved_ARPES_data.html | 1651 ---------- ...tadata_collection_and_export_to_NeXus.html | 1048 ------ sed/latest/tutorial/4_hextof_workflow.html | 2931 ----------------- .../6_binning_with_time-stamped_data.html | 1224 ------- .../7_correcting_orthorhombic_symmetry.html | 937 ------ sed/latest/tutorial/8_jittering_tutorial.html | 1166 ------- .../tutorial/9_hextof_workflow_trXPD.html | 1378 -------- sed/stable | 2 +- sed/switcher.json | 8 +- sed/{latest => v1.0.0}/_modules/index.html | 10 +- .../_modules/sed/binning/binning.html | 10 +- 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21], "how": 3, "id": [15, 21], "import": [15, 16, 20, 21, 25], "inspect": [20, 21], "instal": [27, 28], "instanc": 20, "instrument": [15, 20, 21, 25], "interfac": 13, "io": 12, "jitter": [20, 24], "json": 9, "level": [16, 25], "librari": [15, 16, 20, 21, 25], "load": [15, 16, 18, 19, 20, 21, 22, 23, 24], "loader": [1, 13], "local": 17, "main": 5, "maintain": 3, "metadata": [14, 19], "meti": 26, "mica": 21, "microbunchid": 21, "microscop": 26, "momentum": [6, 18, 23, 26], "mpe": 26, "mpesload": 13, "necessari": [15, 16, 20, 21, 25], "nexu": 19, "normal": 25, "note": 20, "now": 16, "number": 16, "o": 15, "offset": [20, 21], "oper": 10, "optic": 20, "option": 18, "orthorhomb": 23, "our": 16, "panda": 17, "paramet": [15, 16, 20, 21], "partit": 17, "path": [15, 16, 20, 21, 25], "peak": 16, "pipelin": 18, "plot": 20, "posit": 16, "prepar": [15, 16, 20, 21, 25], "previou": [16, 20, 21], "processor": 20, "profil": 20, "pull": 1, "puls": 15, "pulseid": 21, "rang": [17, 18, 22], "read": 25, "refer": 16, "releas": 3, "remov": 9, "request": 1, "resolv": 18, "result": 20, "roi": 16, "run": 20, "sampl": 20, "save": [20, 21], "sb": 16, "scan": 21, "sector": 20, "sed": [0, 1, 27], "see": 16, "seri": [16, 21], "set": [15, 26], "setup": [15, 16, 20, 21, 25], "side": 16, "some": [18, 22], "spectrum": [20, 21], "spline": 23, "spot": 20, "stage": [16, 21], "stamp": 22, "start": 1, "step": 18, "store": [15, 18, 19], "sxp": 21, "sxploader": 13, "symmetri": 23, "t0": [15, 16], "temperatur": 22, "those": 16, "time": [18, 20, 21, 22], "top": 23, "topic": 27, "train": [15, 21], "transform": 17, "trxp": [15, 16], "trxpd": 25, "tutori": [15, 16, 20, 21, 25], "us": [5, 9, 15, 16, 18, 22, 24], "user": [0, 9, 27], "util": 13, "v": 21, "valenc": 23, "valu": 20, "version": 28, "versu": 15, "visual": [16, 18, 20, 22], "volum": [18, 19, 22], "w": 15, "w4f": 25, "warp": 23, "we": [15, 16], "workflow": [1, 18, 20, 29], "xfel": 21, "xpd": 25, "zenodo": 18}}) \ No newline at end of file diff --git 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Tutorial for trXPS for the HEXTOF instrument at FLASH: t0, cross-correlation and BAM correction#

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Preparation#

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Import necessary libraries#

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[1]:
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%load_ext autoreload
-%autoreload 2
-
-from pathlib import Path
-import os
-
-from sed import SedProcessor
-from sed.dataset import dataset
-import numpy as np
-
-%matplotlib widget
-import matplotlib.pyplot as plt
-
-# For peak fitting
-from lmfit.models import GaussianModel
-
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Get data paths#

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If it is your beamtime, you can read the raw data and write to the processed directory. For the public data, you can not write to the processed directory.

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The paths are such that if you are on Maxwell, it uses those. Otherwise, data is downloaded in the current directory from Zenodo: https://zenodo.org/records/12609441

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-
[2]:
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beamtime_dir = "/asap3/flash/gpfs/pg2/2023/data/11019101" # on Maxwell
-if os.path.exists(beamtime_dir) and os.access(beamtime_dir, os.R_OK):
-    path = beamtime_dir + "/raw/hdf/offline/fl1user3"
-    buffer_path = beamtime_dir + "/processed/tutorial/"
-else:
-    # data_path can be defined and used to store the data in a specific location
-    dataset.get("W110") # Put in Path to a storage of at least 10 Byte free space.
-    path = dataset.dir
-    buffer_path = path + "/processed/"
-
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-
-INFO - Not downloading W110 data as it already exists at "/home/runner/work/sed/sed/docs/tutorial/datasets/W110".
-Set 'use_existing' to False if you want to download to a new location.
-INFO - Using existing data path for "W110": "/home/runner/work/sed/sed/docs/tutorial/datasets/W110"
-INFO - W110 data is already present.
-
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Config setup#

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Here, we get the path to the config file and set up the relevant directories. This can also be done directly in the config file.

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[3]:
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# pick the default configuration file for hextof@FLASH
-config_file = Path('../src/sed/config/flash_example_config.yaml')
-assert config_file.exists()
-
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[4]:
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# here we setup a dictionary that will be used to override the path configuration
-config_override = {
-    "core": {
-        "beamtime_id": 11019101,
-        "paths": {
-            "raw": path,
-            "processed": buffer_path
-        },
-    },
-}
-
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[5]:
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energy_cal = {
-    "energy": {
-        "calibration": {
-            "E0": -132.47100427179566,
-            "creation_date": '2024-11-30T20:47:03.305244',
-            "d": 0.8096677238144319,
-            "energy_scale": "kinetic",
-            "t0": 4.0148196706891397e-07,
-        },
-        "offsets":{
-            "constant": 1,
-            "creation_date": '2024-11-30T21:17:07.762199',
-            "columns": {
-                "monochromatorPhotonEnergy": {
-                    "preserve_mean": True,
-                    "weight": -1,
-                },
-                "tofVoltage": {
-                    "preserve_mean": True,
-                    "weight": -1,
-                },
-            },
-        },
-    },
-}
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We use the stored energy calibration parameters and load trXPS data set to define:#

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    -

  • t0 position with respect to delay stage values;

    • -

  • correct accordingly delay stage offset

    • -

  • fit cross-correlation

    • -

  • apply BAM correction and see its effect on cross-correlation

    • -
-
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[6]:
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run_number = 44498
-sp_44498 = SedProcessor(runs=[run_number], config=config_override, folder_config=energy_cal, system_config=config_file, verbose=True)
-
-sp_44498.add_jitter()
-sp_44498.align_dld_sectors()
-sp_44498.append_energy_axis()
-sp_44498.add_energy_offset()
-
-
-
-
-
-
-
-
-INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
-INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
-INFO - Reading files: 0 new files of 14 total.
-loading complete in  0.09 s
-INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
-INFO - Aligning 8s sectors of dataframe
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID
-npartitions=14
-                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-Dask Name: assign, 16 graph layers
-INFO - Adding energy column to dataframe:
-INFO - Using energy calibration parameters generated on 11/30/2024, 20:47:03
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
-npartitions=14
-                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-Dask Name: assign, 31 graph layers
-INFO - Adding energy offset to dataframe:
-INFO - Using energy offset parameters generated on 11/30/2024, 21:17:07
-INFO - Energy offset parameters:
-   Constant: 1.0
-   Column[monochromatorPhotonEnergy]: Weight=-1.0, Preserve Mean: True, Reductions: None.
-   Column[tofVoltage]: Weight=-1.0, Preserve Mean: True, Reductions: None.
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
-npartitions=14
-                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-Dask Name: assign, 64 graph layers
-
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-

Check which channels are included in the dataframe

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[7]:
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sp_44498.dataframe.head()
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[7]:
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-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
trainIdpulseIdelectronIddldPosXdldPosYdldTimeStepspulserSignAdcbamtimeStampmonochromatorPhotonEnergy...delayStagesampleBiastofVoltageextractorVoltageextractorCurrentcryoTemperaturesampleTemperaturedldTimeBinSizedldSectorIDenergy
0162802283010650.764000894.7640004594.76416032919.0-6187.968751.677563e+09116.858299...1448.60205172.99590319.9953566029.299805-0.07385749.20878.9899980.0205763-43.662226
1162802283011650.750341887.7503414595.75048832919.0-6187.968751.677563e+09116.858299...1448.60205172.99590319.9953566029.299805-0.07385749.20878.9899980.0205760-43.675760
2162802283050681.995886671.9958864422.99609432914.0-6170.156251.677563e+09116.858299...1448.60205172.99590319.9953566029.299805-0.07385749.20878.9899980.0205766-40.988670
3162802283051685.282414658.2824144425.28222732914.0-6170.156251.677563e+09116.858299...1448.60205172.99590319.9953566029.299805-0.07385749.20878.9899980.0205763-41.028869
4162802283052669.563147686.5631474423.56298832914.0-6170.156251.677563e+09116.858299...1448.60205172.99590319.9953566029.299805-0.07385749.20878.9899980.0205765-40.998651
-

5 rows × 21 columns

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Data w/o BAM correction#

-

First, we take a look at our sideband measurement before any corrections. The sidebands on the W4f core levels can be used as a measure of the pump and probe cross-correlation, and hence our temporal resolution. We plot the data delay stage position vs Energy data, normalized by acquisition time.

-
-
[8]:
-
-
-
axes = ['energy', 'delayStage']
-ranges = [[-37.5,-27.5], [1446.75,1449.15]]
-bins = [200,40]
-res = sp_44498.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time="delayStage")
-
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-INFO - Calculate normalization histogram for axis 'delayStage'...
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[9]:
-
-
-
fig,ax = plt.subplots(1,2,figsize=(6,2.25), layout='constrained')
-res.plot(robust=True, ax=ax[0], cmap='terrain')
-fig.suptitle(f"Run {run_number}: W 4f, side bands")
-ax[0].set_title('raw')
-bg = res.sel(delayStage=slice(1448.7,1449.1)).mean('delayStage')
-(res.sel(delayStage=slice(1446.8,1449.3))-bg).plot(robust=True, ax=ax[1])
-ax[1].set_title('difference')
-
-
-
-
-
[9]:
-
-
-
-
-Text(0.5, 1.0, 'difference')
-
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-
-
-

Now we make fit to determine precise t\(_0\) position and cross-correlation using lmfit fit models

-
-
[10]:
-
-
-
Gauss_mod = GaussianModel()
-
-#first order sideband:
-x1=res['delayStage']
-y1=res.sel(energy=slice(-30.5,-29.5)).sum('energy')
-y1=y1-np.mean(y1.sel(delayStage=slice(1448.7,1449.1)))
-
-pars1 = Gauss_mod.make_params(amplitude=0.1, center=1447.8, sigma=0.02)
-out1 = Gauss_mod.fit(y1, pars1, x=x1)
-
-#second order sideband
-x2=res['delayStage']
-y2=res.sel(energy=slice(-29.5,-28.5)).sum('energy')
-y2=y2-np.mean(y2.sel(delayStage=slice(1448.7,1449.1)))
-
-pars2 = Gauss_mod.make_params(amplitude=0.1, center=1447.8, sigma=0.02)
-out2 = Gauss_mod.fit(y2, pars2, x=x2)
-
-plt.figure()
-plt.plot(x1,y1,'rx', label='$1^{st}$ order sideband')
-plt.plot(x1,out1.best_fit,'r', label="FWHM = {:.3f} ps".format(out1.values['fwhm']))
-plt.legend(loc="best")
-plt.title('run44498, W4f, sidebands comparison')
-plt.plot(x2,y2,'bx', label='$2^{nd}$ order sideband')
-plt.plot(x2,out2.best_fit,'b', label="FWHM = {:.3f} ps".format(out2.values['fwhm']))
-plt.legend(loc="best")
-plt.xlabel("delayStage [ps]")
-plt.ylabel("Intensity [cts/s]")
-plt.show()
-
-
-
-
-
-
-
-
-
-

As we see the sidebands are quite broad and one of the possible reasons for this could be long or short-term drifts (jitter) of the FEL arrival time with respect to e.g. optical laser or differences in the intra-bunch arrival time. To check and correct for this we can look at beam arrival monitor (BAM). The BAM gives a pulse-resolved measure of the FEL arrival time with respect to a master clock.

-
-
-

Check BAM versus pulse and train IDs#

-
-
[11]:
-
-
-
axes = ['trainId', 'pulseId', 'bam']
-ranges = [[1628022640,1628046700], [0,500], [-6400,100]]
-bins = [250, 100, 1000]
-res_bam = sp_44498.compute(bins=bins, axes=axes, ranges=ranges)
-
-
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-
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-
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-
-

As we can see, jitter between FEL and pump laser is quite significant withing a pulse train as well as over the whole measurement period.

-
-
[12]:
-
-
-

fig,ax = plt.subplots(1,2,figsize=(6,2.25), layout='constrained')
-res_bam.sel(bam=slice(-6400,-5100)).sum('trainId').plot(ax=ax[0],robust=True, cmap='terrain')
-res_bam.sel(bam=slice(-6400,-5100)).sum('pulseId').plot(ax=ax[1],robust=True, cmap='terrain')
-plt.show()
-
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-
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-
-

Apply BAM correction#

-

To correct the SASE jitter, using information from the bam column and to calibrate the pump-probe delay axis, we need to shift the delay stage values to centre the pump-probe-time overlap time zero.

-
-
[13]:
-
-
-
sp_44498.add_delay_offset(
-    constant=-1448, # this is time zero position determined from side band fit
-    flip_delay_axis=True, # invert the direction of the delay axis
-    columns=['bam'], # use the bam to offset the values
-    weights=[-0.001], # bam is in fs, delay in ps
-    preserve_mean=True # preserve the mean of the delay axis to keep t0 position
-)
-
-
-
-
-
-
-
-
-INFO - Adding delay offset to dataframe:
-INFO - Delay offset parameters:
-   Column[bam]: Weight=-0.001, Preserve Mean: True, Reductions: None.
-   Constant: -1448
-   Flip delay axis: True
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
-npartitions=14
-                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float64    float32    float32          float32          float32         float32           float32        float32        int8  float64
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
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-Dask Name: assign, 84 graph layers
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bin in the corrected delay axis#

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[14]:
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axes = ['energy', 'delayStage']
-ranges = [[-37.5,-27.5], [-1.5,1.5]]
-bins = [200,60]
-res_corr = sp_44498.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time="delayStage")
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-INFO - Calculate normalization histogram for axis 'delayStage'...
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fig,ax = plt.subplots(1,2,figsize=(6,2.25), layout='constrained')
-fig.suptitle(f"Run {run_number}: W 4f, side bands")
-res_corr.plot(robust=True, ax=ax[0], cmap='terrain')
-ax[0].set_title('raw')
-bg = res_corr.sel(delayStage=slice(-1.3,-1.0)).mean('delayStage')
-(res_corr-bg).plot(robust=True, ax=ax[1])
-ax[1].set_title('difference')
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We clearly see an effect of BAM corrections - side bands are visible much nicer and width became smaller.

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-INFO - Saved delay offset parameters to "sed_config.yaml".
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Now we can repeat fit procedure to determine true cross-correlation value.

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[17]:
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Gauss_mod = GaussianModel()
-
-#first order sideband:
-x5=res_corr['delayStage'].sel(delayStage=slice(-1.6,1.5))
-y5=res_corr.sel(energy=slice(-30.4,-29.5),delayStage=slice(-1.6,1.5)).sum('energy')
-y5=y5-np.mean(y5.sel(delayStage=slice(-1.4,-1.0)))
-
-pars5 = Gauss_mod.make_params(amplitude=0.1, center=0.0, sigma=0.02)
-out5 = Gauss_mod.fit(y5, pars5, x=x5)
-
-print(out5.fit_report())
-
-#second order sideband
-x6=res_corr['delayStage'].sel(delayStage=slice(-1.6,1.5))
-y6=res_corr.sel(energy=slice(-29.5,-27.5),delayStage=slice(-1.6,1.5)).sum('energy')
-y6=y6-np.mean(y6.sel(delayStage=slice(-1.4,-1.0)))
-
-pars6 = Gauss_mod.make_params(amplitude=0.1, center=0.0, sigma=0.02)
-out6 = Gauss_mod.fit(y6, pars6, x=x6)
-
-print(out6.fit_report())
-
-#comparison plot
-plt.figure()
-plt.plot(x5,y5,'rx', label='$1^{st}$ order sideband')
-plt.plot(x5,out5.best_fit,'r', label="FWHM = {:.3f} ps".format(out5.values['fwhm']))
-plt.legend(loc="best")
-plt.title('run44498, W4f, sidebands comparison')
-plt.plot(x6,y6,'bx', label='$2^{nd}$ order sideband')
-plt.plot(x6,out6.best_fit,'b', label="FWHM = {:.3f} ps".format(out6.values['fwhm']))
-plt.legend(loc="best")
-plt.xlabel("pump probe delay [ps]")
-plt.ylabel("Intensity [cts/s]")
-plt.show()
-
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-[[Model]]
-    Model(gaussian)
-[[Fit Statistics]]
-    # fitting method   = leastsq
-    # function evals   = 108
-    # data points      = 60
-    # variables        = 3
-    chi-square         = 2091.05692
-    reduced chi-square = 36.6852091
-    Akaike info crit   = 219.064821
-    Bayesian info crit = 225.347855
-    R-squared          = 0.76937760
-[[Variables]]
-    amplitude:  16.8977797 +/- 1.15914419 (6.86%) (init = 0.1)
-    center:     0.04413499 +/- 0.01091173 (24.72%) (init = 0)
-    sigma:      0.13775487 +/- 0.01091141 (7.92%) (init = 0.02)
-    fwhm:       0.32438792 +/- 0.02569442 (7.92%) == '2.3548200*sigma'
-    height:     48.9364844 +/- 3.35692180 (6.86%) == '0.3989423*amplitude/max(1e-15, sigma)'
-[[Correlations]] (unreported correlations are < 0.100)
-    C(amplitude, sigma) = +0.5773
-[[Model]]
-    Model(gaussian)
-[[Fit Statistics]]
-    # fitting method   = leastsq
-    # function evals   = 109
-    # data points      = 60
-    # variables        = 3
-    chi-square         = 275.087260
-    reduced chi-square = 4.82609228
-    Akaike info crit   = 97.3646276
-    Bayesian info crit = 103.647661
-    R-squared          = 0.60986862
-[[Variables]]
-    amplitude:  4.40959498 +/- 0.39963939 (9.06%) (init = 0.1)
-    center:     0.02242043 +/- 0.01302554 (58.10%) (init = 0)
-    sigma:      0.12446500 +/- 0.01302544 (10.47%) (init = 0.02)
-    fwhm:       0.29309268 +/- 0.03067258 (10.47%) == '2.3548200*sigma'
-    height:     14.1338843 +/- 1.28095129 (9.06%) == '0.3989423*amplitude/max(1e-15, sigma)'
-[[Correlations]] (unreported correlations are < 0.100)
-    C(amplitude, sigma) = +0.5774
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Comparison of the BAM correction effect#

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[18]:
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fig,ax=plt.subplots(2,2,figsize=(6,6),layout="constrained")
-
-plt.axes(ax[0,0])
-res.plot(cmap='terrain', robust=True)
-plt.title("W4f, no bam correction")
-
-plt.axes(ax[0,1])
-plt.plot(x1,y1,'rx',label='integrated intensity 1. order')
-plt.plot(x1,out1.best_fit,'r',label='1. order fit, FWHM = {:.3f} ps'.format(out1.values['fwhm']))
-plt.plot(x2,y2,'bx',label='integrated intensity 2. order')
-plt.plot(x2,out2.best_fit,'b',label='2. order fit, FWHM = {:.3f} ps'.format(out2.values['fwhm']))
-plt.legend(loc=1)
-plt.title("Sidebands without bam correction")
-
-plt.axes(ax[1,0])
-res_corr.sel(delayStage=slice(-1.6,1.5)).plot(robust=True,cmap='terrain')
-plt.title("W4f, with bam correction")
-
-plt.axes(ax[1,1])
-plt.plot(x5,y5,'rx',label='integrated intensity 1. order')
-plt.plot(x5,out5.best_fit,'r',label='1. order fit, FWHM = {:.3f} ps'.format(out5.values['fwhm']))
-plt.plot(x6,y6,'bx',label='integrated intensity 2. order')
-plt.plot(x6,out6.best_fit,'b',label='2. order fit, FWHM = {:.3f} ps'.format(out6.values['fwhm']))
-plt.legend(loc=1)
-plt.title("Sidebands with bam correction")
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-fig.suptitle(f'Run {run_number}: Effect of BAM correction',fontsize='14')
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Tutorial for trXPS for energy calibration using core level side-bands#

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Preparation#

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Import necessary libraries#

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[1]:
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%load_ext autoreload
-%autoreload 2
-
-from pathlib import Path
-import os
-
-from sed import SedProcessor
-from sed.dataset import dataset
-import numpy as np
-
-%matplotlib widget
-import matplotlib.pyplot as plt
-
-### for automatic peak finding
-from scipy.signal import find_peaks
-
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Get data paths#

-

If it is your beamtime, you can read the raw data and write to the processed directory. For the public data, you can not write to the processed directory.

-

The paths are such that if you are on Maxwell, it uses those. Otherwise, data is downloaded in the current directory from Zenodo: https://zenodo.org/records/12609441

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[2]:
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beamtime_dir = "/asap3/flash/gpfs/pg2/2023/data/11019101" # on Maxwell
-if os.path.exists(beamtime_dir) and os.access(beamtime_dir, os.R_OK):
-    path = beamtime_dir + "/raw/hdf/offline/fl1user3"
-    buffer_path = beamtime_dir + "/processed/tutorial/"
-else:
-    # data_path can be defined and used to store the data in a specific location
-    dataset.get("W110") # Put in Path to a storage of at least 10 GByte free space.
-    path = dataset.dir
-    buffer_path = path + "/processed/"
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-INFO - Not downloading W110 data as it already exists at "/home/runner/work/sed/sed/docs/tutorial/datasets/W110".
-Set 'use_existing' to False if you want to download to a new location.
-INFO - Using existing data path for "W110": "/home/runner/work/sed/sed/docs/tutorial/datasets/W110"
-INFO - W110 data is already present.
-
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Config setup#

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Here, we get the path to the config file and set up the relevant directories. This can also be done directly in the config file.

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[3]:
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# pick the default configuration file for hextof@FLASH
-config_file = Path('../src/sed/config/flash_example_config.yaml')
-assert config_file.exists()
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[4]:
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# here we setup a dictionary that will be used to override the path configuration
-config_override = {
-    "core": {
-        "beamtime_id": 11019101,
-        "paths": {
-            "raw": path,
-            "processed": buffer_path
-        },
-    },
-}
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Reference calibration from a bias series#

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[5]:
-
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-
sp_44455 = SedProcessor(runs=[44455], config=config_override, system_config=config_file)
-sp_44455.add_jitter()
-sp_44455.align_dld_sectors()
-
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-
-INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/sed_config.yaml]
-INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
-INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
-INFO - Reading files: 0 new files of 6 total.
-loading complete in  0.08 s
-INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
-INFO - Aligning 8s sectors of dataframe
-INFO - Dask DataFrame Structure:
-              trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID
-npartitions=6
-               uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8
-                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
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-Dask Name: assign, 16 graph layers
-
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find calibration parameters#

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We now will fit the tof-energy relation. This is done by finding the maxima of a peak in the tof spectrum, and then fitting the square root relation to obtain the calibration parameters.

-
-
[6]:
-
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axes = ['sampleBias','dldTimeSteps']
-bins = [4, 250]
-ranges = [[77.5,81.5],  [4050,4500]]
-res = sp_44455.compute(bins=bins, axes=axes, ranges=ranges)
-sp_44455.load_bias_series(binned_data=res)
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[7]:
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ranges=(4120, 4200)
-ref_id=0
-sp_44455.find_bias_peaks(ranges=ranges, ref_id=ref_id, apply=True)
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-INFO - Use feature ranges: [(4120.2, 4199.4), (4156.2, 4224.6), (4195.8, 4282.2), (4237.2, 4323.6)].
-INFO - Extracted energy features: [[4.1472e+03 1.0000e+00]
- [4.1850e+03 1.0000e+00]
- [4.2246e+03 1.0000e+00]
- [4.2678e+03 1.0000e+00]].
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[8]:
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-
sp_44455.calibrate_energy_axis(
-    ref_energy=-31.4,
-    method="lmfit",
-    energy_scale='kinetic',
-    d={'value':1.0,'min': .7, 'max':1.0, 'vary':True},
-    t0={'value':5e-7, 'min': 1e-7, 'max': 1e-6, 'vary':True},
-    E0={'value': 0., 'min': -200, 'max': 100, 'vary': True},
-)
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-INFO - [[Fit Statistics]]
-    # fitting method   = leastsq
-    # function evals   = 46
-    # data points      = 4
-    # variables        = 3
-    chi-square         = 0.00151332
-    reduced chi-square = 0.00151332
-    Akaike info crit   = -25.5189696
-    Bayesian info crit = -27.3600865
-[[Variables]]
-    d:   0.80966772 +/- 1.56525760 (193.32%) (init = 1)
-    t0:  4.0148e-07 +/- 1.6505e-07 (41.11%) (init = 5e-07)
-    E0: -101.048293 +/- 12.5092127 (12.38%) (init = 0)
-[[Correlations]] (unreported correlations are < 0.100)
-    C(d, t0)  = -0.9999
-    C(d, E0)  = -0.9998
-    C(t0, E0) = +0.9995
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Now that we have the calibration parameters, we can generate the energy axis for each spectrum

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[9]:
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sp_44455.save_energy_calibration("reference_calib.yaml")
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-INFO - Saved energy calibration parameters to "reference_calib.yaml".
-
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Now we can use those parameters and load our trXPS data using the additional config file#

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To obtain a correct energy axis, we offset the energy axis by the difference of photon energy between this run and the energy calibration runs

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[10]:
-
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run_number = 44498
-sp_44498 = SedProcessor(runs=[run_number], config=config_override, folder_config="reference_calib.yaml", system_config=config_file, verbose=True)
-sp_44498.add_jitter()
-sp_44498.append_energy_axis()
-sp_44498.add_energy_offset(
-    constant=1,
-    columns=['monochromatorPhotonEnergy','tofVoltage'],
-    weights=[-1,-1],
-    preserve_mean=[True, True],
-)
-
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-INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/reference_calib.yaml]
-INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
-INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
-INFO - Reading files: 0 new files of 14 total.
-loading complete in  0.08 s
-INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
-INFO - Adding energy column to dataframe:
-INFO - Using energy calibration parameters generated on 03/05/2025, 23:08:29
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
-npartitions=14
-                uint32   int64      int64  float64  float64      float64       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-Dask Name: assign, 29 graph layers
-INFO - Adding energy offset to dataframe:
-INFO - Energy offset parameters:
-   Column[monochromatorPhotonEnergy]: Weight=-1, Preserve Mean: True, Reductions: None.
-   Column[tofVoltage]: Weight=-1, Preserve Mean: True, Reductions: None.
-   Constant: 1
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
-npartitions=14
-                uint32   int64      int64  float64  float64      float64       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
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-Dask Name: assign, 62 graph layers
-
-
-

And bin an energy spectrum for reference

-
-
[11]:
-
-
-
axes = ['energy']
-ranges = [[-37.5,-27.5]]
-bins = [200]
-res_ref = sp_44498.compute(bins=bins, axes=axes, ranges=ranges)
-
-plt.figure()
-res_ref.plot()
-
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-[<matplotlib.lines.Line2D at 0x7f1ab0240340>]
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Energy calibration using side-band peaks#

-
-

Visualize trXPS data bin in the dldTimeSteps and the corrected delay axis to prepare for energy calibration using SB#

-

We now prepare for an alternative energy calibration based on the side-bands of the time-dependent dataset. This is e.g. helpful if no bias series has been obtained.

-
-
[12]:
-
-
-
run_number = 44498
-sp_44498 = SedProcessor(runs=[run_number], config=config_override, system_config=config_file, verbose=True)
-sp_44498.add_jitter()
-
-
-
-
-
-
-
-
-INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/sed_config.yaml]
-INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
-INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
-INFO - Reading files: 0 new files of 14 total.
-loading complete in  0.08 s
-INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
-
-
-
-
-

We correct delay stage, t0 position and BAM (see previous tutorial)#

-
-
[13]:
-
-
-
sp_44498.add_delay_offset(
-    constant=-1448, # this is time zero position determined from side band fit
-    flip_delay_axis=True, # invert the direction of the delay axis
-    columns=['bam'], # use the bam to offset the values
-    weights=[-0.001], # bam is in fs, delay in ps
-    preserve_mean=True # preserve the mean of the delay axis to keep t0 position
-)
-
-
-
-
-
-
-
-
-INFO - Adding delay offset to dataframe:
-INFO - Delay offset parameters:
-   Column[bam]: Weight=-0.001, Preserve Mean: True, Reductions: None.
-   Constant: -1448
-   Flip delay axis: True
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID
-npartitions=14
-                uint32   int64      int64  float64  float64      float64       float32  float32   float64                   float32  float32    float64    float32    float32          float32          float32         float32           float32        float32        int8
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-Dask Name: assign, 34 graph layers
-
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[14]:
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axes = ['dldTimeSteps', 'delayStage']
-ranges = [[3900,4200], [-1.5,1.5]]
-bins = [100,60]
-res_corr = sp_44498.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time="delayStage")
-
-fig,ax = plt.subplots(1,2,figsize=(6,2.25), layout='constrained')
-fig.suptitle(f"Run {run_number}: W 4f, side bands")
-res_corr.plot(ax=ax[0], cmap='terrain')
-ax[0].set_title('raw')
-bg = res_corr.sel(delayStage=slice(-1.3,-1.0)).mean('delayStage')
-(res_corr-bg).plot(ax=ax[1])
-ax[1].set_title('difference')
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-INFO - Calculate normalization histogram for axis 'delayStage'...
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-Text(0.5, 1.0, 'difference')
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Automatically extract number and position of peaks in the ROI around t0#

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[15]:
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# binned data
-roi = slice(3980, 4160)
-delay = slice(-0.5,0.5)
-data = res_corr.sel(dldTimeSteps = roi, delayStage=delay).sum('delayStage')
-distance = 7
-peaks, _ = find_peaks(data, height=None, distance=distance)
-
-p1SB = data[peaks]['dldTimeSteps'][0]
-W4f5 = data[peaks]['dldTimeSteps'][1]
-m1SB = data[peaks]['dldTimeSteps'][2]
-W4f7 = data[peaks]['dldTimeSteps'][3]
-mm1SB = data[peaks]['dldTimeSteps'][4]
-plt.figure()
-data.plot()
-plt.scatter(data[peaks]['dldTimeSteps'], data[peaks], c='r')#, "x")
-plt.vlines([p1SB-7,p1SB+7], 0, 150, color='violet', linestyles='dashed', label='$1^{st}$ order SB')
-plt.vlines([W4f5-7,W4f5+7], 0, 150, color='b', linestyles='dashed', label='W 4f 7/2')
-plt.vlines([m1SB-7,m1SB+7], 0, 150, color='g', linestyles='dashed', label='$-1^{st}$ order SB')
-plt.vlines([W4f7-7,W4f7+7], 0, 150, color='r', linestyles='dashed', label='W 4f 5/2')
-plt.vlines([mm1SB-7,mm1SB+7], 0, 150, color='orange', linestyles='dashed', label='$2nd -1^{st}$ order SB')
-plt.legend()
-plt.show()
-
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-

find calibration parameters#

-

We now will fit the tof-energy relation. This is done using the maxima of a peak in the ToF spectrum and the known kinetic energy of those peaks (kinetic energy of e.g. W4f peaks (-31.4 and -33.6 eV) and their SB of different orders accounting energy of pump beam of 1030 nm = 1.2 eV. The calibration parameters are obtained by fitting the square root relation.

-
-
[16]:
-
-
-
### Kinetic energy of w4f peaks and their SB
-ref_energy = -30.2
-sp_44498.ec.biases = -1*np.array([-30.2,-31.4,-32.6,-33.6,-34.8])
-sp_44498.ec.peaks = np.expand_dims(data[peaks]['dldTimeSteps'].data,1)
-sp_44498.ec.tof = res_corr.dldTimeSteps.data
-
-sp_44498.calibrate_energy_axis(
-    ref_energy=ref_energy,
-    method="lmfit",
-    d={'value':1.0,'min': .8, 'max':1.0, 'vary':True},
-    t0={'value':5e-7, 'min': 1e-7, 'max': 1e-6, 'vary':True},
-    E0={'value': -100., 'min': -200, 'max': 15, 'vary': True},
-)
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-INFO - [[Fit Statistics]]
-    # fitting method   = leastsq
-    # function evals   = 123
-    # data points      = 5
-    # variables        = 3
-    chi-square         = 0.04811488
-    reduced chi-square = 0.02405744
-    Akaike info crit   = -17.2180090
-    Bayesian info crit = -18.3896953
-[[Variables]]
-    d:   0.80482246 +/- 0.56768800 (70.54%) (init = 1)
-    t0:  4.0567e-07 +/- 2.9002e-07 (71.49%) (init = 5e-07)
-    E0: -59.1600349 +/- 29.3415291 (49.60%) (init = -100)
-[[Correlations]] (unreported correlations are < 0.100)
-    C(d, t0)  = -0.9999
-    C(d, E0)  = -0.9997
-    C(t0, E0) = +0.9992
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Append energy axis into a data frame, bin and visualize data in the calibrated energy and corrected delay axis#

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To get a correct energy axis, we undo the shifts imposed by the calibration function

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[17]:
-
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sp_44498.append_energy_axis()
-sp_44498.add_energy_offset(
-    constant=30.2,
-    columns=['monochromatorPhotonEnergy','tofVoltage','sampleBias'],
-    weights=[-1,-1,-1],
-    preserve_mean=[True, True,False],
-)
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-INFO - Adding energy column to dataframe:
-INFO - Using energy calibration parameters generated on 03/05/2025, 23:08:42
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
-npartitions=14
-                uint32   int64      int64  float64  float64      float64       float32  float32   float64                   float32  float32    float64    float32    float32          float32          float32         float32           float32        float32        int8  float64
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-Dask Name: assign, 49 graph layers
-INFO - Adding energy offset to dataframe:
-INFO - Energy offset parameters:
-   Column[monochromatorPhotonEnergy]: Weight=-1, Preserve Mean: True, Reductions: None.
-   Column[tofVoltage]: Weight=-1, Preserve Mean: True, Reductions: None.
-   Column[sampleBias]: Weight=-1, Preserve Mean: False, Reductions: None.
-   Constant: 30.2
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
-npartitions=14
-                uint32   int64      int64  float64  float64      float64       float32  float32   float64                   float32  float32    float64    float32    float32          float32          float32         float32           float32        float32        int8  float64
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-Dask Name: assign, 87 graph layers
-
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[18]:
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axes = ['energy', 'delayStage']
-ranges = [[-37.5,-27.5], [-1.5,1.5]]
-bins = [200,60]
-res_corr = sp_44498.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time="delayStage")
-
-fig,ax = plt.subplots(1,2,figsize=(6,2.25), layout='constrained')
-fig.suptitle(f"Run {run_number}: W 4f, side bands")
-res_corr.plot(ax=ax[0], cmap='terrain')
-ax[0].set_title('raw')
-bg = res_corr.sel(delayStage=slice(-1.3,-1.0)).mean('delayStage')
-(res_corr-bg).plot(ax=ax[1])
-ax[1].set_title('difference')
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-INFO - Calculate normalization histogram for axis 'delayStage'...
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Compare to reference#

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While this calibration methods gives a reasonable approximation to the energy axis, there are some deviations to the bias method, so it should be used with care

-
-
[19]:
-
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axes = ['energy']
-ranges = [[-37.5,-27.5]]
-bins = [200]
-res_1D = sp_44498.compute(bins=bins, axes=axes, ranges=ranges)
-
-plt.figure()
-(res_ref/res_ref.max()).plot(label="bias series calibration")
-(res_1D/res_1D.max()).plot(label="side band calibration")
-plt.legend()
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Binning demonstration on locally generated fake data#

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In this example, we generate a table with random data simulating a single event dataset. We showcase the binning method, first on a simple single table using the bin_partition method and then in the distributed method bin_dataframe, using daks dataframes. The first method is never really called directly, as it is simply the function called by the bin_dataframe on each partition of the dask dataframe.

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[1]:
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import dask
-import numpy as np
-import pandas as pd
-import dask.dataframe
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-import matplotlib.pyplot as plt
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-from sed.binning import bin_partition, bin_dataframe
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-%matplotlib widget
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-/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/dask/dataframe/__init__.py:42: FutureWarning:
-Dask dataframe query planning is disabled because dask-expr is not installed.
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-You can install it with `pip install dask[dataframe]` or `conda install dask`.
-This will raise in a future version.
-
-  warnings.warn(msg, FutureWarning)
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Generate Fake Data#

-
-
[2]:
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n_pts = 100000
-cols = ["posx", "posy", "energy"]
-df = pd.DataFrame(np.random.randn(n_pts, len(cols)), columns=cols)
-df
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posxposyenergy
01.324629-0.5047070.241533
1-0.511322-0.2804290.367342
2-0.666617-0.984490-0.996565
30.395211-0.7820890.291860
4-1.1174830.7983910.441827
............
999951.2082090.1172952.005021
999961.2703482.156440-1.231543
999970.5573131.267318-0.199633
99998-1.1586181.908764-0.985325
999991.0940600.9179470.972665
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100000 rows × 3 columns

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Define the binning range#

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[3]:
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binAxes = ["posx", "posy", "energy"]
-nBins = [120, 120, 120]
-binRanges = [(-2, 2), (-2, 2), (-2, 2)]
-coords = {ax: np.linspace(r[0], r[1], n) for ax, r, n in zip(binAxes, binRanges, nBins)}
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Compute the binning along the pandas dataframe#

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[4]:
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%%time
-res = bin_partition(
-    part=df,
-    bins=nBins,
-    axes=binAxes,
-    ranges=binRanges,
-    hist_mode="numba",
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-CPU times: user 1.15 s, sys: 24.9 ms, total: 1.18 s
-Wall time: 1.18 s
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fig, axs = plt.subplots(1, 3, figsize=(6, 1.875), constrained_layout=True)
-for i in range(3):
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Transform to dask dataframe#

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posxposyenergy
npartitions=50
0float64float64float64
2000.........
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98000.........
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Compute distributed binning on the partitioned dask dataframe#

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In this example, the small dataset does not give significant improvement over the pandas implementation, at least using this number of partitions. A single partition would be faster (you can try…) but we use multiple for demonstration purposes.

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[7]:
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%%time
-res = bin_dataframe(
-    df=ddf,
-    bins=nBins,
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Demonstration of the conversion pipeline using time-resolved ARPES data stored on Zenodo#

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In this example, we pull some time-resolved ARPES data from Zenodo, and load it into the sed package using functions of the mpes package. Then, we run a conversion pipeline on it, containing steps for visualizing the channels, correcting image distortions, calibrating the momentum space, correcting for energy distortions and calibrating the energy axis. Finally, the data are binned in calibrated axes. For performance reasons, best store the data on a locally attached storage (no network drive). -This can also be achieved transparently using the included MirrorUtil class.

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%load_ext autoreload
-%autoreload 2
-import numpy as np
-import matplotlib.pyplot as plt
-import sed
-from sed.dataset import dataset
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-%matplotlib widget
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Load Data#

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dataset.get("WSe2") # Put in Path to a storage of at least 20 GByte free space.
-data_path = dataset.dir # This is the path to the data
-scandir, caldir = dataset.subdirs # scandir contains the data, caldir contains the calibration files
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-INFO - Not downloading WSe2 data as it already exists at "/home/runner/work/sed/sed/docs/tutorial/datasets/WSe2".
-Set 'use_existing' to False if you want to download to a new location.
-INFO - Using existing data path for "WSe2": "/home/runner/work/sed/sed/docs/tutorial/datasets/WSe2"
-INFO - WSe2 data is already present.
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-sp = sed.SedProcessor(folder=scandir, config="../src/sed/config/mpes_example_config.yaml", system_config={}, verbose=True)
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-INFO - Configuration loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/mpes_example_config.yaml]
-INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/sed_config.yaml]
-INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
-WARNING - Entry "KTOF:Lens:Sample:V" for channel "sampleBias" not found. Skipping the channel.
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-rate, secs = sp.loader.get_count_rate(range(100))
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-# axes = ['X', 'Y', 't', 'ADC']
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Distortion correction and Momentum Calibration workflow#

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Distortion correction#

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1. step:#

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Bin and load part of the dataframe in detector coordinates, and choose energy plane where high-symmetry points can well be identified. Either use the interactive tool, or pre-select the range:

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[8]:
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#sp.bin_and_load_momentum_calibration(df_partitions=20, plane=170)
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2. Step:#

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Next, we select a number of features corresponding to the rotational symmetry of the material, plus the center. These can either be auto-detected (for well-isolated points), or provided as a list (these can be read-off the graph in the cell above). These are then symmetrized according to the rotational symmetry, and a spline-warping correction for the x/y coordinates is calculated, which corrects for any geometric distortions from the perfect n-fold rotational symmetry.

-
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[9]:
-
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-
#features = np.array([[203.2, 341.96], [299.16, 345.32], [350.25, 243.70], [304.38, 149.88], [199.52, 152.48], [154.28, 242.27], [248.29, 248.62]])
-#sp.define_features(features=features, rotation_symmetry=6, include_center=True, apply=True)
-# Manual selection: Use a GUI tool to select peaks:
-#sp.define_features(rotation_symmetry=6, include_center=True)
-# Autodetect: Uses the DAOStarFinder routine to locate maxima.
-# Parameters are:
-#   fwhm: Full-width at half maximum of peaks.
-#   sigma: Number of standard deviations above the mean value of the image peaks must have.
-#   sigma_radius: number of standard deviations around a peak that peaks are fitted
-sp.define_features(rotation_symmetry=6, auto_detect=True, include_center=True, fwhm=10, sigma=12, sigma_radius=4, apply=True)
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3. Step:#

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Generate nonlinear correction using splinewarp algorithm. If no landmarks have been defined in previous step, default parameters from the config are used

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[10]:
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# Option whether a central point shall be fixed in the determination fo the correction
-sp.generate_splinewarp(include_center=True)
-
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-INFO - Calculated thin spline correction based on the following landmarks:
-pouter_ord: [[203.0039765  342.99171918]
- [299.87633072 346.19427038]
- [350.95113635 244.77654127]
- [305.64228939 150.20244008]
- [199.5409951  152.78524092]
- [153.41883308 243.04327152]]
-pcent: (249.04108057657348, 249.1877259516608)
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Optional (Step 3a):#

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Save distortion correction parameters to configuration file in current data folder:

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[11]:
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# Save generated distortion correction parameters for later reuse
-sp.save_splinewarp()
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-INFO - Saved momentum correction parameters to "sed_config.yaml".
-
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4. Step:#

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To adjust scaling, position and orientation of the corrected momentum space image, you can apply further affine transformations to the distortion correction field. Here, first a potential scaling is applied, next a translation, and finally a rotation around the center of the image (defined via the config). One can either use an interactive tool, or provide the adjusted values and apply them directly.

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[12]:
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#sp.pose_adjustment(xtrans=14, ytrans=18, angle=2)
-sp.pose_adjustment(xtrans=8, ytrans=7, angle=-4, apply=True)
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-INFO - Applied translation with (xtrans=8.0, ytrans=7.0).
-INFO - Applied rotation with angle=-4.0.
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5. Step:#

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Finally, the momentum correction is applied to the dataframe, and corresponding meta data are stored

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[13]:
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sp.apply_momentum_correction()
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-INFO - Adding corrected X/Y columns to dataframe:
-Calculating inverse deformation field, this might take a moment...
-INFO - Dask DataFrame Structure:
-                       X        Y        t      ADC       Xm       Ym
-npartitions=100
-                 float64  float64  float64  float64  float64  float64
-                     ...      ...      ...      ...      ...      ...
-...                  ...      ...      ...      ...      ...      ...
-                     ...      ...      ...      ...      ...      ...
-                     ...      ...      ...      ...      ...      ...
-Dask Name: apply_dfield, 206 graph layers
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Momentum calibration workflow#

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1. Step:#

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First, the momentum scaling needs to be calibrated. Either, one can provide the coordinates of one point outside the center, and provide its distance to the Brillouin zone center (which is assumed to be located in the center of the image), one can specify two points on the image and their distance (where the 2nd point marks the BZ center),or one can provide absolute k-coordinates of two distinct momentum points.

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If no points are provided, an interactive tool is created. Here, left mouse click selects the off-center point (brillouin_zone_centered=True) or toggle-selects the off-center and center point.

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[14]:
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k_distance = 2/np.sqrt(3)*np.pi/3.28 # k-distance of the K-point in a hexagonal Brillouin zone
-#sp.calibrate_momentum_axes(k_distance = k_distance)
-point_a = [308, 345]
-sp.calibrate_momentum_axes(point_a=point_a, k_distance = k_distance, apply=True)
-#point_b = [247, 249]
-#sp.calibrate_momentum_axes(point_a=point_a, point_b = point_b, k_coord_a = [.5, 1.1], k_coord_b = [0, 0], equiscale=False)
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Optional (Step 1a):#

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Save momentum calibration parameters to configuration file in current data folder:

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[15]:
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# Save generated momentum calibration parameters for later reuse
-sp.save_momentum_calibration()
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-INFO - Saved momentum calibration parameters to sed_config.yaml
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2. Step:#

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Now, the distortion correction and momentum calibration needs to be applied to the dataframe.

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[16]:
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sp.apply_momentum_calibration()
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-INFO - Adding kx/ky columns to dataframe:
-INFO - Using momentum calibration parameters generated on 03/05/2025, 23:10:39
-INFO - Dask DataFrame Structure:
-                       X        Y        t      ADC       Xm       Ym       kx       ky
-npartitions=100
-                 float64  float64  float64  float64  float64  float64  float64  float64
-                     ...      ...      ...      ...      ...      ...      ...      ...
-...                  ...      ...      ...      ...      ...      ...      ...      ...
-                     ...      ...      ...      ...      ...      ...      ...      ...
-                     ...      ...      ...      ...      ...      ...      ...      ...
-Dask Name: assign, 216 graph layers
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Energy Correction and Calibration workflow#

-
-

Energy Correction (optional)#

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The purpose of the energy correction is to correct for any momentum-dependent distortion of the energy axis, e.g. from geometric effects in the flight tube, or from space charge

-
-

1st step:#

-

Here, one can select the functional form to be used, and adjust its parameters. The binned data used for the momentum calibration is plotted around the Fermi energy (defined by tof_fermi), and the correction function is plotted ontop. Possible correction functions are: “spherical” (parameter: diameter), “Lorentzian” (parameter: gamma), “Gaussian” (parameter: sigma), and “Lorentzian_asymmetric” (parameters: gamma, amplitude2, gamma2).

-

One can either use an interactive alignment tool, or provide parameters directly.

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-
[17]:
-
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#sp.adjust_energy_correction(amplitude=2.5, center=(730, 730), gamma=920, tof_fermi = 66200)
-sp.adjust_energy_correction(amplitude=2.5, center=(730, 730), gamma=920, tof_fermi = 66200, apply=True)
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Optional (Step 1a):#

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Save energy correction parameters to configuration file in current data folder:

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[18]:
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# Save generated energy correction parameters for later reuse
-sp.save_energy_correction()
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-INFO - Saved energy correction parameters to sed_config.yaml
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-

2. Step#

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After adjustment, the energy correction is directly applied to the TOF axis.

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[19]:
-
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sp.apply_energy_correction()
-
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-
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-
-
-
-INFO - Applying energy correction to dataframe...
-INFO - Using energy correction parameters generated on 03/05/2025, 23:10:39
-INFO - Dask DataFrame Structure:
-                       X        Y        t      ADC       Xm       Ym       kx       ky       tm
-npartitions=100
-                 float64  float64  float64  float64  float64  float64  float64  float64  float64
-                     ...      ...      ...      ...      ...      ...      ...      ...      ...
-...                  ...      ...      ...      ...      ...      ...      ...      ...      ...
-                     ...      ...      ...      ...      ...      ...      ...      ...      ...
-                     ...      ...      ...      ...      ...      ...      ...      ...      ...
-Dask Name: assign, 230 graph layers
-
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-

Energy calibration#

-

For calibrating the energy axis, a set of data taken at different bias voltages around the value where the measurement was taken is required.

-
-

1. Step:#

-

In a first step, the data are loaded, binned along the TOF dimension, and normalized. The used bias voltages can be either provided, or read from attributes in the source files if present.

-
-
[20]:
-
-
-
# Load energy calibration EDCs
-energycalfolder = caldir
-scans = np.arange(1,12)
-voltages = np.arange(12,23,1)
-files = [energycalfolder + r'/Scan' + str(num).zfill(3) + '_' + str(num+11) + '.h5' for num in scans]
-sp.load_bias_series(data_files=files, normalize=True, biases=voltages, ranges=[(64000, 75000)])
-
-
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-WARNING - Entry "KTOF:Lens:Sample:V" for channel "sampleBias" not found. Skipping the channel.
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2. Step:#

-

Next, the same peak or feature needs to be selected in each curve. For this, one needs to define “ranges” for each curve, within which the peak of interest is located. One can either provide these ranges manually, or provide one range for a “reference” curve, and infer the ranges for the other curves using a dynamic time warping algorithm.

-
-
[21]:
-
-
-
# Option 1 = specify the ranges containing a common feature (e.g an equivalent peak) for all bias scans
-# rg = [(129031.03103103103, 129621.62162162163), (129541.54154154155, 130142.14214214214), (130062.06206206206, 130662.66266266267), (130612.61261261262, 131213.21321321322), (131203.20320320321, 131803.8038038038), (131793.7937937938, 132384.38438438438), (132434.43443443443, 133045.04504504506), (133105.10510510512, 133715.71571571572), (133805.8058058058, 134436.43643643643), (134546.54654654654, 135197.1971971972)]
-# sp.find_bias_peaks(ranges=rg, infer_others=False)
-# Option 2 = specify the range for one curve and infer the others
-# This will open an interactive tool to select the correct ranges for the curves.
-# IMPORTANT: Don't choose the range too narrow about a peak, and choose a refid
-# somewhere in the middle or towards larger biases!
-rg = (66100, 67000)
-sp.find_bias_peaks(ranges=rg, ref_id=5, infer_others=True, apply=True)
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-INFO - Use feature ranges: [(64638.0, 65386.0), (64913.0, 65683.0), (65188.0, 65991.0), (65474.0, 66310.0), (65782.0, 66651.0), (66101.0, 67003.0), (66442.0, 67388.0), (66794.0, 67795.0), (67190.0, 68213.0), (67575.0, 68664.0), (67993.0, 69148.0)].
-INFO - Extracted energy features: [[6.51330000e+04 9.43293095e-01]
- [6.54080000e+04 9.52672958e-01]
- [6.57050000e+04 9.47981834e-01]
- [6.60130000e+04 9.46402431e-01]
- [6.63430000e+04 9.50330198e-01]
- [6.66730000e+04 9.63564813e-01]
- [6.70360000e+04 9.59838033e-01]
- [6.73990000e+04 9.67203319e-01]
- [6.78060000e+04 9.55975950e-01]
- [6.82130000e+04 9.56439197e-01]
- [6.86750000e+04 9.70683038e-01]].
-
-
-
-
-

3. Step:#

-

Next, the detected peak positions and bias voltages are used to determine the calibration function. Essentially, the functional Energy(TOF) is being determined by either least-squares fitting of the functional form d2/(t-t0)2 via lmfit (method: “lmfit”), or by analytically obtaining a polynomial approximation (method: “lstsq” or “lsqr”). The parameter ref_energy is used to define the absolute energy position of the feature used for calibration in the calibrated energy -scale. energy_scale can be either “kinetic” (decreasing energy with increasing TOF), or “binding” (increasing energy with increasing TOF).

-

After calculating the calibration, all traces corrected with the calibration are plotted ontop of each other, and the calibration function (Energy(TOF)) together with the extracted features is being plotted.

-
-
[22]:
-
-
-
# Eref can be used to set the absolute energy (kinetic energy, E-EF, etc.) of the feature used for energy calibration (if known)
-Eref=-1.3
-# the lmfit method uses a fit of (d/(t-t0))**2 to determine the energy calibration
-# limits and starting values for the fitting parameters can be provided as dictionaries
-sp.calibrate_energy_axis(
-    ref_energy=Eref,
-    method="lmfit",
-    energy_scale='kinetic',
-    d={'value':1.0,'min': .7, 'max':1.2, 'vary':True},
-    t0={'value':8e-7, 'min': 1e-7, 'max': 1e-6, 'vary':True},
-    E0={'value': 0., 'min': -100, 'max': 0, 'vary': True},
-)
-
-
-
-
-
-
-
-
-INFO - [[Fit Statistics]]
-    # fitting method   = leastsq
-    # function evals   = 43
-    # data points      = 11
-    # variables        = 3
-    chi-square         = 0.00218781
-    reduced chi-square = 2.7348e-04
-    Akaike info crit   = -87.7502612
-    Bayesian info crit = -86.5565754
-[[Variables]]
-    d:   1.09544523 +/- 0.03646409 (3.33%) (init = 1)
-    t0:  7.6073e-07 +/- 7.5361e-09 (0.99%) (init = 8e-07)
-    E0: -46.6158341 +/- 0.79487877 (1.71%) (init = 0)
-[[Correlations]] (unreported correlations are < 0.100)
-    C(d, t0)  = -0.9997
-    C(d, E0)  = -0.9988
-    C(t0, E0) = +0.9974
-
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Optional (Step 3a):#

-

Save energy calibration parameters to configuration file in current data folder:

-
-
[23]:
-
-
-
# Save generated energy calibration parameters for later reuse
-sp.save_energy_calibration()
-
-
-
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-
-
-INFO - Saved energy calibration parameters to "sed_config.yaml".
-
-
-
-
-

4. Step:#

-

Finally, the the energy axis is added to the dataframe. Here, the applied bias voltages of the measurement is taken into account to provide the correct energy offset. If the bias cannot be read from the file, it can be provided manually.

-
-
[24]:
-
-
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sp.append_energy_axis(bias_voltage=16.8)
-
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-INFO - Adding energy column to dataframe:
-INFO - Using energy calibration parameters generated on 03/05/2025, 23:10:49
-INFO - Dask DataFrame Structure:
-                       X        Y        t      ADC       Xm       Ym       kx       ky       tm   energy
-npartitions=100
-                 float64  float64  float64  float64  float64  float64  float64  float64  float64  float64
-                     ...      ...      ...      ...      ...      ...      ...      ...      ...      ...
-...                  ...      ...      ...      ...      ...      ...      ...      ...      ...      ...
-                     ...      ...      ...      ...      ...      ...      ...      ...      ...      ...
-                     ...      ...      ...      ...      ...      ...      ...      ...      ...      ...
-Dask Name: assign, 243 graph layers
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4. Delay calibration:#

-

The delay axis is calculated from the ADC input column based on the provided delay range. ALternatively, the delay scan range can also be extracted from attributes inside a source file, if present.

-
-
[25]:
-
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sp.dataframe.head()
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[25]:
-
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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
XYtADCXmYmkxkytmenergy
0-0.418660-0.418660-0.418660-0.4186600.0000000.000000-2.060071-2.060071-48.653969-25.224036
1365.3625471002.36254770101.3625476317.362547355.6253291032.935535-1.1061470.71065970084.355914-9.315714
2761.209350818.20935075615.2093506316.209350791.752860839.6554050.0637140.19220775614.323807-16.717439
3691.895476970.89547666454.8954766316.895476713.580659984.782470-0.1459740.58149466449.215844-0.832955
4670.653482711.65348273025.6534826316.653482696.555409741.090835-0.191642-0.07218173025.260945-13.816654
-
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[26]:
-
-
-
#from pathlib import Path
-#datafile = "file.h5"
-#print(datafile)
-#sp.calibrate_delay_axis(datafile=datafile)
-delay_range = (-500, 1500)
-sp.calibrate_delay_axis(delay_range=delay_range, preview=True)
-
-
-
-
-
-
-
-
-INFO - Adding delay column to dataframe:
-INFO - Append delay axis using delay_range = [-500, 1500] and adc_range = [475.0, 6400.0]
-INFO -              X            Y             t          ADC           Xm  \
-0     0.339812     0.339812      0.339812     0.339812   -15.458586
-1   364.795054  1001.795054  70100.795054  6316.795054   355.019408
-2   760.901931   817.901931  75614.901931  6315.901931   791.434474
-3   691.988557   970.988557  66454.988557  6316.988557   713.678591
-4   671.049513   712.049513  73026.049513  6317.049513   696.977862
-5   299.168125  1164.168125  68459.168125  6316.168125   282.219586
-6   570.872978   664.872978  73902.872978  6315.872978   589.521746
-7   822.043494   545.043494  72632.043494  6318.043494   847.503229
-8   817.626889   415.626889  72421.626889  6316.626889   837.022802
-9  1006.434642   667.434642  72802.434642  6317.434642  1039.919639
-
-            Ym        kx        ky            tm     energy        delay
-0    88.716318 -2.101537 -1.822100    -47.851156 -25.223831  -660.222848
-1  1032.439895 -1.107772  0.709329  70083.781471  -9.314670  1471.913942
-2   839.367220  0.062860  0.191434  75614.023721 -16.717146  1471.612466
-3   984.866420 -0.145711  0.581719  66449.305555  -0.833222  1471.979260
-4   741.450570 -0.190509 -0.071216  73025.663175 -13.817170  1471.999836
-5  1185.833415 -1.303050  1.120790  68432.654642  -5.972561  1471.702321
-6   701.332303 -0.478747 -0.178828  73899.953776 -14.887719  1471.602693
-7   586.676810  0.213258 -0.486378  72627.891136 -13.294704  1472.335356
-8   465.917303  0.185146 -0.810302  72411.958883 -13.001104  1471.857178
-9   708.956391  0.729393 -0.158378  72794.936917 -13.516961  1472.129837
-
-
-
-
-

5. Visualization of calibrated histograms#

-

With all calibrated axes present in the dataframe, we can visualize the corresponding histograms, and determine the respective binning ranges

-
-
[27]:
-
-
-
axes = ['kx', 'ky', 'energy', 'delay']
-ranges = [[-3, 3], [-3, 3], [-6, 2], [-600, 1600]]
-sp.view_event_histogram(dfpid=1, axes=axes, ranges=ranges)
-
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Define the binning ranges and compute calibrated data volume#

-
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[28]:
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-
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axes = ['kx', 'ky', 'energy', 'delay']
-bins = [100, 100, 200, 50]
-ranges = [[-2, 2], [-2, 2], [-4, 2], [-600, 1600]]
-res = sp.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time="delay")
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-INFO - Calculate normalization histogram for axis 'delay'...
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Some visualization:#

-
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[29]:
-
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fig, axs = plt.subplots(4, 1, figsize=(6, 18), constrained_layout=True)
-res.loc[{'energy':slice(-.1, 0)}].sum(axis=(2,3)).T.plot(ax=axs[0])
-res.loc[{'kx':slice(-.8, -.5)}].sum(axis=(0,3)).T.plot(ax=axs[1])
-res.loc[{'ky':slice(-.2, .2)}].sum(axis=(1,3)).T.plot(ax=axs[2])
-res.loc[{'kx':slice(-.8, -.5), 'energy':slice(.5, 2)}].sum(axis=(0,1)).plot(ax=axs[3])
-
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[29]:
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-<matplotlib.collections.QuadMesh at 0x7f7b7d13cbe0>
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[30]:
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fig, ax = plt.subplots(1,1)
-(sp._normalization_histogram*90000).plot(ax=ax)
-sp._binned.sum(axis=(0,1,2)).plot(ax=ax)
-plt.show()
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Binning with metadata generation, and storing into a NeXus file#

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In this example, we show how to bin the same data used for example 3, but using the values for correction/calibration parameters generated in the example notebook 3, which are locally saved in the file sed_config.yaml. These data and the corresponding (machine and processing) metadata are then stored to a NeXus file following the NXmpes NeXus standard -(https://fairmat-experimental.github.io/nexus-fairmat-proposal/9636feecb79bb32b828b1a9804269573256d7696/classes/contributed_definitions/NXmpes.html#nxmpes) using the ‘dataconverter’ of the pynxtools package (FAIRmat-NFDI/pynxtools).

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[1]:
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%load_ext autoreload
-%autoreload 2
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-import sed
-from sed.dataset import dataset
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-%matplotlib widget
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Load Data#

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[2]:
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dataset.get("WSe2") # Put in Path to a storage of at least 20 GByte free space.
-data_path = dataset.dir # This is the path to the data
-scandir, _ = dataset.subdirs # scandir contains the data, _ contains the calibration files
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-INFO - Not downloading WSe2 data as it already exists at "/home/runner/work/sed/sed/docs/tutorial/datasets/WSe2".
-Set 'use_existing' to False if you want to download to a new location.
-INFO - Using existing data path for "WSe2": "/home/runner/work/sed/sed/docs/tutorial/datasets/WSe2"
-INFO - WSe2 data is already present.
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[3]:
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metadata = {}
-# manual Meta data. These should ideally come from an Electronic Lab Notebook.
-#General
-metadata['experiment_summary'] = 'WSe2 XUV NIR pump probe data.'
-metadata['entry_title'] = 'Valence Band Dynamics - 800 nm linear s-polarized pump, 0.6 mJ/cm2 absorbed fluence'
-metadata['experiment_title'] = 'Valence band dynamics of 2H-WSe2'
-
-#User
-# Fill general parameters of NXuser
-# TODO: discuss how to deal with multiple users?
-metadata['user0'] = {}
-metadata['user0']['name'] = 'Julian Maklar'
-metadata['user0']['role'] = 'Principal Investigator'
-metadata['user0']['affiliation'] = 'Fritz Haber Institute of the Max Planck Society'
-metadata['user0']['address'] = 'Faradayweg 4-6, 14195 Berlin'
-metadata['user0']['email'] = 'maklar@fhi-berlin.mpg.de'
-
-#NXinstrument
-metadata['instrument'] = {}
-metadata['instrument']['energy_resolution'] = 140.
-#analyzer
-metadata['instrument']['analyzer']={}
-metadata['instrument']['analyzer']['slow_axes'] = "delay" # the scanned axes
-metadata['instrument']['analyzer']['spatial_resolution'] = 10.
-metadata['instrument']['analyzer']['energy_resolution'] = 110.
-metadata['instrument']['analyzer']['momentum_resolution'] = 0.08
-metadata['instrument']['analyzer']['working_distance'] = 4.
-metadata['instrument']['analyzer']['lens_mode'] = "6kV_kmodem4.0_30VTOF.sav"
-
-#probe beam
-metadata['instrument']['beam']={}
-metadata['instrument']['beam']['probe']={}
-metadata['instrument']['beam']['probe']['incident_energy'] = 21.7
-metadata['instrument']['beam']['probe']['incident_energy_spread'] = 0.11
-metadata['instrument']['beam']['probe']['pulse_duration'] = 20.
-metadata['instrument']['beam']['probe']['frequency'] = 500.
-metadata['instrument']['beam']['probe']['incident_polarization'] = [1, 1, 0, 0] # p pol Stokes vector
-metadata['instrument']['beam']['probe']['extent'] = [80., 80.]
-#pump beam
-metadata['instrument']['beam']['pump']={}
-metadata['instrument']['beam']['pump']['incident_energy'] = 1.55
-metadata['instrument']['beam']['pump']['incident_energy_spread'] = 0.08
-metadata['instrument']['beam']['pump']['pulse_duration'] = 35.
-metadata['instrument']['beam']['pump']['frequency'] = 500.
-metadata['instrument']['beam']['pump']['incident_polarization'] = [1, -1, 0, 0] # s pol Stokes vector
-metadata['instrument']['beam']['pump']['incident_wavelength'] = 800.
-metadata['instrument']['beam']['pump']['average_power'] = 300.
-metadata['instrument']['beam']['pump']['pulse_energy'] = metadata['instrument']['beam']['pump']['average_power']/metadata['instrument']['beam']['pump']['frequency']#µJ
-metadata['instrument']['beam']['pump']['extent'] = [230., 265.]
-metadata['instrument']['beam']['pump']['fluence'] = 0.15
-
-#sample
-metadata['sample']={}
-metadata['sample']['preparation_date'] = '2019-01-13T10:00:00+00:00'
-metadata['sample']['preparation_description'] = 'Cleaved'
-metadata['sample']['sample_history'] = 'Cleaved'
-metadata['sample']['chemical_formula'] = 'WSe2'
-metadata['sample']['description'] = 'Sample'
-metadata['sample']['name'] = 'WSe2 Single Crystal'
-
-metadata['file'] = {}
-metadata['file']["trARPES:Carving:TEMP_RBV"] = 300.
-metadata['file']["trARPES:XGS600:PressureAC:P_RD"] = 5.e-11
-metadata['file']["KTOF:Lens:Extr:I"] = -0.12877
-metadata['file']["KTOF:Lens:UDLD:V"] = 399.99905
-metadata['file']["KTOF:Lens:Sample:V"] = 17.19976
-metadata['file']["KTOF:Apertures:m1.RBV"] = 3.729931
-metadata['file']["KTOF:Apertures:m2.RBV"] = -5.200078
-metadata['file']["KTOF:Apertures:m3.RBV"] = -11.000425
-
-# Sample motor positions
-metadata['file']['trARPES:Carving:TRX.RBV'] = 7.1900000000000004
-metadata['file']['trARPES:Carving:TRY.RBV'] = -6.1700200225439552
-metadata['file']['trARPES:Carving:TRZ.RBV'] = 33.4501953125
-metadata['file']['trARPES:Carving:THT.RBV'] = 423.30500940561586
-metadata['file']['trARPES:Carving:PHI.RBV'] = 0.99931647456264949
-metadata['file']['trARPES:Carving:OMG.RBV'] = 11.002500171914066
-
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[4]:
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# create sed processor using the config file, and collect the meta data from the files:
-sp = sed.SedProcessor(folder=scandir, config="../src/sed/config/mpes_example_config.yaml", system_config={}, metadata=metadata, collect_metadata=True)
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-INFO - Configuration loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/mpes_example_config.yaml]
-INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/sed_config.yaml]
-INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
-WARNING - Entry "KTOF:Lens:Sample:V" for channel "sampleBias" not found. Skipping the channel.
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# Apply jittering to X, Y, t, ADC columns.
-sp.add_jitter()
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-INFO - add_jitter: Added jitter to columns ['X', 'Y', 't', 'ADC'].
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# Calculate machine-coordinate data for pose adjustment
-sp.bin_and_load_momentum_calibration(df_partitions=10, plane=33, width=10, apply=True)
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# Adjust pose alignment, using stored distortion correction
-sp.pose_adjustment(xtrans=8, ytrans=7, angle=-4, apply=True, use_correction=True)
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-INFO - No landmarks defined, using momentum correction parameters generated on 03/05/2025, 23:10:32
-INFO - Calculated thin spline correction based on the following landmarks:
-pouter_ord: [[203.2  341.96]
- [299.16 345.32]
- [350.25 243.7 ]
- [304.38 149.88]
- [199.52 152.48]
- [154.28 242.27]]
-pcent: (248.29, 248.62)
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-INFO - Applied translation with (xtrans=8.0, ytrans=7.0).
-INFO - Applied rotation with angle=-4.0.
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# Apply stored momentum correction
-sp.apply_momentum_correction()
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-INFO - Adding corrected X/Y columns to dataframe:
-Calculating inverse deformation field, this might take a moment...
-INFO - Dask DataFrame Structure:
-                       X        Y        t      ADC       Xm       Ym
-npartitions=100
-                 float64  float64  float64  float64  float64  float64
-                     ...      ...      ...      ...      ...      ...
-...                  ...      ...      ...      ...      ...      ...
-                     ...      ...      ...      ...      ...      ...
-                     ...      ...      ...      ...      ...      ...
-Dask Name: apply_dfield, 206 graph layers
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# Apply stored config momentum calibration
-sp.apply_momentum_calibration()
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-INFO - Adding kx/ky columns to dataframe:
-INFO - Using momentum calibration parameters generated on 03/05/2025, 23:10:39
-INFO - Dask DataFrame Structure:
-                       X        Y        t      ADC       Xm       Ym       kx       ky
-npartitions=100
-                 float64  float64  float64  float64  float64  float64  float64  float64
-                     ...      ...      ...      ...      ...      ...      ...      ...
-...                  ...      ...      ...      ...      ...      ...      ...      ...
-                     ...      ...      ...      ...      ...      ...      ...      ...
-                     ...      ...      ...      ...      ...      ...      ...      ...
-Dask Name: assign, 216 graph layers
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# Apply stored config energy correction
-sp.apply_energy_correction()
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-INFO - Applying energy correction to dataframe...
-INFO - Using energy correction parameters generated on 03/05/2025, 23:10:39
-INFO - Dask DataFrame Structure:
-                       X        Y        t      ADC       Xm       Ym       kx       ky       tm
-npartitions=100
-                 float64  float64  float64  float64  float64  float64  float64  float64  float64
-                     ...      ...      ...      ...      ...      ...      ...      ...      ...
-...                  ...      ...      ...      ...      ...      ...      ...      ...      ...
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-Dask Name: assign, 230 graph layers
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# Apply stored config energy calibration
-sp.append_energy_axis(bias_voltage=16.8)
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-INFO - Adding energy column to dataframe:
-INFO - Using energy calibration parameters generated on 03/05/2025, 23:10:49
-INFO - Dask DataFrame Structure:
-                       X        Y        t      ADC       Xm       Ym       kx       ky       tm   energy
-npartitions=100
-                 float64  float64  float64  float64  float64  float64  float64  float64  float64  float64
-                     ...      ...      ...      ...      ...      ...      ...      ...      ...      ...
-...                  ...      ...      ...      ...      ...      ...      ...      ...      ...      ...
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-Dask Name: assign, 243 graph layers
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# Apply delay calibration
-delay_range = (-500, 1500)
-sp.calibrate_delay_axis(delay_range=delay_range, preview=True)
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-INFO - Adding delay column to dataframe:
-INFO - Append delay axis using delay_range = [-500, 1500] and adc_range = [475.0, 6400.0]
-INFO -              X            Y             t          ADC           Xm  \
-0     0.051626     0.051626      0.051626     0.051626   -23.640991
-1   365.144652  1002.144652  70101.144652  6317.144652   353.661627
-2   760.898249   817.898249  75614.898249  6315.898249   791.895896
-3   692.122146   971.122146  66455.122146  6317.122146   714.435564
-4   671.339233   712.339233  73026.339233  6317.339233   697.262755
-5   298.924102  1163.924102  68458.924102  6315.924102   280.596553
-6   570.866497   664.866497  73902.866497  6315.866497   588.334617
-7   822.055127   545.055127  72632.055127  6318.055127   846.748776
-8   818.291515   416.291515  72422.291515  6317.291515   836.277431
-9  1005.967367   666.967367  72801.967367  6316.967367  1037.774218
-
-            Ym        kx        ky            tm     energy        delay
-0    96.997304 -2.123485 -1.799887    -48.156196  -8.260191  -660.320126
-1  1034.759985 -1.111415  0.715553  70084.135361   7.511570  1472.031950
-2   838.562181  0.064098  0.189275  75614.020127   0.223453  1471.611223
-3   984.009799 -0.143680  0.579421  66449.434304  15.954266  1472.024353
-4   741.873516 -0.189744 -0.070081  73025.957393   3.068771  1472.097632
-5  1187.459153 -1.307403  1.125150  68432.410693  10.828692  1471.619950
-6   702.596579 -0.481932 -0.175437  73899.947018   2.017107  1471.600505
-7   586.942188  0.211234 -0.485667  72627.902969   3.583193  1472.339283
-8   467.381412  0.183146 -0.806374  72412.647747   3.871333  1472.081524
-9   707.941775  0.723638 -0.161099  72794.486647   3.365058  1471.972107
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Compute final data volume#

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[13]:
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axes = ['kx', 'ky', 'energy', 'delay']
-bins = [100, 100, 200, 50]
-ranges = [[-2, 2], [-2, 2], [-4, 2], [-600, 1600]]
-res = sp.compute(bins=bins, axes=axes, ranges=ranges)
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[14]:
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# save to NXmpes NeXus (including standardized metadata)
-sp.save(data_path + "/binned.nxs")
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-Using mpes reader to convert the given files:
-• ../src/sed/config/NXmpes_config.json
-The output file generated: /home/runner/work/sed/sed/docs/tutorial/datasets/WSe2/binned.nxs.
-
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[15]:
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# Visualization (requires JupyterLab)
-from jupyterlab_h5web import H5Web
-H5Web(data_path + "/binned.nxs")
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Tutorial for binning data from the HEXTOF instrument at FLASH#

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Preparation#

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Import necessary libraries#

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[1]:
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-
%load_ext autoreload
-%autoreload 2
-from typing import List
-from pathlib import Path
-import os
-
-from sed import SedProcessor
-from sed.dataset import dataset
-import xarray as xr
-
-%matplotlib widget
-import matplotlib.pyplot as plt
-
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Get data paths#

-

The paths are such that if you are on Maxwell, it uses those. Otherwise data is downloaded in current directory from Zenodo.

-

Generally, if it is your beamtime, you can both read the raw data and write to processed directory. However, for the public data, you can not write to processed directory.

-
-
[2]:
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beamtime_dir = "/asap3/flash/gpfs/pg2/2023/data/11019101" # on Maxwell
-if os.path.exists(beamtime_dir) and os.access(beamtime_dir, os.R_OK):
-    path = beamtime_dir + "/raw/hdf/offline/fl1user3"
-    meta_path = beamtime_dir + "/shared"
-    buffer_path = "Gd_W110/processed/"
-else:
-    # data_path can be defined and used to store the data in a specific location
-    dataset.get("Gd_W110") # Put in Path to a storage of at least 10 GByte free space.
-    path = dataset.dir
-    meta_path = path
-    buffer_path = path + "/processed/"
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-INFO - Not downloading Gd_W110 data as it already exists at "/home/runner/work/sed/sed/docs/tutorial/datasets/Gd_W110".
-Set 'use_existing' to False if you want to download to a new location.
-INFO - Using existing data path for "Gd_W110": "/home/runner/work/sed/sed/docs/tutorial/datasets/Gd_W110"
-INFO - Gd_W110 data is already present.
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Config setup#

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Here we get the path to the config file and setup the relevant directories. This can also be done directly in the config file.

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[3]:
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# pick the default configuration file for hextof@FLASH
-config_file = Path('../src/sed/config/flash_example_config.yaml')
-assert config_file.exists()
-
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The path to the processed folder can also be defined as a keyword argument later.

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[4]:
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# here we setup a dictionary that will be used to override the path configuration
-config_override = {
-    "core": {
-        "paths": {
-            "raw": path,
-            "processed": buffer_path,
-        },
-    },
-}
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cleanup previous config files#

-

In this notebook, we will show how calibration parameters can be generated. Therefore we want to clean the local directory of previously generated files.

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WARNING running the cell below will delete the “sed_config.yaml” file in the local directory. If these contain precious calibration parameters, DO NOT RUN THIS CELL.

-
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[5]:
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local_folder_config = Path('./sed_config.yaml')
-if local_folder_config.exists():
-    os.remove(local_folder_config)
-    print(f'deleted local config file {local_folder_config}')
-assert not local_folder_config.exists()
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-deleted local config file sed_config.yaml
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Load a chessy sample run#

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The common starting point at a FLASH beamtime. Look at the Chessy sample!

-
    -

  • run 44762: Chessy - FoV = 450 µm

    • -
-
-

Generate the Processor instance#

-

this cell generates an instance of the SedProcessor class. It will be our workhorse for the entire workflow.

-
-

Important note#

-

The following extra arguments are available for FlashLoader. None of which are necessary to give but helpful to know.

-
    -

  • force_recreate: Probably the most useful. In case the config is changed, this allows to reduce the raw h5 files to the the intermediate parquet format again. Otherwise, the schema between the saved dataframe and config differs.

    • -

  • debug: Setting this runs the reduction process in serial, so the errors are easier to find.

    • -

  • remove_invalid_files: Sometimes some critical channels defined in the config are missing in some raw files. Setting this will make sure to ignore such files.

    • -

  • filter_timed_by_electron: Defaults to True. When True, the timed dataframe will only contain data points where valid electron events were detected. When False, all timed data points are included regardless of electron detection (see OpenCOMPES/sed#307)

    • -

  • processed_dir: Location to save the reduced parquet files.

    • -

  • scicat_token: Token from your scicat account.

    • -

  • detector: ‘1Q’ and ‘4Q’ detector for example. Useful when there are separate raw files for each detector.

    • -
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[6]:
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-
sp = SedProcessor(runs=[44762], config=config_override, system_config=config_file, collect_metadata=False)
-# You can set collect_metadata=True if the scicat_url and scicat_token are defined
-
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-INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
-INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
-INFO - Reading files: 0 new files of 1 total.
-loading complete in  0.08 s
-
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Add Jitter#

-

In order to avoid artifacts arising from incommensurate binning sizes with those imposed during data collection, e.g. by the detector, we jitter all the digital columns.

-
-
[7]:
-
-
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sp.add_jitter()
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-INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
-
-
-
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-

inspect the dataframe#

-

Looking at the dataframe can give quick insight about the columns loaded and the data available.

-
    -

  • sp.dataframe shows the structure of the dataframe without computing anything. Interesting here are the columns, and their type.

    • -

  • The sp.dataframe.head() function accesses the first 5 events in the dataframe, giving us a view of what the values of each column look like, without computing the whole thing. sp.dataframe.tail()does the same from the end.

    • -

  • sp.dataframe.compute() will compute the whole dataframe, and can take a while. We should avoid doing this.

    • -
-
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[8]:
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sp.dataframe
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[8]:
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Dask DataFrame Structure:
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
trainIdpulseIdelectronIddldPosXdldPosYdldTimeStepspulserSignAdcbamtimeStampmonochromatorPhotonEnergygmdBdadelayStagesampleBiastofVoltageextractorVoltageextractorCurrentcryoTemperaturesampleTemperaturedldTimeBinSizedldSectorID
npartitions=1
uint32int64int64float64float64float64float32float32float64float32float32float32float32float32float32float32float32float32float32int8
............................................................
-
-
Dask Name: apply_jitter, 14 graph layers
-
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[9]:
-
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sp.dataframe.head()
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-
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[9]:
-
-
-
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
trainIdpulseIdelectronIddldPosXdldPosYdldTimeStepspulserSignAdcbamtimeStampmonochromatorPhotonEnergygmdBdadelayStagesampleBiastofVoltageextractorVoltageextractorCurrentcryoTemperaturesampleTemperaturedldTimeBinSizedldSectorID
01646339970120781.275094690.2750943050.27509432914.08976.093751.679395e+0951.02345345.5127941448.312988-0.00348929.9990656029.370117-0.070368303.940002304.9400020.0205767
11646339970121780.837700690.8377003048.83770032914.08976.093751.679395e+0951.02345345.5127941448.312988-0.00348929.9990656029.370117-0.070368303.940002304.9400020.0205762
21646339970220562.411873231.4118735729.41187332914.08990.375001.679395e+0951.02345345.5127941448.312988-0.00348929.9990656029.370117-0.070368303.940002304.9400020.0205763
31646339970221938.159477947.1594775730.15947732914.08990.375001.679395e+0951.02345345.5127941448.312988-0.00348929.9990656029.370117-0.070368303.940002304.9400020.0205762
41646339970270536.301326854.3013261571.30132632914.08982.875001.679395e+0951.02345345.5127941448.312988-0.00348929.9990656029.370117-0.070368303.940002304.9400020.0205760
-
-
-
-
-

Visualizing event histograms#

-

For getting a first impression of the data, and to determine binning ranges, the method sp.view_even_histogram() allows visualizing the events in one dataframe partition as histograms. Default axes and ranges are defined in the config, and show the dldPosX, dldPosY, and dldTimeStep columns:

-
-
[10]:
-
-
-
sp.view_event_histogram(dfpid=0)
-
-
-
-
-
-
-
-
-
-
-
-

Binning#

-

Here we define the parameters for binning the dataframe to an n-dimensional histogram, which we can then plot, analyze or save.

-

If you never saw this before, the type after : is a “hint” to what type the object to the left will have. We include them here to make sure you know what each variable should be.

-
a:int = 1 # a is an integer
-b:float = 1.0 # b is a float
-c:str = 1 # we hint c to be a string, but it is still an integer
-
-
-

This is totally optional, but can help you keep track of what you are doing.

-
-
[11]:
-
-
-
# the name of the axes on which we want to bin
-axes: List[str] = ['dldPosY', 'dldPosX']
-# the number of bins for each axis
-bins: List[int] = [480, 480]
-# for each axis, the range of values to consider
-ranges: List[List[int]] = [[420,900], [420,900]]
-# here we compute the histogram
-res_chessy: xr.DataArray = sp.compute(bins=bins, axes=axes, ranges=ranges)
-
-
-
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-
-
-
-
-
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-
-

visualize the result#

-

here we plot the binned histogram. The result is an xarray, which gives us some convenient visualization and simple plotting tools.

-
-
[12]:
-
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res_chessy
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[12]:
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-
- - - - - - - - - - - - - - -
<xarray.DataArray (dldPosY: 480, dldPosX: 480)> Size: 922kB
-array([[0., 0., 0., ..., 0., 0., 0.],
-       [0., 0., 0., ..., 0., 0., 0.],
-       [0., 0., 0., ..., 0., 0., 0.],
-       ...,
-       [0., 0., 0., ..., 0., 0., 0.],
-       [0., 0., 0., ..., 0., 0., 0.],
-       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)
-Coordinates:
-  * dldPosY  (dldPosY) float64 4kB 420.0 421.0 422.0 423.0 ... 897.0 898.0 899.0
-  * dldPosX  (dldPosX) float64 4kB 420.0 421.0 422.0 423.0 ... 897.0 898.0 899.0
-Attributes:
-    units:      counts
-    long_name:  photoelectron counts
-    metadata:   {'file_statistics': {'electron': {'0': {'created_by': 'parque...
-
-
-
[13]:
-
-
-
plt.figure()
-res_chessy.plot(robust=True) # robust is used to avoid outliers to dominate the color scale
-
-
-
-
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[13]:
-
-
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-
-<matplotlib.collections.QuadMesh at 0x7f91041425f0>
-
-
-
-
-
-
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-
-
-
-
-

Optical Spot Profile#

-

Here we load runs 44798 and 44799, which show the profile of the optical spot on the same spatial view as in our chessy run above. The two differ in transmission, being \(T=1.0\) and \(T=0.5\) respectively.

-
-
[14]:
-
-
-
sp = SedProcessor(runs=[44798], config=config_override, system_config=config_file, collect_metadata=False)
-sp.add_jitter()
-res_t05: xr.DataArray = sp.compute(bins=bins, axes=axes, ranges=ranges)
-
-sp = SedProcessor(runs=[44799], config=config_override, system_config=config_file, collect_metadata=False)
-sp.add_jitter()
-res_t10: xr.DataArray = sp.compute(bins=bins, axes=axes, ranges=ranges)
-
-
-
-
-
-
-
-
-INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
-INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
-INFO - Reading files: 0 new files of 1 total.
-loading complete in  0.06 s
-INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
-
-
-
-
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-
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-
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-INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
-INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
-INFO - Reading files: 0 new files of 2 total.
-loading complete in  0.07 s
-INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
-
-
-
-
-
-
-
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-
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[15]:
-
-
-
fig,ax = plt.subplots(1,3,figsize=(6,2), layout='tight')
-res_chessy.plot(ax=ax[0], robust=True, add_colorbar=False)
-res_t05.plot(ax=ax[1], robust=True, add_colorbar=False)
-res_t10.plot(ax=ax[2], robust=True, add_colorbar=False)
-
-
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-
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[15]:
-
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-<matplotlib.collections.QuadMesh at 0x7f910428c670>
-
-
-
-
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-
-
-

TODO: here we can add the evaluation of the spot size.

-
-
-

Energy Calibration#

-

We now load a bias series, where the sample bias was varied, effectively shifting the energy spectra. This allows us to calibrate the conversion between the digital values of the dld and the energy.

-
-
[16]:
-
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sp = SedProcessor(runs=[44797], config=config_override, system_config=config_file, collect_metadata=False)
-sp.add_jitter()
-
-
-
-
-
-
-
-
-INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
-INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
-INFO - Reading files: 0 new files of 5 total.
-loading complete in  0.07 s
-INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
-
-
-

We can use the view_event_histogram() function also to e.g. visualize the events per microbunch along the train, or hit multiplicity per microbunch:

-
-
[17]:
-
-
-
sp.view_event_histogram(dfpid=0, axes=["pulseId", "electronId"], ranges=[[0, 600], [0,10]], bins=[100, 10])
-
-
-
-
-
-
-
-
-
-
-

sector alignment#

-

as usual first we jitter, but here we also align in time the 8 sectors of the dld. This is done by finding the time of the maximum of the signal in each sector, and then shifting the signal in each sector by the difference between the maximum time and the time of the maximum in each sector.

-

For better precision, the photon peak can be used to track the energy shift.

-
-
[18]:
-
-
-
sp.align_dld_sectors()
-
-
-
-
-
-
-
-
-INFO - Aligning 8s sectors of dataframe
-INFO - Dask DataFrame Structure:
-              trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID
-npartitions=5
-               uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8
-                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-...               ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-Dask Name: assign, 16 graph layers
-
-
-
-
-

time-of-flight spectrum#

-

to compare with what we see on the measurement computer, we might want to plot the time-of-flight spectrum. This is done here.

-
-
[19]:
-
-
-
sp.append_tof_ns_axis()
-
-
-
-
-
-
-
-
-INFO - Adding time-of-flight column in nanoseconds to dataframe.
-INFO - Dask DataFrame Structure:
-              trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID  dldTime
-npartitions=5
-               uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64
-                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-...               ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-Dask Name: assign, 22 graph layers
-
-
-

Now, to determine proper binning ranges, let’s have again a look at the event histograms:

-
-
[20]:
-
-
-
sp.view_event_histogram(dfpid=0, axes=["sampleBias", "dldTime"], ranges=[[27, 33], [650,1050]], bins=[50, 100])
-
-
-
-
-
-
-
-
-
-
-
[21]:
-
-
-
axes = ['sampleBias','dldTime']
-bins = [5, 250]
-ranges = [[28,33],  [650,800]]
-res = sp.compute(bins=bins, axes=axes, ranges=ranges)
-
-
-
-
-
-
-
-
-
-

We binned not only in dldTime but also in sampleBias. This allows us to separate the spectra obtained at different bias values.

-
-
[22]:
-
-
-
plt.figure()
-res.plot.line(x='dldTime'); # the ; here is to suppress an annoying output
-
-
-
-
-
-
-
-
-
-
-
-

find calibration parameters#

-

We now will fit the tof-energy relation. This is done by finding the maxima of a peak in the tof spectrum, and then fitting the square root relation to obtain the calibration parameters.

-
-
[23]:
-
-
-
axes = ['sampleBias', 'dldTimeSteps']
-bins = [5, 500]
-ranges = [[28,33], [4000, 4800]]
-res = sp.compute(bins=bins, axes=axes, ranges=ranges)
-
-
-
-
-
-
-
-
-
-
-
[24]:
-
-
-
sp.load_bias_series(binned_data=res)
-
-
-
-
-
-
-
-
-
-
-
[25]:
-
-
-
ranges=(4120, 4200)
-ref_id=0
-sp.find_bias_peaks(ranges=ranges, ref_id=ref_id, apply=True)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
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-
-
-INFO - Use feature ranges: [(4120.0, 4200.0), (4156.8, 4238.4), (4195.2, 4286.4), (4236.8, 4329.6), (4281.6, 4374.4)].
-INFO - Extracted energy features: [[4.1488e+03 1.0000e+00]
- [4.1872e+03 1.0000e+00]
- [4.2272e+03 1.0000e+00]
- [4.2704e+03 1.0000e+00]
- [4.3152e+03 1.0000e+00]].
-
-
-
-
[26]:
-
-
-
sp.calibrate_energy_axis(
-    ref_energy=-.55,
-    method="lmfit",
-    energy_scale='kinetic',
-    d={'value':1.0,'min': .2, 'max':1.0, 'vary':False},
-    t0={'value':5e-7, 'min': 1e-7, 'max': 1e-6, 'vary':True},
-    E0={'value': 0., 'min': -100, 'max': 100, 'vary': True},
-)
-
-
-
-
-
-
-
-
-INFO - [[Fit Statistics]]
-    # fitting method   = leastsq
-    # function evals   = 22
-    # data points      = 5
-    # variables        = 2
-    chi-square         = 1.9886e-04
-    reduced chi-square = 6.6286e-05
-    Akaike info crit   = -46.6618227
-    Bayesian info crit = -47.4429469
-[[Variables]]
-    d:   1 (fixed)
-    t0:  3.5727e-07 +/- 2.9058e-10 (0.08%) (init = 5e-07)
-    E0: -54.7998131 +/- 0.04277721 (0.08%) (init = 0)
-[[Correlations]] (unreported correlations are < 0.100)
-    C(t0, E0) = -0.9964
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-

generate the energy axis#

-

Now that we have the calibration parameters, we can generate the energy axis for each spectrum

-
-
[27]:
-
-
-
sp.append_energy_axis()
-
-
-
-
-
-
-
-
-INFO - Adding energy column to dataframe:
-INFO - Using energy calibration parameters generated on 03/05/2025, 23:17:42
-INFO - Dask DataFrame Structure:
-              trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID  dldTime   energy
-npartitions=5
-               uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64  float64
-                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
-...               ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
-                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
-                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
-Dask Name: assign, 37 graph layers
-
-
-

Lets have a look at the dataset, and the columns we added.

-
-
[28]:
-
-
-
sp.dataframe[['dldTime','dldTimeSteps','energy','dldSectorID']].head()
-
-
-
-
-
[28]:
-
-
-
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
dldTimedldTimeStepsenergydldSectorID
0696.4531194230.952637-2.6459861
1696.7018014232.463379-2.6821810
2697.7753774238.985352-2.8375271
3697.7437894238.793457-2.8329774
4745.1936364527.051270-8.4654171
-
-
-
-
-

Bin in energy#

-

With the newly added column, we can now bin directly in energy

-
-
[29]:
-
-
-
axes: List[str] = ['sampleBias', 'energy']
-bins: List[int] = [5, 500]
-ranges: List[List[int]] = [[28,33], [-10,10]]
-res: xr.DataArray = sp.compute(bins=bins, axes=axes, ranges=ranges)
-
-
-
-
-
-
-
-
-
-
-
[30]:
-
-
-
plt.figure() # if you are using interactive plots, you'll need to generate a new figure explicitly every time.
-res.mean('sampleBias').plot.line(x='energy',linewidth=3)
-res.plot.line(x='energy',linewidth=1,alpha=.5);
-
-
-
-
-
-
-
-
-
-
-
-

correct offsets#

-

The energy axis is now correct, taking the sample bias of the measurement into account. Additionally, we can compensate the photon energy (monochromatorPhotonEnergy) and the tofVoltage.

-
-
[31]:
-
-
-
sp.add_energy_offset(
-    columns=['monochromatorPhotonEnergy','tofVoltage'],
-    weights=[-1,-1],
-    preserve_mean=[True, True],
-)
-
-
-
-
-
-
-
-
-INFO - Adding energy offset to dataframe:
-INFO - Energy offset parameters:
-   Column[monochromatorPhotonEnergy]: Weight=-1, Preserve Mean: True, Reductions: None.
-   Column[tofVoltage]: Weight=-1, Preserve Mean: True, Reductions: None.
-INFO - Dask DataFrame Structure:
-              trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID  dldTime   energy
-npartitions=5
-               uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64  float64
-                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
-...               ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
-                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
-                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
-Dask Name: assign, 67 graph layers
-
-
-

Now we bin again and see the result

-
-
[32]:
-
-
-
axes = ['sampleBias', 'energy']
-bins = [5, 500]
-ranges = [[28,33], [-10,2]]
-res = sp.compute(bins=bins, axes=axes, ranges=ranges)
-
-
-
-
-
-
-
-
-
-
-
[33]:
-
-
-
plt.figure()
-ax = plt.subplot(111)
-res.energy.attrs['unit'] = 'eV' # add units to the axes
-res.mean('sampleBias').plot.line(x='energy',linewidth=3, ax=ax)
-res.plot.line(x='energy',linewidth=1,alpha=.5,label='all',ax=ax);
-
-
-
-
-
-
-
-
-
-
-
-

save the calibration parameters#

-

The parameters we have found can be saved to a file, so that we can use them later. This means the calibration can be used for different runs.

-
-
[34]:
-
-
-
sp.save_energy_calibration()
-sp.save_energy_offset()
-
-
-
-
-
-
-
-
-INFO - Saved energy calibration parameters to "sed_config.yaml".
-INFO - Saved energy offset parameters to "sed_config.yaml".
-
-
-

A more general function, which saves parameters for all the calibrations performed. Use either the above or below function. They are equivalent (and overwrite each other)

-
-
[35]:
-
-
-
sp.save_workflow_params()
-
-
-
-
-
-
-
-
-INFO - Saved energy calibration parameters to "sed_config.yaml".
-INFO - Saved energy offset parameters to "sed_config.yaml".
-
-
-
-
-
-

Correct delay axis#

-

To calibrate the pump-probe delay axis, we need to shift the delay stage values to center the pump-probe-time overlap time zero. Also, we want to correct the SASE jitter, using information from the bam column.

-

Here we load multiple runs at once

-
-
[36]:
-
-
-
sp = SedProcessor(
-    runs=[44824,44825,44826,44827],
-    config=config_override,
-    system_config=config_file,
-    collect_metadata=False,
-)
-
-
-
-
-
-
-
-
-INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/sed_config.yaml]
-INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
-INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
-INFO - Reading files: 0 new files of 62 total.
-loading complete in  0.32 s
-
-
-
-

Run the workflow from the config file#

-

as we have saved some calibration and correction parameters, we can now run the workflow from the config file. This is done by calling each of the correction functions, with no parameters. The functions will then load the parameters from the config file.

-
-
[37]:
-
-
-
sp.add_jitter()
-sp.align_dld_sectors()
-sp.append_energy_axis()
-sp.add_energy_offset()
-
-
-
-
-
-
-
-
-INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
-INFO - Aligning 8s sectors of dataframe
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID
-npartitions=62
-                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-Dask Name: assign, 16 graph layers
-INFO - Adding energy column to dataframe:
-INFO - Using energy calibration parameters generated on 03/05/2025, 23:17:42
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
-npartitions=62
-                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-Dask Name: assign, 31 graph layers
-INFO - Adding energy offset to dataframe:
-INFO - Using energy offset parameters generated on 03/05/2025, 23:17:44
-INFO - Energy offset parameters:
-   Column[monochromatorPhotonEnergy]: Weight=-1.0, Preserve Mean: True, Reductions: None.
-   Column[tofVoltage]: Weight=-1.0, Preserve Mean: True, Reductions: None.
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
-npartitions=62
-                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-Dask Name: assign, 61 graph layers
-
-
-
-
-

plot the delayStage values#

-
-
[38]:
-
-
-
axes = ['energy','delayStage']
-bins = [100,150]
-delay_start,delay_stop=1462.00,1464.85
-ranges = [[-5,2], [delay_start,delay_stop]]
-res = sp.compute(bins=bins, axes=axes, ranges=ranges)
-
-
-
-
-
-
-
-
-
-
-
[39]:
-
-
-
fig,ax = plt.subplots(1,2,figsize=(6,2.25), layout='constrained')
-res.plot(robust=True, ax=ax[0])
-bg = res.isel(delayStage=slice(0,10)).mean('delayStage')
-(res-bg).plot(robust=True, ax=ax[1])
-
-
-
-
-
[39]:
-
-
-
-
-<matplotlib.collections.QuadMesh at 0x7f90d80eaad0>
-
-
-
-
-
-
-
-
-
-
[40]:
-
-
-
sp.add_delay_offset(
-    constant=-1463.7, # this is time zero
-    flip_delay_axis=True, # invert the direction of the delay axis
-    columns=['bam'], # use the bam to offset the values
-    weights=[-0.001], # bam is in fs, delay in ps
-    preserve_mean=True # preserve the mean of the delay axis
-)
-
-
-
-
-
-
-
-
-INFO - Adding delay offset to dataframe:
-INFO - Delay offset parameters:
-   Column[bam]: Weight=-0.001, Preserve Mean: True, Reductions: None.
-   Constant: -1463.7
-   Flip delay axis: True
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
-npartitions=62
-                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float64    float32    float32          float32          float32         float32           float32        float32        int8  float64
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-Dask Name: assign, 81 graph layers
-
-
-
-
[41]:
-
-
-
sp.dataframe # This has generated too many layers, there is room for improvement!
-
-
-
-
-
[41]:
-
-
-
-
Dask DataFrame Structure:
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
trainIdpulseIdelectronIddldPosXdldPosYdldTimeStepspulserSignAdcbamtimeStampmonochromatorPhotonEnergygmdBdadelayStagesampleBiastofVoltageextractorVoltageextractorCurrentcryoTemperaturesampleTemperaturedldTimeBinSizedldSectorIDenergy
npartitions=62
uint32int64int64float64float64float32float32float32float64float32float32float64float32float32float32float32float32float32float32int8float64
...............................................................
..................................................................
...............................................................
...............................................................
-
-
Dask Name: assign, 81 graph layers
-
-
-
-

bin in the corrected delay axis#

-
-
[42]:
-
-
-
axes = ['energy','delayStage']
-bins = [100,150]
-delay_start,delay_stop=1462.00,1464.85
-ranges = [[-3,2], [-1.1, 1.75]]
-res = sp.compute(bins=bins, axes=axes, ranges=ranges)
-
-
-
-
-
-
-
-
-
-
-
[43]:
-
-
-
fig,ax = plt.subplots(1,2,figsize=(6,2.25))
-res.plot(robust=True, ax=ax[0])
-bg = res.sel(delayStage=slice(-1,-0.2)).mean('delayStage')
-(res-bg).plot(robust=True, ax=ax[1])
-fig.tight_layout()
-
-
-
-
-
-
-
-
-
-

You may note some intensity variation along the delay axis. This comes mainly from inhomogeneous speed of the delay stage, and thus inequivalent amounts of time spent on every delay point. This can be corrected for by normalizing the data to the acquisition time per delay point:

-
-
[44]:
-
-
-
res = sp.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time="delayStage")
-fig,ax = plt.subplots(1,2,figsize=(6,2.25), layout='constrained')
-res.plot(robust=True, ax=ax[0])
-bg = res.sel(delayStage=slice(-1,-.2)).mean('delayStage')
-(res-bg).plot(robust=True, ax=ax[1])
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-INFO - Calculate normalization histogram for axis 'delayStage'...
-
-
-
-
-
-
-
-
-
-
[44]:
-
-
-
-
-<matplotlib.collections.QuadMesh at 0x7f90c7bceb90>
-
-
-
-
-
-
-
-
-
-
-

save parameters#

-

as before, we can save the parameters we just used in the config for the next run

-
-
[45]:
-
-
-
sp.save_delay_offsets()
-
-
-
-
-
-
-
-
-INFO - Saved delay offset parameters to "sed_config.yaml".
-
-
-
-
-
-

Run workflow entirely from config.#

-

Once all the calibrations are done, a new run can be loaded by simply calling all the calibration functions.

-
-
[46]:
-
-
-
from sed.core.config import load_config
-import numpy as np
-metadata = load_config(meta_path + "/44824_20230324T060430.json")
-
-# Fix metadata
-metadata["scientificMetadata"]["Laser"]["wavelength"]["value"] = float(metadata["scientificMetadata"]["Laser"]["wavelength"]["value"][:-2])
-metadata["scientificMetadata"]["Laser"]["energy"] = {"value": 1239.84/metadata["scientificMetadata"]["Laser"]["wavelength"]["value"], "unit": "eV"}
-metadata["scientificMetadata"]["Laser"]["polarization"] = [1, 1, 0, 0]
-metadata["scientificMetadata"]["Collection"]["field_aperture_x"] = float(metadata["scientificMetadata"]["Collection"]["field_aperture_x"])
-metadata["scientificMetadata"]["Collection"]["field_aperture_y"] = float(metadata["scientificMetadata"]["Collection"]["field_aperture_y"])
-metadata["pi"] = {"institute": "JGU Mainz"}
-metadata["proposer"] = {"institute": "TU Dortmund"}
-
-
-
-
-
[47]:
-
-
-
sp = SedProcessor(
-    runs=[44824,44825,44826,44827],
-    config=config_override,
-    system_config=config_file,
-    metadata = metadata,
-    collect_metadata=False,
-)
-
-
-
-
-
-
-
-
-INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/sed_config.yaml]
-INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
-INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
-INFO - Reading files: 0 new files of 62 total.
-loading complete in  0.16 s
-
-
-
-
[48]:
-
-
-
sp.add_jitter()
-sp.align_dld_sectors()
-sp.append_tof_ns_axis()
-sp.append_energy_axis()
-sp.add_energy_offset()
-sp.add_delay_offset()
-
-
-
-
-
-
-
-
-INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
-INFO - Aligning 8s sectors of dataframe
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID
-npartitions=62
-                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-Dask Name: assign, 16 graph layers
-INFO - Adding time-of-flight column in nanoseconds to dataframe.
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID  dldTime
-npartitions=62
-                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-Dask Name: assign, 22 graph layers
-INFO - Adding energy column to dataframe:
-INFO - Using energy calibration parameters generated on 03/05/2025, 23:17:42
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID  dldTime   energy
-npartitions=62
-                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64  float64
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
-Dask Name: assign, 37 graph layers
-INFO - Adding energy offset to dataframe:
-INFO - Using energy offset parameters generated on 03/05/2025, 23:17:44
-INFO - Energy offset parameters:
-   Column[monochromatorPhotonEnergy]: Weight=-1.0, Preserve Mean: True, Reductions: None.
-   Column[tofVoltage]: Weight=-1.0, Preserve Mean: True, Reductions: None.
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID  dldTime   energy
-npartitions=62
-                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64  float64
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
-Dask Name: assign, 67 graph layers
-INFO - Adding delay offset to dataframe:
-INFO - Using delay offset parameters generated on 03/05/2025, 23:19:35
-INFO - Delay offset parameters:
-   Constant: -1463.7
-   Flip delay axis: True
-   Column[bam]: Weight=-0.001, Preserve Mean: True, Reductions: None.
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID  dldTime   energy
-npartitions=62
-                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float64    float32    float32          float32          float32         float32           float32        float32        int8  float64  float64
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
-Dask Name: assign, 87 graph layers
-
-
-
-

Compute the results#

-
-
[49]:
-
-
-
axes = ['energy','delayStage']
-bins = [100,150]
-delay_start,delay_stop=1462.00,1464.85
-ranges = [[-5,2], [-1.1, 1.75]]
-res = sp.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time="delayStage")
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-INFO - Calculate normalization histogram for axis 'delayStage'...
-
-
-
-
-
-
-
-
-
-
[50]:
-
-
-
fig,ax = plt.subplots(1,2,figsize=(6,2.25), layout='constrained')
-res.plot(robust=True, ax=ax[0])
-bg = res.sel(delayStage=slice(-1,-.2)).mean('delayStage')
-(res-bg).plot(robust=True, ax=ax[1])
-
-
-
-
-
[50]:
-
-
-
-
-<matplotlib.collections.QuadMesh at 0x7f90d0993f70>
-
-
-
-
-
-
-
-
-
-
-
-

Save results#

-

binned data can now be saved as h5 or tiff. igor binaries soon to come if requested!

-
-
[51]:
-
-
-
sp.save('runs44824-27.h5')
-
-
-
-
-
-
-
-
-saving data to runs44824-27.h5
-Saved creation_date as string.
-Saved creation_date as string.
-Saved creation_date as string.
-Saving complete!
-
-
-
-
[52]:
-
-
-
sp.save('runs44824-27.tiff')
-
-
-
-
-
-
-
-
-Successfully saved runs44824-27.tiff
- Axes order: ['delayStage', 'energy', 'C', 'Y', 'X', 'S']
-
-
-
-
[53]:
-
-
-
sp.save("runs44824-27.nxs")
-
-
-
-
-
-
-
-
-Using mpes reader to convert the given files:
-• ../src/sed/config/NXmpes_config-HEXTOF.json
-The output file generated: runs44824-27.nxs.
-
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Binning of temperature-dependent ARPES data using time-stamped external temperature data#

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In this example, we pull some temperature-dependent ARPES data from Zenodo, which was recorded as a continuous temperature ramp. We then add the respective temperature information from the respective timestamp/temperature values to the dataframe, and bin the data as function of temperature For performance reasons, best store the data on a locally attached storage (no network drive). This can also be achieved transparently using the included MirrorUtil class.

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%load_ext autoreload
-%autoreload 2
-import numpy as np
-import matplotlib.pyplot as plt
-import os
-import glob
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-import sed
-from sed.dataset import dataset
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-%matplotlib widget
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Load Data#

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dataset.get("TaS2") # Put in Path to a storage of at least 20 GByte free space.
-data_path = dataset.dir
-scandir, caldir = dataset.subdirs # scandir contains the data, caldir contains the calibration files
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-# correct timestamps if not correct timezone set
-tzoffset = os.path.getmtime(scandir + '/Scan0121_1.h5') - 1594998158.0
-if tzoffset:
-    for file in glob.glob(scandir +'/*.h5'):
-        os.utime(file, (os.path.getmtime(file)-tzoffset, os.path.getmtime(file)-tzoffset))
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-INFO - Not downloading TaS2 data as it already exists at "/home/runner/work/sed/sed/docs/tutorial/datasets/TaS2".
-Set 'use_existing' to False if you want to download to a new location.
-INFO - Using existing data path for "TaS2": "/home/runner/work/sed/sed/docs/tutorial/datasets/TaS2"
-INFO - TaS2 data is already present.
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# create sed processor using the config file with time-stamps:
-sp = sed.SedProcessor(folder=scandir, user_config="../src/sed/config/mpes_example_config.yaml", system_config={}, time_stamps=True, verbose=True)
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-INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/sed_config.yaml]
-INFO - User config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/mpes_example_config.yaml]
-INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
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# Apply jittering to X, Y, t, ADC columns.
-sp.add_jitter()
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-INFO - add_jitter: Added jitter to columns ['X', 'Y', 't', 'ADC'].
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sp.bin_and_load_momentum_calibration(df_partitions=10, plane=33, width=3, apply=True)
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features = np.array([[337., 242.], [289., 327.], [187., 344.], [137., 258.], [189., 161.], [289., 158.], [236.0, 250.0]])
-sp.define_features(features=features, rotation_symmetry=6, include_center=True, apply=True)
-sp.generate_splinewarp(include_center=True)
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-INFO - Calculated thin spline correction based on the following landmarks:
-pouter_ord: [[137. 258.]
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-pcent: (236.0, 250.0)
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# Adjust pose alignment, using stored distortion correction
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-INFO - Applied translation with (xtrans=15.0, ytrans=8.0).
-INFO - Applied rotation with angle=-5.0.
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# Apply stored momentum correction
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-INFO - Adding corrected X/Y columns to dataframe:
-Calculating inverse deformation field, this might take a moment...
-INFO - Dask DataFrame Structure:
-                      X        Y        t      ADC timeStamps sampleBias       Xm       Ym
-npartitions=97
-                float64  float64  float64  float64    float64    float64  float64  float64
-                    ...      ...      ...      ...        ...        ...      ...      ...
-...                 ...      ...      ...      ...        ...        ...      ...      ...
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-Dask Name: apply_dfield, 492 graph layers
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# Apply stored config momentum calibration
-sp.apply_momentum_calibration()
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-INFO - Adding kx/ky columns to dataframe:
-INFO - Dask DataFrame Structure:
-                      X        Y        t      ADC timeStamps sampleBias       Xm       Ym       kx       ky
-npartitions=97
-                float64  float64  float64  float64    float64    float64  float64  float64  float64  float64
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-Dask Name: assign, 502 graph layers
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# Apply stored config energy correction
-sp.apply_energy_correction()
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-INFO - Applying energy correction to dataframe...
-INFO - Dask DataFrame Structure:
-                      X        Y        t      ADC timeStamps sampleBias       Xm       Ym       kx       ky       tm
-npartitions=97
-                float64  float64  float64  float64    float64    float64  float64  float64  float64  float64  float64
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-Dask Name: assign, 516 graph layers
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# Load energy calibration EDCs
-scans = np.arange(127,136)
-voltages = np.arange(21,12,-1)
-files = [caldir + r'/Scan' + str(num).zfill(4) + '_1.h5' for num in scans]
-sp.load_bias_series(data_files=files, normalize=True, biases=voltages, ranges=[(64000, 76000)])
-rg = (65500, 66000)
-sp.find_bias_peaks(ranges=rg, ref_id=5, infer_others=True, apply=True)
-sp.calibrate_energy_axis(ref_energy=-0.5, energy_scale="kinetic", method="lmfit")
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-INFO - Use feature ranges: [(67180.0, 67780.0), (66820.0, 67384.0), (66436.0, 67012.0), (66088.0, 66652.0), (65764.0, 66316.0), (65500.0, 66004.0), (65188.0, 65704.0), (64864.0, 65416.0), (64624.0, 65140.0)].
-INFO - Extracted energy features: [[6.76360000e+04 4.47140008e-01]
- [6.72520000e+04 4.41972464e-01]
- [6.68800000e+04 4.45905387e-01]
- [6.65320000e+04 4.43017632e-01]
- [6.62080000e+04 4.37122852e-01]
- [6.58960000e+04 4.28882003e-01]
- [6.55960000e+04 4.22135979e-01]
- [6.52960000e+04 4.13137674e-01]
- [6.50320000e+04 4.00443912e-01]].
-INFO - [[Fit Statistics]]
-    # fitting method   = leastsq
-    # function evals   = 163
-    # data points      = 9
-    # variables        = 3
-    chi-square         = 0.00179088
-    reduced chi-square = 2.9848e-04
-    Akaike info crit   = -70.7004554
-    Bayesian info crit = -70.1087817
-[[Variables]]
-    d:   1.09335629 +/- 0.06668048 (6.10%) (init = 1)
-    t0:  7.6176e-07 +/- 1.3448e-08 (1.77%) (init = 1e-06)
-    E0: -48.0855611 +/- 1.25773261 (2.62%) (init = -21)
-[[Correlations]] (unreported correlations are < 0.100)
-    C(d, t0)  = -0.9998
-    C(d, E0)  = -0.9993
-    C(t0, E0) = +0.9985
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# Apply stored config energy calibration
-#sp.append_energy_axis(bias_voltage=17)
-sp.append_energy_axis()
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-INFO - Adding energy column to dataframe:
-INFO - Using energy calibration parameters generated on 03/05/2025, 23:27:36
-INFO - Dask DataFrame Structure:
-                      X        Y        t      ADC timeStamps sampleBias       Xm       Ym       kx       ky       tm   energy
-npartitions=97
-                float64  float64  float64  float64    float64    float64  float64  float64  float64  float64  float64  float64
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-Dask Name: assign, 531 graph layers
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# add time-stamped temperature data
-# either, directly retrieve data from EPICS archiver instance (within FHI network),
-#sp.add_time_stamped_data(dest_column="T_B", archiver_channel="trARPES:Carving:TEMP-B")
-# or use externally provided timestamp/data pairs
-import h5py
-with h5py.File(f"{data_path}/temperature_data.h5", "r") as file:
-    data = file["temperatures"][()]
-    time_stamps = file["timestamps"][()]
-sp.add_time_stamped_data(dest_column="sample_temperature", time_stamps=time_stamps, data=data)
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-INFO - add_time_stamped_data: Added time-stamped data as column sample_temperature.
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# inspect calibrated event histogram
-axes = ['kx', 'ky', 'energy', 'sample_temperature']
-ranges = [[-3, 3], [-3, 3], [-6, 2], [10, 300]]
-sp.view_event_histogram(dfpid=80, axes=axes, ranges=ranges)
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Define the binning ranges and compute calibrated data volume#

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axes = ['kx', 'ky', 'energy', 'sample_temperature']
-bins = [100, 100, 300, 100]
-ranges = [[-2, 2], [-2, 2], [-6, 2], [20, 270]]
-res = sp.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time="sample_temperature")
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-INFO - Calculate normalization histogram for axis 'sample_temperature'...
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Some visualization:#

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fig, axs = plt.subplots(4, 1, figsize=(4, 12), constrained_layout=True)
-res.loc[{'energy':slice(-.1, 0)}].sum(axis=(2,3)).T.plot(ax=axs[0])
-res.loc[{'kx':slice(-.2, .2)}].sum(axis=(0,3)).T.plot(ax=axs[1])
-res.loc[{'ky':slice(-.2, .2)}].sum(axis=(1,3)).T.plot(ax=axs[2])
-res.loc[{'kx':slice(-.2, .2), 'ky':slice(-.2, .2), 'energy':slice(-2, 0.2)}].sum(axis=(0,1)).plot(ax=axs[3])
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# Inspect effect of histogram normalization
-fig, ax = plt.subplots(1,1)
-(sp._normalization_histogram/sp._normalization_histogram.sum()).plot(ax=ax)
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-plt.show()
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# Remaining fluctuations are an effect of the varying count rate throughout the scan
-plt.figure()
-rate, secs = sp.loader.get_count_rate()
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# Normalize for intensity around the Gamma point
-res_norm = res.copy()
-res_norm = res_norm/res_norm.loc[{'kx':slice(-.3, .3), 'ky':slice(-.3, .3)}].sum(axis=(0,1,2))
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fig, axs = plt.subplots(4, 1, figsize=(4, 12), constrained_layout=True)
-res_norm.loc[{'energy':slice(-.1, 0)}].sum(axis=(2,3)).T.plot(ax=axs[0])
-res_norm.loc[{'kx':slice(-.2, .2)}].sum(axis=(0,3)).T.plot(ax=axs[1])
-res_norm.loc[{'ky':slice(-.2, .2)}].sum(axis=(1,3)).T.plot(ax=axs[2])
-res_norm.loc[{'kx':slice(-.2, .2), 'ky':slice(-.2, .2), 'energy':slice(-2, 0.5)}].sum(axis=(0,1)).plot(ax=axs[3])
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# Lower Hubbard band intensity versus temperature
-plt.figure()
-res_norm.loc[{'kx':slice(-.2, .2), 'ky':slice(-.2, .2), 'energy':slice(-.6, 0.1)}].sum(axis=(0,1,2)).plot()
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Distortion correction with orthorhombic symmetry#

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This example showcases how to use the distortion correction workflow with landmarks that are not at symmetry-equivalent positions, such as for orthorhombic systems with different in-plane axis parameters.

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[1]:
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%load_ext autoreload
-%autoreload 2
-import numpy as np
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-from sed.dataset import dataset
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Load Data#

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For this example, we use the example data from WSe2. Even though the system is hexagonal, we will use it for demonstration.

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dataset.get("WSe2") # Put in Path to a storage of at least 20 GByte free space.
-data_path = dataset.dir # This is the path to the data
-scandir, _ = dataset.subdirs # scandir contains the data, _ contains the calibration files
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-INFO - Not downloading WSe2 data as it already exists at "/home/runner/work/sed/sed/docs/tutorial/datasets/WSe2".
-Set 'use_existing' to False if you want to download to a new location.
-INFO - Using existing data path for "WSe2": "/home/runner/work/sed/sed/docs/tutorial/datasets/WSe2"
-INFO - WSe2 data is already present.
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# create sed processor using the config file with time-stamps:
-sp = sed.SedProcessor(folder=scandir, user_config="../src/sed/config/mpes_example_config.yaml", system_config={}, time_stamps=True, verbose=True)
-sp.add_jitter()
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-INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/sed_config.yaml]
-INFO - User config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/mpes_example_config.yaml]
-INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
-WARNING - Entry "KTOF:Lens:Sample:V" for channel "sampleBias" not found. Skipping the channel.
-INFO - add_jitter: Added jitter to columns ['X', 'Y', 't', 'ADC'].
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Get slice for momentum calibration

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sp.bin_and_load_momentum_calibration(df_partitions=100, plane=203, width=10, apply=True)
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Feature definition:#

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We will describe the symmetry of the system with a 4-fold symmetry, and select two K points and two M points as symmetry points (as well as the Gamma point).

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features = np.array([[252., 355.], [361., 251.], [250., 144.], [156., 247.], [254., 247.]])
-sp.define_features(features=features, rotation_symmetry=4, include_center=True, apply=True)
-# Manual selection: Use a GUI tool to select peaks:
-# sp.define_features(rotation_symmetry=4, include_center=True)
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Spline-warp generation:#

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For the spline-warp generation, we need to tell the algorithm the difference in length of Gamma-K and Gamma-M. This we can do using the ascale parameter, which can either be a single number (the ratio), or a list of length rotation_symmetry defining the relative length of the respective vectors.

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gamma_m = np.pi/3.28
-gamma_k = 2/np.sqrt(3)*np.pi/3.28
-# Option 1: Ratio of the two distances:
-#sp.generate_splinewarp(include_center=True, ascale=gamma_k/gamma_m)
-# Option 2: List of distances:
-sp.generate_splinewarp(include_center=True, ascale=[gamma_m, gamma_k, gamma_m, gamma_k])
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-INFO - Calculated thin spline correction based on the following landmarks:
-pouter_ord: [[252. 355.]
- [361. 251.]
- [250. 144.]
- [156. 247.]]
-pcent: (254.0, 247.0)
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sp.pose_adjustment(xtrans=4, ytrans=7, angle=1, apply=True)
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-INFO - Applied translation with (xtrans=4.0, ytrans=7.0).
-INFO - Applied rotation with angle=1.0.
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-INFO - Adding corrected X/Y columns to dataframe:
-Calculating inverse deformation field, this might take a moment...
-INFO - Dask DataFrame Structure:
-                       X        Y        t      ADC timeStamps       Xm       Ym
-npartitions=100
-                 float64  float64  float64  float64    float64  float64  float64
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-Dask Name: apply_dfield, 206 graph layers
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Momentum calibration with orthorhombic axes#

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For the momentum calibration using symmetry points with non-equal distances, the option equiscale can be used:

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[9]:
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point_a = [256, 155]
-point_b = [370, 256]
-sp.calibrate_momentum_axes(point_a=point_a, point_b=point_b, k_coord_a=[0, gamma_m], k_coord_b=[gamma_k, 0], equiscale=False, apply=True)
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sp.apply_momentum_calibration()
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-INFO - Adding kx/ky columns to dataframe:
-INFO - Using momentum calibration parameters generated on 03/05/2025, 23:33:49
-INFO - Dask DataFrame Structure:
-                       X        Y        t      ADC timeStamps       Xm       Ym       kx       ky
-npartitions=100
-                 float64  float64  float64  float64    float64  float64  float64  float64  float64
-                     ...      ...      ...      ...        ...      ...      ...      ...      ...
-...                  ...      ...      ...      ...        ...      ...      ...      ...      ...
-                     ...      ...      ...      ...        ...      ...      ...      ...      ...
-                     ...      ...      ...      ...        ...      ...      ...      ...      ...
-Dask Name: assign, 216 graph layers
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Bin the top of the valence band#

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[11]:
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axes = ['kx', 'ky']
-bins = [100, 100]
-ranges = [[-2, 2], [-2, 2]]
-res = sp.compute(bins=bins, axes=axes, ranges=ranges, filter=[{"col":"t", "lower_bound": 66100, "upper_bound": 66300}])
-plt.figure()
-res.T.plot()
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Correct use of Jittering#

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This tutorial discusses the background and correct use of the jittering/dithering method implemented in the package.

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[1]:
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%load_ext autoreload
-%autoreload 2
-import matplotlib.pyplot as plt
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-import sed
-from sed.dataset import dataset
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-%matplotlib widget
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Load Data#

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[2]:
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dataset.get("WSe2") # Put in Path to a storage of at least 20 GByte free space.
-data_path = dataset.dir # This is the path to the data
-scandir, _ = dataset.subdirs # scandir contains the data, _ contains the calibration files
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-INFO - Not downloading WSe2 data as it already exists at "/home/runner/work/sed/sed/docs/tutorial/datasets/WSe2".
-Set 'use_existing' to False if you want to download to a new location.
-INFO - Using existing data path for "WSe2": "/home/runner/work/sed/sed/docs/tutorial/datasets/WSe2"
-INFO - WSe2 data is already present.
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[3]:
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# create sed processor using the config file:
-sp = sed.SedProcessor(folder=scandir, config="../src/sed/config/mpes_example_config.yaml", system_config={})
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-INFO - Configuration loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/mpes_example_config.yaml]
-INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/sed_config.yaml]
-INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
-WARNING - Entry "KTOF:Lens:Sample:V" for channel "sampleBias" not found. Skipping the channel.
-
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After loading, the dataframe contains the four columns X, Y, t, ADC, which have all integer values. They originate from a time-to-digital converter, and correspond to digital “bins”.

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XYtADC
00.00.00.00.0
1365.01002.070101.06317.0
2761.0818.075615.06316.0
3692.0971.066455.06317.0
4671.0712.073026.06317.0
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Let’s bin these data along the t dimension within a small range:

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[5]:
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axes = ['t']
-bins = [150]
-ranges = [[66000, 67000]]
-res01 = sp.compute(bins=bins, axes=axes, ranges=ranges, df_partitions=20)
-plt.figure()
-res01.plot()
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-[<matplotlib.lines.Line2D at 0x7f826f2ef970>]
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We notice some oscillation ontop of the data. These are re-binning artifacts, originating from a non-integer number of machine-bins per bin, as we can verify by binning with a different number of steps:

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[6]:
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axes = ['t']
-bins = [100]
-ranges = [[66000, 67000]]
-res02 = sp.compute(bins=bins, axes=axes, ranges=ranges, df_partitions=20)
-plt.figure()
-res01.plot(label="6.66/bin")
-res02.plot(label="10/bin")
-plt.legend()
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-<matplotlib.legend.Legend at 0x7f826f3c7e20>
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If we have a very detailed look, with step-sizes smaller than one, we see the digital nature of the original data behind this issue:

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[7]:
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axes = ['t']
-bins = [200]
-ranges = [[66600, 66605]]
-res11 = sp.compute(bins=bins, axes=axes, ranges=ranges, df_partitions=20)
-plt.figure()
-res11.plot()
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-[<matplotlib.lines.Line2D at 0x7f826f4997e0>]
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To mitigate this problem, we can add some randomness to the data, and re-distribute events into the gaps in-between bins. This is also termed dithering and e.g. known from image manipulation. The important factor is to add the right amount and right type of random distribution, to end up at a quasi-continuous uniform distribution, but not lose information.

-

We can use the add_jitter function for this. We can pass it the columns to add jitter to, and the amplitude of a uniform jitter. Importantly, this step should be taken in the very beginning as first step before any dataframe operations are added.

-

Let’s try with a value of 0.2 for the amplitude:

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[8]:
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df_backup = sp.dataframe
-sp.add_jitter(cols=["t"], amps=[0.2])
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-INFO - add_jitter: Added jitter to columns ['t'].
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We see that the t column is no longer integer-valued:

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sp.dataframe.head()
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XYtADC
00.00.00.0357970.0
1365.01002.070101.0268346317.0
2761.0818.075614.8804046316.0
3692.0971.066454.8096326317.0
4671.0712.073025.8784126317.0
-
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[10]:
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axes = ['t']
-bins = [200]
-ranges = [[66600, 66605]]
-res12 = sp.compute(bins=bins, axes=axes, ranges=ranges, df_partitions=20)
-plt.figure()
-res11.plot(label="not jittered")
-res12.plot(label="amplitude 0.2")
-plt.legend()
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-<matplotlib.legend.Legend at 0x7f826f2e3ee0>
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This is clearly not enough jitter to close the gaps. The ideal (and default) amplitude is 0.5, which exactly fills the gaps:

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sp.dataframe = df_backup
-sp.add_jitter(cols=["t"], amps=[0.5])
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-INFO - add_jitter: Added jitter to columns ['t'].
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[12]:
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axes = ['t']
-bins = [200]
-ranges = [[66600, 66605]]
-res13 = sp.compute(bins=bins, axes=axes, ranges=ranges, df_partitions=20)
-plt.figure()
-res11.plot(label="not jittered")
-res12.plot(label="amplitude 0.2")
-res13.plot(label="amplitude 0.5")
-plt.legend()
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This jittering fills the gaps, and produces a continuous uniform distribution. Let’s check again the longer-range binning that gave us the oscillations initially:

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[13]:
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axes = ['t']
-bins = [150]
-ranges = [[66000, 67000]]
-res03 = sp.compute(bins=bins, axes=axes, ranges=ranges, df_partitions=20)
-plt.figure()
-res01.plot(label="not jittered")
-res03.plot(label="jittered")
-plt.legend()
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-<matplotlib.legend.Legend at 0x7f8276bf0f70>
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Now, the artifacts are absent, and similarly will they be in any dataframe columns derived from a column jittered in such a way. Note that this only applies to data present in digital (i.e. machine-binned) format, and not to data that are intrinsically continuous.

-

Also note that too large or not well-aligned jittering amplitudes will

-
    -

  • deteriorate your resolution along the jittered axis

    • -

  • will not solve the problem entirely:

    • -
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[14]:
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sp.dataframe = df_backup
-sp.add_jitter(cols=["t"], amps=[0.7])
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-INFO - add_jitter: Added jitter to columns ['t'].
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[15]:
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axes = ['t']
-bins = [200]
-ranges = [[66600, 66605]]
-res14 = sp.compute(bins=bins, axes=axes, ranges=ranges, df_partitions=20)
-plt.figure()
-res13.plot(label="Amplitude 0.5")
-res14.plot(label="Amplitude 0.7")
-plt.legend()
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If the step-size of digitization is different from 1, the corresponding stepsize (half the distance between digitized values) can be adjusted as shown above.

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Also, alternatively also normally distributed noise can be added, which is less sensitive to the exact right amplitude, but will lead to mixing of neighboring voxels, and thus loss of resolution. Also, normally distributed noise is substantially more computation-intensive to generate. It can nevertheless be helpful in situations where e.g. the stepsize is non-uniform.

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[16]:
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sp.dataframe = df_backup
-sp.add_jitter(cols=["t"], amps=[0.7], jitter_type="normal")
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-INFO - add_jitter: Added jitter to columns ['t'].
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[17]:
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axes = ['t']
-bins = [200]
-ranges = [[66600, 66605]]
-res15 = sp.compute(bins=bins, axes=axes, ranges=ranges, df_partitions=20)
-plt.figure()
-res14.plot(label="Uniform, Amplitude 0.7")
-res15.plot(label="Normal, Amplitude 0.7")
-plt.legend()
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- - - - - - - - \ No newline at end of file diff --git a/sed/latest/tutorial/9_hextof_workflow_trXPD.html b/sed/latest/tutorial/9_hextof_workflow_trXPD.html deleted file mode 100644 index 616a4e4..0000000 --- a/sed/latest/tutorial/9_hextof_workflow_trXPD.html +++ /dev/null @@ -1,1378 +0,0 @@ - - - - - - - - - - - Tutorial for trXPD for the HEXTOF instrument at FLASH with background normalization — SED 1.0.0a1.dev19+gf1bb527 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Tutorial for trXPD for the HEXTOF instrument at FLASH with background normalization#

-
-

Preparation#

-
-

Import necessary libraries#

-
-
[1]:
-
-
-
%load_ext autoreload
-%autoreload 2
-
-from pathlib import Path
-import os
-
-from sed import SedProcessor
-from sed.dataset import dataset
-import xarray as xr
-
-%matplotlib widget
-import matplotlib.pyplot as plt
-from scipy.ndimage import gaussian_filter
-
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-
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Get data paths#

-

If it is your beamtime, you can access both read the raw data and write to processed directory. For the public data, you can not write to processed directory.

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The paths are such that if you are on Maxwell, it uses those. Otherwise data is downloaded in current directory from Zenodo: https://zenodo.org/records/12609441

-
-
[2]:
-
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-
beamtime_dir = "/asap3/flash/gpfs/pg2/2023/data/11019101" # on Maxwell
-if os.path.exists(beamtime_dir) and os.access(beamtime_dir, os.R_OK):
-    path = beamtime_dir + "/raw/hdf/offline/fl1user3"
-    buffer_path = beamtime_dir + "/processed/tutorial/"
-else:
-    # data_path can be defined and used to store the data in a specific location
-    dataset.get("W110") # Put in Path to a storage of at least 10 GByte free space.
-    path = dataset.dir
-    buffer_path = path + "/processed/"
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-
-INFO - Not downloading W110 data as it already exists at "/home/runner/work/sed/sed/docs/tutorial/datasets/W110".
-Set 'use_existing' to False if you want to download to a new location.
-INFO - Using existing data path for "W110": "/home/runner/work/sed/sed/docs/tutorial/datasets/W110"
-INFO - W110 data is already present.
-
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Config setup#

-

Here we get the path to the config file and setup the relevant directories. This can also be done directly in the config file.

-
-
[3]:
-
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-
# pick the default configuration file for hextof@FLASH
-config_file = Path('../src/sed/config/flash_example_config.yaml')
-assert config_file.exists()
-
-
-
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[4]:
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-
-
# here we setup a dictionary that will be used to override the path configuration
-config_override = {
-    "core": {
-        "beamtime_id": 11019101,
-        "paths": {
-            "raw": path,
-            "processed": buffer_path
-        },
-    },
-}
-
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-
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-

Prepare Energy Calibration#

-

Instead of making completely new energy calibration we can take existing values from the calibration made in the previous tutorial. This allows us to calibrate the conversion between the digital values of the dld and the energy.

-

For this we need to add all those parameters as a dictionary and use them during creation of the processor object.

-
-
[5]:
-
-
-
energy_cal = {
-    "energy": {
-        "calibration": {
-            "E0": -132.47100427179566,
-            "creation_date": '2024-11-30T20:47:03.305244',
-            "d": 0.8096677238144319,
-            "energy_scale": "kinetic",
-            "t0": 4.0148196706891397e-07,
-        },
-        "offsets":{
-            "constant": 1,
-            "creation_date": '2024-11-30T21:17:07.762199',
-            "columns": {
-                "monochromatorPhotonEnergy": {
-                    "preserve_mean": True,
-                    "weight": -1,
-                },
-                "tofVoltage": {
-                    "preserve_mean": True,
-                    "weight": -1,
-                },
-            },
-        },
-    },
-}
-
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-

Read data#

-

Now we can use those parameters and load our trXPD data using additional config file

-
-
[6]:
-
-
-
run_number = 44498
-sp_44498 = SedProcessor(runs=[run_number], folder_config=energy_cal, config=config_override, system_config=config_file, verbose=True)
-sp_44498.add_jitter()
-
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-INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
-INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
-INFO - Reading files: 0 new files of 14 total.
-loading complete in  0.15 s
-INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
-
-
-

We can inspect dataframe right after data readout

-
-
[7]:
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-
sp_44498.dataframe.head()
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trainIdpulseIdelectronIddldPosXdldPosYdldTimeStepspulserSignAdcbamtimeStampmonochromatorPhotonEnergygmdBdadelayStagesampleBiastofVoltageextractorVoltageextractorCurrentcryoTemperaturesampleTemperaturedldTimeBinSizedldSectorID
0162802283010651.191991895.1919914595.19199132919.0-6187.968751.677563e+09116.8582992.5214571448.60205172.99590319.9953566029.299805-0.07385749.20878.9899980.0205763
1162802283011651.411319888.4113194596.41131932919.0-6187.968751.677563e+09116.8582992.5214571448.60205172.99590319.9953566029.299805-0.07385749.20878.9899980.0205760
2162802283050682.321267672.3212674423.32126732914.0-6170.156251.677563e+09116.8582992.5214571448.60205172.99590319.9953566029.299805-0.07385749.20878.9899980.0205766
3162802283051684.995221657.9952214424.99522132914.0-6170.156251.677563e+09116.8582992.5214571448.60205172.99590319.9953566029.299805-0.07385749.20878.9899980.0205763
4162802283052670.038803687.0388034424.03880332914.0-6170.156251.677563e+09116.8582992.5214571448.60205172.99590319.9953566029.299805-0.07385749.20878.9899980.0205765
-
-
-

Now we will do energy calibration, add energy offset, jittering and dld sectors alignment

-
-
[8]:
-
-
-
sp_44498.align_dld_sectors()
-sp_44498.append_energy_axis()
-sp_44498.add_energy_offset()
-
-
-
-
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-
-INFO - Aligning 8s sectors of dataframe
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID
-npartitions=14
-                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
-Dask Name: assign, 16 graph layers
-INFO - Adding energy column to dataframe:
-INFO - Using energy calibration parameters generated on 11/30/2024, 20:47:03
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
-npartitions=14
-                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-Dask Name: assign, 31 graph layers
-INFO - Adding energy offset to dataframe:
-INFO - Using energy offset parameters generated on 11/30/2024, 21:17:07
-INFO - Energy offset parameters:
-   Constant: 1.0
-   Column[monochromatorPhotonEnergy]: Weight=-1.0, Preserve Mean: True, Reductions: None.
-   Column[tofVoltage]: Weight=-1.0, Preserve Mean: True, Reductions: None.
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
-npartitions=14
-                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
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-Dask Name: assign, 64 graph layers
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[9]:
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sp_44498.attributes.metadata['energy_calibration']
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[9]:
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-
-{'applied': True,
- 'calibration': {'creation_date': datetime.datetime(2024, 11, 30, 20, 47, 3, 305244),
-  'd': 0.8096677238144319,
-  't0': 4.0148196706891397e-07,
-  'E0': -132.47100427179566,
-  'energy_scale': 'kinetic',
-  'calib_type': 'fit',
-  'fit_function': '(a0/(x0-a1))**2 + a2',
-  'coefficients': array([ 8.09667724e-01,  4.01481967e-07, -1.32471004e+02]),
-  'axis': 0.0},
- 'tof': 0.0}
-
-
-

We can do the SASE jitter correction, using information from the bam column and do calibration of the pump-probe delay axis, we need to shift the delay stage values to center the pump-probe-time overlap time zero.

-
-
[10]:
-
-
-
sp_44498.add_delay_offset(
-    constant=-1448, # this is time zero position determined from side band fit
-    flip_delay_axis=True, # invert the direction of the delay axis
-    columns=['bam'], # use the bam to offset the values
-    weights=[-0.001], # bam is in fs, delay in ps
-    preserve_mean=True # preserve the mean of the delay axis to keep t0 position
-)
-
-
-
-
-
-
-
-
-INFO - Adding delay offset to dataframe:
-INFO - Delay offset parameters:
-   Column[bam]: Weight=-0.001, Preserve Mean: True, Reductions: None.
-   Constant: -1448
-   Flip delay axis: True
-INFO - Dask DataFrame Structure:
-               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
-npartitions=14
-                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float64    float32    float32          float32          float32         float32           float32        float32        int8  float64
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
-Dask Name: assign, 84 graph layers
-
-
-
-

bin in the calibrated energy and corrected delay axis#

-

Visualize trXPS data

-
-
[11]:
-
-
-
axes = ['energy', 'delayStage']
-ranges = [[-37.5,-27.5], [-1.5,1.5]]
-bins = [200,60]
-res_corr = sp_44498.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time="delayStage")
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-INFO - Calculate normalization histogram for axis 'delayStage'...
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[12]:
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fig,ax = plt.subplots(1,2,figsize=(6,2.25), layout='constrained')
-fig.suptitle(f"Run {run_number}: W 4f, side bands")
-res_corr.plot(robust=True, ax=ax[0], cmap='terrain')
-ax[0].set_title('raw')
-bg = res_corr.sel(delayStage=slice(-1.3,-1.0)).mean('delayStage')
-(res_corr-bg).plot(robust=True, ax=ax[1])
-ax[1].set_title('difference')
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[12]:
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-Text(0.5, 1.0, 'difference')
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XPD from W4f core level#

-

Now we can bin not only in energy but also in both momentum directions to get XPD patterns of different core level line of tungsten.

-
-
[13]:
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axes = ['energy', 'dldPosX', 'dldPosY']
-ranges = [[-38,-28], [420,900], [420,900]]
-bins = [100,240,240]
-res_kx_ky = sp_44498.compute(bins=bins, axes=axes, ranges=ranges)
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[14]:
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## EDC and integration region for XPD
-plt.figure()
-res_kx_ky.mean(('dldPosX', 'dldPosY')).plot()
-plt.vlines([-30.3,-29.9], 0, 2.4, color='r', linestyles='dashed')
-plt.vlines([-31.4,-31.2], 0, 2.4, color='orange', linestyles='dashed')
-plt.vlines([-33.6,-33.4], 0, 2.4, color='g', linestyles='dashed')
-plt.vlines([-37.0,-36.0], 0, 2.4, color='b', linestyles='dashed')
-plt.title('EDC and integration regions for XPD')
-plt.show()
-
-## XPD plots
-fig,ax = plt.subplots(2,2,figsize=(6,4.7), layout='constrained')
-res_kx_ky.sel(energy=slice(-30.3,-29.9)).mean('energy').plot(robust=True, ax=ax[0,0], cmap='terrain')
-ax[0,0].set_title("XPD of $1^{st}$ order sidebands")
-res_kx_ky.sel(energy=slice(-31.4,-31.2)).mean('energy').plot(robust=True, ax=ax[0,1], cmap='terrain')
-ax[0,1].set_title("XPD of W4f 7/2 peak")
-res_kx_ky.sel(energy=slice(-33.6,-33.4)).mean('energy').plot(robust=True, ax=ax[1,0], cmap='terrain')
-ax[1,0].set_title("XPD of W4f 5/2 peak")
-res_kx_ky.sel(energy=slice(-37.0,-36.0)).mean('energy').plot(robust=True, ax=ax[1,1], cmap='terrain')
-ax[1,1].set_title("XPD of W5p 3/2 peak")
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[14]:
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-Text(0.5, 1.0, 'XPD of W5p 3/2 peak')
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As we can see there is some structure visible, but it looks very similar to each other. We probably have to do some normalization to remove the detector structure/artefacts. The best option is to divide by a flat-field image. The flat-field image can be obtained from a sample that shows no structure under identical measurement conditions. Unfortunately, we don’t have such a flat-field image.

-

In this case, we can make a flat-field image from the actual dataset using several different approaches.

-

As a first option, we can integrate in energy over the whole region and use this image as a background. Additionally, we introduce the Gaussian Blur for comparison.

-
-
[15]:
-
-
-
## Background image
-bgd = res_kx_ky.mean(('energy'))
-
-## Apply Gaussian Blur to background image
-bgd_blur = xr.apply_ufunc(gaussian_filter, bgd, 15)
-
-fig,ax = plt.subplots(1,2,figsize=(6,2.7), layout='constrained')
-bgd.plot(robust=True, cmap='terrain', ax=ax[0])
-ax[0].set_title('Background image')
-bgd_blur.plot(cmap='terrain', ax=ax[1])
-ax[1].set_title('Gaussian Blur of background image')
-plt.show()
-
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[16]:
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## XPD normalized by background image
-fig,ax = plt.subplots(2,2,figsize=(6,4.7), layout='constrained')
-(res_kx_ky/bgd).sel(energy=slice(-30.3,-29.9)).mean('energy').plot(robust=True, ax=ax[0,0], cmap='terrain')
-(res_kx_ky/bgd).sel(energy=slice(-31.4,-31.2)).mean('energy').plot(robust=True, ax=ax[0,1], cmap='terrain')
-(res_kx_ky/bgd).sel(energy=slice(-33.6,-33.4)).mean('energy').plot(robust=True, ax=ax[1,0], cmap='terrain')
-(res_kx_ky/bgd).sel(energy=slice(-37.0,-36.0)).mean('energy').plot(robust=True, ax=ax[1,1], cmap='terrain')
-fig.suptitle(f'Run {run_number}: XPD patterns after background normalization',fontsize='11')
-
-## XPD normalized by Gaussian-blurred background image
-fig,ax = plt.subplots(2,2,figsize=(6,4.7), layout='constrained')
-(res_kx_ky/bgd_blur).sel(energy=slice(-30.3,-29.9)).mean('energy').plot(robust=True, ax=ax[0,0], cmap='terrain')
-(res_kx_ky/bgd_blur).sel(energy=slice(-31.4,-31.2)).mean('energy').plot(robust=True, ax=ax[0,1], cmap='terrain')
-(res_kx_ky/bgd_blur).sel(energy=slice(-33.6,-33.4)).mean('energy').plot(robust=True, ax=ax[1,0], cmap='terrain')
-(res_kx_ky/bgd_blur).sel(energy=slice(-37.0,-36.0)).mean('energy').plot(robust=True, ax=ax[1,1], cmap='terrain')
-fig.suptitle(f'Run {run_number}: XPD patterns after Gaussian-blurred background normalization',fontsize='11')
-
-## XPD normalized by Gaussian-blurred background image and blurred to improve contrast
-fig,ax = plt.subplots(2,2,figsize=(6,4.7), layout='constrained')
-(xr.apply_ufunc(gaussian_filter, res_kx_ky/bgd_blur, 1)).sel(energy=slice(-30.3,-29.9)).mean('energy').plot(robust=True, ax=ax[0,0], cmap='terrain')
-(xr.apply_ufunc(gaussian_filter, res_kx_ky/bgd_blur, 1)).sel(energy=slice(-31.4,-31.2)).mean('energy').plot(robust=True, ax=ax[0,1], cmap='terrain')
-(xr.apply_ufunc(gaussian_filter, res_kx_ky/bgd_blur, 1)).sel(energy=slice(-33.6,-33.4)).mean('energy').plot(robust=True, ax=ax[1,0], cmap='terrain')
-(xr.apply_ufunc(gaussian_filter, res_kx_ky/bgd_blur, 1)).sel(energy=slice(-37.0,-36.0)).mean('energy').plot(robust=True, ax=ax[1,1], cmap='terrain')
-fig.suptitle(f'Run {run_number}: resulting Gaussian-blurred XPD patterns',fontsize='11')
-
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[16]:
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-Text(0.5, 0.98, 'Run 44498: resulting Gaussian-blurred XPD patterns')
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Sometimes, after this division, you may not be happy with intensity distribution. Thus, other option for background correction is to duplicate the XPD pattern, apply large Gaussian blurring that eliminates the fine structures in the XPD pattern. Then divide the XPD pattern by its blurred version. This process sometimes enhances the visibility of the fine structures a lot.

-
-
[17]:
-
-
-
## XPD normalized by Gaussian-blurred background image
-
-### Define integration regions for XPD
-SB = res_kx_ky.sel(energy=slice(-30.3,-29.9)).mean('energy')
-W_4f_7 = res_kx_ky.sel(energy=slice(-31.4,-31.2)).mean('energy')
-W_4f_5 = res_kx_ky.sel(energy=slice(-33.6,-33.4)).mean('energy')
-W_5p = res_kx_ky.sel(energy=slice(-37.0,-36.0)).mean('energy')
-
-### Make corresponding Gaussian Blur background
-SB_blur = xr.apply_ufunc(gaussian_filter, SB, 15)
-W_4f_7_blur = xr.apply_ufunc(gaussian_filter, W_4f_7, 15)
-W_4f_5_blur = xr.apply_ufunc(gaussian_filter, W_4f_5, 15)
-W_5p_blur = xr.apply_ufunc(gaussian_filter, W_5p, 15)
-
-### Visualize results
-fig,ax = plt.subplots(2,2,figsize=(6,4.7), layout='constrained')
-(SB/SB_blur).plot(robust=True, ax=ax[0,0], cmap='terrain')
-(W_4f_7/W_4f_7_blur).plot(robust=True, ax=ax[0,1], cmap='terrain')
-(W_4f_5/W_4f_5_blur).plot(robust=True, ax=ax[1,0], cmap='terrain')
-(W_5p/W_5p_blur).plot(robust=True, ax=ax[1,1], cmap='terrain')
-fig.suptitle(f'Run {run_number}: XPD patterns after Gaussian Blur normalization',fontsize='11')
-
-### Apply Gaussian Blur to resulted images to improve contrast
-SB_norm = xr.apply_ufunc(gaussian_filter, SB/SB_blur, 1)
-W_4f_7_norm = xr.apply_ufunc(gaussian_filter, W_4f_7/W_4f_7_blur, 1)
-W_4f_5_norm = xr.apply_ufunc(gaussian_filter, W_4f_5/W_4f_5_blur, 1)
-W_5p_norm = xr.apply_ufunc(gaussian_filter, W_5p/W_5p_blur, 1)
-
-### Visualize results
-fig,ax = plt.subplots(2,2,figsize=(6,4.7), layout='constrained')
-SB_norm.plot(robust=True, ax=ax[0,0], cmap='terrain')
-W_4f_7_norm.plot(robust=True, ax=ax[0,1], cmap='terrain')
-W_4f_5_norm.plot(robust=True, ax=ax[1,0], cmap='terrain')
-W_5p_norm.plot(robust=True, ax=ax[1,1], cmap='terrain')
-fig.suptitle(f'Run {run_number}: XPD patterns after Gauss Blur normalization',fontsize='11')
-
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[17]:
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-Text(0.5, 0.98, 'Run 44498: XPD patterns after Gauss Blur normalization')
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Third option for background normalization is to use the simultaneously acquired pre-core level region. As an example for W4f 7/2 peak, we define a region on the high energy side of it and integrate in energy to use as a background

-
-
[18]:
-
-
-
### Define peak and background region on the high energy side of the peak
-W_4f_7 = res_kx_ky.sel(energy=slice(-31.4,-31.2)).mean('energy')
-W_4f_7_bgd = res_kx_ky.sel(energy=slice(-32.0,-31.8)).mean('energy')
-
-### Make normalization by background, add Gaussian Blur to the resulting image
-W_4f_7_nrm1 = W_4f_7/(W_4f_7_bgd+W_4f_7_bgd.max()*0.00001)
-W_4f_7_nrm1_blur = xr.apply_ufunc(gaussian_filter, W_4f_7_nrm1, 1)
-
-### Add Gaussian Blur to the background image, normalize by it and add Gaussian Blur to the resulting image
-W_4f_7_bgd_blur = xr.apply_ufunc(gaussian_filter, W_4f_7_bgd, 15)
-W_4f_7_nrm2 = W_4f_7/W_4f_7_bgd_blur
-W_4f_7_nrm2_blur = xr.apply_ufunc(gaussian_filter, W_4f_7_nrm2, 1)
-
-### Visualize all steps
-fig,ax = plt.subplots(4,2,figsize=(6,8), layout='constrained')
-W_4f_7.plot(robust=True, ax=ax[0,0], cmap='terrain')
-W_4f_7_bgd.plot(robust=True, ax=ax[0,1], cmap='terrain')
-W_4f_7_nrm1.plot(robust=True, ax=ax[1,0], cmap='terrain')
-W_4f_7_nrm1_blur.plot(robust=True, ax=ax[1,1], cmap='terrain')
-W_4f_7_bgd_blur.plot(robust=True, ax=ax[2,0], cmap='terrain')
-W_4f_7_nrm2.plot(robust=True, ax=ax[2,1], cmap='terrain')
-W_4f_7_nrm2_blur.plot(robust=True, ax=ax[3,0], cmap='terrain')
-fig.suptitle(f'Run {run_number}: XPD patterns of W4f7/2 with pre-core level normalization',fontsize='11')
-
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fig,ax = plt.subplots(1,3,figsize=(6,2), layout='constrained')
-(xr.apply_ufunc(gaussian_filter, res_kx_ky/bgd_blur, 1)).sel(energy=slice(-31.4,-31.2)).mean('energy').plot(robust=True, ax=ax[0], cmap='terrain')
-W_4f_7_norm.plot(robust=True, ax=ax[1], cmap='terrain')
-W_4f_7_nrm2_blur.plot(robust=True, ax=ax[2], cmap='terrain')
-fig.suptitle(f'Run {run_number}: comparison of different normalizations\nof XPD pattern for W4f 7/2 peak with Gaussian Blur',fontsize='11')
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- - - - - - - - \ No newline at end of file diff --git a/sed/stable b/sed/stable index be5bf2a..60453e6 120000 --- a/sed/stable +++ b/sed/stable @@ -1 +1 @@ -v0.4.1 \ No newline at end of file +v1.0.0 \ No newline at end of file diff --git a/sed/switcher.json b/sed/switcher.json index f8a8e48..6e8ea2d 100644 --- a/sed/switcher.json +++ b/sed/switcher.json @@ -1,13 +1,13 @@ [ { "name": "latest", - "version": "1.0.0a1.dev19+gf1bb527", - "url": "https://opencompes.github.io/docs/sed/latest" + "version": "1.0.0", + "url": "https://opencompes.github.io/docs/sed/v1.0.0" }, { "name": "stable", - "version": "0.4.1", - "url": "https://opencompes.github.io/docs/sed/v0.4.1", + "version": "1.0.0", + "url": "https://opencompes.github.io/docs/sed/v1.0.0", "preferred": "true" }, { diff --git a/sed/latest/_modules/index.html b/sed/v1.0.0/_modules/index.html similarity index 97% rename from sed/latest/_modules/index.html rename to sed/v1.0.0/_modules/index.html index 1ba289e..2c1e184 100644 --- a/sed/latest/_modules/index.html +++ b/sed/v1.0.0/_modules/index.html @@ -7,7 +7,7 @@ - Overview: module code — SED 1.0.0a1.dev19+gf1bb527 documentation + Overview: module code — SED 1.0.0 documentation @@ -37,7 +37,7 @@ - + @@ -46,7 +46,7 @@ @@ -54,7 +54,7 @@ - + @@ -116,7 +116,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

diff --git a/sed/latest/_modules/sed/binning/binning.html b/sed/v1.0.0/_modules/sed/binning/binning.html similarity index 99% rename from sed/latest/_modules/sed/binning/binning.html rename to sed/v1.0.0/_modules/sed/binning/binning.html index 26621a3..2a19fd4 100644 --- a/sed/latest/_modules/sed/binning/binning.html +++ b/sed/v1.0.0/_modules/sed/binning/binning.html @@ -7,7 +7,7 @@ - sed.binning.binning — SED 1.0.0a1.dev19+gf1bb527 documentation + sed.binning.binning — SED 1.0.0 documentation @@ -37,7 +37,7 @@ - + @@ -46,7 +46,7 @@ @@ -54,7 +54,7 @@ - + @@ -116,7 +116,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

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SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

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SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

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SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

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SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

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SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

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SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

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+

SED 1.0.0 documentation

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SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

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SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

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SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

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+

SED 1.0.0 documentation

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+

SED 1.0.0 documentation

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+

SED 1.0.0 documentation

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+

SED 1.0.0 documentation

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SED 1.0.0 documentation

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SED 1.0.0 documentation

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SED 1.0.0 documentation

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SED 1.0.0 documentation

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SED 1.0.0 documentation

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SED 1.0.0 documentation

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SED 1.0.0 documentation

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SED 1.0.0 documentation

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SED 1.0.0 documentation

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a/sed/latest/_sources/tutorial/3_metadata_collection_and_export_to_NeXus.ipynb.txt b/sed/v1.0.0/_sources/tutorial/3_metadata_collection_and_export_to_NeXus.ipynb.txt similarity index 100% rename from sed/latest/_sources/tutorial/3_metadata_collection_and_export_to_NeXus.ipynb.txt rename to sed/v1.0.0/_sources/tutorial/3_metadata_collection_and_export_to_NeXus.ipynb.txt diff --git a/sed/latest/_sources/tutorial/4_hextof_workflow.ipynb.txt b/sed/v1.0.0/_sources/tutorial/4_hextof_workflow.ipynb.txt similarity index 100% rename from sed/latest/_sources/tutorial/4_hextof_workflow.ipynb.txt rename to sed/v1.0.0/_sources/tutorial/4_hextof_workflow.ipynb.txt diff --git a/sed/latest/_sources/tutorial/5_sxp_workflow.ipynb.txt b/sed/v1.0.0/_sources/tutorial/5_sxp_workflow.ipynb.txt similarity index 100% rename from sed/latest/_sources/tutorial/5_sxp_workflow.ipynb.txt rename to sed/v1.0.0/_sources/tutorial/5_sxp_workflow.ipynb.txt diff --git a/sed/latest/_sources/tutorial/6_binning_with_time-stamped_data.ipynb.txt b/sed/v1.0.0/_sources/tutorial/6_binning_with_time-stamped_data.ipynb.txt similarity index 100% rename from sed/latest/_sources/tutorial/6_binning_with_time-stamped_data.ipynb.txt rename to sed/v1.0.0/_sources/tutorial/6_binning_with_time-stamped_data.ipynb.txt diff --git a/sed/latest/_sources/tutorial/7_correcting_orthorhombic_symmetry.ipynb.txt b/sed/v1.0.0/_sources/tutorial/7_correcting_orthorhombic_symmetry.ipynb.txt similarity index 100% rename from sed/latest/_sources/tutorial/7_correcting_orthorhombic_symmetry.ipynb.txt rename to sed/v1.0.0/_sources/tutorial/7_correcting_orthorhombic_symmetry.ipynb.txt diff --git a/sed/latest/_sources/tutorial/8_jittering_tutorial.ipynb.txt b/sed/v1.0.0/_sources/tutorial/8_jittering_tutorial.ipynb.txt similarity index 100% rename from sed/latest/_sources/tutorial/8_jittering_tutorial.ipynb.txt rename to sed/v1.0.0/_sources/tutorial/8_jittering_tutorial.ipynb.txt diff --git a/sed/latest/_sources/tutorial/9_hextof_workflow_trXPD.ipynb.txt b/sed/v1.0.0/_sources/tutorial/9_hextof_workflow_trXPD.ipynb.txt similarity index 100% rename from sed/latest/_sources/tutorial/9_hextof_workflow_trXPD.ipynb.txt rename to sed/v1.0.0/_sources/tutorial/9_hextof_workflow_trXPD.ipynb.txt diff --git a/sed/latest/_sources/user_guide/config.md.txt b/sed/v1.0.0/_sources/user_guide/config.md.txt similarity index 100% rename from sed/latest/_sources/user_guide/config.md.txt rename to sed/v1.0.0/_sources/user_guide/config.md.txt diff --git a/sed/latest/_sources/user_guide/index.md.txt b/sed/v1.0.0/_sources/user_guide/index.md.txt similarity index 100% rename from sed/latest/_sources/user_guide/index.md.txt rename to sed/v1.0.0/_sources/user_guide/index.md.txt diff --git a/sed/latest/_sources/user_guide/installation.md.txt b/sed/v1.0.0/_sources/user_guide/installation.md.txt similarity index 100% rename from sed/latest/_sources/user_guide/installation.md.txt rename to sed/v1.0.0/_sources/user_guide/installation.md.txt diff --git a/sed/latest/_sources/workflows/index.md.txt b/sed/v1.0.0/_sources/workflows/index.md.txt similarity index 100% rename from sed/latest/_sources/workflows/index.md.txt rename to sed/v1.0.0/_sources/workflows/index.md.txt diff --git a/sed/latest/_static/basic.css b/sed/v1.0.0/_static/basic.css similarity index 100% rename from sed/latest/_static/basic.css rename to sed/v1.0.0/_static/basic.css diff --git a/sed/latest/_static/doctools.js b/sed/v1.0.0/_static/doctools.js similarity index 100% rename from sed/latest/_static/doctools.js rename to sed/v1.0.0/_static/doctools.js diff --git a/sed/latest/_static/documentation_options.js b/sed/v1.0.0/_static/documentation_options.js similarity index 88% rename from sed/latest/_static/documentation_options.js rename to sed/v1.0.0/_static/documentation_options.js index 1cc336c..89435bb 100644 --- a/sed/latest/_static/documentation_options.js +++ b/sed/v1.0.0/_static/documentation_options.js @@ -1,5 +1,5 @@ const DOCUMENTATION_OPTIONS = { - VERSION: '1.0.0a1.dev19+gf1bb527', + VERSION: '1.0.0', LANGUAGE: 'en', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/sed/latest/_static/file.png b/sed/v1.0.0/_static/file.png similarity index 100% rename from sed/latest/_static/file.png rename to sed/v1.0.0/_static/file.png diff --git a/sed/latest/_static/language_data.js b/sed/v1.0.0/_static/language_data.js similarity index 100% rename from sed/latest/_static/language_data.js rename to sed/v1.0.0/_static/language_data.js diff --git a/sed/latest/_static/minus.png b/sed/v1.0.0/_static/minus.png similarity index 100% rename from sed/latest/_static/minus.png rename to sed/v1.0.0/_static/minus.png diff --git a/sed/latest/_static/nbsphinx-broken-thumbnail.svg b/sed/v1.0.0/_static/nbsphinx-broken-thumbnail.svg similarity index 100% rename from sed/latest/_static/nbsphinx-broken-thumbnail.svg rename to 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index 100% rename from sed/latest/_static/pygments.css rename to sed/v1.0.0/_static/pygments.css diff --git a/sed/latest/_static/scripts/bootstrap.js b/sed/v1.0.0/_static/scripts/bootstrap.js similarity index 100% rename from sed/latest/_static/scripts/bootstrap.js rename to sed/v1.0.0/_static/scripts/bootstrap.js diff --git a/sed/latest/_static/scripts/bootstrap.js.LICENSE.txt b/sed/v1.0.0/_static/scripts/bootstrap.js.LICENSE.txt similarity index 100% rename from sed/latest/_static/scripts/bootstrap.js.LICENSE.txt rename to sed/v1.0.0/_static/scripts/bootstrap.js.LICENSE.txt diff --git a/sed/latest/_static/scripts/bootstrap.js.map b/sed/v1.0.0/_static/scripts/bootstrap.js.map similarity index 100% rename from sed/latest/_static/scripts/bootstrap.js.map rename to sed/v1.0.0/_static/scripts/bootstrap.js.map diff --git a/sed/latest/_static/scripts/fontawesome.js b/sed/v1.0.0/_static/scripts/fontawesome.js similarity index 100% rename from sed/latest/_static/scripts/fontawesome.js rename 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sed/latest/_static/scripts/pydata-sphinx-theme.js.map rename to sed/v1.0.0/_static/scripts/pydata-sphinx-theme.js.map diff --git a/sed/latest/_static/searchtools.js b/sed/v1.0.0/_static/searchtools.js similarity index 100% rename from sed/latest/_static/searchtools.js rename to sed/v1.0.0/_static/searchtools.js diff --git a/sed/latest/_static/sphinx_highlight.js b/sed/v1.0.0/_static/sphinx_highlight.js similarity index 100% rename from sed/latest/_static/sphinx_highlight.js rename to sed/v1.0.0/_static/sphinx_highlight.js diff --git a/sed/latest/_static/styles/pydata-sphinx-theme.css b/sed/v1.0.0/_static/styles/pydata-sphinx-theme.css similarity index 100% rename from sed/latest/_static/styles/pydata-sphinx-theme.css rename to sed/v1.0.0/_static/styles/pydata-sphinx-theme.css diff --git a/sed/latest/_static/styles/pydata-sphinx-theme.css.map b/sed/v1.0.0/_static/styles/pydata-sphinx-theme.css.map similarity index 100% rename from sed/latest/_static/styles/pydata-sphinx-theme.css.map 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b/sed/v1.0.0/_static/vendor/fontawesome/webfonts/fa-regular-400.ttf similarity index 100% rename from sed/latest/_static/vendor/fontawesome/webfonts/fa-regular-400.ttf rename to sed/v1.0.0/_static/vendor/fontawesome/webfonts/fa-regular-400.ttf diff --git a/sed/latest/_static/vendor/fontawesome/webfonts/fa-regular-400.woff2 b/sed/v1.0.0/_static/vendor/fontawesome/webfonts/fa-regular-400.woff2 similarity index 100% rename from sed/latest/_static/vendor/fontawesome/webfonts/fa-regular-400.woff2 rename to sed/v1.0.0/_static/vendor/fontawesome/webfonts/fa-regular-400.woff2 diff --git a/sed/latest/_static/vendor/fontawesome/webfonts/fa-solid-900.ttf b/sed/v1.0.0/_static/vendor/fontawesome/webfonts/fa-solid-900.ttf similarity index 100% rename from sed/latest/_static/vendor/fontawesome/webfonts/fa-solid-900.ttf rename to sed/v1.0.0/_static/vendor/fontawesome/webfonts/fa-solid-900.ttf diff --git a/sed/latest/_static/vendor/fontawesome/webfonts/fa-solid-900.woff2 b/sed/v1.0.0/_static/vendor/fontawesome/webfonts/fa-solid-900.woff2 similarity index 100% rename from sed/latest/_static/vendor/fontawesome/webfonts/fa-solid-900.woff2 rename to sed/v1.0.0/_static/vendor/fontawesome/webfonts/fa-solid-900.woff2 diff --git a/sed/latest/_static/webpack-macros.html b/sed/v1.0.0/_static/webpack-macros.html similarity index 100% rename from sed/latest/_static/webpack-macros.html rename to sed/v1.0.0/_static/webpack-macros.html diff --git a/sed/latest/genindex.html b/sed/v1.0.0/genindex.html similarity index 99% rename from sed/latest/genindex.html rename to sed/v1.0.0/genindex.html index 249dbf8..96fada4 100644 --- a/sed/latest/genindex.html +++ b/sed/v1.0.0/genindex.html @@ -7,7 +7,7 @@ - Index — SED 1.0.0a1.dev19+gf1bb527 documentation + Index — SED 1.0.0 documentation @@ -37,7 +37,7 @@ - + @@ -46,7 +46,7 @@ @@ -54,7 +54,7 @@ - + @@ -116,7 +116,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

diff --git a/sed/latest/index.html b/sed/v1.0.0/index.html similarity index 98% rename from sed/latest/index.html rename to sed/v1.0.0/index.html index 87d9832..86d2286 100644 --- a/sed/latest/index.html +++ b/sed/v1.0.0/index.html @@ -9,7 +9,7 @@ - SED documentation — SED 1.0.0a1.dev19+gf1bb527 documentation + SED documentation — SED 1.0.0 documentation @@ -39,7 +39,7 @@ - + @@ -50,7 +50,7 @@ @@ -59,7 +59,7 @@ - + @@ -121,7 +121,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

diff --git a/sed/latest/misc/contributing.html b/sed/v1.0.0/misc/contributing.html similarity index 98% rename from sed/latest/misc/contributing.html rename to sed/v1.0.0/misc/contributing.html index b011fe5..416e797 100644 --- a/sed/latest/misc/contributing.html +++ b/sed/v1.0.0/misc/contributing.html @@ -8,7 +8,7 @@ - Contributing to sed — SED 1.0.0a1.dev19+gf1bb527 documentation + Contributing to sed — SED 1.0.0 documentation @@ -38,7 +38,7 @@ - + @@ -47,7 +47,7 @@ @@ -57,7 +57,7 @@ - + @@ -119,7 +119,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

diff --git a/sed/latest/misc/contribution.html b/sed/v1.0.0/misc/contribution.html similarity index 98% rename from sed/latest/misc/contribution.html rename to sed/v1.0.0/misc/contribution.html index 64c51bf..39bae27 100644 --- a/sed/latest/misc/contribution.html +++ b/sed/v1.0.0/misc/contribution.html @@ -8,7 +8,7 @@ - Development — SED 1.0.0a1.dev19+gf1bb527 documentation + Development — SED 1.0.0 documentation @@ -38,7 +38,7 @@ - + @@ -47,7 +47,7 @@ @@ -57,7 +57,7 @@ - + @@ -119,7 +119,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

diff --git a/sed/latest/misc/maintain.html b/sed/v1.0.0/misc/maintain.html similarity index 98% rename from sed/latest/misc/maintain.html rename to sed/v1.0.0/misc/maintain.html index 2e8ffc5..83af02e 100644 --- a/sed/latest/misc/maintain.html +++ b/sed/v1.0.0/misc/maintain.html @@ -8,7 +8,7 @@ - How to Maintain — SED 1.0.0a1.dev19+gf1bb527 documentation + How to Maintain — SED 1.0.0 documentation @@ -38,7 +38,7 @@ - + @@ -47,7 +47,7 @@ @@ -56,7 +56,7 @@ - + @@ -118,7 +118,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

diff --git a/sed/latest/objects.inv b/sed/v1.0.0/objects.inv similarity index 99% rename from sed/latest/objects.inv rename to sed/v1.0.0/objects.inv index f418b441ba78ddcc6df3ba634e13208532dd091e..6ed9f011390e2b6a1a52fd432e964ac33a101906 100644 GIT binary patch delta 10 RcmaDFb|q|r%|>4dZ2%pP1aklY delta 28 jcmcZ-_B3pQjbNgoUP@}2p`~_unqg9ssge0c8wqUynga=E diff --git a/sed/latest/py-modindex.html b/sed/v1.0.0/py-modindex.html similarity index 98% rename from sed/latest/py-modindex.html rename to sed/v1.0.0/py-modindex.html index 054dbd6..0dce1ad 100644 --- a/sed/latest/py-modindex.html +++ b/sed/v1.0.0/py-modindex.html @@ -7,7 +7,7 @@ - Python Module Index — SED 1.0.0a1.dev19+gf1bb527 documentation + Python Module Index — SED 1.0.0 documentation @@ -37,7 +37,7 @@ - + @@ -46,7 +46,7 @@ @@ -55,7 +55,7 @@ - + @@ -119,7 +119,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

diff --git a/sed/latest/search.html b/sed/v1.0.0/search.html similarity index 97% rename from sed/latest/search.html rename to sed/v1.0.0/search.html index b7534ed..1e19422 100644 --- a/sed/latest/search.html +++ b/sed/v1.0.0/search.html @@ -6,7 +6,7 @@ - Search - SED 1.0.0a1.dev19+gf1bb527 documentation + Search - SED 1.0.0 documentation @@ -36,7 +36,7 @@ - + @@ -45,7 +45,7 @@ @@ -56,7 +56,7 @@ - + @@ -118,7 +118,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

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a/sed/latest/sed/api.html b/sed/v1.0.0/sed/api.html similarity index 98% rename from sed/latest/sed/api.html rename to sed/v1.0.0/sed/api.html index 29421bf..4fd545e 100644 --- a/sed/latest/sed/api.html +++ b/sed/v1.0.0/sed/api.html @@ -8,7 +8,7 @@ - API — SED 1.0.0a1.dev19+gf1bb527 documentation + API — SED 1.0.0 documentation @@ -38,7 +38,7 @@ - + @@ -47,7 +47,7 @@ @@ -57,7 +57,7 @@ - + @@ -119,7 +119,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

diff --git a/sed/latest/sed/binning.html b/sed/v1.0.0/sed/binning.html similarity index 99% rename from sed/latest/sed/binning.html rename to sed/v1.0.0/sed/binning.html index 23c544a..a42c951 100644 --- a/sed/latest/sed/binning.html +++ b/sed/v1.0.0/sed/binning.html @@ -8,7 +8,7 @@ - Binning — SED 1.0.0a1.dev19+gf1bb527 documentation + Binning — SED 1.0.0 documentation @@ -38,7 +38,7 @@ - + @@ -47,7 +47,7 @@ @@ -57,7 +57,7 @@ - + @@ -119,7 +119,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

diff --git a/sed/latest/sed/calibrator.html b/sed/v1.0.0/sed/calibrator.html similarity index 99% rename from sed/latest/sed/calibrator.html rename to sed/v1.0.0/sed/calibrator.html index 3ef9378..1c42b61 100644 --- a/sed/latest/sed/calibrator.html +++ b/sed/v1.0.0/sed/calibrator.html @@ -8,7 +8,7 @@ - Calibrator — SED 1.0.0a1.dev19+gf1bb527 documentation + Calibrator — SED 1.0.0 documentation @@ -38,7 +38,7 @@ - + @@ -47,7 +47,7 @@ @@ -57,7 +57,7 @@ - + @@ -119,7 +119,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

diff --git a/sed/latest/sed/config.html b/sed/v1.0.0/sed/config.html similarity index 99% rename from sed/latest/sed/config.html rename to sed/v1.0.0/sed/config.html index 10d69c3..6655f66 100644 --- a/sed/latest/sed/config.html +++ b/sed/v1.0.0/sed/config.html @@ -8,7 +8,7 @@ - Config — SED 1.0.0a1.dev19+gf1bb527 documentation + Config — SED 1.0.0 documentation @@ -38,7 +38,7 @@ - + @@ -47,7 +47,7 @@ @@ -57,7 +57,7 @@ - + @@ -119,7 +119,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

diff --git a/sed/latest/sed/core.html b/sed/v1.0.0/sed/core.html similarity index 99% rename from sed/latest/sed/core.html rename to sed/v1.0.0/sed/core.html index e4067e3..fa4e1ff 100644 --- a/sed/latest/sed/core.html +++ b/sed/v1.0.0/sed/core.html @@ -8,7 +8,7 @@ - Core — SED 1.0.0a1.dev19+gf1bb527 documentation + Core — SED 1.0.0 documentation @@ -38,7 +38,7 @@ - + @@ -47,7 +47,7 @@ @@ -57,7 +57,7 @@ - + @@ -119,7 +119,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

diff --git a/sed/latest/sed/dataset.html b/sed/v1.0.0/sed/dataset.html similarity index 99% rename from sed/latest/sed/dataset.html rename to sed/v1.0.0/sed/dataset.html index 0af634b..1c0a536 100644 --- a/sed/latest/sed/dataset.html +++ b/sed/v1.0.0/sed/dataset.html @@ -8,7 +8,7 @@ - Dataset — SED 1.0.0a1.dev19+gf1bb527 documentation + Dataset — SED 1.0.0 documentation @@ -38,7 +38,7 @@ - + @@ -47,7 +47,7 @@ @@ -57,7 +57,7 @@ - + @@ -119,7 +119,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

diff --git a/sed/latest/sed/dfops.html b/sed/v1.0.0/sed/dfops.html similarity index 99% rename from sed/latest/sed/dfops.html rename to sed/v1.0.0/sed/dfops.html index 25ab69b..ab4d3ad 100644 --- a/sed/latest/sed/dfops.html +++ b/sed/v1.0.0/sed/dfops.html @@ -8,7 +8,7 @@ - Dataframe Operations — SED 1.0.0a1.dev19+gf1bb527 documentation + Dataframe Operations — SED 1.0.0 documentation @@ -38,7 +38,7 @@ - + @@ -47,7 +47,7 @@ @@ -57,7 +57,7 @@ - + @@ -119,7 +119,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

diff --git a/sed/latest/sed/diagnostic.html b/sed/v1.0.0/sed/diagnostic.html similarity index 98% rename from sed/latest/sed/diagnostic.html rename to sed/v1.0.0/sed/diagnostic.html index 62d9669..effcf8e 100644 --- a/sed/latest/sed/diagnostic.html +++ b/sed/v1.0.0/sed/diagnostic.html @@ -8,7 +8,7 @@ - Diagnostics — SED 1.0.0a1.dev19+gf1bb527 documentation + Diagnostics — SED 1.0.0 documentation @@ -38,7 +38,7 @@ - + @@ -47,7 +47,7 @@ @@ -57,7 +57,7 @@ - + @@ -119,7 +119,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

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SED 1.0.0 documentation

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SED 1.0.0a1.dev19+gf1bb527 documentation

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SED 1.0.0 documentation

diff --git a/sed/latest/sed/loader.html b/sed/v1.0.0/sed/loader.html similarity index 99% rename from sed/latest/sed/loader.html rename to sed/v1.0.0/sed/loader.html index 88f006e..c47f5bf 100644 --- a/sed/latest/sed/loader.html +++ b/sed/v1.0.0/sed/loader.html @@ -8,7 +8,7 @@ - Data loader — SED 1.0.0a1.dev19+gf1bb527 documentation + Data loader — SED 1.0.0 documentation @@ -38,7 +38,7 @@ - + @@ -47,7 +47,7 @@ @@ -57,7 +57,7 @@ - + @@ -119,7 +119,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

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SED 1.0.0 documentation

diff --git a/sed/latest/sed/metadata.html b/sed/v1.0.0/sed/metadata.html similarity index 98% rename from sed/latest/sed/metadata.html rename to sed/v1.0.0/sed/metadata.html index 8aa440d..33c8b1a 100644 --- a/sed/latest/sed/metadata.html +++ b/sed/v1.0.0/sed/metadata.html @@ -8,7 +8,7 @@ - Metadata — SED 1.0.0a1.dev19+gf1bb527 documentation + Metadata — SED 1.0.0 documentation @@ -38,7 +38,7 @@ - + @@ -47,7 +47,7 @@ @@ -57,7 +57,7 @@ - + @@ -119,7 +119,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

diff --git a/sed/v1.0.0/tutorial/10_hextof_workflow_trXPS_bam_correction.html b/sed/v1.0.0/tutorial/10_hextof_workflow_trXPS_bam_correction.html new file mode 100644 index 0000000..6f0bce5 --- /dev/null +++ b/sed/v1.0.0/tutorial/10_hextof_workflow_trXPS_bam_correction.html @@ -0,0 +1,1389 @@ + + + + + + + + + + + Tutorial for trXPS for the HEXTOF instrument at FLASH: t0, cross-correlation and BAM correction — SED 1.0.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

Tutorial for trXPS for the HEXTOF instrument at FLASH: t0, cross-correlation and BAM correction#

+
+

Preparation#

+
+

Import necessary libraries#

+
+
[1]:
+
+
+
%load_ext autoreload
+%autoreload 2
+
+from pathlib import Path
+import os
+
+from sed import SedProcessor
+from sed.dataset import dataset
+import numpy as np
+
+%matplotlib widget
+import matplotlib.pyplot as plt
+
+# For peak fitting
+from lmfit.models import GaussianModel
+
+
+
+
+
+

Get data paths#

+

If it is your beamtime, you can read the raw data and write to the processed directory. For the public data, you can not write to the processed directory.

+

The paths are such that if you are on Maxwell, it uses those. Otherwise, data is downloaded in the current directory from Zenodo: https://zenodo.org/records/12609441

+
+
[2]:
+
+
+
beamtime_dir = "/asap3/flash/gpfs/pg2/2023/data/11019101" # on Maxwell
+if os.path.exists(beamtime_dir) and os.access(beamtime_dir, os.R_OK):
+    path = beamtime_dir + "/raw/hdf/offline/fl1user3"
+    buffer_path = beamtime_dir + "/processed/tutorial/"
+else:
+    # data_path can be defined and used to store the data in a specific location
+    dataset.get("W110") # Put in Path to a storage of at least 10 Byte free space.
+    path = dataset.dir
+    buffer_path = path + "/processed/"
+
+
+
+
+
+
+
+
+INFO - Not downloading W110 data as it already exists at "/home/runner/work/sed/sed/docs/tutorial/datasets/W110".
+Set 'use_existing' to False if you want to download to a new location.
+INFO - Using existing data path for "W110": "/home/runner/work/sed/sed/docs/tutorial/datasets/W110"
+INFO - W110 data is already present.
+
+
+
+
+

Config setup#

+

Here, we get the path to the config file and set up the relevant directories. This can also be done directly in the config file.

+
+
[3]:
+
+
+
# pick the default configuration file for hextof@FLASH
+config_file = Path('../src/sed/config/flash_example_config.yaml')
+assert config_file.exists()
+
+
+
+
+
[4]:
+
+
+
# here we setup a dictionary that will be used to override the path configuration
+config_override = {
+    "core": {
+        "beamtime_id": 11019101,
+        "paths": {
+            "raw": path,
+            "processed": buffer_path
+        },
+    },
+}
+
+
+
+
+
[5]:
+
+
+
energy_cal = {
+    "energy": {
+        "calibration": {
+            "E0": -132.47100427179566,
+            "creation_date": '2024-11-30T20:47:03.305244',
+            "d": 0.8096677238144319,
+            "energy_scale": "kinetic",
+            "t0": 4.0148196706891397e-07,
+        },
+        "offsets":{
+            "constant": 1,
+            "creation_date": '2024-11-30T21:17:07.762199',
+            "columns": {
+                "monochromatorPhotonEnergy": {
+                    "preserve_mean": True,
+                    "weight": -1,
+                },
+                "tofVoltage": {
+                    "preserve_mean": True,
+                    "weight": -1,
+                },
+            },
+        },
+    },
+}
+
+
+
+
+
+

We use the stored energy calibration parameters and load trXPS data set to define:#

+
    +

  • t0 position with respect to delay stage values;

    • +

  • correct accordingly delay stage offset

    • +

  • fit cross-correlation

    • +

  • apply BAM correction and see its effect on cross-correlation

    • +
+
+
[6]:
+
+
+
run_number = 44498
+sp_44498 = SedProcessor(runs=[run_number], config=config_override, folder_config=energy_cal, system_config=config_file, verbose=True)
+
+sp_44498.add_jitter()
+sp_44498.align_dld_sectors()
+sp_44498.append_energy_axis()
+sp_44498.add_energy_offset()
+
+
+
+
+
+
+
+
+INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
+INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
+INFO - Reading files: 0 new files of 14 total.
+loading complete in  0.09 s
+INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
+INFO - Aligning 8s sectors of dataframe
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID
+npartitions=14
+                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+Dask Name: assign, 16 graph layers
+INFO - Adding energy column to dataframe:
+INFO - Using energy calibration parameters generated on 11/30/2024, 20:47:03
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
+npartitions=14
+                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+Dask Name: assign, 31 graph layers
+INFO - Adding energy offset to dataframe:
+INFO - Using energy offset parameters generated on 11/30/2024, 21:17:07
+INFO - Energy offset parameters:
+   Constant: 1.0
+   Column[monochromatorPhotonEnergy]: Weight=-1.0, Preserve Mean: True, Reductions: None.
+   Column[tofVoltage]: Weight=-1.0, Preserve Mean: True, Reductions: None.
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
+npartitions=14
+                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+Dask Name: assign, 64 graph layers
+
+
+

Check which channels are included in the dataframe

+
+
[7]:
+
+
+
sp_44498.dataframe.head()
+
+
+
+
+
[7]:
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
trainIdpulseIdelectronIddldPosXdldPosYdldTimeStepspulserSignAdcbamtimeStampmonochromatorPhotonEnergy...delayStagesampleBiastofVoltageextractorVoltageextractorCurrentcryoTemperaturesampleTemperaturedldTimeBinSizedldSectorIDenergy
0162802283010651.401434895.4014344595.40136732919.0-6187.968751.677563e+09116.858299...1448.60205172.99590319.9953566029.299805-0.07385749.20878.9899980.0205763-43.670972
1162802283011650.594095887.5940954595.59423832919.0-6187.968751.677563e+09116.858299...1448.60205172.99590319.9953566029.299805-0.07385749.20878.9899980.0205760-43.673617
2162802283050682.028937672.0289374423.02880932914.0-6170.156251.677563e+09116.858299...1448.60205172.99590319.9953566029.299805-0.07385749.20878.9899980.0205766-40.989246
3162802283051685.282979658.2829794425.28320332914.0-6170.156251.677563e+09116.858299...1448.60205172.99590319.9953566029.299805-0.07385749.20878.9899980.0205763-41.028886
4162802283052670.118386687.1183864424.11816432914.0-6170.156251.677563e+09116.858299...1448.60205172.99590319.9953566029.299805-0.07385749.20878.9899980.0205765-41.008417
+

5 rows × 21 columns

+
+
+
+
+
+

Data w/o BAM correction#

+

First, we take a look at our sideband measurement before any corrections. The sidebands on the W4f core levels can be used as a measure of the pump and probe cross-correlation, and hence our temporal resolution. We plot the data delay stage position vs Energy data, normalized by acquisition time.

+
+
[8]:
+
+
+
axes = ['energy', 'delayStage']
+ranges = [[-37.5,-27.5], [1446.75,1449.15]]
+bins = [200,40]
+res = sp_44498.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time="delayStage")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+INFO - Calculate normalization histogram for axis 'delayStage'...
+
+
+
+
+
+
+
+
+
+
[9]:
+
+
+
fig,ax = plt.subplots(1,2,figsize=(6,2.25), layout='constrained')
+res.plot(robust=True, ax=ax[0], cmap='terrain')
+fig.suptitle(f"Run {run_number}: W 4f, side bands")
+ax[0].set_title('raw')
+bg = res.sel(delayStage=slice(1448.7,1449.1)).mean('delayStage')
+(res.sel(delayStage=slice(1446.8,1449.3))-bg).plot(robust=True, ax=ax[1])
+ax[1].set_title('difference')
+
+
+
+
+
[9]:
+
+
+
+
+Text(0.5, 1.0, 'difference')
+
+
+
+
+
+
+
+
+

Now we make fit to determine precise t\(_0\) position and cross-correlation using lmfit fit models

+
+
[10]:
+
+
+
Gauss_mod = GaussianModel()
+
+#first order sideband:
+x1=res['delayStage']
+y1=res.sel(energy=slice(-30.5,-29.5)).sum('energy')
+y1=y1-np.mean(y1.sel(delayStage=slice(1448.7,1449.1)))
+
+pars1 = Gauss_mod.make_params(amplitude=0.1, center=1447.8, sigma=0.02)
+out1 = Gauss_mod.fit(y1, pars1, x=x1)
+
+#second order sideband
+x2=res['delayStage']
+y2=res.sel(energy=slice(-29.5,-28.5)).sum('energy')
+y2=y2-np.mean(y2.sel(delayStage=slice(1448.7,1449.1)))
+
+pars2 = Gauss_mod.make_params(amplitude=0.1, center=1447.8, sigma=0.02)
+out2 = Gauss_mod.fit(y2, pars2, x=x2)
+
+plt.figure()
+plt.plot(x1,y1,'rx', label='$1^{st}$ order sideband')
+plt.plot(x1,out1.best_fit,'r', label="FWHM = {:.3f} ps".format(out1.values['fwhm']))
+plt.legend(loc="best")
+plt.title('run44498, W4f, sidebands comparison')
+plt.plot(x2,y2,'bx', label='$2^{nd}$ order sideband')
+plt.plot(x2,out2.best_fit,'b', label="FWHM = {:.3f} ps".format(out2.values['fwhm']))
+plt.legend(loc="best")
+plt.xlabel("delayStage [ps]")
+plt.ylabel("Intensity [cts/s]")
+plt.show()
+
+
+
+
+
+
+
+
+
+

As we see the sidebands are quite broad and one of the possible reasons for this could be long or short-term drifts (jitter) of the FEL arrival time with respect to e.g. optical laser or differences in the intra-bunch arrival time. To check and correct for this we can look at beam arrival monitor (BAM). The BAM gives a pulse-resolved measure of the FEL arrival time with respect to a master clock.

+
+
+

Check BAM versus pulse and train IDs#

+
+
[11]:
+
+
+
axes = ['trainId', 'pulseId', 'bam']
+ranges = [[1628022640,1628046700], [0,500], [-6400,100]]
+bins = [250, 100, 1000]
+res_bam = sp_44498.compute(bins=bins, axes=axes, ranges=ranges)
+
+
+
+
+
+
+
+
+
+

As we can see, jitter between FEL and pump laser is quite significant withing a pulse train as well as over the whole measurement period.

+
+
[12]:
+
+
+

fig,ax = plt.subplots(1,2,figsize=(6,2.25), layout='constrained')
+res_bam.sel(bam=slice(-6400,-5100)).sum('trainId').plot(ax=ax[0],robust=True, cmap='terrain')
+res_bam.sel(bam=slice(-6400,-5100)).sum('pulseId').plot(ax=ax[1],robust=True, cmap='terrain')
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
+

Apply BAM correction#

+

To correct the SASE jitter, using information from the bam column and to calibrate the pump-probe delay axis, we need to shift the delay stage values to centre the pump-probe-time overlap time zero.

+
+
[13]:
+
+
+
sp_44498.add_delay_offset(
+    constant=-1448, # this is time zero position determined from side band fit
+    flip_delay_axis=True, # invert the direction of the delay axis
+    columns=['bam'], # use the bam to offset the values
+    weights=[-0.001], # bam is in fs, delay in ps
+    preserve_mean=True # preserve the mean of the delay axis to keep t0 position
+)
+
+
+
+
+
+
+
+
+INFO - Adding delay offset to dataframe:
+INFO - Delay offset parameters:
+   Column[bam]: Weight=-0.001, Preserve Mean: True, Reductions: None.
+   Constant: -1448
+   Flip delay axis: True
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
+npartitions=14
+                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float64    float32    float32          float32          float32         float32           float32        float32        int8  float64
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+Dask Name: assign, 84 graph layers
+
+
+
+

bin in the corrected delay axis#

+
+
[14]:
+
+
+
axes = ['energy', 'delayStage']
+ranges = [[-37.5,-27.5], [-1.5,1.5]]
+bins = [200,60]
+res_corr = sp_44498.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time="delayStage")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+INFO - Calculate normalization histogram for axis 'delayStage'...
+
+
+
+
+
+
+
+
+
+
[15]:
+
+
+
fig,ax = plt.subplots(1,2,figsize=(6,2.25), layout='constrained')
+fig.suptitle(f"Run {run_number}: W 4f, side bands")
+res_corr.plot(robust=True, ax=ax[0], cmap='terrain')
+ax[0].set_title('raw')
+bg = res_corr.sel(delayStage=slice(-1.3,-1.0)).mean('delayStage')
+(res_corr-bg).plot(robust=True, ax=ax[1])
+ax[1].set_title('difference')
+
+
+
+
+
[15]:
+
+
+
+
+Text(0.5, 1.0, 'difference')
+
+
+
+
+
+
+
+
+

We clearly see an effect of BAM corrections - side bands are visible much nicer and width became smaller.

+
+
[16]:
+
+
+
sp_44498.save_delay_offsets()
+
+
+
+
+
+
+
+
+INFO - Saved delay offset parameters to "sed_config.yaml".
+
+
+

Now we can repeat fit procedure to determine true cross-correlation value.

+
+
[17]:
+
+
+
Gauss_mod = GaussianModel()
+
+#first order sideband:
+x5=res_corr['delayStage'].sel(delayStage=slice(-1.6,1.5))
+y5=res_corr.sel(energy=slice(-30.4,-29.5),delayStage=slice(-1.6,1.5)).sum('energy')
+y5=y5-np.mean(y5.sel(delayStage=slice(-1.4,-1.0)))
+
+pars5 = Gauss_mod.make_params(amplitude=0.1, center=0.0, sigma=0.02)
+out5 = Gauss_mod.fit(y5, pars5, x=x5)
+
+print(out5.fit_report())
+
+#second order sideband
+x6=res_corr['delayStage'].sel(delayStage=slice(-1.6,1.5))
+y6=res_corr.sel(energy=slice(-29.5,-27.5),delayStage=slice(-1.6,1.5)).sum('energy')
+y6=y6-np.mean(y6.sel(delayStage=slice(-1.4,-1.0)))
+
+pars6 = Gauss_mod.make_params(amplitude=0.1, center=0.0, sigma=0.02)
+out6 = Gauss_mod.fit(y6, pars6, x=x6)
+
+print(out6.fit_report())
+
+#comparison plot
+plt.figure()
+plt.plot(x5,y5,'rx', label='$1^{st}$ order sideband')
+plt.plot(x5,out5.best_fit,'r', label="FWHM = {:.3f} ps".format(out5.values['fwhm']))
+plt.legend(loc="best")
+plt.title('run44498, W4f, sidebands comparison')
+plt.plot(x6,y6,'bx', label='$2^{nd}$ order sideband')
+plt.plot(x6,out6.best_fit,'b', label="FWHM = {:.3f} ps".format(out6.values['fwhm']))
+plt.legend(loc="best")
+plt.xlabel("pump probe delay [ps]")
+plt.ylabel("Intensity [cts/s]")
+plt.show()
+
+
+
+
+
+
+
+
+[[Model]]
+    Model(gaussian)
+[[Fit Statistics]]
+    # fitting method   = leastsq
+    # function evals   = 108
+    # data points      = 60
+    # variables        = 3
+    chi-square         = 2115.59934
+    reduced chi-square = 37.1157779
+    Akaike info crit   = 219.764932
+    Bayesian info crit = 226.047966
+    R-squared          = 0.76527879
+[[Variables]]
+    amplitude:  16.8956187 +/- 1.16658096 (6.90%) (init = 0.1)
+    center:     0.04393214 +/- 0.01099542 (25.03%) (init = 0)
+    sigma:      0.13790818 +/- 0.01099506 (7.97%) (init = 0.02)
+    fwhm:       0.32474895 +/- 0.02589138 (7.97%) == '2.3548200*sigma'
+    height:     48.8758304 +/- 3.37470505 (6.90%) == '0.3989423*amplitude/max(1e-15, sigma)'
+[[Correlations]] (unreported correlations are < 0.100)
+    C(amplitude, sigma) = +0.5773
+[[Model]]
+    Model(gaussian)
+[[Fit Statistics]]
+    # fitting method   = leastsq
+    # function evals   = 101
+    # data points      = 60
+    # variables        = 3
+    chi-square         = 255.783501
+    reduced chi-square = 4.48742985
+    Akaike info crit   = 92.9992096
+    Bayesian info crit = 99.2822433
+    R-squared          = 0.63671998
+[[Variables]]
+    amplitude:  4.39032410 +/- 0.38384691 (8.74%) (init = 0.1)
+    center:     0.02190587 +/- 0.01246704 (56.91%) (init = 0)
+    sigma:      0.12348701 +/- 0.01246691 (10.10%) (init = 0.02)
+    fwhm:       0.29078968 +/- 0.02935732 (10.10%) == '2.3548200*sigma'
+    height:     14.1835647 +/- 1.24007666 (8.74%) == '0.3989423*amplitude/max(1e-15, sigma)'
+[[Correlations]] (unreported correlations are < 0.100)
+    C(amplitude, sigma) = +0.5774
+
+
+
+
+
+
+
+
+
+
+
+

Comparison of the BAM correction effect#

+
+
[18]:
+
+
+
fig,ax=plt.subplots(2,2,figsize=(6,6),layout="constrained")
+
+plt.axes(ax[0,0])
+res.plot(cmap='terrain', robust=True)
+plt.title("W4f, no bam correction")
+
+plt.axes(ax[0,1])
+plt.plot(x1,y1,'rx',label='integrated intensity 1. order')
+plt.plot(x1,out1.best_fit,'r',label='1. order fit, FWHM = {:.3f} ps'.format(out1.values['fwhm']))
+plt.plot(x2,y2,'bx',label='integrated intensity 2. order')
+plt.plot(x2,out2.best_fit,'b',label='2. order fit, FWHM = {:.3f} ps'.format(out2.values['fwhm']))
+plt.legend(loc=1)
+plt.title("Sidebands without bam correction")
+
+plt.axes(ax[1,0])
+res_corr.sel(delayStage=slice(-1.6,1.5)).plot(robust=True,cmap='terrain')
+plt.title("W4f, with bam correction")
+
+plt.axes(ax[1,1])
+plt.plot(x5,y5,'rx',label='integrated intensity 1. order')
+plt.plot(x5,out5.best_fit,'r',label='1. order fit, FWHM = {:.3f} ps'.format(out5.values['fwhm']))
+plt.plot(x6,y6,'bx',label='integrated intensity 2. order')
+plt.plot(x6,out6.best_fit,'b',label='2. order fit, FWHM = {:.3f} ps'.format(out6.values['fwhm']))
+plt.legend(loc=1)
+plt.title("Sidebands with bam correction")
+
+fig.suptitle(f'Run {run_number}: Effect of BAM correction',fontsize='14')
+
+
+
+
+
[18]:
+
+
+
+
+Text(0.5, 0.98, 'Run 44498: Effect of BAM correction')
+
+
+
+
+
+
+
+
+
+
[ ]:
+
+
+

+
+
+
+
+
[ ]:
+
+
+

+
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+ + +
+ + + + + + + +
+ + + + + + + +
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+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/sed/v1.0.0/tutorial/11_hextof_workflow_trXPS_energy_calibration_using_SB.html b/sed/v1.0.0/tutorial/11_hextof_workflow_trXPS_energy_calibration_using_SB.html new file mode 100644 index 0000000..da7d93a --- /dev/null +++ b/sed/v1.0.0/tutorial/11_hextof_workflow_trXPS_energy_calibration_using_SB.html @@ -0,0 +1,1318 @@ + + + + + + + + + + + Tutorial for trXPS for energy calibration using core level side-bands — SED 1.0.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + +
+ + + Ctrl+K +
+ + +
+ +
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+ +
+ + +
+
+ + + + + +
+ +
+

Tutorial for trXPS for energy calibration using core level side-bands#

+
+

Preparation#

+
+

Import necessary libraries#

+
+
[1]:
+
+
+
%load_ext autoreload
+%autoreload 2
+
+from pathlib import Path
+import os
+
+from sed import SedProcessor
+from sed.dataset import dataset
+import numpy as np
+
+%matplotlib widget
+import matplotlib.pyplot as plt
+
+### for automatic peak finding
+from scipy.signal import find_peaks
+
+
+
+
+
+

Get data paths#

+

If it is your beamtime, you can read the raw data and write to the processed directory. For the public data, you can not write to the processed directory.

+

The paths are such that if you are on Maxwell, it uses those. Otherwise, data is downloaded in the current directory from Zenodo: https://zenodo.org/records/12609441

+
+
[2]:
+
+
+
beamtime_dir = "/asap3/flash/gpfs/pg2/2023/data/11019101" # on Maxwell
+if os.path.exists(beamtime_dir) and os.access(beamtime_dir, os.R_OK):
+    path = beamtime_dir + "/raw/hdf/offline/fl1user3"
+    buffer_path = beamtime_dir + "/processed/tutorial/"
+else:
+    # data_path can be defined and used to store the data in a specific location
+    dataset.get("W110") # Put in Path to a storage of at least 10 GByte free space.
+    path = dataset.dir
+    buffer_path = path + "/processed/"
+
+
+
+
+
+
+
+
+INFO - Not downloading W110 data as it already exists at "/home/runner/work/sed/sed/docs/tutorial/datasets/W110".
+Set 'use_existing' to False if you want to download to a new location.
+INFO - Using existing data path for "W110": "/home/runner/work/sed/sed/docs/tutorial/datasets/W110"
+INFO - W110 data is already present.
+
+
+
+
+

Config setup#

+

Here, we get the path to the config file and set up the relevant directories. This can also be done directly in the config file.

+
+
[3]:
+
+
+
# pick the default configuration file for hextof@FLASH
+config_file = Path('../src/sed/config/flash_example_config.yaml')
+assert config_file.exists()
+
+
+
+
+
[4]:
+
+
+
# here we setup a dictionary that will be used to override the path configuration
+config_override = {
+    "core": {
+        "beamtime_id": 11019101,
+        "paths": {
+            "raw": path,
+            "processed": buffer_path
+        },
+    },
+}
+
+
+
+
+
+
+

Reference calibration from a bias series#

+
+
[5]:
+
+
+
sp_44455 = SedProcessor(runs=[44455], config=config_override, system_config=config_file)
+sp_44455.add_jitter()
+sp_44455.align_dld_sectors()
+
+
+
+
+
+
+
+
+INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/sed_config.yaml]
+INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
+INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
+INFO - Reading files: 0 new files of 6 total.
+loading complete in  0.07 s
+INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
+INFO - Aligning 8s sectors of dataframe
+INFO - Dask DataFrame Structure:
+              trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID
+npartitions=6
+               uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8
+                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+...               ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+Dask Name: assign, 16 graph layers
+
+
+
+

find calibration parameters#

+

We now will fit the tof-energy relation. This is done by finding the maxima of a peak in the tof spectrum, and then fitting the square root relation to obtain the calibration parameters.

+
+
[6]:
+
+
+
axes = ['sampleBias','dldTimeSteps']
+bins = [4, 250]
+ranges = [[77.5,81.5],  [4050,4500]]
+res = sp_44455.compute(bins=bins, axes=axes, ranges=ranges)
+sp_44455.load_bias_series(binned_data=res)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
[7]:
+
+
+
ranges=(4120, 4200)
+ref_id=0
+sp_44455.find_bias_peaks(ranges=ranges, ref_id=ref_id, apply=True)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+INFO - Use feature ranges: [(4120.2, 4199.4), (4156.2, 4231.8), (4197.6, 4276.8), (4237.2, 4323.6)].
+INFO - Extracted energy features: [[4.1472e+03 1.0000e+00]
+ [4.1850e+03 1.0000e+00]
+ [4.2246e+03 1.0000e+00]
+ [4.2678e+03 1.0000e+00]].
+
+
+
+
[8]:
+
+
+
sp_44455.calibrate_energy_axis(
+    ref_energy=-31.4,
+    method="lmfit",
+    energy_scale='kinetic',
+    d={'value':1.0,'min': .7, 'max':1.0, 'vary':True},
+    t0={'value':5e-7, 'min': 1e-7, 'max': 1e-6, 'vary':True},
+    E0={'value': 0., 'min': -200, 'max': 100, 'vary': True},
+)
+
+
+
+
+
+
+
+
+INFO - [[Fit Statistics]]
+    # fitting method   = leastsq
+    # function evals   = 46
+    # data points      = 4
+    # variables        = 3
+    chi-square         = 0.00151332
+    reduced chi-square = 0.00151332
+    Akaike info crit   = -25.5189696
+    Bayesian info crit = -27.3600865
+[[Variables]]
+    d:   0.80966772 +/- 1.56525760 (193.32%) (init = 1)
+    t0:  4.0148e-07 +/- 1.6505e-07 (41.11%) (init = 5e-07)
+    E0: -101.048293 +/- 12.5092127 (12.38%) (init = 0)
+[[Correlations]] (unreported correlations are < 0.100)
+    C(d, t0)  = -0.9999
+    C(d, E0)  = -0.9998
+    C(t0, E0) = +0.9995
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

Now that we have the calibration parameters, we can generate the energy axis for each spectrum

+
+
[9]:
+
+
+
sp_44455.save_energy_calibration("reference_calib.yaml")
+
+
+
+
+
+
+
+
+INFO - Saved energy calibration parameters to "reference_calib.yaml".
+
+
+
+
+

Now we can use those parameters and load our trXPS data using the additional config file#

+

To obtain a correct energy axis, we offset the energy axis by the difference of photon energy between this run and the energy calibration runs

+
+
[10]:
+
+
+
run_number = 44498
+sp_44498 = SedProcessor(runs=[run_number], config=config_override, folder_config="reference_calib.yaml", system_config=config_file, verbose=True)
+sp_44498.add_jitter()
+sp_44498.append_energy_axis()
+sp_44498.add_energy_offset(
+    constant=1,
+    columns=['monochromatorPhotonEnergy','tofVoltage'],
+    weights=[-1,-1],
+    preserve_mean=[True, True],
+)
+
+
+
+
+
+
+
+
+INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/reference_calib.yaml]
+INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
+INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
+INFO - Reading files: 0 new files of 14 total.
+loading complete in  0.08 s
+INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
+INFO - Adding energy column to dataframe:
+INFO - Using energy calibration parameters generated on 03/06/2025, 09:24:45
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
+npartitions=14
+                uint32   int64      int64  float64  float64      float64       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+Dask Name: assign, 29 graph layers
+INFO - Adding energy offset to dataframe:
+INFO - Energy offset parameters:
+   Column[monochromatorPhotonEnergy]: Weight=-1, Preserve Mean: True, Reductions: None.
+   Column[tofVoltage]: Weight=-1, Preserve Mean: True, Reductions: None.
+   Constant: 1
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
+npartitions=14
+                uint32   int64      int64  float64  float64      float64       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+Dask Name: assign, 62 graph layers
+
+
+

And bin an energy spectrum for reference

+
+
[11]:
+
+
+
axes = ['energy']
+ranges = [[-37.5,-27.5]]
+bins = [200]
+res_ref = sp_44498.compute(bins=bins, axes=axes, ranges=ranges)
+
+plt.figure()
+res_ref.plot()
+
+
+
+
+
+
+
+
+
+
+
[11]:
+
+
+
+
+[<matplotlib.lines.Line2D at 0x7f1d483540d0>]
+
+
+
+
+
+
+
+
+
+
+
+

Energy calibration using side-band peaks#

+
+

Visualize trXPS data bin in the dldTimeSteps and the corrected delay axis to prepare for energy calibration using SB#

+

We now prepare for an alternative energy calibration based on the side-bands of the time-dependent dataset. This is e.g. helpful if no bias series has been obtained.

+
+
[12]:
+
+
+
run_number = 44498
+sp_44498 = SedProcessor(runs=[run_number], config=config_override, system_config=config_file, verbose=True)
+sp_44498.add_jitter()
+
+
+
+
+
+
+
+
+INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/sed_config.yaml]
+INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
+INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
+INFO - Reading files: 0 new files of 14 total.
+loading complete in  0.08 s
+INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
+
+
+
+
+

We correct delay stage, t0 position and BAM (see previous tutorial)#

+
+
[13]:
+
+
+
sp_44498.add_delay_offset(
+    constant=-1448, # this is time zero position determined from side band fit
+    flip_delay_axis=True, # invert the direction of the delay axis
+    columns=['bam'], # use the bam to offset the values
+    weights=[-0.001], # bam is in fs, delay in ps
+    preserve_mean=True # preserve the mean of the delay axis to keep t0 position
+)
+
+
+
+
+
+
+
+
+INFO - Adding delay offset to dataframe:
+INFO - Delay offset parameters:
+   Column[bam]: Weight=-0.001, Preserve Mean: True, Reductions: None.
+   Constant: -1448
+   Flip delay axis: True
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID
+npartitions=14
+                uint32   int64      int64  float64  float64      float64       float32  float32   float64                   float32  float32    float64    float32    float32          float32          float32         float32           float32        float32        int8
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+Dask Name: assign, 34 graph layers
+
+
+
+
[14]:
+
+
+
axes = ['dldTimeSteps', 'delayStage']
+ranges = [[3900,4200], [-1.5,1.5]]
+bins = [100,60]
+res_corr = sp_44498.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time="delayStage")
+
+fig,ax = plt.subplots(1,2,figsize=(6,2.25), layout='constrained')
+fig.suptitle(f"Run {run_number}: W 4f, side bands")
+res_corr.plot(ax=ax[0], cmap='terrain')
+ax[0].set_title('raw')
+bg = res_corr.sel(delayStage=slice(-1.3,-1.0)).mean('delayStage')
+(res_corr-bg).plot(ax=ax[1])
+ax[1].set_title('difference')
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+INFO - Calculate normalization histogram for axis 'delayStage'...
+
+
+
+
+
+
+
+
+
+
[14]:
+
+
+
+
+Text(0.5, 1.0, 'difference')
+
+
+
+
+
+
+
+
+
+
+

Automatically extract number and position of peaks in the ROI around t0#

+
+
[15]:
+
+
+
# binned data
+roi = slice(3980, 4160)
+delay = slice(-0.5,0.5)
+data = res_corr.sel(dldTimeSteps = roi, delayStage=delay).sum('delayStage')
+distance = 7
+peaks, _ = find_peaks(data, height=None, distance=distance)
+
+p1SB = data[peaks]['dldTimeSteps'][0]
+W4f5 = data[peaks]['dldTimeSteps'][1]
+m1SB = data[peaks]['dldTimeSteps'][2]
+W4f7 = data[peaks]['dldTimeSteps'][3]
+mm1SB = data[peaks]['dldTimeSteps'][4]
+plt.figure()
+data.plot()
+plt.scatter(data[peaks]['dldTimeSteps'], data[peaks], c='r')#, "x")
+plt.vlines([p1SB-7,p1SB+7], 0, 150, color='violet', linestyles='dashed', label='$1^{st}$ order SB')
+plt.vlines([W4f5-7,W4f5+7], 0, 150, color='b', linestyles='dashed', label='W 4f 7/2')
+plt.vlines([m1SB-7,m1SB+7], 0, 150, color='g', linestyles='dashed', label='$-1^{st}$ order SB')
+plt.vlines([W4f7-7,W4f7+7], 0, 150, color='r', linestyles='dashed', label='W 4f 5/2')
+plt.vlines([mm1SB-7,mm1SB+7], 0, 150, color='orange', linestyles='dashed', label='$2nd -1^{st}$ order SB')
+plt.legend()
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
+

find calibration parameters#

+

We now will fit the tof-energy relation. This is done using the maxima of a peak in the ToF spectrum and the known kinetic energy of those peaks (kinetic energy of e.g. W4f peaks (-31.4 and -33.6 eV) and their SB of different orders accounting energy of pump beam of 1030 nm = 1.2 eV. The calibration parameters are obtained by fitting the square root relation.

+
+
[16]:
+
+
+
### Kinetic energy of w4f peaks and their SB
+ref_energy = -30.2
+sp_44498.ec.biases = -1*np.array([-30.2,-31.4,-32.6,-33.6,-34.8])
+sp_44498.ec.peaks = np.expand_dims(data[peaks]['dldTimeSteps'].data,1)
+sp_44498.ec.tof = res_corr.dldTimeSteps.data
+
+sp_44498.calibrate_energy_axis(
+    ref_energy=ref_energy,
+    method="lmfit",
+    d={'value':1.0,'min': .8, 'max':1.0, 'vary':True},
+    t0={'value':5e-7, 'min': 1e-7, 'max': 1e-6, 'vary':True},
+    E0={'value': -100., 'min': -200, 'max': 15, 'vary': True},
+)
+
+
+
+
+
+
+
+
+INFO - [[Fit Statistics]]
+    # fitting method   = leastsq
+    # function evals   = 123
+    # data points      = 5
+    # variables        = 3
+    chi-square         = 0.04811488
+    reduced chi-square = 0.02405744
+    Akaike info crit   = -17.2180090
+    Bayesian info crit = -18.3896953
+[[Variables]]
+    d:   0.80482246 +/- 0.56768800 (70.54%) (init = 1)
+    t0:  4.0567e-07 +/- 2.9002e-07 (71.49%) (init = 5e-07)
+    E0: -59.1600349 +/- 29.3415291 (49.60%) (init = -100)
+[[Correlations]] (unreported correlations are < 0.100)
+    C(d, t0)  = -0.9999
+    C(d, E0)  = -0.9997
+    C(t0, E0) = +0.9992
+
+
+
+
+
+
+
+
+
+
+

Append energy axis into a data frame, bin and visualize data in the calibrated energy and corrected delay axis#

+

To get a correct energy axis, we undo the shifts imposed by the calibration function

+
+
[17]:
+
+
+
sp_44498.append_energy_axis()
+sp_44498.add_energy_offset(
+    constant=30.2,
+    columns=['monochromatorPhotonEnergy','tofVoltage','sampleBias'],
+    weights=[-1,-1,-1],
+    preserve_mean=[True, True,False],
+)
+
+
+
+
+
+
+
+
+INFO - Adding energy column to dataframe:
+INFO - Using energy calibration parameters generated on 03/06/2025, 09:24:57
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
+npartitions=14
+                uint32   int64      int64  float64  float64      float64       float32  float32   float64                   float32  float32    float64    float32    float32          float32          float32         float32           float32        float32        int8  float64
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+Dask Name: assign, 49 graph layers
+INFO - Adding energy offset to dataframe:
+INFO - Energy offset parameters:
+   Column[monochromatorPhotonEnergy]: Weight=-1, Preserve Mean: True, Reductions: None.
+   Column[tofVoltage]: Weight=-1, Preserve Mean: True, Reductions: None.
+   Column[sampleBias]: Weight=-1, Preserve Mean: False, Reductions: None.
+   Constant: 30.2
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
+npartitions=14
+                uint32   int64      int64  float64  float64      float64       float32  float32   float64                   float32  float32    float64    float32    float32          float32          float32         float32           float32        float32        int8  float64
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+Dask Name: assign, 87 graph layers
+
+
+
+
[18]:
+
+
+
axes = ['energy', 'delayStage']
+ranges = [[-37.5,-27.5], [-1.5,1.5]]
+bins = [200,60]
+res_corr = sp_44498.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time="delayStage")
+
+fig,ax = plt.subplots(1,2,figsize=(6,2.25), layout='constrained')
+fig.suptitle(f"Run {run_number}: W 4f, side bands")
+res_corr.plot(ax=ax[0], cmap='terrain')
+ax[0].set_title('raw')
+bg = res_corr.sel(delayStage=slice(-1.3,-1.0)).mean('delayStage')
+(res_corr-bg).plot(ax=ax[1])
+ax[1].set_title('difference')
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+INFO - Calculate normalization histogram for axis 'delayStage'...
+
+
+
+
+
+
+
+
+
+
[18]:
+
+
+
+
+Text(0.5, 1.0, 'difference')
+
+
+
+
+
+
+
+
+
+
+
+

Compare to reference#

+

While this calibration methods gives a reasonable approximation to the energy axis, there are some deviations to the bias method, so it should be used with care

+
+
[19]:
+
+
+
axes = ['energy']
+ranges = [[-37.5,-27.5]]
+bins = [200]
+res_1D = sp_44498.compute(bins=bins, axes=axes, ranges=ranges)
+
+plt.figure()
+(res_ref/res_ref.max()).plot(label="bias series calibration")
+(res_1D/res_1D.max()).plot(label="side band calibration")
+plt.legend()
+
+
+
+
+
+
+
+
+
+
+
[19]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f1d483d7580>
+
+
+
+
+
+
+
+
+
+
[ ]:
+
+
+

+
+
+
+
+
+ + +
+ + + + + + + +
+ + + + + + + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/sed/v1.0.0/tutorial/1_binning_fake_data.html b/sed/v1.0.0/tutorial/1_binning_fake_data.html new file mode 100644 index 0000000..cd8ef70 --- /dev/null +++ b/sed/v1.0.0/tutorial/1_binning_fake_data.html @@ -0,0 +1,921 @@ + + + + + + + + + + + Binning demonstration on locally generated fake data — SED 1.0.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + +
+ + + Ctrl+K +
+ + +
+ +
+ + +
+
+ + + +
+ +
+ + + + + +
+ +
+ +
+ +
+ +
+
+ +
+ +
+
+ +
+
+ +
+ + +
+ +
+ + + +
+ + +
+ +
+ +
+ +
+ +
+ + +
+ + +
+ + + + + +
+ +
+ + +
+
+ + + + + + +
+ + +
+
+ +
+
+ +
+ +
+ + +
+ +
+ + +
+
+ + + + + +
+ +
+

Binning demonstration on locally generated fake data#

+

In this example, we generate a table with random data simulating a single event dataset. We showcase the binning method, first on a simple single table using the bin_partition method and then in the distributed method bin_dataframe, using daks dataframes. The first method is never really called directly, as it is simply the function called by the bin_dataframe on each partition of the dask dataframe.

+
+
[1]:
+
+
+
import dask
+import numpy as np
+import pandas as pd
+import dask.dataframe
+
+import matplotlib.pyplot as plt
+
+from sed.binning import bin_partition, bin_dataframe
+
+%matplotlib widget
+
+
+
+
+
+
+
+
+/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/dask/dataframe/__init__.py:42: FutureWarning:
+Dask dataframe query planning is disabled because dask-expr is not installed.
+
+You can install it with `pip install dask[dataframe]` or `conda install dask`.
+This will raise in a future version.
+
+  warnings.warn(msg, FutureWarning)
+
+
+
+

Generate Fake Data#

+
+
[2]:
+
+
+
n_pts = 100000
+cols = ["posx", "posy", "energy"]
+df = pd.DataFrame(np.random.randn(n_pts, len(cols)), columns=cols)
+df
+
+
+
+
+
[2]:
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
posxposyenergy
01.989314-0.8748660.443205
11.0612680.0978740.219086
2-0.403891-2.4656060.674966
3-1.1191251.9740471.703641
40.125598-2.614056-0.433593
............
999950.5631430.812274-1.031682
99996-0.007966-2.026399-1.735395
999970.5210440.873829-0.357709
999980.6331060.670135-0.649792
999991.901023-0.0515280.159558
+

100000 rows × 3 columns

+
+
+
+
+

Define the binning range#

+
+
[3]:
+
+
+
binAxes = ["posx", "posy", "energy"]
+nBins = [120, 120, 120]
+binRanges = [(-2, 2), (-2, 2), (-2, 2)]
+coords = {ax: np.linspace(r[0], r[1], n) for ax, r, n in zip(binAxes, binRanges, nBins)}
+
+
+
+
+
+

Compute the binning along the pandas dataframe#

+
+
[4]:
+
+
+
%%time
+res = bin_partition(
+    part=df,
+    bins=nBins,
+    axes=binAxes,
+    ranges=binRanges,
+    hist_mode="numba",
+)
+
+
+
+
+
+
+
+
+CPU times: user 1.12 s, sys: 23.9 ms, total: 1.14 s
+Wall time: 1.14 s
+
+
+
+
[5]:
+
+
+
fig, axs = plt.subplots(1, 3, figsize=(6, 1.875), constrained_layout=True)
+for i in range(3):
+    axs[i].imshow(res.sum(i))
+
+
+
+
+
+
+
+
+
+
+
+

Transform to dask dataframe#

+
+
[6]:
+
+
+
ddf = dask.dataframe.from_pandas(df, npartitions=50)
+ddf
+
+
+
+
+
[6]:
+
+
+
+
Dask DataFrame Structure:
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
posxposyenergy
npartitions=50
0float64float64float64
2000.........
............
98000.........
99999.........
+
+
Dask Name: from_pandas, 1 graph layer
+
+
+
+

Compute distributed binning on the partitioned dask dataframe#

+

In this example, the small dataset does not give significant improvement over the pandas implementation, at least using this number of partitions. A single partition would be faster (you can try…) but we use multiple for demonstration purposes.

+
+
[7]:
+
+
+
%%time
+res = bin_dataframe(
+    df=ddf,
+    bins=nBins,
+    axes=binAxes,
+    ranges=binRanges,
+    hist_mode="numba",
+)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+CPU times: user 608 ms, sys: 193 ms, total: 801 ms
+Wall time: 689 ms
+
+
+
+
[8]:
+
+
+
fig, axs = plt.subplots(1, 3, figsize=(6, 1.875), constrained_layout=True)
+for dim, ax in zip(binAxes, axs):
+    res.sum(dim).plot(ax=ax)
+
+
+
+
+
+
+
+
+
+
+
[ ]:
+
+
+

+
+
+
+
+
+ + +
+ + + + + + + +
+ + + + + + + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/sed/v1.0.0/tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data.html b/sed/v1.0.0/tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data.html new file mode 100644 index 0000000..5a5b8ca --- /dev/null +++ b/sed/v1.0.0/tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data.html @@ -0,0 +1,1651 @@ + + + + + + + + + + + Demonstration of the conversion pipeline using time-resolved ARPES data stored on Zenodo — SED 1.0.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + +
+ + + Ctrl+K +
+ + +
+ +
+ + +
+
+ + + +
+ +
+ + + + + +
+ +
+ +
+ +
+ +
+
+ +
+ +
+
+ +
+
+ +
+ + +
+ +
+ + + +
+ + +
+ +
+ +
+ +
+ +
+ + +
+ + +
+ + + + + +
+ +
+ + +
+
+ + + + + + +
+ + +
+
+ +
+
+ +
+ +
+ + +
+ +
+ + +
+
+ + + + + +
+ +
+

Demonstration of the conversion pipeline using time-resolved ARPES data stored on Zenodo#

+

In this example, we pull some time-resolved ARPES data from Zenodo, and load it into the sed package using functions of the mpes package. Then, we run a conversion pipeline on it, containing steps for visualizing the channels, correcting image distortions, calibrating the momentum space, correcting for energy distortions and calibrating the energy axis. Finally, the data are binned in calibrated axes. For performance reasons, best store the data on a locally attached storage (no network drive). +This can also be achieved transparently using the included MirrorUtil class.

+
+
[1]:
+
+
+
%load_ext autoreload
+%autoreload 2
+import numpy as np
+import matplotlib.pyplot as plt
+import sed
+from sed.dataset import dataset
+
+%matplotlib widget
+
+
+
+
+

Load Data#

+
+
[2]:
+
+
+
dataset.get("WSe2") # Put in Path to a storage of at least 20 GByte free space.
+data_path = dataset.dir # This is the path to the data
+scandir, caldir = dataset.subdirs # scandir contains the data, caldir contains the calibration files
+
+
+
+
+
+
+
+
+INFO - Not downloading WSe2 data as it already exists at "/home/runner/work/sed/sed/docs/tutorial/datasets/WSe2".
+Set 'use_existing' to False if you want to download to a new location.
+INFO - Using existing data path for "WSe2": "/home/runner/work/sed/sed/docs/tutorial/datasets/WSe2"
+INFO - WSe2 data is already present.
+
+
+
+
[3]:
+
+
+
# create sed processor using the config file:
+sp = sed.SedProcessor(folder=scandir, config="../src/sed/config/mpes_example_config.yaml", system_config={}, verbose=True)
+
+
+
+
+
+
+
+
+INFO - Configuration loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/mpes_example_config.yaml]
+INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/sed_config.yaml]
+INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
+WARNING - Entry "KTOF:Lens:Sample:V" for channel "sampleBias" not found. Skipping the channel.
+
+
+
+
[4]:
+
+
+
# Apply jittering to X, Y, t, ADC columns.
+# Columns are defined in the config, or can be provided as list.
+sp.add_jitter()
+
+
+
+
+
+
+
+
+INFO - add_jitter: Added jitter to columns ['X', 'Y', 't', 'ADC'].
+
+
+
+
[5]:
+
+
+
# Plot of the count rate through the scan
+rate, secs = sp.loader.get_count_rate(range(100))
+plt.plot(secs, rate)
+
+
+
+
+
[5]:
+
+
+
+
+[<matplotlib.lines.Line2D at 0x7f1d4b18ce20>]
+
+
+
+
+
+
+
+
+
+
[6]:
+
+
+
# The time elapsed in the scan
+sp.loader.get_elapsed_time()
+
+
+
+
+
[6]:
+
+
+
+
+2588.4949999999994
+
+
+
+
[7]:
+
+
+
# Inspect data in dataframe Columns:
+# axes = ['X', 'Y', 't', 'ADC']
+# bins = [100, 100, 100, 100]
+# ranges = [(0, 1800), (0, 1800), (130000, 140000), (0, 9000)]
+# sp.view_event_histogram(dfpid=1, axes=axes, bins=bins, ranges=ranges)
+sp.view_event_histogram(dfpid=2)
+
+
+
+
+
+
+
+
+
+
+
+

Distortion correction and Momentum Calibration workflow#

+
+

Distortion correction#

+
+

1. step:#

+

Bin and load part of the dataframe in detector coordinates, and choose energy plane where high-symmetry points can well be identified. Either use the interactive tool, or pre-select the range:

+
+
[8]:
+
+
+
#sp.bin_and_load_momentum_calibration(df_partitions=20, plane=170)
+sp.bin_and_load_momentum_calibration(df_partitions=100, plane=33, width=10, apply=True)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

2. Step:#

+

Next, we select a number of features corresponding to the rotational symmetry of the material, plus the center. These can either be auto-detected (for well-isolated points), or provided as a list (these can be read-off the graph in the cell above). These are then symmetrized according to the rotational symmetry, and a spline-warping correction for the x/y coordinates is calculated, which corrects for any geometric distortions from the perfect n-fold rotational symmetry.

+
+
[9]:
+
+
+
#features = np.array([[203.2, 341.96], [299.16, 345.32], [350.25, 243.70], [304.38, 149.88], [199.52, 152.48], [154.28, 242.27], [248.29, 248.62]])
+#sp.define_features(features=features, rotation_symmetry=6, include_center=True, apply=True)
+# Manual selection: Use a GUI tool to select peaks:
+#sp.define_features(rotation_symmetry=6, include_center=True)
+# Autodetect: Uses the DAOStarFinder routine to locate maxima.
+# Parameters are:
+#   fwhm: Full-width at half maximum of peaks.
+#   sigma: Number of standard deviations above the mean value of the image peaks must have.
+#   sigma_radius: number of standard deviations around a peak that peaks are fitted
+sp.define_features(rotation_symmetry=6, auto_detect=True, include_center=True, fwhm=10, sigma=12, sigma_radius=4, apply=True)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

3. Step:#

+

Generate nonlinear correction using splinewarp algorithm. If no landmarks have been defined in previous step, default parameters from the config are used

+
+
[10]:
+
+
+
# Option whether a central point shall be fixed in the determination fo the correction
+sp.generate_splinewarp(include_center=True)
+
+
+
+
+
+
+
+
+INFO - Calculated thin spline correction based on the following landmarks:
+pouter_ord: [[203.00184761 342.98205366]
+ [299.87630041 346.19474964]
+ [350.95544165 244.77430106]
+ [305.63519239 150.21702617]
+ [199.37691593 152.83212495]
+ [153.41124117 243.05883096]]
+pcent: (249.23240623877487, 249.24332926024232)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

Optional (Step 3a):#

+

Save distortion correction parameters to configuration file in current data folder:

+
+
[11]:
+
+
+
# Save generated distortion correction parameters for later reuse
+sp.save_splinewarp()
+
+
+
+
+
+
+
+
+INFO - Saved momentum correction parameters to "sed_config.yaml".
+
+
+
+
+

4. Step:#

+

To adjust scaling, position and orientation of the corrected momentum space image, you can apply further affine transformations to the distortion correction field. Here, first a potential scaling is applied, next a translation, and finally a rotation around the center of the image (defined via the config). One can either use an interactive tool, or provide the adjusted values and apply them directly.

+
+
[12]:
+
+
+
#sp.pose_adjustment(xtrans=14, ytrans=18, angle=2)
+sp.pose_adjustment(xtrans=8, ytrans=7, angle=-4, apply=True)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+INFO - Applied translation with (xtrans=8.0, ytrans=7.0).
+INFO - Applied rotation with angle=-4.0.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

5. Step:#

+

Finally, the momentum correction is applied to the dataframe, and corresponding meta data are stored

+
+
[13]:
+
+
+
sp.apply_momentum_correction()
+
+
+
+
+
+
+
+
+INFO - Adding corrected X/Y columns to dataframe:
+Calculating inverse deformation field, this might take a moment...
+INFO - Dask DataFrame Structure:
+                       X        Y        t      ADC       Xm       Ym
+npartitions=100
+                 float64  float64  float64  float64  float64  float64
+                     ...      ...      ...      ...      ...      ...
+...                  ...      ...      ...      ...      ...      ...
+                     ...      ...      ...      ...      ...      ...
+                     ...      ...      ...      ...      ...      ...
+Dask Name: apply_dfield, 206 graph layers
+
+
+
+
+
+

Momentum calibration workflow#

+
+

1. Step:#

+

First, the momentum scaling needs to be calibrated. Either, one can provide the coordinates of one point outside the center, and provide its distance to the Brillouin zone center (which is assumed to be located in the center of the image), one can specify two points on the image and their distance (where the 2nd point marks the BZ center),or one can provide absolute k-coordinates of two distinct momentum points.

+

If no points are provided, an interactive tool is created. Here, left mouse click selects the off-center point (brillouin_zone_centered=True) or toggle-selects the off-center and center point.

+
+
[14]:
+
+
+
k_distance = 2/np.sqrt(3)*np.pi/3.28 # k-distance of the K-point in a hexagonal Brillouin zone
+#sp.calibrate_momentum_axes(k_distance = k_distance)
+point_a = [308, 345]
+sp.calibrate_momentum_axes(point_a=point_a, k_distance = k_distance, apply=True)
+#point_b = [247, 249]
+#sp.calibrate_momentum_axes(point_a=point_a, point_b = point_b, k_coord_a = [.5, 1.1], k_coord_b = [0, 0], equiscale=False)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

Optional (Step 1a):#

+

Save momentum calibration parameters to configuration file in current data folder:

+
+
[15]:
+
+
+
# Save generated momentum calibration parameters for later reuse
+sp.save_momentum_calibration()
+
+
+
+
+
+
+
+
+INFO - Saved momentum calibration parameters to sed_config.yaml
+
+
+
+
+

2. Step:#

+

Now, the distortion correction and momentum calibration needs to be applied to the dataframe.

+
+
[16]:
+
+
+
sp.apply_momentum_calibration()
+
+
+
+
+
+
+
+
+INFO - Adding kx/ky columns to dataframe:
+INFO - Using momentum calibration parameters generated on 03/06/2025, 09:26:48
+INFO - Dask DataFrame Structure:
+                       X        Y        t      ADC       Xm       Ym       kx       ky
+npartitions=100
+                 float64  float64  float64  float64  float64  float64  float64  float64
+                     ...      ...      ...      ...      ...      ...      ...      ...
+...                  ...      ...      ...      ...      ...      ...      ...      ...
+                     ...      ...      ...      ...      ...      ...      ...      ...
+                     ...      ...      ...      ...      ...      ...      ...      ...
+Dask Name: assign, 216 graph layers
+
+
+
+
+
+
+

Energy Correction and Calibration workflow#

+
+

Energy Correction (optional)#

+

The purpose of the energy correction is to correct for any momentum-dependent distortion of the energy axis, e.g. from geometric effects in the flight tube, or from space charge

+
+

1st step:#

+

Here, one can select the functional form to be used, and adjust its parameters. The binned data used for the momentum calibration is plotted around the Fermi energy (defined by tof_fermi), and the correction function is plotted ontop. Possible correction functions are: “spherical” (parameter: diameter), “Lorentzian” (parameter: gamma), “Gaussian” (parameter: sigma), and “Lorentzian_asymmetric” (parameters: gamma, amplitude2, gamma2).

+

One can either use an interactive alignment tool, or provide parameters directly.

+
+
[17]:
+
+
+
#sp.adjust_energy_correction(amplitude=2.5, center=(730, 730), gamma=920, tof_fermi = 66200)
+sp.adjust_energy_correction(amplitude=2.5, center=(730, 730), gamma=920, tof_fermi = 66200, apply=True)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

Optional (Step 1a):#

+

Save energy correction parameters to configuration file in current data folder:

+
+
[18]:
+
+
+
# Save generated energy correction parameters for later reuse
+sp.save_energy_correction()
+
+
+
+
+
+
+
+
+INFO - Saved energy correction parameters to sed_config.yaml
+
+
+
+
+

2. Step#

+

After adjustment, the energy correction is directly applied to the TOF axis.

+
+
[19]:
+
+
+
sp.apply_energy_correction()
+
+
+
+
+
+
+
+
+INFO - Applying energy correction to dataframe...
+INFO - Using energy correction parameters generated on 03/06/2025, 09:26:48
+INFO - Dask DataFrame Structure:
+                       X        Y        t      ADC       Xm       Ym       kx       ky       tm
+npartitions=100
+                 float64  float64  float64  float64  float64  float64  float64  float64  float64
+                     ...      ...      ...      ...      ...      ...      ...      ...      ...
+...                  ...      ...      ...      ...      ...      ...      ...      ...      ...
+                     ...      ...      ...      ...      ...      ...      ...      ...      ...
+                     ...      ...      ...      ...      ...      ...      ...      ...      ...
+Dask Name: assign, 230 graph layers
+
+
+
+
+
+

Energy calibration#

+

For calibrating the energy axis, a set of data taken at different bias voltages around the value where the measurement was taken is required.

+
+

1. Step:#

+

In a first step, the data are loaded, binned along the TOF dimension, and normalized. The used bias voltages can be either provided, or read from attributes in the source files if present.

+
+
[20]:
+
+
+
# Load energy calibration EDCs
+energycalfolder = caldir
+scans = np.arange(1,12)
+voltages = np.arange(12,23,1)
+files = [energycalfolder + r'/Scan' + str(num).zfill(3) + '_' + str(num+11) + '.h5' for num in scans]
+sp.load_bias_series(data_files=files, normalize=True, biases=voltages, ranges=[(64000, 75000)])
+
+
+
+
+
+
+
+
+WARNING - Entry "KTOF:Lens:Sample:V" for channel "sampleBias" not found. Skipping the channel.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

2. Step:#

+

Next, the same peak or feature needs to be selected in each curve. For this, one needs to define “ranges” for each curve, within which the peak of interest is located. One can either provide these ranges manually, or provide one range for a “reference” curve, and infer the ranges for the other curves using a dynamic time warping algorithm.

+
+
[21]:
+
+
+
# Option 1 = specify the ranges containing a common feature (e.g an equivalent peak) for all bias scans
+# rg = [(129031.03103103103, 129621.62162162163), (129541.54154154155, 130142.14214214214), (130062.06206206206, 130662.66266266267), (130612.61261261262, 131213.21321321322), (131203.20320320321, 131803.8038038038), (131793.7937937938, 132384.38438438438), (132434.43443443443, 133045.04504504506), (133105.10510510512, 133715.71571571572), (133805.8058058058, 134436.43643643643), (134546.54654654654, 135197.1971971972)]
+# sp.find_bias_peaks(ranges=rg, infer_others=False)
+# Option 2 = specify the range for one curve and infer the others
+# This will open an interactive tool to select the correct ranges for the curves.
+# IMPORTANT: Don't choose the range too narrow about a peak, and choose a refid
+# somewhere in the middle or towards larger biases!
+rg = (66100, 67000)
+sp.find_bias_peaks(ranges=rg, ref_id=5, infer_others=True, apply=True)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+INFO - Use feature ranges: [(64638.0, 65386.0), (64913.0, 65683.0), (65188.0, 65991.0), (65474.0, 66310.0), (65782.0, 66651.0), (66101.0, 67003.0), (66442.0, 67388.0), (66794.0, 67795.0), (67190.0, 68213.0), (67575.0, 68664.0), (67993.0, 69148.0)].
+INFO - Extracted energy features: [[6.51330000e+04 9.43293095e-01]
+ [6.54080000e+04 9.52672958e-01]
+ [6.57050000e+04 9.47981834e-01]
+ [6.60130000e+04 9.46402431e-01]
+ [6.63430000e+04 9.50330198e-01]
+ [6.66730000e+04 9.63564813e-01]
+ [6.70360000e+04 9.59838033e-01]
+ [6.73990000e+04 9.67203319e-01]
+ [6.78060000e+04 9.55975950e-01]
+ [6.82130000e+04 9.56439197e-01]
+ [6.86750000e+04 9.70683038e-01]].
+
+
+
+
+

3. Step:#

+

Next, the detected peak positions and bias voltages are used to determine the calibration function. Essentially, the functional Energy(TOF) is being determined by either least-squares fitting of the functional form d2/(t-t0)2 via lmfit (method: “lmfit”), or by analytically obtaining a polynomial approximation (method: “lstsq” or “lsqr”). The parameter ref_energy is used to define the absolute energy position of the feature used for calibration in the calibrated energy +scale. energy_scale can be either “kinetic” (decreasing energy with increasing TOF), or “binding” (increasing energy with increasing TOF).

+

After calculating the calibration, all traces corrected with the calibration are plotted ontop of each other, and the calibration function (Energy(TOF)) together with the extracted features is being plotted.

+
+
[22]:
+
+
+
# Eref can be used to set the absolute energy (kinetic energy, E-EF, etc.) of the feature used for energy calibration (if known)
+Eref=-1.3
+# the lmfit method uses a fit of (d/(t-t0))**2 to determine the energy calibration
+# limits and starting values for the fitting parameters can be provided as dictionaries
+sp.calibrate_energy_axis(
+    ref_energy=Eref,
+    method="lmfit",
+    energy_scale='kinetic',
+    d={'value':1.0,'min': .7, 'max':1.2, 'vary':True},
+    t0={'value':8e-7, 'min': 1e-7, 'max': 1e-6, 'vary':True},
+    E0={'value': 0., 'min': -100, 'max': 0, 'vary': True},
+)
+
+
+
+
+
+
+
+
+INFO - [[Fit Statistics]]
+    # fitting method   = leastsq
+    # function evals   = 43
+    # data points      = 11
+    # variables        = 3
+    chi-square         = 0.00218781
+    reduced chi-square = 2.7348e-04
+    Akaike info crit   = -87.7502612
+    Bayesian info crit = -86.5565754
+[[Variables]]
+    d:   1.09544523 +/- 0.03646409 (3.33%) (init = 1)
+    t0:  7.6073e-07 +/- 7.5361e-09 (0.99%) (init = 8e-07)
+    E0: -46.6158341 +/- 0.79487877 (1.71%) (init = 0)
+[[Correlations]] (unreported correlations are < 0.100)
+    C(d, t0)  = -0.9997
+    C(d, E0)  = -0.9988
+    C(t0, E0) = +0.9974
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

Optional (Step 3a):#

+

Save energy calibration parameters to configuration file in current data folder:

+
+
[23]:
+
+
+
# Save generated energy calibration parameters for later reuse
+sp.save_energy_calibration()
+
+
+
+
+
+
+
+
+INFO - Saved energy calibration parameters to "sed_config.yaml".
+
+
+
+
+

4. Step:#

+

Finally, the the energy axis is added to the dataframe. Here, the applied bias voltages of the measurement is taken into account to provide the correct energy offset. If the bias cannot be read from the file, it can be provided manually.

+
+
[24]:
+
+
+
sp.append_energy_axis(bias_voltage=16.8)
+
+
+
+
+
+
+
+
+INFO - Adding energy column to dataframe:
+INFO - Using energy calibration parameters generated on 03/06/2025, 09:26:57
+INFO - Dask DataFrame Structure:
+                       X        Y        t      ADC       Xm       Ym       kx       ky       tm   energy
+npartitions=100
+                 float64  float64  float64  float64  float64  float64  float64  float64  float64  float64
+                     ...      ...      ...      ...      ...      ...      ...      ...      ...      ...
+...                  ...      ...      ...      ...      ...      ...      ...      ...      ...      ...
+                     ...      ...      ...      ...      ...      ...      ...      ...      ...      ...
+                     ...      ...      ...      ...      ...      ...      ...      ...      ...      ...
+Dask Name: assign, 243 graph layers
+
+
+
+
+
+
+

4. Delay calibration:#

+

The delay axis is calculated from the ADC input column based on the provided delay range. ALternatively, the delay scan range can also be extracted from attributes inside a source file, if present.

+
+
[25]:
+
+
+
sp.dataframe.head()
+
+
+
+
+
[25]:
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
XYtADCXmYmkxkytmenergy
0-0.245890-0.245890-0.245890-0.2458900.0000000.000000-2.060071-2.060071-48.471104-25.223989
1364.6576301001.65763070100.6576306316.657630355.0685451031.894587-1.1076410.70786770083.642352-9.314417
2760.756182817.75618275614.7561826315.756182791.140442839.3837280.0620720.19147975613.881435-16.717008
3691.787527970.78752766454.7875276316.787527713.322803984.827845-0.1466650.58161666449.111798-0.832646
4671.027618712.02761873026.0276186317.027618697.038665741.371289-0.190346-0.07142873025.640940-13.817142
+
+
+
+
[26]:
+
+
+
#from pathlib import Path
+#datafile = "file.h5"
+#print(datafile)
+#sp.calibrate_delay_axis(datafile=datafile)
+delay_range = (-500, 1500)
+sp.calibrate_delay_axis(delay_range=delay_range, preview=True)
+
+
+
+
+
+
+
+
+INFO - Adding delay column to dataframe:
+INFO - Append delay axis using delay_range = [-500, 1500] and adc_range = [475.0, 6400.0]
+INFO -              X            Y             t          ADC           Xm  \
+0     0.045283     0.045283      0.045283     0.045283   -12.112204
+1   364.623258  1001.623258  70100.623258  6316.623258   355.031915
+2   760.929539   817.929539  75614.929539  6315.929539   791.320179
+3   691.515091   970.515091  66454.515091  6316.515091   713.036149
+4   671.142569   712.142569  73026.142569  6317.142569   697.160720
+5   299.293999  1164.293999  68459.293999  6316.293999   282.452671
+6   571.112618   665.112618  73903.112618  6316.112618   590.222886
+7   822.157798   545.157798  72632.157798  6318.157798   848.334188
+8   817.727541   415.727541  72421.727541  6316.727541   838.418284
+9  1006.060087   667.060087  72802.060087  6317.060087  1040.106139
+
+            Ym        kx        ky            tm     energy        delay
+0    86.832103 -2.092560 -1.827154    -48.162911 -25.223911  -660.322267
+1  1031.864480 -1.107739  0.707786  70083.607555  -9.314354  1471.855952
+2   839.546554  0.062554  0.191915  75614.050672 -16.717173  1471.621785
+3   984.581438 -0.147434  0.580955  66448.849198  -0.831864  1471.819440
+4   741.475972 -0.190018 -0.071148  73025.757679 -13.817292  1472.031247
+5  1185.482292 -1.302424  1.119848  68432.780473  -5.972844  1471.744810
+6   701.294601 -0.476866 -0.178929  73900.203661 -14.888010  1471.683584
+7   586.988787  0.215487 -0.485542  72628.007405 -13.294860  1472.373940
+8   466.206316  0.188889 -0.809527  72412.063214 -13.001247  1471.891153
+9   709.272529  0.729893 -0.157530  72794.575998 -13.516486  1472.003405
+
+
+
+
+

5. Visualization of calibrated histograms#

+

With all calibrated axes present in the dataframe, we can visualize the corresponding histograms, and determine the respective binning ranges

+
+
[27]:
+
+
+
axes = ['kx', 'ky', 'energy', 'delay']
+ranges = [[-3, 3], [-3, 3], [-6, 2], [-600, 1600]]
+sp.view_event_histogram(dfpid=1, axes=axes, ranges=ranges)
+
+
+
+
+
+
+
+
+
+
+
+

Define the binning ranges and compute calibrated data volume#

+
+
[28]:
+
+
+
axes = ['kx', 'ky', 'energy', 'delay']
+bins = [100, 100, 200, 50]
+ranges = [[-2, 2], [-2, 2], [-4, 2], [-600, 1600]]
+res = sp.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time="delay")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+INFO - Calculate normalization histogram for axis 'delay'...
+
+
+
+
+
+
+
+
+
+
+

Some visualization:#

+
+
[29]:
+
+
+
fig, axs = plt.subplots(4, 1, figsize=(6, 18), constrained_layout=True)
+res.loc[{'energy':slice(-.1, 0)}].sum(axis=(2,3)).T.plot(ax=axs[0])
+res.loc[{'kx':slice(-.8, -.5)}].sum(axis=(0,3)).T.plot(ax=axs[1])
+res.loc[{'ky':slice(-.2, .2)}].sum(axis=(1,3)).T.plot(ax=axs[2])
+res.loc[{'kx':slice(-.8, -.5), 'energy':slice(.5, 2)}].sum(axis=(0,1)).plot(ax=axs[3])
+
+
+
+
+
[29]:
+
+
+
+
+<matplotlib.collections.QuadMesh at 0x7f1d93ae0790>
+
+
+
+
+
+
+
+
+
+
[30]:
+
+
+
fig, ax = plt.subplots(1,1)
+(sp._normalization_histogram*90000).plot(ax=ax)
+sp._binned.sum(axis=(0,1,2)).plot(ax=ax)
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
[ ]:
+
+
+

+
+
+
+
+
+ + +
+ + + + + + + +
+ + + + + + + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/sed/v1.0.0/tutorial/3_metadata_collection_and_export_to_NeXus.html b/sed/v1.0.0/tutorial/3_metadata_collection_and_export_to_NeXus.html new file mode 100644 index 0000000..2b408ff --- /dev/null +++ b/sed/v1.0.0/tutorial/3_metadata_collection_and_export_to_NeXus.html @@ -0,0 +1,1048 @@ + + + + + + + + + + + Binning with metadata generation, and storing into a NeXus file — SED 1.0.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + +
+ + + Ctrl+K +
+ + +
+ +
+ + +
+
+ + + +
+ +
+ + + + + +
+ +
+ +
+ +
+ +
+
+ +
+ +
+
+ +
+
+ +
+ + +
+ +
+ + + +
+ + +
+ +
+ +
+ +
+ +
+ + +
+ + +
+ + + + + +
+ +
+ + +
+
+ + + + + + +
+ + +
+
+ +
+
+ +
+ +
+ + +
+ +
+ + +
+
+ + + + + +
+ +
+

Binning with metadata generation, and storing into a NeXus file#

+

In this example, we show how to bin the same data used for example 3, but using the values for correction/calibration parameters generated in the example notebook 3, which are locally saved in the file sed_config.yaml. These data and the corresponding (machine and processing) metadata are then stored to a NeXus file following the NXmpes NeXus standard +(https://fairmat-experimental.github.io/nexus-fairmat-proposal/9636feecb79bb32b828b1a9804269573256d7696/classes/contributed_definitions/NXmpes.html#nxmpes) using the ‘dataconverter’ of the pynxtools package (FAIRmat-NFDI/pynxtools).

+
+
[1]:
+
+
+
%load_ext autoreload
+%autoreload 2
+
+import sed
+from sed.dataset import dataset
+
+%matplotlib widget
+
+
+
+
+

Load Data#

+
+
[2]:
+
+
+
dataset.get("WSe2") # Put in Path to a storage of at least 20 GByte free space.
+data_path = dataset.dir # This is the path to the data
+scandir, _ = dataset.subdirs # scandir contains the data, _ contains the calibration files
+
+
+
+
+
+
+
+
+INFO - Not downloading WSe2 data as it already exists at "/home/runner/work/sed/sed/docs/tutorial/datasets/WSe2".
+Set 'use_existing' to False if you want to download to a new location.
+INFO - Using existing data path for "WSe2": "/home/runner/work/sed/sed/docs/tutorial/datasets/WSe2"
+INFO - WSe2 data is already present.
+
+
+
+
[3]:
+
+
+
metadata = {}
+# manual Meta data. These should ideally come from an Electronic Lab Notebook.
+#General
+metadata['experiment_summary'] = 'WSe2 XUV NIR pump probe data.'
+metadata['entry_title'] = 'Valence Band Dynamics - 800 nm linear s-polarized pump, 0.6 mJ/cm2 absorbed fluence'
+metadata['experiment_title'] = 'Valence band dynamics of 2H-WSe2'
+
+#User
+# Fill general parameters of NXuser
+# TODO: discuss how to deal with multiple users?
+metadata['user0'] = {}
+metadata['user0']['name'] = 'Julian Maklar'
+metadata['user0']['role'] = 'Principal Investigator'
+metadata['user0']['affiliation'] = 'Fritz Haber Institute of the Max Planck Society'
+metadata['user0']['address'] = 'Faradayweg 4-6, 14195 Berlin'
+metadata['user0']['email'] = 'maklar@fhi-berlin.mpg.de'
+
+#NXinstrument
+metadata['instrument'] = {}
+metadata['instrument']['energy_resolution'] = 140.
+#analyzer
+metadata['instrument']['analyzer']={}
+metadata['instrument']['analyzer']['slow_axes'] = "delay" # the scanned axes
+metadata['instrument']['analyzer']['spatial_resolution'] = 10.
+metadata['instrument']['analyzer']['energy_resolution'] = 110.
+metadata['instrument']['analyzer']['momentum_resolution'] = 0.08
+metadata['instrument']['analyzer']['working_distance'] = 4.
+metadata['instrument']['analyzer']['lens_mode'] = "6kV_kmodem4.0_30VTOF.sav"
+
+#probe beam
+metadata['instrument']['beam']={}
+metadata['instrument']['beam']['probe']={}
+metadata['instrument']['beam']['probe']['incident_energy'] = 21.7
+metadata['instrument']['beam']['probe']['incident_energy_spread'] = 0.11
+metadata['instrument']['beam']['probe']['pulse_duration'] = 20.
+metadata['instrument']['beam']['probe']['frequency'] = 500.
+metadata['instrument']['beam']['probe']['incident_polarization'] = [1, 1, 0, 0] # p pol Stokes vector
+metadata['instrument']['beam']['probe']['extent'] = [80., 80.]
+#pump beam
+metadata['instrument']['beam']['pump']={}
+metadata['instrument']['beam']['pump']['incident_energy'] = 1.55
+metadata['instrument']['beam']['pump']['incident_energy_spread'] = 0.08
+metadata['instrument']['beam']['pump']['pulse_duration'] = 35.
+metadata['instrument']['beam']['pump']['frequency'] = 500.
+metadata['instrument']['beam']['pump']['incident_polarization'] = [1, -1, 0, 0] # s pol Stokes vector
+metadata['instrument']['beam']['pump']['incident_wavelength'] = 800.
+metadata['instrument']['beam']['pump']['average_power'] = 300.
+metadata['instrument']['beam']['pump']['pulse_energy'] = metadata['instrument']['beam']['pump']['average_power']/metadata['instrument']['beam']['pump']['frequency']#µJ
+metadata['instrument']['beam']['pump']['extent'] = [230., 265.]
+metadata['instrument']['beam']['pump']['fluence'] = 0.15
+
+#sample
+metadata['sample']={}
+metadata['sample']['preparation_date'] = '2019-01-13T10:00:00+00:00'
+metadata['sample']['preparation_description'] = 'Cleaved'
+metadata['sample']['sample_history'] = 'Cleaved'
+metadata['sample']['chemical_formula'] = 'WSe2'
+metadata['sample']['description'] = 'Sample'
+metadata['sample']['name'] = 'WSe2 Single Crystal'
+
+metadata['file'] = {}
+metadata['file']["trARPES:Carving:TEMP_RBV"] = 300.
+metadata['file']["trARPES:XGS600:PressureAC:P_RD"] = 5.e-11
+metadata['file']["KTOF:Lens:Extr:I"] = -0.12877
+metadata['file']["KTOF:Lens:UDLD:V"] = 399.99905
+metadata['file']["KTOF:Lens:Sample:V"] = 17.19976
+metadata['file']["KTOF:Apertures:m1.RBV"] = 3.729931
+metadata['file']["KTOF:Apertures:m2.RBV"] = -5.200078
+metadata['file']["KTOF:Apertures:m3.RBV"] = -11.000425
+
+# Sample motor positions
+metadata['file']['trARPES:Carving:TRX.RBV'] = 7.1900000000000004
+metadata['file']['trARPES:Carving:TRY.RBV'] = -6.1700200225439552
+metadata['file']['trARPES:Carving:TRZ.RBV'] = 33.4501953125
+metadata['file']['trARPES:Carving:THT.RBV'] = 423.30500940561586
+metadata['file']['trARPES:Carving:PHI.RBV'] = 0.99931647456264949
+metadata['file']['trARPES:Carving:OMG.RBV'] = 11.002500171914066
+
+
+
+
+
[4]:
+
+
+
# create sed processor using the config file, and collect the meta data from the files:
+sp = sed.SedProcessor(folder=scandir, config="../src/sed/config/mpes_example_config.yaml", system_config={}, metadata=metadata, collect_metadata=True)
+
+
+
+
+
+
+
+
+INFO - Configuration loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/mpes_example_config.yaml]
+INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/sed_config.yaml]
+INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
+WARNING - Entry "KTOF:Lens:Sample:V" for channel "sampleBias" not found. Skipping the channel.
+
+
+
+
[5]:
+
+
+
# Apply jittering to X, Y, t, ADC columns.
+sp.add_jitter()
+
+
+
+
+
+
+
+
+INFO - add_jitter: Added jitter to columns ['X', 'Y', 't', 'ADC'].
+
+
+
+
[6]:
+
+
+
# Calculate machine-coordinate data for pose adjustment
+sp.bin_and_load_momentum_calibration(df_partitions=10, plane=33, width=10, apply=True)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
[7]:
+
+
+
# Adjust pose alignment, using stored distortion correction
+sp.pose_adjustment(xtrans=8, ytrans=7, angle=-4, apply=True, use_correction=True)
+
+
+
+
+
+
+
+
+INFO - No landmarks defined, using momentum correction parameters generated on 03/06/2025, 09:26:41
+INFO - Calculated thin spline correction based on the following landmarks:
+pouter_ord: [[203.2  341.96]
+ [299.16 345.32]
+ [350.25 243.7 ]
+ [304.38 149.88]
+ [199.52 152.48]
+ [154.28 242.27]]
+pcent: (248.29, 248.62)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+INFO - Applied translation with (xtrans=8.0, ytrans=7.0).
+INFO - Applied rotation with angle=-4.0.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
[8]:
+
+
+
# Apply stored momentum correction
+sp.apply_momentum_correction()
+
+
+
+
+
+
+
+
+INFO - Adding corrected X/Y columns to dataframe:
+Calculating inverse deformation field, this might take a moment...
+INFO - Dask DataFrame Structure:
+                       X        Y        t      ADC       Xm       Ym
+npartitions=100
+                 float64  float64  float64  float64  float64  float64
+                     ...      ...      ...      ...      ...      ...
+...                  ...      ...      ...      ...      ...      ...
+                     ...      ...      ...      ...      ...      ...
+                     ...      ...      ...      ...      ...      ...
+Dask Name: apply_dfield, 206 graph layers
+
+
+
+
[9]:
+
+
+
# Apply stored config momentum calibration
+sp.apply_momentum_calibration()
+
+
+
+
+
+
+
+
+INFO - Adding kx/ky columns to dataframe:
+INFO - Using momentum calibration parameters generated on 03/06/2025, 09:26:48
+INFO - Dask DataFrame Structure:
+                       X        Y        t      ADC       Xm       Ym       kx       ky
+npartitions=100
+                 float64  float64  float64  float64  float64  float64  float64  float64
+                     ...      ...      ...      ...      ...      ...      ...      ...
+...                  ...      ...      ...      ...      ...      ...      ...      ...
+                     ...      ...      ...      ...      ...      ...      ...      ...
+                     ...      ...      ...      ...      ...      ...      ...      ...
+Dask Name: assign, 216 graph layers
+
+
+
+
[10]:
+
+
+
# Apply stored config energy correction
+sp.apply_energy_correction()
+
+
+
+
+
+
+
+
+INFO - Applying energy correction to dataframe...
+INFO - Using energy correction parameters generated on 03/06/2025, 09:26:48
+INFO - Dask DataFrame Structure:
+                       X        Y        t      ADC       Xm       Ym       kx       ky       tm
+npartitions=100
+                 float64  float64  float64  float64  float64  float64  float64  float64  float64
+                     ...      ...      ...      ...      ...      ...      ...      ...      ...
+...                  ...      ...      ...      ...      ...      ...      ...      ...      ...
+                     ...      ...      ...      ...      ...      ...      ...      ...      ...
+                     ...      ...      ...      ...      ...      ...      ...      ...      ...
+Dask Name: assign, 230 graph layers
+
+
+
+
[11]:
+
+
+
# Apply stored config energy calibration
+sp.append_energy_axis(bias_voltage=16.8)
+
+
+
+
+
+
+
+
+INFO - Adding energy column to dataframe:
+INFO - Using energy calibration parameters generated on 03/06/2025, 09:26:57
+INFO - Dask DataFrame Structure:
+                       X        Y        t      ADC       Xm       Ym       kx       ky       tm   energy
+npartitions=100
+                 float64  float64  float64  float64  float64  float64  float64  float64  float64  float64
+                     ...      ...      ...      ...      ...      ...      ...      ...      ...      ...
+...                  ...      ...      ...      ...      ...      ...      ...      ...      ...      ...
+                     ...      ...      ...      ...      ...      ...      ...      ...      ...      ...
+                     ...      ...      ...      ...      ...      ...      ...      ...      ...      ...
+Dask Name: assign, 243 graph layers
+
+
+
+
[12]:
+
+
+
# Apply delay calibration
+delay_range = (-500, 1500)
+sp.calibrate_delay_axis(delay_range=delay_range, preview=True)
+
+
+
+
+
+
+
+
+INFO - Adding delay column to dataframe:
+INFO - Append delay axis using delay_range = [-500, 1500] and adc_range = [475.0, 6400.0]
+INFO -              X            Y             t          ADC           Xm  \
+0     0.384484     0.384484      0.384484     0.384484   -23.283623
+1   365.244567  1002.244567  70101.244567  6317.244567   353.769545
+2   761.101929   818.101929  75615.101929  6316.101929   792.106877
+3   692.429705   971.429705  66455.429705  6317.429705   714.760810
+4   671.413437   712.413437  73026.413437  6317.413437   697.342750
+5   298.969298  1163.969298  68458.969298  6315.969298   280.645123
+6   570.922107   664.922107  73902.922107  6315.922107   588.394845
+7   822.485227   545.485227  72632.485227  6318.485227   847.200367
+8   817.521308   415.521308  72421.521308  6316.521308   835.486497
+9  1005.828330   666.828330  72801.828330  6316.828330  1037.626783
+
+            Ym        kx        ky            tm     energy        delay
+0    97.299514 -2.122526 -1.799076    -47.803871  -8.260110  -660.207769
+1  1034.845992 -1.111125  0.715783  70084.236496   7.511388  1472.065676
+2   838.751262  0.064664  0.189782  75614.218953   0.223263  1471.679976
+3   984.283338 -0.142808  0.580155  66449.730696  15.953383  1472.128171
+4   741.940029 -0.189530 -0.069903  73026.032742   3.068676  1472.122679
+5  1187.498385 -1.307273  1.125256  68432.455877  10.828590  1471.635206
+6   702.645708 -0.481770 -0.175305  73900.005007   2.017041  1471.619277
+7   587.353173  0.212446 -0.484564  72628.340437   3.582615  1472.484465
+8   466.640482  0.181025 -0.808362  72411.849440   3.872415  1471.821539
+9   707.805877  0.723243 -0.161464  72794.352656   3.365232  1471.925175
+
+
+
+
+

Compute final data volume#

+
+
[13]:
+
+
+
axes = ['kx', 'ky', 'energy', 'delay']
+bins = [100, 100, 200, 50]
+ranges = [[-2, 2], [-2, 2], [-4, 2], [-600, 1600]]
+res = sp.compute(bins=bins, axes=axes, ranges=ranges)
+
+
+
+
+
+
+
+
+
+
+
[14]:
+
+
+
# save to NXmpes NeXus (including standardized metadata)
+sp.save(data_path + "/binned.nxs")
+
+
+
+
+
+
+
+
+Using mpes reader to convert the given files:
+• ../src/sed/config/NXmpes_config.json
+The output file generated: /home/runner/work/sed/sed/docs/tutorial/datasets/WSe2/binned.nxs.
+
+
+
+
[15]:
+
+
+
# Visualization (requires JupyterLab)
+from jupyterlab_h5web import H5Web
+H5Web(data_path + "/binned.nxs")
+
+
+
+
+
[15]:
+
+
+
+
+<jupyterlab_h5web.widget.H5Web object>
+
+
+
+
[ ]:
+
+
+

+
+
+
+
+
+ + +
+ + + + + + + +
+ + + + +
+ + +
+
+ On this page +
+
+ +
+
+

This Page

+ +
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/sed/v1.0.0/tutorial/4_hextof_workflow.html b/sed/v1.0.0/tutorial/4_hextof_workflow.html new file mode 100644 index 0000000..26a09a9 --- /dev/null +++ b/sed/v1.0.0/tutorial/4_hextof_workflow.html @@ -0,0 +1,2931 @@ + + + + + + + + + + + Tutorial for binning data from the HEXTOF instrument at FLASH — SED 1.0.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + +
+ + + Ctrl+K +
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+ + +
+ +
+ + +
+
+ + + + + +
+ +
+

Tutorial for binning data from the HEXTOF instrument at FLASH#

+
+

Preparation#

+
+

Import necessary libraries#

+
+
[1]:
+
+
+
%load_ext autoreload
+%autoreload 2
+from typing import List
+from pathlib import Path
+import os
+
+from sed import SedProcessor
+from sed.dataset import dataset
+import xarray as xr
+
+%matplotlib widget
+import matplotlib.pyplot as plt
+
+
+
+
+
+

Get data paths#

+

The paths are such that if you are on Maxwell, it uses those. Otherwise data is downloaded in current directory from Zenodo.

+

Generally, if it is your beamtime, you can both read the raw data and write to processed directory. However, for the public data, you can not write to processed directory.

+
+
[2]:
+
+
+
beamtime_dir = "/asap3/flash/gpfs/pg2/2023/data/11019101" # on Maxwell
+if os.path.exists(beamtime_dir) and os.access(beamtime_dir, os.R_OK):
+    path = beamtime_dir + "/raw/hdf/offline/fl1user3"
+    meta_path = beamtime_dir + "/shared"
+    buffer_path = "Gd_W110/processed/"
+else:
+    # data_path can be defined and used to store the data in a specific location
+    dataset.get("Gd_W110") # Put in Path to a storage of at least 10 GByte free space.
+    path = dataset.dir
+    meta_path = path
+    buffer_path = path + "/processed/"
+
+
+
+
+
+
+
+
+INFO - Not downloading Gd_W110 data as it already exists at "/home/runner/work/sed/sed/docs/tutorial/datasets/Gd_W110".
+Set 'use_existing' to False if you want to download to a new location.
+INFO - Using existing data path for "Gd_W110": "/home/runner/work/sed/sed/docs/tutorial/datasets/Gd_W110"
+INFO - Gd_W110 data is already present.
+
+
+
+
+

Config setup#

+

Here we get the path to the config file and setup the relevant directories. This can also be done directly in the config file.

+
+
[3]:
+
+
+
# pick the default configuration file for hextof@FLASH
+config_file = Path('../src/sed/config/flash_example_config.yaml')
+assert config_file.exists()
+
+
+
+

The path to the processed folder can also be defined as a keyword argument later.

+
+
[4]:
+
+
+
# here we setup a dictionary that will be used to override the path configuration
+config_override = {
+    "core": {
+        "paths": {
+            "raw": path,
+            "processed": buffer_path,
+        },
+    },
+}
+
+
+
+
+
+

cleanup previous config files#

+

In this notebook, we will show how calibration parameters can be generated. Therefore we want to clean the local directory of previously generated files.

+

WARNING running the cell below will delete the “sed_config.yaml” file in the local directory. If these contain precious calibration parameters, DO NOT RUN THIS CELL.

+
+
[5]:
+
+
+
local_folder_config = Path('./sed_config.yaml')
+if local_folder_config.exists():
+    os.remove(local_folder_config)
+    print(f'deleted local config file {local_folder_config}')
+assert not local_folder_config.exists()
+
+
+
+
+
+
+
+
+deleted local config file sed_config.yaml
+
+
+
+
+
+

Load a chessy sample run#

+

The common starting point at a FLASH beamtime. Look at the Chessy sample!

+
    +

  • run 44762: Chessy - FoV = 450 µm

    • +
+
+

Generate the Processor instance#

+

this cell generates an instance of the SedProcessor class. It will be our workhorse for the entire workflow.

+
+

Important note#

+

The following extra arguments are available for FlashLoader. None of which are necessary to give but helpful to know.

+
    +

  • force_recreate: Probably the most useful. In case the config is changed, this allows to reduce the raw h5 files to the the intermediate parquet format again. Otherwise, the schema between the saved dataframe and config differs.

    • +

  • debug: Setting this runs the reduction process in serial, so the errors are easier to find.

    • +

  • remove_invalid_files: Sometimes some critical channels defined in the config are missing in some raw files. Setting this will make sure to ignore such files.

    • +

  • filter_timed_by_electron: Defaults to True. When True, the timed dataframe will only contain data points where valid electron events were detected. When False, all timed data points are included regardless of electron detection (see OpenCOMPES/sed#307)

    • +

  • processed_dir: Location to save the reduced parquet files.

    • +

  • scicat_token: Token from your scicat account.

    • +

  • detector: ‘1Q’ and ‘4Q’ detector for example. Useful when there are separate raw files for each detector.

    • +
+
+
[6]:
+
+
+
sp = SedProcessor(runs=[44762], config=config_override, system_config=config_file, collect_metadata=False)
+# You can set collect_metadata=True if the scicat_url and scicat_token are defined
+
+
+
+
+
+
+
+
+INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
+INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
+INFO - Reading files: 0 new files of 1 total.
+loading complete in  0.07 s
+
+
+
+
+
+

Add Jitter#

+

In order to avoid artifacts arising from incommensurate binning sizes with those imposed during data collection, e.g. by the detector, we jitter all the digital columns.

+
+
[7]:
+
+
+
sp.add_jitter()
+
+
+
+
+
+
+
+
+INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
+
+
+
+
+

inspect the dataframe#

+

Looking at the dataframe can give quick insight about the columns loaded and the data available.

+
    +

  • sp.dataframe shows the structure of the dataframe without computing anything. Interesting here are the columns, and their type.

    • +

  • The sp.dataframe.head() function accesses the first 5 events in the dataframe, giving us a view of what the values of each column look like, without computing the whole thing. sp.dataframe.tail()does the same from the end.

    • +

  • sp.dataframe.compute() will compute the whole dataframe, and can take a while. We should avoid doing this.

    • +
+
+
[8]:
+
+
+
sp.dataframe
+
+
+
+
+
[8]:
+
+
+
+
Dask DataFrame Structure:
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
trainIdpulseIdelectronIddldPosXdldPosYdldTimeStepspulserSignAdcbamtimeStampmonochromatorPhotonEnergygmdBdadelayStagesampleBiastofVoltageextractorVoltageextractorCurrentcryoTemperaturesampleTemperaturedldTimeBinSizedldSectorID
npartitions=1
uint32int64int64float64float64float64float32float32float64float32float32float32float32float32float32float32float32float32float32int8
............................................................
+
+
Dask Name: apply_jitter, 14 graph layers
+
+
+
[9]:
+
+
+
sp.dataframe.head()
+
+
+
+
+
[9]:
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
trainIdpulseIdelectronIddldPosXdldPosYdldTimeStepspulserSignAdcbamtimeStampmonochromatorPhotonEnergygmdBdadelayStagesampleBiastofVoltageextractorVoltageextractorCurrentcryoTemperaturesampleTemperaturedldTimeBinSizedldSectorID
01646339970120780.864716689.8647163049.86471632914.08976.093751.679395e+0951.02345345.5127941448.312988-0.00348929.9990656029.370117-0.070368303.940002304.9400020.0205767
11646339970121780.946285690.9462853048.94628532914.08976.093751.679395e+0951.02345345.5127941448.312988-0.00348929.9990656029.370117-0.070368303.940002304.9400020.0205762
21646339970220561.656736230.6567365728.65673632914.08990.375001.679395e+0951.02345345.5127941448.312988-0.00348929.9990656029.370117-0.070368303.940002304.9400020.0205763
31646339970221938.219777947.2197775730.21977732914.08990.375001.679395e+0951.02345345.5127941448.312988-0.00348929.9990656029.370117-0.070368303.940002304.9400020.0205762
41646339970270535.522056853.5220561570.52205632914.08982.875001.679395e+0951.02345345.5127941448.312988-0.00348929.9990656029.370117-0.070368303.940002304.9400020.0205760
+
+
+
+
+

Visualizing event histograms#

+

For getting a first impression of the data, and to determine binning ranges, the method sp.view_even_histogram() allows visualizing the events in one dataframe partition as histograms. Default axes and ranges are defined in the config, and show the dldPosX, dldPosY, and dldTimeStep columns:

+
+
[10]:
+
+
+
sp.view_event_histogram(dfpid=0)
+
+
+
+
+
+
+
+
+
+
+
+

Binning#

+

Here we define the parameters for binning the dataframe to an n-dimensional histogram, which we can then plot, analyze or save.

+

If you never saw this before, the type after : is a “hint” to what type the object to the left will have. We include them here to make sure you know what each variable should be.

+
a:int = 1 # a is an integer
+b:float = 1.0 # b is a float
+c:str = 1 # we hint c to be a string, but it is still an integer
+
+
+

This is totally optional, but can help you keep track of what you are doing.

+
+
[11]:
+
+
+
# the name of the axes on which we want to bin
+axes: List[str] = ['dldPosY', 'dldPosX']
+# the number of bins for each axis
+bins: List[int] = [480, 480]
+# for each axis, the range of values to consider
+ranges: List[List[int]] = [[420,900], [420,900]]
+# here we compute the histogram
+res_chessy: xr.DataArray = sp.compute(bins=bins, axes=axes, ranges=ranges)
+
+
+
+
+
+
+
+
+
+
+
+

visualize the result#

+

here we plot the binned histogram. The result is an xarray, which gives us some convenient visualization and simple plotting tools.

+
+
[12]:
+
+
+
res_chessy
+
+
+
+
+
[12]:
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray (dldPosY: 480, dldPosX: 480)> Size: 922kB
+array([[0., 0., 0., ..., 0., 0., 0.],
+       [0., 0., 0., ..., 0., 0., 0.],
+       [0., 0., 0., ..., 0., 0., 0.],
+       ...,
+       [0., 0., 0., ..., 0., 0., 0.],
+       [0., 0., 0., ..., 0., 0., 0.],
+       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)
+Coordinates:
+  * dldPosY  (dldPosY) float64 4kB 420.0 421.0 422.0 423.0 ... 897.0 898.0 899.0
+  * dldPosX  (dldPosX) float64 4kB 420.0 421.0 422.0 423.0 ... 897.0 898.0 899.0
+Attributes:
+    units:      counts
+    long_name:  photoelectron counts
+    metadata:   {'file_statistics': {'electron': {'0': {'created_by': 'parque...
+
+
+
[13]:
+
+
+
plt.figure()
+res_chessy.plot(robust=True) # robust is used to avoid outliers to dominate the color scale
+
+
+
+
+
[13]:
+
+
+
+
+<matplotlib.collections.QuadMesh at 0x7f783c3168f0>
+
+
+
+
+
+
+
+
+
+
+
+

Optical Spot Profile#

+

Here we load runs 44798 and 44799, which show the profile of the optical spot on the same spatial view as in our chessy run above. The two differ in transmission, being \(T=1.0\) and \(T=0.5\) respectively.

+
+
[14]:
+
+
+
sp = SedProcessor(runs=[44798], config=config_override, system_config=config_file, collect_metadata=False)
+sp.add_jitter()
+res_t05: xr.DataArray = sp.compute(bins=bins, axes=axes, ranges=ranges)
+
+sp = SedProcessor(runs=[44799], config=config_override, system_config=config_file, collect_metadata=False)
+sp.add_jitter()
+res_t10: xr.DataArray = sp.compute(bins=bins, axes=axes, ranges=ranges)
+
+
+
+
+
+
+
+
+INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
+INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
+INFO - Reading files: 0 new files of 1 total.
+loading complete in  0.06 s
+INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
+
+
+
+
+
+
+
+
+
+
+
+
+
+INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
+INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
+INFO - Reading files: 0 new files of 2 total.
+loading complete in  0.06 s
+INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
+
+
+
+
+
+
+
+
+
+
[15]:
+
+
+
fig,ax = plt.subplots(1,3,figsize=(6,2), layout='tight')
+res_chessy.plot(ax=ax[0], robust=True, add_colorbar=False)
+res_t05.plot(ax=ax[1], robust=True, add_colorbar=False)
+res_t10.plot(ax=ax[2], robust=True, add_colorbar=False)
+
+
+
+
+
[15]:
+
+
+
+
+<matplotlib.collections.QuadMesh at 0x7f783c5f8850>
+
+
+
+
+
+
+
+
+

TODO: here we can add the evaluation of the spot size.

+
+
+

Energy Calibration#

+

We now load a bias series, where the sample bias was varied, effectively shifting the energy spectra. This allows us to calibrate the conversion between the digital values of the dld and the energy.

+
+
[16]:
+
+
+
sp = SedProcessor(runs=[44797], config=config_override, system_config=config_file, collect_metadata=False)
+sp.add_jitter()
+
+
+
+
+
+
+
+
+INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
+INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
+INFO - Reading files: 0 new files of 5 total.
+loading complete in  0.07 s
+INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
+
+
+

We can use the view_event_histogram() function also to e.g. visualize the events per microbunch along the train, or hit multiplicity per microbunch:

+
+
[17]:
+
+
+
sp.view_event_histogram(dfpid=0, axes=["pulseId", "electronId"], ranges=[[0, 600], [0,10]], bins=[100, 10])
+
+
+
+
+
+
+
+
+
+
+

sector alignment#

+

as usual first we jitter, but here we also align in time the 8 sectors of the dld. This is done by finding the time of the maximum of the signal in each sector, and then shifting the signal in each sector by the difference between the maximum time and the time of the maximum in each sector.

+

For better precision, the photon peak can be used to track the energy shift.

+
+
[18]:
+
+
+
sp.align_dld_sectors()
+
+
+
+
+
+
+
+
+INFO - Aligning 8s sectors of dataframe
+INFO - Dask DataFrame Structure:
+              trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID
+npartitions=5
+               uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8
+                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+...               ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+Dask Name: assign, 16 graph layers
+
+
+
+
+

time-of-flight spectrum#

+

to compare with what we see on the measurement computer, we might want to plot the time-of-flight spectrum. This is done here.

+
+
[19]:
+
+
+
sp.append_tof_ns_axis()
+
+
+
+
+
+
+
+
+INFO - Adding time-of-flight column in nanoseconds to dataframe.
+INFO - Dask DataFrame Structure:
+              trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID  dldTime
+npartitions=5
+               uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64
+                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+...               ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+Dask Name: assign, 22 graph layers
+
+
+

Now, to determine proper binning ranges, let’s have again a look at the event histograms:

+
+
[20]:
+
+
+
sp.view_event_histogram(dfpid=0, axes=["sampleBias", "dldTime"], ranges=[[27, 33], [650,1050]], bins=[50, 100])
+
+
+
+
+
+
+
+
+
+
+
[21]:
+
+
+
axes = ['sampleBias','dldTime']
+bins = [5, 250]
+ranges = [[28,33],  [650,800]]
+res = sp.compute(bins=bins, axes=axes, ranges=ranges)
+
+
+
+
+
+
+
+
+
+

We binned not only in dldTime but also in sampleBias. This allows us to separate the spectra obtained at different bias values.

+
+
[22]:
+
+
+
plt.figure()
+res.plot.line(x='dldTime'); # the ; here is to suppress an annoying output
+
+
+
+
+
+
+
+
+
+
+
+

find calibration parameters#

+

We now will fit the tof-energy relation. This is done by finding the maxima of a peak in the tof spectrum, and then fitting the square root relation to obtain the calibration parameters.

+
+
[23]:
+
+
+
axes = ['sampleBias', 'dldTimeSteps']
+bins = [5, 500]
+ranges = [[28,33], [4000, 4800]]
+res = sp.compute(bins=bins, axes=axes, ranges=ranges)
+
+
+
+
+
+
+
+
+
+
+
[24]:
+
+
+
sp.load_bias_series(binned_data=res)
+
+
+
+
+
+
+
+
+
+
+
[25]:
+
+
+
ranges=(4120, 4200)
+ref_id=0
+sp.find_bias_peaks(ranges=ranges, ref_id=ref_id, apply=True)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+INFO - Use feature ranges: [(4120.0, 4200.0), (4156.8, 4238.4), (4195.2, 4286.4), (4236.8, 4328.0), (4281.6, 4374.4)].
+INFO - Extracted energy features: [[4.1488e+03 1.0000e+00]
+ [4.1872e+03 1.0000e+00]
+ [4.2272e+03 1.0000e+00]
+ [4.2704e+03 1.0000e+00]
+ [4.3152e+03 1.0000e+00]].
+
+
+
+
[26]:
+
+
+
sp.calibrate_energy_axis(
+    ref_energy=-.55,
+    method="lmfit",
+    energy_scale='kinetic',
+    d={'value':1.0,'min': .2, 'max':1.0, 'vary':False},
+    t0={'value':5e-7, 'min': 1e-7, 'max': 1e-6, 'vary':True},
+    E0={'value': 0., 'min': -100, 'max': 100, 'vary': True},
+)
+
+
+
+
+
+
+
+
+INFO - [[Fit Statistics]]
+    # fitting method   = leastsq
+    # function evals   = 22
+    # data points      = 5
+    # variables        = 2
+    chi-square         = 1.9886e-04
+    reduced chi-square = 6.6286e-05
+    Akaike info crit   = -46.6618227
+    Bayesian info crit = -47.4429469
+[[Variables]]
+    d:   1 (fixed)
+    t0:  3.5727e-07 +/- 2.9058e-10 (0.08%) (init = 5e-07)
+    E0: -54.7998131 +/- 0.04277721 (0.08%) (init = 0)
+[[Correlations]] (unreported correlations are < 0.100)
+    C(t0, E0) = -0.9964
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

generate the energy axis#

+

Now that we have the calibration parameters, we can generate the energy axis for each spectrum

+
+
[27]:
+
+
+
sp.append_energy_axis()
+
+
+
+
+
+
+
+
+INFO - Adding energy column to dataframe:
+INFO - Using energy calibration parameters generated on 03/06/2025, 09:33:21
+INFO - Dask DataFrame Structure:
+              trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID  dldTime   energy
+npartitions=5
+               uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64  float64
+                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
+...               ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
+                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
+                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
+Dask Name: assign, 37 graph layers
+
+
+

Lets have a look at the dataset, and the columns we added.

+
+
[28]:
+
+
+
sp.dataframe[['dldTime','dldTimeSteps','energy','dldSectorID']].head()
+
+
+
+
+
[28]:
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
dldTimedldTimeStepsenergydldSectorID
0696.4718474231.066406-2.6487141
1696.5781034231.711914-2.6641870
2697.8375074239.362793-2.8464731
3697.7014324238.536133-2.8268744
4745.1970124527.071777-8.4657461
+
+
+
+
+

Bin in energy#

+

With the newly added column, we can now bin directly in energy

+
+
[29]:
+
+
+
axes: List[str] = ['sampleBias', 'energy']
+bins: List[int] = [5, 500]
+ranges: List[List[int]] = [[28,33], [-10,10]]
+res: xr.DataArray = sp.compute(bins=bins, axes=axes, ranges=ranges)
+
+
+
+
+
+
+
+
+
+
+
[30]:
+
+
+
plt.figure() # if you are using interactive plots, you'll need to generate a new figure explicitly every time.
+res.mean('sampleBias').plot.line(x='energy',linewidth=3)
+res.plot.line(x='energy',linewidth=1,alpha=.5);
+
+
+
+
+
+
+
+
+
+
+
+

correct offsets#

+

The energy axis is now correct, taking the sample bias of the measurement into account. Additionally, we can compensate the photon energy (monochromatorPhotonEnergy) and the tofVoltage.

+
+
[31]:
+
+
+
sp.add_energy_offset(
+    columns=['monochromatorPhotonEnergy','tofVoltage'],
+    weights=[-1,-1],
+    preserve_mean=[True, True],
+)
+
+
+
+
+
+
+
+
+INFO - Adding energy offset to dataframe:
+INFO - Energy offset parameters:
+   Column[monochromatorPhotonEnergy]: Weight=-1, Preserve Mean: True, Reductions: None.
+   Column[tofVoltage]: Weight=-1, Preserve Mean: True, Reductions: None.
+INFO - Dask DataFrame Structure:
+              trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID  dldTime   energy
+npartitions=5
+               uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64  float64
+                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
+...               ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
+                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
+                  ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
+Dask Name: assign, 67 graph layers
+
+
+

Now we bin again and see the result

+
+
[32]:
+
+
+
axes = ['sampleBias', 'energy']
+bins = [5, 500]
+ranges = [[28,33], [-10,2]]
+res = sp.compute(bins=bins, axes=axes, ranges=ranges)
+
+
+
+
+
+
+
+
+
+
+
[33]:
+
+
+
plt.figure()
+ax = plt.subplot(111)
+res.energy.attrs['unit'] = 'eV' # add units to the axes
+res.mean('sampleBias').plot.line(x='energy',linewidth=3, ax=ax)
+res.plot.line(x='energy',linewidth=1,alpha=.5,label='all',ax=ax);
+
+
+
+
+
+
+
+
+
+
+
+

save the calibration parameters#

+

The parameters we have found can be saved to a file, so that we can use them later. This means the calibration can be used for different runs.

+
+
[34]:
+
+
+
sp.save_energy_calibration()
+sp.save_energy_offset()
+
+
+
+
+
+
+
+
+INFO - Saved energy calibration parameters to "sed_config.yaml".
+INFO - Saved energy offset parameters to "sed_config.yaml".
+
+
+

A more general function, which saves parameters for all the calibrations performed. Use either the above or below function. They are equivalent (and overwrite each other)

+
+
[35]:
+
+
+
sp.save_workflow_params()
+
+
+
+
+
+
+
+
+INFO - Saved energy calibration parameters to "sed_config.yaml".
+INFO - Saved energy offset parameters to "sed_config.yaml".
+
+
+
+
+
+

Correct delay axis#

+

To calibrate the pump-probe delay axis, we need to shift the delay stage values to center the pump-probe-time overlap time zero. Also, we want to correct the SASE jitter, using information from the bam column.

+

Here we load multiple runs at once

+
+
[36]:
+
+
+
sp = SedProcessor(
+    runs=[44824,44825,44826,44827],
+    config=config_override,
+    system_config=config_file,
+    collect_metadata=False,
+)
+
+
+
+
+
+
+
+
+INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/sed_config.yaml]
+INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
+INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
+INFO - Reading files: 0 new files of 62 total.
+loading complete in  0.24 s
+
+
+
+

Run the workflow from the config file#

+

as we have saved some calibration and correction parameters, we can now run the workflow from the config file. This is done by calling each of the correction functions, with no parameters. The functions will then load the parameters from the config file.

+
+
[37]:
+
+
+
sp.add_jitter()
+sp.align_dld_sectors()
+sp.append_energy_axis()
+sp.add_energy_offset()
+
+
+
+
+
+
+
+
+INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
+INFO - Aligning 8s sectors of dataframe
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID
+npartitions=62
+                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+Dask Name: assign, 16 graph layers
+INFO - Adding energy column to dataframe:
+INFO - Using energy calibration parameters generated on 03/06/2025, 09:33:21
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
+npartitions=62
+                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+Dask Name: assign, 31 graph layers
+INFO - Adding energy offset to dataframe:
+INFO - Using energy offset parameters generated on 03/06/2025, 09:33:23
+INFO - Energy offset parameters:
+   Column[monochromatorPhotonEnergy]: Weight=-1.0, Preserve Mean: True, Reductions: None.
+   Column[tofVoltage]: Weight=-1.0, Preserve Mean: True, Reductions: None.
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
+npartitions=62
+                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+Dask Name: assign, 61 graph layers
+
+
+
+
+

plot the delayStage values#

+
+
[38]:
+
+
+
axes = ['energy','delayStage']
+bins = [100,150]
+delay_start,delay_stop=1462.00,1464.85
+ranges = [[-5,2], [delay_start,delay_stop]]
+res = sp.compute(bins=bins, axes=axes, ranges=ranges)
+
+
+
+
+
+
+
+
+
+
+
[39]:
+
+
+
fig,ax = plt.subplots(1,2,figsize=(6,2.25), layout='constrained')
+res.plot(robust=True, ax=ax[0])
+bg = res.isel(delayStage=slice(0,10)).mean('delayStage')
+(res-bg).plot(robust=True, ax=ax[1])
+
+
+
+
+
[39]:
+
+
+
+
+<matplotlib.collections.QuadMesh at 0x7f781c920100>
+
+
+
+
+
+
+
+
+
+
[40]:
+
+
+
sp.add_delay_offset(
+    constant=-1463.7, # this is time zero
+    flip_delay_axis=True, # invert the direction of the delay axis
+    columns=['bam'], # use the bam to offset the values
+    weights=[-0.001], # bam is in fs, delay in ps
+    preserve_mean=True # preserve the mean of the delay axis
+)
+
+
+
+
+
+
+
+
+INFO - Adding delay offset to dataframe:
+INFO - Delay offset parameters:
+   Column[bam]: Weight=-0.001, Preserve Mean: True, Reductions: None.
+   Constant: -1463.7
+   Flip delay axis: True
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
+npartitions=62
+                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float64    float32    float32          float32          float32         float32           float32        float32        int8  float64
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+Dask Name: assign, 81 graph layers
+
+
+
+
[41]:
+
+
+
sp.dataframe # This has generated too many layers, there is room for improvement!
+
+
+
+
+
[41]:
+
+
+
+
Dask DataFrame Structure:
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
trainIdpulseIdelectronIddldPosXdldPosYdldTimeStepspulserSignAdcbamtimeStampmonochromatorPhotonEnergygmdBdadelayStagesampleBiastofVoltageextractorVoltageextractorCurrentcryoTemperaturesampleTemperaturedldTimeBinSizedldSectorIDenergy
npartitions=62
uint32int64int64float64float64float32float32float32float64float32float32float64float32float32float32float32float32float32float32int8float64
...............................................................
..................................................................
...............................................................
...............................................................
+
+
Dask Name: assign, 81 graph layers
+
+
+
+

bin in the corrected delay axis#

+
+
[42]:
+
+
+
axes = ['energy','delayStage']
+bins = [100,150]
+delay_start,delay_stop=1462.00,1464.85
+ranges = [[-3,2], [-1.1, 1.75]]
+res = sp.compute(bins=bins, axes=axes, ranges=ranges)
+
+
+
+
+
+
+
+
+
+
+
[43]:
+
+
+
fig,ax = plt.subplots(1,2,figsize=(6,2.25))
+res.plot(robust=True, ax=ax[0])
+bg = res.sel(delayStage=slice(-1,-0.2)).mean('delayStage')
+(res-bg).plot(robust=True, ax=ax[1])
+fig.tight_layout()
+
+
+
+
+
+
+
+
+
+

You may note some intensity variation along the delay axis. This comes mainly from inhomogeneous speed of the delay stage, and thus inequivalent amounts of time spent on every delay point. This can be corrected for by normalizing the data to the acquisition time per delay point:

+
+
[44]:
+
+
+
res = sp.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time="delayStage")
+fig,ax = plt.subplots(1,2,figsize=(6,2.25), layout='constrained')
+res.plot(robust=True, ax=ax[0])
+bg = res.sel(delayStage=slice(-1,-.2)).mean('delayStage')
+(res-bg).plot(robust=True, ax=ax[1])
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+INFO - Calculate normalization histogram for axis 'delayStage'...
+
+
+
+
+
+
+
+
+
+
[44]:
+
+
+
+
+<matplotlib.collections.QuadMesh at 0x7f7810090340>
+
+
+
+
+
+
+
+
+
+
+

save parameters#

+

as before, we can save the parameters we just used in the config for the next run

+
+
[45]:
+
+
+
sp.save_delay_offsets()
+
+
+
+
+
+
+
+
+INFO - Saved delay offset parameters to "sed_config.yaml".
+
+
+
+
+
+

Run workflow entirely from config.#

+

Once all the calibrations are done, a new run can be loaded by simply calling all the calibration functions.

+
+
[46]:
+
+
+
from sed.core.config import load_config
+import numpy as np
+metadata = load_config(meta_path + "/44824_20230324T060430.json")
+
+# Fix metadata
+metadata["scientificMetadata"]["Laser"]["wavelength"]["value"] = float(metadata["scientificMetadata"]["Laser"]["wavelength"]["value"][:-2])
+metadata["scientificMetadata"]["Laser"]["energy"] = {"value": 1239.84/metadata["scientificMetadata"]["Laser"]["wavelength"]["value"], "unit": "eV"}
+metadata["scientificMetadata"]["Laser"]["polarization"] = [1, 1, 0, 0]
+metadata["scientificMetadata"]["Collection"]["field_aperture_x"] = float(metadata["scientificMetadata"]["Collection"]["field_aperture_x"])
+metadata["scientificMetadata"]["Collection"]["field_aperture_y"] = float(metadata["scientificMetadata"]["Collection"]["field_aperture_y"])
+metadata["pi"] = {"institute": "JGU Mainz"}
+metadata["proposer"] = {"institute": "TU Dortmund"}
+
+
+
+
+
[47]:
+
+
+
sp = SedProcessor(
+    runs=[44824,44825,44826,44827],
+    config=config_override,
+    system_config=config_file,
+    metadata = metadata,
+    collect_metadata=False,
+)
+
+
+
+
+
+
+
+
+INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/sed_config.yaml]
+INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
+INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
+INFO - Reading files: 0 new files of 62 total.
+loading complete in  0.16 s
+
+
+
+
[48]:
+
+
+
sp.add_jitter()
+sp.align_dld_sectors()
+sp.append_tof_ns_axis()
+sp.append_energy_axis()
+sp.add_energy_offset()
+sp.add_delay_offset()
+
+
+
+
+
+
+
+
+INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
+INFO - Aligning 8s sectors of dataframe
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID
+npartitions=62
+                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+Dask Name: assign, 16 graph layers
+INFO - Adding time-of-flight column in nanoseconds to dataframe.
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID  dldTime
+npartitions=62
+                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+Dask Name: assign, 22 graph layers
+INFO - Adding energy column to dataframe:
+INFO - Using energy calibration parameters generated on 03/06/2025, 09:33:21
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID  dldTime   energy
+npartitions=62
+                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64  float64
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
+Dask Name: assign, 37 graph layers
+INFO - Adding energy offset to dataframe:
+INFO - Using energy offset parameters generated on 03/06/2025, 09:33:23
+INFO - Energy offset parameters:
+   Column[monochromatorPhotonEnergy]: Weight=-1.0, Preserve Mean: True, Reductions: None.
+   Column[tofVoltage]: Weight=-1.0, Preserve Mean: True, Reductions: None.
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID  dldTime   energy
+npartitions=62
+                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64  float64
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
+Dask Name: assign, 67 graph layers
+INFO - Adding delay offset to dataframe:
+INFO - Using delay offset parameters generated on 03/06/2025, 09:35:12
+INFO - Delay offset parameters:
+   Constant: -1463.7
+   Flip delay axis: True
+   Column[bam]: Weight=-0.001, Preserve Mean: True, Reductions: None.
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID  dldTime   energy
+npartitions=62
+                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float64    float32    float32          float32          float32         float32           float32        float32        int8  float64  float64
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...      ...
+Dask Name: assign, 87 graph layers
+
+
+
+

Compute the results#

+
+
[49]:
+
+
+
axes = ['energy','delayStage']
+bins = [100,150]
+delay_start,delay_stop=1462.00,1464.85
+ranges = [[-5,2], [-1.1, 1.75]]
+res = sp.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time="delayStage")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+INFO - Calculate normalization histogram for axis 'delayStage'...
+
+
+
+
+
+
+
+
+
+
[50]:
+
+
+
fig,ax = plt.subplots(1,2,figsize=(6,2.25), layout='constrained')
+res.plot(robust=True, ax=ax[0])
+bg = res.sel(delayStage=slice(-1,-.2)).mean('delayStage')
+(res-bg).plot(robust=True, ax=ax[1])
+
+
+
+
+
[50]:
+
+
+
+
+<matplotlib.collections.QuadMesh at 0x7f7800bfe050>
+
+
+
+
+
+
+
+
+
+
+
+

Save results#

+

binned data can now be saved as h5 or tiff. igor binaries soon to come if requested!

+
+
[51]:
+
+
+
sp.save('runs44824-27.h5')
+
+
+
+
+
+
+
+
+saving data to runs44824-27.h5
+Saved creation_date as string.
+Saved creation_date as string.
+Saved creation_date as string.
+Saving complete!
+
+
+
+
[52]:
+
+
+
sp.save('runs44824-27.tiff')
+
+
+
+
+
+
+
+
+Successfully saved runs44824-27.tiff
+ Axes order: ['delayStage', 'energy', 'C', 'Y', 'X', 'S']
+
+
+
+
[53]:
+
+
+
sp.save("runs44824-27.nxs")
+
+
+
+
+
+
+
+
+Using mpes reader to convert the given files:
+• ../src/sed/config/NXmpes_config-HEXTOF.json
+The output file generated: runs44824-27.nxs.
+
+
+
+
[ ]:
+
+
+

+
+
+
+
+
+ + +
+ + + + + + + +
+ + + + + + + +
+
+ +
+ +
+
+
+ + + + + + + + \ No newline at end of file diff --git a/sed/latest/tutorial/5_sxp_workflow.html b/sed/v1.0.0/tutorial/5_sxp_workflow.html similarity index 69% rename from sed/latest/tutorial/5_sxp_workflow.html rename to sed/v1.0.0/tutorial/5_sxp_workflow.html index 4394139..84a7e5a 100644 --- a/sed/latest/tutorial/5_sxp_workflow.html +++ b/sed/v1.0.0/tutorial/5_sxp_workflow.html @@ -8,7 +8,7 @@ - Tutorial for binning data from the SXP instrument at the European XFEL — SED 1.0.0a1.dev19+gf1bb527 documentation + Tutorial for binning data from the SXP instrument at the European XFEL — SED 1.0.0 documentation @@ -39,7 +39,7 @@ - + @@ -50,7 +50,7 @@ @@ -60,7 +60,7 @@ - + @@ -122,7 +122,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

@@ -576,7 +576,7 @@

Load Au/Mica data
-
+
@@ -720,7 +720,7 @@

Channel Histograms
-
+

@@ -741,7 +741,7 @@

PulseIds, ElectronIds
-
+

We can also inspect the counts per train as function of the trainId and the pulseId, which gives us a good idea about the evolution of the count rate over the run(s)

@@ -761,7 +761,7 @@

PulseIds, ElectronIds

-
+ 
[11]:
@@ -769,14 +769,14 @@ 
PulseIds, ElectronIds
 

 
-<matplotlib.collections.QuadMesh at 0x7f7de414c940>
+<matplotlib.collections.QuadMesh at 0x7fd694149840>
-
+

@@ -800,7 +800,7 @@

Spectrum vs. MicrobunchId
-
+ 
[12]:
@@ -808,14 +808,14 @@ 
Spectrum vs. MicrobunchId
 
-<matplotlib.collections.QuadMesh at 0x7f7dd0093340>
+<matplotlib.collections.QuadMesh at 0x7fd6b8a81ba0>
-
+

We see that the background below the Au 4f core levels slightly changes with microbunch ID. The origin of this is not quite clear yet.

@@ -835,14 +835,14 @@

Spectrum vs. MicrobunchId 
-<matplotlib.legend.Legend at 0x7f7dd01985b0>
+<matplotlib.legend.Legend at 0x7fd69594eb00>

-
+
@@ -892,7 +892,7 @@

time-of-flight spectrum
-
+

@@ -932,7 +932,7 @@

Load energy calibration files
-
+
@@ -949,7 +949,7 @@

Load energy calibration files

-
+
@@ -966,7 +966,7 @@

Load energy calibration files

-
+
@@ -983,7 +983,7 @@

Load energy calibration files

-
+
@@ -1000,7 +1000,7 @@

Load energy calibration files

-
+
@@ -1017,7 +1017,7 @@

Load energy calibration files

-
+
@@ -1034,7 +1034,7 @@

Load energy calibration files

-
+
@@ -1051,7 +1051,7 @@

Load energy calibration files

-
+
@@ -1068,7 +1068,7 @@

Load energy calibration files

-
+
@@ -1085,7 +1085,7 @@

Load energy calibration files

-
+
@@ -1102,7 +1102,7 @@

Load energy calibration files

-
+

@@ -1120,7 +1120,7 @@

Load bias series
-
+

@@ -1140,19 +1140,19 @@

find calibration parameters
-
+
-
+
-
+
@@ -1214,13 +1214,13 @@

find calibration parameters

-
+
-
+

@@ -1266,10 +1266,10 @@

Bin data with energy axis
-
+
@@ -1312,7 +1312,7 @@

Bin data with energy axis

-
+
[23]:
@@ -1329,14 +1329,14 @@ 
Bin data with energy axis
 
-<matplotlib.collections.QuadMesh at 0x7f7dc41f0af0>
+<matplotlib.collections.QuadMesh at 0x7fd6885cfa90>
-
+

@@ -1384,7 +1384,7 @@

Correct delay stage offset.
-
+
@@ -1398,7 +1398,7 @@

Correct delay stage offset.

-
+
[26]:
@@ -1418,14 +1418,14 @@ 
Correct delay stage offset.
 
-[<matplotlib.lines.Line2D at 0x7f7daedae500>]
+[<matplotlib.lines.Line2D at 0x7fd688b66cb0>]
-
+
[ ]:
@@ -1436,7 +1436,7 @@ 
Correct delay stage offset.
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", 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", 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", 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", 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", 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", 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", 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+68EfAzYIKfaEKFKDOVaV0KjkKhUCgUSnNRKv8aQQUNEDLXoQrgFOCkaDTqBdvIsW7zb6YKY8ar8qSld7OEl1EldItXmyurY+pVDZxUIy8043pNlfLhRi1160nrJn+7fJplG+elrm7VdS9qYKPKnVOd3Hr+asc329O9Vv5u6tVIfWarx68ViqpAbvCj0AHgnIcqgBQKhUKhzCEUqA0P4KgCOPehCuAUUEslsHtzbkQRcmPT5FZ9q0cVc5OPnb1bvW/x5n1OKqFX7027a2HncVyrXC/X0UlZscpvqrwjmxEfspandLPzt6PZ7VWrjHqPn8oZAq/qoV2bWT2vnJ4/dp7Cte7JRiMeuPG8tzrHuaQEUihOUAWQQqFQKJQ5BFEAqQ0gxRmqADaRejxKrb673ddIHbS8vShjbuxy6lHz6jl/O7XE/HbP8ALYUMg2Hzfta2X/5FatrcfL2o1q5iUWm9syvSiqzYINhTzV1UlRrWW3OdUenl7zN5+7GwW/nv5q5/XsVhE0q3XNiNHodC5ePaSd6lSP1+9c8ARWoEJWG/sodPw356EDwCYyEx4cXqdvvT447aZJ6gll4WUq1Xyc3WDUalrK69SwneNOrWmuZjl1OOHGscZum1OeVvV3M5hv1FBfyec99VmnAfVMuP+M1Gobp3NvBLcvmLXa3TgVa3XfezURqOdesLvPGzFPmGn9ZCrQbAAb+VAFcO5DB4AUCoVCoVAohxl0ADgNWKknXp0UnNLZTQE1C6f8tVAsbs6t3gCxbqdV3aqSTqF6zNN0tQzI7epaL051axZ2yqbdVJz5GlhNu5vz8eoMYy7fjBfj/mbhVfVu5Fp5NaWo93yd7uVafUItWyvGzbzf7Y5zu7RerX1zFQUq5AY/VP+b+1AnEAqFQqFQ5hA0DAzFDVQBPMTYqSdubNqccAqv4TaoqlVd3RqUa2hv5m7DfVjVjWtpcUxvV1c3eXvJE5hsp+UljIlXe0yrNF7tChu1ybOjVv8y/u2kHNnZGXq1KbSySfNybZzytsOL81ajOPXdeu0frRRScznNcFZqpvLZqBOIcZ+bmZd6y6dQZiNUAaRQKBQKZQ4hq4CsNqbgNRZEhjIboApgE2m2smKVv+b9Vk+Yj3rq5kb1aFQJMR+vZHM1j7HyVDR+t8u7UVvLenBjx+fW1q+WXaGba+H2erlVQ5rRdl7UI69Kn1vbQDdqpNc2aKRtaoVDqZW32VPW3G5OKnM9ePXqd5rxqDUzUisfK9w8u+aKh7AKMoBr5EMngOc+VAGkUCgUCmUOoTlyNAK1AZz7UAWwiTSivrhREIwx7ZoZc67Z6pfbGF1O6WopHmbFyEkJdGPj5PXNX/MOnip1wk6N0hSRQ+Hl2mg8Oae83e5rBlZql536ZFS97RQjO1s6u7KdcKscWqmBtdQxtVxjfxOfIVp+tVRLDS04ey07RyvF29z/nfJxozI6MVcUQQrFCqoAUigUCoUyh1BUYgfYaB6UuQ1VAGcIXhQEOwWoHq/TZsSqM+N2CTG7dG7i0tWiUY/QmjHeyqJtPc31qNf+0up/c571enfbldmsWHpW9mdWtnV2qlGzVEE7ha5W+zmprHa2dLXqUCsvp/ra7atlD+pVnfWiptX6Xsu73imNXTtYRSGwe4Y1w152ttKo/R+1ATw8oAoghUKhUChzCBkMZDAN5aE0eDxl5kMVwGmkli2XlzhgTkz1W7CbuFqNrN3pxuatHju0WsqMkxLiRvk5FHi5fm68Pp1sr7xgpe7a2dY52XaacdvmbtI5qn++iX3m1WDMuFGB3Zbv9l6tx9vWrv+7vc61bELdetw71dEuL6cy7O7FRp9tM+H+plCmEjoAbBL1PCy8/ngbQ8A0MgVY6ziv4S/c/Gi5nRa2y6PZButuBji1foSmevBstc1uMFzrRx5wN11pTOumzl5p9Ie6GS8/xoGPnamBVQBwL44GdrhxSLLL1+lYN/en2wFbs6ZGp/K62h3XyKCzWXWaCagqseFr5EOngOc+dAqYQqFQKJQ5hAw0PAVMB4BzH6oANol6HQ3slAWzQqWpEuay3JZrFTrBjkbe3KfyrdmslnlVBesJydGMaSAv6qmbUCx202DG9G7VxHqoZ9rZeKwbpwmjounGUcOLA0ItddNsrlBLvdbyqzVVbEzrJQSJG7W+Gfeddm20j7EdvKqfTn2tkbxqmcWYp8mt+od2XjPFjINCmS6oAkihUCgUyhxCoU4gFBdQBbDJeHm7dbIlM6oKbvKvhVNICLs8uZaWSfXxcrxb3Ni0GdVSJ/s9t6qgU5trxzTiCGG8bk51Nauy9YRicat2GfuAlfrs9Tq6rZtZSXJje6nVzanfGvfV6ttezk1T/NwozsZ0WmggN3hx6GjE5rQem0nto+TzVQqjm/vILr3VrEU9jh9eZyes2s6o6BqfKXMJRWUa/jS4lDBlFkAVQAqFQqFQ5hDNCAOjUgVwzkMVwCZjfrutV0kyqgpW+bs93mqb2zdvOZl0la8b2zqvtnlGBcHN23kjtn92x9Syz3M6L7PdmNtzsLL9dIuV4lLLvtSNMmNXltu6mRUXt3i18fSqslsdb9f2dnWxa0svipKx3b3awTVi++dVXfZ6fk6KaTMU6GbhpDJTKHMZqgBSKBQKhTKHUMBCblDfoTaAcx+qADYJO1XN6c3SGNfPrHYZbaCcjrUq265cJ6XCjWemXb5Ox9ey43LCyoaH8VXbAhrLcFsnr2mM9bGrn9djrba7UR3dluF0vWopL26usRf1xss5aP3aLnC4U/tYqcZ2ZVvlb6fe2+VTyz7RqXy7Y9zYxzVLrWq2Aufk+Wsu10mBdsq7GfVtVl+dyZBYfg3aAE73SVCmHKoAUigUCoUyh6BewBQ3UAWwSbj1xDRijOvn9o1Ye0Ot167Krm5uPDMbyd+M2zdts7qneSaaPUVr5VuPN2itejUzrZNa4uY4O8XIyQawlqrrpS5O19nL9dFsXs2euE7tY6UC1lLQjR6udnlaKtAubPTMeZmptSyilX2cXRnN7M9218nLDENVHr76+71decbrYHdNauVZTz+mUOYidADYBKZiUOHG6Nvrg89rOi+4Hai4TW9MN+k8fdbtYZfemM5qwFCrrnb1cpu+0R8VNw4udtN5bk0BapXjVHatNF6cFqyup91xdudY66XG6jgv/dPtgMgO48tbrSnmWmXU07e8XI9azj529Wd4wdUg3u1UuXnQbPWibHbKcYPXAf1sQQYLWW3so6reFcD33nsPn/nMZ9DW1oZgMIhVq1bh+eef1/erqorrrrsOPT09CAaDWL9+Pd58881mnjrFA4d8AHjLLbfgxBNPRDQaRWdnJ84++2y8/vrrVWnOOOMMMAxT9bn00kur0uzduxdnnXUWQqEQOjs78dWvfhWSJFWlefrpp3HcccfB7/dj6dKluPvuuyfVZ+vWrVi0aBECgQDWrl2LP/7xj00/ZwqFQqFQDhUK2IY/XsPAjI+P49RTT4UgCHjsscfw2muv4V//9V/R2tqqp/nWt76F733ve7jjjjvw3HPPIRwOY8OGDSgWi81uAooLDvkAcNu2bdi4cSOeffZZPPHEExBFEWeeeSZyuVxVus9//vMYGBjQP9/61rf0fbIs46yzzkK5XMYf/vAH/PjHP8bdd9+N6667Tk+zZ88enHXWWXj/+9+Pl156CZdffjkuueQS/OpXv9LT/PSnP8XmzZtx/fXXY8eOHVi9ejU2bNiAoaEhT+dU643TacrH6AiiYX6L1f52egtmQ6GmTOVNxZuv17dycz3MxxqVhVpqn5v87I53agur69YIzZiWcms+YLetWdNfVmqaW5XU7po6HWOk1vSquSztfy9t7KWdai0P58b0w0npr8cpwip/c5vYmVfYKaVOamUj/crK5MVqv9uyzA4+tZx4KO75l3/5F/T19eGuu+7CSSedhP7+fpx55plYsmQJAKL+bdmyBddeey0+8YlP4JhjjsF//dd/4cCBA/if//mf6a38YcohHwA+/vjjuOCCC3D00Udj9erVuPvuu7F371688MILVelCoRC6u7v1TywW0/f9+te/xmuvvYb//u//xrHHHouPfOQj+MY3voGtW7eiXC4DAO644w709/fjX//1X3HUUUdh06ZN+Ju/+Rt897vf1fP5zne+g89//vO48MILsWLFCtxxxx0IhUL40Y9+dGgag0KhUCiUJqM5gTTyUTyW+b//+7844YQT8P/9f/8fOjs7sWbNGtx55536/j179mBwcBDr16/Xt8Xjcaxduxbbt29v0plTvDDtNoCpVAoAkEgkqrb/5Cc/QXt7O1auXIlrrrkGecMb4Pbt27Fq1Sp0dXXp2zZs2IB0Oo1XX31VT2PsaFoaraOVy2W88MILVWlYlsX69ettO2OpVEI6na76uMFpMflajiBe89Ewv627MSCvR6lzyr8R3KpGbm2I3JyXkwpr3me+bm5UGuO2WmF8rPLy6gRgPs7KKH8qlA6vjj9mFbKRPujk3AFUK7e1lGK7Onrp60o+77qP2vUhJ3XUq9JWy9bQKT+rvt6oY4qdEuek0Nmp1m7a2e78vCjPswFZZZpiAyjL8qTfvFKpZFnm7t278f3vfx9HHHEEfvWrX+GLX/wivvSlL+HHP/4xAGBwcBAAqn63te/aPsqhZVrDwCiKgssvvxynnnoqVq5cqW//u7/7OyxcuBDz5s3Dn//8Z1x11VV4/fXX8eCDDwIgHcmqE2n7nNKk02kUCgWMj49DlmXLNLt27bKs7y233IIbbrihsZOmUCgUCmUKUcE0HMZFBYNXX30V8Xi8avv111+Pr3/965PSK4qCE044ATfffDMAYM2aNdi5cyfuuOMOnH/++Q3VhTI1TOsAcOPGjdi5cyd+//vfV23/whe+oP+9atUq9PT04IMf/CDefvtt3Z5gOrjmmmuwefNm/Xs6nUZfX19VmnpVNLu34Gbl5dZjs96yzG/TmlLA+ASoZRFsKOSoYDZSn3ry9lq2FxstqzRmrBakd7JHslMnnfqM+X9jCB1zWjvctm2tvOyUHKOq5KQceum/TmmNyq3b/O1scmthZVtn3ud0Leu5h63qa1QS67HFdaqnm2eW3XWuxwbPTV/w+qwzK62UCY4++mj87ne/q9rm9/st0/b09GDFihVV24466ij8/Oc/BwB0d3cDAA4ePIienh49zcGDB3Hsscc2sdYUt0zbFPCmTZvwyCOP4De/+Q16e3sd065duxYA8NZbbwEgHengwYNVabTvWiezSxOLxRAMBtHe3g6O4yzTaHmY8fv9iMViVR8KhUKhUGYScmUpuEY+ChhwHDfpN89uAHjqqadOiujxxhtvYOHChQCA/v5+dHd348knn9T3p9NpPPfcc1i3bt3UNQbFlkM+AFRVFZs2bcJDDz2Ep556Cv39/TWPeemllwBAf2tYt24dXnnllSpv3SeeeAKxWEx/A1m3bl1VR9PSaB3N5/Ph+OOPr0qjKAqefPLJhjpjLXuiWnY+dnlZYWez0gxv4EYw5qvZQDVLobM650byNtsT1WMHZKWAmvfbKSPGNG7LcKO0uekbVsqi+bh6vS9r2URaqWqN9Od6bfTMZbu9D2vd21ZKl5Vy1ox70O5aeFH+rK6XMbKAl2eKW8/cWqqrl2Ma8Yyudf/ORpQm2AB6nUK+4oor8Oyzz+Lmm2/GW2+9hXvvvRf/+Z//iY0bNwIAGIbB5Zdfjptuugn/+7//i1deeQWf+9znMG/ePJx99tlT0AqUWhzyKeCNGzfi3nvvxcMPP4xoNKrb7MXjcQSDQbz99tu499578dGPfhRtbW3485//jCuuuAKnn346jjnmGADAmWeeiRUrVuCzn/0svvWtb2FwcBDXXnstNm7cqL+dXHrppbj99ttx5ZVX4qKLLsJTTz2F+++/H48++qhel82bN+P888/HCSecgJNOOglbtmxBLpfDhRdeeKibhUKhUCiUpqDF8msEFYynIeCJJ56Ihx56CNdccw1uvPFG9Pf3Y8uWLfj7v/97Pc2VV16JXC6HL3zhC0gmk3jf+96Hxx9/HIFAoKG6UuqDUVX1kK75zDDWXequu+7CBRdcgH379uEzn/kMdu7ciVwuh76+Pnzyk5/EtddeWzXl+u677+KLX/winn76aYTDYZx//vn45je/CZ6fGNM+/fTTuOKKK/Daa6+ht7cXX/va13DBBRdUlXv77bfj1ltvxeDgII499lh873vf06eca5FOpxGPx3EGPgGesVYEvNhbeUnXCE7edVNVRy9tYbY78lreoWjDQ11ePX3pUGFlc9Zo/Zptl+WkwNZje+uUppbdZKPt08jxU9V3mtE/m9Vv6nlmHCokVcTTeBipVGrKTIjC4TC+9T996F3S2KDqgX8/CObgetxzzz1NqhllpnHIFcBa482+vj5s27atZj4LFy7EL3/5S8c0Z5xxBl588UXHNJs2bcKmTZtqlkehUCgUymyAhIFp0AtY9aYAUmYf0x4HcC5Tj+fgobBFqccmR6PeN+tanoNW9nDm/922hxs7oWZgVJCmKm8rlcqtTZrbMqzK85Jvvd6qTnWqZVPoFS993k1/c+NprOHFXtKpTK+qqJ3nsXm/mzJrpavnWWeXh5Utqtc83NpiN1LWTEZpkhMIZW5DB4AzmGb90DfjWI16pn5r7bMyUm/2A9o4NeQmrRvcOjHUg9d2rmfAZOeUUMs5xe2AwW6fk+OJ3UDCqyOIGxoJJ6I5Sbiph5dyGgl/4pSv3X6nfK0cddxgd+/Werkwtmc9L3HG453yMj8LZuJUMYVyKJjWOIAUCoVCoVCai6IyUNTGnUAocxuqAB5ipmoqy0059YRdmErchDNxG/7ESik0KhF2CpdVPs2YwjL+b66P3fdG8Ko6OdXFrWJl1eZu2s6sKjlNubptI2PIEivs8qmlMDuFI7Fz9JjK0C610hmXunNTLzf3oFN5TsdNCq/icz4nqyDdXuqrHW/lhGOlCFrNPMwVmjEFTAeAcx+qAFIoFAqFModohhOIckjjg1CmA6oATiFWNkKH6o3TqwLUbKN7K/uaWnm6sfdzUoncOiPUqzBYlWmntFgpiW7yNis4Wl5erocbw/1mOG54UYjcKKuW+fnsr7exLCs1zq1aVW9/qPeauMFtuU4q2qFU9d2UVStgu5OKXwvjs9ZYjn4fla37qlsnFwplLkIVQAqFQqFQ5hDNCgRNmdtQBXAK8bJE2aF++2zEA9JL/m5sy5y8gd3UqxEVx6oebqhll+jWFs5cppUdlHZ+9YThONQ2TvW2h53nqNU95Naes1Y7eFWbrFRzt3nU49VqlbaWkurFlrNWHrXCyBjxovTbYWUP6vYcrFQ/4/FunhvNngWZTpqxFBwdAM59qAJIoVAoFMocQgHTcBw/Ggdw7kMVwBmCW4XAyfasXhrxvqyVr1evUC9519pnF6fNbT3q8RD1EmewEY9jL2W4vZb1qlGN9BWz0uNG7ayVl9tj3dhB2ilqXu1O7fL1cv3rVci9Yqeauc27GWpnI+ql03X0cn/OVQ9hCkWDKoAUCoVCocwhFJBp3EZQG/Qipsx8qALYJKYqxpvdW3iz7FXs3pi9qmFWTMUbtJu3c22fZhdUr1I3KY6ZyWPQqo3c2ELVsh+sVT+vsdi8HuMGY9uYy7Brb7ex6bzUxcqGy2u/c2tH6LTdypvUTb7N9CRuNC/jNiv13It6ZvVdlUTXq6c0YkfotN2rbe5stQOUwdCl4Cg1oQNACoVCoVAolMMMOgBsEma7JS8qhFPsNLs0bjwS68WLUtMsnNqglh1WrXzdKJy1yjUf0yx7MC9eo/XaNrmxHXPCLm+zmuOkTjvVoZYa5TU+nFe1uhn3jrlv2K1M4tSntP31eBIb+3m9aq9xm5LP16yrVT1q3QNW8fiMx3spq559btPVqybPFMhScI19aBzouQ8dADaReh8WdtNJTiEK6hnUuCnbKR83U5ZWP9Rep67MPwR2P9DNfDhb/dg1avTezGPNjiNWzhN2x3jJ2wwbClkGYwbcBfa1q5sRpz5oN3h0GuwY+4zx3LiWFsv62dXdq0OPMb3bEFB2Lzi1ppRr3SNO9W/Gi2K996PTs8Xq3J0cubwOlJ2OdXrZno00Zyk4OjyY61AnEAqFQqFQ5hCKykKhTiCUGtAhfpNwazRcz9tlPQ4YU4nT27/VVLimIJnf5o0KkXa8cerM7ZRQPUb8XtLVUgcOxbUxK31mg/p6FBkrFdGcj5LP26pZtcLsNDMkiZv95nvQ7KgiJ5O2x5rzsVIda/V7Lw5Axu+1trlR6Y3Tr1Z5GpcYdKsWG+9DJ5MTp3vCq7JmXgpRm4o251NLoWd4wbF/mhVHK1WdQpnrUAWQQqFQKJQ5hAIGcsOBoClzHaoANgntDbKWIXY9djJOdnDmstwqVF5UK6/G4Ob9moJkVpLMKhZQbSQ+VTZ4XlUJs1Jgtd+LvZjXOtrtN7ZnPQqpWbFy03+17WwopJdfS4m0a+9GbNSsFJ5a9pC16mnOR0tnVobc3E9u1EOvGO0arRQxq/TaOZvvPbv6Wil3dmqbU3tb7bOrf9VxZdFSAa2liJrPS5VES0cWI1rbzHaHDyuIIwfb0IcuBTf3oQoghUKhUChzCLkJCiAdAM59qALYRBq1I6mlElh5+pnLquVR6ZTOrVJkRz0qh5UqWAuvS7yZqTofGw9XwJuq41aVcPI+NJdVr2pkVrvsrrWTyuPkrQlUXzc3SqRZbTH/baW0WWFWeGqlM5ZVr7pcr3drPV7GtQIwa/Wpda3MNnTGtGabN6vzqGXjZy7HjWJsrL+b4+3ayCmtud5W9+xMsqemUKYTqgBSKBQKhTKHUJvgBaxQL+A5D1UAm0C9ypoZL16H2t9Ob7SN2LrZ5eukhtgphV7fuK1snIwYlR+3edulc7IT8mrr6KZOVvaddnk4qYC1jrNTx4wKmpvr3QyM9am3rb3Wz0otr2XfaEzrpa5e7i3jd6tztrLVc6NAWp2rlSpmVRe7fNwqdVpaK3XbrBjr+fgme6Cby6xle6ihRQ6YdF181s8QKxXb6jxns1ooqwxklW3oQ6eA5z50AEihUCgUCoVymEEHgE3CSbFyo25Y4db2yMleq943WLe2T07HA+4UDEt7LZdef27VES1dPfaD9Sqpdt+9lOflWHNb17IXNR7jdL3rsfu0s3Wst0+a6+jmmtgpdcbtbChkqW65Vd6sytHz8FWrSWbMMe8mHe9R8TT/bWwvY/+3sse0yse4zWw76MZe087GE3C/ioz5b6v87OyI7WxT3Tyra90TMx0VDJQGP3QpuLkPHQA2AS8DFrvjgfqcKNwE4zVPkRzKqQ23bWFepmuq6mA57ePgCGI+3s1g1usg0zwA8dIfnH6MrertNMgxDobs8jHX2a6eVnWpNWiw+5GumdY00PJiuqDk85PKtWt/p0GcXd7GAbY5D60vOl0bL23iBiWfr3KGcTsdbsTNwNq8Xx9E1hgUG8twyreegZnVtah1r83aKWA0Nv0rqyy1ATwMoE4gFAqFQqHMIUgcQBoGhuIMVQCnELeKAdfS4slI3Zi/1VJJZqxCQXiZVnWjhNSD8Q3fvExXvSqlnTLlpX1qHVPLIL7W9JbdsWZlwsqRwW7Kyq2S4zQ1rwXhdcrLKniul6lSt9fUzXS0Xpdy7Wk7uyldu/vCSnmzOt7Yd2r12VpT9LWUZrfmDsa/rZ43tcqxMkFwql+telkp8IzP3snJjaJodw/WUhXtvs/WqV4KpRGoAkihUCgUyhyCLAXXmL5DFcC5D1UADwHGt3ArdUhOJnVbPcC7Y0MtWyE3zgHa37VsfrwYx9vVx2pfLfunenBSu8zlu1F8zNud6udFUdSuvVW55r+tVEGr7VZlmI9xSmvXF5R83tZWUMvbDqd+6CYv83ejjV2tPJ3OyXys3XU2Hm8VaLuWame8RnZKrhuc2sVsO2e097OilkLotX52bWx17azydavUagqi+brWOtbqu3GbpprWUiFnOtoUcCMflXqBzHmoAkihUCgUyhxCAQtlDiiAiUTCU3qGYbBjxw4sXLhwimo0t6ADwCZjVuaA6iCoTm+UdoFJ7d7Infa5/e7lzd7Og65Wnb3aETm9xZvPu1HbKbcKRK18rNJ6sZtyYzdol4+dYqptd2ovOwXNsc3K9sd7aWurejC8AMYn6N65VveTE17uC3P72JVj2ffLzv3Prp2dzsXLPeWEl37qJhyLU5uyoZCeh/FvL3Uyl+G5D1b6ixPGurntB7NdBZwLJJNJbNmyBfF4vGZaVVXxj//4j5Bl+RDUbG5AB4AUCoVCocwhFJWB3KAXsDIDFEAAOPfcc9HZ2ekq7WWXXTbFtZlbUBvAJmHlZWne78X+yeoYM7XeTN16XGo2NE62hFbqjZ0tjVUe9dgOmsuzUk80+zkNtzH4nGzF3KRzqqddXrXqY1eWm1iPTtvtFCpj2do+OyXRnK/bvue2/Yx1tVPCnezLarWRk61brXOx6vtWx1rZBdp9N+Jkh6r/77O+F2u1r5tynfZbKXNGjLMbdiqcVdtr11KLUWqsq1X/clLlrJbPs6qnuf/Usul1089nKnPFBlBRFNeDPwDIZDJYvHjxFNZobkEHgBQKhUKhUCiHGYd8AHjLLbfgxBNPRDQaRWdnJ84++2y8/vrr+v6xsTFcdtllOPLIIxEMBrFgwQJ86UtfQiqVqsqHYZhJn/vuu68qzdNPP43jjjsOfr8fS5cuxd133z2pPlu3bsWiRYsQCASwdu1a/PGPf6zrvLQ3RTtPXvMbtFt7JidqebeZ35qtPBi1dMb61VIz7epnpfJ5sd+qFzcqhBmv9anlQeolH+P/xvrUUlnc5OsWs9ridLwXu7RaqooRu2XYnOppVb4qVXu7WvVlpzb00nZ295BTGbW8Yp3aXztnO5XLTqUyK11aWzs9g+z6uPla2qmpVtfSeE9alW13XuY8GrHH89LX5wqqykBR2YY+6gzTh3784x/j0Ucf1b9feeWVaGlpwSmnnIJ33313Gms2eznkV3jbtm3YuHEjnn32WTzxxBMQRRFnnnkmcrkcAODAgQM4cOAAvv3tb2Pnzp24++678fjjj+Piiy+elNddd92FgYEB/XP22Wfr+/bs2YOzzjoL73//+/HSSy/h8ssvxyWXXIJf/epXepqf/vSn2Lx5M66//nrs2LEDq1evxoYNGzA0NDTl7UChUCgUylQgg2n4o0z3SZi4+eabEQwGAQDbt2/H1q1b8a1vfQvt7e244oorprl2sxNGVad3pn94eBidnZ3Ytm0bTj/9dMs0DzzwAD7zmc8gl8uB54nfCsMweOihh6oGfUauuuoqPProo9i5c6e+7dxzz0UymcTjjz8OAFi7di1OPPFE3H777QCIvUFfXx8uu+wyXH311TXrnk6nEY/HcQY+AZ6xj4vmxgPODV68Ob3kdahwoxh5Od7uHJzaqdaxXuvgNT836bzU3679nGxOnbw6rTB6ULotr9Z2K2p5wTI+oWrVj0b6sF1f1GxKjaui1HtfOZXh1JftbO+c6sG1tFiupmNlO+fmmWQuz67etTDn5eQpbFXvRtI5tb9VHY31nCokVcTTeBipVAqxWGxKygiHw/jYf30QrYtqe846seNHO3FkeiXuueeeJtWsMUKhEHbt2oUFCxbgqquuwsDAAP7rv/4Lr776Ks444wwMDw9PdxVnHdOu8WpTu07xfrSbRRv8aWzcuBHt7e046aST8KMf/QjGsez27duxfv36qvQbNmzA9u3bAQDlchkvvPBCVRqWZbF+/Xo9jZlSqYR0Ol31MeJkYGyVxmq/FVbTyrWMpM3HNmuayyqd0Qjf7VSi0bjd7UPX7gfJKZ25PKc05vRW01F2x7mdWnKaVre7zlZTc7WmtazKqceoneGFSdOrWnlWTg9uzQjcXjcjdmE8zPk5XQetLe3aR5uyNE972k3jGs/XzcDFS980Drjs+oF2vHnwZ95vLJeNhCeVXaufmOukHWNXJ2O5xrzcOGzY9SWrerrJx5zG6rnllJ4yM4hEIhgdHQUA/PrXv8aHPvQhAEAgEEChUJjOqs1apjUMjKIouPzyy3Hqqadi5cqVlmlGRkbwjW98A1/4wheqtt944434wAc+gFAohF//+tf4x3/8R2SzWXzpS18CAAwODqKrq6vqmK6uLqTTaRQKBYyPj0OWZcs0u3btsqzLLbfcghtuuKHe06VQKBQKZcpRK3Z8DeUxQ8LAaHzoQx/CJZdcgjVr1uCNN97ARz/6UQDAq6++ikWLFk1v5WYp06oAbty4ETt37pzkvKGRTqdx1llnYcWKFfj6179ete9rX/saTj31VKxZswZXXXUVrrzyStx6661TWt9rrrkGqVRK/+zbtw+Ae7UPQFU4B6d0RowLztfCXAcr1aSWQmJnoG1XliqJjuqME14CINuVbaaWYmdlYF5LDXDKw+4YJ1XKSlkytoVX5a6WAmfcb6eA2Z2TVd/Rrnmt8pzydRuyxxgqxJinkzmBXR83K6Z2SpfVceZyjNudVDPtGLt+qUpkaTPzEnvmc7Qqw0l1NF8L7X85mZykNrt9PliVUc+0qZ1CbPzfqm5297O5f1spzsbl47S8lHwebCRsme9UTwdPJQqYhj8zIApMFVu3bsW6deswPDyMn//852hrawMAvPDCCzjvvPOmuXazk2kbAG7atAmPPPIIfvOb36C3t3fS/kwmgw9/+MOIRqN46KGHIAjOD6W1a9di//79KJVKAIDu7m4cPHiwKs3BgwcRi8UQDAbR3t4OjuMs03R3d1uW4ff7EYvFqj4UCoVCocwkZBWQK8Gg6/2oHgNJf/3rX58UmWP58uX6/mKxiI0bN6KtrQ2RSATnnHPOpN9fJ9LpNL73ve/h4Ycfxoc//OGqcj/72c96qiuFcMgHgKqqYtOmTXjooYfw1FNPob+/f1KadDqNM888Ez6fD//7v/+LQCBQM9+XXnoJra2t8Pv9AIB169bhySefrErzxBNPYN26dQAAn8+H448/viqNoih48skn9TSuz8nD26OVmme2qzH+Xys/I3b2QcDkILVulA67MmrZ/nipozkvp3NwS63rYVQLrFRMJzXLqQwnFdFJuXCy8fOKU5217Xb7rNRpu/zdtJHVcUbM7W13na1UYiu1ze4aG6m3z9qpfl7ydGojJZ+vsj20Su/VbtLpHjW3qdGGt1aetv3HQg22UjW1fVbXxq4/me8tI7WUV22b3WyDk/0kxRtHH310VWSO3//+9/q+K664Ar/4xS/wwAMPYNu2bThw4AA+9alPuc67v78fIyMjk7aPjY1ZjiMotTnkNoAbN27Evffei4cffhjRaBSDg4MAgHg8jmAwqA/+8vk8/vu//7vK2aKjowMcx+EXv/gFDh48iJNPPhmBQABPPPEEbr75ZnzlK1/Ry7n00ktx++2348orr8RFF12Ep556Cvfff39VHKHNmzfj/PPPxwknnICTTjoJW7ZsQS6Xw4UXXnhoG4VCoVAolCYxXTaAPM9bzqClUin88Ic/xL333osPfOADAEgYt6OOOgrPPvssTj755Nr1sQlYks1mXYlElMkc8gHg97//fQDAGWecUbX9rrvuwgUXXIAdO3bgueeeAwAsXbq0Ks2ePXuwaNEiCIKArVu34oorroCqqli6dCm+853v4POf/7yetr+/H48++iiuuOIK3Hbbbejt7cUPfvADbNiwQU/z6U9/GsPDw7juuuswODiIY489Fo8//vgkx5B6qUdlM3r+mXHjZWhXllFt8ZKHlR1SI9jZJTnVzYvdk5v9xnyt0lrVyS58hVFhcGMPZU5vVWY9GNVEL3m5OcapjazKtfvbqQ7G/53K9nqOXtOb05oVK/P/xtA0TnnZtYc5T3M4l3rq7qSk2eHWhtfpumvbjZ79xucO47MO1K7dW05Kqna/GNvczbPCqR+6adt6nrkzAW05t0aoJ0Dcm2++iXnz5iEQCGDdunW45ZZbsGDBArzwwgsQRbEq6sby5cuxYMECbN++3XEAuHnzZgAAwzC47rrrEDIowbIs47nnnsOxxx7rvbKUQz8ArBV28IwzzqiZ5sMf/nCVDYBTXi+++KJjmk2bNmHTpk0186JQKBQK5XBCluVJ4c78fr9uamVk7dq1uPvuu3HkkUdiYGAAN9xwA0477TTs3LkTg4OD8Pl8aGlpqTqmq6tLnwW0Q/sNV1UVr7zyCnw+n77P5/Nh9erVVbN/FPdMaxiYuUS9b4racW4VK+0Y43fzfjd5GMuuVTc3NKoselV1GtlvlbaW+mhnP2Rly6Th5hyd1LJ62qReFRCwPkcnb1tzucZtxn5dK/ivG09Wq21u7NHs7i27dnZzT5nPzw7jPmNwaas02v9mezS7srx4/3rF3Cet8rK77nZY1V/fV67Oy0nNtivDHKzc7jg3gajnAponbyOoYPDqq68iHq8OKH399ddPisoBAB/5yEf0v4855hisXbsWCxcuxP3336+v4FEPv/nNbwAAF154IW677TbqfNlE6ACQQqFQKJQ5hNqEKWBFBVYefTR+97vfVW23Uv+saGlpwbJly/DWW2/hQx/6EMrlMpLJZJUK6BR1w8xdd93luu4Ud0z7SiBzBbu3WyeMb7pWednlY3ybtvNydPKmtXurN3vr1VJXzHWqdd5Gr0SrN/Ra5+LFg9POA9KpTd2U4cUT04uaZP7bKV9zXWp5ENulc9OednX2on659fY1Y05v9gJ1uufM19Ou39lts6pLLbXLyttV+9/s4WtXhlucnhv1YNXvtPiLzbKBM5dRS6m0a2OrOhltCJ36hVH9s7pPmtmm0wmxAWQb+qhgwHHcpNBnbgeA2WwWb7/9Nnp6enD88cdDEISqqBuvv/469u7d6zrqRi6Xw9e+9jWccsopWLp0KRYvXlz1oXiHKoBNxMt0LuAuZIfbPIzla9udphytpkysptY8TcNEwpCTyZrTqbXOxfwgr2d60+uPurENnM7fzY+41Q+Q02DazcDCaEBvvl5u29Vu0G3ebjTK9/oDWI+xvZv+z/D2zhZ2edi1az3n5bbPOT0D7Mq163vaPrs87M6pVp+zqotVWrcmAXb7zdOttdpQS++1jZzyd3sdal0bSm2+8pWv4K//+q+xcOFCHDhwANdffz04jsN5552HeDyOiy++GJs3b0YikUAsFsNll12GdevWufIABoBLLrkE27Ztw2c/+1n09PSAYWbWSiWzEToApFAoFAplDqGgGV7A3o7fv38/zjvvPIyOjqKjowPve9/78Oyzz6KjowMA8N3vfhcsy+Kcc85BqVTChg0b8O///u+u83/sscfw6KOP4tRTT/VUL4o9dADYRJr5plhPiAIrRcDOGN5tWBgvb8WaAbubY8zfjWqB3RRmM6ajzO1lpSzUUnKs6mWVv9V3q/q4USS8KClW196pfDNuQ4I4qcNWSpWxTmZ1yIvhv1tVph4lulHFx6nuxvAoduXVUirdtIVduBWrfOzU7npVfOOxTs8Yq21OqrNVG9Uyl7B75ni5vrNV/VOb4gTiDbslXTUCgQC2bt2KrVu31lWf1tZWJBKJuo6lWENtACkUCoVCmUNocQAb+dQTCHoq+cY3voHrrrsO+cPAi/tQQRXAKcSNAmOHVzs3uzAItezR7N743drBOSk3dnW1UqicFAtzPbyoNF7q5UXdcLIZ0vJqxF7R6zUCattc1bIT86p+1WpXJ3vIWupvI/eOdrzXY52UqlrXu1ZdjTaMxmDPdse4vR/N/zO8MCmocrOfP/Xc63bHOz1n3NhMOtXNUY31aNtLmX7+9V//FW+//Ta6urr0BSGM7NixY5pqNnuhA0AKhUKhUOYQ07USyFRy9tlnT3cV5hy2A0Bt+RUvXHvttYflHD3DC2D9kwOM1mND5Ta9GxsxNzY9VnW1enP2okA4vZmblQmzl6Mxvbk+dm/25jLszssLtVQfNyqo27LdBko2t7G5TCfbPXMd3fTNWufg1Ke8XAsr714trRdbQTdlOeHV5q0exZThhUnBnt3akrq5dlb3Y61y3HjYmu95LSRPLVtiu212fdrp2Frt7cWT183zyq7+s4FmxAGcaVPA119//XRXYc5hOwDcsmUL1q1bV7XsihO///3vsWnTpsNyAEihUCgUCoUym3CcAn7ooYfQ2dnpKqNoNNqUCs1GVEmEIk9WKdwc57WcRrxTvdgFuVWz6jnXem1v6rU9qtez00m9MOZrp0wyvODKI7NRj003ONXRSeGtpywr20RtGTSvKq1d7L96lRknmzQnJbZWG9Uqw6v9WT3XgO/sgDQ0bFuuneLu5rli3m432+Hmung9t1r3mV0djdtreQSbn42zVfnTUFTMuSlglmUdY//JsnwIazM3sB0A3nXXXZPWAHTiP/7jP9DV1dWUSlEoFAqFQqmPZq0FPJN46KGHqr6LoogXX3wRP/7xj3HDDTdMU61mN7YDwPPPP99TRn/3d3/XcGXmCk62K/WqUW7ytktTa7tVPm6VNrvzMq8mYbYzMitj9baRm7TNeJt3snt0KrfR86hVbj3n5jVPu+tiVJTs7MoAbzEMzZjLtlLitHRGpdF4jJNnqHGbnT2bVdlOaqnVdqvyzMe4uQZmjOXIY0lXKrndM8OtfaVTvZ3S2l0DN/3eTZ3ctLmTAmyVn139ZjrNsQGcWXziE5+YtO1v/uZvcPTRR+OnP/0pLr744mmo1ezGkxfw888/j7/85S8AgKOOOgonnHDClFSKQqFQKBQKpRYnn3wyvvCFL0x3NWYlrgaA2hIvzzzzDFpaWgAAyWQSp5xyCu677z709vZOZR1nPW4Um0aVQTdvtk5par2Fm+24jOdjla+TR3QtZcxJcbJK69WuzMqz1OoYJ9XC6XrV8uq1wu461VI/6uk3XlSeWmW4vVZePDS9lsOGQqRPlcWa/cVN2U7qmNs2c2ovu7Zyul/t1Ll61OBa17LRfIxY2RzWo9rXUvC0NF7shI3bvD5DZjrTsRTcdFAoFPC9730P8+fPn+6qzEpcrQRyySWXQBRF/OUvf8HY2BjGxsbwl7/8BYqi4JJLLpnqOs5JzA9Gpx/4eo9380AzD+jM+9hQCAwvTJrGs5qOM5Zhl6fduVjVw7jN/L/VeTG84Kpcs2OBeUpPC3Hh9YfE2FbmunhpD3N6r/VwQ6MvG27Pxzy4dtvv7bDKx60TjdV2t+fhVE9z3/Q6uLHbbnVf2Q1+zNuMdTKnsTumHlzdb1L1i6OXa+7UX6z6ltV+qzytrn29bTATmYsrgWhLwWmf1tZWRKNR/OhHP8Ktt9463dWblbhSALdt24Y//OEPOPLII/VtRx55JP7t3/4Np5122pRVjkKhUCgUijfmYiDoLVu2VH1nWRYdHR1Yu3YtWltbp6dSsxxXA8C+vj6I4uS3NlmWMW/evKZX6nDAaUrDKp3ddrMiUs9UodN0XS0Dfq/TJ1bTo05TT+b8G1XDak2Lm6f5vDinOAXj9nJdvEyV1mO4X0vNsquP1TWodX52ebmdJnczTetletaNWYGxfm7vz3qnuJ3uWzdT/16mc53C8dRTd+04p3vKzb3j1Ied+rZxn5tnhzFto/cd5dDj1TGVUhtXA8Bbb70Vl112GbZu3ao7fjz//PP48pe/jG9/+9tTWkEKhUKhUCgeUBu34ZtpU8AA8T344Q9/qDujHn300bjooos8hayjTMCoam2ht7W1Ffl8HpIkgefJmFH7OxwOV6UdGxubmprOQNLpNOLxOM7AJ8AzM8d+xEuoA6djGZ6EazEb10/VW7FXFeVQvZ17ccKw++61rl6UR/NxgHuF2azEWalEM0UFsVLl6lE0azHd59vI/WtO62QzW29dau334gRltsdz6m/m7W7KcXvvmkMI1Tq2XiRVxNN4GKlUCrFYrKl5a4TDYRx1+6cRWtDWUD57730Wp5UX4Z577mlSzRrj+eefx4YNGxAMBnHSSScBAP70pz+hUCjg17/+NY477rhpruHsw5UCaJ57p1AoFAqFQjlUXHHFFfj4xz+OO++8s0qIuuSSS3D55Zfjt7/97TTXcPbhagBI597rY7oUFC+2erXysfKWa/RcvKqR5rdyLYi0W/vHRutTy2bQKa15u1NZRkXDiwLkZHPF+Mj/dmqJORyOlR2jUztb5VtLFTVvc6t42bWNE7WUJPM96kXtdUM96rWTfaNbuzyrv+upq1Pd7drMSwgkc13tPJStngOaGmxXT227G6XQav9MUL3rpTlOIDNrCvj555+vGvwBAM/zuPLKK2lM4jpxFQYGAN5++21ce+21OO+88zA0NAQAeOyxx/Dqq69OWeUoFAqFQqF4Q1WZhj8zjVgshr17907avm/fPkSj0Wmo0ezH1QBw27ZtWLVqFZ577jk8+OCDyGazAICXX34Z119//ZRWcDZi91Y6XXGmjOVqf9vVxU49tMuvHtwoH051Mb75a9vsbJ7c1NVOebA63iqtVfua62XcZluPsrt2sSrHvF1X78oVhaZc+xzdKEt2qpyWj1ubM7Pdl5tjjHU1p/PSJ60UMvP/tY5zg1uVzwm3SrmX7W7KcnpOmFU/L/dPrbLd3jNW6e3uQ7ONK8MLevDwuYpaI8afqziAM2wQ+OlPfxoXX3wxfvrTn2Lfvn3Yt28f7rvvPlxyySU477zzprt6sxJXU8BXX301brrpJmzevLlqpP2BD3wAt99++5RVjkKhUCgUCuXb3/42GIbB5z73OUiSBAAQBAFf/OIX8c1vfnOaazc7caUAvvLKK/jkJz85aXtnZydGRkaaXqnZjvmNuJa6UEuVq0Uthc/qbbnW26+dmmNl3+X22Gbi1a7KiLbSh3aME4xvcts6KRS1yjeWbaZeO0SrdMa6mm0Bjbi5nm7RyvJi3+mlXDtbQa1sp+PcbjN+aqW32mdsX/O1cJuHVV7mPI3HuVUZNfXLTimzOtZOFbR6xnnxOHa6D4zU+2y0K1ezHbazCTwUz65DgYrGp4BnWBxo+Hw+3HbbbRgfH8dLL72El156CWNjY/jud78Lv98/3dWblbhSAFtaWjAwMID+/v6q7S+++CJdg49CoVAolBnEXHQCSaVSkGUZiUQCq1at0rePjY2B5/kpC6szl3GlAJ577rm46qqrMDg4CIZhoCgKnnnmGXzlK1/B5z73uamu46yjHm/bej0M7bxSG7VvsVNynDxCzcfW8ybdiBpq9k61qp/mPci1tFTtd1IMrPKvFzs7PzNONlVu6mFnq2e3Covd91po7exV3XKjaNrZ+zkd50aFclK9nFRMO5XOyQPWymPWrmxzGWbbNafjatVTy8/q3qzHTs+tx7Ax/yrFsGxdB7tr7nQNa6nA5rROirAqiWAjYcu6zCZUtfHPTOPcc8/FfffdN2n7/fffj3PPPXcaajT7cTUAvPnmm7F8+XL09fUhm81ixYoVOP3003HKKafg2muvneo6znjMDxUnw3S7qaV6BhdWP1RuHuxO0xy1jjfXt94pzVr5upmidtpuN8Wj/XjJyaSr/Oql1g99Lez6UD3tUutHzw12/dYpNItV3/YyTWg3heqUr939YJ6+dOvw4JQ3UDvkiRsnE3PdLPMpTz6veq6f1X1Wq09pL0tO+Tr9bTVgNA9EvZqmGPtGrWPMaWu92GrPhnpfYilTw3PPPYf3v//9k7afccYZeO6556ahRrMfV1PAPp8Pd955J6677jq88soryGazWLNmDY444oiprh+FQqFQKBQPqACUBpdym2kiYKlU0p0/jIiiiEKhMA01mv24UgBvvPFG5PN59PX14aMf/Sj+9m//FkcccQQKhQJuvPHGqa7jjKfWW2WtY2sZnHuZrrF7M7ZSKGvts1Kh7KYW3eBUd68OAW4N363Kt2pzu+O4lhbH62GlgNYznW+Vt3Ha0E4FM/9t5ehhNyXv1llDS+vUD6zq0ij1lG3VfsZj6lXa7ermFivFznx+dveA1T4n5c7ttTY7Q9mdk5xM1pyaNatyVnUy4nbmw6odnNQ/t84lbChkab7QqEnETKEZcQBnmg3gSSedhP/8z/+ctP2OO+7A8ccfPw01mv24UgBvuOEGXHrppQiZbq58Po8bbrgB11133ZRUjkKhUCgUCuWmm27C+vXr8fLLL+ODH/wgAODJJ5/En/70J/z617+e5trNTlwpgKqqgmEmvw28/PLLSCQSTa/UXMaNUmgMPaLtd1KvnMox28yY86ylPlrWz4X9kRcVwIhdgNZadlp2+6xskaz2GxUBI3IyaasKMHx1gOVa18hN3TUFz1gvs+JhZU9lzNtKlXVjq2VXn1r2deb0dvWyw40NXi3HDG2/G9swu/Kc1J5aKqRZTbLqE07qklk9c2o74z67e7GWamleHs1OJXaTH8MLuuOEU3o3OPUFp/tQ+98cmNwuvZLPT7Jf9XrPzmQaDQKtzMAwMKeeeiq2b9+Ovr4+3H///fjFL36BpUuX4s9//jNOO+206a7erMRRAWxtbQXDMGAYBsuWLasaBMqyjGw2i0svvXTKK0mhUCgUCsUdTfHknWkjQADHHnssfvKTn0x3NeYMjgrgli1b8J3vfAeqquKGG27Ad7/7Xf1zxx134Pe//z22bt3qqcBbbrkFJ554IqLRKDo7O3H22Wfj9ddfr0pTLBaxceNGtLW1IRKJ4JxzzsHBgwer0uzduxdnnXUWQqEQOjs78dWvfnWSgejTTz+N4447Dn6/H0uXLsXdd989qT5bt27FokWLEAgEsHbtWvzxj3/0dD4aVsqSUcWxemO3e4u3Cv/gxSPN6c3XmK8bL0o7nN6arfJxq1LYqVdubIWc2sh87oyvWhnVFAG3tnZa/Y3Kjll9qhUixViGlbqhShMBa42qsDGtVbgbu7bW6mSlPBnhWlr09tHKturf5nxqBZk2llcrP2PaWmqWlQpm7gtWSrAxf1cKc6UdNK9YY7lmNcku71rqubGtzX3IUrH3Tc7bqe/q9TIo11bXbVK9TM8z8znKyaT+nKs1Y2BMY7ZDdJqpMAayrjpPw71s3O6k+GsYn83afuO2WWsD2IxA0DPABjCdTntKn8lkpqgmcxNHBfD8888HAPT39+PUU08Fz7syGXRk27Zt2LhxI0488URIkoT/9//+H84880y89tprCIfJNMIVV1yBRx99FA888ADi8Tg2bdqET33qU3jmmWcAEPXxrLPOQnd3N/7whz9gYGAAn/vc5yAIAm6++WYAwJ49e3DWWWfh0ksvxU9+8hM8+eSTuOSSS9DT04MNGzYAAH76059i8+bNuOOOO7B27Vps2bIFGzZswOuvv47Ozs6Gz5VCoVAoFEp9tLa2YmBgwPXv8fz58/HSSy9h8eLFU1yzuQGjqrWF4h07dkAQBD369sMPP4y77roLK1aswNe//nX4fL66KzA8PIzOzk5s27YNp59+OlKpFDo6OnDvvffib/7mbwAAu3btwlFHHYXt27fj5JNPxmOPPYaPfexjOHDgALq6ugAQT6CrrroKw8PD8Pl8uOqqq/Doo49i586delnnnnsukskkHn/8cQDA2rVrceKJJ+rrGSuKgr6+Plx22WW4+uqra9Y9nU4jHo/jDHwCPGN4E/cJlgF/zSoN4xMsA8dq241KkvbdvM+NrZaWrzGdsWzjm7SSz+tv5drfVnY1VcqGoU7GfZNsdAztoh3DtbRMislnhGtpgVouk3zLYlUek1SeihqilsVJdQAqqmfZOnYiGwnr9dDSmc/FfE66olauVn20NrNqOztbMrtra8xTO3/tHM04qRXma8NGwlDLZb084/lbnadVnYz1NraBuZ2NbWSsg1X9rcoGAL6zA/JY0rJe5jx0hcqiDua6q5JY1ceM/dhcJ+1cjenMbc61tEDJ5qruW6c2MF5zrR3N97xVnY3ladfReIxVGcZ+qdWFjYShZHOTrouVUmt13QGAbW2BfHDY8r6yek4Yny/mMsxtbr6HtLYk+fmqnh1Wzxzzedm1v1XbWl3fRpFUEU/jYaRSqSlbuSIcDmP+rRfA39veUD5DD/wOZ2I+7rnnnibVzDssy+Kmm25CJBJxlf6qq67Cq6++SgeALnEl6f3DP/wDrr76aqxatQq7d+/Gpz/9aXzqU5/CAw88gHw+jy1bttRdgVQqBQC6M8kLL7wAURSxfv16Pc3y5cuxYMECfQC4fft2rFq1Sh/8AcCGDRvwxS9+Ea+++irWrFmD7du3V+Whpbn88ssBAOVyGS+88AKuueYafT/Lsli/fj22b99e9/lQKBQKhTKdNG0puGmeBV6wYAHuvPNO1+m7u7shCLPTcWc6cDUAfOONN3DssccCAB544AH81V/9Fe69914888wzOPfcc+seACqKgssvvxynnnoqVq5cCQAYHByEz+dDiyn6fFdXFwYHB/U0xsGftl/b55QmnU6jUChgfHwcsixbptm1a5dlfUulEkqlkv5ds09geAGQDSqLSQ1hI2FIQ8P6cdobuPY2bHyTVSWx2v6KF6rswIwKmpZey1NXEgxqglmx0I41qguaCgIQo1DjcVreWh7a8Ua1woimJBnrZ6yDMS+GF3T1wVyO8e3b7K1oVkT1fQaFRVfMHOyttPZlfBP1YHihSkE056OrGhZKnN6e5QkVzKzKaEqW2abPaEtozMvYLppqZ25rvrMD0tCwrbJipTRXKSeGumgKlv490aIrb1aqM5doAQC9fNI2PlK3sSS4RAukoWFd6Z1Qbya3n9Y25nqpkghpaHjCPsuk/BgxKoWTbNt8pP78/Hm6YsV3dhDFrjyhNAKoOmfdzs5wT2ntZKWM6t/LE2oU29oC6b0Dk+znjM8AJZvT24vx+SbuRZPKXaXoZXNgfEKVqqflrauR5v5rVCIN/cmIfn6m54h+fj5Bf67pKnLlWUeOI+djVGMZnw9MWaxSOSf2TVbyybmSdqh6XlXSa+2vtYfxOaaWy3o67RyMaqeuCJfFSX3eSjWdbczU5dy88s4770x3FeY0rsPAKIoCAPi///s/fPSjHwUA9PX1YWRkpO7CN27ciJ07d1qu7zcTueWWWxCPx/VPX18fAICNVIcQ4RItE+tJ+nzkB583GZAbjb3LZf14YoA/MaVuNcCxm6bRjbQjYT2t5tyg/Tibp6IA6D8karlMHtLaQ18fQPiqBjFKPj/xgzl/XlVe2o+8ua76gM8UfkH78eI7O6ocMbhESyWfcpURuN4ufHXoCe1HRZXIg17bx0bC5Iehq0P/kdDqbR5A2g0WtR87rquD/N/SUpWXdi5cogV8Z4dpoO7Ty9EGRWwkDLa1BQDAdZEBB9/ZYRjgTAwWq87ZOJCuDAa1AYyxHYyG8ObrrA/SDMbualnUf6yVbA5sJKzv115ctOO4lom+zYZCkMeSkMfIIFT7MQWgD+S0H2l9sKT/KPuqBixcV4c+4NDqbxwY6I47lYEF39mh18U4YDDWVz/vSLiqz0rvHdDbVx5L6oMDbTCrDdD5zg59wKG1LSnPpw8mjf2GDYUmBU3W6i0fHNYHE1o7aX3c2I+rBiKV54Dx5UC7t42De61u2oBWuwZaXtrzaNJycr6J54XZ0UfrB2pZ1PuoltY4eGJ4Qe/zWv825q3k8+Raa21luJ76oNDwgsK2tlS/8FWeSVxLy8Tzy5CX8SVJmw43Dlq162U8RmtvLtFiGmxOPGNm46CPQvGKqwHgCSecgJtuugn33HMPtm3bhrPOOgsAcbQwK2hu2bRpEx555BH85je/QW9vr769u7sb5XIZSdMb/sGDB9Hd3a2nMXsFa99rpYnFYggGg2hvbwfHcZZptDzMXHPNNUilUvpn37593k+cQqFQKJQphCiAjXoBT/dZUKYaVwPALVu2YMeOHdi0aRP++Z//GUuXLgUA/OxnP8Mpp5ziqUBVVbFp0yY89NBDeOqpp9Df31+1//jjj4cgCHjyySf1ba+//jr27t2LdevWAQDWrVuHV155BUNDQ3qaJ554ArFYDCtWrNDTGPPQ0mh5+Hw+HH/88VVpFEXBk08+qacx4/f7EYvFqj5A9XSDKomQx5JVb/L6uUuiPj2iqQkA9LdUAAYlzjDdYeHoYJ7u1fbpUyOGN1pjKAldgeEFomhVTS+Kk6ZOAFRN3WkKgF6/XL5KGWJDoQl1K0He2jVnD6PyxLW06FNf2nkb24m0q1ilAin5PPiFfQZFhtRVK88YvsE4lch3dkAZT05MU4VCUMbJ9KSmyGgKkKYK8J0d1SpiZaqsSpGqtL3eThU1rMrRRTOwL4tAT6eueirjSVJ2rqKsxqL69KnWdhCqlUQtb+PUnjENgElqjXashtHpw6iaVk9zlifUE0Pf09XrcEhvOy7RUqXeqeXyhHpk5YxkMmPg5neTaVdxQmnj5nfrx7Mdbbq6qKlFxuur9QFufjcYnw/8/Hmk31X6jm6SoF0rXiDqr08AhAk1SKu3WhZ1xUzJ5nSljvEJukKoZHPkWE21n9+ttxcbClV/r9zb7Iql1Q4a5TKZNm+daFPdMeqoZZXzKut9TQvNo6mTRkXPqOAZpzi18vVp68q1Mivn2nlr6bmujqrpdzWXB9vRVqWYaefIJVqAYABqWay65zSlm2tpARMOVa5huUqx1dpBU8n1/mqYeTDe61xLC9hWTWX36e2tnYfWFtpUut7nK9dR22+c9lbLIpiwQS1ubdGfZ+bp8tlEc0LATH8YGMrU4soG8JhjjsErr7wyafutt94KjuM8Fbhx40bce++9ePjhhxGNRnWbvXg8jmAwiHg8josvvhibN29GIpFALBbDZZddhnXr1uHkk08GAJx55plYsWIFPvvZz+Jb3/oWBgcHce2112Ljxo3w+/0AgEsvvRS33347rrzySlx00UV46qmncP/99+PRRx/V67J582acf/75OOGEE3DSSSdhy5YtyOVyuPDCCz2dE4VCoVAoFMpsoqHAfoFAwPMx3//+9wEAZ5xxRtX2u+66CxdccAEA4Lvf/S5YlsU555yDUqmEDRs24N///d/1tBzH4ZFHHsEXv/hFrFu3DuFwGOeffz5uvPFGPU1/fz8effRRXHHFFbjtttvQ29uLH/zgB3oMQAD49Kc/jeHhYVx33XUYHBzEsccei8cff7zuaW0AkxQ5fbvBGJkJh6Amk7pqotvNGJxI2EgYnM8HJmxQqyrqoWZro9lETah+Pv14Y7gHq/ASfGcHUZ8qZRgN1LmuDsgHhydUjI42yO8NGtQSkqdmTA9MKHhsJAxlnJwPKt5Yum2OMYxGJVQGb1BgjLZy1QboPl3JkN7dN0kR1eva0QYUilVOFhNq6IQKprWv5qSh5aOpIcZj2UgYCAaAbE5XqTQlQzew15TW1pZKmwr6+auxCJjRcZJudJyoXcKEbRJA1BT5nX3g588jaktrCyCKgEjsr9RcnihElXAfupISDkHN5fU2VrI5/XoA0B1ENNVQLZf1a8b4iAKsjCcnXV/9+IoTh9rWCiZfgDI8StoqTGwF+fnzSNukM/q5q+WyrsAZbbIm+ma5qm3VVGZC6dL6QmUb19ICNZWpUnS0Ok1yFikUyf8icRjR2lkZn2gDJh4l+Wn9UyT3IhcOERu9I/qhvLlHV7U0Jwwlm5u4JphQWfXwJ6mM4b7wAYUiuf8M54u33iXqdSUtUfx8eptpfRwAmHQW8PlIG8zvJtemtUVXLeWDw1VKmrE/a8oh29pCrm2l3mpbK5Q39+j9RjtnrqtD76P6MyQYAMu3QU1lwC/sg/TuPrCa0trVMXEvGeqi1cdoL4kFPcDeAdJHUFGihep7V1fZj+gH8gWoqYxeR4giuEV9QKE40Y4iURq1a1D1/ItHoQyPkucAAEa7dpX8lPFkldqn98HKPcRGwvr9o91j2n00G5mDC4FQmoztADCRSOCNN95Ae7u7WEILFizA7373OyxcuNAxnYuwgwgEAti6davjKiMLFy7EL3/5S8d8zjjjDLz44ouOaTZt2oRNmzbVrBOFQqFQKLMBbSWQBjOZUTz++OOIRCJ43/veB4Cs4nXnnXdixYoV2Lp1K1pbW6e5hrMP2wFgMpnEY489hng87iqj0dFRyLLctIrNRibexI2Bi40evZW/RVEP3aHZ0mmer9pxcjJJVB3tLZ7nwAL626/RIxKYsH3R3sQ1FUZTAOSx5IQNl+YhrNndGfKQx5JQc3ldeUIwoCs/2nlpaiR4rtqTUHsD15ScXF5XX8j5TygEWv0gCFAMSpP67n5SZiWsCBsJg2lPQDkwCLVMQnho9nNaftwi4o2NdKbKrsv49g+eA1IZMBXlQ1MndC9HUdTVKg1NCUKhaLiegq7amm03jeobl6hcu4pqpC+V5fMBprAXkGSiah0crrIzYzvaAIncU1JfB5iDw7rnqqZcGM/RaNCrecoabag072HtXLR218rX7S0FoqRp6hMnCLoyOaGQiPrfuvqK6rAuWp/X8lTGk0TpMpyXdry2X1NxNaq8OuNRXbXU+qdZ4eISLURJjUXApLNgFvZCHRgieaQyYNoTkNuj4FIFIF/Qy2J8AhhRmlBcBYNdbaVfa/0cokj6hSSTvpfO6IqV1nZsKAR2XvfEtkr/1FRbXcULh4CeTjDpLDn/ynVl4lEwhSKps08g5fEcUChOnHskDMSikNqj4PcNQzXcxxAnbIwRDOjqn9H+Te9rb+6ZsA0UBCCdmVADC0Wi9FbKFvs7Ibzxnq60G2cm9P4TDIDt6YT61rtg53VDemv3RLQAnpvkkaxkc1DHkuAW9UFd0gt2hMSGVXN5MJW6sB1tFWW4EnqI50jbxKPkOrW1AobrjZ5OaFZs8nuD4MJGhd43oQSKEwqvmsuDWX0k8PLrE2l4DpiNCqCKGTeAa5SvfvWr+Jd/+RcAwCuvvIJ/+qd/wubNm/Gb3/wGmzdvxl133TXNNZx9uFoKjkKhUCgUCmW62LNnj+7k+fOf/xwf+9jHcPPNN2PHjh16aDqKN2y9gBVF8fw5XJdfUcsSgAkVjG1t0b1KmXi0yiPNqBhpipAxFphmx8MvXaynV8aTumLCJUhAWc2W0Gh/o9smdbQRRSWbm7ARq6hkugJgUDgAYkMmDQ3r56C9GSvDo7rapuVNbOx8+j5dySgUdSVAs6WThoYnbKgqbcTN7654tJZ1mzklmwMGKl7dhSL4hX0Ty5OlM/q5q21EkdbaTpVEqCNjUA4M6ufL+ARi59TVoZ+LhqayMKMpvT5au2vx+YzehygUAcGgtlY8HrlEi64iGD1Eda/gYGBC6RBI/EdmYa9+7Yn3bhlMewLgOV3VQzCgHwtJJmpRMgl+JKN7BnOL+nS7Ks0bUxlPEtWjUid2xVJS73CIqE4Ve0clmyPHtCf0/JTxJLHz05SmYIC0ZSqjK5NyMkls5iplcV0d4OZ3E4/ded16/zQG6mXiUVJ2G5ma0ezY1FSGKCuV6zHRF4nihFiUqG6aotXRRq5jpQw2EiYKULlMVMhYFMzoOOn/sYoilM6S/0fHSb2624kNoMAT9W9giFyDtlbigV0WgXSG9K8FPfo9orUDO4+cp9rWSo6RZF0VI/nESb8fr0QAOIrcv2o8ottzAiD17ekEN78SaornwOQLRLGb163f/1JPQrdr01RRTQHT2g6CAHVkDPzuA0RZ62gj/bSvS++nmr0jGwkDPWQ9VXn5QmBBT5XXtf5MEKs9i9W21on7JxYFv/MdvY9wi/omFPcFPRP3sTAxKwAA3FHLSN9pT0DqSZD+0Z4gfb8CN7+bKKRv79dVWSWbI59jjgAkeUK1E4jNp7RyEelLheLEs2NgiPS70XGIfW0T94tYabeeTjDtCWJ7mZuIPai1KbvvIOnzyWTVc3e20Qwv4EamkL/5zW+CYRh95S0AKBaL2LhxI9ra2hCJRHDOOedMCsPmhM/nQ74Sn/H//u//cOaZZwIg5mraogwUb7gKA0OhUCgUCmV2oK0E0sinXv70pz/hP/7jP3DMMcdUbb/iiivwi1/8Ag888AC2bduGAwcO4FOf+pTrfN/3vvdh8+bN+MY3voE//vGPejziN954oyqWMMU9dADYBBgfP2EXV1EG9Phalbd2Tf2o8vq1WNKKqdiqSG/t1r0tNW9BBAOAIIBfunjCVqg9Qd7Uu4mzjlouA5I84dWo2fkVimDaE2BbSVwzNZcHt6iPqJVHkFiMfGfHxFt2LAr2iH6w87rBHbWM2MiURaBQrPJA1Gx7NCVHt8lZulD3FOUSLUThaK2oZoKg2xxV2cIBugqmDI+SdEctg9rWSlQmQQAzmqryKNVXIZjXPWG3EwwQu7BcnigEglCtHvV06oqEWi7rahpiUd0rEzDE9YtFiGrT1aG3o5LN6StKaIqZMcaadt012zAmHCLqhI8oEmosQuyaRsaIXdeiPr3t1ZCfKFkVlYtf2KeroEp3Qrcr09tO87JMZydUjsERKN0VpSMWIdc6EgazsBdKe1y3l4QoglnYC0aU9OurjoyRa9We0G0stfiLmrqNYED3aEU6Q/KJR4ly3dOp28ipbXEwoqRfD8SiZH+hSNrAoL6gp5O0d8gPZXh0oj0LRaih4MQ1LYtQ4xEwC3uJcqTZaMYiUOIVuy2eI2peLEruu8GRCVu8ikqEQpEohOkMyUcQwKSyYFJZ0s7G/pImShMjSuR8DOenqdLa/cUs7AU7OEYUrVTWYENb8aJNZ4kyWLkv9WtYucbMwl5wqQJpG0Egx1falwmHSF/u6SR2oe0J0qaSTOzyli8kNnSVe5Gd1z1R1sAQ2EgY/EgG2DtAjhVF0lfb4rpto6ZaQxD0c1VDQaghP1FR45GJttT6Xiqrq+gQice70tdF/k5nddtCfoTY7akjY5Dao1C728mzJZUBggH9HlDa4yQe47xuonACpH49neT6xqIQ9o0CC3omZhv6OvTrKi2eB37nO1BHxqAKvK4aqiEfuecW9JDoB4vnAaIIsa+N3E+xKHnGVp5rmko422iOAui93Gw2i7//+7/HnXfeWeWUkUql8MMf/hDf+c538IEPfADHH3887rrrLvzhD3/As88+6yrv22+/HTzP42c/+xm+//3vY/78+QCAxx57DB/+8Ie9V5bSWBgYCoVCoVAocxNZlidNr/r9fj3erpmNGzfirLPOwvr163HTTTfp21944QWIooj169fr25YvX44FCxZg+/bteoxfJxYsWIBHHnlk0vbvfve7bk+HYoIqgE2ADVdiyh3Rrysj+koKFUVMGU/qqxNoypucTBL7GGDC/qpiL6THhNM89yqqCUSi0jDxKPHsPDCoKxd6+ly+Yt/FEY/BMslTHRmbiIG2oEfPj8kXqmy19Hhvo+PkuJBP9yqUFs8jymRfl65MMvEopJOWk7dlTdHYO6CrAfqKFbGIbk+kxiZUBN3ubkmvrtZpMbkYUSKKyciYrlAw7QmwHW1EIdEUC6ONVUXtq1IXgwFyLYSJ2Gma+oVCscqej9hSCcQjECD2RQNDutqhCjxRUpYuJiqKpkBV1Cm2o63KHg8AsfVqT0BaPA/KgUFdFdFUNhSKpP0kmcROqygzyjFHkGuirT4xOEbUEoNdIyR5wuO0ct3UtlawKXJtNPsokgcHdt/BCVuyWBRMOotyb4tuk8XEoxP2bZpaFwpOKJRtrRNx9yo2e1rfAaArXFJfB6SYn6QJBoiN2IFB0p8EQbfT023oQj7dVpXtaNMVb7WtlfTFYIAoN/EoUbAqx4v9nbodIDs4pueJvQP6eSt9XeRe0RAEco7BAKl7RQ1VQ0FSfqpiI6bVs3Ltxe4YSSeQd2cmlSWqdL5ElKe+DkAgCiQEAWo8ons9q0t6Sb8HoAqkP2hpIAi6/SIEjiiysQjUkJ8oxVr7Vp4RzOg48fwVeNK+lWvJ7XpXPwexr42odoMjpD9W+r8a8k88T2JRSCsXIbcoCqWvC1IPsc1TxpMTyrggkHMCyP58iXgKx0MQu2PELjKVgbRyka6gqd3tkKIVe+dKXmp3u97GTDxK1MDBESAYQGHdEYAgoHwEsQ9mU/mJ8oMB0q4AGFEi9pHxELHxA5ltULvbwaUKUOMRqG2t4PcRW0ss6AGTL+jtzohEPWXyJYDnwA+Q/iLsGSL1qyjT5Fng05/fsw6VafwDBq+++iri8XjV55ZbbrEs8r777sOOHTss9w8ODsLn86GlEolCo6urS18MohYcx1Wt/qUxOjrqeUEKCoEOAJuAWjGOZ0bHJwZAlQeh9iOqTVsy4ZA+ZcYvXQwmXyCDtsqPqh62QwsQ254g05uipC/7pRt2d7TpgyylPT4xwOmp/CAOjkxUUjO0X7qQTM3sHZgIW1IJ6Mqks/pATzkwOPFD//Z+Ms3SniAPzGAA7EhqImyKJEMYK+hTKNpAQjOwR08nGShWAiIrcTIdqk2vqLEImfrWfrwrAz1ufrc+8NMMzFWB1398tSlYbVoZC3qqDdoFAeqSXtJehilPbSqWTCmTgYUyniT7YpUprlgUTL5MpuzaE2TArA26BobI1JF2/dta9cG+FmxYOw+lOzHxQ1Yogh/JkAFBZaCvxEPkmLa47rDChEN6iAsuVdCdUADoU7wA9B+sqrpXptIAQGqP6oNMJR4iA9tUVp8+VkNBffDge3OQDAyCAfIjWhnAYWBInw6dcD7gdDMHbUChhnxk8Fq5dnKI1JcfnTBd0MJ5MPmyPkWr/9imsmS7KAKiTKYcK9dZjgdJWZKs70NPJ9S2OMr9HRDeeA9KPFRx0IjrU9zqkl7SxsccASZfJoNkkAGuGvKTQVllsJRd0Q5p8Txkl7ciu7pHLxvBQGUgFgSCAfA730F2dWVQkcuTtopFkD+ijQwo9g2TQa8gkCl4gUN+RbfedowoQerrQDkRIGXwHFSBI204MjbRp0IVhWXvwIRTRHucXNNggFzrWAQQOBRXzCODoUIR6pJeMgAXeDKoETiIy+YDAkfqmsuTAas2wBR4MJKKwFAJ7EiKDJwqDikAoMRDpI/mS1AFDkqg4rzT1wUmXwafLpHzak9A2DdK6p3OACJ5JkAgL1SqwAN7ByD1kL4k9pFtSl8XVIFH8J0UeS6ABNou97ZMDMrjEbCiTO7TeIi0cdQHlWeBvQPILYqCGU1BDfmQPjJeuScr4ctEGUp7HIVFcajd7WTgXXlxkHrIy5va1loZWMukTQeGdLOP2UpTbABV4Oijj0Yqlar6XHPNNZPK27dvH7785S/jJz/5SV0LRLg7J+s56VKpBJ8h3BrFPa6mgHfs2AFBELBq1SoAwMMPP4y77roLK1aswNe//nXa+BQKhUKhzDE4jtPXvHfihRdewNDQEI477jh9myzL+O1vf4vbb78dv/rVr1Aul5FMJqtUwIMHD6K7u9sx7+9973sAAIZh8IMf/ACRSGRSGcuXL/d4ZhTA5QDwH/7hH3D11Vdj1apV2L17N84991x88pOfxAMPPIB8Po8tW7ZMcTVnNnIyBX71Qn0aCICu8KhlEXI8CH50IsCu2tZKtu0+oE+N6Ebq2vJF87pJINiQn4Qs4bmJKb6FvUDlLVYLqaAZfgMToS/0kBiohD+Z1w2MpsgS3+0JcJpaJfBk+roydciIEpiyOGHs3tYKZmBIn37WDNO14KnobgczmkJ5RR+Eg1kS1mJBD1GbKuWjEkyXWboQisCB16ZODVPCTJoY3zOirLelZnSuChw5bmCMqDGSTBwOCkVyzNv7wcSjKC7rQuCNg2SaKV8AO5JC8dQV8O9PQw35oFSCAKsAqWeljdh5xJlA6kmAH8lUDMeJ0lXubYHvtX2kbSR50hLpTL4wMT0cjwChIFFh2lrB5MtEzQuHiCL08utkej5WUfvECRVKMx1QuhNgRypTsKEgUe8qU1Pqu/vJtHA8CFaUwcSiYGNRoniFgkQBGhwjdYoHIbVHweXLZEozFAQjSkQtzpehhnxg8kTRUXN5MJpSuXeAhCI5dQW4BT1k2qyiIAGAFPODF2UgFIQUDxKHhXwJUl8HUYhFkTgaVK6v0keWVmTRTRSn0XFdjWHzJTIlvrAXEDhIPQlw+fKEgihKJC+fALG/kyiKAgdmNAWlOzFxXd7eT5TafEG/PorAgQmR+mnT4szoOFHPBA5MMED6hMARBUyUERwgKm25twW+0XGoAg85HgSXL5M+055A+J0MlPY4WEGAWmmbwFBBdz4AiHImRX3gM2WyDwA7OAalm6jorEgUquzqHoTey5P7X1POAcghHylzSS+YFFHPmXwA5WVdUPk24gAR8pP7gmfgK0hQ2+ITU9cxokwy+TLkRAD8aJ70yQU9QL6EzKoOBAeK4PJlFHoCiOwaJ/1Q4EjZqQJ5Ro2Qqd5ybwuEg1n4UnkoBwYhdy+DkMpD0vrhCJl+lUM+cG2tkNpC4EfzKPe2gM9UnKpWLoLKs+DyHFSeRWERaYPQCwcrin0cvv1JMsNQlCF2x8j1BgnPona3I7cgjECQB1uUwe8bRn7dEeR6dJNnYEyUibo5MARp5SJwBQnFziACQwVIMT+4SjvlFkXBiiq47nZ9OyPKpF8s6NH7n9oWAvPuPoen/wzmEAaC/uAHP4hXXnmlatuFF16I5cuX46qrrkJfXx8EQcCTTz6Jc845BwDw+uuvY+/evVi3bp1j3pqNn6qquOOOO6qme30+HxYtWoQ77rijyWd0eOBqAPjGG2/g2GOPBQA88MADOP3003HvvffimWeewbnnnnvYDwApFAqFQpkpkGncxpaC83J8NBrFypUrq7aFw2G0tbXp2y+++GJs3rwZiUQCsVgMl112GdatW1fTAWTPnj0AgPe///148MEH6ZJvTcSVDaCqqlAUBQAJwKhF3e7r68PIyIjToYcF/OJFFdskP3nr1pU1AfLyheB2vasbnRNVqEBsWoCKUlXSA67qwVErypaYILZHmu0MEw4RFYPnIPZ36iEV1IpSpBmTK9lcxXjfD6mvg4SDqNg1afYxui1VyKerMkyeqBWMr7IkVMUhBAt6oLTHdRtFdl73RNDlfAlqLELsAPOFCQN8idjf6AbbRy2GKhA7KRSKyB8zX1dMmHyBBLMdqRjUV0J+lPs7AFEGI8rg8pUg1gNDyB8zn6heFZVDXdILNRSE/2COKFyiBLGvDWJfGwJ7xiDHicrGj2Qq9pREbVIFHmJ3jHz6O/WQE0RxKQGCAOFglthAVpwBiqsXghVl/ViA2Gcp7XGoAkfUMs0GaWBID/bM7jsIZvWRut0TCsWKE8E45JAPhXVHIHtyP+Qgr9shkrYpk2XU8gUSIiRfBj8wRrbHQ1AFnjjniBIUgfQLNZUBv29YN3JX4iEUFsUhdscgB3kwokQcSuIRotQt6AHSGRIOIx4Fs7AX/j+9RezTKqoQRGKDx0gqsYHTVKqQD6rAE+W0omYCQPmIbojL5kOK+sg5jZDroLa1gkllIUV9KPYnyPJdIR/kkA+sKCO7JKafIwpF0q+DAWJvpjkj9HVAESpOLKJEghsDugOQuGw+yq0TpilqWytKvTHihBMPgcmXkT+ijeQBgMuXiU1bRbkUDmYhLpsPtVInVJQ+gCigjCgjtaaTXJM8sYOTFs+DFBXAFSTIwYqTiCiD3XcQUk8CxRXzkJ9fCdSdykNqC8GXlpCfHyJ2ehWYVBaFngCSK1sAAJlVHSiv6EO5vwO+sSK4goRyP3GwYVN5om5FfSjMj+j3anY5ec4wokRUr5APaixCnicAhKwMlWfA5Etg5InrWeoiYWJKvaRfS+1RogBjIrg229GGYgexUWRFGVLUh3J/B+SQDyrPoNQbg7BvlNRBUogSW7EXVATyk6MILPiCDL4gQ21rRXHFPEgxP8TuGDKrOsDly2TJOYEjdnx9XZBDPkRfGQYjqSh2+iH2d8I3XibXI18GBAHZJTH9OSpGBSgCByEj6tdZc0zxpSUE30lBivmh8iyKnUEU5kegCBwK84nNIT+SAVeQMCtRm/BpMt/97nfxsY99DOeccw5OP/10dHd348EHH3R9/G9+8xs6+GsyrhTAE044ATfddBPWr1+Pbdu24fvf/z4AMjLv6uqa0gpSKBQKhUKZXTz99NNV3wOBALZu3YqtW7fWlZ8sy7j77rvx5JNPYmhoSBelNJ566ql6q3rY4koB3LJlC3bs2IFNmzbhn//5n7F06VIAwM9+9jOccsopU1rBWYEk6koAU1lgnvEJJBzLrneJOiGKxAYunYXUQ+xVyiv6oL67Xw/FgiDxDGTndZMguSMp+N4cJN6juw8QRSgYIDZVhSL4dIkExI1HIceDFTsv8hbNLF1YCReRqtjN+XUPRE2x0MJNsKk8yiv6iMpRsdvSlTmfj6TT7PnaiWqmhvwonbiUKBeiqL+pA5gIEQKigDCiDK5A1Ck2lSfepTwH33hZV/XUeIR4b0oyCRAbj0A8djHYoqwrD3LF01Rd0ktUj8XzIMeJXZsc5HXVThE4YvtWkIhqlM6g2OnXF5lHoYjC/Ai4Xe9C7IqAK0gQowL40TxR50J+PbC2vjRWJSyJGvLDvz+t29BxBaI0ElWNqCHYO0CUQED3RBX7SCiPcoJ4UDOihPKKPqIc9nUR72BJhS8tQRhMT4RPEThyXksXknLiQXItJGLrpF1HlWd0L0o5yOsejFJPAnLFzk3IkHPh0yVio1YJ06JWbDg1dVdpj+t1Z0SZeHxXlB2pLQQuVUC5vwNMvgwmlYUcJHZySjwEqS1EPIjbo/DtT4IfzcO3Z5ioUEuIeim1haCGgsSOKyPqyqVUUWwCwyVAlImC29+J3AISDNqoQqo8AykqoNifgBryE8/QdmJXKfUkwEgqWEklylfIp6t3XKpQuU98CAwVwOXLxNNalEm9BJ4o66IEYd8oUkcSb/D0yko7xkNgJBWqwCG6O4v8im7iKStwRDXKiCh2BiEHefj2J8HkS8gfvwi5viCkIHnclvs7kF3RXrknWIgRDvxoHlJ7lLRhTwKRXeMIDotg8mVE3k7D9+YgFIHVrwcAZBcEkV3RDnbfQd3OTmqPQkwEEX1lGFJPAmJ3DPmeABSBQ6k3Bt/+JJR4CL6xIhhJhdQeReTZPXpIFQAo9ifAZ0SoPAMuRWznhINZFFfMQ7m3BWooCCFLbPQYUYZvzzCEMTKrwUgq/PvTyK/oRnTHATCSCiVAyi7MJ/eaFPODFRX43hwknsIA+MxEWKPgQBFiIojsyf2AKKOwKI7R1RPhWIqdRH0U9gyBSxUwuiqE5BriVVxsZVFc1kVsDwsyKTvhw8iaMFSehSKwyC6JQRgrkOvNs2CLMhSBQfCdFMqtPoT+/B4YUUa5t0VXDGcdzVgG7hDaELrhy1/+Mr785S9DlmWsXLkSq1evrvpQvONKATzmmGMmGXgCwK233krj71AoFAqFMpOYomnc6eS+++7D/fffr5ugURrHUxzA559/Hvfccw/uuecePP/88wgEAhC0JYEOY/JHdun2ZFJPQlfqsKCHeKJV7FOYhb0kVthIBmqc2MyxHW0kvldPQvdc1TxQM8dVliNqi+sexGJ3DOy+gyQO1+AIUUPiEag8Q1S+fAnCYBpyyAdp8TyUjyBLQYmJIMTuGAo9AT22l2a3qMRDxH5PlIi6KAhEFdEQZRLPax9ZuFuOB6EKHLGtC/LEbiyVRe4Iog7kjyH2TFJfh66EaTZ82RXt4PJlXWVi8iXI8aDudYdgoGJfx8G3PwlWlKF0J4jn6QiJcacInK6CcKkCCWIsqZAWz0P+iDaiMlXUJIgyEIsiMFTSFRH54DACQwWUTlyqn6IiMJX/OTD5EsQE8aCVehLEflCSocRDxFZvEQlazKbyKHYGIQymwadLpO3HCsQrO5Wv2GGVcXBDH4TBNAqL4hVv5KDe5gCxB1PiIbCiAilIvI7FqIDxvyKe5dLieSTWmUSe6Ep7HGJ/J4r9xC40uySmK5AAiCrZReL4camKypUvE8WkIBE7vnwZKs+AT1fOta8DudOXgZFU5OeHdBswOeRDcVkXuIIEVlTASCrErgh8r+2r8gzWbBI1dSyzmPQxsYsou0rFi1uK+SEHeRQWxaEESFy5/BFt4EcyukIGAOmVCag8C5VnEXk7rdvIiV0R3cPUvz9NzjURJMGgAYyvX4pyK7FFY0UFo6ujUCp2jFyB2H7qQZxFmdgitpHz1TyMxYqyWO7vQMtOYnsZf3EIKs9ADvIot/qQnx8Cm8qDlVQwlf5WTgRImxYqdojdMeSPaINvvIzQQBF8QYF/rAzhYBah9/IkD1FB+AC5B1SeIWonQDyL3zgIRpSQPjIOqY/Y/0kxP7nfJAXBYRH+sTKKq4n9o5ARoQQ4lGPENpWrXA9/UiI2cxEOxf4EsdlLBFDomQj0rMXzy/UI4DMiyq0+MJJaiWCQR+6IFrAimXLLLm9FrkdAKUHsNg98vI/UMeqDEuB0e9tyfweUAAe2KKPQIUDIiBCj5LjMAj+UbqKqlnpjSC0LQQ7y4NOlStkK+IICOU48eMMDEviBMd3mli8oxEa6K4LgCGlDqS2E4IgMhWdQahXge3MQjKSAFVW0vFWGMFZApk+Af1xEqSsMMTrx28WKKulXBRnFFfMAUQafKevK6uyDadJn5uDz+fTZR0pzcKUA7t+/H+eddx6eeeYZPYZPMpnEKaecgvvuu48uxEyhUCgUCmXK+Kd/+ifcdtttuP3228EwM2twOltxpQBecsklEEURf/nLXzA2NoaxsTH85S9/gaIouOSSS6a6jjMevigTb7i2EFH7CkUS061iNwcASncCpd4Yyv0dUEfGUOoKE/ureATM2/tJrLJ0VvdiLMyPILw3R1ZhqESoV0NBorBU4p5Ji+cR+yZAj4qvLw0FQOUZCAezJOZVUQa34w0EB4q6XZ4cD+qKjLYagSIQRUZqC5HlnFYuqths+XWvVCVA1ByxO4bAnjHwo3mIfW0Iv5PR39ilxfOIXVsqDzER1L1Jw++QVTLKiYDuScuPZIhSMJKqeFaSuHXF/oTuQZjvCRD7I4EnbSwROzAIHBSBJceLMoSMCCZfgv9Pb4GtnCNAvBWVAAc5SGIe5ueHEHjjIHz7kygnAvCPlXXVS+yOgZEUlFtJPLRCTwDlI7p1dSb05ijYkRSxcxsnnrhSzI/Mqg7kFkUhdsfISgYA1JAPrW8UkV3RDiEj6moTOzgGiDLKiYCe1rc/idAL70AKcgi+k0LrHw9WlEEZoffyUAIcUU8FYtek8AykqA+B4RICbxyEmCAx74TBNPhMWVdjmHwJcsg3ocy2EVs9OchDTAQxviyAcqsPvrQEVpThS0vgDZ6TfEaEFPUR+zHDShC5RVGUusJgJBXZ1T0o9cZ0m04hS+whhbECMouJQq0EOKJIHczBN15GOcZDEVj4xsukHxRIm8tBHkKWrCShCKxeRq4vCEVgUezwkxhyFbWOLVaUz3wZLTtJG5YSPuJpWiT9NbcgjOSyIFFSK7aTYiIIpT0OrkA8ZUfWtRHbywpsUSa2aKt7kD+iDYykEvWqT0BgqITRU7rAZ0SkVybAFST496eRXUDq6N+fhhgVkOkTkFpGbDPFCOl/6ZUJlBMBhN7LE8VYUpBZTFRNzTu31BUGJBmZVR3wJyVIUQH5+cT+kCtIKCXIfa+pWEpF4SzHePjSZL8U80MJcOAzIlEfx0VwBQmFDh9YUUFwoEiuS0WJG10dRXhA1K9hudWHQg+JlcgXFKKmFWVE3k4jurcEVlTBD4whsasMrkT2lVonVDUpqHneCggfKFVs8BjkunmEB0Tk54egBDhk+gTwBXJeQye3gpVUiFEBqX4fpKgAdiSF4HvE3jLfE4AY4VBq4Ynnc4cP0d1ZlFqJismKKuQACynIkmXlgjzYinIuJoJoeaOA3DxyjQNDBZRjPIl5GGDBSCr4TFlX0OUgr98Ds45meAHPsCnk3//+9/jJT36CJUuW4K//+q/xqU99qupD8Y6rAaDm+XvkkUfq24488kj827/9G377299OWeVmC773UmBTef2hq7bF9XVtVZ6F+ta7AAApyEI4mEVh3RHwjZFBWm5RFLnTl5EfhSMmIqKXWnnygBTIj3ipN0amqQbG9EDLmkE7ky+Bz5QhL1+IUm8Mai6PYqdfD6Kqoa0rW+oK66EONOQgj+ySmO6MoE0N8qN53SlBCzni258kP1IJn+6ooPIsxEQQxc5KSAYA5d4WFPsTEA5myYApSqaMCvMjUHhGPzcAlSlFAdHdWfLD3h0jU6JRAWrIR34EgyTEitbGwr5RDJ3cCjHC6eeg8iyUeAjpDy3XHQv00CeSQgYzbSFEXtiPcn8HMqvIGqPaD7h2XipPwlSUemOI7jgAtigjvDdHgkgLPBnIV4zk5SAPriAhvDeHwFAJxQ4/pCAHIUsGeAAg+8mtpvLE6F/s7yRhQMZIyJEDp5HpwuzJ/foPlhIng7Txo6OQomRaTLtu2jkzkoJ8TwDFZV3ILPBDjgcx+KFuEjQ7Q6Z5tWl3McKhnAhAjJJ+yYoKhLECWt8oQg6w4DNlZBZHUGoViNOMWHmxiQoQxgpIremEMFYAnykjtyAMlSPTrKwoVwIpK8gtqgTWFsk5SDE/wvsKyPeQ4L7lVh+yS2JQeQbphTwCe8YgRQXStyuOALke4yCCJQ4kPIPwvgIZ9FamikdOaEGplUdmcQhjK0LILonpzgOsqCLTJ6BlZxKlVgFSkEXbyxkEhkrkBacyOJWiPkhRH/I9AbS+ltfvPYAMgBSBhX+MtB2XKkD2s4jvKev3TrHTj+juLMSogPTKBBSeDJjHTmxDoYNH4rU8wgPkvIIDxUq/Z5DrIQO6UhdxTojsJYGXpSCL0VVkwPjOBYvgHxeh8ETtCL+TgcozKCcC8KXJIDb05mhlgE6moCMvD6DUKiD0HnkelWM8Cj0BlFp4ffDsT0pgizKKnX6kFwchRX0YXxFCfHcJYoTTl1PjCzKkIEte9iIcsgtI0Oehk1tRaiVTuvkV3RAjHPxjZQyeTF6Qcn1BZPrI4FcRWP16KgKLwFAJsh8otZCBamaBH/HdJRRbyQAs8VoeuR4BrKQiMK4guZSYIKSPjIMvyMjMJ23lT0oQxgqIvZ5CsTOIyK5xjB3bgvQiAZG30/CPEwebTJ+A5FIfSi08cvP8KCXIYLbQIWB0dRT+sTJyR7Rg5BiuagpeOJgFP5onL5mzlTk0+AOAlpYWfPKTn8Rf/dVfob29fdIaxRTvuJoC7uvrg2hcfL6CLMuYN2+exREUCoVCoVAozeGuu+6a7irMOVwpgLfeeisuu+wyPP/88/q2559/Hl/+8pfx7W9/e8oqN1uQ2iJk+asCMZLWplVTazrh2zOM8qkr9LdzJl+Ab5woZHy6hPA7RJWIvZ4iylobCWIcPlBC+M0kUZwqb6EKz0ANBTF+ai+ZVg35cPADEwGelQAH//40pJWLEBiaCPUSGCrp04caQkbUnQb4dAmlhA+RXeMo9icQeOOgnq6wKI4DH+muTN9wYPIFiN0xqDxLpnODAYiJIHxvDqLQQd6upTYybSkFucoSSyRwrTBGziX4XhbBd1IodvorCp8fxdULMX5SF+QgT4LRVoLGcgUSpoYrSPCNl4nKExUAUUZxWRdCwzL84yJ8e4aR7wkgs8APKepDeF+BqH+VaTc5yEMYTGN8BVFXpL4OsEUZgeESqWOeTAEzkkrOoTJ9lesRKkoug/z8EAmW2xsDW5TJFGpXBMJgWg/9UegJQOEZFDp4oswEWUhBDrHXidOKb88wGFlFscOPsRVE4c0ub0XLbgmBoQL8Y2WwIjF+VwQSHiTxUhKMpBDVruKkwYqq7mwQHCbX1Z8k39tfzAEAip1BZBcE4UtLSK5sQez1lG7IzxZlMBJR7MaOCuhTtgDgH68ocQvCyM8PodAhEKeiIKOrQyPHcAgOFFHo8CF1ZJQop62Cfg+IEU6fmh08OQwhK+vTx1xRIc4UMeDAR4jqXWohbecbLyM4LCH4XhbJlS2Q/cQRRA7ykKIC+IKM4LAI33gZra+RKVO+oCA8KKHYyiK12K+X3/ZyBmPHtkDhGYQPlPSpaCEjYnxZALlFUSgCS6ZEJRWFngBxRCgQ5anUyiPXI0CMCvAnJd1EAAAOnBZDeECEypFpeFZSEd5XACsBqWUhsBLINGdPAL6xIrILgnr4Er6gQOEB/1iZBC4XWGQXBDF6TBilFg7BEaJQBcaAkVUB4jxRUeb4dAlSkKi1hQ6ibmcWh4iqHOEg9rUhtZCYaKgcA19aAldUIAWJcipkiao3eHIYsp8FKwHjywIID0i6UgoAhQ4eUpBD/HUSQqkcZcGKpO/F95Sh8AyyC4IQI6QfFDv8aNktQcjKCO8rIDCuIPFaHoUOAf6kjLGjyNRtoSeA6H4Z5SiDQocPwWEyJR0elJBaFkK+J1DZJiA4XEbiL0UE9pBg5lKQQ/xd0qeSS31IrmzByAktEDIiCovikAIMGBkYOrmVqPctPPgiIPmBYis591w3CRUT2VvQn318QUHLm6puVqHyLJlJCfmqHEVmFSrThM90nwRlqnGlAF5wwQXI5/NYu3YteJ4cIkkSeJ7HRRddhIsuukhPOzY2NjU1pVAoFAqFUhOyFFyDeTSnKk2jv7/f0flj9+7dh7A2cwNXA0C61q8zxQ4/eD4AVlTAp0v6kkjhfQUUl3VBCrIIv5lEfkU3AkMFEqqh1YfQm6N60FUmX4Ia8pNjY37wmTKYfAEHPt6H6H5ieJ1bFEVhURytz+wnqpTAI76nDCadhRzv0EO5aKghEu7BN0YCSUuL5+khUsSogGKHH8GBIrJLiJ1bub8D/v1pKO1xfSkrISNCyBH7OSkqQAl0EKcLngEvcGAApBb7ERXmIbo7i2JnEEolbyErg0+ThefLURZt+5NgQj6kj4zrqpUWGFaMcCTwraRCCrLwVYyvhcE0Ums6EXs9RZwcigpKrTx840EoPAOuqEARWKjxCLgSUe6kIIdApgyF94GRK7ZoQRbF/gTiu0sYXhOCwgG9jwxi+PQuBEdIPRWeAZcv68tbAQBfUJFd0Q7/WBm+tITBU2KI7icBfxWeAVdQKkuKMaRNW1m0bx/V1aLAUKmiCIUrdlS9iOwtoJQgqtHoMWFE94lQBGbChlEgCo8U9aHQ04qxIzlEDqjgCyS4MVdUwFeWqNKuIZ8pQ5ofQrnVh1wPUT6CwyLCB0oox3j4xyVkl8TAyEThZEUZCohKFB4kgbpJwGAGwlgBI6taEd1P1Bb/uITM4hDS/Qza/0yW9up9Mo9yK1EFY7sLxPmBJ3ZumcURlKMMoqKM0RVRdL5UhBTkMHByALG9KoLDIlL9PrS+oSC2cwxiF0kfGFcxviKEtpczSK5sQWRvgTh8VBS6YiuL8IAERWAgBf1IL+TR+acccn1BgGfAF4nziewnak9+fgi+jAJGVpFZUFEGowKSSwW0v1IkNqGjeRQ62lCOMmAlINPLIfauBDEMCERIhRxgkekg/TE8KCG51IfgmApWVOAfV1COkXul1ELaQAwz8KdUoh6GGQCRSpurxJawpICVSF3yPQEUW4my2/m7ESTXtEEKkh+5zj+mkVsQ1p1iSi2kHDHCIbM4AlZUkVwWhOxn4B9XUOjgkesOIf4uUVuJvSFxstGdLwYlos7KgD8pIdXvg+wn9n6KwBAbxoIKLQTI8PExhIZlsBIJleRPKsj1CCRdESi0s/AniUom+4l9axAAV1JQSvgQfz2j29Xluom66U8StVYRGJRaSd9kJRXDa3h0vFhxgmtjUI4Q+9lwrB3lKIORVQISu1QceF8IkQMVO1mewXt/FUJwuHKt/IA/pYItypCCDMQwg86XisjN8yM7n4c/RY5LLgsiPCDpzyK+oCDXzYMvqkgt9pPz5RmkFwlor+M3Ydpphh3fDBsBXn755VXfRVHEiy++iMcffxxf/epXp6dSsxxXA8Dzzz9/qutBoVAoFAqFYsmXv/xly+1bt26tMk+juMd1IOi3334b1157Lc477zwMDQ0BAB577DG8+uqrU1a52UL47RRRIUDCt7B/fhPZBUFw+TICe8bgHyPLXwWGCsjPD1XCXzDEGzFIQphI7VFikxTgdJsnNR5Bz7ZxRHaNQxU48AUFwfeyUNrjGD+pS1eZ1FgEXL6M9JHxiaWqeAZMvgzfWJEs5dXWCpVn9MXgyds+Qzw4h0tQ2uNgizKSa9pIKJieAFn+qcMPvghdESx0VBZTHy+jnAggu6IdYpiBGCF2R3KAeM9qHo9iIghGVpF4iSxBlesLwp+UJlSThA+lhI+oIJrXL0c8HRWeQWZVB8L7CpBDPvjeHIRvvAwhS7xTg++k9JAPuUVRYgs2IEIOkG6tLX2m8Az4gkIC6QY5dP4pB39aRWpNJwLjCkLv5ZFbFIWQEYmtW9SHUquA8WUBBIfLukfl2FEBdLxI7M7KrT7IARalhA+FDh7hdzIQIxziu0sYO7ENfEZEZjFR5FgJxKZruITgsIjksiBUjoHsZxDdJyLTV1FQgsRTkRUV5BaEUWolHo2xvSr8SRmx11NQeAahP79HAvG2kqC2UlQg9oftHEqtPBSe0RWj8WUB8AWimnLFibUzU0dGMXpMGPkO4q2cXEb6hX9cQqkrDH9KhcIzSC4mYTT4goL+u/ch8nYaSoBDchmx//InZQyvCUEKEBvEgfeF9TJyC8LgibM7Ch082l6TIAVIW4QHib0YI0oYXxZAfHeJqLk8g6GTYkj1E0VU4RkEh8u6J2x6EbHr1LyMCz0BlKPEsza6O4/sfB7R3VnIAWKzluuuqJwDIqQAg+HVAoQcOXZ4TYgE5x6XwBeJzV5iVxmFdg5tr4pofTWDtj8cRL6DI968gxJKLRxi74goxRnk5vmhCKQc2c+i0M7Cl6kES57HIN3PgCupyHWT/sgXZIysIn/nO0nAaK6k6Apbck0bsZXkAYUnNpyZXhImZfAkH7iSovfzYiuLzHxiwxd7R0SplQdfUBEelFGOsii1CkRha+GhBDhk5/MIjCsYX8YjsreA9leKGFvuQ2hYRmJXGanFft1TXQqyUCrSQHxPmah1PDmnQgfpq74MaUMhp6LUwuuKnhQAMn3kHk4v5JFZHMH+DwLhQRmMDLCSipGjfSgmGP0ajh1Jzi80ACQX8ySgNQcUEwBfVDG+jEcpxqDzRRmsqKLjZRGsRBTu0LCM+B6ijEf3iRDDQHRvCZnFIeQ7SfsfODUAKUDqWo4Qe1DZT9Q9RWCQWeBHvoNDaFiGFCD3pC+jVNTaGSaDuaUpNoCzwwP6Ix/5CH7+859PdzVmJa7DwKxatQrPPfccHnzwQWSzJL7dyy+/jOuvv35KK0ihUCgUCsU9jNqEz3SfhEt+9rOfIZFITHc1ZiWupoCvvvpq3HTTTdi8eTOi0YlFuT/wgQ/g9ttvn7LKzRbEzjD8SYkESpVYKMccgdZn9kONR6DEQyT21jgJpks8AMmtpS2NxGdI4ONQxSs3sziC2M4xFBaR2FdacNfAnjESg66LeFX6xoi9FZ8JVmKWEY/EsaMCiL0jAiAer/nVPQgMkUCsjKQAArFJlNpCxIOxKCN1ZBTx1zOI7iax0ILDZYhdEUTeTqPUFYZvrIjUkeTaixEOhQ4BLS+OorCmDe2vFDF0bACRt8sIDpBlwYY/2Etsk1K8ripp9k9SkIXKEQXSnyS2bNF9CgodPhTaWUTeI9v4ggy+ICPXFyQ2gJXlvgAS+DZ1Yhv4gorY22Tpu1xfkLTbmjbioVtREzPdPELDMnI9UbASUSE0BSPydhpiIojQe3mkjowisreAzAI/wgMiokUFpVahosoRdSXfE4AUJIoSm52Ix5ddEiOepBXPxmKnHy07k2The0nFwCk+5DpD8KdVBEeIksHInL4EnZaP5kWr2TrmFkUR3Z1HalkI6pIYct0suON7ER4QcfB4H3qercSJC/LwZVRwJQXlCA9WVKFyFVUkwqEUZyBkiaIHAC27JbAii+BwmZyfROy2hIyI4TUhJHYR1S3xuqzbfBU/2Kvb2Sk8A65E+quQIzHb/GNlBMY4FFtZiCGALxLlSMtfjBC7tOQRQM8fFAyv9qHU2gVfVoUUJEpXaJh4qYphBsmlPjAyAPiQXMLCnwIYmZQ/erSA2LuKblfGVgJFA0R5lIIsiq0sYu8QRViMcBWljbzzHjwhgNi75HyzC4IoRxiU4j5wJRWyn4EcYDG6OorgSAiBcQVixaM8O4+BP8lA4Yhamur3QQwBrAzE3pWQXkjszCLvyUgvJG0d3S+j2Moi1x2ALw3kunnE3lUwdGwA836XJh7FQypYUUV2Pg8xDMTeJTZ9AND+fBLh+RGMHSlAyAO+rKpfQ3Iv8kguZRAeIPac4QGR2CmOKBWVkFw/VlIRGlKR7wlgfBmL1jeIfWSuR4AvoyIzn7RNeEgFXyRqa8tbZfgyRGErxTldCex4WQJfkHX7TF+G9B++wCDfwRGP45yK4HAZkXcCyHcAgXEFmV4OKg+0viFhfBkPIQd0vlQkdqcntKD9VXI95ADQ9hpRXIUcUUSTSziEhlSwEotSnEF6gQ+lhApfikF8j6or6ZkFfkgBBoExlcQ2DAf0c+AL5H7jSipYCRhbTpRFhWcR2TWO5JJ2JJf60PIW8bKW/bNlGGTBHLMBXLNmTZUTiKqqGBwcxPDwMP793/99Gms2e3E1AHzllVdw7733Ttre2dmJkZGRpleKQqFQKBQKRePss8+u+s6yLDo6OnDGGWdg+fLl01OpWY6rAWBLSwsGBgbQ399ftf3FF1/E/Pnzp6RiswlhKAcmIejLLrGiDLGvDQCxcSPxt1g9ZpYeny5APGWZfBmZVR0QshNx3aS2EISMCJUn0fPLrT4U1nQSL8cAC66oILOYeL7m+oIIDZDVHEZWBdD9h7Su1ikCo6tb/nEJcqCyGHpvDO+d5sOSH+5DdnVPJd5XUF+FYvAkH+Zvy6MwPwIhIyK3IIzgsKivZMAXFLIQ+7AIRWDR/WwOTL6E8vwIRo/pA19UER4QK6se8BUbL6KslKMsEi8loQocip3ElkzIyojtHIO/N4ZSC4/0Qh5cCYjuE8nyWJXlz2Q/i9jOMUhtIcR2F8CP5jF2ImlrViLLnJXiDOIFCenFQQSHJQTGyVtjvpNB1/Mkdp0UZCAFGLBLYvqKHeEDJQiDaSTSJWSXxBAYLiG9KFSxX1IrcdNURPcSmymFZ4jylSSep/6kROwqJRWKwKAwPwI5wCIwXEJsj4BSnEFwRMb4MqIAxfeUkesR9P9VjsRqK0f98CeJLVium6i/5G8O4UEFhXYO7c9nENtLFI/AUAHlBFEmibokVezzJJRaeN3jWuUYRN8jCmNyMQ8pDASHoV9XOcAi3xMAVyJqXbGVRXx3CcFhosZqMfZKLRxZrquiWgZHiBI6uioEhQfCgwpUjqhJRHkCpADxGg2MKyh0sEj1+9D2Kukf2fk80gsEJF6XdXvNjpdlZPqIAiNkZcT2Mii0MRDjAF/k0PK2jFw3Kb/QTpTG4Kiq2y6W4iQm3PBqQfegVgQG2T4GQgrwpUlsuHI0CFaCHk9yZFUA+R5A4Tm9HcoRBlKQQb6TQcvbCtILeRQ7gcIYj2ICCA2pFW9fgKuE30wv5MFK5HylIIvAuIJ8BwdfGuh4IY333h9Dy9sy3nt/DKEhFeEDJaQW+1FMAG2vyUguIZ7fQk7FwVNbiWIlEy9XflhBoY3D8Boe/jHS3oldQDnCwD8ukph7YUDIMQiOqRVFlZyv7GfgyyiI7lMxvoyFFGbR/ayEQjuHYicQfYdcq9RSBq27KvZ6nUDHiwq4Ermesb1EbSOx+gAxBMgBgJVYBMZUlGOAL0u8oUutAvLzVEQOAJn5LFQeyM9TERoi7Sv5SRQBvrI8W6rfB5UD/Cmg0M6BLxIbx1wPj55tSew/sxUKzyLbB/jHgLadQClO1Nj0AnKtiR0m6RNSkIOQI9d+eA0L/xggBf0Qwwy4EhDZT2yEiwkGcjxIlNd20j9ZSZ39NoBzCGpu1nxcDQDPPfdcXHXVVXjggQfAMAwURcEzzzyDr3zlK/jc5z431XWkUCgUCoXiljkYBgYgq4/9z//8D/7yl78AAI4++mh8/OMfB8fN0jWbpxlXTiA333wzli9fjr6+PmSzWaxYsQKnn346TjnlFFx77bVTXccZj9gZhhLg9JUumHyZLPq+OAhWVOEfJzZt4b05fQ3R8N4cylEW/oM5iF0RcEVFX5ReChIv3PTiIMoxHuVWH3zjZT1eGFn9QCZreooq/EmJxBTr4BAcJdH6wwdKCL6XxegKXv+uxVLT1sRd8l+DUEPEIxUAcj08/Adz4AoSFjyeBgA9vh0jq/AfzIEvyAZvQa4SC0/SPZkVgXjRAcTGkS8oiO8u6XHYSBw6FdklMYyujhIbxyhRzTTvWU2xIisJ8ESZmh9Cdj4PrqQgu7wV+Z4AxlaEkF6ZqMQtA8IHSkgvDiLyngRF4BB/I195iyd2l7F3FWQW+MGViGoX3Sei2ErOJdVP1n0d/FA3couiRC0J8kTR2pvTr3VyCYlnF99dImvCSipy3Tz8SQnvnebDwZO0tUeJfV9wgKwCIQUYJHaReGO+NGlXVlQQ3VtCqt+HfCexicx1MsjOY4i36e4CQsMyUVE6GQg5stpEMcGQdXDjDBiJKMG+sSLynQyi+4ntWWigiORSHzLzWez7QACZXg6phRzKUQbphSzkAFH/ho4NYGy5D+lF5BqnF7IIDRObNb6IykoZPAZODkD2Ey9PViJeoKyoohQndoW5TgbBEQXR/TKG17BEsRKJreXYcgbjy4FSnChp3X8sV86Lx/AaHsERBaEh0q8VgUE5ymLk6Ir3dCeQXCrAPy6h0AFwRWKDlumtrIUsQ4/tptmv+pMSwoPkPuOKgBRgdE/W0AAQfU9BOUY8TMUwif+X7+Awvoyon4EhYrdX6ABynQwKHUTNCoypKEeJctTxooLgsAR/Csj1MBDDQPjNJFJL1cp2VVdtiYJI7CLFELD7nBgCYypGjuEghUn99p4ZQL6T3IcjxxBbu3KEqI6Snyhy/pQK2Q+MHclB9gNsCSjHgOQSFoNnipD9ZOWQyHsSCp3Ec5yVyDmwEjmH8aOJml1IkPMIDRBFtxRjENkHqBw5H/8YML6cKMbxt4j9XOsbRd12VuVI28f3lCGFiWLHVGLnKTyJ4+dPqThwGouWXUSRjb5HVET/KElTSgC5BSqyfWR/YFxBvgdILldQipM+lupndK/+t89thRQm/S/+FrFnzPSRcsaOJCunjK6Efk8DxMMYAJJHMGAkIDBG6s+ViOobGtbsWIF9G8IoRxmUKsvK+jIqUkvnloo2m3nrrbdw1FFH4XOf+xwefPBBPPjgg/jMZz6Do48+Gm+//fZ0V29W4moA6PP5cOedd2L37t145JFH8N///d/YtWsX7rnnHjryBsBIJBixFPODK0jIHdECAIjtJsu+Bd9JQQqySB0ZhSJwepiP4DBxjmAkpZKPSpwXIhzC+woIDxAD/tGjBRQ7/cQYPUKmmbVl0kqtJD0rqeh4gSyC7kuTxd7f/etWzH96YvCiTdmJUQGpZSGUe1tQ6o0hOFBEbgEJSCzF/GRJtcURDK8JgS8o+kAOIjH6JlNnrO7AIAd5FDp8UAIcSi0cCSrLk+0HjychVUpx8iD3pSU9BE18N5kvi+8uwTdWRPyNPMmjVUBwlAS51QZ3I6t44mwwVEI5SgL9hgclhAaKYCVi0F2O8YjtLiAwVEKujyy9JQVJmA6upKAcZRB/I4/RFSRkCiupaHmjgEIHWXx+eDUxnPelJQTGFX3ZtNSRUbCiiujuLDpeLqPtNQljRwX0Re4D42SAGdlPfpSLCTLYKbVwYEUZ4QMltL5RRK6HDA7b/pwDX1DIcmTz/IjvKcOXJlOZQh56kNtihx+MrOrG75k+BiNrwmh9QyJT4zmypFw5ymD86ChCQyRItJBTSRiikgopDCR2qQiOquBLQClGBsJaGS27JT2UiOxn0fqGhHKUOGGU4gwGT/IhO49Bz7NF8qMdI9sL7SxxSCmpiL8rIzxEBo+5bg4Lfl1EfHcJuW7iZBIcBlp3EWcRKTCxrGE5wiC2h7yUFBMMMn0CpABIwOlXy8TxpIe8PKUXCVj4yxwJYJwiU6HhQRmyf2KAs29DGFIAGDnah6E1HDKLgO5nSf8nDgBkAEWCRZPBQnS/jFwng3wPKlOXpJ3yHRzCA6SNfORdCMUEg9HVKvgiGQgeOI2ElOErt9ibF7ahbSew//08svNInTQHFVYk18KfVtG6iziaJF5TIFTOpetPMoQc0P6KhNAAoPjJADU0RI4LDZAwO+UYmYJlJNIO8T0qYntV+Pb6wJVIG+//IIue7WSqOtXPgJUr938YCA5w2HcmAzlAzsufUpGdx5BrWAnVEt9D2qn7WQlcSSEOOZUXn2Iri+AwecFLvC4jubSyfGBKRSlBBlVckbSlFGDQsotBMQFE3pOQ62YRHlQQHCbmGC1vquh7QoLCkang0RUcIvtUBAdYiDFgbAULPkdeGseXk4F5ZB/pQ7KfhNnRiL6ngC+qaN2lhX1SUYoxkMJk4Ktw5HzHlwOZPgYqBwyt4fRwOrF3RIQGyDWO7SXnrwiMfm1nHWoTPjOML33pS1iyZAn27duHHTt2YMeOHdi7dy/6+/vxpS99abqrNytxNQV844034itf+Qr6+vrQ19enby8UCrj11ltx3XXXTVkFKRQKhUKheGAOTgFv27YNzz77bFXIl7a2Nnzzm9/EqaeeOo01m724UgBvuOEGPfafkXw+jxtuuKHplZptcEVJD+2iBUxWeAbFDj9RodpCCAyVSGDaviBKrWS6rdTKo9RCwpWEXngHub4gpCBRTbSpYr6goPdR4mktBRmE9+YgRQVwBQlykEd0d76yULuC0dVR5HomxvTxPSoyi0NkaayxAvIdnB6Oo/XVDBSBOKbk+oIQKiFN+HSJBD8eKKL9lSJKLTxZIoxnMHRaOwklMSyjkKicb4BFcikxmC61koXfB04O6Mbvsb0qMr0c2l8pkmnFDJneLbayyM0jy3wpAlFHc31BsEUSmDr+eoY4DwTJcm/BYTL1U+gJILa7gFIrry+FpSmCuW6ylJpaCYSsBQwudBDFz5dRUegJIFQJcyFGODCSilwnoytjUpCBFOTAFRUMnBwgBuQ8Oc/33h9Dqt+HXDePlrfKSPxpFP5xkUwxCwzKMaDjZRHRfcR4Pzwg6uro+LIARlcSJWT0mDC4InECAQzKbLjiNBAghvqB4RIOnsBXDNtVdO4Q4U+pGFnFV0KqoOKcwqCQIMteKQKDtpczGFvOIDwgIb6HBHEODou6IkiceMh1VDkSLFcRyDSpGOF0ZUULmBzbW5liH1QQHlIh+YHxo2W9n+lhjSpqycETAqQNS+Tv8KCCUqwS7iYEjC336eYMmT6ivoWGVLS+lkcxwSCyj5THF4H2P3J6XQbeF0YpQZS/yAEV5WhFURojzh+hAaIs5eepaHlLRWQfMHRiGMUEadeDJxJVbuxIAVKYqJLlKIvgmAqFI0rc4JkigiNETVJ4Rs+7FK84QLzFYnw5WXYu+k7lPntXJipcikzZ+lIMOl4WK04IRO0eX8bqalK+k0FyOVGXo+8pCI6SgNVciTg+lGMAn6uoyDEGvjSZstz3USD2DumvxU7owZqz80iaMokLj5ZdRKGX/UBgDJX/Vagc+T5vGwOuSKZxWQkoJUhbkueDiuQRZHq41MKh0M5B4YgqHRoifea900i4HClInDECY8DBtaTNpSAxL+BKQKGDqJiFHgUH3kccmMaXsVA5QIwBoysZ7H8/cWTJzyPXPNvHIDSkIjAECClyPoMn8xByDFSeKHTZXiBb0SFUnkxVkyXpyJR3MQGML2NR7AS6/1gGVwJa3lLR9qoI/xijK7qR/YDsZ5DtV4hq20vOM9XPEAXQlTwyQ2lKEOiZNf3t9/uRyWQmbc9ms/D5fNNQo9mPqwGgqqqWizC//PLLNAAjhUKhUCiUKeVjH/sYvvCFL+C5556DqqpQVRXPPvssLr30Unz84x+f7urNShwHgK2trUgkEmAYBsuWLUMikdA/8XgcH/rQh/C3f/u3ngr87W9/i7/+67/GvHnzwDAM/ud//qdqP8Mwlp9bb71VT7No0aJJ+7/5zW9W5fPnP/8Zp512GgKBAPr6+vCtb31rUl0eeOABLF++HIFAAKtWrcIvf/lLT+eiUWwnoUzEqIBSwgf/GFF2tIDH+Z4AVJ5BaKCI4HAZXFFB+J1MZQFyFnKAhdLXRRZQb+EgBTnkFoSJXdmucZR6Ywi/mURsdwFS1IdMHymHFRWkloXgT8rIzSOhDQLjCtgiWaosvYAY7Ud3ZzFyQgsSr+URfyOP6O4sMosjyPUQJZIEMBYgZGUcPLUVpVYBxQ4/2KJMQtJUFqfXloUCiOoBECP3xF/Iel9k+TEOC345Dq6oIDufrwRQVlCO8Wh7OYPM4gjCB0q68TVXUirOJWR5q0JPACrPothJHCfyncQ2jC+qE0tHrSBLjykCUb00O8HQMAmanV0QRHoRcRwQMiKCwxJa3iiAK5FQHIFxoogFB4oodvrR+VIRnS8VERooQgqQsDWKQOzecj0MgsNE4Q0NkcDKfFFFqt+HvZ9oRzlGzlGMEDu9UiuRDfgiUbryPcRuqu3POYQOkCXz+KJaCY9DbN6ELHFICYyRZcO0YMjJZUHE3lH1IL6ZPgF8gRjS57qJopmdz4ORSfBeRgZkP4vxo6OIvaPqxvPlKItCh0BUq4q6ll7IVpxCKvZ+LRzEEBDZW0DXH0UUOogCxeeIQf/uT5I37FKcQctuCa2vcoi9K6EUY1COkv4ReU/SHTpKrWTpMV+alMVXQoj408ReEyD2hOEBFbE9pE6jx4QRe1dBsaIulyNEsYu9K0EKMBByQHCI2IoNnQAScLuVJfdFgOTZ9poEX4r0iUInUaICY0Cml0NsDwmULAeIwhbfXQJfIPXh80Qxj77kw8gqFsmK8b8/SdRuViJKaHS/jMAQOZfx04ooJBi8dwZJGxgjKigjk+XQxMqqeOUYCe0i5InCJeSAtpeJKpheyCJVUVyTy4nS60sTxa7QQcKrAMD40Qq6f0uueXBMhZAmYW805waFJ04Ysp844xQSRJHW1NJ8J4PAGFHOct1E9SwmgKETSCgVKcCAL5Ly+RwpPzuP1IsvTTzrkktIIGct7M2834lEBRyqBO8+goEUIu2uhcQJDrDoeIk4q7S+oSB1BKl/aICEqhHjRMkLD6goJRTwReIgwsqk7Og7xLZWCgNtr4rwpYG2nUS19Y8BcoDcI8NrSOgdhfiboetPMgZPImFl0v0MBtYJyC8nz+bUEWrF4QUQUizkThFcCUgvIEqjL036F2c491lFE1YCmWlTwN/73vewZMkSrFu3DoFAAIFAAKeeeiqWLl2K2267bbqrNytxFLm3bNkCVVVx0UUX4YYbbkA8Htf3+Xw+LFq0COvWrfNUYC6Xw+rVq3HRRRfhU5/61KT9AwMDVd8fe+wxXHzxxTjnnHOqtt944434/Oc/r383rlCSTqdx5plnYv369bjjjjvwyiuv4KKLLkJLSwu+8IUvAAD+8Ic/4LzzzsMtt9yCj33sY7j33ntx9tlnY8eOHVi5cqWnc6JQKBQKZUYxwwZwjdLS0vL/s/fmUXKd9Z335+731l7VXb1JraUtS7K8yMbG2KxhCQYyJAROTkiYGd7Ek8yZQAiBQ+blJcmbnMwMQ+YkE94kQ96ZgYRwYJKXd94EJpOwxIAxYMcLli1LSLL2VquX6q696u73vn/8qq7QAEYOJrZIfc/pI3XV7VvPXarr6e/zXfjUpz7FyZMnsxiY6667jj179jzLI7t6oaRp+l1vk3vvvZcXvehF6PozK4pQFIW/+Iu/+JaE72/GG97wBnq9Hvfcc0/22K5du3jnO9/JO9/5zm/7Mx/60Id43/vex9raWqYN+N//9/+dv/zLv+TYsWMA/ORP/iSDwYC/+qu/yn7ujjvu4Oabb+aP/uiPrmj83W6XcrnMTT/z77ASS8rYl10Agqq8bmyrRI5K8fSQ02/MUT6pUH+km4U4W82AVFdHpfKiC0oMCSKdeaBFd1+Z/LKbuWM1T1yF9ol1es9bwGqFrN5hs/j5LrGjo0Qpqa6Q6qKxcxrhKLA1JHLEhevWTay2RM6Ma+rGwcbVE1LrVj4Xo3kJsa3S2y46oDF7I6yJ3DZKLDEoxWWp3PKqKtWjQ9QwFqfzqsdg0SG/7DJYdCSyZqR5s1ohei+gt1Qgt+rRPJDL9qPEsv/YUgmKEi2T6JdCpf2qkTl83WmV2jc8gpJo/UC0QpVToicE0Waqobi1xwHDflkYm9qxgMhRM+Z2/bYc5XMxw/olh7vuSbxHc58h4bq2QvlMQGe3Sa4hAcbutMpgXrZf/IKX1Y2NA6J1N6Z5nT2qyIroLRqYfSmo1z1hXKyOaAFzGxLVEeVh5uviumzvMdB8sm1L5y9FDG1db1B/LGDzejOr1koNjcatJXQvZesGcDYUCRce6SOb+5URgyLslhpKdVdrr01vl7AsIAyg5otmLL8mx7p5o0rhAgznoXY0ucRej5iksdt3OC/MlNMQhrazUyO2RSu5db0cT34toXlAxeiIJq55QKV0Rlid6fu36N5QEwZ0u4T6DuZFo2U1ZXuJ1ElpHlCpPHnp+Iy+aByDolSHGQOJffFr8rOlcxGbN+pMHZVjCoqiuTOGjDSAch9NHZEg4v6iMKLGALq7U6aeED1flIegnFJ+Uo556vCQlZflcBpyL/R2QfWYXM8xu2T25Xy3r1EzjeVgRp4H0aJt3aAw94Bc3/42iSQqnYtoL+nEtmj3Uh3y5xWsrhxbbMn4rI5oBfu7E2b/ThzXresTqkdGNX0jRtaty7kYO4X9GtQflZo2syuazeJKMnJqy33Z3aVQO5biTklEjtMguz+UWPSWgznRIGo+owga+X+iyf2WWBDmxbXb2i8spO6Ku9qrKVRPRPS36bgzogUc7EiZPgQbL4wpH5G6vDGTrESyT2Mgj3V3y/mcemJ0TGWJ6amcunSf9fZHTD+o0bwBak8IcxrmRS+Z6rDtXo/NG216u1Ouedf9V/R5cCWI0pAv8Sk6nQ6lUukZ2+83I5/PU/nlt2POznz3jZ8C7c/dwz+pTPGxj33sGRrZBM81POUScBRF+L7Py172smzyt76+zm/+5m/yK7/yK3zlK1/5vg5ufX2d//k//yd33333tzz37//9v2dqaopbbrmF//Af/gNRFGXP3X///bz0pS+9TBh61113cfz4cVqtVrbNq171qsv2edddd3H//c/cm32CCSaYYIIJJvje8aY3vYkPfOAD3/L4b//2b/MTP/ETz8KIrn485QTw537u5y7L1+n1ejz/+c/nD//wD/nsZz/Ly1/+8r+3bu5K8NGPfpRisfgtS8XveMc7+LM/+zO++MUv8i//5b/k3/27f8ev/MqvZM+vra0xOzt72c+Mv19bW3vKbcbPfzv4vk+3273sC2DmngvkVyXAeLDooIYSaGu2AuyGT+WJNp29OWYfSsmviXvXakfkViS4trfDGlWsyeVQw4Ta0SEbd1RxGgHuvI1fNTI2Sw0TwsUpYS1KOqXzKUHNRolSvBkRwAwWLMnuY6zNE3aseZ2N0Y8xmx5eXXLyWnulLH2sy5s+EmQMnN3wmT7sUT4Xo3uiibLaEUqcZu7PXEOOd1gXjVJQNRluy2G1QtLRNp29OQlNnjcIilKPFpR0tg4WyS+7DOftkZNYJ9GlHizVFNrXSB1Z5KhsXS+5e+Pz5JcvuVX9qjw3DrwtnUtoX6MROzqae4l1jByV/GpEmJccszGLYHYjjLUuetdn4b4uVivE7KeYfQnAtdrCKjlNCdgds39jaH7C9KMDKidTasdSEkOldC4hv+yyeb3JYF7cwGNXsVvXs2OoHR2S6BJa7GwmGAMwe4mc84HUsCWGQn4twW5JeLTmSbitGkkIrjGQMGu/JvdT/5oSreuLBCXRMi18Jcbsyr2wdUDH6McsfsHDGAobB6LV6+2w0L2UqSeEoUr0S+7k0tmQYV3DnVbZdl9AUILZB8OsFs7sJRJQHAlzpEYp049LtlyqQWenhtVNmToa0d1lUDonmYVeVZg7kLowoyPH75cUzv/YNIM5yReceWhAokvFm9MQ3WNQVNi6Qe4ToyOOWHdao7tDYe0OXcY7Cj2OLWEBa0clcDjVZF+DOY2Vu2K8mmTmeTUyXanmi9bUqwn7J4xljOaJ/jTKC4NmdhTRkw1Szr86R3FZKtFSTTRs+dWQsDxmqcRp2tqrEuXEDZvol1i4cbZhbEumnzstju/+YiqO2CF4Mynz9ycUz4gmM8wr9BdT7KZoQQHCMuSXVYKCOGe3fSmlvT8dBTKPHN5nhUWMLWHCc6tSExhbl9gwNZL3AQjjqcYyvjAvY1YjYTnHrtnuThW/Bu7MSKfXEQeuMZDMSL8mj6e67C93UaFxq1TT9RcV3JmUi28OUWJhDAGSXExkK0w/KKsRY9Y0ykN/d0JiidbSq8H2L0YUliUncDijZPtoHlCpPxphdVPKR0Za3b5cW3fkqlZiKJ1JR8HnMHf/1bmO+r3q/zId4HMIX/7yl3nd6173LY+/9rWv5ctf/vKzMKKrH0+5pvvVr36VP/iDP8i+/9M//VPiOObJJ5+kXC7zr//1v+Y//If/8G0vyjOBj3zkI7zlLW/Btu3LHn/Xu96V/f+mm27CNE3+5b/8l7z//e/Hsqzvy1gA3v/+909ibyaYYIIJJnhuI+UHrgv4O8W9GIaRkTETPD08JQO4srLCtddem31/zz338KY3vSkzg7z1rW/lyJEj35eB3XfffRw/fpx/8S/+xXfd9gUveAFRFHH27FkA5ubmWF9fv2yb8fdzc3NPuc34+W+H9773vXQ6nexreXkZgHBbDb0XokQJuVUPb8bB7EZ09uYAGOwqUjrtkhhK5go2mx6t64uERYPieT9jx1QvRokS1m+Tn1U9qW6yGz79HQ5qlKJECamuZg0DVitC74VERQN7wyfVJa3f6MfCFhY0ND9h6rEeU4elXq2zr4jmJRRPD8k1hNXTvERaTXSF3MqQ3qJBf4eTPVY47xJbKoN5A6sZjBoVVDRPXiPXiNH8hNzjK5J5uGDRuCVHYihYbdFZqREUz/sM52VS72zGdPbmGMyp2K2EwnmX0hNNSmelpq1yKqGzZJFqkt9VOTHSWBYlvT9yFPKrIZqXkFsRJq14ISa/7FI5Jce/9sISVivMWMKwoLHti12CojRZ+BWdwYLF8Nop4pxJb6mQtbM4jZDeDov2krC2Zi9hMCd/N/W3y30ROZKfl9iSs2j0YzavN+nuVImKl5jJxJAWjfJpn96iaAjzazH9HQ66GKkpnu5T+4bHYE7DWfUorkhjiL3hj2rSFDZutony0oqw+kJzVMUluszZhyPyqyFKLDo8uynsXH+bTq4Rj5hVOQeDBWmXiW01y5KTSjqkQWLUsFI7HtPdqWY1f0oMfkWnckpY0c5ODb98qZ4t0YVF7uwRVtgYCLM5ZnzcaU2cqtPiHM01YvoLSuY2HTvM1Vh0W2okrNvG8/OoUYrVkfPZXxDN3rhFw5sRPVh/URyv1WPi9HSawsb5ZWEBvapK+WSK1QopnU9xNhN2/Xdhk4oXJNOvfY36TbowlakjIbMPe1gdybrThbzPmMvSGcnBG84oRAXRaI7bVsx+SnuPKQ0futzz5TPC1NWOjfIKb0/o3u5hdsWpXTkdUTmm4KyqOJvx6Poq2BvCdE0fEkeuX4PWXp3+YkrpjLBZsSXnpnRGch+DEllGX/lJuYcG88Iow8hxPCNJAM2bYjkPoyxLuznK+1sQF/zU0Shj70D0n5El51ru85T6YwGVJ1NyFyVHsbNbYet6gzCvMJgTN7Yaw8zDch2NIRTOqFm93dQTsOs/y+RF84XJLB6TnL7Wfrmegxkly0usPyJO6FQHbyZh63ppaEm0S/pAqwNeXdjv/qKwpbon+6+eiAjKKVFOnNyRLTmJpXMJF1/2HKPB/hHjxhtv5M///M+/5fE/+7M/48CBA8/CiK5+POUE0LZtXNfNvn/ggQd4wQtecNnz3y4g+pnAhz/8YW699VYOHjz4Xbc9dOgQqqoyMyOi1zvvvJMvf/nLhGGYbfP5z3+effv2Ua1Ws22+2Vgy3uapXM2WZVEqlS77AvCmLTp7c4RFg1SXWJegpGO145H5IGU4b0uEhyeVZL2lArqbYrbEeJDoMpEJqiaprrLts5tSzTaa1IVFI4sbAenhlUmNju7G9JZyuHUjWwI2mx5u3SByNHQ3kYDZmo1Xt6gdapO/6BPbarZEGxY0ia4oGjgrfZQwxm4lWK2IVFcpHe9IqPG03DLtvY4ce1Wlu0vG5qx6qGFK466dhAWN4ukh04dlZqOGslwncRXy4R9bKq298klidVKUWKrNoimZ/OoelJ5oUlwO6W2X+JRUV/Erejahyq+KOcSv6gx25LNzmdgaw7pMcionQ9y6/OU4mNNxGgFrL5RrZ/bENOBOyT60YSBLfvVLTLJMgsRMM47NUcOE0tlRYPFoqb2zJKab2FKZOeRhNyUupnwupnjex51SmD4s4nKzC27dkOXuBTkvldMRnX1Fejss8msx7ryN0wgIcxKRYvRlEq37UD0hr7ntXo+Zr4d0ditUTri0l3Tae0yJJcrJhMedVnFnZAnW2UzIr6ZiDpqSbt6tAxqVU3HW66pG8kEYFFUim5H5QpYGw4L00BZP99E8EfursUzwtFE3rRrB2u0m1WMieXC20iw+KLchZoXIUegvyvZBUZbL3ZlLhoOxAcerirFm3HMNoyVYP0XzZaLprHo4WynFszJ5nH5cqt5ADBntPTIRLlyUSU2Yk0li8zr5I8SdVmkcNPDql8KtxzVuVkeWpTeeZ5AY0ifc3SFmHasjk5vihdEE1k2pnIqpHJMJqO4mWB0x+YAsBwclmbBZrQi7Ke+LMA+Fsxr1z1uZ+WQwJxOe2UcCujt12nsUqsdS1FgmbNIDLbE4ZhespkJhJcqWbnMbKa39CrlVmeCokYxb82WS6s7LH2syyVWYegKpkWxqzHw9xK0pMoEf9RObXXmvXHzxqIM5kmXo7k6JlNm6QQwqagSDeal+tLoyhnGQdVCC4hsvZp3M7WuV0blOsbop+lDMIWoEy6+wMfsp5TMykaucjsivQvmkvFdSXYKcjQGsv1R0HEZHQrBhZAI7KRPgsCQT7+JJjSgnhp3cRYXuDiUz6pgdhdJZuV+DkkwyI0chd+EqrTp9JqrgnmNz31/7tV/jt37rt3jrW9/KRz/6UT760Y/yz//5P+ff/tt/y6/92q8928O7KvGUE8Cbb745cwDdd999rK+v84pXvCJ7/tSpUywsLDytF+z3+xw6dIhDhw4BcObMGQ4dOsT58+ezbbrdLp/85Ce/Lft3//3383u/93s89thjnD59mo9//OP88i//Mv/0n/7TbHL30z/905imyd13382RI0f48z//cz74wQ9etnT8S7/0S3zmM5/hd37ndzh27Bi/8Ru/wcMPP8zb3/72p3U8E0wwwQQTTPCcwg/Y5A/g9a9/PX/5l3/JyZMn+YVf+AXe/e53c+HCBf72b//2KZNEJvjOeMoYmHvvvZfXvva1zM/Ps7q6yk/91E/x4Q9/OHv+F37hFxgMBnz0ox+94hf80pe+xMtf/vJvefytb30rf/InfwLAf/7P/5l3vvOdrK6uXpY9CPD1r3+dX/iFX+DYsWP4vs/u3bv5Z//sn/Gud73rMv3f448/ztve9jYeeughpqen+cVf/EX+9b/+15ft65Of/CS/+qu/ytmzZ7n22mv57d/+7aelZxzHwLz4Fb9B4XSHYHcdAL0XENTsLAi6eLjB1gtnqT20BYZGnDMJqib2hosSxrjbCuhujNF0CWsOiaGiucK8jWvjYksq4sbLv8LsyXJx45YcMw8N0Douac4kMWQ5MnI07A2XoGajuRL7EhVN3LpJUFSoHh2idVz87SX0Xog3Y2Vhy35VWApnpU/z5ooE606rTD86kKVOL8abkaXZMfLnB1mAdX7ZZfXFeea/MkANY1kWb4mhRYll6droCUPb3+FkcSbjUOoxWxoWNPLLLt6MReGxVby9s/gVnfyyS+tALjN9FFbk59tLOmosS3kAmpfg1nVqh9oAbN5WobgcyjJ2Ox5V9ElkjRKnuNMSFK15Ei9idiN6Oyysdkz+bA9/Np+FZstSZkz+yTYXfmSawkWpXbMbPmt35Jh92MvMOImuMJhTKV6IGcxpWB1Zul+906B0NqW/qFB5UqI1KidDWaKdU7E6Kbp7KdrEakesvtCkdlRY1LHg324JcyYh1wF+1cgkAL0dEhJeOiv79aoq+bUI3U3o7Dazn23vT0lyMTs/lV52HcbsndlLL4v3yK/JOYpsuQaVkxLtEpRg9mGJEhkHAqtRSuWES+zodHabYoay1CzCpbdNpXwuxquqJLpCqsmS7cJXhvg1MzvXrb1SjdfdoYjwf8TmzD7scfFFNuVRqHR/QWHugSFe3eLiS1RyFyVaZPNGneKyRLLoPmi+xJrMPhyhhnLcqy80ZfnUVghzYjYZs7SaRxYDpEYpXk0MR629Kk4DOntSCssSNTP9eDxiUSUaxy8rWJ2U4nkxQPW36eTXYpr7NFJNljOjPKPaQ6n4A2E8e9tFXlBYibLwb68mTK3uyVL96isiZr+sZ68H0FsUBs7ZknvL6qRZjaM3IwYVsy/3Ymyr32QYU4ksWZ4dzosZY8zkFS/IeMasXm4jzX4/GAPZ/9gc4mxK9IrVlPt0OC/s3/ie8EtyrmYfjnCntSzKpnAxHQWBC2NZOSXnuLic0t4j8UWV03IurM5o+Xm0ygAwmJfjTjQ5p3aT7JwZAzE2jWNjnM0kizUaRzpd/JGI6S8b+CWFud/72hV/Jnw3/EPFwNTe/nbM/8Xk+HTR+tu/5UemJjEwP8h4ShPIy172Mh555BE+97nPMTc39y1W65tvvpnbb7/9ab3gD/3QD/Hdogd//ud/Pgts/l/xvOc9jwceeOC7vs5NN93Efffd95Tb/MRP/MTEPj7BBBNMMMEEE/yjw3dNdr7uuuu47rrrvu1z32mS9o8NZssjLYtxICwaGOsB1jAg2VXGaAaEcyVKp10G11YoPLZKNDsr7F0Y07y5QvnEkKBqYg593FHwc1A1MXpibgDIn+0Q50w6e3M4Dak4S3UVJZKqtVRXcHeVMXoh7b3OyHCRAg72hktiaPSWChlLZLcS9M0ew2unSAyFRDfJrQzZOlikfNrPtIaDXUWK530iR4wk2jDAnS+R6CZGP2YwCl7WPfBmnOycpLrCti922bi9hDEQhqC9J0/xQozuJqhhgle3RP912sWrW8Kqjdg1sxthNF3YViComqhhirdX/qJNDPkrv7gcovfCLCTbWfUoawrOqoc2DPBnhY2MbIWw5uDWTRJd6tjKJ4b0lnKAsJ6RI2HBU4eHtPc6mD05f4MFC7OXYG/49K8RM8m4Yi7V5GdXXzVN9UREqiliCCkao/gQlcJ5l/4OB6MfE5RUnFWPRJfzpEYSGWP0YyonAobzNqVzEvli9GM0XwruY0tFd2NiW5jhnX/lMdiRl8iLewZow4DN2yq4dSifidi42ZYw46KK7grj1Nsh7LjTCPCqNqkmVXT1R+Qalc6GBCWD3IZGYsQYvRCrNWZ3VXRPzC1WR4w8iS5xH7mNVGJ1Chpb1xvkNlK2f65Nd18Zu3npOQC/ZmJ2I9GWNgP6OxzcKYXYEnYuLAj7GjliYgJhhwczCvkNYY2njoTYGy5QJNFlHLEFKy+zqR1N6G1TKa4kVE4lmdGoeEahdC6iu1MnvyoM0syjwrwVLsL04YTYUvErYtJxNsQIkGqiSYwciZGpPxYwmDckGicHsS3nwp1WsUcMV/mkgtlPSTQxPIzjVsZB1G5NQXdNWnuFvS2dTTAG2igeSEwTUv0nUTjutFT2VU7L+Vm/Tad2TLSHUv0HnT2iidv134XxU6NL7GX5jBgm3CklY2zL54SZdJpy/trXKhSWdWF458W0NP3ogFRXaO91mH0wwq3rtPYrFM+Cs+qxdSCPPhCjxGBOpXFrir0B7swlJhtg7U6FufuFvbObl4Khzb6w5cMZDTVGrs1akjGqXlXFrYPmi/lkMKeSXxX9qNWUwGq/qTGYl3OrRtDflWB0VZRITC/JyHQUW+DOiFFH8y4xjABbN0gdndOQUGzQMPspta+ZmL2E/OqlfNmrDs/BZdwJnlt4Sg3gN+NjH/sYL3rRi1hYWODcuXOAVMV96lOf+r4NboIJJphgggkmeJr4ATSBTPDM44omgB/60Id417vexete9zra7TZxLMKjSqXC7/3e730/x3dVoHGraDn0raFod8oOg2srxLawQX7NxKtbor/aO5tFxqSGhtOIRkHPOuFcifKjGyPnr4tfM8k9uSU6q5JFqitUj/QwW8KeGE1xaGu+sB1mK6C75FA54aJGqdSnGQrDbeKqLT3RZOrxAUZfrl80XcRsBZjdiPYeg8GOfFazllv1iG2pjgMJI44tldTQMn2c0QspLoeUT/tYrShjrsY1cyAuTLMnLGbtmARj6z2pnhsHW4PUwgEUzrvkz/ZIDJX2DRVyR9fwq7roB+sGF15uorsJg0VhOVsHchhNFyVO6S3lRENmayNGzEB3xd2aGCq5VY/icojd8EU/+E2uat1NhFFxpGoOxJ3qNGT7oGpSONVlsCDXMdXVrCpv+rCX6fx0Vx7LryUM5iVGZ+zqXPiqR1A1iRxhafyKEPCpptDfIaxgbKn0t+nEtmjt3GnR7BlNl8KxFqmusvbCEpqX4DQgKhqkhsb0/VssfFUc11ZXQr2L532J/FiwKJ738as6kSMMR6JLNM9wWw5jIGHSpXMJZi+hvaTT3+Gwdb1Bd5cwQkFBGYV0CxMY5sVZK65PcVZXT0QMZxT82Tz5ZRfdEy1p+xqV8oMrXHyxRmfJwp1WaR7IZS7ZyimJ5+juEAa1fLzH+q0mvUV57eJKghqmbDxPvt+4vYRfHjG7edG1KSNXeHElyYKjx9rFMC/O26AkDvHKyVTiSM7L+EtPNEkMBacRUToXocSQX4uEFS1KoLBbH8X95CVCpXwuJiyJLi6yJAA61UTjpoYpxlAe0z1x/yYalE/7xLYwhsXlFL+WsnW9wdzXujibMWqU0t2tYPYuVekpsbB7YUGidgoXxM2bX00zLZvVVMifVzL9ZWwJy6XGwvDlN8RJq/mJMNFFle5uOXdmT4KxY0viYXRXmLPNW/J0lxxiS2HjeRLhMo7VefKf2mi+sHCJoTCch9m/k6BsZeQ2Ll6IcWdg599IVFOUF71l5clLQdWDOQ3NE02gBIinmTZQjWS7KD9KSFhL5H4qQ+/mQOJh8sLM2s1RhM9JDXsD9KEwocMZBW9G3MWzD4Zs3SDvjbHTvb+gUD4p17dyMsRpSLSTXxb2V/MTln/4ma0//YfCMxIE/WwfxATfd1zRBPD3f//3+S//5b/wvve9D027ZIu/7bbbOHz48PdtcBNMMMEEE0wwwQQTPPO4oj9vzpw5wy233PItj1uWxWAweMYHdbWhfDogNTTUTh8ooXVc4kUHzU8wW4F8X3YIqpJF587b5M8PUIYBsS35blYrlCq33TU0N8KbccQlWc4xmNPxKxql0y69pQKaL/l8g11FdDdBdxPyZ3ts3lahfNpHcyOcTXGzxZZK5CjorpG5d81WQGJrmXbOaoVMH/YyF7Ld8NG7PoYj9WVWO8Kv6JSOdwhrDkYvxOiF6FtD4u3Cfhq9kLBo0N5jMvfpswTXSqC2WzfR/ASnFRKUdNp7cpljV4nSUaaXSmKoElpdNLJaOKsVES5OUTjvMlh0KJ4eoobClDkNyU+ceuxSTmXhvEtYlOxDu5UQFFXMC206t8yweaPKzKMahVNdCGNAQrUTQ5HcxqJK+UyAX9HRfGEsx9rHwZJD8bzPuddXMx1TYljCeo4q/CJHHJSNgwbzD4j7F4Rl2PbFLlHRJChJjp4aCZukhqJrG8xJPp/RF51fYUUcqcZApbgc0t5jsnFHFbMv248dzsXlUHIGAc3RJUD8Dpsdf90iKkkId/GCMD6dJYvhjIJVNrA64rq1uqOMt16KMZT/B0XR0AFUT4ycxyOHdnuPgbOV0t+mUzob0ls0MPoRgxm5T1p7VfTRr4PTb8yx4/Mhm9eb2E1ovWg7sw/JmpI3cvmCVK4FRRWrA5WTgTgwX1IityHbSr2YMMszX4/xq/La3V3GyOmqEBRGlW4zCsWVlNK5iMYtutToeQpTRyPMbsRgwWIwo1A94bN+m01kCTPVuHmK+fsT/KqeBWK3l3Rqx0PsVgKoWQbejs8HbF5vEjkqxbPigh3ryYDs+MeuX2crJb8m18uvGlgdYe0SXRi1qce69JYKqFGavdb68xWmnpBrpMZyv/llOUa7laB7Cu09oltLLAmjbl8rrF/5pLCOTiNk/VaTzvUxM1/TKJ+LadyiU380wq0pmcs5coRJ9csQlhSKZyWYu7ddozcK1HYaonl0Z0QfOfc1FUgJugqDGQniDoojp3hXzslgTqPyZMrm9SZOc6SDLClUToiru79Np7ASMZjTUaOUwkUyZ3Fvu7jkp45GrN0hz1vtiMZBUzI5v2aO9M2gD+Reyq3KOW/cmjL/FWHv2ks6+fMKxlBYagnKluo+v6wQ5cBDAtXXbzeYfjwm1TTyazGrd6oosU75ySv5BHgOIlW+9yaQ51iTSBzH/Mmf/An33HMPGxsbJEly2fNf+MIXnqWRXb24ogng7t27OXToEDt37rzs8c985jPf0SAywQQTTDDBBBM8C/gB1PD90i/9En/yJ3/Cj/zIj3DDDTegKM+tCerViCuaAL7rXe/ibW97G57nkaYpDz74IP/tv/033v/+9/Nf/+t//X6P8TkPzY0Y7CjjHSxSOSFZfMXTfdTOkOG1U6hFA+tCF2cYMNhVlMaMzhBvd01cobYmLQOGZKBpQGyLZi2oSSJ+f0GhfCKldLxD/5oSfkHLdGdqlOLP5qkeHUqWnyf5dvpmjzRn4W4riD6tFxMVDcnWOz8gKprYDR+/ZmI1AzbuqJJfE7dk6Zw4b8uPbuDtrmX/9haNTCeoFQ3MVkBvKUf13nPoU2V01yGZq2U5hWqUMpjTGczp1B/pYnZNlChBd3WiopHp7fReiBrGBKWc1OnVLYxeSH+HQ/6ibKN1XLQZi8GcjhKLfi/RLdSILL8wtuUcguh8tl44i9lLmL9/5Cq9piT6r7PhN+WeaeRXQyJHxZ1WcTYhKCpovo7uJpROC7NYPiPOxaCokr8ozlrdFS2fOKklty7VVSpPtHG3FSidDmldX8w0R7MPe1LtNsrr27jZZu6BAVs35QmKUrWmhgluXfL59F6IMTDQ3TQ7L9aITQ0Loucb6xib+01K51PWX1QdtVjIOXEaEX5Vp3JKWmi6O5Ts/85mTKpdYn2KF+LMjV063mP9RVW6O4S50nxh2VIdzFZAvEdyAq2uZMCN8+vWbjexmsi9Msr26+xWWPiq3DexRZZ1GBTFBazEZJmETjPFasdZVmSqC7sUOaKD88sGtW+I3rG3w8JpROJ2nTfo7lQxuyr1RyUjbuwwd+sm/QWFyqmYyJHz5TRT1A1GjSiq6EAjaTqpnI7o7jLQfNFLdncqWZad05T3oxIJ+ydjkozAyqmYzk4Nqyv1c9OHhUVuHDSyJhJQqJwM0Nwoc2AnhpKxw7MPjRnaFGczzvLx8psJ7WtUasdjUl0jv5YQ5qWtxdkQfZzdSujs1AADuwlTRyHVxFldf1SyF8e5hkoEvV1yXON6vTGzJlVqclyVU8I65jfk+Mfua7uV0LoerK6aVdCNmzXcGbnGIK0gU09Irp9Xt4gcybe0mgHtJZ3Zh122bszh1oWdC8uQaAqRrWM1RdPY2S2rJ+Pcwd6iwvThCDXSMse2Go2bQKT5yB+xff3tUDorzOvswx69HZbUFR5Oae1V8Usj57cjrTOFlRSrOfq9mptMMp4r+LM/+zP+n//n/3laWb0TPDWuaAL4L/7Fv8BxHH71V3+V4XDIT//0T7OwsMAHP/hB3vzmN3+/xzjBBBNMMMEEE1whxkaO7wnPMQbRNE327NnzbA/jBwpXHAPzlre8hSeffJJ+v8/a2hoXLlzg7rvv/n6O7arBcM7GWfVGGh5QO0NiR8fbXROtXC+ke0Mt0+wltkY4V8JsengzFn5V3I16T1iRsGhQONUd9QgnOI2Q+a8M6C3lCGtjDVyI0Y/R3Tj72c7eHPaGT2dvDm0YEGyvoAyl83cwr5Pqkv1WONbKenP1rSFmN8KvmVRPeMSWyvzfbmZuVm93DYDOLTOSn3faR/ViCsda6K4wisXTQ7ZeuRN/Nk9vh8VwW47YlrYSJU4pnwmkZ7ZoYl5oC+O4PkDvhfR2WAQlncGiQ+v6IoDk6HkJfm3k+F2wpN1iewmrGZBfk8y9wnmX/GpI4bxLbtUTDeXI+Vl6oonuJlSP9Igc6Sv2qzrFww2MQcpgXsdu+KOGlQhrfcBgTmfmwS75ZZfq0eEoG05Hc6ORSzTEaoXobsryK2ycRoQaCTPnNEJKp4XJaO216V9TIixoaG6E7oqecfZhD7cu59lqR6hezMwhD3feln7dZWEhg5LoEAunusKSnhhi9GP6O5zs+Lo7dfIXfZr7FYKijLN2LBg1iUifsRpBc5+WuWnFfRoy/4CwZ7GlYDUDvKqKEqc4W6n0+84bBAXJTvRqwmxZHXGV5zdSSucStm7KE+aFdSwuh1idFLMvnxh2U1ia4nJIZ6eG2UtxGhCUdLYOaNSOBQzmday2ZAIWViJSTbLhxuMe9yuHRYPmdTZuXfqNvZpoBYOSOJoT/RLr6zQizK64bscNEf0dDoM5YRrtpuQ9xraK5ov+0J0SnZ5fVsRtPZT3dFgQHVp+VcY498Aw0/pJV3LK9JEA3U0wBuLItbrj/YtDVR9A8zqb3qKwf2FetHt+WbqSU13aNvyqTmuvXM/IVuhtUzOnamyNMhgH0nJROZWwdUCj/qiwgcZAsu7CvLCRSpyS6khH8fKl5henEeJOa6IrDFPmHhiS30ipHhOm0upIV7I01ghDKk5muW/MXkJkM2rnkOMbzKmUTqqokVzvsYtWiaH+aMT6rSbta6V/OLJFo9fbrqGGkmN4/tU5Ylt6xcd5iVFeOn2HC+IEzm2IhjLMQW9/yOzDkXQXH73EjLaul2sQFISFTwxhthe/4OE0U8KyuJznHhgyWLCyfECvqmJ25Tj9sjD0M4+OEhJy4GzG2f1w1eFZiID50Ic+xE033USpVKJUKnHnnXfyN3/zN9nznufxtre9jampKQqFAm9605tYX1+/4v2/+93v5oMf/OB3LZKY4MpxxRPAMXK5HDMzM9+PsVy1KB1tkepS+RYWDYLtFVJdRe+FMonZ7GXhxuN4FJDg5MIDZ7Kw56go8Sv2hosy9EeTpAA1lKDnwnkXvRdk29sn1jHPNOjsltew2lLPll8N6e6TCr00J2YENZKl6nEgdfHrFzEvtBlcW8GvGhSObgJgtSPSnJmZGhJdwWx65FY9+d6QW8bdVSYxVKx1CYwtnh8t5bophaObI3OEkoUj+xUxCgTbK5jdiKhk0VuSKrdUk+o4pxGNAoplSdVqBhSOtSicF6PHN8emaF6CMqorGxtHAKYek3MdzhbQvISgZmO1IpnYXfRZf8UcpdMywQuLhiyDnu3gz+YpnwnwZhzceZvBooQ3q1FKZ18xm5C09tpZMLDuynJp80AuW4bNr8VZ1I4apQQ1O4vd8auGROWM4nsGiw6dJQnDrh0LsqXr1l5dKvbCmMhR2bwlny3N9RfFdDJ9WCaTM4/G1A61SQwFt25QvBCPJkMhTiNk9pFAzC66jK253xTDTZQy82AXr35pCd2dUrLjyDVi3LrJ9OGI8jkJ7x7MSz2g5ifk1yKMAdQfHdLZPQoFn5HJQelsiLMZ095jMnPIk8lXKyHVFOYelOOceqyHGkrlXn+bTLzHS4tBQUENxZTR3WXQ3S0Tw+5OlamjYkyxN/xMjgAyCewtGtlk1BjIpMPoxzibCc5mjNmTJfxxrRpIRZoapqPXlmXM2rGAwYxci+Y+AzVKaY5qBxsHZSlyOKOw+kL5f/G8T5iXMPT+goLVGe03lpqx8fvP2UykwuxUwtyDAZ0li9rxEN1NKFxMaV5no3sSISNyA1kK1t0Ev6SQX41G9YQyKcttSGyKMQpkbu1XRrIIMWPEtkz2zF5Ke4/JYF7q77yqxPB0dyiUTrtsuy8gsmXsEkUjRhKQZX93Rkwdia7Q2SPLx/3FdDQRlNDscfD09OEEqyP3cOVUki2b+yWFrRvk/80DIheYPpxkz3k1MbU4DYmtmXpCJpluTaF9jYbTTKl/Tae9pBONGj/VSCaulWMSmK5GKWFelvRTTaGzJBKN/LL8zlKiNAset5vQ2yV1hrlGnJm7ets1+tt0Fr/g0dqrj8Khrz4oPDNRME8H27dv59//+3/PI488wsMPP8wrXvEKfuzHfowjR44A8Mu//Mv8j//xP/jkJz/Jvffey8WLF3njG994xfv/yle+wsc//nGuueYaXv/61/PGN77xsq8Jnj6uaAl4a2uLX//1X+eLX/zit3XfNJvN78vgJphgggkmmGCC5z5e//rXX/b9v/23/5YPfehDPPDAA2zfvp0Pf/jDfOITn+AVr3gFAH/8x3/MddddxwMPPMAdd9zxXfdfqVT48R//8e/L2P+x4oomgP/sn/0zTp48yd13383s7OzEffO/QHFdEn2KpJyT4OZyLqtUy5/tyXJvS2JLtGGAMvSJpotS2bS0gNETg0J3yWHqa02i6SLxXCljCyNHYzBvYfbSTLgPEC5OobkRs19Yo3OLsLL5ZTdjr5QooXlzhdqhNqmh4c04YswIU4LddVQvJn+2x2BXkf6B6Ux0HxXNrIpt9WVVpt04izqxN3wxmoSpxLbUHPSeMGdGL8QJE3o31nEaAcZ6H3dXeXSWEmEnvinuRQ1T8svChrUO5Kh/9hz9W7fjbMas3aFTf1RFrVsUTnXxZ/OUjgtTlz8/GEVnGNmSj+bL0nrrQA7dg+4uAyWG8hlhwMbxOcVlMRVobjAKNlYIZwsZ86V5EseiuRHNAznyaxG6m2QVdblGzLCuUT2RsHmjjdVJqZ4YC8ttND+hdSAnkSsdAI3aN4StU6NUIm12FXGnJaom0SVSQw0lAiS21Uu1clMSAZRfE1Yvf9HHamn4FR2jL+xje4+BV63gbMYSqREqGFFKc79Jfi3JjjksaMLgarIs7Vf0rKavdNqlu+QIG7Lq0bglhxqJ0SUxVNp7TPwybPuSsL2A1PiVZPlOjVK2Dgjjo/nCFOqeBP+u3mFjN8GrKuTXIty6LC839xnkN9LMkLLy8hJzDww5/+ocTgM2b7RHS6sp5ZMKiS5LlJs3jeJFbsmT6BIzNF4K9WuQagZh/pIZQ400+gsKhYvCJtWOhwQljfIZYRXlOWF/isthdk84zZTGzVA6Q7bsroYSVwKyzG32ZcnXq1rUjscjptegv6DgbKYUL4j5wm5JpVvpbITuq1jtKAuVdusSru2XpO4uGf1GVkMZnzcDaqwxfUQiihJdzBXjc5zowtZ1d6pZvInVIWM0uztVSudkmb2wItKBxILa8ZBUM7j44hx+TRi3yikxAtUPyRLtzNflvnHrKsZAlrDn7xcT1NQRMcnYTTGDeFUxrahRmhlivOpoqd2T8VSPSeCz1ZRl+tZeNauqy22kEiJeg7gpDHuiyZJw9UTC+vMVSmck0sXsX5J6DGckZmn5h0vZddE9uU8Kyylrdyponiwfa35OXr8jlXy5VRmjVxM2sXBRxlI6J5FB1RMRF198Kff2qsIz4QL+Hn4+jmM++clPMhgMuPPOO3nkkUcIw5BXvepV2Tb79+9nx44d3H///Vc0AfzjP/7jv/+AJvi2uKIJ4H333cdXvvIVDh48+P0ezwQTTDDBBBNM8L3gGYqBieOYbrd72WOWZWFZ1rfd/vDhw9x55514nkehUOAv/uIvOHDgAIcOHcI0TSqVymXbz87Osra29rTG1Gg0OH78OAD79u2jXr9K1+mfA7giDeD+/ftxXfe7b/iPFbqB6sXEjk6wvQJA/Z4LGE2XqGShjPRbahjjbivQuWVG/l8Xcb4SpehbQ9HAGWLW0NyIqGiO9GcxzmaM0wgon/ZJNQWzFZDqKt6MQ7C9IlqnRoA7b2cB1HrXZ+reC/izebwZYWqUKMGt6/hVg95SjsGuIvaGjxqm2Bsu8ShQOCjpEvo8SGnttXHrBlZTAqQBnLMd0SeOjts525Hj6AXC2FUN2rdMYbaEgbOaAZVHt+jsNokcjbCgUXqiSWdvTkKel0PC3TPYG2LM2PXpgWjoiir+bJ71W00Gu4qYTW8UqyLsS/nEkNyKfKmenCejH0uQ7OMDIkels1vYN3vDx96Q+3jrYDETpkeOxrAuYvnEEKOMXzPJNeJRVInoIVNNAonza8LMzt/bonTapbXXpnjeH+mzImJLYeErQ5xNYV5ae0UHqMRyH1jNALslWrTa0aFU93mi91PiFL8idXte3RqxhMKYRY6GXxWDSGyrtPcY1B8VlbrZjcg1pIYPhNFIDIX+Np3IUeltFw2XGo+DtxUKKxFmL5GondWQxi0q7b0Oc1/r0l+QCrnN60XnVn8sZOumvFQaFoUNtZvyOmYvpf5YKGaBboQaiY5v6rEeqSZsj91KslBps59m9W7zD3hs3qhiN1OG8zbWSIslESKiS6ue8PDLCmqYUn/0UsVgUJJjzG2kVE6GTD8eU/uGh91Ms0Dr/GpI4aLEyjhNYaimjsbZ/twZMYDkGjG9RWEnB/O61Ix5Ytgwe1KtN5hTCQoKkS3ByGo4NjeIRm4c7m2OPi8jRx3V2ynCoBU0KicDYdWmNRJNmDE1ktq29RcIM1U+I8acyIb6ownOltw3zQNixhjMy9/tkS2MX/ufXArjtzopmi9auMQQ40N3p0prr4TJj1m12BYDihoJM2f2xpFSsk+nARdfIprV6cNRtu9hXd7/iSHRPUEJ2teowsrVFHrbNQoXpW4tKEmsjOju5Dx2rhXtoFeDqaMx6y9I0UdB11NHQowOlM7J6w0XUqYPRwRFqWwrnQ0Jc1I1p3vC/pXOJTRuLRHloHY8xqvJ47WjCf1FBXtDmD5FT2ntF/POxu0JsQ39xZSt20OsjjwOEvETW5c+Fp2Nq3O16xnR/6Vw5MgRyuXyZV/vf//7v+Pr7tu3j0OHDvF3f/d3/Kt/9a9461vfytGjR5+RYxoMBvzsz/4s8/PzvPSlL+WlL30pCwsL3H333QyHV6tb59nFFU0A/9N/+k+8733v495772Vra4tut3vZ1wQTTDDBBBNM8IOF66+/nk6nc9nXe9/73u+4/Tiq5dZbb+X9738/Bw8e5IMf/CBzc3MEQUC73b5s+/X1debm5q5oLO9617u49957+R//43/Qbrdpt9t86lOf4t577+Xd737393KY/2hxRRPASqVCt9vlFa94BTMzM1SrVarVKpVKhWq1+v0e43Meac5CDWOMtS5KlDDYkcfbO8tgV5HY0WkdyJHYGkHNxlnpo/kJUdGkfLxHVDTQN3v420sS6WKIRmu4LYfRdLFaEXpP4iZUL84iYwCM9X7mDh1r2JxVD3vDJ9UV/Nk80XwN60IXt66j90I0N6J6pCeMhS3MkQRRKwQ1WyJrlhzsDT+rVMuvRTgN0c65dRN7w8fdVWawI09nb45UVwlnC0RFg9b1RQmUPj/Ijm8c4rrxkmmmHh/gnO2g+Qn9/XLv5Fa9zM3rzUjkixqKzrF8YohbN1j8fJdUkzGWTwyxWiH5sz1aB3IADLflJFKnIg5RqxWR6sLI6V6K1QqJigbejEPkSDTJ9MNtEl3YzOJyiNkTR2j5xBB3WhjBoCAO5/aSjtUKGcyPwo/bUsWX6upIOybXpbfDYvarLcKisCcA1RNeFvR78cU5+jscrFY4CgUGvesTVE1STdyLVjsmMRTsho87LZoxp5nS3WWMHLLCcBkDcaeaPWExI0elud/MtKNWKyKyxJE5DoU2BsICF09LzM2YaYoclYX7QvKrEbGjo/lSrVdcSYgsqdLyauKqHczrDOZFY9lfUDLdn0SayHmYuW+TqGii+eI+ViPR+w3mdSJbtFdWO6K3w6JyUlghd3ocfyJsmd0Sd+zmjba4aBsBfkW0jH7NpHzm0hpX46BUGm7eaMt1HIUaa25EokssTJhX6C0abN6kMXV4iBqlzN+f4M6I+zPVyO4DJYbdn+qieYxc1Eq2T78kbtrBnMpgXsEvSwzLyg9pdHeqxJYcnxqmlM5LaLEajbc3JHTag+JKIrq3smjeSieFtWzuN3HrMJhXRu8FCRtXImhfoxFbCt1d4qyuPzpk5r85VE7F5DaEbRvMK6KJLCs0D+QonUuonpAKuOJKIqHbOzWMISgjN3Rnp0b7GhXNl2PPNWLsDWFytw4I4ziYkcBlpyEVbronbOfU0Qh3SphYNRIGbupozNSRcDQWqedTo5TpQ9DdnZJfTTH6MfNfERbU6qQs/7AusS0FuRZWU6G7U66b7qX4VZ2wLKyl0Y+pHY+JHHEmBzsCBnMaU0fHummpu6ucSgjzsP1TGtVj8rOLn5N4mR2fD8mfFJe5X5L3le6mtK9R6S9cclT/Y4amaVmsy/jrOy3/fjskSYLv+9x6660YhsE999yTPXf8+HHOnz/PnXfeeUX7+u///b/z4Q9/mNe+9rXZWF73utfxX/7Lf+H//X//36d9bBNcoQbwLW95C4Zh8IlPfGJiAplgggkmmGCC5zKehSq49773vbz2ta9lx44d9Ho9PvGJT/ClL32Jz372s5TLZe6++27e9a53UavVKJVK/OIv/iJ33nnnFRlAAIbDIbOzs9/y+MzMzGQJ+O+JK5oAPvHEEzz66KPs27fv+z2eqxKxY6AHMf0D0+SfbGM4OmbTw+7ITamGlcwRG+dMcitDoqKZhTGbZWGxlCihu69MbtVDDVNSQ+rUgpqdsTpeVWXqsR7ejEM8b+NVhYEa/7zWceneUBMdYFd+ZvPOKaYfbuPP5kl0CdR1NhPUKMW80BZHsqOhhglB1cRqxwRVE+dsh+4NNXINP9MxAiS2Ru7oGtF8Dbuh4NekNk5K22NMTSEqmhIKPXLpRUWp+dI6Lv39VaxWyPptNlNHQobzNk5DKtDUVkxnbw41srBaERvPz1O8ELN1UEKix0xeoisEu4rijK7Z5M/2AEivKZHYmjA1oyBpqy2ZdpqfEFuSgZcYCt19ZcqnfQbXVi57HMRBabcSjH6U5bUlhmiw3LqRZdZZzYCwYGZZe7WHtvC3l8g9uYW6uybMkRcTLljobkLtuOy7s2She+DXTC6+OEfhYkrxvE9nyWIwr1A9kdDe6zB1eEjzQI7icojuamj+SEvXjkeaulBczqMMPKcBw3mbwTw4m5po7UZaSqMXkge2bsyJs3gUuhzZCrGl0t+mMvulTdq3TKFGcOGVOWpHE4or4vKsnEqyrMJEFx1X5VTC5o266OpK2qhSLcXdVaa7y6D+WIBbF2dsbiOls0dh+xeDEctl0FuUbEC3bmQ1acXlkN6iwXBGpf6YHFPukbO0XrWH4nlhpt26Tm9RmCajnxJb+ii30GPjZpviilSiaZ6B3UqwWiHN64RJ3HavJyHMjkqiQ5hPya0qmU4xKArjdP41JTmfdS0LeY+tS07UsVYuEAMq04ckUFiN5HmpFUxQYmH5jIEwZIMZcfxGjsLU0Yj2kk6+ldDbJgsywtbKeR4sWAQFBbcOM1+X81I7OsTZtBnMaXR25tB9yeMbs3nVEwmDOXnt9ecrqMcU8v0UpymsW+GikuUg6h5Y7Ri7Jdma7T2GZAv2Y3RfVhXGIdZOU455MK+jRukoqFoYZncmZTgv17a9x2RY10g1Dc2XQOWtAzqpJiHRVhNa+1O8mokxkPPVW1SYeVjCnKceH7D+Uw7zX5Hfd2Y/pblfoXBBnMGSg6hl18PsgHPSJCjJisbUkRS3ruPWFHRfXMDDuoSIFy6Kxtbspay+0KSwnOLXJAx68yeGlD6TJ8pBWE6YOhIzfOmE7LhSbGxs8M//+T9ndXWVcrnMTTfdxGc/+1l++Id/GID/+B//I6qq8qY3vQnf97nrrrv4T//pP13x/u+8807+z//z/+RP//RPsW3JfXVdl9/8zd+8YhZxgstxRRPA2267jeXl5ckEcIIJJphgggme63gGquCe7s9/+MMffsrnbdvmD//wD/nDP/zDv9d4fu/3fo/XvOY1bN++PUskeeyxx7Btm89+9rN/r33+Y4eSXkGvyic/+Ul+4zd+g/e85z3ceOONGIZx2fM33XTT922Az2V0u13K5TIv/OHfJNdXSXUpOy8+uMzwpm2jRo+ANGeibnaI5qVWLdWFNSs8tkoyXSZ2dIy1LqmhE5cdtI60ddgbog1LDAXdTTCbHupak3QwJLphF6mu0tprM/X4ADWMhVVcEOZMjUQ3ZLUj1DBBiZJRO4nkESrDgHC2QFDSM/Zu7Eo2+jGxpVI83ScxNPTNHq3bZ7PHS8c7NG+uUD4xJKgKo5hfdomKBvaJdRqv3C5aqqZHb6kwahCRY/hfG0KCgoLZl4YGzRuxkk0vyxVs3JKjcjIkMZTMnaf5CfknpcUk1ZRsn0ZfMg7NVkBUNLAudBlcW0ENU7auN7CbUu3V3WWg+Sl2K5HqLE3+yh//P3JUrFbEYF6nfNrHr4rr1a/oRI7UTRWXw+x1gexajbWYQVFcsCsvL1E9IXSL7ib4FR2nEdDaa1M+E2TMbn+HQ1BQqB0d0rglR+1YgH1infW7Fkk1iCxhKfJrEe60RvXokKhoEBY0Ojs1tn2hRfuGimgaG9GIJZTcPb8sjs0xxmzWOI/Oagas35YjvyFMy8yjMUFRzSrZlFi0kaIDVEaVYbIvpxGhuzGDBStzSnd2aljdNMsm9Gow+0hA5KhsHdCzDD2jLyxmf0HaLXq7oHhWnNnDGYXZhz2CkrhXv5lBc2sKVldYqcpJYcWCkrRhBEUl06EpkeTdDeZ18qsRbl0Gbfbk+L2qmjFhbk0ZuYRHOsc5nVwjzo59/XaD6cclj7F9jYrmyfX45oaW3Cp09qQsfCXGagY0bsll2svieZ/WXmEtwpy0XeQv+lx8kU31RELkyLgTXcY3Poe6J9dg8yYNfSAsnzGU+69x0JDxDiRzcTgj18YvK+TXZJ+RLe0e45aS3IY8L+8jGb/dSrj4EpXpQ8LEFZdFB3vhlXnyq+PGD4X82qgmbcScujVhQ2NLMhjHDKTmC0s39fiAlR/Kk46i9KonEpoHVKYfl/Pa36bjbArzaQzBrUOqC0PYX5T8xzAnFXt+SSEcRYrOfy3I8iRDWTwh1WUM+oBRRqdKqkNu9fLf2c6W6CQ710eUj+jCyM7LeO2mnKegoIzYRLlWc7/3te/2UXDFiNKQL/EpOp0OpVLpGdvvNyOfzzPzM7+INf2ty6VPB1v3/S2vWajxsY997Bka2feO4XDIxz/+cY4dOwbAddddx1ve8hYcx3mWR3Z14ooYwJ/8yZ8E4Gd/9mezxxRFIU1TFEUhjuPv9KMTTDDBBBNMMME/JJ7lIOhnGmEYsn//fv7qr/6Kn/u5n3u2h/MDgyuaAJ45c+b7PY6rGrnj6+hWgXCuhN3wCXfPENsqShgTl6VbVtmWQ3cT7DPS9BEWNLy98hdaWNBQoiJBVdyyTscVh+aM6MaMkXt362CRiqMTO5LtFhY0ph4f4M1Y5FaGJIaa9c6aTY+wkEdzI8l5u+jj1k2sURMHRRO9F6BEkjeXxCnl4z2G23JYzQCvbtFbKuA0ArzdtazLOCjpdPeVqR1qs/KKKts+u4m6vcTWTXlp2cg5VI8Ocedt1FC0cbGt4qx6ma7Q317CbHrEjtx+fk2O263rOI1I+njrujSLbEk+YVS8lB9odiOiqRyFo5sE2ysokWTZjbMMh6Nz3d9flXE3PewRmwdgdXQK512UKCWxNZQoIXZ03LrBYEahfE5cq2ZPHLOal2TsaH5VmL+woGU9tlYnpb8tn/W+an6CHYljuXJKWLGgKOylNBiYlxjEokF3l0HlZECiGzQP5Jh5aCB9xC/bLk7LXsKwrlFcDtm63qC/K8Zu2SO9XUJ+I2XjjpGjuhHj1oXZCAsaQUHJ9J4gbld3WmXq8JDukoPTiFh5WY6ZrwtTNn1YWFrdHbmG+5KP59UU6o8FxJboKod1jVwjxq/qxLbk3e34VJvm86cyzaDuJqSauEQjR6Vxi07pm5y77SUd3YfcRiqsZc4g1cS9anbF5Q4Fujt1Ykv0XoWL0pUrbTgazX0Gui/sU2+bSliGwnLK1JGQ9h4Dt65njRthfqznku7YMWMGwshFtujhzK64XLcOaJhdyaWbfjxmMCfaWWMA04c91m+zqZ6I6e7UqR1NRmyaMNXdJSdjmzRfmk0610fUvq5TOS26P3dKWlLcaXF0q2FKUFBxa8L+iQsYujskv89uioaSDTmKVAMispaOwkXp6a0/Joy5Oy0M29qdCmZHjj3MSyaf3RRns2RMakwfusTqhnmFjefnSXU5514Npo5IS0pQUDJG2q9JfuFwHgrL0nXcuEWluCxsZG8phzEQfSKIM7ewLK8bVFUG85DoKuVzcm7dxYjCSZ3hPBgD0T3qAzL9oRoLcxcWRgkGfWHs/LKCNhTmMcyL21rzIeaSPtNpkLGFANs/q7Bxizyejo7b7IkDOLHA6Mj59Sa1988JGIaB53nP9jB+4HBFMTA7d+58yq9/7OgdnMfbLcu74+XcwhePkxoaQdUkf36A7ibovZDUuDTnlogKhcKpLoktho/xkmJ+2ZUPonkDfWvI1kExPOhbQyJHFbNJP0YbBphdmTSZTQ93WgwQQc0mKKqkukr1wXX0XpAtBwclnciRarjY0SkcaxEWNGJHloPHQb/5ZZfEUNF7YVbjFhZk/6khBgN3V5mwoDFz36YIr3eV0YaBHHNP/nVWPbShBEKnOVM+AGo23SUHfWuI7ibkVobkV8XMorsxU19bJ9UlSqNxa4nBgiUTkaose3eXHFJDx1jvZ9u5dV2OyVJpHDTRPJnMDLflUCMxyRhNF6chr6MNA4KSTnuvQ+TIW2H24SG97Vq2VBs7ejZ5AugtGjQOGtgNmUxGlizRlc6GaL7Ew7T26nR2aqPqLgUllniJVJMl3vF+jJ58UI+r3syeFNN39uawGz7DmdG9cH5ArhGzdb2B2YXdn5IlWndKYViXqArdS6k/0pUl3XZM4bw7ii5J0XyZsGlektWrNW7JZaHC2+8Z4Nb1kelF7sH2NfLHRGe3idlLmX3YY+12qYRT4lSWaesagxmFwZzG/AMeq6+apnTapbdNJdVg7XaZ2I+Xxee/FlA71JZl3Ckli5hxa2IscJopU4eHo6XelM07p1i/zSa2oHoiIspL+DHA5o3yPkp1MV5Y3ZTyuRglEpONX9VxtlIiW87heHl1vHSZagp2U773y5eW9bs7ddZvkxfxp9LR5E2nfY1GOIqoKZ2LRBYwENNBfi1h9cVkQeJjM9RgRpZ1ZXk6YvGvyc6HMbw0OcmvxTiNKIuQ0UfGiYWvesSWTETU0SJL+aRMgAfzYrwxBpdiVGAUyVLXcac1NF9MTZVjsjxdO5pgdSS6pbgc4tZFDmB1oLtb4mP0gdzTzlaK6su5rZyK2XieSAnslsTmGEMyU4ZU0MkEfOqJlNZ+heqJaGSwkSXq8TLwcEaht01lOKOw868lwHpsLJr5mkZuQ46jdEbiaKxumlW3JZps09mpZUvjwxmF2rGAXCOWKsNRFE2qy/jyq6lM8jR5rLNbpARBUUUfyn2RW5U/IIKiLJEXllPCskxow4XgaX0WPFfwjARBP8fwtre9jQ984ANEUfRsD+UHBt+RAfz0pz/Na1/7WgzD4NOf/vRT7uRHf/RHn/GBTTDBBBNMMMEEfw/8gC0BAzz00EPcc889fO5zn+PGG28kn89f9vz/9//9f8/SyK5efMcJ4Bve8AbW1taYmZnhDW94w3fcwUQDCIUTbbp3LFA9IvESvRvraJ7UoDkrfUD+ytWGQbYkXDjvkuoqZivAn82j94SVUh0ti3EpHN2kFJWIpnI4mzH2hg+Ghu4mRFM5qR3LmVKBtt4nLjuXBP7rA/SeiTdjERyYBiB/tkdUssg/2SaayuHVLWJAm8pReUJiYmJb6siMXpgtSQOjUGeTyIbiaS9jxnQ3JrZV3F3lUUSKjtGzSHUVo+lCGBPsKhPP2zirHlsHi1L7NooyictOFnwdW8I6+VUDtz6D0Y8pPdEkev4UxdPDzDyTWxnizTj420uoYULzOpvZr7bYuKOasWpTR+IsfDl/0cfe8KVyzo3Q3AivbkHdwm74pJrFYE7H7KekukrtWJBVzjmNS2aQZLR8rkZyfH5VZ+aQR2+HhTUykNhnmlQK9ctMI5GjEeZHVVYjdjUoKiijwOP8RR/Vi+kt5aQqDVkWnzoSZvV8iX7JfDFm1ErnohHjK6HPsaNTPD2kdSBHeymH1b08JHxcIQZQvBBnS4C9JWFIvaqK7qk4jZDCeTlHxeWQ7i6DxLBY+KqYMtpLOtu+0GKwq0h/m44SSwC2s5Vy8cU5qeSqqkwfjjPjT2xL7IpWsohsWbZ0GgGRY0lszLSK7qVZ/V1npyaM4JEwO4bpx+X3jNUKsVumhCRHFpGtSBxQUZYfM4bxQowaCiM3mNPwa7I8PH4/2q1kFA0ky8N+Rc9q6Jr7DIpn5GfnHgxp7zHJrwnzGtkK+bVIWK8dFmoES3/hs3GzjVeD3IbEBYVlSAfCoAH4FY0wJ9ErEqgsIeFnX2cx+5AsFYOwZqmmkBgqc1/rcv41JSpPXvo03rpB6tuCoopXA6ehENmyPFw9phHZcmxBUYxXoGYSkKAgS8Cb15uYXWjtHUXPNIVFHF+L8vEeuptnMCc1ifEo+1eNUoJySmwpWE1h97y81LpJ3aDsq3GLTuXJlCjHiJWTgPPiealGjPJI6PtWyuqLoXLskhFn4b6QjecZzHxdfk94VRU1lvGFeYXYhva1CoVlMaOMqx5br/LY/jEdt66TfzyhfY2G3YTmTTHVIxq9/SGL/1Md1dmlDPaEqMdE2jFmHt2a1CUWlsWkZJ6/9DvwasIzweI91wJwKpUKb3rTm57tYfxA4TtOAJMk4fz586RpSpIk/5BjmmCCCSaYYIIJJsjwx3/8x8/2EH7g8JQawN27d9NoNP6hxnLVQnFdANTNTqZ7szdcWgdyKB1hAHU3pruvnGkC4VJws+ZKHVuqK9gbLpovmrjhtVMAGMtbWM2AVFeIRxq64byNWzdRw5hUV2jfMkVQNSl+/SIAUckisUe1aM2A3MoQfzaPV7eIpnLoW0OMfoyz6gkbhrAihWOt0WsJw6WOjBKJIRrA6lFh4lJdxWqF2fGCGAaK5338molbHxk7dkl2g93wUcOY6tEhapgQOSqxpRJUTZRoFDMypxLbckuWjndEl1h2KJ8Q3VxYlL/WBzvyopfUFfReQGElkliaM0GmYzR6Ifkn21Qe3ULvSbWc1Y6kpq9uSRVb/xJznWvEI7YE3LpUxhXPCyUz9VhvpJtUGSxYEqsSJQQFhcGCRfVID78iIcjhnAi77DNNuSciiQCpfcMjLIxMLK0AZzMe1d4Jw9XZm0MNJdBZ8xJSTdjLoGYznLfRfKlvqx7pETnCQvkVCSgOCqJ5S3UVd16q0IoriTBpe4QFyjViqTDzRIc1DpCunvBwGqFEsfRSnEYo18bR2bxJxisaRblf3WmN/EZK/5qSaNkGKeUzQaavMwZkesrYEv1od5dxyQRTt8ivRuiesGf5VWFsjUE6iqtJcVY9rK4EY8e23BOJccmw0FmyCIoKF19kC/vXF6rDL4t5xWpFTB/28KoqTiOgt13MG7WjCcXlkOrRoehhI4kfKqxEFFYi1p+vjGr9xKgxjp2JHAlV7m2XOJricojZjbCbYhoZs9ZWV/R1oilUqDyZUn8sICiqdHeKwUmN5Wf6i6K/86sGuYsSJ1Q5GVI6F1F/LJDj3G2y/MMl8qtiUPHLEnVkdCWuJcwr5FdTghJsPi+hekzMNGMUl0OU+JsC3I2xEURq6GJLTBbF5ZREk/vCq6rk12JWXl7K3h9jtlLzUzo7NXZ/2mf2IdEfyuukon/URpq6jVTYP1uMGbonjO/mjTobN9sEJQjKKYM54R8qx6SCDuS4eosGdhPWbzcYzKnk16JRDZ+wgFZHTBpqBLXjMf3tkF+NKN3n0DhoEOYVVn5I6vA6exSqR0QPWXnMwFn10D25rvWv6VKZJ/JtIlsh1RnV1kFzn2hur0qkz9DXcwiveMUrvqVLGMhqaid4+nhKF/AVRAROMMEEE0wwwQTPNfyAfXx/6UtfIgi+1ZTjeR733XffszCiqx/f1QU86f397gi31ag9tEWac1DXRszP8jrTD7fx9s6SGqLBKp7uS0XYWhO965PqKnHZQYlS0csBUdGk8MgFAPyqzM+T6TKaG6F3fRJbk1iVRkDpeIfBDol6KR/vYW+4pOWCBCmPWD01jNG3hvSWClgXuqLHcnTC2QJ6LySxxR0alSzCgsbg2gp6V+q2QJzKgwULNYzR3AhtKOHFRtPFfHLtUpRLVWJUEkPiP3KrHqmhYfRCYXEcndb1RalxM1QK511Kxzs4K/1Ma1g7FkhV1akuqaHhNAKpdQvj0c9JYbvVCglKOvaGS2+pQGypGUuohglmSxjMwbUVoqkcw205BjvymE0PqxWieQn6g8ew1gekukr+/ACvqlI41kKJhGkbsyWAOKy7EVZTmK7asYCwKNVrhfOi0SuddolsSHXZTzRdzCr9gpKO3guIHAWnEWTHO9bH+VUDqx3jNAJqR+W1NS8RJ3jTE3dxMxA9Z82W6Jc1cVkWTnWpHR1SPt6T83pKKAujH2M3fOYeiLBaIVYrpHZcWE+J9DDo7JRr3Fs0mH3YI7fq0dlt4lfk8fqjEYM50e45Dbn/isuyD3kspHpEKvh0TxjA/JqwqPlV+dmgamIMLrmgvao4ycvHe0S2QnuPnIuxkzW21FGVHySG/HoazInb2KuqTB2NyK/Kl+bDzAMt/LK4lmvHAoqn+/hVnc0bbZzNmKAkur6gqJBb9UYRJCaDeSMbCwhbuf2LUXaPjY/JLyu40xqtvSr5NXFRu3Vx0ReXQ/yKhjst7G/lhIvuiUt4fP8095uUTrs4WxKk7WzJeageS+ltU4UpPC2s3Zj9DguiXcs1YgoX0yzYWvOFNbOb4m6dOjwkvxqR20jZfg8jliwd6QKDbF9OI6S3Q5jr8T5721TqjwWjMGtxGY9jYJRYmMvmfpPKyYDKyYDYkhWC/IZEIyW6jCfMC6M5DtQunh7S3S2u6u4uibEBeY3qCYksspspU0/IWK2WaCm3DqYs3Nclv5pmOubCckrtWIBf0ejuFkbRnRLX/Di0O3JUdv61uNhLZ0NyG+JyL51UKaxE5FbH7u9RhaEtely3ptDaD4WVCHtDmMdUu6TXDPPiUh+znBM8e3j88cd5/PHHATh69Gj2/eOPP86jjz7Khz/8YbZt2/Ysj/LqxHfNAfy1X/s1crncU27zu7/7u8/YgCaYYIIJJphggr8/npEol+cIg3jzzTejKAqKonzbpV7Hcfj93//9Z2FkVz++6wTw8OHDmOZ3dkJNGEIw1nuktSqJoeFdW6FwdBMcm9TQsM80SXMWei9gsCOP3dDRuxbKVgcDcLcVsDfc7N+gZhNcOwdA4bzoA6PipfOvRFJsDxDnTAqnuvizeewT64SLU+hhjLrZwajZwjoVTVRDo3hatIil4x2UoZ9p1fyqIcxXGFHq+hDGhLMFnLMddLdAf4eT6dSMMxskczWUMEYZ+my9cie1Q21AXKjO2Q5KKFRAOFdCCWP6+4pUHt1CCSPUsELkaOSOruHtnUWJUlJdwWwJu6WOXLFq6BA5WuaA7ewrZjVj5oZPVDRINWEyrba8njdjYTWFnUxGjGvh6CZJOQeYonlbXseYm0bNmQzuujFjBfJeTOWES/P5U5eOp6mgd32oOei9IKu0qz20RVx2SHVx8QJERYPBvJEFYG/eOE3lZIhztkN+dIyDHXnUCMwLbYychT8rEQbO2Q7urjK6G6N6oudUw5Tck1t0bpEU2vyyizsvuY5WUxzKnX1Fph7r0b+mJIzm1pBoWw615qD5Eh6ud0WPGZR0Uk3JwrjtDZfBjjz5jZTeDoupxyQAHKD+SJetg0UA0VIOUiqPbjG4tsL6iyRsWnLrRCuYzEgFk9MIUcOEjZttUh2cRsr8vS02b6uM2DwFd1pl+tEBm7fkMfoO9Ue6dPYVM4fqmLVy6yb51VAYal3BGKjUvuETlC79utq63qBwMSWsOZTOhhJ+HSYENZv8RZ/C+QQlSkUT2Rdt43DexmpHo3tEtJFu3UANJSsxttWR/nJ0XW2YeWhAZ2+ObfcO6e9wKJ/20XsBG7eXUGJhsYrLcuzdJQezJ/rNcd1afi3h4otzTB2NCAo6Rj8i1RTaexTm7w8zXehYb6e7Cb1Fg1zjUvC01Y6oHvG4+JKS6Ax7KVY7IhxVAaqRBHwnGgQFE80j0+D6ZQW/LCys5otWr3zaZzhjjxztKUHhmxzIOXCnNeyWjL+3aGD2UrZ9sUvr+iJWK6K75GC1Y/L9lOGMvBfDvELteMjG8/PkVkF3U2rHhH3rLyjYzRQzlP9rvuQHVk5LqHPhYkqqqVx8SYnKaTk/lZMBF15uUjkREdkm9UPiyk30S3V+mic5lq0DueweczZj+tt00UXeqOM0hEnVvAS/qtPaa2fnNDwn7K0aC6MaW5KrmV+LcDalSm8c7n7V4Tmo4fv74syZM6RpytLSEg8++CD1ej17zjRNZmZm0DTtWRzh1YvvOgH8i7/4C2ZmJnHoE0wwwQQTTHBV4AcoB3BcNjFJI3nm8ZQawAm7d2VIDR21M0QbBhQeuUBSzmU1byBMHUDxcEM0c2FMtFgnrDnisjWkBSR29Ev6ujMNNDdCCWPMJ9cwmi5+VVpBzAttQJgnkMw/EIelu60AgP3YuYw53LwlT2JoRFM5CGOSci5jr4qHG/jbSwTbK6JVNDRhonJSFVd9cB2z6aF2hqRTZdTOUF4jFP2X0ukz2FXEbAWEswWi6SLDa6fQ3Ah3W0G0XlM5htdOXRr3vNjutI6bHUfhsVX8ii5MnReTO7oGgN71KZyX7Yx+TFA1UUNhQVNd9Gn2iXVxTc/bqJsdqVFbsBheO4U342A1A4ymy/DWXYQ1J3Nij3WUQKbjSw0NfzaPsbyVXbfEEIZmfIzejCXO3GFAVDTwKzq6m4rerR8zfdhDd2P6+6ukuoLmRllziLe7RneftKe4dQN/e4nYVlG9mM1b8lktnre7Rm7Vw2pHpLpyyWm9vCUO5dNDgpotbSeGQjhbICxo+FVhhYbzNpu3VdDdBL+iSeafrYmbdsSQVp5oUzzv01uSn001hd5SgfyqNNQYIwYunBWdJUDlZIDd8LEudBnM6VnuYm/RQIkS5h4YkNtI8Ss6g12S+ZhfDXEaIc6muN6tTorZusSqBiVpXdF78uVVJW+xt5TDr+rS1lE1iC1xjqtRSm5DHLxBSZzqbm3kcB011sSOztZNeZxVj0SH9h7JihvMG8SOTnenzub15kjPKI5qJRYXNsBgTqdywqWzN4fTiIQ9bEUkhspgR57aNzzya1GWH6hESVbZN77W048OJMvyfIrZjbI2jMhRKZ1NR2NVRuxpSntJWMzicijtQLrUCnZ2mzRuLVE+F2P20lGdINlYrVaEs5Wi+5Bfi7C6aeaABmFsS6ddcS/3hPWtnhBtb341Ege8l1K8EJPqon0c1rWRJlMcyp19RdHSGcqoTk5SAkrnU5Q4pbASEdsqVkc0fImhEBTElT11RO6jwZyaNXXEtrCdRj/GasdMH46Ye2BAqgkLt3m9Sf3RhLU7cpTPBOJU36ZTPSE63sgRZ7jdErd7f0Fc892d+siZnpJfFYd68esX8as6ViuifEa0uCDHaPZS5r7WxasphHmplzO7EZEjY29fc0VlWRP8A+D9738/H/nIR77l8Y985CN84AMfeBZGdPXjKe/uiQv46UEZyi9+tTPEPrGOMpRfwPpyA2U08dJ7IXHZQQ1jzDMN9FHgsnmmgXFmA31rKFEv02USQ0NZ2ySZqxHnZNISTeVIc2LwsE+sE5Ws7PWLhxs4Z2VCs/WaPZhPrmFeaFNYuWTeGMOdl7qrYHtlFMuioW7K8nBUNFA3OzJpnC7izTgk5Rzp6HujF5KWC6idIcFuoePVMMZoumjDIBPWOyt96UN2xLCRlHMyYbA1WdqeymVhyESyBKZ6YvjAlbDpqGShd/1MmO+s9DHW+yNThfQCy7kPxGCxu47d8Kk+uI694eKc7RAWDQnMjlKJhIlSWcaOEtxtBfTNHp3dEkcTFU2s9QGd27dJPV9PTBu6G2ff664ECI+NM05DzAdGb/zBpKJEiYRM69IJ7dfMbJnPakeybOsm2TXRhgGzX9qUpW1blS7nooEaSs/xYEee8okh/YPzOGc7qGGMGibo7qhruKTLh2krzEwslROu1AauhjiNQP6AcBO8GQnAHuySpd7BnJr9rNMIskrC9dtkWXiwYKH5CbVveGxeb+LXTKIp+WD2a2IaqX95XZabqybF8z6an2TXbDAvk+T8ssZE3ScAAQAASURBVCuh5o2QqGiQW/VoL+lYTbmWsaPTWypQPhMwWLDEGNKKMHspkSNjNPoxei/E7AkbYHYj2ntMYltiZcavFxY0+bAvGtQe2sLZTLKJ2WDeoHYsyJYbQZYqzW6UhVBXT8j5D/MSsaP5EnLu1g2ZzCxIT/f8vS2RLhSNLFwapEbMnbdp7zGwWhFu3aR0NsTe8CV+aTPGnVZlUjmKf5p7YMBgwcJsehDGlM5F2Bs+upeSatDZqY1iWiKa+01yjZjcqvyxUTjvUjkp96azGWcRM86m/LHk1S3ae0y8qkrtUJtUU+TctgKsVigRPv2Y0jm5L82+dFDn1+SeLZ4Wk1WqybLo2OSiu0n2x4H0P8tSrdMISTXZZ1iQAOz6o0OJE4rB6kjcUuOgLCH7FQ1vxspkGU4zJXKkUq6z25ToI0uu8fptMhkfzOkERRW74VM5JeMunYtwGhGF8y76qDp2/Md4bIvpx+wl+BUdryZj7i0VKJ1LsJspgxl5L7k1RULcn1r+/pzFM1EF91yjf/7v//v/Zv/+/d/y+PXXX88f/dEfPQsjuvrxlEvAf/zHf0y5XP6HGssEE0wwwQQTTPC94gdoCXiMtbU15ufnv+Xxer3O6urqszCiqx/fkQH89Kc/TbVa5bOf/Syf/vSnn/Lr6eDLX/4yr3/961lYWEBRFP7yL//ysuf/t//tf8scP+Ov17zmNZdt02w2ectb3kKpVKJSqXD33XfT7/cv2+bxxx/nJS95CbZts7i4yG//9m9/y1g++clPsn//fmzb5sYbb+Sv//qvn9axfDOSco50s0m4e0aMB1GM0u3L46UCcc5EGYU2g8TEhItTbN6Sx2wFJNNl0lJBll5PX5Tl4GEAJWFpgqqJMgwyhlD1YpLpMnrXR1nbJM05pDkJefb2zlI+MWR40za83TUKj62SGhpqZ0g0lSOo2SjxyIDx5JosX7UC0nKBNGehuRFpuYC+2iSxJcpFXWuihMIGaG5E+4YK/QPT6L0Ae8MnMTTinElYcyg/uIIylIq71JDlx6hoZj+fGKq8hq7KEqmXgC5GFX21iRompHPT6F1fImtqDrknt4gcFWXok+ZMhttymC1Z2o3ma/jbxdRirPeJHZ3htVNERZNwtoDZCtBXmxkzaZ9pMthVzCJsouki9Ue69JZyRI7GYJdEuJgX2rKP0c8lhoraGZLoitTR1S0Kx1oEJZ2gZtPe6zCY09F7YjjoLjm09grTmmoK9oYvzOcoKNlu+OhbQwqnusQ5k7jsUDzcGB1nIJEkXpwtgQdVE7MbMbi2grrZyeJlrJbEkaSaBFRrwwA1TIkdXSrumh5KlDCY07DWB7SXdIbz9ii8WiO/JmzdYEEYtM0bbWJLZfqIxNKAsGNu3SS/kWI1hfnT3EjCnt0Ub3cNsxvR3SXLkvkn26iRMHdjdmm8xK2GwpJt3Zhj5pDUJ7p1CbJ2GgG9RYPIlugQ3ZUl2vxFn8ZBiQLyZiw5R7EsZxZWohEDJJ9YaiQ1aeOqu2gqh1eVe072KeMKC1q2RKxG0Npr41VViiuytNxbNNB8qd+zN4Q9DAoSR5PowmA1b65kLFdiKATFy81JVieVJf4olXifEUNub/hUTgajmj2FoGbLEr0OnX1FujfUhBktGjiNiPxazOzDci3caTF8eFWVrRtz+FWDxi053LqRVUpGjjJaKr3E45TOhuheyuZtFbyqSneXgTtv01mSyj+JdpGIlzF7C2Ik2bopL0u7o20kvmXEnhoKZjeS2sCdKr1FIwsQH4fJV05LVaHuQfmMhLcHBYWpo1HGHFrNgMGcSu3oMAv1NrsR+bWI1l4dYyih6pVTMe09BrElY2vckssYyXGA+todORL9Ehucv+izdUAY1PY18n7ObaSY/ZTNG1Xa16jkVyMqpyO2DuiUz0n4+ezDl1ZNriY8Ewzgcw2Li4t89atf/ZbHv/rVr7KwsPAsjOjqx1N2AX8zFEW5bEn4m/WBT6cLeDAYcPDgQX72Z3+WN77xjd92m9e85jWX1b5YlnXZ8295y1tYXV3l85//PGEY8jM/8zP8/M//PJ/4xCcASQZ/9atfzate9Sr+6I/+iMOHD/OzP/uzVCoVfv7nfx6Ar33ta/zUT/0U73//+/kn/+Sf8IlPfII3vOENfP3rX+eGG2644uOZYIIJJphgggm+v/i5n/s53vnOdxKGYRYHc8899/Arv/IrvPvd736WR3d14jsygEmSZF+f+9znuPnmm/mbv/kb2u027Xabv/7rv+Z5z3sen/nMZ57WC772ta/l3/ybf8OP//iPf8dtLMtibm4u+6pWq9lz3/jGN/jMZz7Df/2v/5UXvOAFvPjFL+b3f//3+bM/+zMuXpQatI9//OMEQcBHPvIRrr/+et785jfzjne847K8wg9+8IO85jWv4T3veQ/XXXcdv/Vbv8Xznvc8/uAP/uBpHQ+AEkYow4Dohl3oXZ/Bjjw4NtFindjRUYYuWsfNoj8AoqUF9K7P7Fdb6KsSHp3mTGEG924TdszQwPVQhgFmK8DfXiIdGRP0zR5KGBOVLLyDO1G2WpkecGwKyT2+IpqaKBYThyEmk0RXyD/ZxjizQVoqoERiYBj//NiIMDwwhxIl+DWT3u2LxDkzM54UzrvkVoYZq6mvNtGGAeaFNmnOwd1VHunhNJyVPuaFNnHOlBq29QFBzc7YOmelT1ouSATO7noWADw2YZhHl4mmixISXM7hzTiie6uaMGIVrfUBRtPF3VXGrRvkHl/JGLJUVxgemKN4uMHaC8XwYjVH+kxXxh87OuXjPcxWcInt2V7JArrFoBCSGqJnVDtDIkdl884pcitDeosG5dM+Zj9lsOiIaaSXUDs6pH9NScKrqyaprmavGzs6cdkhKlkEVZPE1gi2VwDo76+SP9sjKhqZYcdsBfR2WKSaQjRfIyxoeDMOzetsnIboCo0TK7RvqOA0AiJHZerxAYMdecKiQX4tZvO2CrXjIZqfUDrtkhgKxdN9YlvCuZUoweqkmWFirF1s7ZUIFTVMUUYVal7dwquqIxOFzmDBYubBLpGj0Xz+FH5Fx69oYioZ1b3pbkLzOmFFZx7sit6qfinmKDGEibFbwko2r7OpHulhrPfRPEZGiTQzt1SP9Eg1OQY1Ev1Z4bwrYb91uVZh0RBt374ilSfaGP2Y/JNtdDdhsOhg9sSYkGvEVE9I8HZY0Mg1YsqnfWqH2mIcWg2ZenxAZ48EW0e2sImJrowYNwmo9iuiFcyCtrdLUHSqKWzcURXdny0moPxaLGHSdQOj6VI71BaDT1Ghu+SIUagq+k6vbpFrxEw91kP3pUouvxbjVVVK5yKMfkxnb47cypD+giKMdSS6ufY1wlCWTrskukL16BBjkIqGrpXQ3G/i1vVRrIzEzIwZ1MJKhNlPGdaFpXenNfyqTmElQomFIV15mU3joMn2z7Uw+ynNfcL8626CV1VxpzUJln6ifYmVWxMNpu7B5o0qXt0i0aC/w2HmIfkd1lmyWH61Qumc6BI7S3IsYR7m/3aToCTawv6CmEe2Dsj4p46EDGeEpBhX9TkNCXiuPybsen5VWEinIZVyaiQh3VNH5bjUSO63qxY/YFVw73nPe7j77rv5hV/4BZaWllhaWuIXf/EXecc73sF73/veZ3t4VyWuyOL0zne+kw9+8IPcddddlEolSqUSd911F7/7u7/LO97xjmd8UF/60peYmZlh3759/Kt/9a/Y2trKnrv//vupVCrcdttt2WOvetWrUFWVv/u7v8u2eelLX3pZfuFdd93F8ePHabVa2TavetWrLnvdu+66i/vvv/8ZP54JJphgggkm+AfDD9jkD2TV8QMf+ACNRoMHHniAxx57jGazya//+q8/20O7anFFE8BTp05RqVS+5fFyuczZs2ef0QG95jWv4U//9E+55557+MAHPsC9997La1/72myZeW1t7VtyCXVdp1arsba2lm0zOzt72Tbj77/bNuPnvx1836fb7V72BYDnEc5K/EpqaFkd17g+LVycQgkjCWseMWhqGEsky9GTpOUCShgLA3ZgEc2NMM9IZExw7ZzUti2LY1gJY6lGmy6idPqybdMjnZuW2Jgoxd1WIKiahLtnRJs0VyPYLrVoIE5af3tJ2L+hK3Vxrow1Kom7Mc6J9k0cvOJcHLN/amco2sNhINtvDek9byFzCSthhLPSJ3+2R6or2eNax5U4k5GDdXBtBetCl7DmENYczKaH0RSGoruvTFAVR653cCdR0RAXadHE6IU4qx7Oiug+vRkLwpjBriLO2Q6VJ9qga+iuaM2UKBXncs5i/t4Wei/ItHhBScdYk+sVFYWF04ZBFqDsz+bJrQoLGxUNBtdW6OwrsvXCWRIdnE25L6ce66FE41gLCbi1GxLE7FUl5kWNUty6KRq6qoF9pikxMjWT3JNb9HZYoju0JaIizpm4ddFpOaseaihxHponTt78RR+jF1JYGYVvFzQa/2QP+YsSmqyGKb2lnGgWn9xC8xLCvES2qGGKXzNJNQmpVuKUxi05cR5HUgcGoqnqb9OpHR0KK7bq0Tog91H+/ID8WjTS3CkUzru0rhfNqtWOR2HLKc5KH91NsrqwyCJzP2/eqIvesie6u7AgkUjFww1xpZ4N6S0V6N5Qo7iSUDkd4Vd1lFhYuOG2nLjji+IUNZti/dT8lOJ5n4svKUmUzCj4uX9NCbeu099fZVjXRmONyF/0pSZuFDY9jigZO4vVKB3pVhUKy8IsFZfDjHn0y0pW3TjWLOY3UpxGxPRhqQQcx7dsXW+weoeN0Y9HUSTCdvavKTHYVWTlh/KUT/uXjaOz28RZ9bI4ICUm00GqkegCB3M6+VXRANaOy3tMDVMiW1iv2FLpLjl4NUh1heqRHokuDCVIuLJfVgiKKn5Fp7tTzdi7cYTKYEeeygmX/oIiwezdiP6CQv3RiMLFdMR2R9SOxwRFZaQ1FJav/uiQ5s0VyieGEsI9rREUpVKwdFbYyPyG3He9pZwwdyUFo6sROQqd3cJIxhbMPyC/c5UY2nsd7KZU4E0fjuht12juM0Tj15Ow7952jeG8BFA3DppSWbfLwKvJY0FRpXHQJNHlfo8tleJ5H28Sgfucw9raGs1mk2uuuQbLsiZpJd8DrmgC+PznP593vetdrK+vZ4+tr6/znve8h9tvv/0ZHdCb3/xmfvRHf5Qbb7yRN7zhDfzVX/0VDz30EF/60pee0df5++D9738/5XI5+1pcXHy2hzTBBBNMMMEE3wLlGfh6LmFra4tXvvKV7N27l9e97nWZ8/fuu++eaAD/nriiCeBHPvIRVldX2bFjB3v27GHPnj3s2LGDlZUVPvzhD39fB7i0tMT09DQnT54EYG5ujo2Njcu2iaKIZrPJ3Nxcts03T1aB7Pvvts34+W+H9773vXQ6nexreXlZntAlH86vmeJ27fRJDR1vxkEZBhjLW+B6hItTmSNVCWMG11bwf/hm0pPnUDrCZplnGiSGRrg4BYDRdIWFcMQl6M/mUTc76MuNEbMo+r44Z4rOT1dwznawN1z0rSG5VQlx1nsBSpTSWyrgz+ZFzzbKG1SGl3LYlJFzU9/s4TQC0fFs9jCbHtZDJ7FPrJMaOqmhEZcdyfGblUDfxNBGurwQwpj+NSWUKEUZBnT25nB3lRnsyBOXHRJDNFEYEtxrNKUGLx2FYhv9mNhWMx0gCGuRGKIVGrupQRya/vYS9oYvNW2GRrC7jtn0MqevsbxFamiX3NBFE91NyD/ZpndjHa9uSQaho9HdJ9FHapgKA1m/pK00u9FI6xXiNCJ0N0EZBjRuLWX6Ps0VPZbe9ck/2aZywiXVFaz1QeYO1byEpJyTc+0mpDmL4nmf/oFpKidc6o90UcOY/EUfZ9VjsOhIYHPRwFnpExY0YcyKIzavqtPbpkrY75mGZLm1giy8N81ZwqxdkNDf7L0zYpH8ikblpFSqjWH2xPXpbCY0bskJ81iXrDbrgtS4mV3JXCuf9jPtaHOfjEny4EYs8cidOpg3mD4SEDvy/tjxmS5h0cBqhRQeW830WtF0MXN2Gv2Y0vEO+WX30nVpBgRFcXxefEmJ5nWSDbdxe4nhvE1xOUSJEmYOeRJCPa6u06WmTfMkcDm/7NI4aOLWJSfSagZcfLGWjdVpRFlwd3fJobvkULwQo/lS2dZbKhA5KrVjElasRinDGYXNG23UMMWt6/R2WKI7XBUtXf2xgNlHglHlWpIdo9TQKdSOx5kjW/MScZZ7KYmtsfbCEmsvLBFbkl04zjEcM9GNgya9pYIcZ9UgKCoUl+XYvapK9UgPuykO7N5SgdhSmD7sUTkZZnq//GoomYsDcezqHlnVntUKae91MLswmNOE9R5IcLbuShC2W9fl98Yoh89pyFj9monTiOjszY3ur5T8aohbF42mGkmlYGe3gtGPces6xZWE8sl09JwENW+7d4hbl2tWOx6je+LiLp0XDV9+TZhiNWIUKi4h10oEqQ6zDwuLbQxSSudTrK68H5RIgrelNk+yUcsnr1J26ZlYAn6OHfov//IvYxgG58+fJ5e7FND4kz/5k0/bizCB4IoUrnv27OHxxx/n85//PMeOHQPguuuu41WvetX3vS3kwoULbG1tZfk/d955J+12m0ceeYRbb70VgC984QskScILXvCCbJv3ve99hGGIYciy5ec//3n27duXGUruvPNO7rnnHt75zndmr/X5z3+eO++88zuOxbKsb3EkTzDBBBNMMMFzCc9ElMtzLQrmc5/7HJ/97GfZvn37ZY9fe+21nDt37lka1dWNK+65URSFV7/61bzjHe/gHe94Bz/8wz/895r89ft9Dh06xKFDhwApej506BDnz5+n3+/znve8hwceeICzZ89yzz338GM/9mPs2bOHu+66C5CJ52te8xp+7ud+jgcffJCvfvWrvP3tb+fNb35zlgX00z/905imyd13382RI0f48z//cz74wQ/yrne9KxvHL/3SL/GZz3yG3/md3+HYsWP8xm/8Bg8//DBvf/vbn/YxpSXR/xWObmKeaQDg7irjnO2gdPvC5jk2mhuhdVzSnIOy1SG3MpTWhZuuBcA+ehEMA32zh7HWRYlS4pxJ7aEthtdOYaz3pRni4DzegQVxh4Zx5hwNtlcw1rr091dRl9dRhq64iUfNIcrDRyk/KM7gVFdQ15rEjk6aM6XmrGhI7djZHtF0EfNCG6MX0ruxjtoZ0n/5PqL5mriVR2yjOnLhit4oxlnp0z84j7urTP78AG/GYuMl05RPDHFW+pJNVpS8MrvhE+dGeXI5E70XsnWwiN4LMs2aNhQW0mxKXtw4Qw6gf02JcLZAYmuYTQ+t42b1cnovQBm1n0RFg+GBOTZuLxE7OuuvmCMxVNFUDl3shk/+/EDaNaKU/LIrDQxujD+bp3Cqi7urTFhzpIXEFX2V2RIn8eDaClOPD1AiaUVQInHRtm8Q3eWYhYxzJr1FQ2rmbJWgZpM/2xO37Wwe1YvJn+1dYk33FWWcmz3UKMVpSItJWHOwWhG5lSGDeclZU8OU6SPi/E2mxYG9dVOe0mk5H919ZfyqkWnIurvEiWm1xNmbNWuM9GjutNTfBUXJfaucFHbLbvioUUrz+VNUHt0iKOn0dziZczuyFYyhtC6Mndbj+yrRRRsYOWpW5RU7Mn6/atA/OI8+qp9LR1l07T0GRi9k5RVV3Hmb3MoQpxFkzt7y8R6zDw/xavKazmYirScVyRVUvZjBvLR0OA1h/OqPDnHrOoN5ndaBHNUTEblVj6CokuoqC1+JGcxpI/2YZPoN6xr5VXGWjt2tINpHd1qjvcfMXLOVU9LiofkJTkMY39iWrMP+gtzL7T0mzqYwiePr0Vs0spoz3RWXtlsX9i+/GmI0ZezOlnwy51fD0WvK+2Hq8QGFi2nW7DK+1mokNXO5RkxUFKZzrBt1NhM2b7SzthMQ5nPs2m7uF1a3eqSH3gtoXifsamxJxVx3yUGNIMwJWwlyjdWRU9wvKdQfHTKckfo4ty61iV5VGMnBvIFfVlDilPxFn96iwdTRGKsZCDs4rRJbCv0FJauEa+91KB3v0NqrsnVAk3Pak2xCubcUtg7oRDa09gurFxRV7KawfL0dFv1FBach+sXiciha1aYwiGoEfkWjt2iwdfA5Ngv6R4zBYHAZ8zdGs9mcEDN/T3xHBvD/+r/+ryveydNxAj/88MO8/OUvz74fT8re+ta38qEPfYjHH3+cj370o7TbbRYWFnj1q1/Nb/3Wb112gT/+8Y/z9re/nVe+8pWoqsqb3vSmy8ZbLpf53Oc+x9ve9jZuvfVWpqen+fVf//UsAxDghS98IZ/4xCf41V/9Vf6P/+P/4Nprr+Uv//IvJxmAE0wwwQQTXN14Di7hfq94yUtewp/+6Z/yW7/1W4CQUkmS8Nu//duXzSkmuHIo6Xew0OzevfvKdqAonD59+hkd1NWCbrdLuVzmlfvejZFopIaOEkaikctJc0c0lUPfkgT/uOygDQOUoU9SzqGuNfEOLGAfvUi0WJfntjqkU2XinPSq2hsuyjBAGboQxaSlAu6ucsY+qWE8ckBqOGc7RFM5NDciqNlY6wNpzjB0oqkc/R3CHOluTFCSDlYlSvFmrFE3qLAMqSF6QH1rmPX1qmEs7RvdCKPpZpmA39wvrHfFuehuK0j3ryH9nvaGP2I+TWn2GLuJI8lQM3ryV/30/VtEUzmG8za5VdHvhQWN4oPLpFNl6d4d/Wzu6BrDA3Po7qUOYW/GovDIBYY3bcNsSYevc7aTnS/JXjMpPdEEQ6N/TQln1cObscitDEkMDX2zx/DaKcKCRumJpugbm650Brsxbt1E8xPMboRfNbIWCLshLR+9pQKl451L98i+MkFROmLHztrcypDBjnzmEh5rwcZdtePcP3deGlvUMGUwr2P2RJtkb/iXHNlhQm+HRem0KxmLrRC/amRsTGyL7s3oxwzmpYnBaYRY6wMGu4pYzQCvbqF5yagrOUQbBgx2FaVxYqS1zDXiEaPrEBbElam7adb5295jULwQCyNlCCvlNCJ6iwbF5XDEpMXElkpQVJh6rMfFl5TQfckDHG679Jd9bAmDpUYpnd0mZl/YpKCoZq/pTmuEeQVtlIeXGMLwqWFy2XXRPHkuttQsJ9CtSz/v+Nw4DXkvBSU9y4D0ZizMbsTqHTaVU+LElY5kVRo3Ru7ccZc3iE6udNrN3n/rt5rMPiLNH0ZfXPbdJSdrHhmfn9iCyslw1Hoyyg5cFIazfY2K5kH53KWwfasVEpTEqepVJcdvMKdSORnS3WVQOhuOHOeXjrO3XaN4QZjN2jc8BgsW7pSwfHZLzm3ptMtw3h5lQw5ZfXGeqSOi3bz4EoO5B6KsEcZqR6zfalI5leBOC/Na+vwxNt94HWokLGCqKdn57i8o2E3R8uVXQwbzBmYvQfNES5lq4tzOr0YjRjuhs2SRX5Xxa14iWZXHWrRvmcJphPgVndZelfyq/B4x++LGrn3Do7XXxuzLe0cdvZ8Gc3Ju/bLoBcfX0K/oBEWF6YfbDHYV0V0Z0/T9W6z/0DTTH/ra0/pceCpEaciX+BSdTodSqfSM7febkc/n2f7jv4hdm/3uGz8FNh76W161p8bHPvaxZ2hk3xueeOIJXvnKV/K85z2PL3zhC/zoj/4oR44codls8tWvfpVrrrnm2R7iVYfvyACeOXPmH3IcVzXSnEFYkGXg8WQvMTQ0Q0PfGqJ0+3gHFlDDhMR2MEaTNsWZwbrQFRPJZg/CMJsIRkUjCxzG9UjmalKldn4Vs+yghjHejCPbeDGmF5PmTCmwLxqjD7cCzopE0xhrXayikX2I5c/2soDi8YfeOJoGQxvFvLikswWM5cZospVgnhHzibHWRetIyHXveQsUjrWIR+MCGOzIy6QilAmmXjSIHFWE/uPzVpZzFpUsph7rEZedS0uCNYlLCQsavdsXKZzq4tfMUQCwjrd3VqI9NsRQEFRl+97ti/KhuCFLnxgauUfOgmPTe96CGAkMDUKJGukfmM6E9EHVJCrWMFsBuSd79G6s46x6RCUrm3DnVj3CooFbNyVA2ZFw7fEyr9OQCjy3LkdZeaIt52P0wSJL0mYWXaK7CWZPJjyprtLaazNz32ZWX1Y9OmTrpjy5hhhjmvvN7ANK9yQmpHxiyGDRuVQ/NpogjCvsQKJ/1CjPYF7G1d1XluXk0UQyMSQ4Wa0bQI7y8R5q6GSTl+4ug6BYxGlEo9fRJfy4nWA2PWrjSWvdoHh6mIVrF5dDEkOMF/nVZGQOgcatJZymLG1u3F7C6sjkLNUkVFnzlSyM2W74Ms6inKf2HoPaMVnm3bxRJb+WoPmydFl7aIvBwpRMris6yaiiTcwFJrWGT+F8hF8zsRs+Xt2itdceLQMmuPP2aFKgYTXFrNHZbY4iXAzMforVijKzTG/RIMwrJBos3NelcWuJ+iNdBvMG9cfCzPyi90LceVvOw5pMznU3pnwmpb3HHE2UoXHQYOpoRKohsUdODt1NR7E3CZ3dpkTH9FOCgpJVzBmDlN6iQWFFQrd7iwblMyIJ6G3XMAayD2OQsnGzTeV0xGBGR42kklDzDIbzNpEjk/fekkzIx9V+sw/FNG7RmX0wJSgqRI6B5skSeO0bHuu32VjP30PzBpi7PyW2RBLQ2W1itxKMgYJfUnCaEjAe5hWGMxrDBZXi6KMmshTUSCfRxSCS6HJ8W9dogEblVMyFH5mmekLCtnvbVKaOyu+bYHRvVB5tZ9uM3yfl0wGrd9jUjsd0dmrs+OsW515fxW5qKLFGbEFQAm6rkF+NWLvdRB9IML97lcbAKDz3NHzfK2644QZOnDjBH/zBH1AsFun3+7zxjW/kbW9727ftCJ7gu+MqjjmfYIIJJphgggn+saBcLvO+973v2R7GDwy+4wTwmw0T3w3fXLH2jxFqx0XVbGGBDI3k6Em0A3vg/CrKdI3+rdvJP9kmzUlMTDhbwDzTwNs7S7irjBqViBxh5YKqidNxsU+sE+yuozg6xhoopy7AjnmUcpHE1tCXGzjDQrbEPN73eKnTWemTGhrKVgemyoRzJcyW1KLZR5cJ927DaoXovYCoKKHHhcdWwfWIywtivpiWOJRgdx3nbIe47NA/OE/+bA9vdw0Atz4lLJdxiW2yN1zUzQ4YBt7umsSW1Bziik5avhRDM2YLjbVuZmTR3GgUuKuiuRF2A4l9MTRyKxLfUn50QyrZwohoukiqq8S2SnNfjtrxEN0Tg4XZCkgNDffWXcS2xJ2M42P87SXCgoR2d/eVUdeaGM4Mfs3EbErQduFUF3dbQcK0R8tiY6Yvt+rJknHXp7uvTGIoFM5HDBYsiUU5Ls9395Wz+qzicsLWjTlKZ0NAlu2NXojFJYZo6vEBzedPoUZQPTpEDeOMyRE2SZYbnU1ZTrUbvkT/NILMiKF6Me2bbWYOeaheTG8pR2poWOsDeosV9F6I2fRo3FqiuCxL02FBE/H+aMksqIkxoHhaGMhEl9oz3ZWIkvESr3VBDDJ+Vadw3iUomJmkwG74mKNl8cpJWbJzGgGbN9rUjom5x6/oGfs3XqZVI4kVcetQPZFkMoPYluucaBJfY7UjZh8Ww4RUmEE4W8BphGzdmBtVzwljN644ix1dXrMtLOBgTsMvQ3FZ2MtIWuoonxjSW8qhhqnUv+3NUTzvy/LyiNXrLRr0dkH1mDCZay8skV9L6C0V5BjmdfyqjtWKWPmh/KhuTIKipaZMDBmlsyGxrRIWNGrHhQUtrESs3ZGnfC7GWfUIqiZ+RcfsS/VaoovxorgipqXE1mjttUk1MVTYLWELa0eHqNv0rI4vzCtYXbnexlD2M5y3cadVEp1sSThyVIwB2TlU4pTZB8OMVdbdFBDTy+odNqkmbN/CfTFKnNLfpuNsCtOZGApKDJXTch27O3WKF2La12jM3Z/iTivZsrUsiSu05qWirbnfHIU8KwzmNHKjoGh3WqV2POTCy3WmnpDxbB3QgQqlc4nU/I3MP+u32RQuSpWd1U3ZuKNKcVmWi/ObssxePpNm0VPTh8UY1bi1RHLp19rVhWdCA/gcYBAff/zxK972pptu+j6O5AcT33EC+Oijj172/de//nWiKGLfvn0AnDhxAk3TsiiWCSaYYIIJJpjg2cczEgPzzAzle8LNN9+Moijfte1DUZSsLWyCK8d3nAB+8YtfzP7/u7/7uxSLRT760Y9mOXqtVouf+Zmf4SUvecn3f5TPcfT3T1HaSLI4E+3AHpShz/DOa9FdifaIyw4ASpRI2PJ8DWt9INt3XLSpHITyF2iaM3F3lTF6oWjGpovo3R6JoaEA5tFlksXZ0f7SzHBC0URfbZJMlzOWzbtpG7obo0QJ+mqTtNMD0yDVVRnL6OcKRzcZHpAQbDWS0FklSjCaLv5sPjODjKM9xpVbZtMjzpmkuiLRHBsugx15rKKJ3gvQeyGDXUVSTaF4ui8aQ0RvGNRs9F5IPFdC9WLRPpasTFcHjOJwiihhTJwzMdb7BNsro+DtEnovHIVcJ+TPRwy35ag8ukXz+VPU1gdiaLFVCqekck7v+plJBWwx2xQVosU6SpSODBbCzoWLzohZE7OBO29Lrdqql40vKlmZDivVVayWMEtqKDpMzU9oXV/E7MkvsMJKhNEL0bs+0TbRQLp1A7UilWhjhrJ4WrSkUdFkMG9QPjEksTWszqWKNndapThiVXpLEsZtNoXRLa5IkK27w8LsJXT2SUVb+bQv0SdHh5k5A0S0r7sS/Kz3QgaLDk4jJLHFoDJmGIOSsIKDRbmf05w5MvzkMtZtjHGwuOZLnEpkC6tjdS6ZOwrnXcLiKHakL/dGoivYrZTymWjEvmqkupo97zRVujt1jEEqtWQ7ZCzl4z2Cmi3BzN/w6O2wcKdVMRTZFsVlYSE1P6G9x6T2DY+ZlSEXX1IavS6ZIaJwzCWyc6i6gjtvE9kKkaMROSrtvQ5eTWHqSEj5TIpf0WkcNClclHiT+iNdCWMuSGyR7saUzutSp2hp1L7hA1Zm4IiqehYrM6yPmEpL4nSCogrYbB3Q0AcwfUR0sePatMhRaB3IMZhXqJ5IRtVqEamWkl+LGM7bhHmpnytcTHE2xbThl8BpSvyPV5Xz1bzOJraUTHPpThmjaxaNmL8EzU9IqjqRA8UV+T7XEHZOjaTOrXqkR2wVMnOIO63i1UCNNPKrEVZH9H1TR8S0YnWkqnHtjhy5jZQwL6HPwkQqRLZC6ZzsJ8wruDWd8jnRgpZPQun0kP4OiaNp7dWpPxbQOGiiRGB1U+zmmLGEMK8Q5iG2RIvpVVVyGxJB49bld0F+NaJx0KB0PiW2n/bHwQTPICZehO8vrkgD+Du/8zt87nOfyyZ/ANVqlX/zb/4Nr371qyc1LBNMMMEEE0zwXMEPyBLwzp07n+0h/EDjioKgu90ujUbjWx5vNBr0er1nfFBXGwpPrKF2hqib/z97fx4lV37Xd+Ovu99be1V3lbpa3dpH0kiakTQbM2N7PLYHb7GBxNgBk/CADTwhTgCbJA4nwcRJeMYnxAEHgjnmYPj9WAI/A8ExyYMxxh4vM+PxzGg0I8naW1JLvVV37VX33rrb749P9ZUnBryM8CzU+5w6UldX3frepau+9f6+lw7acARBRGLoGD2JVQkL1zMME10l0ZVxZVpEmDdQAtG9+XMFCdpd7+BckiiR4daMRL3smmVUsYmni3iHt9PZl0cZit5vM8xZCWPCeoXh1kwaOr25Hb9iSnXcdAUMA/PcSsoexoZKOJ1PK+TUIJbA441hWsWW6BJInOgq/pYsynAkVVKOLtEpdRujFxAbGvlnGsSGynBrBrdukz3XJne6ReugsFCbrtnQkctPCRNaBzL4W7Ki+zu1RKKrUn02U0ANIqlx05VUa9jZl5d9HI4Ybs2Q6CpezcFqjlINXViwGGzLkn+mITEuvRHumHWLDW3sytbEqWprrB/NkuhKqs3LX+wzKuiiiwsS8k8ukb/Yx6sKSzncKiHPdsOndKKNWzXF8VqSMdoNH3vNp/JUG6s9PscljSBv0DxSIjYU+tsc/KKC1Q4lkqdq4TRC0dGNq++choRZb0aEgDh9S+dHjCo2G4fluLb3GAy2ZSWqZFr0cvkrvlS3tcJxUK7sr1u3ae4zsNoR2eUAsxsyqBvC1OUNYYlKOqOCMFei+1MYzOh09opDOTYUOvvyDLZlAQkGttrC0nW2awzqhriTm6M0HNitmgRZhUxD9IZe1cIfM2CbYclWW/RqaiDVYhu3Zults9Kg5fwVH6uT4KxHtPc6qIEwOKOKnbqvVS+ifLJH7lrIxi2Z1BWshlJ756zHjAq6OJC7oj0rnh2mgc8YcpyCrDJmJCUmJ3fFpXTWFUaqqqehyeWzcnyLCyNaB6XGLtOI6G5Xx2NHtIddOQZuRYKNY0POzWbcTmSBX1DIrkRkGhHtPUoa6ZO/Jg749u7r10GQVYTNWh67c22FxmGDYVWju13HnVYpXYgonw3JXxyihglKdP25w6rGoK7QvNmmdH5E7lrI6h0ZevMGhcshkcWY4RVXdmSp6F7C2r0RoaPQ3qWnjGtnj7i91+4q0N2uokRSxeZVQPPBqyh0d0j8yyivsHyPgVuD9k0KjaMZlJA02ie7EuKVVUJ77NDPiy7SGCT09kTkTrcIsrJa0TyQwWqFKCEYA6nDiyzoHgrGAdTClqshTD0zxG4mKZNePuvhFxSWXmbjlVW5pvYYqON0K7PzQlgI/eaxuQT8XG4vRPz2b/82L3vZy5idnU3bP37pl36Jj3/848/zyF6c+IYmgH//7/99fviHf5g//uM/5urVq1y9epU/+qM/4p3vfCf/4B/8g7/tMU4wwQQTTDDBBN8oXoJdwB/+8Id5z3vewxvf+Eba7Xaq+SuVSvzSL/3S8zu4Fym+oQngr/3ar/GGN7yBt7/97Wzfvp3t27fz9re/nde//vX86q/+6t/2GF/40A2SjEWScQgLFkoQomy0JPh1XxFjcYNEV4QdRAKTN8NJrdUBo7lSykhpT54lyTgkGZNEV8meawOQjLVZ6noHs+mRvzgk2JLDL4ueb1MntVnlFuQNkoxJ91AF1Yuw13z5vaFLVt6BWbSOK7l7gVSuJRmTwbYsbtXEnyuwfs9UGjishDGtu7YwKuiYTY/BTSWcxghjcYNwOk/hRDNl8hr3bZHi+ObY6TlXwJ8rUD7ZI9iSSx2+m65nfb1Hdjlk/aCwjcHOmhSxH1tDCRMG27Ip27bJpuYvDlHCBH9LVnR7Y3YsyBsULrrkLw4lq9CL2bh3C2qYEOZl+2qYSL2aF+GXpRLNWO1jdZLUUay5oYQ1r/lSMXapQ+eurUSOMGJKmIwDkVXJqAuiNFBYDeU1ursc3LqNuzXHoG7gVS0qX94gNhSKZ4forrgVS+flebGtkX9yKb2shnURIBnjejSjF4xdpKLf7M0b+CVRcRj9iFFB/lWDGGdd9HyqF+GVhVnzyzrNfQb2qSU62zWKlyPcadkHv2wQ61L51Zs3MAbJmIVLUoYuNhSyKyFWW9gpvyQZgZsB1sOqnIPIEn1adjlglFfwqha6G2O3YlbuUbA64ir1ymqaFdneY46z7hKpidMhNlTKZz1haHUIcpoEfBdkn4Ocltaprd8ix0rCoEXD6tWEHdysPQPR1PVnRW9oNUdofoLZu+703fy77O4rYjY9sitRGiDdmzfob3Pwqhb5i33MXkxoSy3cxgFdnOj7TakSKwpzmFtKxuMKxxl7EpTtNBPWD5pYrZDuDgOvrJK/2Cd/NcIQ+SdeWaVwKWHjoEF+Uc6H1Q6xmwn9WTk3hUtBqqkLsgpeBbY8McLsJxQuh2RXxG27fovOYN5hlFfJNCIiGwqXAnQvIdGhdF7yDtu7RF9n9iXLL7si+tFBTaF9k5Kycvt+fUh/ViGyJYty6WU2W74sLmfNF6ey2U9INJg6FWF2GTN04sZ11iPqjwTMfXpI6VyS7rdbExbUnRZNodlPCDKiB+xulxq53CWNwU0lYktYRbslLvAwKxrbqZMBtScDsucNrA4okVTehTa09zronlQWutPCzmbXEvQBDOuwfotoSzUf1ED+neCFgV/+5V/m13/91/k3/+bfoGnXWfA77riDZ5555nkc2YsX35AGMJPJ8Ku/+qv8wi/8AhcuXABg9+7dZLPZv9XBTTDBBBNMMMEE3wJeYAzec8XCwgJHjx79mvsty2IwGDwPI3rx4xtiADeRzWa59dZbufXWWyeTv69CsCWPuzVHVHSkCSQICPZuJTY0io9dI5ifGrNvGkbTpb9b3KvBlhwE0syhr/cwmi7xrTcRFR2UIELvjdL/t/c6jMomvdtmUYKIRFcwz61gtQJx0HZcRgUdr2qlLle1M6Rwook2HKWMVziVkfaP45cJtuTwqhbm1TZhwWK4NUPuQpfCmQ7W6oDqp68yqBuogTiXc1dcEk0aHsxumObOxbZGf78YhPKLAflF0T4qY+bEutoVR3LtuhN6uDVDlDHJnNsgyYiTNrsm+XORo5M5tUKSsQjzBrnTLdQgwWx6+BVx6Ma23DQ3lNzDSx2MXoBzqcOwbjMqm6IfDK+/C4aOJnl3V7uA1H1tNggsPzCdPs7oR+mxCPMGVitg9f7pdFtqEEvzSCskc21Id4eBP1cgdDRyp9bFPasrWO0IzYvxyzpOIxQn8pYckaUS5iWjLP9Mg/YeU5ydBZ3Wy+YAUldoqg2zVRJdxWlIBl13l0N2OSSzLG5st6qTXZYqt8GsJe7MZS/NESxcdMku+Wx5fMjq39tB5UyQbj/RFHpzwso0bi9g9hKCrLBkzqWOMCFeTGRJVl1rr06sC4tpNUc0jmbQ3ZhMI5L8vmmV/MUhV19lYvYSnGUPvRfgLHtUTsi+Wc0RmUZE5twGgxmVqWeGaQ6g2Q1p71FYP2iyfoudVgZqfkxvV4b2ruvfW/2SnNP8VWlJUYOYQV2nt02Y4u4Og8hSU6ew7ibklqS6LhifA6MfyfU0bqDozRsMZlTW7pJ8zlFepTdv0Nmj0NmpEDpjvWlLGLRYV9J8OjUUN7GzHtPae/3ttb1HHLWJRupmFj2gnurRhlszaF5MdiWkN6eNmWTSbfdnFdp7TPyCbGezvcWdUiTvL5SKs85OEzVIcKeFJcmsJUydiuTxFdEUZtYSmvsM3IpC6VzC8r2im9tkH/2iQuOoSne7yignjmR9AF4FdC+hsy9PmNn8u1Ixu1K1NqiLhrFwJWFQk2t3WNXQvQSrK8yg7iV0t+sM6jrNA7KRtaPC+JXOjfd1XhhNvyi5haEjGr7eVpXIgpW7dYwOqfN6lBcX9NXXqPhlfewuFsf5qADd7eIgdtbHzSEFYQt1VxpVpk+OyCwzbpRR0veBwXz8177vv5BxIzSALzQd4M6dO3nqqae+5v4/+7M/4+abb/72D+glgG9qAjjBXw3NC1OzBUBYrxDkDbSOC658QOs9EexHGZPchS5qEGGs9gH5EA5mClIXtt6T5dhxH68aRPhbspRPDcmc28BqBShDWZdICmJsSHSFcCqDvebjLHvpcmdczBBOyUTL21kZR7skmFfb9F62C3OhgTOeQChhklaiRRmTwY5xbMjZcbWdrWEsrKF5sUxGx0vBYb0iE58LXQmo7kkfa1iwxj3ALv5cgWRc62Q0paLN7IZ09mZY/J4ZBjvE0JG/OERzQ9yqwWhnlaDiyKRvrkBkS7izGshSmERySBDzqKAzuKlE5Oi4O4o4jVE6YbKudjF7MWoQp3Vu/lyB3i1V3GmNylNtYl2hemwoQv2y9BurgSwvx7rCYNai8hUPJUrkd944wLoXMKrY5K5JNdWgrtM5Kt1RuitLjLor8SrdHQb+liyqF6X9qIO6zsa9W7A6CVZLjBibz411hVFewWxJV688RyaKQU6jcNGlu8NIl7TKJ3symRtPaNxpjf42h8XvLKC5IcHYRNLf5mC34rRfNb8YMKxqVI8NUQP5sASofMUjyGksPzBNd7uK2RqN+39jtn6my2BGZeXuLJGjU/vyIF3CzTQiNH8ssD8txgSvZtHZm5FomfWI3pxGe68jPbC3VNOl91FeJsTNm21qxyLy12Kmn/Fwq1LBFuQ0QlsChTcOMe7nTQht6M1pFC6HjAo6pbMuQVauZWddlq5BApjdaZVhTUmX6YsLEqsymLVS4b+zHo2XqWWyOqzJMmP1WMz0M+H1nuJdFlY7+qrtqxiDRCJ7xtFBsS7dt6XzMuG2Osl4oivLmkFWJpVmP6G1VyYufkniftyKmDqGNZkMFS7HGIMEp5lQPhungdD5qxF+xRRTUE7G0jygpsc6tBWu3a/Qn5XJ1PI90s+cvxYTZmWpc8tjAcZAomMGMxpKBLlFmRCZ/YTaoy1mHh2SWZOgbt2NqR2LKF2QpVXNlwnV9DMxagSjnELlTDCOXJHJq1+QG5AurSaaPNZpkG5XDWHu0zKhNQZJWnno1uTcFxcScovSjxza0lPtrMvf1bY/k79NJZKbX5RleGMgx16+vMiYNg7o4+DxhNXbJZZI82Wiq/nQ3LdZWvkixEtM/wdSTvGud72LP/iDPyBJEh577DF+/ud/np/5mZ/hX/2rf/V8D+9FiUkV3AQTTDDBBBNM8ILGj/zIj+A4Dv/23/5bhsMhb3/725mdneVDH/oQ3/d93/d8D+9FiQkDeAMQ6xpKEKIGEe6OIl7NQncjMXLMTItJALDPrhLbGomhCRtYdAi2yHKtsbCG2Q0Z3jQFV5YZVew0LkUNZBliMwC5d0sVAKUrDKLmhugbQwlSzhtYV7uyTGoI4+fVLKzVAaonS8ejuRK5C106d21FDSKCmQKtAxKV4W7NSfjvmk8wP4VbtyXKxtFkaXssvNfdGCWQpWcJVRaW0x0bF2TcwkRaV7t4VUvYva05urscVC/CaYRs/UyX7KUeSpgwKptorsRpxIZKbKjp/tsNX9i38RKhc6kj9W4dOQZWU0Kn/bIuy89Rki6hO8sexmqfMG/gNEbYp5bEvNCKiTImuiusQXY5QPNkudsv62lsiNGXZfr+Vp3WwTzacIRb1Ul0ldBRyR1fFmH/urBBg7rBqKDT3+akS5Gl86M0dmZQl+9d3R3jZdSWsIqho5G/OJQA56qOsx4xmHfSGJDNpcPski9xMetirOhuU/BqDplGxMYtGQYz43DnVsjUKWH/Ng4aNPcZZJd8NF+215sXFlL3EjZuyRDkNGYeHabslu4K42Q3EwbzDm5FasY6+/LUH2qRvybi+0RX0Hxh6JRIwpk7e2VpL78YoLtxGucBULgssSebIdRi/NAIsgr9A9MYA1ny88oqzZttrFaIO6WkQdUAxfOydD2YUccRLdA4quNOa+mxATFSOI0Ad0qhu1PBGCQS/LsSsX7QTJeWQcKwhSWU5WA1TMbMooQQh468phoIY5RdDhnM6HS3CaNYOrYhQcV1m9AGswtmT8bcOCysm1zbCoPaeOlZh/yixAwpYwYyyCqYvZjSxZBRQbYTZGVfdFcMIJusY3Y5ZP1WjcGMxLHkF2VZ2lkTZq+3MyG/GFA6rTIqgO5B7ZgYWkZ5he2faNHbKkvcZi9hMKOTXYnQvQS3JkzYKKfQ313gwlttgqycB6MfsX6rltbLAbR36XhlMY7oXiIB7EsJ0TgFS41kidkvipnI7CV4Fam0izUY1pSxQSRm5e4MznqMXxTmcpRXqZyKMbshuhtT+Yo3DsmGjQMaK3fr9OYNiXAJEoZ1WWo2Bgmr35GMzT7CeEr0UUKYEdZ2UFcwBtDdJtdHdk3q8KZPjpg/sPJNfx68EKAkyXO+8XXaN54P/MAP/ADnzp2j3++zsrLC1atXeec73/l8D+tFiwkDOMEEE0wwwQQvJbxAl3FvBBqNBmfOnAFAVVWmp6e/zjMm+OswYQBvANRQWDRlOMK51BEdixcRGxoEkcR3rHdIirk0INlaHTAqm2kdWzxTQe+NxGiwrY7mhng1B2NFDAubFWGjik32ijiegp01Ikfm8OFUJjWXhFMZMuc2UAMRt0eWSpQx09Bo1YvSOJXOvjxKKCyB5oZkTq0wKugkuiI1Xl6MMpTYmv7dO8ksizZMDWL6uwsSury4SpQx6e3KYbUCYkNChGNDkXDqKWEX9Q1hl7LLAV7NwmyNUMb1b529mbRoPrLlstwMigbRKEaOzurtEuWCoWG1AoL5KTEpuCGDeYfyF6+yfkcp1dOFeYPergzBlhz6OJh7dJNU3imRsI5G05Wokp4U0q8fzWK1wlSrtxmSPH1sQP6KT/NICTWE9VtsIkulc9dWcldcsufa6G6M0xBzSPFMj/6s1GTFukJrr81g1mL6kQ2MfsSuP2wxKkt49Khsis5x/LMaik5y0+ShuSEbB41UC2i1AgYzaho1kjm3QW9Oo3R+hO4lZJfD9Hjaay4zjw5JdGjeLDpKr6xSXBgx/cgGhYsula94OI0Rq3dkUKKE8qkhvTmN8thcoIaJsDcljdwVNw2y1ryYYd0mvxhQPNMj0ST2JbQVYl1ijvySTnFhlDKYQW5sCmhHcg7yCr15A82X3wVZRRi0vtSC+WU9ZQ0jSx6TXQlFw3hVtll7rEtm+XqVG8DabQa6JxEzhcshhYWE3rzo6gYz4xicqs7GATFSJJoyrgpT0D1h10CMByDMXGe7sF5Tp0L8sk52JcRuyiW5/MA0zrrUiwVZRcxQfWGFtzwxSs0JflGh9pTHoC41aLq7WSUo2svCpYCNA2K2UULRuJUuSDTLKK9gN2HtDnArEs5sdGS/BzM67T0S7l24FEgY9ZJCZKsM6jD9TMygJlEuIGzglTeWsboJ/TlhmBNNGDW/qFB/WN6PdE+Y0C1fUugcDGnv1ujsNMksC8uYXYkZFcCvgFsVrWV/Vt6vdDfG7ELpQpwyg7EG3e3y/lBckLq14mU5j62DMe3dKsNZqbkLssKAxrowoKt32LR3i2ZzE2ZX9KZqmOBsJDQPqBhjSbZfVNj62QSvorB6p8bWhzwGMzpBVqFyOsEvyvHMrsRonhhARJM4wq0arH969pv8NPi7iwcffJA777yTfD5PrVbje77ne9KJ2iY8z+Nd73oXU1NT5HI53vKWt7C6uvoNbX8wGPCOd7yDer3Offfdx3333Ue9Xued73wnw+Hwb2OXXvKYTAAnmGCCCSaY4KWE58EF/NBDD/Gud72LRx99lE996lMEQcBrX/vaZ0W0vPvd7+YTn/gEH/vYx3jooYdYWlr6hssk3vOe9/DQQw/xiU98gna7Tbvd5uMf/zgPPfTQpI72W4SSJC/Ahf4XCbrdLsVikdfs+2kMX2rYRmUT51KHcCqT6tqMnjgA9a6funv1jSHhVAa966eu3nBanLdu3ZaA3DCRWItZi9KJtoQeLzQZ3jSFc61Pf3eB/DMNOkdrZJY9iaAxtJTdixydIKeRO93C3VHE6AUSfWEo5I4vE9YrqEFE5OhEjo7mSh3ZZkSJW9UxezH5ZxokGYv2oVLqPJx+ZAN/roC1OiDKmKKL25pLK6NKxzboHqrgNEbovVEawtzbZlF5qg1AUJFoDgkhVsguung1i9ypdTbu3SIRDXmF6cfbRBmTjVuz5BcDIlsd66fEWRzktFTLlT3Xxt1RxGyNxq7kUbp/m7BWBwx25Olv1XHW4zRmZJNta+91MHsJmh9jr/m4dTsNO/YrJokmUSBfrUnTfIlJ2XSIho6KveYzmJd9VMMkZaaGNakOa+/Sya4lOI2A9h6T6WMDCecuOqzcnSV/bcwmZaByJmBQ1ymfGqJ1XNpHpzD6Ee1dOpENlTPi+KycHjGoGziNkMhW6c1p+BXY+pBH82ZxLJvdMD3moSOMWGQxDhSG4kWfzi6L8qkhyy/PUrgco/kSxFs5PWLlLlOcmFdc2nsdgqxC6XyQXletl80R2s/+W1FD0cOFjiraUEfBaYgeLnupR1iw8CsmZjfErZrkL/bp7cql48svir7TL8rxFuZMtGidnSZqmBBZEjK9GcMjtXIRkSXVeJqfMKwJGzeoG/TmhYFypxT8ClhNcDaEFTIGwlAmmujSMg1hKzevGXdaTdk+EDfr5s9mPyF/ccjGrVmyK1L/F+vXj0N/VsbQ3G9SOS0BzPlFOX5XX6NSOi1jLZ8a0tmboTd/3VFcuiB6xdU7FYrn5bgM6jqdPfKYwoJoBHNLSao/bO9W05+HVY3IkhgUfQBOUyJ/wnEEjO4ldHfK9bDrj7rEhsbK3dn0d/krcm14FXm90vmAxmEDuymMbfsmhdyiMG6ioxxrIfsStxJk5DVDW3SAIOHLZlfO6ebxKV2QAGuzKxpB3R9H0AwkPqc/r2A1obMvonxSo3UwQh2pFM8ptPfHOMsqVkdCqfvzCtNPRwyrGm5VtqFG18/1ZmTP5vGw2tFYCxmy+FqFm/7Zl771D4n/A2ES8Fk+TqfToVAo3LDtfjWy2Sw7X/vPcEpbntN2lp/+C151YIrf/u3f/pae32g0qNVqPPTQQ9x33310Oh2q1Sq/93u/x/d+7/cCcPr0aW6++WYeeeQR7r777r9xe9PT0/zhH/4h999//7Pu/8xnPsPb3va2v7KudoK/GRMGcIIJJphgggleQlC4AexfAlEU0e12n3Xz/W+sHqXTkXX4SqUCwBNPPEEQBDzwwAPpY/bv38+2bdt45JFHvu72hsMhW7Z87aS2VqtNloC/RUwmgDcAQS1LUswxKpvYay7K0EVz5dutGkrG2bBukxgaw63CDEZFJ33+aK6U5vRpHVcy4VrCUhhNqTVrHilhNj2CmQJGL2D5lWU0LyYuZsguSuiyP1dIXbGaG6YhtsnY6bo5pkRTGB6YoXUgk4Yza25I84BktcW6gu6KS9dqBfQPTDPYkRe9z7KwK0nGlAqxLdcDwYOcRqIpqdYrf7GPWzWJHD3V7BQuuiy/UkKj/bKBWzXRvBirLRo+sxsSFzNUvrxBdtGldNaVXMJ5cbn65es1YPaai7U6kPy1fiRl9UWHyFZZuzNLbKionSGJrqKP69TUIE7HrPkJmWWPwYyG0xgR5CUcuHjRF+Ytp7F2Z1b+nzdYufu6wxZIa72stujt1DDB6AUpO7hxa5ZRfrPMXsVqR/hFCQ32Sxqli6Ho6fabKBEM5h1W759m/WiW2lOeMJCtmC2PD/HLOmoIG7dmGdxUEudwII7F3FKS6iX9kuScDeo6vTmN2pcHbHlMGOipZ+RNUu+N0mNWPtkjyJBq0byKQmyIs3bj1ixTJwOstrCGzkaCWzWYfiYktJEAaE8cnI3D4nTv3TaLX1SILLmJ01Mhf8UX16gOgxmV3rxCd4c4Nq+8sUx7r0N7l45fNlDDhItvKRAbEgTcOii5he2bFHLXwrTWa1jVGNTldTedqLob45fEERraCv2tOqt3KpIhd9Yl0aC53yTISnAwyPiNDtS+PMBpSB5ea7/C+i06SiR1ZoMZjY0DujBl/YjQIq0qkzw/0b7ZLam26+zNkF8MJI9xrIfbZMU0D9p7TJyNJNW0Duo6w/+7zdbPinMV5Hro7lTQB6RZdoMZcSOXT8s1uOkWL59OyCwLy2p15HgMqxrt3XJdNG5PUsY8fzUiv5hQvBzRn1XS3MbIkudVTsVEdoJXc7j8xiy6LzVpflFh6WU2fkHBrQlbt3HwOvsHUDklrKPZBWMg7F9kMXZKJ6iRMHyJJu9D3e06Xk3OQ39WzneYheV71JQ1Bbk+N7MDm4fAq0ZMP+NRPKMRWpC7pJG9IvuSW5Ccxe7OcaB5B5ZeIUx6dlkCqWGzrk/Y1PziOIy7ptDaq6e5k2+++xh/l3Hy5EmKxeKzbg8++ODXfV4cx/zUT/0UL3vZyzh06BAAKysrmKZJqVR61mO3bNnCysrXd1vfc889/NzP/Rye56X3ua7L+9//fu65555vbscmACYu4AkmmGCCCSZ4aeEGuYAPHjzI5z//+WfdZ1nWX/Po63jXu97FiRMn+MIXvvDcBzHGL/3SL/H617+eubk5Dh8+DMDx48exbZtPfvKTN+x1/i5hwgDeIPhbsqL9y5v0D9eJHJ1R2cStGihhTP5iP22ySHSF2NYI8gbNIyWMpjCG5rkVBjeV8MsGybhqq79bdCL5K75o+vIGfsVk+hlp/Nh8HW04wlod0D9cp73XIcyLnqryVJvh1gyxIfVVmy0TzrU+pbNumoGnhAnlsx6FMx26O4SZM1sj+bcrbJPVDmWc42/7mWtDQkcltjX8LVmpTprRsJojKWs3NDQ/xq+YossypJKremxId19R9uvidcbSaofovRFezWFwU4nWgQzNAxk6ezNklj2cZS/VlsW6Qm9XDqXTJ9bF6RrrwpBZrWDMyKiM5kq4VZPWgUzKHrpVaVtw1iOuviaDMUjwywa6K47tzi6L8lkPqyWs12Y9mDGA4sK4TaQ4ZjSCJGXTrFbAxi2Zcc2VQeXUkOJFn9ZelcJFyUosXA7H2iwYzEh13/QzHvnFgFFeoXR+hBKBWzVTtnHl7gyjnGjhROumMqjrGD3J2NtkMqxOwmBGpXTWTSuvBvNOmuvXOJrBndbo7coJS2erhHkTNRLmZ+UeaZtYuctkWBVGU3ejlGULbcmEC3Iag/p1XVpkw9SpkNZeYR0LlwK6OxOc9ZhRXvRkg1mL3GIyZqgSpp8RNrpx2KBwRdip7FpCZ7vGYEYjsyyskbOR4CyrNPeN79uuEzrSKBFk5Dwkmrg4NyvV3GlV3LmtGCUCYyB6sWHdxmlIu4TVEX3pJssUZqF1ICPXRvF6ZuHGAR3NT8bHF4KiuNTVCDRPXLWho1A9PkINhU1q71ZxKwqDurhN/YqwoH5FjnMyfs3+rILVEfZMDSH+U4mz2DgkbOEorwg7OQR3SmFUuM7SRZbst1tRWL1DZ1hT6O0Qp6xbG+cGVsBuQvV4gOpLC0rprEtnu8awJk7oMCPNJ5UzAc5GkrKBmSWF9h4jzSBUQtEN2k0Zz+wXIvrzsh+JBo0j4kzubRUNpFsV1rC3Y1yPZzHW6UJQkJaOpVeoDOswdVzOc6JBfx4yy3wVwynnZvU7JCfRr4C1oTDzsGRExpqcE7uZkOjQ2SMMYqxD9Sk5134F7DW5Xw0hshSyKzFeRX6fW1RYe7U0o2y2hPhFyXv85P++41v7QHiecaOq4DRNo1AoPOv29SaA/+yf/TP+9E//lM985jPMzc2l98/MzDAajWi32896/OrqKjMzM193n2655RbOnTvHgw8+yJEjRzhy5Agf+MAHOHfuHAcPHvyWjtPfdUwYwAkmmGCCCSZ4KeF5yAFMkoR//s//Of/jf/wPPvvZz7Jz585n/f7222/HMAw+/elP85a3vAWAM2fOcOXKlW9oCfdzn/sc9957Lz/6oz/6rPvDMORzn/sc9913343bmb8jmDCANwChrUnf6pYcIK0UIExB6dgGSpgQGxrW6gB7zUXfGKL3JBcsf0WcwfrGkHC+itUcpf28Xk2+aalBxKgg7F/K+pQNzKZHd5eD0QuIMibKyjpqkFC86EvnbFP0c/aaj3W1O3anSo5Zd1+RRFfTporBvPT79ncXyF0LGcyodPZmUMMEtypO5iAnHa65K640mYxdxsBY9xZR+YpHoqskmvTSJppCZKljV6aB1QrxK6bo2xo+vV3ShBKMncfDrRnM1gg1EFdn5dQQsxcT5A28mkVkKWlPLsBoZ5XyyR6jio3uSmZZ82abqVMh7rSGsdrHaYyk37UVsnZEmiV0V5i0uU8P0zw6t2oS5A3UUBi49h5j3E0q281dE9bKagW4Nehs17DaIdlz7bRPtvIVj/zVKB2z6kVMnYro7nLwSxp+ScPoR2m/rNUKGMxa9OaFZWvuN9E9YfLMvrSQZNYSKqeGVJ/oYrdicdCuR6zekcErq+MO1YTCRZfsinQ1g7gcBzMq115hUj41xNkQpskrq+SuhdIT62j050V3pnmiaSyfjSkuyDHr7LLSzEOzJyyNOyVMjhqKK7R0IcYvibs01iR/r/oUxIYwVbUng3Sfm/skY667XcfqiH7R6EcpmxfZ4Bc3WTpo7hdHayKXWcrO9PZE6P7mz/I7oz/uvrVg5W59nKeYoA+EvfLKKr0d0k7iTil09ox1g74wW4O6gtMI8CvSg1tcSChdiCUTcCVmUBcmbP1WGczSKwyya3IM1g+adLcpmB2FMIM4uCsKbhWcNdHEKaGwtGFWGLDMWoJfFGY11mWfr74uof5IjDEQpk+NoL1/fH58Ybq6OxP682PXbATJmLUSDWCC0RH9XeVMRG+HdNoWFoTtvPzGDE4zweyCW4PsMvS36qzdZtA4Iixdd7uaHutNHR88u793/RadoBBhN4U1zCwplE4rDGcTutvEXV0+Lce+dTjE6EieoVuFoBISWTDziOgWN/uBp05FhFlh5oY1haBwfd9Kp1X6O2M0/3rHb2iJI7h0LqG1X8aq+YyvTWEN+/PCuAZjw22QVWh9x4ilVybYzTE7qUHxCZPWXpXhnLSEgPQpawe7z+mz4e8S3vWud/E7v/M7/N7v/R75fJ6VlRVWVlZwXVn9KBaLvPOd7+Q973kPn/nMZ3jiiSf44R/+Ye65556v6wAGeNWrXkWz2fya+zudDq961atu+P78XcBkAngDEBtKWvemhBKMq3d9lCgBQ8OrWSS6gr8ly3BrhiRjEhsSqGus9okNjWBLDjUQo8ZmCDPIG3KYN8mea6cTJb0XYDd8OvvyVJ5qEzk6Xs1iePuOdJlz8/XUQOrf/DkJiY4NBaslAcN6b0T+is+wbjPKSxyHV1Zp7ZW4j8JFF6Mf4TRkQhs6KsWLfjrB8CsmpRNtOrssCaRtbu6PTPiUUAKHs4uuVMg1xNwSWSqRpaK5EgMyqozr42wxT8jSuUwQ2nsdia4Y/27qGQmTBhjlFQazFp19eQnOLquYPakvUwNZuu3vF8PJxgGZrNae8saRLLJ01ziaIbLUcbSLBBJvBh87GzI5GeVViVWxNgOupZaqcibArRq0j04xyl1fNtw8b5Gt0tuVIdYVrLYI7vNXfGJdgoQ1PyY2pKrMbol4fvoZD6sdpfuQBmdXLQbbsqmZJNEU8tdiyqeGUjMXJnhVmcwHWVmm7c/KUtfWz4/o7cqQvzhkWFPSJfzNSVz9EVlGnv18QH/2erSOX9IonXVp36SkS6abFWt2UyaTftlIa8kUWXVm+hmRDEhIsNSOBTktnZSALIW7U/K8yFKJbBjU5QPZbkqwsdVJKJ2X/c8uy7JiZi1hVBDRf3dnkkaNeGW5ptQQsmsJ1WMx7Zsk1HlzEqOGCflLsHKXSel8QPVYjFtRxgaDhOxyIvVwmuzLoKbQ2qsS2rK0GRRlSTnWZPJlNWXydu2VNrovy+B2k3SJ124mZJflOA/roA9loqeEsn9uRUHzoLVfJjxuRSF3XpbRYx368zKByi/IfgxnEwZ1iX+xmrIc71Zle2ZXHu+VVYyhjL83p1E8L5NEJZJJcHF8PDef56zH9OfFHFE8L+c+tyTLrWImGo99IMfeq8h9ZheKZ2QpeViTibtXkcmu5smxM3sx+gC0rkZvv5jS3HpE5pJOkJWlepBJ3Op3JGwc0NjxpyGJBsO5CKspk1o1knHaa6qEQlsyNjWC/rzC6ht8wsrYdDeeACoh2Gsq7nww/r+cl9ACMzOieEaT5eGiTBD98XJ5+aQEkbs1MbR4Q/O5fTg8T7ghGYDfJIP44Q9/mE6nw/3330+9Xk9vf/AHf5A+5hd/8Rd505vexFve8hbuu+8+ZmZm+OM//uNvaPtJkqAoytfcv7GxQTab/SueMcHXw2QJeIIJJphgggleSkj4tnf5fiORwrZt89/+23/jv/23//YNb3czKFpRFH7oh37oWRrEKIp4+umnuffee7/5AU8wYQBvBLIXOjiXOunPhTMd3K2yHOxuzWF2QxJdxV4Q+lrtDAnzBsbCGv5cATWIMM+tEDk6saEJc1azpHh+zHZFRYfh1gwgy7V610d3YwY78hLHUpKl3VFOIXQ0hnVh1QbbsmhuiN4LcOs22Us93KqOXzEZVaSaLH+xT34xICxYlM66VI+P0FxZqrVWBwxmJaQ31mVptHjRH0c9hAQVJ63MCgsW/lwBv6RjtUN6uzKybDhesnWrBp29GTRfYl82DucZ5RTsU0tiwOhHwhJUdWGvlnwiS6G9X0wCQU7Dr5gY/Sg1OuiuCPEjR6f6RBerFVA8O0R3I7yyirPsSXVaRxig1l45LnZLKttmHu6SWfbQvFhCrxdliX3TcKIGMaEtzODmsnNn53VWYNNEkF8MyF1xGcxahI6K1Q7HsTMSeqwGCVueGKWvH+Q02rt0CTEOJGg5vxjQ22bhlzTsltwXOiqdnSbt3ZoYbnSpY5PlW4Uwb7B+0MQrS+TPpnFAd2Psprx2rCuUTrTp7cqghFLftYnevCy9l84H6G5E4fL1cGu3otDd5bDjf7QAMSZ0t6tovgjp1VBqw4x+hO7Kcmn+Wow7rbF+i8SVbMJpiMkgdFSsToI7rclSe0Uek1tKcBrC3uiejG+TwVs/KLEtTkMMEmZX2DVhrMYMWD+ht1WldH4khom8grMm2wuyjA0rCsWFEbNf9Fh6hUF3u5oaLIKsQnfHs9mFRBdmbFAXA4E+kHBhNWLMCEskiV+JaR2M6G7Xya5ExHrC6p3CjAXZccC2BoUrEktSXJDqNzUSA4MxkP1QIzE8WB0xqUydIDVhRBZkr4ihZpMJDTJQOZ2M2U0xTngVeezGXYEwjzXZp94OGBUT1u+KuPYKk+yymCaWXy41ap09Cs3bQryKQuctPQbb5HxqvgRkh1mRGZhdiGxhx4MseLWEzJpE1GienJfBNtn26p0aw1lZHp5+TGNQU8hc1bA6cr7l/UTGWDiv4jSEnY0smH5SrnVvHNC99MokDbAunUvo3D7CrwjjN/UZix1/BM3bA5EoVEnHoHV1rCb09kSpoYbT8t7cn1Xo3uWly/PdnYlU6GnyGpsh3xM8v9iMn0mShHw+/6xImpmZGX7sx36M3/md33m+h/mixIQBnGCCCSaYYIKXEDaDoJ/TNl4gHWG/+Zu/CcCOHTv4F//iX0yWe28gJl9xbgCigk1UdOhts6ROrdNPdVSbNXCqFxHMFMgdXyYxdEJHJdhZkyDjvEnjddvpb3PQFxvCCHbDNFpE9UQ3ZTVHEujsxyidPvknl/BLIkh3GiGJJsYCv6xTONOhs1PiPCJHJ7Y17IZPWLBwGmPGzlGxWiGxIVVqkaOT6KLN29Q0JoZG8UxPApp1sNohRlNEvaOCjt4b4VzqUDzTI8gbaZzL5raziy7Duk1+UY5D4aKLX5IAX2c9orgwIinkCB25L9GEjcktybvP1DNDZj8fk1n2JN4lpxHkNKnVG9d0WV1hlkYVm1FBp7M3w/LdNplGxMatWWEcOwmDGY3sikTZxLqwemt3FWgeyBDkNKxWQHeHgRIltA6Idk/vif5RDZO0Hm6zwkruF3ZrUNfp7nLQXQkpdqsSaOxOq1K1Zsufmu4l+GUd3Y2xuhK941ZN/JI2rrSD7HKA5km1XGuvPmYDxeSQXZbz4GwIg9KbNyhdDMdsqIzRaUqcirMeYbUj2nsM1u8oEdpSdTXKK2wc0CWepS7xIN0dBp1d1ph1lKq77FqCW1G4+toy7T2ipTMGsg/ZlYhRTmHqVJSGf4eOMNZeRaFyOnlWkG+sK6lOsjev4I21d/lrMYMZNRXjTz/jkV0O2DigkVsU80Nki2bQq0D9kfG5rEsMitmF6ZMjqeCLxETjbCQSUD02EGyyinZLQqI7uyyK58VU0ToYMypA9diQymmJDJl+JsatQvmsnGcxJwhLFFqih/PLOk4DZj8fk11UsRuiKQsdlS1flmgREOZs7a6YwqWEtTuEzWvul2MhsTBynW9WoSmhGBx0VyrJdE8YNruZYAwhKIoGc+3eiEQfa+26sq9BVphEq5Ow44+ELcstJXT2yftH8byC0dRRI9Ed5hYT6l+A9SOitXMWdUYF4MkC009JpI0SyTWeXU7GOkvQPCWNY8ovKGwcFnautyeSijjverxOVAvketwjOsHMWkJ7f0xrv0S/wJjNzIrWUImgcCmh9YCHMdYdRpZo8yqnEzFUbVPQTNEIap6YSK7+45DCCQOnAbVjEXpfSVlTp5mgDVX8WWEIa8ciNF+2Xf+fwjhKcLXC4nfqDLYlbByS6ytfcL+lz4TnHckNur2A8HM/93Nks1kajQZf+MIX+MIXvjCpf3uOmEwAJ5hgggkmmOAlBCV+7rcX2gRwOBzyjne8g3q9zn333cd9993H7Ows73znOydVcN8iJhPAG4AwbxDmDcxejDIckRRzuFVj/C3fTIOdjZUuSTEHhoYaJOgbQ3HqehHlU0OKZ3rEM5W0ni3IaYwKOr1dGfSLS0SOjrnQSKvchgdmcBohmWtDcc22pLbLaQQEFYfKqSGZRkTkyNdxJUxQxuG+ZlfYDWOsDYwNBc0NGRV0mgcyohMsacSGhldzCB1xhFpXu4QFcf3qbkxnXx5/rsBwq7BozQMZcSmv+cIG1SyMfpQyQJGjM/XwKtllYfM6O038ucLYJRzS2a6RXYnIX/HxyxJ6DaBvDBnMWuhuTPbKgPahEkqUkGlETD/eprtdJ9YVBjNSmTb7BQmpLi6MsJoj+rNSIxZZ6lhrKQyB1UmoPiFRD82b7bG7U1i/4sKI4VaJwimddQkdqRYDcX53dxgY/Qi/pNPdoaQ1eRLvEqJEEh2je8LIuVUDqx2JEzQSpmwwa4lWsRHiNEZkl0Oa+yV2x2qHaL44XDfrwTYOGmmkjbMeiRO3pNHfqtPdrtLbqlI+2QMYR+moVI8NMXuiP9u8lS4IkzT7RY/CpQDNF9Yse66d6saa+8VpXLoQUTkt+xKMQ3ZDRxXN4pw23jcd3U0YVjXMrjhUNzWCbhXaewypVKspZJeFHYx1WL9FFcbOjYVxrMr5zi8mab3blidGtHfpY92YsEtmV6r8+nNSf9c4qqds38YhYaiMQUJvp7BL7T3i8nanVYY1hfxiIDE0C/IWuPTyzJhhlUq3MAvtXTrNQ6Qh1LmroiHLrCU0jgir2N6toYRjVrAgVWardwq7WboQo4RQeVoTVvSEOIWdhrBd1WMhUydExzYqyPHYjDEZ5SUqJ7QV+vOKOIXrkFtQSXRQfGHjEl30fZtaRyWUOrjL3y3Hya0ozD4kdXJeBaZOSPSK1RSNYne7Su1xcRCbXWHHQJ6XWRLWF4QFNPsSYWN2ZV832UWjI8eweEYY7DAbExSgeizGvGLiFxWM7vUQZmdZpXg+YVBTCIoxG4cl5qdyKpbX6SVkHnNwq/J/JZSxS3i6JtFBZ+T3Xk0cvHGk0DsyIrJg7ajGlsflbyeyYe3VAZklBXWg0Z+X+KYgAxuHRTfav8cdXycJZkchNmOK5xNCC3rd65WdEzy/ePe7381DDz3EJz7xCdrtNu12m49//OM89NBD/PRP//TzPbwXJSYawAkmmGCCCSZ4KeEFuIT7XPFHf/RH/OEf/iH3339/et8b3/hGHMfhbW97Gx/+8Iefv8G9SDFhAG8ANDfEbEp4sz9XwN+SpXSizdTTg7QuLcgbeDsrAAx25NHdiKjooLkhWsdl/agIW2NDS8OV7YZP7tQ6oQ39uyVVPZ4ukr0yIPPEJTLnNlDDhMG2LNnlgFFB5vP22VUA9K64de2zqyhhjFu3SXQF1YtQwvhZblC/pKVMYXFhxNTDqxQuuozKJmqYkDm3gV8xCacyDOs2Rl+YgtKJNmogzs/8xT6JJgHWyy/Piq7QEn2ZGibjUGmD1l1bhK2rKWQa4vx1GiOMXkDpYshgRiM2VOyGT2TJJdq8cyod73BrhtwVl0RTGOVV2odKTD/jEdlSAaYGCSt3Z9KaNq9qictvu7iTB3VhZ7MrUv105fUFrHZI7pro64K8QemsS3O/SXe7MIpLL88Q2sJIZa4NUQNhsYKc1N2VzktdnDutMqxqNPcZmF2pR9usS3OnxMkc2grutEbpgjifI0syENdvsenuMMiuxDSOigZRwpuTcQizVI5du1+uj8GMPtb4KRiDhKmTAdMnR2wczpNdk2O1eqc4eZsHxH3rrAvLMphRKVwWFtitynm3WiHujmKqKSudlxxEryxZeH5JI8yOtWaB7G+sQeliiFvZ1P9JUHBkCYtUXEiY+/QAYyDHS/fl+ZUzEcWzQ7LLwhZ6ZZXGEdD8mMZhk968VJ8Nawp+ScanhqKD9Cqyv8Pa9Vq1ICtO1MgSFgzEoVt7XPLdCpeStDLPbgqDGdpSPzd1KqJ6PMCd1iT3riaMlTGUCjE1FDbR7Avj2JtXmDpBqossXo7EiRuJk7R4XjR8va1y7dotcWSbvYT1W2TAmWXJP9w4RKrxax6C0gUJxY71cfZiQdhFNVQwOvJapXMJpdMqytg1nFsUFhDk+K7eqWEvj49ZJGyiPoDBnkB0oztEO1e4kqR1fpqnjK8LYXATXfSiolMUnWBnp4wBJLsPYPv/iglz8jhnIyEeJ3QYXclOzF0d/zwQh3Csg1uPsVsxg20JU8cV7DVxAIeO6CJDRzIPC5cSll55Pey6vUfO39pdMe58QOm85DoWLiU4J22KT5i4Vfl59Y4xI+yBtmYwnE3QB6qMa79cIEqk0L/dI25Y6H0FqynHKXNV8g3z12KSjvE3vfW/cHGDquBeSBgOh2zZsuVr7q/VapMl4G8RkwngBBNMMMEEE7yUkCTP/fYCoxDvuecefu7nfg7P89L7XNfl/e9//zdUJTfB12IyAbwBiBydyNFRokRy8+oGYcFiVJasvdwVF3vNxV5o4m/JSttFQWdUNvGqFuFUhty1kOHWDPp6TzLkDJXuLofE0Jl+ZAM1SLAXmnT25Rlsy9K/eydJxkINRBNnNj3sNZ/2HpPRziqjgs7qy8oUH7vG6uvm0dwQu+FLTZuusHGLVK5tVrA5jZAgp5F74ipu1SCYKaTNEtbqgNVXz6AGCYkuWXNBTsO62sXfkhW2rhUTG+KEzFwbUj0uOjphx0ap6zfWRZumuxFbHh8K09EcMSrobNySwV7zya4IK9jd5WC15dt6djkQlrAvrtP+Nidt2DD6kWT9tYTFiQ2FwuWYUV6q6LyyitVNmHm4S3uPKW7ZfkL+Yh+3IlVlfkkn0WR7sSFaRc2H0vmAwkUXa1xlVbgUEOZN8hf7OBsJVkvc105DNHlKBNmVEN2H1l471UvZ4wYjox9h9sWRqnlSoaaGUi+nhpBdiVi9U5i/2FBo7lcY1JVxjZ/K8r0mpdNy/DcbPeqPXG/w6Ow0iXXJUPTLulST2QrTT0cEWYXmAZXsSkTl9IiVu3XRWRaVtI4rsuVcAnR3KrT3CAMi+YgJucWE/FU55v05YaSs5ginKfq/+qPX35w3WaLWgYy4cjVSLePyPWrKegs7LC7MRFNQxq7T2pMBhcsx7rScv+42cW6HWXEvly7EzH1mlOYNurWx43cIrQc8iueEZU40YQOdDdEENm+VzL5hXZo8hlUtbdCpHRM2r3ImItGEudp8vdCW3yuRsGZOQzSk7d2a5NV1Enb8b5/8YsBgRt5alUgqxbo7RG+XXZZMwNZB2a/qU5KvGGQV8gsKvTmNwbaEYV20fKULkTCbdoLuwyivMqgpdHcmaJ7oBwHylyDMSC1eZhmmn5bnlc4HDOrCrDmLBq39wm5m18SVrI7Z0uyyPHezhs+vyPlPa+VOyOOcZkJ/R0RkSdPHpbeMWdY1ydUzOsKsGQNhUDdzCTfZ3PzViMJ5aQoqnZY6vvyiaAB78zK21n45n0FWwehqDOvC8GaXobsnFk1jU/SRvR2y3UQTpnJUiUSfePV6+4e0kygEhYipUyHTj2mokbCezkkbe010lV4twa0l+BVxXi+/HLbtW31uHw4T3DB86EMf4otf/CJzc3O85jWv4TWveQ3z8/M8/PDDfOhDH3q+h/eixGQCeANgrQ3R3JDspR5RxiR/xZfKNjfCbHoM67ZErRQzqIF8uCaagl8W4wZA7tQ6mWtCY7tVA2t1QPHsUKrMDE1iZWYKlB9bRfNidDemfaiEebVNZ1+e2NBIdIXpYwNiQyXIaRQuBQTzU2RXQsK8ybBus3bEJtGlC1YNpKYrNq6P5epbd6C7MV7VInehK9VtGRHmBzkNJYzHE8YRzTunxiHUMvnt7M3gNEIG27IM6gZuVR8vqxnkr/i0d+mYPQmB9ssSGZNZ9uhvc9g4oKdVYnbDTydVZtMT08cecxySLB/Uo5z01m4aHQYzKrGhYvZkqW8TdsOXCVgrZrg1w/SxAU5jJCGzNQenKZPRwYxK7nRLJrmaTLDsVoxblXgXzR8HctvyOptGnchW0bw43VeznzCY0SlcClDDhOLCSJYI+wmFy2FqnvHKqsT1XHSZ+dQKftkgyMoS3OwXZKLsTmuUzifMftGjs12jcQQKC7IkHBtKOpmObJXShZjevIHdkliT1Tuk87h8VrqMN+vFnDXGIdIqW74cYTd8NF8+KP2yzrCqMZhR0d0EZ41xLZ4cS3daJcgqrN4pk2yrKdvq7nJQQ+jsSbjyWluWezWZNG5W8vnFcfBxVnqEM0sK2ZWI1sEIfTxnzCzLBHmzR7e9R5bUNV8miMmdXfpzMumZfiakvVulvcfE7MVMndg0EchkIvOYI8vR4/qv4jmFtTtA7yvUHhPjSW5R4mSA9JrZOCDn0CurOOtSKzgqJuNlcYX2bqm08wuyDN3dJmaI3JJUrK0dsdk4KHWCkS3L4fpgbCAZjKsds1A+qTKoi9EmsqC3c1z9qEl0SWZZJqa9OY3m7QHZK/J6QVahdDEkrvvovkxUY10MGlseD/FqEpsSOmray7xpLPErEicUFGQ5t3Ra+nSDrEJ7fywVfONOZKMLwT6X/k7pQA6yMlELbQlz9otw7X4FZ9FgUJOold5+eS+zNq4HVmseDGcllDn8qqXtpVfKeTK6MvEb1GWiCFJXFxUiOgdDlFAeE2WkI3jquEJ7t0phQf6mM8tSDZfoMNwRMvOwilcTw8xwVkwrvf0B1vgL2GBGZ1iTsPRYlz5gsyvX3u4ji2ieQv2RmPX7AqaOKxTN619oXkzYzAH8dlbB/W3j0KFDnDt3jgcffJAjR45w5MgRPvCBD3Du3DkOHjz4fA/vRYlv+wTwc5/7HG9+85uZnZ1FURT+5E/+JP1dEAS8973v5ZZbbiGbzTI7O8sP/uAPsrS09Kxt7NixA0VRnnX7wAc+8KzHPP3007ziFa/Atm3m5+f5T//pP33NWD72sY+xf/9+bNvmlltu4X//7//9t7LPE0wwwQQTTPBtw0ssA3ATmUyGH/3RH+WDH/wgH/zgB/mRH/kRHGfi1P5W8W13AQ8GAw4fPsw73vGOtONvE8PhkCeffJKf/dmf5fDhw7RaLX7yJ3+S7/qu7+Lxxx9/1mP//b//9/zoj/5o+nM+n0//3+12ee1rX8sDDzzAr/3ar/HMM8/wjne8g1KpxI/92I8B8PDDD/P93//9PPjgg7zpTW/i937v9/ie7/kennzySQ4dOvRN7VPkGERlG+tqF63jkugZItuQaBNkGTTRFbyyg9ELUAFnOSDMG6heRGxr9A9MA6BXbLJLPomh4dZtcqdbYGj05g2mjnsEM4VxCK1Ulg1vmiK75OPVJCLFrRrjJVZhGpUwwS9pWM2RLMdejFG9CBPYOJynfNbDWO0TTmUI8gYzjw5w6zZeWcUuWBLifLGfhh9vBkTrvRFOQ8wIQU7Basty5mYcSP6Kj1s1iQ1hVlQvYvrkCLPpMdiWJXe6RTiVYeXuDFMnA7ZeHNE6kKGzNyO1X+sxGtDblcPoR/KzH6fGj+LCCLdqYPSCcTi2jls1yC75WG1hJZ31iPZeB90XNgVIQ4sBzNYIszWityuD5sPaK6bJNKKUlfXLBmZP2CAJ2VWx2hL7kln2xvuusX6XQWEhoXjRZzBrMSqIsSLIioGhu12lfDZMI2ScRkRvTiO7HNA4msHqOOSv+NhNNa3V090kXfYFEffPfiFK9z+0GVeXicnEbsUUL8qye3e7TflsSGSrqGGC7snycJAV9qU/q1xnTXUbNRQmREKsVfKLIX5ZGFndS8ZLwSaV0/KpEGTFxJK/FlM402H1ZWW6O6XyS43Aake4FdnX9m6pT9s0bRQuhwxm5HfDqkb5JAxqEuehDyDRTJz1mDALWx738MsGXlmiW8zPFiidD3Crsly/uawOsiSrhgl+QSW/KCHK7rSwuGtHNbKnE0qnlfHS/5CNW2X52ezK8uQmwwdSQeeswfLhBKupoIzrGDt7xODhVoWtcprjYGw3ZjCjsXyvibMGRlOOtTGQbRcux/S2qkQ2+JWEwoKMQx8qxJawbt5+n05GZ+ZzGt1tchyHdalps5YM+jtjgqI6jkTRyR6X2rnOdo2gCJ19MZWndTJLciwaRyDRE2IzBj1B62o4y+NraSBGmf6cHDu1ITV3Zl9MP8O6nJ9kxcbeUBgVEzRfoTevjA0pY/PJgkpQlP+vbwNryUD35VpVIln+Hc6Ox4Asu7b2qpTPxtgNWTYHUiPPqBJRuKQyqCvUHpa/1968mEYyVzXceoxbT8hdEgZSiSQKR/XHj7mkM6hdr66LChFqqKM3dbwaaEOVqe+7wtVPb6O/U94fezsU9H1deLLAxcfnKV6VZXbziqy6LPULVFj5Op8AE3y7cO7cOT7zmc+wtrZGHMfP+t373ve+52lUL1582yeAb3jDG3jDG97wV/6uWCzyqU996ln3/cqv/Ap33XUXV65cYdu2ben9+XyemZmZv3I7v/u7v8toNOKjH/0opmly8OBBnnrqKf7Lf/kv6QTwQx/6EK9//ev5l//yXwLwH/7Df+BTn/oUv/Irv8Kv/dqv3YhdnWCCCSaYYIJvO26Ei/eF5gL+9V//dX78x3+c6elpZmZmUJTrUh9FUSYTwG8BL3gNYKfTQVEUSqXSs+7/wAc+wNTUFEePHuUXfuEXCMPr9VyPPPII9913H6Zppve97nWv48yZM7RarfQxDzzwwLO2+brXvY5HHnnkrx2L7/t0u91n3QBGJRHeJxkTd0dRzAwNH6sdooRxGrqsu1FqrBiVzTQ6pnmzjb3mp7VuftkQluxCl6joEGVMyqeGjCo2kaNjta5r49QwESF/6Xp8jBpe16u5dZvscoBfMUWj1hXtn+qJ9mpU0HF3FOnuEhrdrdsSoHzRp73XIbPs4dWcMWOko7sRmUZE62CeICffkjcZtfwVn8JFqU5SPakGcxoBRj9iMO8Q5LRUO+fuKBI5+phBiSSMehzy66zHonEzlHRf3Onrl6rZi1GDWEKYK5vaQA2rJce5s1OYx0RTKF70cdZjMo1oHNAcMKgbFC66uHVbjs+SL7Vi/YRRXqW1V3quvLLo++yWaME2jTKFM5KFERsKo7zC3KeHuBWF2FBRA4nWaO8Rtk3YtSQ9N5WveLhVY3yedUrnA/yiInVwBSXd71FeDAGhDWtHbJQIFt8oLJUagu6JIaE/q+Csi2mmt82iebONEorWKdYVNg5odHaKYL8/D91tClMnA0Z50eKFjmip7FZMb04jskR7190mTBdA/eFRGg0zyon5wm6JjrF5pITmJ1SfIo2e6W6X75VXvtNIa776s2JEcKc1RgXRx40K0J+X/+cvSfhzdiXCnVbRBxA6Guu3Sk1hbikhuxKzfI9BaEtVXWdPgu4lNPeJkaY3p5Ho8lrX7pfw5d6cxJoMagq6lzDKKbh1W851ASqnR2TW5DwN6+NKtqbsq9VUUX3ILSoEWalSCzJQezIgd1XMCtkVYf8GY6OCEl2PKzEGCdnlJDWEDPePUu2jO6WgefJafhGqn7JwFnX8ojCbm7q93rywgYXzaqpF3Kwu680Jq1Y6l2A3JHDbrSUEBSifFi3h9JPy2uXTYHWEgZT3BjGxZJdFq5e/BCv3xjSO6ugDcOsR1oaEdif6WM/nQ1CEwXyMV5W/eaMjhp1Nk0iQFTPOqCDM5tQJMLoSlh1ZchvMqFhjvaHmg1cPGVUiZh5Wx6w24/cx8KcSrKZKokHptErmqmgwvVqSBlOrIRgdeYwxhP4e0Q5WntRRQqg+JYxnlIk5e7GOt9/D6KhU9giFnDxdSM0iXkXiiwqXhLFud16kvbM3xAX8wsJ//I//kZ//+Z9nZWWFp556imPHjqW3J5988vke3osSL+ggaM/zeO9738v3f//3UygU0vt/4id+gttuu41KpcLDDz/Mz/zMz7C8vMx/+S//BYCVlRV27tz5rG1t5getrKxQLpdZWVn5mkyhLVu2sLLy19P9Dz74IO9///tv1O5NMMEEE0wwwQ3HDcnxe4HNAVutFm9961uf72G8pPCCZQCDIOBtb3sbSZJ8TcL3e97zHu6//35uvfVW/sk/+Sd88IMf5Jd/+Zfxff9vdUw/8zM/Q6fTSW+Li4sA6F6EveYSG6KBMwZS86YGMXpXxmSsdOnsEvYvNlRiQ8GrOaiB6NsSXeI61EDiQUJH2JVEVxjMO8S2MEh6L2BQl9iZUcXGWh1gN752vwczWvp/t2qQaAqxruCXDYZbMxIIPdYKquG4mstQGFY1nEaAebWN3ZLxx4bUaBUXRoSORu5Cl/wVn9AZx8E0R/glnVFBJ9FV3GmV1oEMVjti5S6TIKelgcJGL2Awo4luzw3R/ITBrEWsK5TPejjrEU5jNI7vULAbPu09Bs66aAxzp1vCgnkS5bEZyOw0wtShq3sJTiOgvVujt80aM2oqmh8TOhq6m+BXTHKnW2hezPotNrlrIaOcQv6KT3ZFYniKF33cqjicI0tBdyNCG8KCRXuvQ3+rjtlLWL0jg+6PnZeGuDUrpxPM3tjtO45YqXzFo7XXxi9KHEZoS7Wb1ZFIGhBNodkNyS6HFC6H6J7EqahhwvRjGr2tqkTD1BQ626/rGkNb2KLQkmiWZHz684viCo11hfwlCR1u7xFtoxqKHnAwo6XMYpCF/FVhd5yGbBMgt3S9mq23VaW9W+q0evMSIbNxSKFxBLIrwpZGtrA/xYs+bk0iS/SBOExzSwmVMxJTUj49/p0nDtNhVSMcM1yDuk6siZM2tIWt3NSLuVMKzppCd6fo6Db1jcZAHj/ziLBD2ZWYzJqMpz8rDN76rRrN/QqlCxGXfjAmyMrfhuqLCxck0sWrh1gdOSa5JQmBViO48nZhwWM9obVX3LxWE7Z8OSHISAzNcBwovekWBsicNolsicBRQ9HKuVXZdm9ewWkIe9bbITExEhYt2+7tlJqyyJLzkmlIDWB3T0xnp0KQHbuVO3KO+vOKaAP3iJ5u/a6I0BJdnBKKI7d9eOzabcLGXQHOsobqC1uWu6QRWaIzNTsKZld0dVYT7DUVchHDWTmuw5rCxuFEIlgqcp5B9kPY0ASnIa7bwiVxiFePDenclFA+G6IVAtSRijulUDov1+v6EdExFs8peDtHUs13r0dQEFe32VHo7JTIoE0N5ybDqGRD3HpE62CMGonTGmD+zxOcRQOlYRHtcfE/M01uEZRbu3j1ML3O/Ypcp5m1hKj7Ig2CfgnirW99K3/+53/+fA/jJYUXJAO4Ofm7fPkyf/mXf/ks9u+vwnd8x3cQhiGXLl1i3759zMzMsLr67PymzZ83dYN/3WP+Ol0hgGVZWJb1rezSBBNMMMEEE3x78AJ28n4z+K//9b+m/9+zZw8/+7M/y6OPPsott9yCYTx7cv4TP/ET3+7hvejxgpsAbk7+Nt0+U1NTX/c5Tz31FKqqUqvVAEkM/zf/5t8QBEF6kXzqU59i3759lMvl9DGf/vSn+amf+ql0O5/61Ke+pURx1YuIHBMllCwwzd2s2LquQQxmCkwd7zGq2JhND39XDmvsiHUaI7yqxZZPLjLaWU1DaTe1eyB6KL+skw1iSmddEl1Fc0OaR0oULrqUT/YI8yahI2xa7lpI7tEFWg/swehHZK4NcZY1RmVh5DQ3lPy/ks4or5BfDCTEeqaMGsSM5kpSAXaoRP7ikP7WLH5JxyurRHZBGKUrPqOCTn+bQ2iD48uxsDqbuj2NbX/Wpbcrh9kSh2qiq5TOjyQH0Itx1iVIeOXuDGCmx3Pm0QHrR7MMZjJpDl3+is+lt02TWQY1tJl+xkP1IvyyaBMjW02z8NyqQfmsOE43HbChrZNfDMaMoIbZzdE4LNdHkBNXYmeXhdlL2Digj8OJQQ0lU7G115bA2bxB+dQw1Uuq42Dg/lY9ZYs2a+iCrIIxFO2dGsj+bZbag1R/Wa2AUcVG9yXXr7PLYlgTRq58NqQ/q2M3E7yK6Pa2PB5K4LUu2xYGTzRVbg2UKBlfMxJsnbuqEzqi6XMakoHW3a6i+ZvZdJK/1zygUj0W0t6lk1sSDdT0MyG9eQO/IFlvm/uje8IeamPyObcoLGNvq2j9asci1o5q9GdtCguQuybb1X1Y/Y6E7KLowlLn7R7JFbS6CWFW2CtnTfRvoQWhJfeVTye4FRlLe49Cdll0hJsZkpvHoXlA9Hub+js1lGPtlVX8ijJmpCQIeFCHMJdgbci+jQrCFtYe1hjWhC3z3tIm97/K6F5C9rhNezdUn0po3zSumStIyLY7H1A4Ychx1YQx9AtSOde+SfYxzCi4NdFW+kVz7MoW7a4SKuTWwC+qjAqSH9jZIw7mICMavNFdfYbncyih5Al6FamKU0LR7uXPa+nraT40vtNHbVhk1xK8mk6iy7jUgUZ/HoJCROm41Pr19ofkzuv0D43IP2VKlt5YHwdybLLLCUpkkllLaB5KyC8oqIuqrCKMWTivFpNdVMd6RYXOHnE/N+4NUAcaup/B2oBr92uUPqvT2yFMa2+nBDizf4CmxTQrDmpHdInWcZvIkmMQ7HNJVmys7T2CocmoY6COVIKhgrpskeiS+agPJAewcMJg8R/ETH8uoY9CPCcMprWhkDxdIN8BvwL++G8sOtynUcswNd/+pj8PXgjYzAF8Ttt4AUwgf/EXf/FZP+dyOR566CEeeuihZ92vKMpkAvgt4Ns+Aez3+5w/fz79eWFhgaeeeopKpUK9Xud7v/d7efLJJ/nTP/1ToihKNXmVSgXTNHnkkUf40pe+xKte9Sry+TyPPPII7373u/lH/+gfpZO7t7/97bz//e/nne98J+9973s5ceIEH/rQh551Mf3kT/4kr3zlK/ngBz/I3/t7f4/f//3f5/HHH+cjH/nIt/eATDDBBBNMMMGNRJzI7bngBWAEWVhYeL6H8JLGt10D+Pjjj3P06FGOHj0KiJ7v6NGjvO997+PatWv8z//5P7l69SpHjhyhXq+nt4cffhiQZdjf//3f55WvfCUHDx7k53/+53n3u9/9rIlbsVjkz//8z1lYWOD222/np3/6p3nf+96XRsAA3Hvvvfze7/0eH/nIRzh8+DB/+Id/yJ/8yZ980xmAIC5gvesT28LKbLJ2m1lxShiT6HKo/dJYv5dX0Ls+mhdjNF3shs/wwAytvTZGP0INEgZ1g9zxZTLLHrEhjloAr2oxmLXwKyZWO0IJE9T1DqGjkV3ycRojzG7I8PYdUglW0tg4nMerWem4ertyqEGC0xiRXQ5Rg5iwYFFcGBEbKoNZS9yv/YjWgUy6r3YrRonkeX5Zsg4lgw1ae/U0jzDWofrpqwy2ZYVxK5tjLaFGe49J6KhEtkqiKazcnSG3lKD50hKyfjQrrSLrUiM39cwQzY+JDZUd/6NF5ZQ0prT22qwflWq9zi6LUV7Fq4iea5RT6G7X08dGFtQebdGbN8a6QdFDli7ETJ0KcRpSxeZVxHlcuhCTW0oILWHl1FC0SYVLAe09Bmt3ZrFamxVsouUS9knq4TQ/pn2T5N55FWEFN53MwrwlGAPJGNzUhhoDcYzarVhcyV1o79IpXJb2lciSaq/udv16O0dVnrep33PWrjOR7T2S95doMrbIkmy2/LWYwuVYmkmyoi9r7jOwmqIn1Mcu5tyStKxElty3el9I7lpIkBGGzOqMK75mE5x1aSAB0Yk192nkrsp+K5GwwU4zkcaJNRX3NpfsWkJ3T4xXE6YrEvM1+cWEIJvg1sQhuqk1MzpSTwaiO3MaSCWaD+39kleo+dfZQM0TLR7IsRxWNVr7Sc9Bb6vKYE9AsNPD7Mhz1VCqzoICbBwSV60+BPPjJbyK5DOKw1UY0FExobtDSce35XOSOdfdE2MM5dwERTlesSZ/J9rBrjBT8wZBQdy5VlN0jPpAWNzhjhCzK0yrGkrV2WCb6Oeyn8kRZkXfBvL8MBsT7PQgF4n2b6eCV40YFRNyT9hkryi4U9Lw0d8TEmQTnGWVVz5wnNpjKm5VmDWjKRWIxpKJV4PYSlBmPFoHo1QfuX5fgOaJvjCzpNDZJ9o/96CHV41SVtgYQHFBXPBTJ6B1MKJwwpD8wCx4+z3yC4pU8s17DPYEJJq4mDmdY3QhT2bBIM5GhFnRILo3jfCqEZnHHBItQXukwNRnLGZ2b2BtiIM6t6hQOSHXlFtL0DNyDWlrBv15+Tuq5IdoniLbq4ckulwTVnPsGA410TpO8IJEkiQkL4AJ6osd33YG8P777/8bT9zXO6m33XYbjz766Nd9nVtvvZXPf/7zf+Nj3vrWt05cRRNMMMEEE7y08BLRAP6f+I3f+A1+8Rd/kXPnzgFw00038VM/9VP8yI/8yPM8shcnJl9xbgDsdZeg4mA0Xcl9W/PB0Ni4JYNbNQnyBn7ZkNy7fsSwbpNdDrn2almyVobSHWyvuWQakWT1hdKfGtYlLt9e8wlyGp1dlriEbTC7oqty6zZhvUJsSJ5cZ5eF6kXjzliVypc3xq0UGvaaS+SILk4NRW/ml687eDe7fZ1GgObFqEEi+sCOMHSaH2M1R7hVE82LJV/NFW1YZi3B7IYokfy88co5enNCG7hVHas5IrLHmqwgYVjVJGevmaSdvqEjHawgurSVu0y6u8Rx65d0+rsLqYPXq0hmYO5Cl+xySHY5IDPOojP7khunuSF2S9g8d2tuzKgklM96ae/txgGdzk6TLY97FC7HDKsa3e0qRj9CH7MZ6wdNcXe7Ec6G9Pq29toYA3GMTj8TE2vCkBo9YemMjjBfU6dC+rOiy8wvBmNXqzLWfgmD2JuX/tj81YilV6g4DWHrrK7sT3+OtPkisiSDzm7FlM/GhLY4YMtnx2zjtZDckjgue1vFpeqNs83qj3oMZlR6W1XJTuwI4+GJfJbOdnHhTj8j5761V7p4Y13YrfYunfy1mNKFmNbBiNqxCGOg4JVVigsJ2TXJjVNDceOuH5H9sFvCChsdlTArXb1OIyC3IO0WbhVKF2IiS8GtCMunDyBzSSe3qDDccT3nc1OnF2vSjqKE4grd8liA7glTq4Ry7Irnx3rUKilD6TTk/4kOlScMqp+y5FzVpBtYiRSCQkT9kRhnTfL6gqz829qrEhRj+vPS6WuvKehCMjPYJm7eWE/QByr9OXHRelVhsJwGdF/hEp4p0Nsf0N2hEGYln9GrkfYHgzhZIws2Xu9JL29W9mlUiXBr4CyLxrF574juzgTVFz1j5rRJkE3wp6QxQ/MUGdeREZ2Dcgyd6pDYki7eTz9yC6v3CdvorJG6avWBaAMTKyZuWJhNDachuXzmFRO/AvEOV85pV8PoQjLQqTytpS0hg7p0LA9rCo3bZTxhFnp7ItzbXJyTNsO66Pmyx20qTxiovrjVza7oGkfFhNJxQ8ZVTHDOmWiVUXqcRgXYeJVP5ws1qvcvyfaysHE4obMnQfMU1EsOYSXEGMj24h0ua1+pYnQBT0XxVdRXtNB86O+IUCIwTzqYXag4w+f24fA84bn2AL8Qu4Df97738ZM/+ZO8+c1v5mMf+xgf+9jHePOb38y73/3uSQj0t4jJBPAGQAliRgWdKCNLv1rHJSxYTD0zJLsowcijvJg21DAZL2dKqTtA/8A0QU4jzJs4y1J/pUQJzkaCV7PEZJE3iHWF7LKYN+yWTJJqT3kSbjzvoAYSypxflJq5QV2WaLuHKhQuhxQuugy2ZSU0eE7DagVpRIk7reGXZXnUK8sk0K3qUrXWCxjlFPpbdSJLHS89h+huRHYcousXxaQxmLUYzOg463E6oZG4GYleGeVVrI4sdUqItU52OcSdlpgWd1ojNhSyywFmayTRGBWFyldk4tLZrpFoCs07p7A64yW73QWMXoDZ9HArUvVm9CM0P8arOWJmsUnNNYMZjcGsLLtanURMG+PJcOiIISazJuHNQRbWb1EpXQwxewnrt9jorkyCdU+WPhtHJKjaGMoEaunlGVmWjUgn3pvHWHejtCbLbsqHVazLkmZow9pRjemnSJeBI0th5R6FymkJVB6Ma7pyi1Kz1jwgFV5WK6S1V+rbNuNxzF6C1U0onU/SZeTmzTb+eGLZn5VQ4uJ5heJ5WUZWI5nIqGHC0iuTtCLNHwfkRrYYLK69LsLoarR3a+QWE7yKhER3dkoFmtkV80aiyfFt7pNaNqspkwvdl8Du/s4YfSiTMXdaTWvgcosSPZJoIs5P9IT+7SKp0D35ZBrOJly7XyO/mJBZhuY+gyCrpCHEXkUm2JuTveGsTAj697j0D43SCWFvXuJlMksK3NbFXlNxlkXOMSpA6WKIXyFdhrTn+ugDmYhn1hIGewL8ioQhe7WY4nmFcN4jzMkk3FnWyCxL7IpxxiG3KJNpqwlTxxWqx+JxxZuYKEZFMZp098QUP+sQFOS1jIFC7pKG0YGgIBNGe8EkthKiWsBwLqJwRerrzI4yDtxOsNcU8k+Z5E/rDPePcBsZ7Ln++PgmaF2dMAuDV/XJXZWl2UQHpRjI5MiX94jOvgjNG5tBto3IZj3C6+oQlGxIooFXlS9OwU4Pd2yiSYqhTPzvXafytEbUNPErCUEhwnraYVSQJeLsvjbN2zaPd4zVVNKYl0STysI4ki8EzrKKPgTnpI1bj1j7wqwEiN/Wx2qqcryuyjGob21JHFImIlmxmbtlGbceQygT/sHAZjibUDyjpWHZowJcbZVu2OfEBM8NH/7wh/n1X/91HnzwQb7ru76L7/qu7+LBBx/kIx/5CL/6q7/6fA/vRYkXnAt4ggkmmGCCCSZ4DniBtnk8FwRBwB133PE1999+++3PagKb4BvHhAG8AYgcg8y1IV7Nwi/prL1iGq9qobkhg3mHQV2XyJSKTXO/idUcibEgSIhsFd0Vs0VsSICy0Y/SxySaklacab4sL0aWysYBDdWL6G2z6G2z6G5XMVujsSlDSSNInGVPljLdGK9qYbUCNg7oFC6HjAo6aiRLrbon1WdBTsPsy9JhkFWIHJ0wb6B7CVu+2ELzY7rbpWbMrZoUz/QY5VWMAeNAZwmpDR2pwxpWNdxpCUhWA3lDUoOE/NUIZ11U7LEhDN8mU9bdptDeYzIqm2LIuBiy9DIbqyNLjKEjkRNWJ2HtqEZzn8bK3RmGWzNjA4PB0vcFtPbqLN8jS6tqKMtRsS5Mi19UaN4my8b5K77UsOmy/OhW9bT2LLeUUDsWsX6LntbSxbpCf5sjQbzbVQoLEsCcXRExuTEYhwBXhLkJsrIdr6KweofNaMzeBBlZdmweEsaqtV+WXoc1iRQBMZ1MP0UaFdI8JGJ1vyjMSmFBmLzVuwxGBbnPGCRce11Eb6tKf16hNy9LtG5VnqcPILsc4tUSNC+W41FQaO+PCbISnwFQPKMRa2KkyC2KmSHMQGYZqg/r5C8JI+asS/C0W73OasKYLVtTcSsiD1g7qtE9FKBEMo7+nCwVap6wXkFWWE2vJoYStyrHz50PsJYkwNdqyvK5NRb7J1rC2qsDRgVhCkFMJH5xvFR+OETzpS4trgRYTVAvOdDXxKxye4Bbl1Dq/s4Y55MFsssSqrz8XSNG20a0d+l49XBcSwbhmYKwjGtyPvKnDZw1hf68gtFRGdYhiVT0vjCQYVbYpMyCgTsf0J+HtXsjrK4YSJZfLuHR/lSCV42ICiGDwx6qL+yX0QV3XpaE/QqEr+gSZmN6+wNGxSQ1K2hDCQn3aglKCEFRVgmG+0f0d8ZENmhrBmbFw7+cp7czIbFjzI4czyjUaN4WQlNWMpyTNvpAFROKDupIxTnYItrjoq0ZDAY2kZ0Q68KCqssW/XlAFwOP0rBw5wMqd66Bp2Le1iL8s2ncKtx04JowlU0Ntx4z2jaSpffHyuTO66mRRL+nxagoxpukGNK5SUwt9rIuodAZ8G915bn7XAaHPfyuRZBNCPa5tA7GWBsK7UdrhPMeM9ubKDMe1xplVF9BKQbocwPMkw5ztyzTn0/YenSZ6HAfpwFR9OL9iLwhy8AvIPzjf/yPv6YUAuAjH/kIP/ADP/A8jOjFjwkDOMEEE0wwwQQvJbwANXw3Ar/xG7/Bn//5n3P33XcD8KUvfYkrV67wgz/4g7znPe9JH7dZCzvB34wX79ebFxjUjoiFNT/Gbo3r3PJmqskbFXQGdYPsSsywbks4cU5DiZK0Qmz9oEl2OcQv6QzrtpguPPkW3zhsorvyXKcxYtufdQEwezG6m5BZS0h0hfzFPr15g/5W0YMluoJf1tHccMwCmRgDUratdD5gMCPfA8qnJG7FL4o+UerXxuNeDli7u0yiKTgbMvbQUVi7q4DZi9Nqs9BWaO6TbftF0SqGllR3uVUd3RUNpBIJuznKKTSO6hLoXFQY1BWqx4Ulau8R3eP6LTrls8KMWa1wzOIJm2c1JQbD7Er8CYCzkVD8rEPhckzuqoxD6r6EeVMiuZWP6zQOm/S2WajRddNFb15JTQwr98as3qmRXRbTijFIWH45DGoK7ZsU7GaC5ktMSeOoTvlsSOX0CDUUUX1kyWutH5HoELMrWrTNaq3cokL2igTlWk2F9VskPNdZk1Bft6rT3iOvU1yQQOCUHRlKLMpgXmrDgp0eQTHGLyjoTWF3R8XrJoipU1If1j80YuOggdlRaO4ThrR4OaJ0WsWrJeSuSl2aW5XX8apRWpU2qoX054T5Mvuy7fVb9JSh2qxjAzkn/lRCb0/E+l0RldMSdDx1Kkx/H2ahd2SE3pfzs/lczRemMrITMgsGmi9Gj2EdmrdGdA8FDOvjWjJP3saCojCY3Z0KpQsx7nyIvawTWbB2B+ROSBWb1QSz5jLYJuMBOfdURvR2iIEAwFiwsRfE8DD9mIbeV+jvCUVnlkvwamBt7zE47GE3JbrGaYBfibEXTLSDXdza9feIMEtqaNi2e43WftFcxtmIeFxvBqB4KtnjNlZTfvYrCYovwd2aDyNfolTUgUZmSSE63MdYEtZuVInYfWRRNHOWvHcoWkxixYQZMVaEV7NkryhEBTkPo2JCZ1/Ega3LGE2dxIrxdo5wXrFOUAkZ1QLiHS56X7RyxhkHq6lQ+LyDGkoln7lmEFZCMeSs6ag+aJ5CZsEg/FiVT77hF+lfKNGfT3BvGrHayzOciwiKMfkdHXInTIyOSvHla/R3RPiVGKOj4h0vc+8rTsH+PuYVkzgT4d/bT80c219+hagrUTEAsa+hN3WY8wiHOs6yKszqzhH5gsvKaonC5x3ihpUe32A5w+igy+XLVbYeXebKmS2M1hxaB2P+3ZE//SY+BSb428SJEye47bbbqFarXLhwgQsXLjA9Pc1tt93GiRMnOHbsGMeOHeOpp556vof6osGEAZxgggkmmGCClxCUJEF5rhrAF5iG8DOf+czzPYSXHCYM4A1AmDeIixnUIKG/Vae7fax58+RbqRqIa7R8sjeOZpEKtk1dXKKJwzZ/LSayVay2OH2DnDBpzX0GpQsRQU7D6EeEjoZXcxiVTQYzWlr51d7r0NmXJ8gIE2P2E1bvyOA0pFLOWY/G8S+kQcJ+WcduCevn1cTBm12JiCw1HavTkAdHFuN6LwWrHWK1IzRfGEyQAvXKqaE49XTRooWOQvFyJOxQO6K7XWJXIkvFWfbINCLRZQUSr1I+G9ObNzD7EvA7KkDtyYD2btEZdncYWO0IsytBvUHx+nkoXBLmUA0SdE90jGqYpCHNm0G8INu1OsKchrYyDmYWZjHRhUXyKpA/r6UhvUFG9H+l0ypWV5yncF3jl1sUV+qlN+kSq7EeY3alyqtyQh43rMPMIzImfTAOn+0mGANhrbY8LlqzICvxGq39UofW2i/7kGjXHcSFK6IZU30Jry5/3kaJFKxugrMm1W+VE/IaZhfauyW027xi0t8R4ayB00xwawlXXyOhyqXTCs1bJVold3UcTzISXZtfSZj7pEJkS4WWO6WQW0wIs6B1JSaku03BL5LqHK0NBaOrYS/rdMYhzouvFaewMRCWU20aEsTryTmx19Q0YNpqKqIZnLr+YVQ8ozFd76Dv6zLcGYAdE1kIk7SUoPqw/HJQh1rKgOZ2t2WbO4RBTs7nMDsKii+O30QX3R6I1iy7r40ayTka1ULW7wuEjbykEx3uSzVbJSR5uoCxYLN+W4zmKaKrK4aMignucpbay5dSvZ6ypy/az50ea1+YBUTfWKz28eohqg+ZqxqF+a5oIOvyvNhKiLMRhYUxy/ikw2BbQqIl9PeEJOdzWE1h3BI75uzFOvrr1+VvthCSRCrqUCPMyd9UbMaEr+hSOGFgFXzMjjzv9Bd3MaoFosFbMzDUGK2roXgq4VBntG2E86RDfLCP1YHOTQnKnj7DWfm3WO2z/eVXGNVCYS339HHnQ5qH4M1f+nGyVxQOfMcCmhnRaeQwuhqVPU36F0q4NRlbZ2hTmO+S39FBGzuJP3dyL8FyBqspETbWwzn0uQHqJYezF+ts272GOtAoftZBHWgw5xFHClpXTx29Skenf6GEmRkRvb4FlRGDPQHGgg0Vqak01wxWWgW0oYo6UsktqPzm1XtvzIfEtxvxDbi9sOZ/E/wtYMIATjDBBBNMMMFLCDeCAXyhmUAmuPGYMIA3ALGmsHE4T5DT0hBev6QR5g2a+wxae22CvMFwqzh8RzklrXbrzUvm3/SxAU5jhNUKUIOY0ok2sS4MnTFk7HxVxoHB47Dj1ojCpYDG7QntXTq6B7ob4zQTsks+sQ4zjw7wSzq6K3qgwYwE/epuTKIp+EVhj5RIgqWDDKJfdIStCW24+iqd9YMm1WNDclehcDlOWb/+vOj4NC9m6lRE80BGmENdnr/JuJld0jEkGoSOQutAht6chhrCtVcV6M8quNNShda+SZ4fWZJ9ZwwkVy7ISl3ZqCCaqdyiaPDKZ72xni2heUAltMf6nqwE9W5mvoU2ac2a7sbCImbEFetsCKNSORVL6GxD8uPM7liT5kNxYYSzHtN6wJMw4vF+9mcl8DazpGA11XQcblV0fn5B2CEllH3fzFqLLNIsxsGMyuIbxYWrhJL1l1kGb+cIsyM6RmMgjsthXXSIlac1oozoI4d1YdyCrMLorj6V0wnDmmgG3apo/zY1fkokTN36ERnf7EOb50ty69z5kFGBNEg5PtgHYPkeNR33qCCMaW4RrKZCfz4ZZ7uBe5tL67AEDI9qAX5Fjqm5pqOOhOELsjIOEK2f+Z3rtPYraS3X5vWjeZBUfWJNHhdkYfjwNP7lPOaaQaE8EK1h16GzUwKblUhB8xSs7T30AXSX8zgNsJd1dr/hIsHsCNUX/V2ij9nYNYMwGzNV7ZIxZczRPV0yl3TUpoG2Y8Bw/4go1GjvT9C6Gqov1wh6wqgSkVR9lM74e3Uu4lqjjNFRUUIF9WSOIAuqluBXYmJLjq3nG4AwfsO5CPdkGbMr++AsGhj1IVpXp7V/fJ0c9ogzEepIRcmGGF3o7Q+I9ripphEkO1Fv6jh5T/IYd7cJ9rnp73t7IpzP5+Q4WxGRnXDrTYt87w9+FjVUWFktEdd9zKaGtWRgL5gMDnuYj+WkGq4i1ZTKjId6Mod3vMziQ9so1HsS4ty0wY6JKwHRpSyDbQnn/nIXmh5hFXyC2RHry0VqNzfEPX9XC3c5S7eVJfmMhOQ7J220rk6cjejvjAnPFBjOJrxu12kKh9fZu2uZK9emMepD0c4uq0Rdgy3TXf7efU+w645F4kyEPdcnKoSM1hx6l4okkUqx2ifY6WFYIdmsx92vOol6Mkdc9ymeU9jx9y9SMr1v7ENggglehJgwgBNMMMEEE0zwUsJL1AU8wY3FZAJ4A9CfM8j2RO/W3S5O0ERT6Ow0pSkjSOjNG+QXA/ySTnFhNM7Rk2/+7V06RU0hd6FLd1+R2FAId1lSPzUuqHenFDRfdGlgonsJy/cYWE3ILko91todMPOI5L75RZsgC34xixpCbBiMcgpBFnJLCYMZDSUSVu3a/QrZRRgVpBFg8Tt1KidEM6b5UD6dYPZimgcyRJZk35ldddzSEFN/OKRx2MTsjp20HWGq+rOS/+ZOCcvnVg3JW9sRMSqqFM9LVp6zRupwDDKw9JqYypMqxYURo4J5nfmqSxbetfuVsYt4XLm1M2ZYs7E60pyhD4TlM4ayveGOkPxpHd2/ztjpXkJ7t4YxEB3h5X+YAAHlL5m0d4PZ2WycUOg/0Ec7niPRwS9Kg0GyYpNZk7q+zj5pxVDC6zVjw5qM1ehKdlusSW1Y4UqC7sZUP2Wx9uoRzjlT8uU25PGZS7owpFnIX4vp/6MO+c+UmT454vIbDKwNYYXc+YBgX0ipOCDz+WmGcxGZqxrDuQgl0oguZWkcgdiKGVqi5TKvmNJOkhF2yRiAvrtNPywRZFUgYf2+AGPJRPFV3NtcekMdvakTLGdQdNGZGU3JXwuzouEzmxqRncCch3HGYVRMmP4zm9X7hEXUujpKKE0WIG0MniYNE4kVS3VaJmTomcR6grJnwGjNwehqqK9o4R0vk3vCprdHWC/3oEcSqWhmRLRi454sk2RjaJqMDrqYjziAsIXh0GTL69bwexk6t5u8+65P8V+fejW5EyaD+Rh1oOHtFA2Y0tFJtITmeQkUVCKFYcvBHLPHUaiROS3nv3xwg41GgSA0Cec9NC1B0yNGQxOzozKqhUxVu/QGtvx/vs3o9DTdQwHFrMe+bYtc7FTImiNeveUs/9+/eCW53W36F0oEsyPiHRFx08SvKMRdi8z2HsWMR6OVJ+kaKL5Ked8G7U4W96DHdKXP+nIRZ1nHnQ8YeiZ37V9g1c1x+XIVc6AyOFMirIS86/5PA/DM7Fa+ODrAwdsX8CKd3rTNmdUal9vl68ejGOLPBuiZkOiqTdIx6O0PsAo+9+24wKqb5/z6NKN9EDWlps1rZTH39HG0mENbVnjisZuI6z6VSp+hZ5K3RwSh6Jsr+SH3zZznE0ObINRQigH5gsuoOHY1FxPmblmmPXQIyhovm1/gxEadTzx+BK0QEIQamhnhdy14RZ981sMBGq08e7av8tC13WDH3DG7yOPMY+gRhh5xtHqNY42tGAs2wU6P6FKWL3Qdkp0j6BgMXtXnzEO7OP0jH+Z1HP5b++z4W8VLzAQywY3HZAl4ggkmmGCCCSaY4O8YlCSZTPO/VXS7XYrFIt/95+/AyJos9kpsHKvxygeO8xePHYJcxFS1y71bLvGJE7fywIGvcLlfpmR6HF+a5TvmL2NpIX9x6mYULUbVEn7iyF/y3y/fyXy+zWOnd/LDd32RTyweAmDombxsfgGAW3JXuebLN/VjrTnOXZnhbYcf51hrjo/e9Pu849z3cfZinb27lnnTzDOcH27h4dUdAOwqNll1c4xCnddvPcX3F7/M9zz+f5OxR/z47s/xX8+8il7X4f86/ChlfcAvPvadOHmPzPib+28d/i1+6PgPcVf9Cn/x5EEeuO0kf/HYIX7ovs8zjEz2Oiu8o7DKj129lzdXnuLSaJq/aNzMFqfH3swqv3nmbg5tWeHXdnyCM4HJl91dtMIs7516mn+1fC+nuzXev/PjfHjl1bRHDt9ff4y/bN/MnN1iGJk8ur6DV285y8nuLHeWLvGny4comh4l08XSQn6u/kkakbCrD179e/zBrk9zLujzbxe/mx+rf5Zteof/uPRGXlk+w3898yp+7dbfwVZCfr/1HUwbfSp6n2aYo6L3eV32PG898UP86s2/yxHT5qPdLWw31vlU9xDfV/4SWUVEjlfCIi+3Pb7rzPfw0Zt+n3968Xu5ubDC95Se4G5L51E/5G5L51zQ588GB/ija0f55IE/ZJgEvHfpAf5p7S/JKiG/sPpaPjL3MB/tbuF4fxs/PP15jpg2n3Y1prQBWSXkJiPHv1+/mfdOPc1XgggvESL/bkvnWtRjq5YH4NOuxsttj3+1fC8PlE7y5syQVjykrGb4aHcLZ90ZjmSv8NRgG++tPkJZlWLXc4Ho/W4yRB/2k0t38q+2/GW63Y92t/COwirngj7Tmsp6FPMrjVfxQOkkx4bbed/0V/j9fpmrowrfX3yKYaywEdvM6266jU8MM5zx6vyLygXuPPZW/uTWj/LJwR4OWNe429L5aHcLfz+3QEYxsBTjr/z7+9erh/nAluN/5e/8JOALns2UNuBmQ+Mjnd3889IVAJ4aeRwx7fRxX739R/2Qed1lMXS423r2AslXH9uvPp83Gbmv+XkTm8do89h+9ev9fr/M9+VanAv6ZNSErVo+3c5X78dXj/3/xOb5/Gbw1fu/uU//5+t+9fhuNDavy7/uvILs1zCJ0uN9I/HV5+iv2u9NbO7/62ZvHAMYJgGf5eN0Oh0KhcIN2+5XI5vNcuuBHyObqT6n7Vy68lnuefkWfvu3f/sGjWyCFxomE8DngM0J4Px//g+ojs3UzhZBqJExA1ZWS9gLJtm71tlYkImaVhlhnHHwKxJ+2mnkUAca+kAlmB2haDHzM00uX65SrPZlO5crbNu9xpVr08xsaRPEKoYaM/hkDfM71zlQWeVzJ/eiZ2Q5cGOhjFIMqFT6/MPtT/IHl2+jeb5CnI3QMyEHti7TGdk0ujmKGY/Vs9PYc31eNr/Ap8/s446dV7C1gM+fuYlX7DvHl//sIF41wqkPGPkGcaSkY5yud6g4EoDtRzrtoUOv65AvuPQuFcnv6NBtZXnFvnPcUbzEb5y7F0OP2FVs0h7ZrPbyBKGGNzQ5tG2JkunyhqlnyKo+H1z4Tt5UP8F354/z9hM/zNHqNSrGgK1Wiz+6dpTtuRbfW32c31m5h4OFJb64voumm6HZzLFn6xqX1yscnl3Ci+TN/YHqVzg/3MKKV8DWRLz+4zN/yaWgyiPdPfyn+sP8bm+Oq6MKGVWWBH/nwp3865s/ySPdPfzpVw5xx84rvG76BD+Qv8ovt/bzm2fu5mXzC3RGDj8z978A+MXl13KuXeVnbvp/mdeb/OjJf8wvH/jvHDUThknA73T3c364hf9Uf5j/MZCU4O/LtfjEMMOl0TQ/VrzADy68npdXznHAusblYDq93v7jo2+iUB7wW4d/iye97VT1Hm/ODPn9fplGmOefl67wk0t38kDpJF/s3QTAB7YcpxUP+Y32LVT0Pu8orPJpV+M1TvSsD/9LQZXvy7WeNaH4vy7fx8MLu3jLzcfSidZ/bu7mqHOJ1zgR333udXz8pk/yy+1t/PPSFf5zczffnT/+rAnQ/Se/m3+y/XPPmkg8NRJh/eYkBL52IvZpV6MRFdIJ0n/v3Mn7pr/Co37IUTP5aycPm5OLK6HPTUYunST+nxOlzTF/Nf79+s28b/orX/PYzcm2pRjPmjD8VZOzTwwzvDkzfNa+fvV+bqIVD1mP4mcdq78O38ok71t57jfz2HNBn2269TdO4r4VPJd9/dvCi3ICePOP3pgJ4CtmJhPAlzAmGsAJJphgggkmeAlBieX2nDChhl7ymDCAzwGbDOD+n/h/yHcsnEaIGias3m5KFdW0SnYlwiurUs/lQ+5ayPotOvnFhO5OBaMjQcAAznrEyt06UyfEQGF2IdOI6M1pdPZFVJ7WGBWgdCFC82KuvkrH7CjUngxYvUsMIWooAa1bPxtJXEpepT8r4vvMWkJkKVidhOZ+hdJ5qT3zpxIyS1JaH2tQOZ3gNAIah00ya0lqaMguixEDpN4ss5Zg9hK621XCLATZhNyibL83L89xGtCfH+/fmtR95ZYkjNovSshvZMljMksSerwZMRIUY3ILKrovAcGNN3kkKzb2mhgY2ocDlFCh8qR8j9m4S37Gjik+YYoRZV6RkN2hJtEZQ43iOYX+vESsaD60DsY4yypqeP11Eysmf1on0cGtJajjeA17TWrTVu/UCCohmUs6sSXHbVQRQ8SoJq9ndhS8aoQSKSkbqmhiVnCWVan/uq1Fd7GA0ZXA6fGKMt1DgcRfVAIULSZ73MYXbwKjWiCxHIdb9C4Vhd1t6mT3tQkfKeNXEmIrwWqqeNXrYzKaOvU7l7h2rE6iQ1wJmNnSpvFMFTVUeOUDx3n0/3cYt5ZgdhTcekRhvisxJadzaD54tRh9oKLs6RMsZ0isGMVXUas+W6strlybhr6GEimoVR/TCvAv51FCiAoRhXqP3qUi+kAl3uHyjw99if/PZ+4jv6ND/0IJkHq18EyBeIdElkRNk9wljf6hEdu2rnPlzBamdrbYaAiDctO2FS5/YRujijDVUaQyatpohQBNjzAfy9E/NKJQHrBvqkF7ZHPuykzKqM/mumxxejz0F4fZcfcVzp2dBTtmZkubZi/Dvi1rnFmtEfg6SaTywIGv8NjyNjJmQN7yOHdqK2bNxbYCXr/tK3xuZQ/DkYHnG3zPTU9zrDXHpUe3Ec4L82lnRrg9GzwVpzqkWujT6OZwezZT1S5BqOGeLBPOeygNi91HFvEjncHI5Kdv+gv+3VNvwrbEABFFKjPlLteO1dl6dGyWGLPqv/vyX+ftn/xxpubbYkZZc9CGKluPLnP1mTovu+cU59pVVi5M8etv+A1++sRb8XwD2wrYku8x4/Q41dxCxRmmDH/GDGj2MoyaNlPzbQ5UVvn8mZvSce+bavDY6Z3s3bWMH+lsz7U4vjbLXfUr/NPaX/LWh/8JccPi0JFLnFmt8e+O/CkfPPcAs7ku59enMfSIjBlg6iFbnD4/Vv8s//r0P+BPbv0oP3D6H7HSKvBbd/4mH23cx+V+maab4R9uf5LfPHM3GXvEbK7LjmyTS4MKO7JNHrq2m8HARjvvMHPPEm+qn2DObPKbV++lZHqc2ahyV/0KjhrwyYv72bdljZLpcnxtliDUOHnP774oGcDD+547A7iw+FnuuW/CAL6UMWEAJ5hgggkmmOClhCSZuHgn+LqYuIBvAMyexH609xis3GVSORMROgqan9Cb0zB7Cbovwbf9rVJO71YkrsQYgu5JGHBkqcw8GtLdJuxff05CmTUfZh9SaO+XIOFhVSPISRF8cSHBL0vMhubDoA7TT0m0zGBGw61ITdnUqRCvInEqflHBagoDF2YlnqR6fITRgdqxiNCG5XtNqsdHtPYL25ZdHodPd6WGDGSfl18uob+qLzEfkQWt/fKv5pOyVs6agluTurXG7QmhLdVjzVujcW2YQmRD9y6PyILS+USK3CvQOhwS2sBVmzgzLn3XJF6k8qSe1qM5i6JHshdMgiys3xYTaxKtovcV1KFEtYSWhCyroRzj7KKKW5fjLLE6Up3VPRSk44/shDAraypLr1Al9iRUSLQx+1cLMboao20jEl0iamINnGUNZ1nFPVmGvkYykO9cw7mIm159ke5iAaUYEMyOcGsJw9mE7FqCuWaQVH00M4KmSZiFVz5wnKAQkSm7VO5cI2MGGPUhii4MpYQXC3un+gr+Hg8lUqjcuYZWCNj/sotcO1YH4ODtC9DXaH65hjFQCAoRf3HqZu5+m2j93PkQpz7A0McRG/v7jA66WE0VJYTDs0tQkX2lMiJZEZ2bsWQytbOF0ZH6sChSye1uU72lQeaSjnuyLNvY00c77/BbT97Llr3rdBcLxKaEBo8u5Dly31l2zTSolnuoI5Xoni5mZsRKq8CdRy7QPS6aWIDz12rYh1vceeQCGXuE37VQhxIz8rpdp6m/8QpKR8fQI44vzXL2Yh36GsORQbuTleiTfpnC4XXOX6sxNd8mCRVWLlfIZz1OXaujaTF2ZgSeytlOlW4ry53VK6z28mzbt0o+69FdzvOV7gzz+TaebxD4Osdac7LNo2vkCy6v2XeGkW/g5D1ecfgMxYzHtUaZkS+B1oPHpglCjXDe4003n+A/vukPKJleyuz97OPfzfZp+QOcK7f5kQMPA/Cq+59m8dQMva5DeKaAqiX89417uOXmK/zD7U+yfbrJA3edoHpLg9/d/zsoMx6WFhLEKm972Zd41xNv5z8c/J/YVsDh2hIl0+Nyv8zR6jV6vs0o1AlCjfl8G02LIVQ4UFnF0kK+8p2/xpvnT2DoEbYm1XYAe4sNLvfLBKHGq0tf4fse+1F+5rb/l8qeJo1hju+Yv8zPPv7dvGb2LL+66w8x9Igt+R7z+Tav3nKWounyHy6+iaw54gdO/yP5Oww1fuB/vgtHDdhfWOMD+/+YZ3pbuWN2kQ/s/2N2ZJuc7tZoDHMczl3htw7/Ftmsx32vP861Rpk/XjzCb169l/PXauzMrnNX/QpvrjzFvN3k8OwSp67VWXHzbC+1iKIX+cdjcgNuE7ykMWEAJ5hgggkmmOAlhBtSBTeZAb7kMdEAPgdsagC3feR9ZBdKae3Z6K4+yfkc9hr49/ZRT+bQB8KUWU87uPUYe65PMePR/HKN7LI8r70/JimGUjvlKaKz8mWOvmW6S//TNYazCbWbGzRaeTQ94nW7TvPJ/30HagTf+ebH+V+f+/+z9+Zhcpz1tf+nuqqrq/dlpmdfNYtG0sijxVotydiSLQM2NjaOzBpWx0kwm/kRyA2EGG4MZAUHcNjBgO0Y4x28yAZLtmTt22iZTbOvPdP7Ut1d1fX745WGODc3F4LDJb59nqcfSV3Vb71V3ep6+3y/55y1qAmJgl+YCxdW5DALMr5gRvQGTXio7pxn7mxY9Js16KgOwbqYLsFwOacFe1JYkcOa0SiFhDlwy8YxJmIBsjGn6E9TS9gK4ldyyWUu9jONzwjazD6sEVo3x/RkEJvDJBRKE3JmGThTT03bAuvCYzz1zKWs2tZP72wNpmmjp26KE893Elo3R7ZgJ5PRYEJDbsnQUzfFocEWJLlEe/0ceVNhMhLknd0HeHy8m4WIj3XtI7S653mwdw1eX46iIRP2pWn2xAjYc/ysfwVv6DzNjO4jXtAYnQ+RTzq4pGOcqbSP6xp7eWl+CQFV51MNT3LzvtsI+MW1u7x+iH2zLWyuHgFgR+A0H979dny1KZLjPmraFojEvNy76Vvct7CJE7E6APyqztrgGA1qlAemLiWV1+gIRHDIBnlTIWDPcS5ZxcBYDbaoHU9bnBtbTzCUCTOaDnJtbS87PKf5k7NvpyMQ4Uy0mu11/RyLNZA3Fb7Z+SPOFat4PLqKkD2DSy7wwMAaLmscZqNviLCSAuBYtpkJPciekTa+uvbHvJTpZLVrlDWOWW44+V5Whyf5Yt1urj7xh/xx2x6+PrSN5aFZarUE07qf0XSQjZUjuOQCPx3u4cFV31q0gRkz/JzJ13M40cI/Nj7Fi3olnzzxZn609tsc1Zt5er6bVvc8LrlA1lS5vfJF9uaa6FKn+cDpdy5e00ORJrIFO5fXD9GoRQW74x/BbcszUQix2jXK3w1fxY+6fsh9iVX0Z6v5k6rn0S2FiOmjT6/FYStyo/c0N5x8L8/0fJ+ns/WLSuu7Bl7Ppzp+znWuLHfHm7jRe5rX7b2dezd9a9FGJ1uSeCqznN2RZTza8TSbT9zEtppBcqbKjO6j1T2/qIq+qOZtUhw8k/MzUqjkocnV3Na8h4lCiI+HhvjbaBsfDw3xh6Pb+Iu6n7E318Z7fbOAUAnrlrJokXPrxGZ2VRxgu9Pk5bzBi5mlfDw0xMt5A00yeCy5ms9UnuV4QWfcCPFSqmNxLvenhdvAv7Vu+bfK2ouK81PpBu6uf4Fncv5XKJcvKpnvnF9GpzbDTtckAEGbi+MFne/Ob+XLdYcW1dl/VnGSYwWJjQ6Fu+NNvMN3jqDN9R8qei9uu6i8vmgt9G+x89wbebrrycX9/70xn8vJnMnXL6qx81aR+ZK+aCHzr212Lm7/t+rl4wWdBdPNFk3nbNFklarx4al1fLnu0H/LHsBVHe/D7fwtewAnX2Dj5bW/dg/gnj17+Ju/+RuOHDnC9PQ0Dz/8MDfccMPidsuy+Mu//Eu++c1vEo/Hueyyy/j6179OR0fHbzXPMv7z+G/OcZdRRhlllFFGGa/AxR7A3+rxmx0yk8nQ09PDV7/61X93+5e+9CW+8pWvcM8993DgwAHcbjc7d+5E18t5y/+3UGYAfwtcZABvfeFm/rp93+Iv0/vTQdy2PI1KlHEjhEvKcyzXwlJtmqudCRySnV3nt/PAkue4Px0kW1KJGh4+Hhp6BQMA4tfqxV+kF3F3vAm3Lb/o6dakJBbNaCOmjy3aPKOGjWalRNDmWvR9g18Zv945v4zL3P2Lr/23+Pd+JV80yQUWf01f/EX+b/ePlbI8nG7lvb7Z/8VQ9n/HDPxro93/LvjvOOcyyijj18d/SwZwyXt/ewZwag8br/j1GcB/DUmSXsEAWpZFXV0dd9xxBx//+McBSCQSVFdX873vfY9bbrnlt5prGf85lBnAMsooo4wyyijjvwzDw8PMzMywY8eOxef8fj8bNmxg//79/xdn9v82yiKQVwH7713NTmujUJXWSbjmLBQdCp4LXngLFpFV0PALg/8ZVjA0iXQDbPxGB/F2iQtJXvz0gv/fkUwP6Uao6BWvUxMSzggousXsNgPXsJ18hcVfA44F4aVnz4B7RngPGg4o+oWiNdVqETgnka0Fz7hFskWMld6U44ndl1NSJGIrStjyYh7uMeF3l2my8A5LwsOvQYcJDXtG+Np5B2VSqwqLYfHqmIrNZNHzTpuzYSmgtxb48nGV/OY0nx5xU3IIdawWkcmHSmhz4vdHwW8ht2SwBkWvpF5VouQ2UefsSO0i4H1hOMjWtec4/sAKUl0GaKJfsaY6zuy8D9u0A3tGQl0TIxHxIOk25KwNz7hEYqmJWpVDPuEh1yHm7Rm2kewWvoFqSITBy7pEIWRiT8oYbuEFKCdlbIaEr2ee6GBI+N65Dba0DxEvODkzWbvYI6jn7Tj2eXjdOw7T7prlvtF1zIyG2LHmNEe+20N0bZHa+hgdgQgn5uqo9orevNmUl+LBIGZPmp66KXZW9jJRCPH4eDdutcD4mRoal88Q+WUdu3b9cvH5mZgPr1tnc/XIYm/ac1Od1HmSnBpuoKNphi7fHD/rX8GapnH6FsK41CJhl1BpJgpC3XmxR9PuMPjq2h/zoeO3cFnjMHlTYSAeJuxKc2q4ASmhcOnaQeIFjS7fHE+c7WZL+xAD8TCJrIaeVfnj1Xt4YHQNS/xRarQkALtHOzm68XvcOradFwfbaKyJcm1tL8/OdZHKa9zYeJx/PrmVN3Se5oXJNnqqphiIh/nash9x18QbOZ8I4VYLNHtii0kxF3vY+hNheoJTOOUC0aKbY5F65qf9PLzjnxg3Qnz69JsIuHI8vfwn3Dq2nQPjzWxrGeLu+hfofuEDWDMa79rxAgsFDy9Mti1GIL40v4S8qdATnOKD4V/w8ZGbuL76ON8a3kJHIAJArZZgOFPJusAIh+ItrAuMsHehnWW+Ga7y9S728X1y8Cb86ivLXNdXHwfgRLqJkUyIHeGzxAw3CwWhXn052cZf1j7NT1MrABjMVtPjGeMrfVfw4Kpv0aQ4eDhTxf5kO065wLFYA1dVnaNBFQrhWzwx7k8H2Z9s54XJNp5c883FeMRVqsbd8SZa1HkaFbH/Ub2ZE+kmAJxygU7nDD8Y38jGypHFfs2fL6zkL+p+xlOZ5bSo82zR5vl2fCUfDw0tntdFRvwi638xCeViP2FYTvJssps9M+28v/VFvjW8hX09DzFppnBJMh8ZvwbdtPOOmv2MFCq5PTDGy3mDZ1Ireav/EJWyjXcPvZm31h7kFk+MvFXk4UwVblueTMmB25bnOld2MWpwi7uPiOmjyz5HpWzjRb2SLvvcK6LgPnZuF+9vfZH3+mb5w9FtfKPpOT4xvZnP1rzwe5dK8uvi1RCBgIVpmiSTyVc863A4cDgcv9FIMzMzAFRXV7/i+erq6sVtZfzuUWYAXyVoMbEgsWcg3SgMj0Es/uIdEkt+mmVqq/gCLviE2XLe/ytzYUcUZjdYBIZM4l0lPOOw0C2hJoRVS7oBkq0S3nN2JBNKagnJEJYsFw2MS3aJRMcFy5mlOWwGmD6D6BqD0JkSiQuLzXwIrBkN0yEWf9qcDd+whCVbFP3gnSyhJiQSKwxshkTJlDBCBqYDvIMy5oVqtGRK2KdU5PyFaxCRKblN5LwwWXadU0l1GRgTbmHynJeQszZsedDmbGhRYRVjKVCcdqHNCRNoz7ANdc6OPQmFOSfJE5VI/iJ9X19OalUBOSnjP6LS0TTDzFAFmqtAyWFRXJrDrghTZHtSRtYl4j1F1KiMY5+HXKNYrNprs6TaTQIn7FQ0xgl5s5hVRQpVBnLWhrJUfOGpIR3TZ2I06sT6KvAM28CQcJ7W2Ld3OaePtCIPCnuOVNIJ5zwoeSFk+Pt9V5PIigu1+8wy4l0l1ncNMz0ZZCbnpdqb4vxMmIlYgNSIH/+WOQpzTk4838mX/uVGfrD7cqJRD5Gkh/aVE8IouNbk8fFuUhmN8ZkQGxpHiUY97JttoXe2hgf3bGRhPIBuKkhyifOHG3lhso36cIx4QSM9FMDr0NFkg8H5SiYjQWbnfVzSMY7Xl+PxDV/njx79AIW8nV8MdjKaDqIqBqeGG1jZOoEtL6GbChOxADO6j1AojUM2UBUDv0unsSbKA6Nr2NV8lEO9S2h3zfJ47yU0BONsOf42Vnonqa5MEs86eWK6m9mUl2zBzgOja7iivZ99sy38XfeD6BcWKm8/8r7F2L6CofDiYBv3jG7j06ffxFeOX0neVLi2tpd9sy1M634OTjexxB9l24p+/uz8TXxk/y6+1/M9mj0x3tR3A7VaAtOQmc15uTvWxTu7D9CwcpqgkmEkE+Ly+iH+ePWexSjATy95ghndxwf6386HGnbzlb4rFhd/lwf7iBbdaHKRb53ZzKcanuRQvIVTZ5t4eb6FBxY2cHe8iTP5evyqzl81PwrA1opBptI+XogtJWp4aNSi/G3LQ+yOLON7e7Yyo4uy4LFIPTf3vhu3Tfzn2uQb5O3eCT634jH+ZvZq3tR3A9mSyibfIMOZSmqcKQ7FW/js8Wv5+cJKPjy1jls8MXYETnN5/RB/PvFGFkw3e7OdANzqH+Kfxq5glapxf2wD7/XN4pQLnIjV8VdVhwG4traXy7wDZEsSLfYIbwkf5gP9byespPhJ5FL6iiogRCPXD+zk8ayLj43ewN9G29jqHOPO+WXszXay7tjNuGwFWuwRIqaPv6o6zPtbXxTn0/kIxwtCsPHGU++kVkvwwJLn6NNrWe6YJFYS4pSrvaf4p8gVPJxu5dGOp2mxRxgophfbTq52JujXa4gYQkjzk8il9GeryZTEQqXD7uHb8ZW4pDwZSyFvFflOspoXM0t5cuW99OdquH5gJyu9k5wtmmzyDbLlwAd+29vC/128Cj2Ap0+fxu/3v+Jx1113/d8+szJeJZQZwDLKKKOMMsp4LeFVMYK2WLFiBXv37n3Fs78p+wdQU1MDwOzsLLW1tYvPz87OsmrVqt9qlmX851FmAF8FlGQWI9fMC7FgFy1hchUi7m30De7FqDbTIdg974SJmgTDLeLRHFEb2fAF4+AqFpk1OQ/uabBdMFYODJUuRIeJSLOCT0SzzWwzCZyTyFWBkVVItgpDYQyJZLMNOS8MkO0JwQzaM6Ik6562iPUY2PIStjzE24TZr2tEwZ4Udi6uYTveEVFaLvqE2bJ/QKJQVaTgt9BrDdzr5/H12snWWSTaRdwbWomSWiLdWsKekXBEJfQqC72qRLyrRD4kStwlt0l2fQ5LgVS7EKwYLpCzNmHAHFWJt0uoYyqyLpjT8RdEycqumHhbEphJO/PTfuwZiYrVc8grkkiKhdmeWywbe9fOU4hqWFqJzl39ZA5WMjMawrpQCrZnJApDXsJHJIrTLtSQju+ghpKWyNZZ4DHRqyzUthTanCgZ63k7ztMaWk+Mq27dR+RUGH84jUsr4BxXWNk6gZKxiRJsMEc05+L8TBjHSSfmaR/tKycIu9KsWzWEvCIJXWmscB7naY3u6hkGxmrIxpz4GpPEE24KWcG8HJ5qhKj4uz7hEdFrIZ3zM2G2L+1DbskslpkHztQDEFB1ju/ppL1ynpIp0d00xamzTfRUTfE3s1dTUkvcs+Fe3tB5mslIkLGhKgB2hM9Sv3qawflKcimN2ZyHeMLNsyeXA7AuPIZf1VnijzJf9LCu+zz3ja6jo2mG/vO1vKv1AE9Md/OhtudZXztGtTPN0ooISysi1HmSrPRMsDw0yx29N9O3EF5k2gL2HKmMxrrwGNuX9jHWV03AlWNJTYTXV5zi2bku5qf9vL7iFDe2nmA8FSBgF9d4S/sQ/zB9NQF7jvc07MMlF2BCY2vFIFvcfbw0vwS/qnPP2a2cGm5gRvfx8dAQp1L1DPTX8bnz13LwXCuTESHq+uSypwnYc4ymg5xIN/HFut0MxMP8afcLfG3uSmZzHr6840c0XxA85Ut2wkqKv2p+lHOFWlrcUX44tI5Dqx/kUv8IHw8N8excFzcc/iP+qvlR/mLHo9RoSSYKIXY1H+Wa+jO8EFvKVlc/t3hi3B3rImJ4cdqKaLLBGm2U705sRjcV2twRHljyHDVBwV73eMZYdegWwnKSQ5EmvtH0HBHTx6l0A+uO3YxDsnNtzSkez7p4X+glwRgGD/DNzh9xd6yLrc4h3hc4RcTw8miqhxczS8mUHIwNVZEtqXy/eQ9/OXw92ZLKda4sX1vyE65zZelbCJMtqdw9v4W3+g+x1dXPodUPcipVz0aHQlhO8sWFS1jumOQbiTayloNVqsbfRtvY1/MQX6g+wUAxzalUPRVyhj+b2sFq1eJQbglfrjtEs32eSTNFhU2nUha3L7ctz7GCxJFYE8sdk+yqOMD3m/cQsmfEtmwzrzt9PUu1ac7k61mlanwjIax4Ph4aYt4scUvwAB9q2M1Px1exN9uJ25bnssbhV/1e8d8Nsizj8/le8fjPLABbW1upqanhueeeW3wumUxy4MABNm3a9GpOuYzfAGUGsIwyyiijjDJeSyhdePw2+A0JxHQ6zeDg4OK/h4eHOX78OKFQiKamJj7ykY/w+c9/no6ODlpbW/n0pz9NXV3dK7wCy/jdoswAvgrIB2GhW/xdTYqHoYElC8bOkbRwXOh3y1WICDj/oIUeFEybpYj94Ve9hIEBi8qTJmZPmsCAhR6CXG2JmpcN5lfaKPpM7BlQMuAbuRDTNqewsL5I5UkTW0am5Lhw3FCBwJCJkoFEhyVYtzk78XYJ14TM/HqT4AkF55yEIyHmIZmCgSv6RMxZwW8R26pTkkEyhMAj7wdbRjCR9qhC4dlK8pvTOJpTmD6T3JocsmqiRWR8gzb0WgPDJfoXbeE8zmnBSubbddyDdoys+D1iT4rItkLIRG1LoWRsiyIVutK4pyEXFnOwJ2WM/UGM/UHsUQXJEEKVRFbDOulDnrPDxK8sdBYiPmqao9TWxzh8pB3zwo9ZyZAozDnJNRZR21LMbjOwHCWKeYXC65IUQiaBFQuQllEaMhh9PvIhCzlUQJZLKJtiJMd9PH6+mzWbB0glnSxEfFgK9I4JQ+iiIbOr4ygAthEnmfYiG646zcBYDadONXPoeBvXLekV22ULZVOMQ4Mt+IIZJMUilXRy07JjOL061ZVJGoJxbt72Mtc19mL5DcyCjDXo4VNrfs4Lu3v47KonyJsKnf4IO9b3smLtMOsCIxTrRI9be/0c1c4U1116nJmcl18MdqJW5TiWa+FcsopwMEVNc5Truk/y1d7LF5ktSS7xrsaXeXDzPTh8eZo9MZ4+38Uy3wyzOQ97ZtrRTYXuimmiORedS6b5xyPbKRgKD8+toXehFk0ussI3RY2WZGvFIN8e2IxDNvi77gdZXysMfW9btpd9sy3UBJPEi06ctiJ/seNR/KrO6HyI5+PLqHGmcAVzuG15HhhYw+y8jyfOdlPnSXJ4qpG9fR3Ei07CcpKfDvdw01X72eE5zddnrkSTDa6vPs4Xeh6mpjrOA0ue48NT63DIBn+y5XmaPTGuuuQMv9x6Nx87t4uJQohcyU4k6eHLdYe4beQ6Gr1xljsmSRSc3NH6LHf2vZHLg30kCk5CSprHo6tYMN38fGEl8aKTz614jDvnlxEz3Hx4ah1Pdz3J6U0/4paDH6DZPs8m3yBv8h1jqTbNT4d7+H7zHgC+k6zmqy9fQX+uhi/V7uOttQcJy0UeW/oIH2rYzQMDa5g0U3x6yRPUagmihoe/636QjQ6FfT0P8YnpzdziifGNhn38uPu73J8OEjPc/HBmExlL4ct1hziqN5OxFLa4+1goabz57Fv5wfhGVjtHeKv/OPuT7Zx/0zcJKyl2nd/Oexr2kTVV8laRL81eSd4qcnzd/Xym8iw3BI7waKqHZXaZu+NN/HXDkwBsd5q4bAX+YXIntwfG2B1fwfGCvigkiZWy3JdYx6X+ER5LruYva59m2bO3catfbM9aDp7OtHMk38i34ysBuM6VZbVq8aGG3VTYdCKmj1gpyxeqT6BJBpe5+/nlikfJlBysc54H4B2+c/xttA1gURByJl/Pvp6HuD0wxtXOBE5b8T95R/g9wAURyG/3+M0OefjwYVavXs3q1asB+NjHPsbq1av5zGc+A8AnPvEJbr/9dm699VbWrVtHOp3mqaeeQtO0/2jYMv4LUWYAyyijjDLKKOO1hFepB/A3wete9zr+I1thSZK48847ufPOO3/LeZXxaqHMAL4KcM6Da0rCMyV63wz3r5TAtjwU3RL6hZ4+OQ+VpwskWyT0kPSKcQIDFlPbS2QbTKavNJhbLVN1nxNDE2rfwDkb8ysV3NNCcZutFf2D2SoJwwXVhw2c43ZSDTKSKRE8bRNzGHGSDctCfdwLRX8JJQOGx0KvsnCNKOghyNZZJFstsi0GRZ/YLufBOa5Ag44UcSDnBcPpPa6SbTBRMjbyIcE05kOgHvQg7/fhGVRwnHRizWgU3Zboi8zKqElwTciEgymsdUlKMpTyMrIuWEk5D8W6AmZ7jqals+gTHkqKRak2T9Fnkk86yFyRpugvgS76A7MtBrkVOoa7xLru8zhrMyh7fRTdFjZDQm7JgFaiqX5e9PopBtOTQao755Ha07hGFDo6p3DWZrA5RP+hzWGihnSUcY1szInkLxI/XYHkLyLLJTZecZrwyggBf4awL00q6cSelMlNuzk02ILXl6OmOo66JkZjTRTnihhhX5rnZztZiPgoVBW56pIz6KYdWTVx1mawtBL/8tIG8kkHZlQlGXNTUx0nOe1FSii018/x+Plu2ivnubHxOADPTXXyvaOb6WiawRfM4FwR4+n5bt78+n38+TM3M/diHXtG2th9dAVrg2M8O9fFuvYRzhxoZeBMPf2JME65QN5UuKK9H69bZ77oIW+K34brwmP0eAQj9xd1P+MLPQ9jmTaenu9m3Ahx4rLvoJt2di45R6U9zZXV/TR64/TNVrH7zDKW+KOsDk7whz0v43Xo+NUcX1v2I/aeWMqRWBO7Rzv5eGiIpRURZnNenk120+kS8XO7I8vYXtePX9WJF5x8tuYFPv/LNwHwtq7DdLpm0U07Lq1An17LZY3DDG3/LkPbv0sk6+E9S19mx/KzxAtOnk12c88lP+S5qU7uj20gXnCiyQYn0k3c2ffGxet5IlZHgyaYzjZ3hNF0kKP5ambnfTSoUXZVHODba77PumM3Ey9orPBN8adH3sbOyl7BRIUnCSspxlMB3uwZ5r3hPdx24J18o+k5AvYc17myZE2VTm2GdpewHXk5b/Cn3S/wUqaTWzwxxo0Qx7LN3HPJD4mVstwf28DbvRMMv+FbXOXrxSHZcdvyfGn2Su6OdXHbgXdyWeMwHxu9gS+NXsMNgSNscffxWGwN96eDTJop4kUnxws6u85vZ2+ujVs8MT5TeZZPNTzJgunmb6Nt7HQPskrVuG9hExU2nU8veYKHl91Hlxqn0qbxpuDRRXsVv5rjeKaJW4IH+OLCJQA4JDt3x5vIW0Ueia/leu8JPjG9mdsDY/zJ+bew6/x2AG4PnmOFb4rjBR2nXHiFyb1LsnN76AhhJcWfVZwkYtp5+oov45Ds5K0ix7LNvNc3S1hOLrKGxws6X1y4hIjpw2WzuMUTW7RvaVZKnMmL/teI4WWjQyFWyvLDZNcr7GtWqRphJcVAMb14LjO6j+OFckpFGa9dlBnAMsooo4wyyngt4dVgAMsZYa95lBnAVwH+8wXyIfF315SEZ/xXPX+WDImlglVKN4ArYhLtUnFPs+gVqNca6CGJXIWEZ1Ch9VET57gdzwSkGmTx2kZItlpIJjgSglErVBXR5oSy2DMBs5cqeMYt3DMlSi6TXFj08Ml50EOCuUs3Skim8BZ0TUl4hyWyDSa5RgPvsGAk1TkxMW1O9NMZbjCjQn2bazTIh4Qa2NIEk2gpwv8QxLEMN6S7CxR9oKSFh2A+BLIukenRyXYVmJkNoE948I1Y2DIy6dYSpmZRdFs4B1SY0Bg/U0N15zwAVkZB8hexZWRM44LfYFJGydjQpsV8S26T3tka9AkP7p1z2AwJRxTcv/Bgi9qZjARZ3zXM6GgYJSoMkE1Dxrl1nqHjjZimjVAojdHnQ4o4sAY9SO1pHL68UNs26GjnhDL3xFwdXodOPOEmkvSwfWkfrmnwNSaREgrJmJtiycb62jEmTtWi5+2MjoaZiQmfN8VlLH5+SqbEro6j+MNp8JjsWnsIPCayahJNuZB9RbpXjfDBpl8Q9qU5M1nLPc9cxeh8iCX+KLJqMhELkIy5sSsmuqlwLNbAX1/9IO+9+Rlspz2oIZ0f7L6cgf46Dh1v496b/4mfXvsVRkfDPDW2jIKhsPvMMq5r7GXPTDuTkSDZgp2RTAiXrcDS6jm+Hb2MT59+E+9esw/dVPjI/l0sf/5WxlMBDkWaeGB0DUdiTWhykZ1LzvHlLffRO1vD4+e7We0a5Tsd97Or4gDjRghfbYpqZ4pL68ZZdegW/GqO3rE6pnU/Pxxax7TuZ0f4LE+NLeOttQfRZIPbRq7DV5siUdB4YGANAH0LYTZXj4j+uESY6wd2suv8doolG9/t28juoyv425aHuL3yRb4+cyUvrvox0aKbFneUo2ONfLnuEO9qPUB/tppVh27hR10/pEGNssNzmnt7N3BV1Tm67HPc3H2Uz516A1s0nTP5euy2Eqm8xoQe5PENX+flZBsfnlpHyJ6hT6/lxsbjbNr3R4wUw3zu0ke5ffJyvlS7j8ezLl6eb+EWT4ywkuL1FaeosOm0qPN8pvIsxws6L6U6eGl+CRsdCk9n67khcASHZOc7yWpeynTyt9E2rnNlGcmEaFCjPLj5Hr7RsI+dlb2sDk4wUgyjSQaNWpSH59ZwrhBgpXeScSNEq3ue9/pm+U6ymkkzxYLpZo0jxe3Bc9yXWMUfjm7jrRX7eTTVw2OxNfQVVeplLw7JzjemX8enTwsGNmTPcJWvl3OFWv6s4iRfrjtErJRlMh9kzMjzheoTLJQ0GrUoH55ax/faHmZLaAAQ7NpnKs/yWHI1q9yCXZ40U3wnWc3OM28haHPRYo8wX9JZMN2LMZfzJZ3TyTpipSzHci2AiMVcpWpkTZXn48uol72vYO2CNhduW56X8wZbXf0AjBq2xV5AELGXn5ztwW3L02H38FxOJm8V0eTiK9jJ/154NbKAyyvA1zrKC8AyyiijjDLKKKOM/8fwO18A7tmzh+uuu466ujokSeKRRx55xXbLsvjMZz5DbW0tTqeTHTt2MDAw8Ip9otEob3/72/H5fAQCAd73vveRTqdfsc/JkyfZunUrmqbR2NjIl770pf9lLg8++CBdXV1omsbKlSv52c9+9p86p4UulcpTJYruX/X02TMWuTD4hy2Cp2WC5wQruLBcpugW2+t/KWgzbVqhdCGhIx+C+RUqjqhgD90zJfSQhGccPONi/GiXhGcCKg7+KlnEdAj2LdF+wfMvKZjDYsgQyRtRcM1ZIqFjSsI7ItS82VqhEnaNKBeSQ0wqT4kkEpsBwdNinLoXJIp1BYInFJSM6G0EyLYWCZyThOdgUrCdemuB4AEV1zSYmoVjyk7onEWpJQdRFf8RFVvUji0vkWi/EB3nKOFYkHDOSRT8FpYCSsZG/OUqnHMSGBLKuCbUw0edBE7YRfpHXYHQmRKWacM1bBf9eqZEJOaleIF5zFWJ3kd50MlszoMrmENNSCgug+X108QTbizZIuTN4lYLFEMGJbVEMWRQnHZRiGpIpoTzqJNsa5FDgy2U9gYZf6EJ1VGkkLfTnwiTWFsg0xegfeUEliExP+1nz0gbJZfJtpYhJMVi55Jz+IIZzILMnpE2emdr8BzR+N6+LWQyGlJC4V8OrKemOo5txElhzomRVTh9pJUP/fJtgOjpLIWKhLxZ4gWNK9r70Sc8rGydIHmiktF4kIGxGj615yZ+9M9Xkw+V8Lp1rHAetBKSv8jbXvgj3vLTj6DO2an2plAVgx3Lz/LT4R6iKRc3LTvGNU1nAXh4bg0fatjNvxxYj56389TkcnrH6giF0rTXzxFNuegIRNjVfJTB+UoAziWraFSimKaNXR1H+eSJN7M318SfHnkbEcNLKumkd6GWNneETy57mkTBieYqMJPzck3TWS71j3DP2a2srx0T6tOEYK8urxd9W5c1DnPPM1fxvo59PHG2G4cvz7W1vby19iA1WpLV4UmuW9JLR+cU1zxyB38+IdS5t45tp0GLkSvZuXfTt+h68Z3sjiyj0zXLL9Z+h3rZywNTl3J/bAN/dMleQkqa+xLreHm+hYZgnC8uXEKm5GBfz0Pc2HicP6l6nnNF4ZVYoaa5JXiAQ/EWxvUQb+s6zJ/vu5F+vYYrA2f5UaqBPr2WL7Q/xJ3zy7hvej1d6jR/MX49mZKDx7Mu/uz8TUSLbt7TsI+BYpr7ptfzYmYpL+cFYzyhB5kvevjD0W18r+1h3uye42tzV/J41kWm5GCVe4yfL6zkL0eFR59fzXEmX8+zc10cyzbT6ZzhuZzMmz3D3D2/hS+NXsNHxq/hE9ObCSlpLg/2cSZfzxZ3Hx8M/4KI6eOTsz08nnWxJTTAg6u+xcdDQzw31UnE9PHdic3cHeti57k3cjTv5fHz3dyXWMfmEzfxTGolW9x9i4rpyXxwsUcwVsqy2jXKTtek+B4pSTw93803O3/EznNvpMImWLzHYmu4O96EJhlkSxKfaniSp7P1zBc9vO709dweGCNvFVnlHuMbF67ZXRNvJG8VuT8d5M75ZbzXN8tSe2HRbzAsFxd7AWOlLA7JzvtCL3Es27yY3vKXc5cSsOcWlcL/7VB6FR5lAvA1j9/5AjCTydDT08NXv/rVf3f7l770Jb7yla9wzz33cODAAdxuNzt37kTXf0Xrv/3tb+f06dM8++yzPPHEE+zZs4dbb711cXsymeTqq6+mubmZI0eO8Dd/8zd89rOf5Rvf+MbiPvv27eOtb30r73vf+zh27Bg33HADN9xwA729vf91J19GGWWUUUYZ/8WQLH5rG5gyXvuQrP9It/1ffXBJ4uGHH140grQsi7q6Ou644w4+/vGPA5BIJKiuruZ73/set9xyC2fPnmX58uUcOnSISy+9FICnnnqKN7zhDUxMTFBXV8fXv/51/sf/+B/MzMygqiIp4ZOf/CSPPPII586dA2DXrl1kMhmeeOKJxfls3LiRVatWcc899/xa808mk/j9fla+/6+RVY1MrfDksxmiv2+hx6LhuRK2okWq0U50jUHjzyBToxBbUSJwzoZxwYfOP2oyt1qm+rDB7KUKzgjEe4rUPq+Qq5DIh37F8tkTIq83utyGkhHHsmSoPGUQ6xT9cHJeMIP5CgvJEGkb+QqLUqhI8IBK0S1Uv44FCS0KmVqwmSLFRMlC3YtZIqtd5MLi39kGE++gLF4TFX6BuaoLqSLar/KIlaxINYl1Scj5C56BVSXc4zZyVRb2jCRUwQ7xsbPlJSp6hVI6J4gUlAykuoq4B+3IOqS6DFwjCtkWAzkpYyngHhNsqOEG1+Z54gk38qCTQpWBPapQDInXaFGQrp8nu68SSxH9lgBS3oYlW9iTMiXFIrwywsxoCFtWpqSWsLQSDl+eQlRDTsqYrhLO2gyFIS/upXHSQwEaVk4zOhpGMiTQSji9OnpWZUv7ECfm6kgPBQguXSCrq+RSGk6vTkMwzvmZMGbSDlqJla0TbK0Y5IdD60iN+GlfOcFELEAupWEZEs3NERyyweCpBiyH8Ih89+YXOZ2so0ZLCvZrUKO0Ii0SThSLP934C344tA79RBBTE2zqW67Yz7Tup1ZL8C8nLmVd+whbQgM8NLmaa2t7OZWq50y0mjpPkkRBo9qZ5tBgCzXVcRJZjYZgnIGxGv5i4xPcdfT1qI4iDcE4g5NVdDdNMZX2MT/tx+HL8/7l+/jp+Co6AhFWeid5Yrobv6ozOF/Je5a+zE/HV/Hkynv5YmQTD51djdutU+1NMThZRXv9HKPzIULeLI3eOB+tf5pH4mupd8T4hz07Wdd9nhpN+AKemKvjk8ueZn+ynTcFj5K1HHzyxJvxu3TCrjS6qTA6HyKfdLBr7SH2zLQTTbmoCSaZPFZLYMUCq8OTNGgxrvae4lBuCWElRYs9wl1jb6Rvtoq3dR3m3t4N/P36f+Hvhq/i00ueIGs5CMtJbj/zVpb4o9RoSTb5BunXa3hpfglXVYnvmHE9xKFIE43eODsre3khtpSZnHfxPGucKd5V9RJZy8Hj0VU4bUVyJTsbfUO4bAW+cHYnf9f9IFnLwbcmt3JmspafXfZP/M3s1Yymg7ynYR/fndjM6uAENwSOcCi3hBejHYvzeT6+jJWeCQ4nWvh+8x7WHbuZP24TvoKPzq4ioOb4fvMedp3fTo2WpEJNc7X31GJ+7g/mLuP1Fafo12t4anI5+3oe4jvJaprt82QtB5mSgze753BIdibNFHtzTdziiXG8oHOuUMtW5xhH89UAhOUkz6RW8pnKszyXkxktVhJWUnTZ5xZ9+JY89gGefv0/cF9iHZ+pPMukmeLu+S1U2tOsdo4wWqzkRLqJXMnOrooDjBYrea9vlvvTQbrUacJykb25JiKGl1PpBn4x2MmDm+9hlapx5/wyTifr+FTDk4wbITIl8R6ucaT4dnwltwfP8cWFS3hqcjmf6vg5XfY5nsos5/bAGDvren6t+8GvA8Mq8kseJZFI4PP5XrVx/zXcbjdrqt+Ox1HxW40zNL+PDdc0ce+9975KMyvj9w2/Vz2Aw8PDzMzMsGPHjsXn/H4/GzZsYP/+/QDs37+fQCCwuPgD2LFjBzabjQMHDizus23btsXFH8DOnTvp6+sjFost7vOvj3Nxn4vH+feQz+dJJpOveABoC+LGXH3YINYFyVaJolui4bkS8SUKqUY7lgyhowrxJQrpBqh9EZzzJQy3MIpeWC7jG7FINitUHTOxZyxCR+wkmyTiPUXCxwyqD5m4psVcMjW2C6VdC1kXQox0vYI9IxZ/NgP0Ll3ErNXmUZNQs9/Ce1wl7xcLJ21OouagMHR2RKHgt1CyoFdZzGwUiz+bKcby98koeSjV5qk4Y1BSxGuKfmEYnW43xGJDhshaC+8IiwKR6gMS6e4CpquEaxrsGQl7woZn2IaakEg1Xoi081uLC1qbwyQfsjA1sBSL4oXvyovCldSqAvnNaZSMMHd2HnWiJgFDwmjU8QyKBWO6Aean/RRW5FAyYI8qyEmZUHtUjHehJD49GSR4QkGq0ZH8RSTdRmHOCRdsZOSsjULejukqkYy5scJ5xvqqkZMK6pyCPGcnP+rFd1DjxcE2llZEsMJ54qcryKU0OppmyE27mYgFsGY0IVzRbZw61cw/n9xKeiiA5SgxdLwRv0tHVk0k3YZf1ZmIBXBEbci+IvaowpFYE30LYZ44280f9rxMwW9hGjKWYiEpFt/t24iet1NoKmBWFQkuXWCVe4wz0WqeGlvG1qUDjKcCTOaDFAyFvQvtBOw5iobMVNqHQzY4NNhCR9MM0ZSLHc39RHMuZNXk0dlVLKkRMW3NnhiXto5xffVxAD62+RkRi/ZLIcII2HN87cUrARicr8SlFfjai1fyobbnufrEH+KSC7yzW/yfnYgFGNr+XQYnq+ipmyKRFXFzb33uNla5x5jMB6lojLMlNMCM7uPEXB3V3hSfO/UGPlvzAo/F1vBPY1fgd+k8ufJerq8+zmWV56kJJnl4xz9xe+WL7Ot5iPcv30c862T5hmGua+ylPxGmQY3yYmYpD02u5u8GdggLk+rjnNtyL53aDA9uvofHo6v4ZueP+MHcZWRKDj45eBPb6/r5aP3TvKdyL/16DUOZMJps8NPxVfRnxcJnW80gre55Hp1dxT82PkVA1bm5+yhdvjleX3GKT567kcejq+hdqGWTb5BjkXq+0ncF902v5/L6Ib4ysYN/GruC77U9TO/l3+TzU28Q5XLZ4OcLK/mr1kd5/Hw3j8TXcs/ZrbyjZj9frjvELZ4YeVPhHb5z1GoJ7pxfxh0du9npHuRLJ6+m2pnio7XPcLyg84PWp/hy3SGCSobvRLZxLNeC25ZnIB4mLCdpUKOoisHfRtv4+tA2Pnf+WsJykls8Mc4WTY4XdOplL7d4YtyfDvLm3R+kXxfZr316Lf80dgW3nXwHL80vAVhcPP4kcikddg/HCzrHCzrXXXqcDw7twmUTRuV3z29hlXuMj4eGWONIsdU5xIzuY6NviC41znt9wkbnFk+M3ekVjBtO1jrGcdvydLpmGXjd91ilanwnWU3WVHlgyXOsUjWOZZu5xROjQs7w7bgoU/8o1UBQybCv5yH69FoWShoPTa7+394LyijjtYDfqwXgzMwMANXV1a94vrq6enHbzMwMVVVVr9iuKAqhUOgV+/x7Y/zrY/zv9rm4/d/DXXfdhd/vX3w0Njb+pqdYRhlllFFGGf+1sCwo/ZaPchX4NY/fqwXg7zs+9alPkUgkFh/j4+MAJFpt5P2CgQsfh8qTgsEDUPJQFJ6kWLIo3SpZcI/n0IM2Ks6YFN2iXKpcaHMsKRJarERJAVmHwAk7sU6F6FIZm2EROG+QCwvmTdFFCdY5B3LewjNpUJLBcEDLD2y4piG4V6MkQ6rehjMq5mVPiLFTjXa0KIu2MJ4pC/eYYOycEcHySQYU3RBbYVL/UzvpeoVcGJwLFvaEMIwOHVWwZyTUJFh+A0sWJWvJgJnNJUjLoIhIOyXDonG03qVjOkCbExFx2TqLTHuR2sdUSg6LXG0JW0ZGmxOG1EU/JNYWcA6o2E57SK/VkeQSmUbBpromZOzDGqmu4oX9S8JyZUIjV2VR9JmoCQnr0UqCp2VyYTEPX6+d2IYC8qATzxENX2MSLSLjHZTxPeXGOSdhFmS0iIxzQMU+rGFPypg+g0KVQclhoaQl0ptySBEHJ57vRBnXcEQlLEOi/3wtllZCn/Bw2aYz6K0F1DkFSyuJCDfZwlIsLNlitr8SJjQkf5FTZ5tor5xHyUB9OIavZ5631h4kNeIH4LsHL8PRnEJWTCTFYuvSAS6tG6eYF6yklFCIJ9x8+vD1xBNuUkknLw62kchqPHR2Napi0DdbxeOHVxFw5XCrwhRaSigMTlZxQ8dJnnhpDamMRsCfIVHQOD8TpiEY51ikHk0u8vmXryXWV8EPhjfw7YHNqFWCTXz85dV8dNvTzL1YJ1jEQWH/8ufPvwWA20NHuLd3Az1VU1zWOEzrz95PKJSmRkvid+lsrBxBTir06zUMZyopGjJfOX4lJ6ZEtN5Afx3XLenlI+PX4JQLdPnmCLvSXPrQR/nW8BZ+fO5SNlaOsGC6eTrTzpJn3gfAPZf8kLfWHuT7v9hGpz9CWEnxxHQ3X2h/iOSJSq7y9fLFn97I605fD8CuH38UgG9HL0M37fx8YSUFQ2Fa9zNSDPORgV0MZcLsPbGUrRWDdFdMc13oOCdidRyLNTCcqUSTDb4dX0m8oJEzVb5Uu4+tzjHu6NjNaDrINfVn+MrQldR5kvRUTaGbCo1alL7ZKq6tOcUt/TfzjUQbMzkv6wIj6KZCwJ7jY+d2cd2SXt4XeolC3s5LqQ7+cHTb4nfWu4fejEsu8PxsJ1udYzydaacmmCRvKtw18UbumngjDsnOJ2d7eDHawUbfELcHz3HX2Bv5/vLv84O5ywD45YpHyZZUvrniXuJZJ0vtgqVzSwaPJVdzd7yJ53IyYTnJt674LkOZsLCPsRW5v/NBDlx6LwFVX7ymt/qHeFfVS9wdbwKEEXOFmuaLSx7i46EhvpOspt4R43imiTvnl/GiXsnnp97ArbW/pD9XQ8QUc3bIBnmrSH+2mqX2Ah12D+/1zQLC3gWg2S6spO5PB9l1fjtBJcOkmeK781sZ10NU2HS2Ooe4PTDGrvPbCSlpHomvpdMf+e1vGv+38KpYwJRXgK91/F4ZQdfUiLLB7OwstbW1i8/Pzs6yatWqxX3m5uZe8TrDMIhGo4uvr6mpYXZ29hX7XPz3/2mfi9v/PTgcDhwOx3/izMooo4wyyijjd4SyEXQZvwZ+rxjA1tZWampqeO655xafSyaTHDhwgE2bNgGwadMm4vE4R44cWdzn+eefp1QqsWHDhsV99uzZQ7H4qzDvZ599lqVLlxIMBhf3+dfHubjPxeP8JggOmPjGBPt2Ee5pg0yNInr5+ooEBgs4EhbOSIFQn4le5cAVMSl4bTgSFlrUwtDAN2pgOCUMpw3zwlqzdIFNy7YYaLESsU7RJ+ieBkMTJswlRQgpYp0KdXuTWIpgEi+OYbiF0ERNiX5FSxHMYd4nYcnCJDowZJKuk7BnwXVOXbSXcSQt7BnwjMgUvDZiK0ycEbAVReydJYveP1te2Mp4elUytWJMOQ+Sv4i/T8Y1In5vqEkxH2dEsJM2U7Bw3kEZ95iEY8qOnBfCkaanTOp/aWEzQTJZNIo23IJFrP65A6IqzmnRE2nJUGzVcUwJixzntA31tBPXlLCYcU7LmA7IhYXwRcmC2Z4jH4LgARU1KcQxpb1Bco1FEY93TYZ0d4GGR4UljpyHQsjEaNTxnhPHsfwGZntOiDsQrGhJEVF7towsBC1JBVteYu+JpdiidvJ1otdQdRWw/AauYTuSKWE5SsgtGTxHNJzjCqeGG8jWWVQ702T3VfKXP7kFR9QmrG+COfQJD+aIG4DRdJC9fR3YHQZqWwoQJt6ug05kxaS6Msm1y4TS3SzIzOyvI+TN4qtNMRPzMTZZSaagYgvn2b60j2OxBmzhPJqjSHQwxOSxWmwjTs7PhIkOhjjw7ApICyYUID0UACAZcyP5i3z5qTew8qp+do92YgvnSSWd/PWVPyF5opK1u2/H3udk74mlHIvUI+k2iobMiVgd0ZSLRx7fzIq1w9zbuwHdVEglndSHY1iDHlJJJ55Bhcd/spl9w0t4/Hw3J2KCGbSF83gdOj11Uzy4ZyOfO38tL8SWIsklxvUQ9y1s4vn4Mm7e9jK7zyzjWLaZTEHlL4evp9SS47YD76TrsvPMxHx89vi1bLziNC+Nt3Is1oBfzbFveMnitf7z599CpqAymg4iuQ3mix6ePbmc3fEVOGSDVF5Dk4scPNfKfNHD+ZkwFWqaDYffyblCgGxJ5f7OB3l+tpMbG4+TKGjsPbGUgbEaDsVb2LnkHLcHxhicrOK+0XXkTYUHRoUJ9puCR1EVg5ypcsPhP8LIKgxnKgnYc4vs2JnJWu7t3YBf1fnS7JV8a3gLnf4IumlnPBVANxXujjcRLbrR5CJrtFHeNXwNo/Eg9yXW4bgQl3d/Oijm+MSHAPizqR1Mmimu2XO76KGMdjBarOST527kgYUNfL95D3fOL+OhydW8e+jNvKhrnE+E6AlOcdfA6/niwiVUyBlOpRv4s/M3cf3ATk4n67hr4o1cP7CTF2JLiRluOp0zXObuJ2J4Wemd5LHYGla5x7jxqdvJmSp317/Aj1IN5E1hmv2609fz4al1OGxF3jV8zaLB8yr3GFudY6wLjJAv2TlXCBAvOnHKBf4pcgUfH7mJv422cWKqjhPpJh7sXcPug92/8b2gjDL+O+F3vgBMp9McP36c48ePA0L4cfz4ccbGxpAkiY985CN8/vOf57HHHuPUqVO8613voq6ublEpvGzZMq655ho+8IEPcPDgQV566SU++MEPcsstt1BXJ24Ab3vb21BVlfe9732cPn2aBx54gC9/+ct87GMfW5zHhz/8YZ566in+7u/+jnPnzvHZz36Ww4cP88EPfvB3fUnKKKOMMsoo49VDuQRcxq+B3/kC8PDhw6xevZrVq4XC6mMf+xirV6/mM5/5DACf+MQnuP3227n11ltZt24d6XSap556Ck37VSTPj370I7q6uti+fTtveMMb2LJlyys8/vx+P8888wzDw8OsXbuWO+64g8985jOv8ArcvHkzP/7xj/nGN75BT08PP/nJT3jkkUfo7v7Nf/W5JzI4YoIBMTRwxIqU7BIVJzOUFFByJplaO5kqiVSTg2xYxla0yIZlnBGDoltiocci75NYWK7gjIixKk4X8Y+aOKMWzY/H8J9WkPUSrjmLTI2MnLcoKRIlGbwT5qJNTKZJsEH5oIJ7pkSwX8c7Lv4zS6awqVGT4EgI5u1ivFzBa6PijDi2diHKznSAd1wwqel2g3i7hBoVTNjMJglnRLBs9oxgCSVDjKlkL/QIZiCwT0POC3uYfEjMI9dRIFsrmLjAgLAqyTRZuOeEofTCcqFoHrkJokvlC1FygrkEwf45EhYL3RK+QRslB/jGxDiOQfFZ8Y5beKYs9C6dol+ci6WIuVkKuKchH7Lw/9IJXWmK4rJhMyQkQ/QFyrqEOeJGnrOTapBRMpBpLC32GkrGBWXxnB0zqiK5DYJLFyiFitSvnhb9hn4DwwXeYQlHVMI1omD6DGwOE8mUUI56QLeRrxA9gBgSvqfcpLoM9C5djO0zOHyk/YJBNuhhE8uQ0Cc8eFsSwlYnLTM+E0Kes4sewP0+GpfPEDwtk+wuYhoy3RXTPPHSGrIxJzXVcehKEzkVxtgfpJgXvX+xvgpKEQe/GOxk8pkmjKxg37Q5GzToFKqKuN06tryEvEIo4W0ZmaIhozRkkE94IC3T3TSF6TPQTQXTtIleR9PGpx/ZRbGugC1qJ9dYRE7KQsldm6GnaopMQcUccWO25zj30hLMqIpuKthki0jSQ7GugOYqYG5K0rB9DNVRJBdxMX6mhkjWQ304xkB/Hbqp8I9vvJeCoXB4qhGvL0e86OSJl9bwi8FO/uWlDfiCGU4n64hGPaTyGmuaxnG7dXRT4ROXPENNMEmtlsA0bZyfCbP7zDLu2XAvXodOJOnBGc4CMDZUheYqcEPgCG9adYLHX15Nl2+OYsnGTM6LzWFyQ+AIf3TJXib0IPqJIO//xXt4OdnG2t230xOc4lSqntHRMJZiUVMdp0ZLcijSxIr9b6e7aQqvQ8ev6iwPzaLJBn965G1kCsLpwK6IuMlDgy0ArHKPcSxST8CfwZrRSBQ0RjIhrqk/w56RNm6t/SWqYtDijvLtgc28NN4KwM37bmNLaIBqb4qsqfLsyeU45QL3jG7j2tpe1KqciDVMB3nvwC1sX9rHt4a38NH6p5kohPhC10/Jmwq7zm9nKCOiDwNqjsdia6jzJHlrxX6eXHkvp5N1/MP01YTsGZo9MRIFjfFUQBzbmWJvXwdPTS4nanj4dP8N3HX09QCMZELcN72eP9nyPI+/vJovLlxC1PAwEA/z58+/hdua93AiVke+ZOdTDU9yla+XFzNLuWd0G/clVnEqVc8Do2s4k68nXnBSaU+zyTfIVNrH3oV2bug4yb7ZFtrr57AVbFw/sPM3vh/8XuC3FYCURSD/T+B33gP4ute9jv/IelCSJO68807uvPPO/+0+oVCIH//4x//hcS655BL27t37H+5z8803c/PNN//HEy6jjDLKKKOMMsp4jeH3qgfwvyvmV/lIttiJLxHr6UydA0uWyNVqlBSJ6DIN93SRUF+RoltCTYsFsBYrUbILQ+OG50o4oxZ1L2ZJNdqxp81XHCNX78GRsMgHlQuqYcH+eceLOKOCvav/RRLTIXr/7BnI+yVS9TbyQTtKThxz9lIFNQk2wyLvF314NsMSvn5xk/mVCtlaYeos5wXbNL1ZxT1TomaPjM0UZtclGfwDYu72zIU5hiF0zsKeFL14eb8wd3YkLAo+cE8Xcc6J8w/tU9HmhHl1uk7CNQ1WOI8zUkRNCi/CkgLqnF0Ya8vgjAqlrV5VIldlkWyVMDVLzNdvYU+b5EMl7Akxn3SdRC4kUfELB65pYUgNUHHGwDdsYTh+Fa+XTzpwXDFPusVEzkO6tYSpiSg702diMySytZDp0ak+IIywi/4SehX4B8U+oZMy2jmN7L5KSMtMHqul6LaQ5+xoPTHSjULhDCAnFYiq2BM2DBeocwoll0nj8hm8gzJ5n4i/I6oSWLGAa9gOIaG81KtEnJvDl8c5bSMR8dC4fAbJFOfimpKwEnZya3LMP1dHuhEkQ0I97eTZk8txREXvoNehU8iqXPG6k2QbTKyE6F8suUwIFXC7dbINwo+wsSaKXlVCdYj5Fw8GWb5hmNy0m6als1R3zhNw5ShENXIdBfCYnDrVjD+cpm+2ikJUQ3UVhBq8QSi3AWrrY7iXxrFPqeQiLkbTQRaGg8gt4kNlNOq8e9teJmIBmNCQ9/uQVZPLGofJj3qZTXnJTQvqdsv6s2QLdiYjoh8P4GMH/4DZeR9Gnw+7YnKpf4Ttm06xvH6aitYYdsXkUO8S/ni1MEhudQvF6PmZMC/EllLtTOOSC/TUTWEm7Ti9OrcdeCeDk8KKyjTFV2hH5xRGn4+PndvFC5NtNC2d5fHeSygaMrMpL401Ud763G18/dg2XhpvpdiqY3OYPHtyOTXVcXaPdnLsoRVU1iboXDJNNOXi6fNdFEs2rlvSy1Tax666w1Q7UwzEwwzOVxLyZlkYD7B7tFO878Ec13WfpN01y/PxZcRPV5DVVaQanUxBpdqZ4ntHNxPyZnlgYQM9wSkORZpYWhHBpRU4E61mTdM495zdiiYb1DtidC6Z5pGBS+gJTvHPJ7eiOYrIcolUXiOgCtsCVTF4JL6Wz1Se5Uy+nndVvUS8oBGw59jQOMpbwoeJF51srRjkvoVN3NIvfnTXagnqHTFC9gwFQ+FDbc+z3DHJnpE2rus+SbFk44dD6+iumOaK9n5CSpplvhm2Vgzy9WPbqGlbAKA/W82H2p6nqW2Ona5JMgWVHw6t46ZHP8w3pl/HE9PdbKwcASBecBJyZvn7fVejyQZ7F9q5b3o9d3TsRpMNVrnHOLT6QVYHJ6haFuGvmh/9P98Afh9hWWCVfstHmQJ8reP3SgVcRhlllFFGGWX8tngVVMDlGvBrHmUG8FWAb7SAZ9LAPWeRbpSwFQUbJesl1LRFYLBASZFIttiR8xa2omDy7GmTXKWMe8ZEyQnfP9OpYIkWO2yGheG0oeQsih4ZQ5OEN2Dewj1toOgWubBgBA1NYvIKHyVFpIQEBgv4RgRbk6mRKdkl9KCNwKCF6RCK4cBQCTkvGEM9JGE6bPiHhbefFrVwLoieOs+4hR60oeQEazO/3sRShDfgRY/DWJeEb8Qi0SrGNR3gmTRwzsHsBovAkMnYVXZccxYFj7SYMmLJwgPRkbDwHNGYX6ESOpNdvLaOCx6FoXMWuZCEZ0Iki9gzgjV0LEgY7hJKWmJmo4I9YUOvAs8E5GpLWIqYb/QSwWqFzpTIVcpkqySydYKZtBkidWVhOIhnREYywDNsI9NexLgQKeeICqbPeVpj7lKId5VQMjZKinjPPYMK0c0F5DyEXzcFimAFLyaxpEb8oj9xyk7RJ+ZtywsGs9iqk68ros4pzOyvI9NkUfSDPSljC+eZn/aL9ykvehBt4TylvExx2kW2q4CkWEycqkXJ2LAyCunWElpExnXQSW6FjqmJL/JsaxHHlF2wpIrJ4KkGpITCsyeXo1blUKMylt9AnVOwTTtIjfhx1mawFWxMRoKU3CbZmFMoj7t0pr/fSk3bApFf1jEzGmIyEgSthDxnxxfMIIcKpIcCNFdG8dWmhFJZsXC7dUp5GUdzivjLVdgVk5aNY2zt6SNTUPE1Jtm55BylC4zmj89dij7hwZ6R8Gyfw+3WefbkchpWTpMc92FpJTo6p9BNO4mIB2tGw31Co2+2CrMgQ1SlWFcgldH4xyPb6U+EiWQ9LIwHKBoyznCWQ/EWiiUb07ofPW/H7dbZc7oTTS6SNVVOTNWxY81p3rP0ZYysgpVRyEVcfOKSZzCeqmR0PkTF6jmiKRfra8eIJD1sXSp66QKuHGN91dTWx6iuTNIQjNNYE+Uja5/jvu33MDMaIj/qxbY1Rp1HePTdv/6bNFdGmZ/2cyzWwPa6fl6ILcVpKwqvw7pxpieDdHRO4XfpfHLZ07i0AjO6jxejHfQnwqhtKUzTRsmU+MzSJ3n+xUsAmJ0XaRpPn++iu2Iav5rjC10/ZSEi4nayMSfVzhSD2WpSeW2xL051FFlaEeGOFbvprphGNxU2+obIFFSGM5XsOr+dfMnOD+YuY1fdYRFTKBv83fBVvCV8mL0L7RyKNLE6OAFAtOjmocnV7JlpR1UM7hndxh29N1MTTPL44VXUeZJUe1OMpoVzw+d3X88q9xgA1y7r5Zr6M6x2jfJc31L+fN+NxLNO7o6u5brGXtbXjlHdKdjcTn+EPTPtfOvMZqbSPi6rPM8zV/0jszmPOM/5Sh6eW8P5RAi3Lb/Y9zczGmKV+qve8/9WeFV6AMsLwNc6ygvAVwHpejuWLFHwSISPGbimdfIBhZJdLNgMp418UJCtznkTOV8i3iFd2C6RDcvkAwr+83lKioRzvsTCcgU5Z+AZy2E4JRxxseBzRgxMh0TRI1NSIFsl4Z4xsWQI9hu45izcMyXmV6iU7BKWAvaMRa5CLPKKbgnvhCgvG04JRRfmzIEhk1ylDVvRumAXI/7zB4bEvpYsxBiSIcqVF3N+060lKk/pVB8y0UPiePMrxUJz9lKFXBUEztlINcj4B0VJNtUiSs/umRKZGhnngkW2StjPAMxsdOEek0i1WqRbTOwJiHZJKBdEKfEOCVte7JurNXFO23BGRKxbocrAOyK2uccvWOloJfx9Mu4TGql6G3pIouAD37CwyYnt0IltKKBGZdQkuKfFAkxxGVQcFEbZkgGxLiFkqdlvETxtI3jOwggZmA4hDJESCvkQzMR8eM+JiDg1CURVIaBAlJQNd4lcrYmsS6gJCWVcwz1ox2zPUQiJ5/XWAiXFohRxoETFZ0dSLMxNSZj41U3JdU7FFrVTChUxGnVcIwqWbJEPlUh1GVimjVKoiDOcxeYwMTwWSsZGtTd1wbBbxMflkw5C6+aQVZNCyBSiklCBQt4uhB2KiWRIOHx5chEXmqtAbIdOJObFvj6GpViYURXSMnJLBmN/kJIpUVJLDJ5qIJV0QoMOhoSet+MPp8lNuymtSFPnSXL+cCMHxptZGA+g5+08fb6Lm7uP0lgThXMe3OM2SjLMnQ2zvnYMfzjN5LFaVq4c5apLzjCb8nLoeBtyUqGklrBtjeF161iGRPvKCXYsP4vmKNLdNIVDNpjtr2TlsjECrhy5aTefaniShYiP0XQQ9aAHuyJK3wfGm3luqhOvW5Q7v3b4dTh8ebb29GEplsim3ZpmabVY/FmDHhIFJ3es2M2+4SUMnKkHYOXKUWbnfczO+6hxpohnndw3uo7bTr6DP9hwkKplEQKuHJpsMD4T4s27P0gqr+Hw5RmdDzGt+5nJecmV7OimwruqXgIgmnMxM1TBPaPbKBoyR8caRVZy1klu2s2GxlGqK5PcNfB61IRERTiJ15fjfx56A5qjyO6jK9h9sJtjuRa2Lh1gNufhko5xGrQYTrkgyrsDl7C5egS/S6dGS3Ii3cR7w3tocUd5YOpS3GqB3tka+hbCLNWmCdhzTBRC7HndVzgWqWfyWC2fPPFmesfqmJkN4JIL1GhJehdqccgGYVeaO1qf5ZudP+KaprM0e2L8wYaD9I7VcVnleQYnq8SCtipHiz3CuB7iibPduGwFfjizCa8vR0fTDM2BGD8+dylXe0/R6ZqlWLJRoyX5k6rnuab+DH/a/QJFQ2ZCFxY372p8mf5EmPbKeXRTYVfzUf6/ozdR7UxxNlmDrzbFd5KvTIsqo4zXEsol4DLKKKOMMsp4LaFsBF3Gr4EyA/gqwDVjYDhtBPt1MjUKlmJDD9pINiuEjsdxTWZxRor4RoqYDhumw0blSRNb0UKLiTKxM1KgZLdhajZKdiEyiC53kW5y4h0TzKCSE6XjkgIFr2D0JANSDTLO+RKZGgXTIZFstuFIWuQqZcInCuRCEuFjWdSksHyRTAvfqIHtV77Vi4bUBa9gJTM1NkoKzK4TgoSiG8InijgjUH1YvNaeFeze3CohdsmHRCRd5akSaqqEIypYuaIb0o2i3JoPQfDcBSuYSvHxS7SK81B0CyUvys/ZOovAOYmG50ScnpIVxs2lC4bTRZ9gJT0jMtkWg8BgUQhTTisouoWathYFHc4BFdMB6RYTUxNsnpwH53wJZwRKEQeucyqeCRYFOq5p8Ox3Er3EFPF1eajohflVMLtOMIhKzkKJKtiToM3ZcI/b0OZAPegR17SpQLyniL02K2LxanRMn4lv0IY9Ker87mlhGF30IUqeioWSAXQbRsigunOekkMYbsuqSSEvrGnkpIJkShhr0kiG2F8Z18g2mKhRUTpGK1ERTiKrJvlRL/ZhDSucx9QsBsZqsE+puCZk5Dk7rmCOyKkwAX+GyqM2LEcJ+7BgGovTLkLerIiqG/RgKRbtlfPIg05uWnaM1IifSzrGcU7LqFEZY8KNsSYNgBaR2b7plGAiTQl7UqYmmCQ57cXXmCSfdDA4XwkNOjXBJGpIxxxxk086eG6qk5mYD3lFksL6ND1X9mPJFrvPLBNl2qVxIlkPB6ebSI342br2HFduOQlAKulkftqP4jLIm8pi+RMgbypUd87Te7yFTEFl5cpRbt53G1uXDjA2WUluTY6F4SB4TLxunehgCIDn+pZCWqanborXV5yic8k055JVvK3rMIPzleSTDrouO0/vbA0vxJZSMiVcEzKd/gij8SChUJpSXmbvkS7sikmxZOPy+iEeOruaubNh4lknh4ebuHZZL5W1CbIFO82VUfJJB3tPLGXk5SbypsJELMBLmU6amyOsDk/y9HV/D0BzIIZtxMm+2RYurx/io1c+Ra2WwOvQ6QhEeNMN+7iusZdM5sL7asjUNEdx1mZ4YHQNA/Ewk5Eg1c4Up5N1vC/0EtXONNtahnhhso2wK02jFuWJs938w+ROziWrOD8Tpic4RdiXRs/b+UnkUtpds7hsBe5LrOKP2/ZwxetOsqvjKP9zwyMc2XE3l7n7ORGrI1sQoqNI1sNdA6/nL8av5/Hz3byr6iUu8w7QXj/HkVgT7fVzPL38J2iOImfy9WzyDXLtsl7G9RA1WpL3dewjmnOhyQbPbfoaX5+5kvmiB7utRLzo5JaDH2Ch4OGhydUkx304bUVOJ+t4OdlGtTPNp5qeJFHQCClp/mbNQ+yqOEDfbBXV3hRhJfVq3ip+dyj7AJbxa6DMAJZRRhlllFHGawkWZQawjP8jygzgqwA1XqDgkSjZbYsMkqJbBAaLzG0MklriEbFsmo14m41cpWAI80GFglciUyWRWOJgfoUwdXXEDOwZwQ4C5IN2ki32xZ7CohtshujP806WCJ0rkKoXb2WuSjBcRbfo98sHFJxRMSfTIQQPgim0YU+blBRhi+KImyRaJZzzJpkaGwUfGJqEZxyKfsGYLayw454xmF8pevtyYcGi2bNgT5vYk0LMkaoX52fPiuuQD4EjKvrtHFHIhSSKLnEeeb/o48vWWcS6hG2MzQA1ccF2pUYm3lXCZggWUK8SFjGFqiKGQ0TgOccVFlbYSXYXyYVhoVsIT1wTsuhVNEWPnj0pi8g680Kcm1ci3Sh63RwJiHeJXsm8TzB8iRUGkilhMyDdCNXvG8Y/IOGaEoxlwSvi5UJ9JloU0t0FMk0WuaoLjN2cXcSbTbtEX13EgRrSyYeg6BPsZKxLWNEUQwZSxCFMoh3inGwZmZnZALa86AlUHUVURxH5Qv+jVKNTiGrYMxL2qILRqOOclnFGQD3tRFZNUkcqqQ/HkGqE0EQZ15B1CcuQcK6IkWkvIrdkyI96MUIG0cEQmVoJSxHxe2ZUxZaXKJZsOHyCPQwdsXPqVDNme45/ObCektvk5EAjZk8aS4G2VeNwziOEKm6L5/pE9J3dYVBqyRHPOqlojJPJaKIXMKURDqYYnxFMm82QaG6OUDRkClmV9sp5NEeRvoUw1152FNVVEFYkI34SWQ2XWiTUHmU0HRQsncfEMm3YHCZm0k706Tos2cLYH6TvhSVsrBwhcipM+8oJUhkN3VRYUhMBwOnVsfc5sRVscCGazvIbbK4eIRQSrGbvbA0Pz60hbyoEVJ3Hx7u5tG6cziXTnBpuIOxLM5oOsqV9CGVTjGOReqq9KVIZjfVdw6xcOcoSf5T46Qoe772Eza3n+fM3PIxdMWmvnyNXspM8Ucn62jEGxmr42OZnWNd9ntp1UwTsOdor5/ne0c3c1ryHXwx28vmpN+CQDSJZDx940zNsrh5hk2+QhyZXc5l3gP7ztew53cnL8y08Nbmcza3n2dI+hEsTPX4AdZ4kf9/1AJ+79FGOReo5MVXHdQf+GE0ustIzwTVNZzk50MipVD3t9SKLPZXXuGnZMb5cd4grq/v50+4XGIiHudU/xGrnCON6iG8NbwFgQg/Sr9fw7fhKtjtN3tX4Ms0BYdh9Tf0ZvA6d84kQuzqO8o3p1/HDmU1ossH76/dybc0pel56LwFXji+dvJovnN3Jl+sOMZIJcSJWx0OTq9nVfBRNLvKx0Ru41D/Cg71rWBceYybn5cRl32FG93FldT9/cNkBdgROo5sKvxjsZDbn4ZH4WsYmK/lK3xVEDC9n8vUUohqDk1Vc5/qVIK2MMl5rKDOAZZRRRhlllPFaglWCUum3HKNMAb7WUWYAXwXYDBObYaFGhVIwH7RfeN4idCZLstmGPVXEcNoI9Zk450sU3RKOmIGig3eyhB6SCPUVccSEdYsjfoGJ8wj7Fud86cKYIvJMyYkoOZthkam144wKxjAwIFS8joSFPWORbBZvsR52kFhh4J4Wv/iTzTaKHplki0SuUmZhuUKwX1ik2AyoOGNSdIF7xsQ3LFg8OQ/5gIx3XFjFOCNC8Ws6wNRseKaEDYx/1CRTK5FuEGyfZIDhEmNm1+fI1lk4kuLLJdRnEjwtY8+I/VzTwkC64oxQ/yZbLRxRG2pSxLaZmoV/QMIxZb/QLyjmJecRZskII2TntI18SMTe5WpLlNwmxbrCYrxdruqCfcu4hPO06InS5kSsHrAY8WZP2CisT2O4Sww/vkSwryakVhWIdgsGcm61TKYW7FMqSkMGR1RaZOnUqhzelgRy1oZn2IZ60INea6BFhKWL6SqRWGHgPadQChXJtBcvxNSJ66OOCVbYOaDCUR+5iItsq/iMqKedhI6K33DFkEF7/RyGG5Lrdcye9KJp88SpWtTTTqECdpeoXTeFc9yOnrdjc5gsrZ7DPSbhHrTTuHwG1+Z5autj5BqLSKbE8g3DRKMetrUMUXKZeG+cQo3KQhmctyEpFpIhYTvtoRgyGH2xCbrS2DIyZlURmywi7pZWzyErJkVDXGQzqlI4GkRWTaIpF9WVSdSDHpSMiFYrGjLoNgafWULRkLmx9cSi6XHYl6Zx+Qx6ViVbsDM/7cchGzTWRFHHVFRXge6mKdSQjuEGe22WzTeeoGbTFC65wE1X7V+0I0nlNfKmwkA8TEMwTr5dZ+36Af5ky/Okkk4kucS5ZBVutcDWtef4yqr7WeGbYibmYzbnYXtdPwfGmwmoOlJCET1/o2EOTzVSNGSiUQ8TsQDbWobona3h1NkmdFPhnde8wMrWCV4cbOOuo68n5MySymvsqjhA7bopRtNBaqrjnEo3sLOyF4dsUKGmOTNZS1P9PN+d2MySmggzOS+j8yESWW3RauXTh69nY+UIx7LNNDdHqK2PkSmoeB06DtlAN+1kdZWCodBdPcOnmp7k6zNX8rlTb2B7XT9Lq+fYueSc+D4r2XlqbBmSIdHmjvB015OMpwJEUy6mdT/3p4MciTVxo/c0jd44D2eqcNvy7Jtt4UNtzzOb89K7UMvzs50cirewYv/bCSsptlYM0tQ2x1AmjCYb7Go+ykvzS+hbCNM7W4NuKjweXcVgthrNUeSbnT9i/+Z/ZmlFhF3nt7O1YpArq/t5evlP+G7fRnTTzpurjnLf6Dq2tA/R7pplIhbgiwuXcGvtL1koeIgW3RzLNpMoaLjdOrc17yFadLNj+Vlyp4P052oYzFazctkYVkZhoJh+NW4Rv3uUewDL+DVQXgCWUUYZZZRRRhll/D+G8gLwVUCmQURRmU6h0JVMwTzNrFdJLnES7DfIh1RsRQtHrIgjbiDnIRdWcEaKxNts+EZL5MIKklHCZlhYsoRzQXj0mQ7hA5irEP2ChgauaR09JDwGcyFhBG04hXFuNizjHcuj6OAbLZFqlJBMi+ZHLVKNdtHrVmXhGcvhmRDjGy4o2SWyVULxm2iWMTXBbiVbJXzD4pzi7RJ5v/DkK/gEC5hutEg0y6TrJLSo6NuTTNHvZ11oMvBMiOviPOrEEZVItIu4umxYRg+JfR1RSLRbpBstJrYLZtQ/KHoHUy1g+kxKoSJ5P6hJ0e94ETZD9PpdZN4uxtMV3WDLS7iG7YT2qaJ3zwfOOeElmK2FkkOMpVeJvjd7VvgbaqLVCcc+D5ZDmFun24Xvn6dXxT0mUXIIbz+60lSsnsO51yMU0hmQdQnHPg/JcR9mVRFTE6bWtqxMPvSr8oyvNkW2zkKSS/h67Yu9gReNotW2FHpViXyFhZQXMW5WOE+u0SDVAnqtgT2qUONMoSxN4hjUkE94yE27KbYKVrqwIoeVsKM1pBmfCZEPlbAGPXh9OXqPtyBdEcNal8QhG6QyGqpiICcV3OM2Tg034PXl2H2wGwyJ8ZkQWk8M5ai4LvYpFYwLDO6IgqlZ9NRNoTWkkeQSAX+GUHuUkwONbGsZQs+qLIwHUKMyK6/qZ0lNhJpgktl5H7k1ObKtRaS8jYZgHClvI9dRwO/S+d6erfhdOsW8wvShOjr9EWyyYCk6l0xT40wxNlmJd60wAN4RPossl8i3C4Vxf0Iobb9/YiOPDFzCY49spjDnRFUM3tX4Mn/f9QATsQDVlUnGUwHmix4+svY57A6DVF4jkvSgm3ZeynTyvaObKWRVNlaOsGemHa9bZzwVoHH5DLvPLEOds3Np3ThhX5r/ueERdnUc5VikHtO0sXLZ2KLSttqZortpijVN41xbc4qZ2QAf+Pn7KBhH5tg3AAA5lElEQVSiv7C7YpqXxlv5x2/dSP/5Wh4YWMOapnEmI0GaPTGaPTG6fHMUsip2xeT66uNcU38Gt1vnuSnBlk6cqmVmNsD8tJ9mj4heO3iulfcsfZlGb5xW9zzfiWzjo7XPcNuyvfzLSxvoHatj32wLB8abyZZUnlzzTSS3welkHXfOL8Pr0Ll//TcJ2HNsdY6hyQb3JVbRtxDm5wsr+YfJnWyv66dfr2E0HuRry37EZCS4eNxj2WYOxVto9sR4fcUpAmqOb53ZTLMnxtKKCN9e832uqjrHRt8QhyJNJMd9dNg93NJ/M7M5obIf10N8b89Wrjh5C5fWjbMuMMJO1yQAr684xWC2mmfW3wPAD+Yuo0JN0+ma5WrvKXqCU1R7U/yPAzcQsmdIFJz0vfvrHIs1sG+2hRZ3FMlt0GH3vDo3id81Xg0GsEwAvuZR7gEso4wyyiijjNcSLqZ5/DYo9wC+5iFZVvld/s8imUzi9/vZtvUzmGEvkikUtq6ISapBxjthkg3LVJzMYCkSCytd2DMWaqok4teCtkWlb8F7UYEr1uT/egzDaSPZJCHrUHm6QLRLxZEQjJyt+Ku3z2ZYpBpkwseyJJc4yfsFeyYZ4EhaqCmLkl1i7lLwDkuLDJ6aFurX0LkC8XaVwGCBSI/oPdOiFnpIIjAk5pNutFATEv5hi8gqwaSVFNGXCMLTz1JYZAwXukU/nD3BYtJH0SXmk2iXcETFMfI+kQTinjHI1Cgk2i2cc4IlLF34mZIXIlHsCfGn4Rasn80Q55gPCcZRyYjewLxf9A06ouLaZess7JkLzF+L6IW8mGqiRSFzRXrRw89wgx4WKSi2go2SKtJEku0lLEcJdU4RqRppidC6OSIxL8HdGrEuMTe1LYV1UkRr2fKCZSy6RbSeVKNjJu1gSMihAuppJ7nGC5FzCXC8aY6Z0RD2qIigy63JYe9zUpKhEDKxtBKSYiGrJvY+J55xC9MhEV1bxJaRBXNYVYS08OUzNYvAigVSGY1CVMNXmyKVdFJdmWRmqEIoXgFHc4r2ynlOH2mlpJaQTAl7bZaeuimOHOzA0yaUuyVT9E1muwo4vTq5aTdyqEB9OEY86ySVdGIl7MihAmZBxhfMiOcyCjXNUbbVDPL4+W4AchEXsq+IGRWehJn2Iuu7hjk61ojqKKJnVaork0xPBrnqkjP0J8I4ZIOBM/U0LZ0l9dM6YitMVq4c5cxkLW63Tk/VFAfGm+mpm+LQ8TbUqhw1wSRjfdWsXDnK4Hyl6PUzxQdrbLISp1cwpeZpH4WQCR4Tec6OpYDpE2xo26px8qZCsyfGmWg1RUPGrpikMhpLq+fQZAO/muOl8VYuaxzm4HQTPVVTvDjYhteXA6Dam6LGmcIhC3/CeMFJJOthW80gD/auwe4w0BxF3tF2iPmih+emOtle18/j57txaQXqPEla3FF2j3bi0grYbSVm533846YH+NjBP8BM2pF9RcLBFLPzPrqbpmhxRzkUaWJdWMSo7ZttYYk/yvlEiLuX38dtJ99BKumksSaKX9VZGxzjgYE1mKaNpdVzRLIeuiumORapZ3loFodsELJniBbd7D66gh/v/Dr/MLmT2ZyHTn+E/kSY8ZkQ25f20emaZe9CO8t8M3Q6Z3DZCvz582+ho3MKYPFazOa8bK0YZFwPkSvZuS50nE+ffhOX1w/xxNlurIzCN6/6Ds8mu8mZ4rvpZ/0rFt/vld5JTqXqccgGiYITgHhB9PaOzofoqbtwPC3JiVgdtzXv4eG5NWwJDXCN+wz3JdbRoEa56+jreUPnaSrUNE9NLmdfz0PsrOv5je4L/xEMq8gveZREIoHP53vVxv3XcLvdrFbfiEcO/FbjnM8eY8NNXdx7772vzsTK+L1DuQRcRhlllFFGGWWU8f8YygvAVwH5gF0kaUQLAJQUkbcrmRaWDIbXTrrJiT1jka6TyNTIFLwS3vEielAkf9gMUHIlFN0itqKEPW3inilhOG04YgaeKdGflmhVcc+UyFYJ5k0PXkwdEX/aDJEgMrdZvL7idHGRebvoI+gdlhb98bK1UPBIVB3KEG8XzGKq0U6uysI3WiLvE/umGmSytSINI1drEu+QaPlZHtecJXroKiTiHWLfi/1thibYOBCMWrzLIt0AzqhFskWi4RcFTIfIMwZIvC5HfImCJYM9I+EbLVG6kGVsM0TKiC0vWD57FvzDgnXMhwSjKOdFyojhFqkh2a4CvmEJ15xFsruImhCMo+GC4AkF14hyoTcR0Ye4z0O6u0BhvUjXcE3IoFhIBngHZUwHVJwQc5VXJPG0xbGZkH+sCiYE+2cpwvOwMORd/HxkWwz0sIkjKqGkJex9gqGwtBL2Pie5jgLqnCLeu7VFZoYqhLq2PS16DqMqetjEu3YeNSrjHrQjz9lRHUWKS3Ms9FjEu0o4x+3Ya7PQoGOL2rG0EjYTSg6L5IlKCnNOLMWip2oK+7BG/rGqC96I4joW8nZ6x+p447YjoFhUd85jDXrof6ATT1scl1rELMhorgL1V49hGeJauCZkAv7Moo+f3WFw3cZjAGxdOkAq6cTuMKhojBNNuXhwz0bsiskjl/4zGBJm0o5alcO5dR6bw6R3toablh3DpRW4dlkv22oGsWVknLYiY0NVpPIa9qTMWF818a4SeExOnWpmef001d4Ue053Yky4OTFVh+QvsqFxFIds4KzNcGq4gVxKY3VwgkhSMFYfXf8sYZ/wI3zbDb+kY/kkFeEkN121H09bnObmCJ62OF2+OWZiPs5Eqwk5sxQNmZAzSyGqMRoPcnSskef6lmKaNmZzXjIZjX17l9NeP0fRkAm4cgz01/HXDU/Snwiz0juJJhvMzvtwyQW2L+0D4MbWEzwxLZiukDPLtO7nssZhttf1o5sK+2ZbyKU0iobM33c9QHv9HGE5iZm009Q2x+bW8zR642iuAr1jdZxLVjE9GWT3aCfnklXc0bEbTS6yEPHxvqN/iEstYmUUNlaOMJX20anN8Mil/8yGxlE02WBmNsDug90s8Ud58eAyntu/krPJGhIFJx/d9jRvfe42toQG+PSSJ7gudJx3Nb5MdWWSXwx28t2+jQDkTJVvDW9hf7KdmuYo72nYRyqvoZt2Ol2zTKV9fLdvI065wJWBszweXcX62jFGMiE+svY5frzz63zu/LXcEDjCSEawhPXhGO9oO8RbwocBGIiH2X1mGe+o2c/Oyl5WByd4uutJQt4sNVqSW2t/yUgmxD92PMAXzu7kzVVHAbjuwB/ToEb5wfhG6sMxdo920qBGmRmqIG8VX7X7xO8UF0vAv82jXBt8zaO8AHwVIJnClsVSRBxcstlGdKmMJUvYMxaRHhVDE5YovjELz6QoP+bCIrbMVhQLw0iPKoyNp8Xb4owUyPslih4ZQxM3Wz0kFnKuOYu8XywizQuGyIoOct7CdEDjz4RFS7zdjpoWCyhDE4utXFiM45qzKMmgxUoMvtWJ4YBkk0SmVizuYp02TE2YOxfdF8qsfgnvoBAxTG/UmF8l4tnsGQvnnBA/2PISqUaJ+fXC7Ng1DQW/hWNBoui/sDj0WIvG154pYQsTfkIj02SR94vXzGwS8XKRzQaZWiHU0KLCxqXoEuXmbFcB7wgkr8kgmcIEuqRYFFt15Dk78S5huaNElUXBSqHKILk1h80Q4zi3zuOetij6he2K7bSHXEdBxKqFdJxzEqkuA8mAZIuENq1gmjaSMTclWSwgTVcJI2SgJiSKFyo7+XadfLuOc1xBrcphuKBQZVCShdGz4hJjqmMqhSqDgg98vXZWrhzFlpeQ5RJqSJQm1aocS/xRLEWIVYyQgT7hwZrRUDI27AkbHVeeFyXsCQ2pRscxZUe6JInlN1CWJqlojXFJxziHpxoxPBbJrTlKioUtnMexIKE6itgdBk+f72JrTx+RU2Gk9jTON4gFzMxsAF8wg2naGBir4apLztAQjOPcOs9CxEcolCbTFwDghck2ZMVkb18HW9qHyCcdZHUVY8ItLHkMmZuPv5+O5ZM4w1lkuUQ06sEybegTHqZ1P0VD5vHDq3hqbBmWX8wLTZQ8K1bP4WtM0r1qhL/Y+AR4TPpmqxicrKJzyTSlUJHCnBPNVeBMtJrZlJdszCmMnqdUhjOVXNY4TO9CLV/tvZyZ/XXIqsll7n6iORfLQ7PUO4QgIZL0UDRknj7fRcibJaurzKa8tFfO852O+3n35hdxqcKkOxRKozmKnBxoZHn9NLIucVXVOdor5+n0R2hqm+Pm3neLUmm2Gr+ao7tJlCcPTjdxQ8dJJvQgGytH2D3aiSYbvHhwGX9S9TyPPbKZaM7Fruaj4jwUk9tOvoMvLnmIu8beiDOcFeXxeJiD51r5yqr76W6a4j0N+/j0lscJ+9IMTgqLlpXeSa7rPsmldeMUSzbWdZ/n5fkW3tV6gIfn1pCxRHl6NudBSihsXSssYW7e9jJb1p9lmW8G3VR4aHI1AMsdk3xg77uJGF6+dPJquiumuaK9n+7qGQB6PGOsC4/x1or9NHrj7E+282D39xhPBfj6sW0s8UexKyZfqD5BWBaCndF0kEc7nuaes1s5lFtCtTPNdyLbeLTjaRIFJ59e8gQNanTxe/hry37En1z6S45lmwkrKeodMY4XdK6pPwPAs8luAmqOr81dyT2X/JB7Rrexznme+9d/kzd7hikYCg7Z4I4Vu3k52YallXBI9lf7dvG7QdkGpoxfA2URSBlllFFGGWW8llAqgVQ2gi7jP0aZAXwV4IgVyFXayIVVEkscaFFR+i14bRTdopTpHS/inSzhPZ8l1qlgOqQLbJUQgJQcwtoELggYAgrzKzXsGQtfbxRXxKToFmKCaJdEslXYxER67BTd4EhAZBXoIRGhlquU8Y0IM+iSIo5zUSxx0VQ53SihZGFyp4l73IZ3skT+khySCeNXaviHLbINJvOrxGtNzSLeU8RwQ/UBEYFmycKcOVMr7GNyVdDwCwP3tIVnUFmMn7MZEkoG7Akbeb+EJVtk6yxyHQVyIYlYF8xsLqE0ZCg5QLp+HjUh4Rm38PXacU+zaAeTbS2SD4FeVcJ/REV98xzGhFsYRbtK2DMSVsIuysiDNkqyKMvmdiZxRCXUOQW3W6ekCEPn0hOVLFyRx54QNjKVJ038RwQ7yTkPuSoL/2mFZHeRor+EuiZGPulAVk2K/hLRS0wkU0JOKui1gm2zZyRKeZngXg29qrQYB6fOCZuUwoocZlTF7ElTs2kKZzhL0V8i3Vqib7aKUm2eQt5OPulAMiUKWZWjY40U6wq0rRoX7KEpUbUsgtGoo/XEGPvJEnJrcsJypiDEH4W8HdVVWPysnpmsJZfSaFg5jRlVWbN5ALdbp/3q89gVk2JeoZBVmcl5WbN5AM1RpFiykR/1Qlomk9EoTrvYsfwsu88sI5pzEY16kOQSsb4KPnTtz/C6dd7RdohCVMPp1dl7pGsx8m3F2mFcwRyFvGBWUnkNv0snP+rFNu0AwNuSYCYnSujOcJZMRowjyyWa6ueF2bNi8LkVj9E3W8VdR19PTXWcbS1DhEJpZlNediw/C4pFLqUxP+3n8vohFJdBQzBOqSXHocGWRbHA+5fvY8NVp7mivZ/3/+I9RAdD1GoJvtp7OesCI7RXzvOjtd8W12WoAr9L53MrHkM3Fbb98kPc+9TlqIqBedqHWy3gUou8Z/1LaLJBz5X9hJQ0Le4ona5Zmj0xGr1CgPLSeCv9iTCRrBAeVXtTPDJwyaI45Qs9D7MjfJb2lRO4JYOqLVO41QKnUvX4XTp/3LaHpRUR/mH6apb5ZrhjxW402aAjEMEfTvODucsIqDmOZ5pw2Qp0+iN8ZO1zAJxK1XMo0sTevg7+uG0PK3xTNHti7I4so0ZL8g/TV9M3W4Vf1bn2sqOs9E7y+cZHucw7wGg6SKU9zVtrD1LtTFNbH+PT/Tewd/s/8oPxjWiOIht9Q2z0DbGzspdHO56mP1cDwEgxvFjGvi+ximvqz3Dtsl7eXHWUa5rOcne8ia9M7CCS9PCehn18J1lNe+U8blt+sWS7Yv/b2VnZy6f7b2B/sh2XlOeJ6W7+7PxN9GercdkKRAwvg9lqvjZ3JT8d7qHHM8a07kc37fx/1c/wncg27mh9lpFimGV2maxlcmPjcZo9MdZoo3yjYR9yssyPlPHaRvkTXkYZZZRRRhmvJSyWcX+bMV6dqZTx+4syA/gq4PxNDgwHi2IOmyHEABeFGo64QaJVpWSXSHS6kAyId5UInoO8T6LolgidKWHJosdMTVvE28RbE+uSmHhjJfE2GUdC2Ks4ooLJy1VIeKYsZF2M5xkX1i7anGAC9ZCE6bjAFs6bxNsl9CrxWt+wRD5UouQQEWquOYvochtMaDi3zmNqwhrmokWIPQM2Q0JSxLdCtEtiepMN97gNe0bCcEO6RcTGjdwE6UbxXL7CYn5NCUcUUu0mpmaRbBelCdNVwhYVMXamT1iuGBNu1DUxsvsqKbotFnosMk0WiQ7BNJZacriCwsDaOW0j3Wgx219JSS1hMyQhfMgL6xbTAfGeIql2k2xXQfTMrRM0a6YvQLbBxOnViW/WkSIOsnUXrGyaZQoX+vjM9hyl2jy5sPi3PSF6/5SoQjiYwnKUQLGw5SVKaglLsYQB89IctfUxpOvnCbVHseUlUCzM9hzOOQkmNNSoTHOlsN5waQUh4AgVKOYV2utFbJpkCBbT6dXx7HeCLt4PIyui47bVDLKlfYjkuI/ECgOzICPP2ZESCuGVEUqmJMarnCfWV0HAn8Hp1Wn2xLC0EoeHmwA4NdxAJqMRCqXxBYVy53wixNKKCPPTfjxtcfCYmEk7//P1/8LBafG67XX9WAk7RFXWrh/gcKKFoiHzwOgaOjqnME0bO9b3AlARThJQc5imjTVN41R7U6iKQeLFKt647Qimz8QXzFA0ZGqcKQKuHNct6WVN0zjvWfoyfpdOtTONX9XJFFTu7Hsjjn0ePrTqeborpuldqGVz9QgBV47RdJCmtjkkucRd2x7iibPdLK+fZjbl5Z3dB/iDnsO8o2Y/22oGORRvYe+RLv6y9mm2reinunOex89386fdL/DPJ7eytWKQm//lI7x7zT7Uqhyf6vg535rcSkDVuXZZL39/4w+4srqfP33Lk1xZ3U9HIILLVkA3FXRTIWp4yJXsnErVc2C8mRW+Kc5Eqwn70vQEpyiWbNzbu4Grqs5RzCt8tPYZokU3dw28nuWOSVYHJ/j81BsoGAoPL7uPNneERm+cZvs8flXYy+RMlWb7PKPxIAF7jsvrhwCIF5y8PN/Cdyc2kyg4GcxW83h0FW8JHybsSrOydYL3+mYJKhlG00GqnSneFDxKwJ7js6ueECIQ3UdISTNm+Omyz/HwsvuYL3p4Pr6Mo2ONhF1prqk/w95cE59e8gRLKyK8nGz7/9u78/Aoqnzh49/e00mns5CdbBAChEUCJEAEHDUZ0eF1EL1eZPAVweVRQXB0RnHuqzi+770wV0dR9OLLoOC9eIXRERBHRWSTHVmCLIJhTViyQJbel6o67x9lGgLo6wiCTc7nefoJVJ0+9ftVdZPDqXNOATDScZjJJ0oZHF/FzqYs3j05gN3VWdyas5tTYQePJm/Dr1lY2VzEplP53O3cR/+kat7s9zYNit4L/B+d32e8s44/rPwnVh3oyp6yd/BqNkpTq+kSW8cz39zGM50/4rWChWTHNDH/YClD7QcZ3WEjs7M3kB7vpoftOL3jjzMkuYpcs40bE78GoEGJZ5E3jeHbH6AmkMzs7A2cVuOY2ZzLPRVrLs0viCtBCISmXdxL3gK+6skGoCRJkiRJUjsjG4CXQNIe/bFpIYf+KLXTvcBRI/TeuSC4cyy486G+BOoHaLi76GPuWroYCCfodYTiDTT1UQh3ClA3UBBbL/Cn6o9HCzn1XjtvJniyDMTVf1tvJ332sBoDIkGJLOzs7qJGerOaeqoYg1Bzu4YpqI9x82WJyGPUwt30R2819BdYWvQFjF07U0jap8/sjanXx+t5cvQYQO/VUxwCNS1MyAlaT4/+CDaHSuCOZgxmgT8njNkLwiRI/sqE7YZT4FAhO4Ct0Uiv4iNgFmix+hhDYjScOd/2znn1cXOmfC9anD7O0OzRe9gSVtsJHHNgHVmvj/lzqvojxxLCKHEaeXkNaDYQqUHCyQoJqR7smV6y/m7G7DVSklWDMENiz9N06NTE4JzD5GQ04ihoxlrgxjKgCXcXlWAHgTPHhaiNId7px6iCyWVm4C/34EzyotkEpanVZOQ1gmJASVbIKDhN184ncea40FQDtXWJuL36ch2qU8WZ6UZTDXh6hUjseRq6e/hl2j5iTArNLXGEg2aE10y8009VdQZBlw1LoxnzzacI+Kz4h3owxCnEmBQmDFoFwMJtpWw9kQMOfcbyvf02YMr3cud1m2h0xyJaLDzWfwUnPE4Gl+0lzhrC747hqCeJrp1PMqTLQfqkncCZ5OV/lyyhc0Ijt3faSZ07nuaWOOr8DkwuMxaz3juXkddIgxJPerwbZ5KXpYd6YQwZ6dL7GNurc9h6IgeLWcW7JYUYk4KqmGgJ2RmQWR1ZRPiFfn+jU9wpujv1Z+2pfTysOV5Aaa9DtDQ4SIgNsLM+C5tJ4dPqIgKqmb72I6TGegC4v+NaZvZ4l8ZGB0nDT9CkxLG+phO351Sys0l/RNyfOv+N2iYn6SkuRsbV0yv3BPd3XEteYhPZ1kY62proZ6sDIMHqp7DHce7cfS8zcj5lUsFKpvdZxNaWfPrl1jB3/yCuHbqXWGOIgTlHyTHrM09/23EZR7zJpJpcnA45SDW7STJ7KUk4wuiESkZnbmF3dRYf1BRT54+nOWRnQq813BS/i7/0/C8WFb3Lr5O2M737B7x37RtkWxv529BZPFY1iuyYJkpTq8k1t/DHtK38NvMz/t77vxiy+QFGJ3zJkOQqyu0qozps5u28L6gN6F3WFrPK0q3F+DULVc36+MJ0u4c/dloCQEXiHno7jrG0sZj/6Pw+Qzsc4PlTRXzeUMT/yNzNn7I+xydsjO6wkbscTWTEuHgjfylzDg9hvbdr5HFs09N3cmtyJb/quoclhcs4HXJwl6OJo+EUShOPMDt7A3mWUzxWczOvZH3JDl8eeY4mipy1zChbyOmQgz+mbeWUqvGnrM+5MfFr8hxNvNncm2dTvubxfaN4NLGa8c46Yg0mKkMBXql4hzuKdrApqHBz3F7Gpazl5ri9PF34CeV2lbF7xwIwpWgZEw+OYpDNzIuNBSzo+h6zam9kRPxOHk2sxmaw8E0ggznHh/JoYjWL6vtxd8GXjEtZC8CQmACfNxTx9qrrLvWvistHzgKWfgA5BlCSJEmSribaJWjAyfbfVU8+Cu4itD4KLueN54hpcKI4BPGHDZEFfc2xCsYjdsJZ+ixMY6MFU0Bfz8/i0scKerooxO8z4+kfQLRYMCToi7KiGDCoBuJqjPpYumQNY9BA+pf6WD1/jr5Aqe2EhWBWGHOjWZ/p2jOAfY/+CKTWx49pmUFi9sWgxJ3pUTQFIfbaU2gfpeiL6SaHEF4zJpcJJVnBXmPBXq/3ILY+Ai0m24P/ZBwWl4nwt2P2cOg/DSYN55YYQk59oWhDQhgtaIrM+LR9ZUfp5yHospGX18DRo6kYFAP2GjOhnn5Sk9zU1iVisurjzOw1Zvw5CvZUHw8VrWXWwuFoPT10S69ndOYW/rDyn8jIa6T2aDLOTDeBoAVVMemPWIvRMFlVRG0MNw75ipaQnS93dyZhjxl3J/3jbs72Eh+nr7HX3BKH2qjP+rWm+emTdYIadyKnd6QRTlawJgf02bCKiZKsGtbuL8Rg0hCqEYNJQwua+HXxTprD9shnw2ZS+OJIATG2MK4aJ9Y0PzG2MO4jCRiDBuK6NRMIWshIcpFgDVDkrGXpoV76TFzFhL8hloy8Rm7PqSTb2sifqyooz/qGFSe60iO5jqrmVHp1OMlRTxJV1RngMVExYDfJFi8nAwk8nLGSd0+Xsc+VRlA1U70/HRGjkZLZQueERnaeyOKFfn/j99vv4LbCr7gveT2/2T2Ohwu+oFFxMCJ+J4UWB5NPlPL50a50STnF3uOZ/M9em5m3YQjOTDc3537Nwm2lLKp4jc89Pdnl7khVcyr3d1rHmqZulCQcYZcnm0fSVjLt2HDuztjI0sZi7MYwr2R9yZS6PnxaXcTdBV8CMCJ+J9VKAnuDHfmotjd9k47x1/UDuXXQDiamruLOyvsjZecfLGVSt1W8uv8G3rhmPuu83RgSt59p1cPpn1RNrDHE2tNdmFewiD81lNHR1kSTEsfpkIMusXWkmt1sdHVhnyuNcdkb+OR0b0oSjtCkxHEskMSoDpv5sKkfT6avZF8okeWuXgyOr+K16htItAYoTTzCRyd7sbqn3rP24LFrmZ29gQe/HWv3f3KWUK0kUG5Xmdmcy81xe0kxGXmzuTc+zcqnx3vwUveFvHu6jF8n6bNb13u7ciyQxOzsDQRFmHfc2SypK2ZEeiWxxhAj4/QeU5vBwuQTpW3O4aRuqxjvrGNKXR/uS17Pm42DuS95PQCFFgfPnyri2ZSvqQp7KLQ4eMuVzn/WDGJ1zyU8f6qIwXHfsMOfH7kG5XaVylCAfaFM4oxBUk0uPnP35nTIwStZX0Y+52+50hnvrGOF30S5XY3UvymoMMhmZqkvlv2BTCoceyiymLAZLGwKKuSY/XQ06WP8FniSuMvRxHHVDRDZvsJvIs4Y5EhYH4B7l6OJ508V8WjyNt5s7s3vkg9G8s012zilBWhQLaSawizzdqFfzFE+9/Tkd8n6eMiZzbmU2g8xyHbhfo8VfhM7/Pkkmz2MdBxmwBePUHX9vOh8FFz4BuKMCRdVzyFlD4NG95aPgruKyVvAkiRJkiRJ7YzsAbwIrT2Af9nen38/OIL7e2ygr/0Iy129aAzHMTt7A2OPXsfDGStpUJ3cGuujKqyPY6pWEmhQnXwTyMCnWtl0Kp90u4c38peyyNOJ/6wZRJ+kE0xMXUWsURBrMDHf1Z3jwSSmp+9khd9Eg+pkWOxxkoyxNGk+3mzuTV/7Ecrteq9ck6Y/A25dIIVJq3/DZ7+cQaHFwVJfLDnmRoqtMcxsziXV7I78L7/1f8dLfbHcGuuL9ESMd+rjpUp33MmovO1UOPZQbI3hLVc6S+qKAfhj3pJInY8mVkdybe1xGBZ3gBrFTjdLiHWBFObXlrGw8wo6f3Yf//2L//ud/zNvjac15nO92FjA75IPclx109EUH+mNOPu9t8b6Llhva6ytPSQ/RJPmI8kYe8F9QRFmR8gQyaW1R+TcMt/3hIHWeL/vOJfC2XG09thIktRWNPYAFoduIM5wcfUfVvcw6DfXyB7Aq5jsAZQkSZKkq4nQLv71IwcBvv766+Tn5xMTE8PAgQPZsmXLpc1NumRkA/ASOBFO4slrPmN5fXcmbPsNv3TupmtsHdfuvIP/lfUx06qHs7SxmJnNudy6+WEA/v3ozVR6c8m2NrKjKZt7cjaxuy6DZb6OzDk8hGc6f8SGunxea7iBMfvu5s3m3qSa3Rz2pjDqUDkPLB9PgxJPyaoJXL9nBCO/Hs2sHdfxxO47ebGxgIIV43jqRAWLPJ2YX1vGnBvm8kLdTYw9eh1ezcZdWx5g8onSyCy4pY3FgN779JYrnfcbSph8opQdIQPLTvXiwWPXUhkKML37B9QEkgkIM6MOlTMs7gDpdjdLCpexPZBH58/uo4ftONfuvIMX6m5iibsPU+r6sMlVwLstxewNdqRk1QQalPjIGmaHbnqTDsYACzxJVIYCVIYClO64M/Ig9udPFXFrrI8ii4njqptRh8qZ2Zwbifd3yQd5y5WOT9PHVx4Np9Ck+VjgSWKF38Sc40MJijAzm3OpDOnj/oIizFuudHZ5shl1qJynOnwV6TFd4EniuOqOHH/UoXIAptT1YfKJUua7un/nZ8FmsDDIZo4cp1o5Mw5nZnMuVWEPNoMlcqzWPFq92FjATfYWptT14ahijOQIZ3p0W20KKucd/+wyrTG0jq0627m9kP+/3r/Wes+N4efiQjm2aj0Pl1Jr7/YP0fo5kqTLRWji4l8/4ubgwoULefzxx5k6dSrbt2+nT58+DBs2jPr6+p8gS+liyVvAFyEyCWTG8xjtMVgazWhm/XFk9kwvyn4nSs6ZXz4WmwL7HIQ7BbAcjiGUG6J3p2Psrs7CcjgGxSHQrPrCwiaXCTUtjMmqYt9ux5clMGd7Me5xkPOLaqqqMxjarYr1G3tgzvYS8ln1CRQhE9bYECGflcLcWg7VpqKGTAjFgCFgpHfvo+yuzMeYGkRttJJRcJpGdyyhxhh6F1XT4HPQ0BSPphpITvaQ5XDxVVUOsUl+nun9Ma8evJFeHU6SbPHy/qoyBpftZe3+QjqkumhsdJCe4sL/cRqiogmAxFg/3pCV0w1OTFaVzhkNxJj0hsvR5iQsZpU4a4h0u4dDLcnc02kzr1beyKTileRbT/HY3/8nyV0aeaLwc/6w4XZ+O2A5nzcUMbTDAXa5O9IcsrNrVx7OHBfvFc/h3ZZSFlb147MBb/D40dvo6TzB0ppeTO/+Af9ZP5je8ceZf7CUlgYHEwatYu3pLuyuziIno5Fmn50+aSe4J209671d+e99JYR8Vn47YDm3x+/hkUP/REsohpBiprYukcLcWganHOJ0yBGZTLCyuYjdpzO5v9M6GhUH3WJO8n5DCZkxLXztyuD+jmt5rfoGYkwKk7I/Z0hMgK/DKnNPDaU24KQ08QjZVn2plVL7Id5quI7Z2Rso/vIu3iuew1p/AWuaunFP2npyzS3kmm08efJaypwH2OjqwriUtdQoycQaggyJCbAuEMPsk9dzqCWZLIeLeQWLOKoYKbbG8PypIm6K38Ws2hsBKIhroGtMLcdCyZEJFf/R+X22B9PxarbIcIXp6TupCntY6y+gh+14JMZRh8pZ2HkFb7nSORZKJsnsZZcnm9nZG3ixsYBHk/ZhM1jo/Nl9fHD967x88iZm567AZrBEGkmtjdKqsIcl7j4kmz2R4QegTzoY6TgcGfaQZIylMhSIDA1o3dZ6G711koBXmCm2xkQavpuCCg2qE69miwyjCIowjx7/Bb9P/4xtwRxGxtW3uUXewRiI3M4/rrqJNZjYH7YyyGZmU1DhSDiVuxxNbf6NOPvWelCEqVaCfOrtwe3xewCINZgit/lX+E10tzbT0RRPk+Yj1mDBZrC0yS8owpzSAm0mUADEGYORYQ6VoUBkwkVrrK3lz1UZCrA9kNfmHH+Xmc25PJhwkM/8Cezw5Z03ZOJCwx0u5NzhFme/ryrsIdYovjPeH+rsc3YhrZ+D1gko0HbYy+yWAj5vKGJJ4bKovAXcx3/dxd8CFnspG1P8D90CHjhwIKWlpbz22msAaJpGTk4Ojz76KFOmTLmoeKRLTw76uQitbWctEPj2p94A1Awaqi+AFrCi+c40AFVVgYAZzRdADYDmDxH2hs783SzQVL0BaAiY0PxhDIqKGjSgBQSqL4AImFG8QTR/gJA3hBYI6MfyaxgUFS1sQiWE5tf0cr4AWvhMAzDsDaH5A+ALRsqoPiOaH8LeEIoviOazoKkG1JggYYNeXrUF8LlVFG+QkC1E0BJGC3wbgz+A6gui+fTY1GAA4QsCoIggalig+QMYFP39YZM+Pk/1BTGaVZRwiLAWQvUFCXgUNF+AgEfBZ1Ejdfvc+p8DHoWwN0TAphDyhgiHTJEyHrdG0BNG9QVwuzXC3hBBYxjVF8TrVgl5QwQMih6r3xypS/MF9Lj9RkLeEF63StAXjpzXgEfBjV6fEjKgKHosijdIMCZMKBRG8eoxhjwhFG8Qv0choCj4wvpxg0qYsDcUOYdhk4rXreIKa3jCGiFPmHAwRMCs5x1QFbyKRsgTwuVWI/n5A0okRo9Zw2XW3+szqIQ8YTw2DZ+iIgx63d6AStirn9uwIYTbreFRwGVVCXrCeNEIefVZ6kERxhdWCYQVvJqer9ut4Quq+DSVYDBMUDXgilXxhPVYvKEzMYa9+k+/RyEYChMwK5F9AY+Cy6xhM+jnzuPWj+ty69uCQn86jM2gfzY8Yf28+80KLsOZ8Zx+j4JbaJiMKm5N/+kJabisepnWbT6fikvVc/RYNLxCLxMU+vG8QQ2fquflVvX3BIWeiydWz9mlaZF4vEENm1HDZfn2OKqGajDgDWu4Qt/WF1ZxiTOxtr7PFVK/Pb8aHkUj4NM/TwCqwYDJ+O0x/OC2arhMem6KQT/+2fkFhYZb08sA+DzfzsI36vkCenmLIRK7Wz1T/lyekIY/2PYcf5eAR8Fl1PD5VYL+MC5b2/d4wmfOz/cJetq+9+z3ecIaqlF8Z7w/1Nnn7IIxfPs58HnOXLPIZ0ZokX8bXG4V5RL24Crodf3U/S4N4jgu0XhRdbhpRlVVXC5Xm+02mw2bzXZe+VAoxLZt23j66acj24xGIxUVFWzcuPGiYpF+GrIH8CIcOnSIgoKCKx2GJEmSFEVqamrIzs7+SeqeNGkSO3fuvCR1paen895777XZNnXqVJ577rnzyp44cYKOHTuyYcMGysrKItuffPJJ1qxZw+bNmy9JTNKlI3sAL0JycjIA1dXVJCRc3JpLV5LL5SInJ4eampqf7LbET+1qyAFkHj8nV0MOcHXkcTXkAHrPn9vtJisr6yc7xquvvnrJ6goGg8yZM6fNtgv1/knRSTYAL4LRqA/ST0hIiOp/lFo5nc6oz+NqyAFkHj8nV0MOcHXkcTXkEE2dBd91u/dCUlJSMJlM1NW1HU9aV1dHRkbGTxGedJHkLGBJkiRJki6K1Wqlf//+rFixIrJN0zRWrFjR5paw9PMhewAlSZIkSbpojz/+OGPHjqWkpIQBAwYwY8YMvF4v48aNu9KhSRcgG4AXwWazMXXq1KgfE3E15HE15AAyj5+TqyEHuDryuBpyaA9GjRpFQ0MDzz77LLW1tRQXF/Ppp5+Snp5+pUOTLkDOApYkSZIkSWpn5BhASZIkSZKkdkY2ACVJkiRJktoZ2QCUJEmSJElqZ2QDUJIkSZIkqZ2RDcCL8Prrr5Ofn09MTAwDBw5ky5YtVzqkiC+++IJbb72VrKwsDAYDixcvbrNfCMGzzz5LZmYmdrudiooKqqqq2pRpbGxkzJgxOJ1OEhMTue+++/B4PJcth2nTplFaWkp8fDxpaWncdttt7N+/v02ZQCDAhAkT6NChAw6HgzvuuOO8hUirq6sZPnw4sbGxpKWl8fvf/x5FUS5bHrNmzeKaa66JLGJbVlbGJ598ElU5nGv69OkYDAYee+yxyLZoyOO5557DYDC0eXXv3j2qcmh1/Phx7r77bjp06IDdbqd3795s3bo1sv/n/h3Pz88/71oYDAYmTJgARNe1kKSoJKQfZcGCBcJqtYq33npL7NmzRzzwwAMiMTFR1NXVXenQhBBCfPzxx+Jf/uVfxAcffCAAsWjRojb7p0+fLhISEsTixYvFzp07xa9//WvRqVMn4ff7I2Vuvvlm0adPH7Fp0yaxdu1a0aVLFzF69OjLlsOwYcPE3Llzxe7du0VlZaX41a9+JXJzc4XH44mUeeihh0ROTo5YsWKF2Lp1qxg0aJC49tprI/sVRRG9evUSFRUVYseOHeLjjz8WKSkp4umnn75seXz44Yfi73//u/jmm2/E/v37xR/+8AdhsVjE7t27oyaHs23ZskXk5+eLa665RkyePDmyPRrymDp1qujZs6c4efJk5NXQ0BBVOQghRGNjo8jLyxP33nuv2Lx5szh06JBYtmyZOHDgQKTMz/07Xl9f3+Y6LF++XABi1apVQojouRaSFK1kA/BHGjBggJgwYULk76qqiqysLDFt2rQrGNWFndsA1DRNZGRkiBdeeCGyrbm5WdhsNvHuu+8KIYTYu3evAMSXX34ZKfPJJ58Ig8Egjh8/ftliP1t9fb0AxJo1ayIxWywW8d5770XKfP311wIQGzduFELoDWGj0Shqa2sjZWbNmiWcTqcIBoOXN4GzJCUliTlz5kRdDm63WxQWForly5eLX/ziF5EGYLTkMXXqVNGnT58L7ouWHIQQ4qmnnhJDhgz5zv3R+B2fPHmyKCgoEJqmRdW1kKRoJW8B/wihUIht27ZRUVER2WY0GqmoqGDjxo1XMLIf5vDhw9TW1raJPyEhgYEDB0bi37hxI4mJiZSUlETKVFRUYDQa2bx582WPGaClpQWA5ORkALZt20Y4HG6TR/fu3cnNzW2TR+/evdssRDps2DBcLhd79uy5jNHrVFVlwYIFeL1eysrKoi6HCRMmMHz48DbxQnRdi6qqKrKysujcuTNjxoyhuro66nL48MMPKSkp4c477yQtLY2+ffvyl7/8JbI/2r7joVCI+fPnM378eAwGQ1RdC0mKVrIB+COcOnUKVVXPW908PT2d2traKxTVD9ca4/fFX1tbS1paWpv9ZrOZ5OTkK5Kjpmk89thjDB48mF69ekVitFqtJCYmtil7bh4XyrN13+Wya9cuHA4HNpuNhx56iEWLFtGjR4+oymHBggVs376dadOmnbcvWvIYOHAg8+bN49NPP2XWrFkcPnyYoUOH4na7oyYHgEOHDjFr1iwKCwtZtmwZDz/8MJMmTeLtt99uE0u0fMcXL15Mc3Mz9957byS2aLkWkhSt5KPgpKgwYcIEdu/ezbp16650KD9Kt27dqKyspKWlhffff5+xY8eyZs2aKx3WD1ZTU8PkyZNZvnw5MTExVzqcH+2WW26J/Pmaa65h4MCB5OXl8de//hW73X4FI/vHaJpGSUkJ//Zv/wZA37592b17N2+88QZjx469wtH94958801uueUWsrKyrnQoktRuyB7AHyElJQWTyXTejLS6ujoyMjKuUFQ/XGuM3xd/RkYG9fX1bfYrikJjY+Nlz3HixIl89NFHrFq1iuzs7Mj2jIwMQqEQzc3Nbcqfm8eF8mzdd7lYrVa6dOlC//79mTZtGn369OGVV16Jmhy2bdtGfX09/fr1w2w2YzabWbNmDa+++ipms5n09PSoyONciYmJdO3alQMHDkTNtQDIzMykR48ebbYVFRVFbmdH03f86NGjfP7559x///2RbdF0LSQpWskG4I9gtVrp378/K1asiGzTNI0VK1ZQVlZ2BSP7YTp16kRGRkab+F0uF5s3b47EX1ZWRnNzM9u2bYuUWblyJZqmMXDgwMsSpxCCiRMnsmjRIlauXEmnTp3a7O/fvz8Wi6VNHvv376e6urpNHrt27Wrzi2758uU4nc7zfoFeTpqmEQwGoyaH8vJydu3aRWVlZeRVUlLCmDFjIn+OhjzO5fF4OHjwIJmZmVFzLQAGDx583pJI33zzDXl5eUD0fMcB5s6dS1paGsOHD49si6ZrIUlR60rPQolWCxYsEDabTcybN0/s3btXPPjggyIxMbHNjLQrye12ix07dogdO3YIQLz00ktix44d4ujRo0IIfYmIxMREsWTJEvHVV1+JESNGXHCJiL59+4rNmzeLdevWicLCwsu6DMzDDz8sEhISxOrVq9ssF+Hz+SJlHnroIZGbmytWrlwptm7dKsrKykRZWVlkf+tSETfddJOorKwUn376qUhNTb2sS0VMmTJFrFmzRhw+fFh89dVXYsqUKcJgMIjPPvssanK4kLNnAQsRHXk88cQTYvXq1eLw4cNi/fr1oqKiQqSkpIj6+vqoyUEIfSkes9ks/vVf/1VUVVWJd955R8TGxor58+dHykTDd1xVVZGbmyueeuqp8/ZFy7WQpGglG4AXYebMmSI3N1dYrVYxYMAAsWnTpisdUsSqVasEcN5r7NixQgh9mYhnnnlGpKenC5vNJsrLy8X+/fvb1HH69GkxevRo4XA4hNPpFOPGjRNut/uy5XCh+AExd+7cSBm/3y8eeeQRkZSUJGJjY8XIkSPFyZMn29Rz5MgRccsttwi73S5SUlLEE088IcLh8GXLY/z48SIvL09YrVaRmpoqysvLI42/aMnhQs5tAEZDHqNGjRKZmZnCarWKjh07ilGjRrVZOy8acmi1dOlS0atXL2Gz2UT37t3F7Nmz2+yPhu/4smXLBHBeXEJE17WQpGhkEEKIK9L1KEmSJEmSJF0RcgygJEmSJElSOyMbgJIkSZIkSe2MbABKkiRJkiS1M7IBKEmSJEmS1M7IBqAkSZIkSVI7IxuAkiRJkiRJ7YxsAEqSJEmSJLUzsgEoSdKPcuTIEQwGA5WVlZftmPfeey+33XbbZTueJEnS1Uo2ACVJ+ll47rnnKC4uvtJhSJIktQuyAShJkiRJktTOyAagJLVT119/PRMnTmTixIkkJCSQkpLCM888Q+vTIQ0GA4sXL27znsTERObNm3fB+pqamhgzZgypqanY7XYKCwuZO3duZH9NTQ3//M//TGJiIsnJyYwYMYIjR458Z3yqqvL444+TmJhIhw4dePLJJ5FPrpQkSbo0ZANQktqxt99+G7PZzJYtW3jllVd46aWXmDNnzo+q65lnnmHv3r188sknfP3118yaNYuUlBQAwuEww4YNIz4+nrVr17J+/XocDgc333wzoVDogvX9+c9/Zt68ebz11lusW7eOxsZGFi1a9KNzlSRJks4wX+kAJEm6cnJycnj55ZcxGAx069aNXbt28fLLL/PAAw/8w3VVV1fTt29fSkpKAMjPz4/sW7hwIZqmMWfOHAwGAwBz584lMTGR1atXc9NNN51X34wZM3j66ae5/fbbAXjjjTdYtmzZj8hSkiRJOpfsAZSkdmzQoEGRBhlAWVkZVVVVqKr6D9f18MMPs2DBAoqLi3nyySfZsGFDZN/OnTs5cOAA8fHxOBwOHA4HycnJBAIBDh48eF5dLS0tnDx5koEDB0a2mc3mSONSkiRJujiyB1CSpAsyGAznjbkLh8PfWf6WW27h6NGjfPzxxyxfvpzy8nImTJjAiy++iMfjoX///rzzzjvnvS81NfWSxy5JkiR9P9kDKEnt2ObNm9v8fdOmTRQWFmIymUhNTeXkyZORfVVVVfh8vu+tLzU1lbFjxzJ//nxmzJjB7NmzAejXrx9VVVWkpaXRpUuXNq+EhITz6klISCAzM7NNfIqisG3btotJV5IkSfqWbABKUjtWXV3N448/zv79+3n33XeZOXMmkydPBuDGG2/ktddeY8eOHWzdupWHHnoIi8XynXU9++yzLFmyhAMHDrBnzx4++ugjioqKABgzZgwpKSmMGDGCtWvXcvjwYVavXs2kSZM4duzYBeubPHky06dPZ/Hixezbt49HHnmE5ubmS34OJEmS2iN5C1iS2rF77rkHv9/PgAEDMJlMTJ48mQcffBDQZ+GOGzeOoUOHkpWVxSuvvPK9PXBWq5Wnn36aI0eOYLfbGTp0KAsWLAAgNjaWL774gqeeeorbb78dt9tNx44dKS8vx+l0XrC+J554gpMnTzJ27FiMRiPjx49n5MiRtLS0XPoTIUmS1M4YhFxYS5Lapeuvv57i4mJmzJhxpUORJEmSLjN5C1iSJEmSJKmdkQ1ASZIkSZKkdkbeApYkSZIkSWpnZA+gJEmSJElSOyMbgJIkSZIkSe2MbABKkiRJkiS1M7IBKEmSJEmS1M7IBqAkSZIkSVI7IxuAkiRJkiRJ7YxsAEqSJEmSJLUzsgEoSZIkSZLUzsgGoCRJkiRJUjvz/wDA4r5BmjQ3fwAAAABJRU5ErkJggg==", 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sEHH0S8d8iDDz5Is2bNGDBgAA8//HBE9/Ps2bM59thj8Xg8Ee+9fPlydu/evd/vL5UStgs4EAhwxBFHhEvbAwYMYMmSJTz99NNccskltT5n/PjxjBs3Lny/oKDgkITAkogxgImxDIzf58MZumM0BlAkLlWUwAOto/Pet28CT9q+z6uif//+3HnnnYD99+DBBx8kJycn3G1511138dRTT7Fo0aLwc+6++25OPvlkwA6Qbdu2DVffDkSPHj0AezxfixYtOP300xkyZMhen9OmTZtaj7/wwgv07NmTYcOGhY9t2bIlIvwB4ftbtmzZ6zkFBQWUlpaSkpKyz+t46aWXyMjI4Mwzz4w4fv3113P44YfTtGlTvv76a8aPH8/mzZvDFb4tW7bUGKZVtX2xOH4/1iVsAGzVqhW9evWKONazZ0/++9//1vmcpKQkkpKSGrppNZQl4E4ggSpdwKoAikgs6NevX/h7p9NJs2bNIsbkhQLJtm3byMzMBGDo0KHhx5s2bUr37t1ZtmzZAb+3CVYsLcsC7O7SqmPk9ldpaSmvvfYaf/7znw/4ufvy6quvcuWVV4bvf/TRRxxzzDER50ycOJELL7yQ5OTkiONViyv9+vXD4/Fw5ZVXMmHChKj83U0ECRsAjzrqKJYvXx5xbMWKFbRv3z5KLapbaQLuBeyvUvq3NAZQJD65U+1KXLTe+0Cf4nZH3LcsK+JYKJwFGqCnJhQaO3ToANQMW7WpLYC99dZblJSUcPHFF0ccz83N5Ztvvok4tnXr1vBjoa+hY1XPyczMJCUlpUZVsnoFcubMmSxfvpzXX399r+0GGDJkCD6fj7Vr19K9e/c637tq++TAJGwAvOmmmxg2bBgPPPAA5557Lt988w3PPvsszz77bLSbVkNijgGsOglEXcAiccmyDrgbtrGZM2cO7dq1A+zlx1asWEHPnj0P6DVKS0t59tlnOfbYY2nevDnAL+4CfuGFFzj99NPDrxMydOhQ7r//frZt20aLFi0AmDp1KpmZmeHesqFDh/Lhhx9GPG/q1KnhKue+qpIvvPACAwcOpH///vu4Yli4cCEOhyPclqFDh3LHHXdQUVERDt1Tp06le/fu6v79hRI2AA4aNIgpU6Ywfvx47rvvPjp27Mg//vEPLrzwwmg3LUIgYCirqPy/yUTZCSRQtQKYIOMeRST+3HfffTRr1oyWLVtyxx13kJOTw+jRo/f6nG3btlFWVkZhYSELFizgoYceYseOHbz99tvhc35JF/CqVav48ssva4Q4gBEjRtCrVy8uuugiHnroIbZs2cKdd97J2LFjw12wV111FY8//ji33norf/jDH/jss8944403+OCDD/b53gUFBbz55ps88sgjNR6bPXs2c+fO5YQTTiAjI4PZs2dz00038bvf/S4c7i644ALuvfdexowZw2233caSJUv45z//uc8ZyFK3hA2AAL/+9a/59a9/He1m7FW5LzL8JEwF0F9lFrAqgCLSSD344IPccMMNrFy5ksMOO4z//e9/ETNZa9O9e3csyyI9PZ1OnToxYsQIxo0bd9BdnRMnTqRt27aMGDGixmNOp5P333+fq6++mqFDh5KWlsYll1zCfffdFz6nY8eOfPDBB9x0003885//pG3btjz//PN7XQMwZPLkyRhjOP/882s8lpSUxOTJk7nnnnsoLy+nY8eO3HTTTRHjArOysvj0008ZO3YsAwcOJCcnh7vuuktrAB4Ey5gEKSk1gIKCArKyssjPzw8P+K1vu4q9HP6XqeH7Vxzbidt/dWDdB43Rjp9XkvPcEQDMSz2GQbe+H+UWicjBKisrY82aNXTs2LHGJACR+ra337dD8fc71iXsOoCNRdXxf5BAewFHVAA1CURERKQ+KQDGuFJvZPdnwqwDWCUAOhQARURE6pUCYIwr9SbqGEAtAyMiItJQFABjXPUu4MTZC1gVQBERkYaiABjjaowBTJAAGPBX7pWpCqCIiEj9UgCMcaVeVQBVARSJL1p8Qg4F/Z7tnQJgjCtL2Apg1VnAiTHxRSTehXZwKCkpiXJLJBGEfs+qb+EntoReCLoxqDkGMDHCUNUA6EQVQJF44HQ6yc7OZtu2bQCkpqaG988VqS/GGEpKSti2bRvZ2dk4nc5oNykmKQDGuMTtAq4cA6guYJH4EdrNIhQCRRpKdnb2Qe+eEs8UAGNcqAJoWWBMIgXAKl3AqgCKxA3LsmjVqhUtWrSgoqJi308Q+QXcbrcqf/ugABjjQhXAdI+LwnJfQo4BdKoCKBJ3nE6n/kCLRJEmgcS4UAUwLcnO6olTAaxcCNqhSSAiIiL1SgEwxoUCYHqyHQATZS9gU3UrOHUBi4iI1CsFwBhXFuwCzkhOrApgoEoFULOARURE6pcCYIwLVwCDXcC+BFkGhkDVZWAS5JpFREQOEQXAGBcKgIlXAay6E4gCoIiISH1SAIxx4VnA4QpgYgRAE9BC0CIiIg1FATDGlYW7gO2tbBKlAohfYwBFREQaigJgjCvxRs4CTpQAGLEMDAFt6i0iIlKPFABjXHgMYMKtA1g57s9FgAS5bBERkUNCATDGlVVfBzBRkpCJHAOYMLOfRUREDgEFwBhXfRJIwlQAI8YABhLmukVERA4FBcAYZoypuRNIolTCqlQAXVYAnz9BrltEROQQUACMYV5/5di3xBsD6Iu47/f56jhTREREDpQCYAwr81ZWvRJtDGDVvYABfAqAIiIi9UYBMIaFun9dDotklxMAvz8xAmDVLmCAgL8iSg0RERGJPwqAMSwUAFPcTpwOC0icCmDVvYABfD4FQBERkfqiABjDSrx2t2eKx4nLaQfARBkDWD0ABvzqAhYREakvCoAxLLQGYHKVCqA/UXbEqNYFrEkgIiIi9UcBMIaVV9iTQJLdDlwO+6PyB0xibItWrQLo1xhAERGReqMAGMPKfXYATHI5cVpW+HhCdANXWwYmUG1WsIiIiPxyCoAxLBQAPS4HTmdlAEyIiSDVZwFrEoiIiEi9UQCMYeU+OwQluRy4HIlWAYzc+cOvSSAiIiL1RgEwhlV2ATvCk0AgQSuACoAiIiL1RgEwhnmrdgEn3BhAdQGLiIg0FAXAGFZ1EojDYREqAvqqdY/GJVUARUREGowCYAyrOgYQiFgKJt5Z1SuAAc0CFhERqS8KgDEs1AWc5LY/pvB2cImwH3C1CqDROoAiIiL1RgEwhoWXgXE6AcIzgROhAlhjJxB1AYuIiNQbBcAYFtoJJFwBdCbOdnCOGhVABUAREZH6ktAB8J577sGyrIhbjx49ot2sMK8/cgxgaCZwQlQAq0100SQQERGR+uOKdgOirXfv3kybNi183+WKnR9JqALocSXeGECregUwoAAoIiJSX2In7USJy+UiNzc32s2oVdVlYCCxxwAGfJoFLCIiUl8SugsYYOXKlbRu3ZpOnTpx4YUXsn79+mg3Kaz6MjChMYCJsA6gKoAiIiINJ6ErgEOGDOHFF1+ke/fubN68mXvvvZdjjjmGJUuWkJGRUeP88vJyysvLw/cLCgoatH1VdwKBBFsHUJNAREREGkxCB8BRo0aFv+/Xrx9Dhgyhffv2vPHGG4wZM6bG+RMmTODee+89ZO2ruhcwVBkDmBABMLLKaYwCoIiISH1J+C7gqrKzs+nWrRurVq2q9fHx48eTn58fvm3YsKFB25PIYwCrVwBRBVBERKTeKABWUVRUxOrVq2nVqlWtjyclJZGZmRlxa0heVQDD1AUsIiJSfxI6AN5888188cUXrF27lq+//prf/OY3OJ1Ozj///Gg3DahtL+BQBTARJ4FoFrCIiEh9SegxgBs3buT8889n586dNG/enKOPPpo5c+bQvHnzaDcNqNIFHNwJxBEOgFFr0iFTYycQzQIWERGpNwkdACdPnhztJuyVt869gOM/AVbvAkYBUEREpN4kdBdwrKteAUysMYB2BdBr7PCrLmAREZH6owAYw8orqo8BTJx1AENdwBWhIrUqgCIiIvVGATCGef2Ry8Ak1F7A2NdeYbkBjQEUERGpTwqAMcofMFQEg56nxizgBAiA4QqgHQBRF7CIiEi9UQCMUaEJIJCY6wA6qlUAFQBFRETqjwJgjKotALqciTMLODQG0BesAFrqAhYREak3CoAxKrQItMMClzNUAbS/JkIFMLQMTOUYQFUARURE6osCYIyqvg8wJNYYQAd24PMHA6BlVAEUERGpLwqAMSq8DZy78iNyWAkUAIMVQJ/DA4ClCqCIiEi9UQCMUeXhXUCCH9HHt3PphjuwCCREF3BoEkioAojZdwA0Jv5/LiIiIvVBATBGRewC4q+AOU/Sp3AmbaydCVIBtANfwLF/s4AXrNvNEX+dxpTvNjZ000RERBo9BcAYVV5RZQxg4WbADn1JeBOrAhjqAt7HGMBZq3aws9jLzBU7GrxtIiIijZ0CYIwK7QLicTqgYFP4uAdfYiwDEwyAgWAAxOz9movK7YCYCOFYRETkYCkAxqjwPsBuBxT8HD7uoSIhQk5oEkgoADr2sQ5gYVkFkBgTZERERA6WAmCMqlwGppYKYALsBewMLgNjnKEu4L2PASwsC1UA4786KiIicrAUAGNUaCcQj8sZGQCtBKkAhrqAnaEu4P0LgKoAioiI7JsCYIyKqADmV85stccAxn/ICQVAwl3Aew+AGgMoIiKy/xQAY1R4IegaXcCJVQE0riRgf7qANQZQRERkfykAxihv1a3gqgTApESYBWwMzlAFcD/HABYFu4Ar/HH+sxEREakHCoAxKtQFnOwMQNGW8HGPVUHcZ5wq3b2WK9gFrDGAIiIi9UYBMEaFuoCbmT0Ra+AlxDqAVcNeqAJI3QEwEDAUeTUGUEREZH8pAMaoUBdws0DkzhYJMQaw6oSP4BjAvVUAi70+QtsAqwIoIiKybwqAMSrUBdzUty3iuIeK+A85pmoXcDKw9wAY6v4F8CXAGokiIiIHSwEwRoX2Am7ij6wAuvEnQAWwMtDtzxjA0BIwoAqgiIjI/lAAjFGhMYBZFdUqgFYCVACrjHF0uIPLwFD3uMfQEjAAFfE+PlJERKQeKADGKG9wqm+GNxgAHW7AXgYm7iuAVap9zmAF0LmfXcBxH45FRETqgQJgjAp1AaeXBwNgk/ZAaAxgnFe5gpNAKowTp8sOvo69zALWGEAREZEDowAYo0KTQFLLgmsANukIBGcBx3vICY4BDODA5XQB4DB76wJWBVBERORAKADGKK8vgIMAyWXb7QNNQwEwAfYCDnb3+nHgdO+7AlhUXjkGMO67x0VEROqBAmCMKvf5ySHfnv1qOSErDwhOAjHxHXIC/soA6Ap2Ae//GMA47x4XERGpBwqAMarcFyDX2mXfSW8J7hQgMSqAfn9wWzccuFzBLuC9zgKuMgYwzn82IiIi9UEBMEZ5fQGyrGL7Tmqz8I4YiTAGMBARAIMVQE0CERERqTcKgDGq3BcgnVL7TlIGOEMBMP4rgIEqk0Dc7uAyMPgxdXR9Vx0DGO8/GxERkfqgABijyn1+0q0qATC4Hl6SVYEvzse5BXyVFUBnlS7gurJdZBdwfP9sRERE6oMCYIwq9wXISNAKoAlOAvHhDI8BdBGoM9xVDYABA4E4//mIiIgcLAXAGFWjCzhYAfRQEfcTHfzBhaADxoroAq4r+FbdCxiI+1nSIiIiB0sBMAYZY/D6ApFdwAlUAQz47TF9VWcBOwnUed1V9wIGjQMUERHZFwXAGBTaBzidEvtAUmbkLOA4DzgmVAHEgStcAdxbAIysAMb7z0dERORgKQDGoNA2cBkRFcBgF7AV/xVAE5oFbDlwOJ0AuPDXGuy8vkD45xXi82siiIiIyN4oAMag8opQBbDqGMDKCmC8B8DKnUCcWI7KWcC1XXf18X+gCqCIiMi+KADGoHKfHYAyHWX2gaoVwAQYAxiaBRzAAY7QLODaK4Ch8X+pHicuhwVoDKCIiMi+KADGIG9tXcARYwDju4uzciFoZzgAOi2D31fzuh1L3uRtz1108uzBGQyAqgCKiIjsnQJg0IMPPohlWdx4443Rbkp4TFtlF3BmeBZwkuXDH+8BMFQBtBxgVf6K+vwVNc7NWPYGhztWcbZjBm6nfa5f28GJiIjslQIgMG/ePJ555hn69esX7aYAtQXAynUAARyBmkEorgQqdwIJVQDtwzWv2/jKARgWWFClAhjfAVlERORgJXwALCoq4sILL+S5556jSZMm0W4OEOoCNqSFl4GpXAcQwG0q4nq3i0B4GRhnRAD0+WtO+DB+LwBdfCvJsQoAjQEUERHZl4QPgGPHjuXUU09l+PDh+zy3vLycgoKCiFtDKPf5ScaLk2Alq8okELAngsTzODcTDHrGcoDDGT4eqC0A+uwA6MAwzFoEQIW6gEVERPYqoQPg5MmT+fbbb5kwYcJ+nT9hwgSysrLCt7y8vAZpV3lFlX2AscCTBg4HxuEG4n8pmPA6gNW6gP2+mgGQKuMCjzbf2Yfi+GcjIiJSHxI2AG7YsIEbbriBV199leTk5P16zvjx48nPzw/fNmzY0CBt8/qrbgOXCZY9ti08E9jyxfU4NxO8toDljJgEEgh291ZlBSqPDQl8h4NAXP9sRERE6oNr36fEpwULFrBt2zYOP/zw8DG/38+XX37J448/Tnl5OU6nM+I5SUlJJCUlVX+pelfu80dOAAkJrwWYKBVAJ1gWfhz2VnDB9RGrsqpUALMppJ/1E/7AUYesrSIiIo1RwgbAk046icWLF0ccu/TSS+nRowe33XZbjfB3KJVXVK0A1gyASfE+BjBQZQwg9o4gTgK1jgEMzYjOT8kjq3QDxzsX4gv87tA1VkREpBFK2ACYkZFBnz59Io6lpaXRrFmzGscPNa8/QEbVGcBBlquyAhjPs4BN1XUAsQMgVGBqWQfQYexju5odTtbGDbSztsV1dVRERKQ+JOwYwFhWXhGoows4tBtIfFcACS4DY4K/nuEgGKjZBew0we7ipEwA3Pio8GsMoIiIyN4kbAWwNjNmzIh2E4DgGMDauoDDk0DifQxgqAJo/3raFcDaF4J2m+AkEHea/QV/XP9sRERE6oMqgDHI66urAljZBRzPFcDKABiqAAYDYPUxgMbgJjhe0BMKgHFeHRUREakHqgDGoAuPbA9FObAIexmYEFdlF3Bc7wccngQSDH7BCmBobGD184AqFUAf5QqAIiIie6UAGINaZiZDUrBrM4ErgMaKHAMYqD4JpMq6gFaSHQDtNRLj92cjIiJSH9QFHKvKC+2vtY4B9OGL4+3OTPVJINTRBVx1YWhPOmBXAOO6OioiIlIPFABjVTgAplceC68DGN+TQCwTqgAGg1/oa6B6AKysCDo9qUBwDGAch2MREZH6oAAYq/ZWAYzziQ7hnUCCwS8UBE0dFcBy48LhsX82bvxx/bMRERGpDwqAsaq8wP5adRJIeB3A+K4AhtYBpFoF0FRfBzAYACtw4XSHAmB8h2MREZH6oAAYq2qtAIYmgfgSIgCaasvA1NgJJHi/AhcuV2UA9GshaBERkb1SAIxVtQVAZ4IsBG3sABdaCDocBOvoAq7AhSvUBaxZwCIiIvukABir9lEB9MXzTNdwF7D962mCQZBqXcCBCjsAenHhqtIFHM/hWEREpD4oAMYinxd8Zfb3te4FHN8VwMqFoKvtBFJtFnBFhf0z8hoXTk/VcBzHPxsREZF6oAAYi7xFld97apsFHN8LQVNtGZjQV6rtBOL32gGwAhdudzKgvYBFRET2hwJgLArNAHangrPKZi2hdQCtOO/mDE0CcYTGANY+C9jvqxwD6HbbPxt7HcA47h4XERGpBwqAsai28X+QcBXAyjGAwQpgtS5gf0U5AD5cOEJjAC0/vup7BouIiEgEBcBYVFcAdFZdBiZ+q1xWtXUAqSMA+rx2BdBnucDprnyg+nIxIiIiEkEBMBbtRwWwIo63OwtvBedwRn6tXgH0BSuAljscjgFM1T2CRUREpAYFwFhUZwWwciu4kvJqa+LFk+A6gKHKX3gsoL/6MjB2APRbrogASPX1AkVERCSCAmAsqm0bOKhcB9CqYGdxHFe5ApEVQCv4NVCtazc0CcRnucHhJBD8dTb+8kPVUhERkUZJATAW7UcFcEdR/IYcK9jVa1mhAGhXAP3VKnuhhaBDO4YEHMFxgBoDKCIislcKgLGozjGAoUkgFewoiuMKYLAL2DjsX08ruBRO9a3gAsFKnz8Y/PyW/dX44vhnIyIiUg9c+z5FDrl+v4VW/SErL/J4lQrgzniuAJrIWcCOOgKgCVYA/ZYdjAMOF/jBCqgCKCIisjcKgLEop6t9qy44Czgp3scAhgJgsOu3zgAYnO0b6vqt7AKO45+NiIhIPVAXcGNSZR3AnXHcBWyFuoBDYwCdoVnA1QKgLzQGMNj1GwyAViB+fzYiIiL1QQGwMamyDmBRuY+yivjc8cIywUkgwTGAzn0FwFAFMBgELU0CERER2SsFwMakSgUQiNtu4FAFEEdoDGAw4FVbCDrU1Wuc1SqACoAiIiJ7pQDYmIQqgJYdcHYUxudEkOqTQOqsAIYCoMMOxqEgiCaBiIiI7JUCYGMSnAXsIoCDADuL4zUA2hXA0Pp/TldwrlKgWpd3aLKHM3ISiENjAEVERPZKAbAxcVVudxbPawGGxgCGuoCdrtDkjmqVvWBXb6jrN1QJ1FZwIiIie6cA2JgEK4BgB8B4nQlcfQygMykVALcpizwvPAYwsgvYoS5gERGRvVIAbExCY9yApMa6GHTRdijds9dTwl3AoYWgk9IBSDbl+AOm8rxg0LOCFUJCXcBGAVBERGRvFAAbE8uqshtII1wMumg7PDEIXhgBxtR5WmgSiAlO/nAl2wEwlTLKfZXjAMOzfYM/k1AlUBVAERGRvVMAbGzCM4F97GhsFcAf3obS3bBjOez6qc7THNUqgO4Ue0/kVMopqwiEzwsv+ByqjKoLWEREZL8oADY24bUAG+EkkMVvVX6/cX6dp1nYVT7LEdkFnGaVRSx+HZ4UEvyZoAqgiIjIflEAbGzCu4E0sjGAu9fBxm8q7/9cdwB0mMgAiNueBJJCOeW+ygpgKOhZ4QBoVwCdGgMoIiKyVwqAjU2VCuCuYi+BQN1j6WLKkv/aX13J9teN8+o8tXIdwGAA9NReAQwFQIc7ODvaqUkgIiIi+0MBsLGpMgbQFzAUlDWSsBMKgEPH2l+3LIaK0lpPdRCs8oXG9nkqK4BVA2Co0ucIhuJQJdBVfcs4ERERiaAA2NgEQ04Tjx2SGsU4wG0/wtYl9jItQ6+FtOYQ8MHmRbWeXqML2JMGQBplEV3AzvAyMNXGAKoCKCIislcKgI1NsALYLNiT2ihmAq/81P7a6XhIbQptB9n36xgHGFoGxuEI/nq67QCYbFVQVl4ZeENBz+EOVgCDQdClACgiIrJXCoCNTXDNu6ZJ9ti/RrEbyPrZ9teOx9pf2wy0v9YxDjDcBRzcCzhUAQSoKCsOfx/uAg6GYitcAVQXsIiIyN4oADY2wSpXk+C8h53FMV4BDARg/Rz7+/bD7K+hCuDGBbU+JbwOYCgAupIIBH9VA2WF4fNcwaDndEVWAJ0KgCIiInulANjYpDQBoI1jJ9AIxgDuXAmlu8CVArn97GOtBwAW5K+Hwq01nuIIrQPoDI4BtCzKLLvP219etQIYDICeYAVQXcAiIiL7JaED4FNPPUW/fv3IzMwkMzOToUOH8tFHH0W7WXvX5ggAOpcvA4j9tQBD3b9tjwhXL0nOhJZ97O9X1Px5h7qAwxVAwOtIASBQXrUCGBoDaIfD0GxgF6oAioiI7E1CB8C2bdvy4IMPsmDBAubPn8+JJ57IGWecwQ8//BDtptWt3RAA2hQtxiLA9sIYD4DrggGw3dDI4/3Osb9+92qNp4S6gB2hWcCA12kHwHAF0Jhw0KveBexSF7CIiMheJXQAPO200/jVr35F165d6datG/fffz/p6enMmTMn2k2rW24/cKWQVJFPJ2sz22I9AIYqgO2OjDze7zywnPbuINuXRzwU6gJ2OCsDYEWwAkgoAAb8OLAnwriCC0E7XPa6gS58jWeBbBERkShI6ABYld/vZ/LkyRQXFzN06NB9PyFanG5oczgAAx0rYqsCuGoabF1aeb9gE+xZB5ajcuJHSEZL6DrC/v67VyqPG4Ozli5gn9NeDJqKYAD0V459dHlCAbBymzyfAqCIiEidEj4ALl68mPT0dJKSkrjqqquYMmUKvXr1qvXc8vJyCgoKIm5RkWd3Aw+0VrK9sBxjYiDsbPgGXjkLXjoNKsrsY6HqX25fe9xfdQN+Z3/9fjL4gxM3TOVCz1aVCqDPFQyA3r0FQLsL2I0PvwKgiIhInRI+AHbv3p2FCxcyd+5crr76ai655BKWLl1a67kTJkwgKysrfMvLyzvErQ0KBsAjHMvx+gPsKYmBWa/zXrC/luyApe/a3/80w/5affxfSLeR9q4gxdvs6iFAoMpev87KCmDAZXcBW74S+4C/8prdweAXWhDabfmoCFQGSREREYmU8AHQ4/HQpUsXBg4cyIQJE+jfvz///Oc/az13/Pjx5Ofnh28bNmw4xK0NyhsMQGfHZppQwNbCsui0I6RkF/wwpfL+/Imwe51d2QPo/qvan+d0Q7/f2t+HuoFNlQBYZRKI32UvBu2oCAVAu+u73LjwuO1fY2ewC9iND79fFUAREZG6JHwArC4QCFBeXvu4uqSkpPCSMaFbVKQ2hZxuAAxwrGJbQZTHAX7/HzuQNe1s796xYQ68fbndTdvxuModQGpz2IX21xUfQ9F2qCgNP1R1DKBx2xVAZ7UxgBW4cDvtX+NQF7AHv8YAioiI7IVr36fEr/HjxzNq1CjatWtHYWEhr732GjNmzOCTTz6JdtP2LW8w7FjBQMeK6M4ENgbmT7K/H3at3e279F3YMNc+NvwesKy6n9+yl7013M8LYNHr4C0CYHWgFQTX9wMwwf2Anb5gQAx2AVcNgFRZB1BjAEVEROqW0BXAbdu2cfHFF9O9e3dOOukk5s2bxyeffMLJJ58c7abtW3gc4Aq2RbMLeN0se7cPTzr0PQeO+EPlY73PDM9Y3qtQFXDe8/D1YwA84jsHp6PKr6fHngTi8ttdwMZnh97aAqAbHz6NARQREalTQlcAX3jhhWg34ZfLs9fV62+t5pP84n2c3IBWf2Z/7Xk6JGXYXb6tD4edq+HEO/fvNfqcBZ/cDrvXALDM0ZUPA0P4g6Oycmh50gFw++0KoN/nxQV4cZHhCgVAex1At+VXBVBERGQvEroC2Kg160KZO4tkq4KkHVHcuWTLYvtrqNJnWXDph3Dj99Cs8/69Rkq2HSCDnnRdBFg4qgRAkuwuYHfADoA+r10B9BoXnmoVQA8+KjQJREREpE4KgI2Vw0F+s8MAaLHnu+i1Y8sS+2tuv8pj7hRIaXJgrzP4CnsCSc/TWGDZ+wQ7q4wddCbZFUBPwO7uDgVAuws4eF6oAqgxgCIiInulANiIVbS2l4NpX7IkOg0o3gmFm+zvW9a+ePZ+yxsE436EsycRym7OKhVAR7ACmBSqAFYZAxg+r0oA1BhAERGRuikANmKuDvY4wN6+ZZhoBJ6twe7fpp3s8X8HK705ON34gzubOKpWAJODFUATHANYYS8D47NcWKHznNoJREREZH8oADZiWZ2H4DMOWlq7Kdq+9tA3IDT+r2Wfen3ZQDC8Va0AuoIBMMXYXcD+YBewH3flE4MBMMny4fOrAigiIlIXBcBGLCUtgx/pCEDxqq8PfQNqG/9XD0IVQGeV3053sl1hDAfAYBewz6oykd1ZGQYD/hjYHk9ERCRGKQA2css99tg7s37OoX/zUAUwt34rgKHuW6tKF7A7NRgAKccYEw6A/ogA6Kl8Da+3XtskIiISTxQAG7kN6X0ByNg4A4q2Hbo39pXDjuX29w3VBVwlAHpS7C7gVKuc8gofAW+oAlizCxgg4Ivy9ngiIiIxTAGwkdvadDAFJpX04g3w1FGVCzM3tO0/QsAHydmQ1bZeX7qyC7gyACalVu67XF5aRMBnV/gCjioBsMrewX51AYuIiNRJAbCRS2vSkrO897AtpRMUb4NXzoK1sxr+jcPj//rufa/fXyA0obnqQtDupFQCxr5fUVKICQZAf9UKoGVREdzcxlSoC1hERKQuCoCNXIvMJFaatvxf26eg1xlgAjDlSijd07BvHB7/17feXzpcAawSLC2Hg1KSAPCWFhHwBwNg1QoglV3CoQqhiIiI1KQA2Mi1zEwG4OdiA2c8AU06QP4G+PCWhn3jDXPtrwcQAI0xTJq1hvcXbYo4tq2wLOK80CQQR7XfzhLLvlZvaSEmOMYvUC0A+nHar+tXABQREamLAmAj1zzDroptKyy3F2M+8zmwnLD4DVj2fsO8acEm2PQtYEHnk/b7aTOWb+fe/y3lxskL2VNiB7TnZ65h8P3TeXP+BsAOhCHOal3L5dgB0F9WFO4CDli1VwCNJoGIiIjUSQGwkcsNVgA37ymzw1PeYDjqevvBGQ+CaYAdMZZ/aH9tewRktNyvpxhj+Me0FQD4AobPl2/DGMMrc9cB8MyXP9nLu1TZwaPqJBCAMkeK/fyywnCFzzirVQCDy8KoAigiIlI3BcBGrm2TVBwWlFb47SogwLDrwZNub9W24pP6f9MfgwGwx6n7/ZQZy7fz/cb88P1Pf9jKD5sKWLezBIBV24qYt3Z3ePwfRE4CASgPdgH7y4vBZ8/yNQ5PxDnhCqAmgYiIiNRJAbCR87gctG2SCsBP24vtg6lNYdAY+/svH67fKmBZAaz50v6++/4FwKrVv2O65gDwxYrtvP3tzxHn/eeb9VTd0rh6F7A3WAEMlBVVVgCrjwEMVwC1DIyIiEhdFADjQMecNADW7iyuPDj0WnAlw8/z4acZ9fdmq6ZBoAKadYHm3fbrKV+ssKt/KW4nfz/3MFpnJVPi9fPy7LUAXH6MvZ3dB4s38/nyysWsHdUCYIXDrgAabxFWMOAZZ2QFMLwsjF9jAEVEROqiABgHwgFwR5UAmN4CDr/Y/v6tS+GHKfXzZsv33f370/YivlmzK3z/ha/WAHDBkHY0z0hiRO9cwB4LmORycOPwbvRqlYnXF+CaV78FoFPzNJLdkb+eXqdd6TTeEgjUPgYwNCtYFUAREZG6KQDGgVAA/KlqAAQ47jbI7Qelu+HN38PLo2HOU/DzAti9FsoLK88tL4LZT8D8ibV3GRsD8yfB0nft+3V0//oDhgufn8u5z8zm8+XbWLm1kJkrd+Cw4PfDOgAwolflxJETe7QgLcnFhUe2Cx87f3AeU645KmIvYACf0+4CxlsEwS5gq0YXsH3f0iQQERGROrn2fYrEug61VQAB0nLgsunw5UMw8xH46XP7VlVuP8gbYge74mD3687VMOKvlTt8+Lzw7jWw+E37fs/Toe2gWtuycMMeNufb6/rd8fZiBndsCsDJvVqS19Su4A3q2JSsFDf5pRX8qm8rAM4b1I6Age4tM8LPqc7vsp9PRQmOOrqAAw6NARQREdkXBcA40LGZHQDX7SrBHzCRy6e4PHDindD3XFjxEaz+HLYtg7I94CuDLYvsG0BmGyj4GWY/bt8/+S92CPzwj3b4s5ww/G4Yel3NVZqDPvtxa/j7TfllvLPQXvT50qM6ho+7nQ7+dk5/Fm7Yzag+dnew02Fx0ZHt93qdfrcdAB3eYqxgFzB1jQEMKACKiIjURQEwDrRpkoLbaeH1Bdi0pzRcaYvQvJt9O+qGymNF22H1dFg/B1r1hwG/g29fhg/G2SFwyyK7Ovjty2A54Pz/QLeRe23L9GV2FfGcgW15c8FGAHq2ymRItareyb1acnKv/VtDMCQQrAA6fCXhSSCWKyniHBOsAKKt4EREROqkABgHnA6Ldk1TWb29mLU7i2sPgLVJbw79z7NvIYPG2LOHP7zZXu4ltOTLyfftM/xt3F3Cj1sKcVhw+696kuR28Mqc9Vx3Ypca4/l+CROsADp9JThMMABWnwQSGgMYUAAUERGpiyaBxInQRJA11ccB/hIDLoSrZ0H7o+37h/3OXlZmHz7/0a7+DWzfhCZpHv5yRh/m3n5SeJzfwTJu+xqdvlIcgVAFsNoYwFCXsMYAioiI1EkVwDhRrwEQoGknuOR/sHuN/f1+VPCmBbt/T+ppd+1alkXL4FZ19cJjX6PLX4JVRwA04QqgAqCIiEhdFADjRJ0zgQ+GwwHNOu/XqYVlFcz+aScAJ/VoUX9tqMLypAOQ4ivAj9M+Vm0MYMAZWgZGAVBERKQuCoBxot4rgAfo71NX4PUF6NQ8jS4t0hvkPYqa9MBvLFp611NqBSeEuKtVAIPrAjo0BlBERKROGgMYJ0IBcMPuUir8gX2cXb++Xb+bF79eC8Ddp/WulwkftUprzpxALwBSTAkAzupdwA4tAyMiIrIvCoBxomVGMiluJ/6AYd3OkkP2vl5fgD/9dxHGwJkD2nBct+YN9l5JbifvB46MOOaoHgCDk0Ac6gIWERGpkwJgnHA4LPq0yQSI2Ie3ob2z8GdWbC2iWZqHP/+6V4O+V7LLwcf+Qfir/No6aqwDGOoCVgAUERGpiwJgHDmqSw4As1btOGTvuWxzAQBnHt6GJmmefZx9cJLdTnaTyXeuw8LHnO5qATA0CcQoAIqIiNRFATCOHNM1GABX7yAQMIfkPUPdzaFZyA2pTZMUAN4qHxw+5qoWAC1VAEVERPZJATCO9GubTXqSiz0lFSwNVuYa2tqd9qzjDs0aPgB2ykmjZWYSH1YMpNR4KDdurOSsiHPCYwADvgZvj4iISGOlABhH3E4HR3ay99ydubLhu4H9AcOGXXYFsH2z/dx+7iBYlsWwzjkUkMYF3ju4tOIWHCmZkScFu4Cd6gIWERGpkwJgnDmU4wA355dS4Td4nA5aZaU0+PsBDOvcDIDvTFe+DvTB7ar2KxyuACoAioiI1EUBMM6ExgF+s3YXZRX+Bn2v0Pi/vKYpOB0NtPZfNcOCATfE46weAFUBFBER2RcFwDjTuXk6LTOT8PoC/Oeb9fgbcDJIaPxf+0Mw/i+kTXYKHap0N7trBEC7AqgAKCIiUjcFwDhjWRYje+cCcO//lnL83z5nxvJtDfJeoQrgoRj/V9XQzpVVQE+1LmAruDC0w2gSiIiISF0UAOPQ+FE9uf7ELjRJdbNhVylXvbKAxRvz6/191u44dDOAqzqqS7Pw925nZNdzKAC6VAEUERGpkwJgHErxOBk3ojtf/+kkju/enLKKAJe9PI+tBWX1+j7rD+EM4KqGdqoMgMluZ8RjVrgLWBVAERGRuigAxrEUj5N/nT+Ari3S2VpQzthXv6231zbGHNI1AKtqlp7EHb/qyVXHdSYnvdpC0E5VAEVERPZFATDOZSa7eeGSQbgcFvPX7Q6v23ewthWWU1YRwOmwwjt0HEqXH9uJP43qUeN4aG9gp2nYGdAiIiKNWUIHwAkTJjBo0CAyMjJo0aIFo0ePZvny5dFuVr1r1yyV/nnZAMxevbNeXjM0/q9NdkrNmbhRZGkZGBERkX2Knb/cUfDFF18wduxY5syZw9SpU6moqGDEiBEUFxdHu2n1LjRu7uvVv2yBaGMMxlQuKROtGcD7lJwBQJYpgEAgyo0RERGJTa5oNyCaPv7444j7L774Ii1atGDBggUce+yxUWpVwxjWuRmPf76K2T/txBiDZe3/ws3bCso4/fFZbC8qJ9XjpH2zVJJc9uSLQz3+b18qsjtSajykWmWwcyU07x7tJomIiMSchK4AVpefby+V0rRp01ofLy8vp6CgIOLWWBzevgkel4OtBeX8tKOYdTuLmfDhMtbt3He187/f/syWgjL8AUNhmY8lPxewYN1uIPYqgG6Xh8Wmo33n5/qb9CIiIhJPFACDAoEAN954I0cddRR9+vSp9ZwJEyaQlZUVvuXl5R3iVv5yyW4nh7fLBuDLFdu54uUFPPPlT1zw3Fw255fu9bnvL9oEwO2/6sG0cccx4cy+HNmpKZ1y0sKLTseKnAwP3wc6A+DfOD/KrREREYlNCd0FXNXYsWNZsmQJX331VZ3njB8/nnHjxoXvFxQUNKoQOKxzDnN+2sXDnyynxGvPkv15TymXTPyGN64cSnaqp8Zz1uwo5odNBTgdFmcPzKNpmocuLdI5f3C7Q938/ZKbmcwKV1cAvOvmc+jnJ4uIiMQ+VQCBa6+9lvfff5/PP/+ctm3b1nleUlISmZmZEbfGZFhneyJIKPz9aVQPWmQksWJrEX984/uIc0MTPj4IVv+GdW5G07SaATHWWJZFSU5/ADw7fwBfeZRbJCIiEnsSOgAaY7j22muZMmUKn332GR07dox2kxpUv7bZpAR3zjipRwuuPLYTL146GKfDYvqP21iwbhcAd7+7hCP+Oo23Fmzk/UWbATitX+uotftANWnTlV0mHWegArYuiXZzREREYk5CB8CxY8fyyiuv8Nprr5GRkcGWLVvYsmULpaV7HxPXWHlcDq44thP922bx19/0wbIserXO5OzD7arnI5+u4H/fb+Kl2evYWezl5je/58cthbidVsyN9dub7q2yWBQcB6iJICIiIjUldAB86qmnyM/P5/jjj6dVq1bh2+uvvx7tpjWYm07uxrvXHk2rrMrRcded1AW30+Lr1Tu59a1FAAzu0BRHcKWYY7o2JyvVHY3m/iI9cjP43igAioiI1CWhJ4FUXdg4kbVtksp5g9rx7znrKK3w07dNFq9ePoT5a3fz7zlrueb4LtFu4gHp1jKDpwKdAPBvXIAzyu0RERGJNQkdAKXS2BO68N9vNxIwhkd/2x+308HQzs0YGpw40phkpbjZmt4LvODYuQLK8iE5K9rNEhERiRkKgAJAblYyH1x/DAAdc2Jrd49fokWrPFavaUVnx2ZY/CYMuizaTRIREYkZCT0GUCJ1zEmLi/AH0D03k3/7T7bvzHlK+wKLiIhUoQAocalHbgZv+o+j2EqDnatg5afRbpKIiEjMUACUuNStZQbFpPCGOdE+MOeJ6DZIREQkhigASlzq3CINp8PiubKTMZYT1nwJXz8Opbuj3TQREZGoUwCUuJTkcjKoQxM2kcOy5qPsg5/eAX/rDm+NgZ9maFygiIgkLAVAiVvnDWoHwNj8iwic8n/Qsi/4y2HJW/DyGTD5AtBakCIikoAUACVundInl6wUN2vy/cxsehZlY2Yw84S3KD/sUnC4YcVHsGVRtJspIiJyyCkAStxKdjv5zYA2AEz8ag0XT5rHRR95+d2WczE9fmWftOiNKLZQREQkOhQAJa79dlAeAF+s2M43a3YBMG/tbn5sERwXuOS/EPBHq3kiIiJRoQAoca1nq0z652UD0CIjieE9WwBw//K2kJwNhZth7VfRa6CIiEgUKABK3PvrGX24ZGh73r5mGPee0Qe30+KrNQVsbxesAi5WN3Ci2l3s5eMlW/j3nHXsLCqPdnNERA4ZyxhNg/ylCgoKyMrKIj8/n8zMzGg3R/bTn/67iMnzNnBZ3ibu3H4zuFKgZS/we+H0x6D1gGg3URrY1oIybn1rEV+s2B4+luJ2cvGw9lx/YlfSkrRNukg8099vBcCDol+gxmn9zhJO+vsMfH4/i5uOJ71kQ+WDXYbD7/4bvcZJg1i0cQ8PfbycDjmpdG6ezuOfrWJnsReAri3ScTosftxSCMBZh7flkXP7R7O5ItLA9PdbAfCg6Beo8Xri81U8/Mlyeidt55WT/TTJSIMpVwEGxs6D5t1gz3pwuCCzdbSbKwdhR1E5p/5rJlsLIrt4e7bK5LHzD6NLiwyMMbz3/SZumLwQj8vBN7efRHaqJ0otFpGGpr/fGgMoCerKYztxeLtsfihvzjVLe+Prcy50D44J/OZZ2DAPHh8E/xoAP34Y3cbKL+YPGG6Y/B1bC8rp1DyNS4/qwMD2Tbj8mI5MuWYYXVpkAGBZFqf3b02vVpl4fQGmfPdzlFsuItKwVAE8CPo/iMZt7Y5iRv1zJqUVfk7u1ZInjizA89pvwJ0GnjQo3mafaDng1/+AgZdEtb1y4B6bvpJHpq4gxe3k3WuPolvLjL2e/+/Za/nzuz/QvWUGH994DJZlHaKWisihpL/fqgBKAuuQk8a/zh+Ax+Vg6tKtXDAtCX9OD6gotsNfy75w2IVgAvC/62H5x9FushyAQMDw0ux1ANx7Ru99hj+A0w9rQ7LbwfKthSzcsKeBWygiEj0KgJLQTu7VklfGDCEz2cX89Xt4x3Oa/UBaCzj/P3DGE3DEH+xjH4yDsoLoNVYOyPcb97CjqJz0JBejD2uzX8/JSnHzq76tAJj8zYZ9nC0i0ngpAErCG9yxKS/8fhAAt67pz+bj/wZ/+Biy88CyYMT90KQjFPwM0+6JbmNlv01bthWA47o3x+Pa/3/qzh/cDoC3vt3IzJXb93G2iEjjpAAoAgzq0JRT+7bCbxz86af+0Kxz5YOeVDj9X/b381+wJ4c83AU++2t0Giv7ZfoyewxnaPeX/XVE+yacdXhb/AHDNa9+y4J1u3hlzjruee8HdgWXjhERaew0CeQgaBBpfFm3s5jhf/+CCr9heM8WLNtcSJvsFO4b3ZseuZnwvxtgwYuVT3C44Y8/QlpO1Nostduwq4RjHvocp8NiwZ3DD3hJl3Kfnwufm8v8dbsjjp8/OI8JZ/arz6aKSBTo77cqgCJh7ZulcfHQDgBMW7aNn/eU8s3aXZz+2Cxue2sRp646gz9U3MKng56H3H4QqICFrwFQ6vWzbLPGB8aKUPfvwPZNftF6fkkuJ09fNJC8pimAvVg0wH8X/My2grL6a6iISJRovyORKm46uRv+gCEz2cWA9k349+x1fPbjNl6fb08I+IEBfPW1gy9PupDcLYvg25fwDRnLxRPnMm/tbh45pz9nDWwb8ZpeX4CAMSS7ndG4pLjj9QUwGJJcdf88Q92/J/ds+YvfJyc9iQ+vP4b80graNknlrKe+ZsG63UyctZY/jerxi19XRCQWqAv4IKiEHP+MMbz97c8sWL+bQR2a8O7CTcxYvp2BuS7eKvkDlreIt3v9k/xFH3KS41s2OlrRf/DxpHU7DtoNZVeFm98+M5udxV5mXNWbzA3TIbsdtOwDac2ifXmNzmc/buWm17+neUYSb18zjMxkd8Tj2wrKuP/DZby7cJN9/h+Po1Pz9Hp576lLt3L5y/PJSHIxa/yJNd5bRBoP/f1WADwo+gVKPNsKyxj56JfsLqngpZxXOK7oQyqME7flr3Gucbh5O+VM/rjzDMCwoO0/aLZjnv0YFr7h9+E++vpDfAWNkzGGxz9bxd+nrSD0L9bZA9vyt3P6s3ZHMRNnreH7jfks21SA1x/AsuCa4ztzy8j9qNRt/h5+mALH3mpP+KlDIGAY+Y8vWbmtiD+N6sFVx3Wu81wRiW36+60xgCIHpEVGMhPO7AvA33YOA8Bt+Sl0ZrPxhH9yp28Mb/iOY6ezOVaggrOKX2eo4weOcSy2w5/TQ3FqWywMTLsPs3stYE9amL16J/H2/2NfrdzBZS/N49+z11LuqxmS99cHizfzyFQ7/J3SOxfLgrcWbOTe//3Aqf+aycuz1/H9hj14/QH652Xz7tij9i/8+bzw+u/gq0dh/sS9nupwWFxxbCcAXvhqDWUVv/x6RESiTQFQ5ACd0qcVb18zjCOGnsBk92i+SDoB66qZtD3u9zQ59kpu9V3JwOJ/8LLvZAD+mfkaN7vesJ886DLG5kxklr83bipY99Yd/LilgF/9cybnPzeHtxZsjOKV1Z+yCj/3/u8HfvfCXKYt28af3/2B4x6awYzl22o9PxDYe/ANLcp82dEdefqigVx5rF19mzRrLcVeP4M7NOWx8wcw4+bjeeeaYfRrm71/Df32Jdiz3v5+xb53ejnjsDa0ykpme2G59gsWkUZNXcAHQSVkqc4Yw9erd/LT9iIKdm/niu/PxV2+C4ASk8T2MfM4+ZmldAus4v2kOwlg8Qf3w8wobA1AitvJe9ceRdf92LYslt0w+bvwOLxT+7ZiwbrdbCkoIyc9iXl3nBSxx+7HS7Zw/X++Y+wJXbhheNcar7VpTylH/d9nGAMzbz2BvKaplPv8nPP0bBb/nM+1J3ThhpO64nIe4P/Peovhn4dV2fPZCbf+BCnZe33a8zN/4q8fLKNjThrTxh2H06H9gkUaG/39VgVQpF5ZlsVRXXK4aGgHxv5qEO6R94Yfm+Qfyf0ztuP1Byhp1pcvko7DgeHP5Y9we+bHnN2umNIKP9e+9l2j7l7cVljG+4s2A/DsRQN54sLD+fzm40lxO9lRVM7yrYXhc9fvLOGWN7/H6w/wr89W8sOm/BqvN+W7nzHG3rElr6k9Ri/J5eTNq4YyZ/xJ/HFE932Hv8VvwVNHwXvXw5qZsO1H+PwBO/xlt4dmXcH4YfX0fV7feYPbkZnsYs2OYj79YcsB/GRERGKHAqBIQzrsd9D9V2xN6cwzvl/z6VJ7fbqRfXJp/ZsHyDdpdHZs5grvyzy87Qr+lvIim7du4fHPVkW54b/cu99twh8wDGiXzYjeuQCkeJwc0aEJAF+v2gnYy7lc+59vKSz34XJY+AOG26cswV+lO9gYw3+/tbvFzz48cnmdJJeTlpnJe2+M3wef3AH/HQNbl9hdvi/9Gp4cArMft8854Q7o8Sv7+xWf7PP60pNc4fUin5ixCp8/sM/niIjEGgVAkYbkcMD5/2HxaR9RQOVyJKf0zqVrjz5UXDEL74j/g84nYWE423zK9KSbsWb9gx1bN0Wx4b+MMYY3F9jj9c6uth7iUV3sHVO+Xr0DgH9OX8Gijflkp7p56+phZCS5+H7DHl6Zsy78nO835vPT9mKS3Q5G9c098AZ9cFNl0BtyFQy4CJKzITkLWvSCI8ZA37Oh2yn2OSs/hcC+q6+/P6oDaR4nS34u4F+NOKyLSOJSABQ5BAZ1bEpoqFirrGT6tc0CIKdNRzzDroKL3oZL3sc060pzK58/Ol4j++n+8O3LUWz1gVu0MZ8VW4tIcjk4rX/riMeO6mwHwLk/7WJPiZcXZ60F4IHf9OWwvGxuPaU7YLjv/R944as1rN5exB1TFgMwsncuGftad8/nhe9ehZ2r7fv5G+37AGdPglH/B2c8Dn9aB39aD9fMhl//HRxOaDvYDoalu2Hxm7D0vcrXqUVOehIPBGeDP/bZSr5eteOAfk4iItGmAChyCGSluOnd2g59I3q1jJgEEdbxGKyrZ7Fy6P+xKNARl/ES+PgOKC86xK395UKzmE/pk1tjoeRerTPJTHZRWO7jjneWUOz107VFOqP62JW9C/qk8V36TUxyTuCv7y9hxKNf8sOmArJS3Ptecy/gh7cvh3evgRdPhfJCe1kX44f2R0OfM/f+fKcLutqztplyJbxxETxzHPy8oM6nnHFYG849oi3GwA2vL2R3sXfv7yEiEkMUAEUOkbEndOHwdtn84eiOdZ/kSqLryKt4KO9JVgda4fAWwMJXD10jD0JRuY93F9pLo/yumx8WvAhllfsjOx0WQzvbu598EJwkMubojuEw7FwwkSa+bRzrXMyvXd/gDxiO7pLDJzceS89We5mlFwjA/66Hpe/Y9ws3w7R77fcHGHLF/l3A4ZeA0wNJWZDRCryF8O8zYcuSOp9yz+m96dw8je2F5Tw5Q13BItJ4aBmYg6Bp5NJQFm7Yw1tP38Nf3ZPwZrbHc+N3dldlDPvHtBX8Y9pKOjVLZXrqeKzty+wgdcLtsGMFLJlCWWkhW8qT+cG053H3GKaMP8feI9nnhX/0gSJ7kkx5Zkc+P+k9RvTNw7G3ZVb2rIcPboaVn4DlgMFXwtynKh/PbAs3fG9X+PaH32f/nL1Fdvjb+A2kNYdr5ta5dd+M5dv4/aR5eJwOPrv5ONo2qXs3ERGJDfr7Dfv5r6KIHEqH5WXzQrez2P3TmzQpWAfLP4Kev452s+q0q9jL8zPXAPDXw/OxZi6zHyjcDO9dFz4vGejgyKcDWznRsZzk1Zn2DNwfptjhLz0XAj6SCtZwSsU0cPwBfOWwcqr9MyjeDhUlYALgcMHGefZ9h9se39f/PMjfAD++b7/hoDH7H/6g8tykDLjwTXhhBOxYDvOeg+P/VOtTjuvWnGGdm/H16p38feoK/n7uYQf40xMROfRUATwI+j8IaUgrtxby6WNjGet6l6Ksrnzd7mq+SxrImYM6x9xC0fd/sJTnZq6hd+tM3m81EeuHt6H/BZCdB4teh5Z94LALME07cce/p3FR4UR68pP95N6/gR0r7WVaTrwTkjLho1vBnQaZre1gWF5Q95u3P9qezNG8u31/z3p4cqhdEbx+YZ2Vu/2y+C17CZnUZnDjkjr3Cv5+wx7OeGIWlgUfXHcMvVrr3wORWKa/3wqAB0W/QNLQ7nl1GjevuJB0qwyAXSadKyr+SMs+x3N0lxw65aRxRIemUd2NYnN+Kcc9PAOvL8Br53Vg2HvHQaACrpwJrfrVOL+swo/PW0b6Vw/AnCftah6AKxlu+sGuvj05FHZVmYWb0Qr6nGWHPHeq3U3rr4C0HOh0AlSfVLN7LRgDTfcy3nJ/+H3w2AA7VJ76CAy6rM5Tx772LR8s2kzv1pm8fc0wklyx3WUvksj091tdwCIxbcyoYZz74/2caaZxhmcezQM7eNH9f/x+sWH8oh4AnNijBS/8rj+W5Tiw7s568q/pK/H6Agzu2JSh+R/a4a/toFrDH2CP+XOnwcj77S7bj8fD2pn2mnxp9lIxjJkKWxfbXbtJ6XYF8UDGQDbpcPAXBvbP88ix8PFt8PXj9nIxpbugzRF2u6q469e9+HrVDn7YVMAjn67g9l/1rJ82iIg0gISuAH755Zc8/PDDLFiwgM2bNzNlyhRGjx6938/X/0HIobBmRzFeX4DuTZ3wn/NgzRd4HSmsd7XHW15GM/bQwsrHcqdA3mDocjKrOl7IrDX5/KpvK5pnJAHw0/YiCst89M/L3u/3NsYw+6ed9MzNpEmaB7Arfj9tL2ZY52as2VHMyY9+iT8QYNqoQrrMvRNKdsBvnrHD3f69iV1hy8qzF86ONd5ieLS3vUZgSN9z4aznapw6delWLn95PgCvjBnC0V1zDlUrReQA6O93gi8DU1xcTP/+/XniiSei3RSROnXMSaN7boY9/uz8ydDpeDyBUrp4f6SXtZaW1h4sjD0Z4qcZ8OkdfPbEWO5+7wd+9/xcisp9LN6Yz68f+4ozn/qalVX24t2XNxds5ILn5nLbfxeFj1317wVc+Pxcbpi8kP/7+EdSAsV8lP0QXT6/yg5/zbpAr9H7f4GWBU3ax2b4A/CkwXF/siedpDS1j/34gT05pZqTe7XkwiHtAHjgw2WHspUiIgckobuAR40axahRo6LdDJH950mFC9+CNV+Cr5xin8Vlb61lVVkGx+e5aL5pOre63+AK1wfMc/Rn6tY+XP3KAn7cUkiJ197i7IWv1vDgWZXds2UVfq77z3ekJ7m456gksip2QMdj8PkDPPbZSgC+WLGdsgo/BaUVfL8xH4D3vre3qrvR9SE9y74HVwoMuxaOugHc+9ijt7E58ioYcqX9/d972rOb134FXU6qceotI7vz+rwNLN1cwOrtRXRunl7jHBGRaIvR/+UWkTo53Xbw6PEr0vqM4oQTR7CdJry5IYMn/aOZ3cze9eLJtOdo5Spk5sodbC8sp1WWHcre/u5ndhZVVq/e+e5npi7dyvcL52E9fxK89Gv4aQbvLtzEhl2lAJT7Asxbu4uZK+0tz9pkp9AszUMKZVzumWa/0Ogng7N4Y2uGcr2xLPvWdYR9f8UntZ6WneoJd/2GFrwWEYk1CoAHoLy8nIKCgoibSLRdPLQDfdpk0iormUm/H8TQq56E5j1xl27ng+ZPkkYpLTOT+O/Vw+jfNguvL8Arc9ZD4VbMR7fx84yJtGA3kzx/I5NiADb/9zae/HwFABlJdkfBzJU7mLlyOwCjB7Tmg+uP4eXDlpMWKICmnaDXGdH5ARxq3U6xv674yB6/6PdF7HgCcGrfVgC8v2jToW6diMh+UQA8ABMmTCArKyt8y8vLi3aTREh2O3l37NF8/acTOaFHC3CnwDkvQnI2TXd/z7xOzzP1ukG0zk5hzDGdAHh79lIC/z4Ta+7T/LHkUWYnXUt7awu73LkUmhRaFf9Iz12fkZ3q5vZT7dmsXyzfzler7ArgMV2bk5vuZNDm1+xGDLsu5ncqqTedjgNnkj1xZeM8mDgCHukOOyuXrRnROxeP08GKrUWsOIAxlyIih4oC4AEYP348+fn54duGDRui3SQRwN5n16q6Fl6LHnDRFEjKJHXTbDKfGQyTL+TU3a9wZvpS/up9CMe2JRQ4svnZNMNpGfCk0/Syt9nax94791bXZP6d+ybnzD+fB9zPk791LTuKvKR6HAx0rIJ3rrZ33UhrYS/6nCg8aXYIBHu7uJ8X2BNwvvt3+JSsFDfHdrO7gd//XlVAEYk9Cb0MTFWWZWkZGIk/6+fAa+dCWX6Nh4pNEr/1/pllpj3TRlt07NIDcrpCeRGBfw3AUbwt4vwy42aFaUtX51ZSTEnlA6MehiFXNPSVxJZ5z8MHf7S/txz2YtaZbeHGxeHZzFO+28hNr39PTrqHc47I48hOzTi2a05kUBeRqNDf7wSvABYVFbFw4UIWLlwIwJo1a1i4cCHr16+PbsNE6ku7I+GmpfD7D2DE/dD3HGjWlYqkJtzmvIUlphN98prR8cjT7PAHkJSO49ePQovecPjFMPopNmb0J9mqoJ9jjR3+3KnQ7zy45P3EC38A3UbZi1S7ku1Z2UlZULAR1n0VPmV4z5ZkJLvYUeTlqRmruWTiN3yzZlcUGy0iUimhK4AzZszghBNOqHH8kksu4cUXX9zn8/V/ENKYbckv4/V5Gxg9oDXtm6Xt9dw5q3fwyPMv0dQq4M5LziCvc29wJR2ilsaojQvs7uAWPeC96+Hbl+Cw38HoynVFN+wqYebKHbw8ey0/bink1lO6c83xXaLYaBEB/f2GBF8H8PjjjyeB868kuNysZG4Y3nW/zh3YoSnJXY7GleKmbbcBNffeTURtB1Z+3/98OwAufceeEFPwM7QeQF7TplwwpB17Sr38+PFylm7SygEiEhsSOgCKyP5xOx38e8yQaDcjdrU7ErLbw5518GTw59R1JFz4BgC9WtkVhmWbFQBFJDYk9BhAEZF6YVkw5Cr7e6e9ZzI/zYCKMqAyAK7ZUUxpcEcWEZFoUgAUEakPR14NN6+C2zfbS+P4y+0lYoDmGUk0S/MQMLBc6wKKSAxQABQRqQ+WBenNwemCDkfZx9bNCj5k0au1uoFFJHYoAIqI1LcOR9tf184MH+oZ7AbWRBARiQUKgCIi9a19MABumAc+L6CJICISWxQARUTqW/PukJoDvlLY9C1QWQH8cUshgYCWnxKR6FIAFBGpb5ZVOQ4w2A3cqXkaHqeDonIfG3aX7OXJIiINTwFQRKQhhLqB19oTQdxOB91y0wF1A4tI9CkAiog0hNBEkA1zoWATAD1z7W7ghz5Zzr9nr6W43Bet1olIglMAFBFpCM17QKv+UFECb40Bv4/T+rcmyeXgp+3F/PndH/jNk7PYU+KNdktFJAEpAIqINASHA86eBJ4MWP81TL2LYzM2Me+yltx1ag+aZySxYmsRY16ar91BROSQUwAUEWkozTrD6f+yv5/zBDxzLJkvnsAfSiby6mVDyEx2sWDdbq597VvNDBaRQ0oBUESkIfU5E469BdKaQ3qufWz2E3QrXcTE3w8iyeVg+o/b+HDJ5ui2U0QSigKgiEhDO/FOuGUV3LwcBlwEGHjnao5o5eGa47sA8PAny/H6AtFtp4gkDAVAEZFDaeQDkJUHe9bBtHu47JiO5KQnsW5nCZPnrY9260QkQSgAiogcSsmZcPpj9vfzJ5JWtI4bhncF4J/TVlKkpWFE5BBQABQROdQ6nwBdR4Dxwxf/x3mD8uiYk8bOYi//nr0u2q0TkQSgACgiEg0n3G5/XfQG7p0rGHuCPRZw4qw1lFVoWRgRaVgKgCIi0dB6APT4NWBgxgRO79+aVlnJbC8s5+1vf45260QkzikAiohEywm3AxYsfQfPxq8Zc3RHAJ79cjV+rQsoIg1IAVBEJFpa9obDL7a/f+96zh/QnKwUN2t3lvDJD1ui2zYRiWsKgCIi0XTyffYC0btWkzbnES4e2h6Ap79YjTGqAopIw1AAFBGJppRsOPUR+/tZ/+KythtIcjlYtDGf2at3RrVpIhK/FABFRKKt56+hz1lg/GS9fSF3dNsAwFNfrI5yw0QkXikAiojEgjOehG6jwFfGRWtv52Tnt8xcuYMlP+dHu2UiEocUAEVEYoE7GX77b+hzFlaggsc8T9DF2sg/pq3QjGARqXcKgCIiscLpht88Cx2OIdmU8qz7UeYuW8vlL8/XFnEiUq8UAEVEYonTBee8CJlt6eTYzMOeZ/nsx22c8/RsCsoqot06EYkTCoAiIrEmLcfuDna4OMXxDb9K/ZFlmwu49c1FWhpGROqFAqCISCxqczgMugyAR7LfItkJH/+whednrolyw0QkHigAiojEquNug+QsUnYtZeKAVQA8+PGPfLVyR5QbJiKNnQKgiEisSm0Kx94CwNC1T3BNzzL8AcPlL89n/tpdUW6ciDRmCoAiIrFs8BXQrAtW0VZuWX8VD+Z+RllFBZdOmqc1AkXkF1MAFBGJZa4k+P2H0G0Ult/LeXue57Gm/6Ww3McNk7/D6wtEu4Ui0ggpAIqIxLqMlnD+f8J7Bv+6ZAqXpXzB6u3FvDx7bXTbJiKNkivaDRARkf1gWfas4JJd8Pn93M4LLHM05R/TXPRqlclLs9eyo8jLMxcNJCc9KdqtFZEYpwqgiEhjcuwt0OdsHMbHi56HObbiKy54fi6f/LCVBet286/pK6PdQhFpBBQARUQaE8uCM56AHr/GjY8nPf/iaud7HNUpG4DX5q5n7Y7i6LZRRGKeAqCISGPjToZzX7ZnCAO3uSfzqhnPpR124QsY/vbp8ig3UERinQKgiEhj5HDCqIfgtH9BchZs/p67tl7PWNc7fLDoZ+b8tDPaLRSRGKYAKCLSWFkWDLwErp0Pfc7CMgFucb3Bi+6HuHHiND5cvDnaLRSRGJXwAfCJJ56gQ4cOJCcnM2TIEL755ptoN0lE5MCkt4CzJ8IZT2BcKRznXMQ7ztuY9NprjH97MUs3FUS7hSISYxI6AL7++uuMGzeOu+++m2+//Zb+/fszcuRItm3bFu2miYgcuAG/w7r8M0xON3Kt3fzH81eO+W4c7z1xC3c++TKb80uj3UIRiRGWMcZEuxHRMmTIEAYNGsTjjz8OQCAQIC8vj+uuu44//elP+3x+QUEBWVlZ5Ofnk5mZ2dDNFRHZP+VF8ME4WPR6xOHv6QZ9zyHDWUGgNJ+dKR35Ob0PJc5MHMaH3+elrKwUAn5aNc2ibZNkmpevJ61gFUmeZDzNO1KWlMPGQj/biwN4kpJJTkmhaXoqOdnp+HCxrTiAHwd5TVNJdjkIlOymcPsGnJ4kUjJzMFiUFBfi9fkIpLXEh4sdReXs3L2HjIKVNCv8EbxF7KAJ2wLZbLeasj2QQXHBLvyF28hN8dM7N5W8LBfOQAXlfljjb8GSsubsKfPjKy8myXjJclfg9JdTVFREeVkxbpeLJLebdjkZ9G6TTXEFzFmzm50lfvq3a0L/tlns2r6ZbVs3kZLdgq69Dsfh9PDjskXk795B07wedOrcndRkNxgoqwhQVO7D43LQrmkqHpejxmdQUV5Cid+JywqQFigEbzEkZUByNmXONBb9XMiu4nIAHJZFmsdJljtA56ZuUhx+1hZZvPX9Toq9PoZ0bMrh7ZvQPD0Jf8Aw+6edfPLDFnaXVGAZPylWBZluQzOPj15NDF2zDclZzfGnt6KcZIrKK/A4oUPTVJyWARMAY8CdggE25ZexeGM+u0u8tG+WSodmaWSluEn1OLEsC4A9JV5+2lHMpj2lFJf7KPcFaJ2VQoecNPwBw9aCMkq8PgCSXE56tMogNzM5/HxjDD9uKWR3sZderTPJTvXs9dfY5w+wo8hLstuxz3MPlP5+J3AA9Hq9pKam8tZbbzF69Ojw8UsuuYQ9e/bw7rvv1nhOeXk55eXl4fsFBQXk5eUl9C+QiMQoY2DDXFg/h+I1c/Gs/hQ3vkPy1n5j4cMFFiRRUed5AWOxhzRSKSfZqvu8WFFu3JSQRAUuKnBSYVw4CJBqleOyDLsdTSl0ZpPj20Jrs3Wvr+U3FoWk4sOJhwqSqCDJqvn5FJoUvLhIpRw3PspIogwPTvzh57msX74doNe42EkmASyyKCaF8uD12dfow0USFaRQhgODHyc+HNW+OvHjwGeCX3GGH7ccLpwuN06XC2d5PjmBHWRQQgUufJYbn+UiYLnwWS58uPHhpNw48QaclAQcVBgX5X1+ywm/vfEXX2NtFAATeCeQHTt24Pf7admyZcTxli1b8uOPP9b6nAkTJnDvvfceiuaJiBwcy4J2R0K7I0k7Gsr3bGLmG3/DvXUhRc5MAs4UOvl/ooN3Fa5gMPTjxO9wY7CwjB+H8bOBliwPtMUyAdpZW2lmFeKx/LgtPy7jw4UPJ5EBxGkZnFWC326Tjgs/GZbdBV1uXFgYPJafphSFz9tjZbHc0ZkiK4Nc5x6amd1k+XaREijC50imPKkppVYKBV4o9TupwIXH8tGOzaQHCsOvE7Cc+KwkKpzJGGcyuDwYYzABPxUVFfh8PhyWIcUJTiuA3+fDHzAUOTOpSGpKincnzX1bcViG3Y4mVLjSaeLdTJJVERlorcgfeXagEALrav04ik0SpSSRTinJVgVOy5DNvtdrDP3MQtIpJZ26u/L9OCm00ikIJNGEwhrPr85j+WjFrohjTipIriO4uwhQ5z4zVh3HfcFblXPs348KMNi32gSLql8X/VzXO8pBSNgA+EuMHz+ecePGhe+HKoAiIrEuKbs1x1zx95oP+H12d6DTjdOycFZ7uGPw5vUFKC734XJaZCS7I84xfh8FxaU4qSDdBcZXzu7CYrYXlpGR04ac7CwCxrCztAwLSEtNxuN0QPEOKN4OnjRIziI7OYshVi0pwufF5XTjsizSgJzqjxsDZXvAcoA7FYfTjQc40E7DrCrfl5cVYwIBmqRmVP6cCn4GXxn4vcGbDwPsqnCxpbCCQP4mTNE2XE3ySMvrR1bT5qQ6DYXlPpZsLWPN9iKS3U4y3X56NQnQPtWLZQLgSganx/7qSmJriWHFjjI6ZxpauwrBXwGeVLzGye78fIqKimjXPAu3Jzn8HFxJ4EzC6XSRDWSHLqSsAHzlYDnwY7FmZwnrd5fTJM1D8/QkknwFOEq2k57kIimjGbhTwV+B8XspKy+jtLSMgNODcaeSlpJEqhMI+CDgD36teos85vV6+XlXEXuKSyksLiUzuyk9u/cgKb0ZBSWlbNi+h7LyMspKSyHgwxHw4bF8pDgNaS4/TZIg3Q3DWvQ8wE9S9oe6gA+gC7g6lZBFREQaH/39TuBZwB6Ph4EDBzJ9+vTwsUAgwPTp0xk6dGgUWyYiIiLSsBK6C3jcuHFccsklHHHEEQwePJh//OMfFBcXc+mll0a7aSIiIiINJqED4G9/+1u2b9/OXXfdxZYtWzjssMP4+OOPa0wMEREREYknCTsGsD5oDIGIiEjjo7/fCTwGUERERCRRKQCKiIiIJBgFQBEREZEEowAoIiIikmAUAEVEREQSjAKgiIiISIJRABQRERFJMAqAIiIiIglGAVBEREQkwST0VnAHK7SJSkFBQZRbIiIiIvsr9Hc7kTdDUwA8CIWFhQDk5eVFuSUiIiJyoAoLC8nKyop2M6JCewEfhEAgwKZNm8jIyMCyrL2eW1BQQF5eHhs2bIjbfQd1jY1fvF8fxP81xvv1ga4xHkT7+owxFBYW0rp1axyOxBwNpwrgQXA4HLRt2/aAnpOZmRmX/zFXpWts/OL9+iD+rzHerw90jfEgmteXqJW/kMSMvSIiIiIJTAFQREREJMEoAB4iSUlJ3H333SQlJUW7KQ1G19j4xfv1QfxfY7xfH+ga40G8X19joEkgIiIiIglGFUARERGRBKMAKCIiIpJgFABFREREEowCoIiIiEiCUQA8AB06dMCyrBq3sWPHAnD88cfXeOyqq66KeI3169dz6qmnkpqaSosWLbjlllvw+XwR58yYMYPDDz+cpKQkunTpwosvvtgg1/Pll19y2mmn0bp1ayzL4p133ol43BjDXXfdRatWrUhJSWH48OGsXLky4pxdu3Zx4YUXkpmZSXZ2NmPGjKGoqCjinEWLFnHMMceQnJxMXl4eDz30UI22vPnmm/To0YPk5GT69u3Lhx9+2ODXWFFRwW233Ubfvn1JS0ujdevWXHzxxWzatCniNWr73B988MFGcY0Av//972u0/5RTTok4J5Y/x31dX23/TVqWxcMPPxw+J9Y/wwkTJjBo0CAyMjJo0aIFo0ePZvny5RHnlJWVMXbsWJo1a0Z6ejpnnXUWW7dujTinvv59eeKJJ+jQoQPJyckMGTKEb775pkGvb9euXVx33XV0796dlJQU2rVrx/XXX09+fn7E69T2OU+ePDnq17c/1wiH9m9ENK5x7dq1df73+Oabb4bPi+XPMaEY2W/btm0zmzdvDt+mTp1qAPP5558bY4w57rjjzOWXXx5xTn5+fvj5Pp/P9OnTxwwfPtx899135sMPPzQ5OTlm/Pjx4XN++uknk5qaasaNG2eWLl1qHnvsMeN0Os3HH39c79fz4YcfmjvuuMO8/fbbBjBTpkyJePzBBx80WVlZ5p133jHff/+9Of30003Hjh1NaWlp+JxTTjnF9O/f38yZM8fMnDnTdOnSxZx//vnhx/Pz803Lli3NhRdeaJYsWWL+85//mJSUFPPMM8+Ez5k1a5ZxOp3moYceMkuXLjV33nmncbvdZvHixQ16jXv27DHDhw83r7/+uvnxxx/N7NmzzeDBg83AgQMjXqN9+/bmvvvui/hci4qKGsU1GmPMJZdcYk455ZSI9u/atSvinFj+HPd1fVWva/PmzWbixInGsiyzevXq8Dmx/hmOHDnSTJo0ySxZssQsXLjQ/OpXvzLt2rWLaONVV11l8vLyzPTp0838+fPNkUceaYYNGxZ+vL7+fZk8ebLxeDxm4sSJ5ocffjCXX365yc7ONlu3bm2w61u8eLE588wzzXvvvWdWrVplpk+fbrp27WrOOuusiNcBzKRJkyI+x6r/HkXr+vbnGo05dH8jonWNPp+vxn+P9957r0lPTzeFhYXh14nlzzGRKAAehBtuuMF07tzZBAIBY4z9H/cNN9xQ5/kffvihcTgcZsuWLeFjTz31lMnMzDTl5eXGGGNuvfVW07t374jn/fa3vzUjR46s/wuoovof1kAgYHJzc83DDz8cPrZnzx6TlJRk/vOf/xhjjFm6dKkBzLx588LnfPTRR8ayLPPzzz8bY4x58sknTZMmTcLXZ4wxt912m+nevXv4/rnnnmtOPfXUiPYMGTLEXHnllQ16jbX55ptvDGDWrVsXPta+fXvz6KOP1vmcWL/GSy65xJxxxhl1PqcxfY778xmeccYZ5sQTT4w41pg+Q2Ps/9kEzBdffGGMsf/bc7vd5s033wyfs2zZMgOY2bNnG2Pq79+XwYMHm7Fjx4bv+/1+07p1azNhwoQGu77avPHGG8bj8ZiKiorwsX19/rFyfcbUfo2H6m9ENK+xusMOO8z84Q9/iDjWmD7HeKYu4F/I6/Xyyiuv8Ic//AHLssLHX331VXJycujTpw/jx4+npKQk/Njs2bPp27cvLVu2DB8bOXIkBQUF/PDDD+Fzhg8fHvFeI0eOZPbs2Q18RZHWrFnDli1bItqSlZXFkCFDwm2ZPXs22dnZHHHEEeFzhg8fjsPhYO7cueFzjj32WDweT/ickSNHsnz5cnbv3h0+JxauGSA/Px/LssjOzo44/uCDD9KsWTMGDBjAww8/HNEl0xiuccaMGbRo0YLu3btz9dVXs3Pnzoj2x8vnuHXrVj744APGjBlT47HG9BmGuj6bNm0KwIIFC6ioqIh4/x49etCuXbuI/x4P9t8Xr9fLggULIs5xOBwMHz68Xq+z+vXVdU5mZiYuV+SW9WPHjiUnJ4fBgwczceJETJWlbGPl+kLth5rX2NB/I2LhGkMWLFjAwoULa/3vsbF8jvHMte9TpDbvvPMOe/bs4fe//3342AUXXED79u1p3bo1ixYt4rbbbmP58uW8/fbbAGzZsiXiP2wgfH/Lli17PaegoIDS0lJSUlIa8KoqhdpTW1uqtrVFixYRj7tcLpo2bRpxTseOHWu8RuixJk2a1HnNodc4VMrKyrjttts4//zzIzYnv/766zn88MNp2rQpX3/9NePHj2fz5s38/e9/D19HLF/jKaecwplnnknHjh1ZvXo1t99+O6NGjWL27Nk4nc64+hxfeuklMjIyOPPMMyOON6bPMBAIcOONN3LUUUfRp0+fcBs8Hk+N/zGp/t/jwf77snv3bvx+f63n/Pjjjw12fdXt2LGDv/zlL1xxxRURx++77z5OPPFEUlNT+fTTT7nmmmsoKiri+uuvj5nr29s1Hoq/EdG+xqpeeOEFevbsybBhwyKON5bPMd4pAP5CL7zwAqNGjaJ169bhY1X/serbty+tWrXipJNOYvXq1XTu3DkazZT9VFFRwbnnnosxhqeeeirisXHjxoW/79evHx6PhyuvvJIJEyY0im2MzjvvvPD3ffv2pV+/fnTu3JkZM2Zw0kknRbFl9W/ixIlceOGFJCcnRxxvTJ/h2LFjWbJkCV999VW0m9Ig9nV9BQUFnHrqqfTq1Yt77rkn4rE///nP4e8HDBhAcXExDz/8cDg4xIq6rjGe/kbs63MsLS3ltddei/jMQhrL5xjv1AX8C6xbt45p06Zx2WWX7fW8IUOGALBq1SoAcnNza8zaC93Pzc3d6zmZmZmHrPpXtT21taVqW7dt2xbxuM/nY9euXfu8nqrvUdc5occbWij8rVu3jqlTp0ZU/2ozZMgQfD4fa9euBRrHNVbVqVMncnJyIn4v4+FznDlzJsuXL9/nf5cQu5/htddey/vvv8/nn39O27Ztw8dzc3Pxer3s2bOnzvevj39fcnJycDqdDXaddV1fSGFhIaeccgoZGRlMmTIFt9u919cbMmQIGzdupLy8HIj+9cG+r7F6+6F+/0bEyjW+9dZblJSUcPHFF+/z9WLxc0wECoC/wKRJk2jRogWnnnrqXs9buHAhAK1atQJg6NChLF68OOKPbShw9OrVK3zO9OnTI15n6tSpDB06tB6vYN86duxIbm5uRFsKCgqYO3duuC1Dhw5lz549LFiwIHzOZ599RiAQCP/DNnToUL788ksqKirC50ydOpXu3bvTpEmT8DnRuuZQ+Fu5ciXTpk2jWbNm+3zOwoULcTgc4W7TWL/G6jZu3MjOnTsjfi8b++cIdlV+4MCB9O/ff5/nxtpnaIzh2muvZcqUKXz22Wc1uqMHDhyI2+2OeP/ly5ezfv36iP8eD/bfF4/Hw8CBAyPOCQQCTJ8+/aCuc1/XB/a/LyNGjMDj8fDee+/VqOLWZuHChTRp0iRcxY3W9cH+XWNt7Yf6/RsRK9f4wgsvcPrpp9O8efN9vm4sfY4JJYoTUBolv99v2rVrZ2677baI46tWrTL33XefmT9/vlmzZo159913TadOncyxxx4bPic0xX/EiBFm4cKF5uOPPzbNmzevdYr/LbfcYpYtW2aeeOKJBlsGprCw0Hz33Xfmu+++M4D5+9//br777rvwDNgHH3zQZGdnm3fffdcsWrTInHHGGbUuAzNgwAAzd+5c89VXX5muXbtGLB+yZ88e07JlS3PRRReZJUuWmMmTJ5vU1NQay2u4XC7zt7/9zSxbtszcfffd9ba8xt6u0ev1mtNPP920bdvWLFy4MGJJgtCMu6+//to8+uijZuHChWb16tXmlVdeMc2bNzcXX3xxo7jGwsJCc/PNN5vZs2ebNWvWmGnTppnDDz/cdO3a1ZSVlYVfI5Y/x339nhpjL+OSmppqnnrqqRrPbwyf4dVXX22ysrLMjBkzIn4PS0pKwudcddVVpl27duazzz4z8+fPN0OHDjVDhw4NP15f/75MnjzZJCUlmRdffNEsXbrUXHHFFSY7OztiZmp9X19+fr4ZMmSI6du3r1m1alXEOT6fzxhjzHvvvWeee+45s3jxYrNy5Urz5JNPmtTUVHPXXXdF/fr25xoP5d+IaF1jyMqVK41lWeajjz6q8Rqx/jkmEgXAA/TJJ58YwCxfvjzi+Pr1682xxx5rmjZtapKSkkyXLl3MLbfcErHGkzHGrF271owaNcqkpKSYnJwc88c//jFimQNjjPn888/NYYcdZjwej+nUqZOZNGlSg1zL559/boAat0suucQYYy8F8+c//9m0bNnSJCUlmZNOOqnGde/cudOcf/75Jj093WRmZppLL700Yr0nY4z5/vvvzdFHH22SkpJMmzZtzIMPPlijLW+88Ybp1q2b8Xg8pnfv3uaDDz5o8Gtcs2ZNrY9RZW3HBQsWmCFDhpisrCyTnJxsevbsaR544IGI8BTL11hSUmJGjBhhmjdvbtxut2nfvr25/PLLa/wjGcuf475+T40x5plnnjEpKSlmz549NZ7fGD7Dun4Pq/63X1paaq655hrTpEkTk5qaan7zm9+YzZs3R7xOff378thjj5l27doZj8djBg8ebObMmdOg11fXZwyYNWvWGGPspYkOO+wwk56ebtLS0kz//v3N008/bfx+f9Svb3+u8VD/jYjGNYaMHz/e5OXl1fhsjIn9zzGRWMZUmXstIiIiInFPYwBFREREEowCoIiIiEiCUQAUERERSTAKgCIiIiIJRgFQREREJMEoAIqIiIgkGAVAERERkQSjACgidTr++OO58cYbw/c7dOjAP/7xj70+x7Is3nnnnf1+jwM9PxFYloVlWWRnZ0e7Kbz44ovh9lT9XRCRxk0BUEQaRNXgUNdt7dq1b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", 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", 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", 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", 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diff --git a/sed/v1.0.0/tutorial/6_binning_with_time-stamped_data.html b/sed/v1.0.0/tutorial/6_binning_with_time-stamped_data.html new file mode 100644 index 0000000..439ee0a --- /dev/null +++ b/sed/v1.0.0/tutorial/6_binning_with_time-stamped_data.html @@ -0,0 +1,1224 @@ + + + + + + + + + + + Binning of temperature-dependent ARPES data using time-stamped external temperature data — SED 1.0.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Binning of temperature-dependent ARPES data using time-stamped external temperature data#

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In this example, we pull some temperature-dependent ARPES data from Zenodo, which was recorded as a continuous temperature ramp. We then add the respective temperature information from the respective timestamp/temperature values to the dataframe, and bin the data as function of temperature For performance reasons, best store the data on a locally attached storage (no network drive). This can also be achieved transparently using the included MirrorUtil class.

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[1]:
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%load_ext autoreload
+%autoreload 2
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+import glob
+
+import sed
+from sed.dataset import dataset
+
+%matplotlib widget
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+

Load Data#

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dataset.get("TaS2") # Put in Path to a storage of at least 20 GByte free space.
+data_path = dataset.dir
+scandir, caldir = dataset.subdirs # scandir contains the data, caldir contains the calibration files
+
+# correct timestamps if not correct timezone set
+tzoffset = os.path.getmtime(scandir + '/Scan0121_1.h5') - 1594998158.0
+if tzoffset:
+    for file in glob.glob(scandir +'/*.h5'):
+        os.utime(file, (os.path.getmtime(file)-tzoffset, os.path.getmtime(file)-tzoffset))
+
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+INFO - Not downloading TaS2 data as it already exists at "/home/runner/work/sed/sed/docs/tutorial/datasets/TaS2".
+Set 'use_existing' to False if you want to download to a new location.
+INFO - Using existing data path for "TaS2": "/home/runner/work/sed/sed/docs/tutorial/datasets/TaS2"
+INFO - TaS2 data is already present.
+
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[3]:
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# create sed processor using the config file with time-stamps:
+sp = sed.SedProcessor(folder=scandir, user_config="../src/sed/config/mpes_example_config.yaml", system_config={}, time_stamps=True, verbose=True)
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+INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/sed_config.yaml]
+INFO - User config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/mpes_example_config.yaml]
+INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
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# Apply jittering to X, Y, t, ADC columns.
+sp.add_jitter()
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+INFO - add_jitter: Added jitter to columns ['X', 'Y', 't', 'ADC'].
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sp.bin_and_load_momentum_calibration(df_partitions=10, plane=33, width=3, apply=True)
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features = np.array([[337., 242.], [289., 327.], [187., 344.], [137., 258.], [189., 161.], [289., 158.], [236.0, 250.0]])
+sp.define_features(features=features, rotation_symmetry=6, include_center=True, apply=True)
+sp.generate_splinewarp(include_center=True)
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+INFO - Calculated thin spline correction based on the following landmarks:
+pouter_ord: [[137. 258.]
+ [187. 344.]
+ [289. 327.]
+ [337. 242.]
+ [289. 158.]
+ [189. 161.]]
+pcent: (236.0, 250.0)
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+
# Adjust pose alignment, using stored distortion correction
+sp.pose_adjustment(xtrans=15, ytrans=8, angle=-5, apply=True)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+INFO - Applied translation with (xtrans=15.0, ytrans=8.0).
+INFO - Applied rotation with angle=-5.0.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
[8]:
+
+
+
# Apply stored momentum correction
+sp.apply_momentum_correction()
+
+
+
+
+
+
+
+
+INFO - Adding corrected X/Y columns to dataframe:
+Calculating inverse deformation field, this might take a moment...
+INFO - Dask DataFrame Structure:
+                      X        Y        t      ADC timeStamps sampleBias       Xm       Ym
+npartitions=97
+                float64  float64  float64  float64    float64    float64  float64  float64
+                    ...      ...      ...      ...        ...        ...      ...      ...
+...                 ...      ...      ...      ...        ...        ...      ...      ...
+                    ...      ...      ...      ...        ...        ...      ...      ...
+                    ...      ...      ...      ...        ...        ...      ...      ...
+Dask Name: apply_dfield, 492 graph layers
+
+
+
+
[9]:
+
+
+
# Apply stored config momentum calibration
+sp.apply_momentum_calibration()
+
+
+
+
+
+
+
+
+INFO - Adding kx/ky columns to dataframe:
+INFO - Dask DataFrame Structure:
+                      X        Y        t      ADC timeStamps sampleBias       Xm       Ym       kx       ky
+npartitions=97
+                float64  float64  float64  float64    float64    float64  float64  float64  float64  float64
+                    ...      ...      ...      ...        ...        ...      ...      ...      ...      ...
+...                 ...      ...      ...      ...        ...        ...      ...      ...      ...      ...
+                    ...      ...      ...      ...        ...        ...      ...      ...      ...      ...
+                    ...      ...      ...      ...        ...        ...      ...      ...      ...      ...
+Dask Name: assign, 502 graph layers
+
+
+
+
[10]:
+
+
+
# Apply stored config energy correction
+sp.apply_energy_correction()
+
+
+
+
+
+
+
+
+INFO - Applying energy correction to dataframe...
+INFO - Dask DataFrame Structure:
+                      X        Y        t      ADC timeStamps sampleBias       Xm       Ym       kx       ky       tm
+npartitions=97
+                float64  float64  float64  float64    float64    float64  float64  float64  float64  float64  float64
+                    ...      ...      ...      ...        ...        ...      ...      ...      ...      ...      ...
+...                 ...      ...      ...      ...        ...        ...      ...      ...      ...      ...      ...
+                    ...      ...      ...      ...        ...        ...      ...      ...      ...      ...      ...
+                    ...      ...      ...      ...        ...        ...      ...      ...      ...      ...      ...
+Dask Name: assign, 516 graph layers
+
+
+
+
[11]:
+
+
+
# Load energy calibration EDCs
+scans = np.arange(127,136)
+voltages = np.arange(21,12,-1)
+files = [caldir + r'/Scan' + str(num).zfill(4) + '_1.h5' for num in scans]
+sp.load_bias_series(data_files=files, normalize=True, biases=voltages, ranges=[(64000, 76000)])
+rg = (65500, 66000)
+sp.find_bias_peaks(ranges=rg, ref_id=5, infer_others=True, apply=True)
+sp.calibrate_energy_axis(ref_energy=-0.5, energy_scale="kinetic", method="lmfit")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+INFO - Use feature ranges: [(67180.0, 67780.0), (66820.0, 67384.0), (66436.0, 67012.0), (66088.0, 66652.0), (65764.0, 66316.0), (65500.0, 66004.0), (65188.0, 65704.0), (64864.0, 65416.0), (64624.0, 65140.0)].
+INFO - Extracted energy features: [[6.76360000e+04 4.47140008e-01]
+ [6.72520000e+04 4.41972464e-01]
+ [6.68800000e+04 4.45905387e-01]
+ [6.65320000e+04 4.43017632e-01]
+ [6.62080000e+04 4.37122852e-01]
+ [6.58960000e+04 4.28882003e-01]
+ [6.55960000e+04 4.22135979e-01]
+ [6.52960000e+04 4.13137674e-01]
+ [6.50320000e+04 4.00443912e-01]].
+INFO - [[Fit Statistics]]
+    # fitting method   = leastsq
+    # function evals   = 163
+    # data points      = 9
+    # variables        = 3
+    chi-square         = 0.00179088
+    reduced chi-square = 2.9848e-04
+    Akaike info crit   = -70.7004554
+    Bayesian info crit = -70.1087817
+[[Variables]]
+    d:   1.09335629 +/- 0.06668048 (6.10%) (init = 1)
+    t0:  7.6176e-07 +/- 1.3448e-08 (1.77%) (init = 1e-06)
+    E0: -48.0855611 +/- 1.25773261 (2.62%) (init = -21)
+[[Correlations]] (unreported correlations are < 0.100)
+    C(d, t0)  = -0.9998
+    C(d, E0)  = -0.9993
+    C(t0, E0) = +0.9985
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
[12]:
+
+
+
# Apply stored config energy calibration
+#sp.append_energy_axis(bias_voltage=17)
+sp.append_energy_axis()
+
+
+
+
+
+
+
+
+INFO - Adding energy column to dataframe:
+INFO - Using energy calibration parameters generated on 03/06/2025, 09:43:01
+INFO - Dask DataFrame Structure:
+                      X        Y        t      ADC timeStamps sampleBias       Xm       Ym       kx       ky       tm   energy
+npartitions=97
+                float64  float64  float64  float64    float64    float64  float64  float64  float64  float64  float64  float64
+                    ...      ...      ...      ...        ...        ...      ...      ...      ...      ...      ...      ...
+...                 ...      ...      ...      ...        ...        ...      ...      ...      ...      ...      ...      ...
+                    ...      ...      ...      ...        ...        ...      ...      ...      ...      ...      ...      ...
+                    ...      ...      ...      ...        ...        ...      ...      ...      ...      ...      ...      ...
+Dask Name: assign, 531 graph layers
+
+
+
+
[13]:
+
+
+
# add time-stamped temperature data
+# either, directly retrieve data from EPICS archiver instance (within FHI network),
+#sp.add_time_stamped_data(dest_column="T_B", archiver_channel="trARPES:Carving:TEMP-B")
+# or use externally provided timestamp/data pairs
+import h5py
+with h5py.File(f"{data_path}/temperature_data.h5", "r") as file:
+    data = file["temperatures"][()]
+    time_stamps = file["timestamps"][()]
+sp.add_time_stamped_data(dest_column="sample_temperature", time_stamps=time_stamps, data=data)
+
+
+
+
+
+
+
+
+INFO - add_time_stamped_data: Added time-stamped data as column sample_temperature.
+
+
+
+
[14]:
+
+
+
# inspect calibrated event histogram
+axes = ['kx', 'ky', 'energy', 'sample_temperature']
+ranges = [[-3, 3], [-3, 3], [-6, 2], [10, 300]]
+sp.view_event_histogram(dfpid=80, axes=axes, ranges=ranges)
+
+
+
+
+
+
+
+
+
+
+
+

Define the binning ranges and compute calibrated data volume#

+
+
[15]:
+
+
+
axes = ['kx', 'ky', 'energy', 'sample_temperature']
+bins = [100, 100, 300, 100]
+ranges = [[-2, 2], [-2, 2], [-6, 2], [20, 270]]
+res = sp.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time="sample_temperature")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+INFO - Calculate normalization histogram for axis 'sample_temperature'...
+
+
+
+
+
+
+
+
+
+
+

Some visualization:#

+
+
[16]:
+
+
+
fig, axs = plt.subplots(4, 1, figsize=(4, 12), constrained_layout=True)
+res.loc[{'energy':slice(-.1, 0)}].sum(axis=(2,3)).T.plot(ax=axs[0])
+res.loc[{'kx':slice(-.2, .2)}].sum(axis=(0,3)).T.plot(ax=axs[1])
+res.loc[{'ky':slice(-.2, .2)}].sum(axis=(1,3)).T.plot(ax=axs[2])
+res.loc[{'kx':slice(-.2, .2), 'ky':slice(-.2, .2), 'energy':slice(-2, 0.2)}].sum(axis=(0,1)).plot(ax=axs[3])
+
+
+
+
+
[16]:
+
+
+
+
+<matplotlib.collections.QuadMesh at 0x7f2655700d60>
+
+
+
+
+
+
+
+
+
+
[17]:
+
+
+
# Inspect effect of histogram normalization
+fig, ax = plt.subplots(1,1)
+(sp._normalization_histogram/sp._normalization_histogram.sum()).plot(ax=ax)
+(sp._binned.sum(axis=(0,1,2))/sp._binned.sum(axis=(0,1,2,3))).plot(ax=ax)
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
[18]:
+
+
+
# Remaining fluctuations are an effect of the varying count rate throughout the scan
+plt.figure()
+rate, secs = sp.loader.get_count_rate()
+plt.plot(secs, rate)
+
+
+
+
+
[18]:
+
+
+
+
+[<matplotlib.lines.Line2D at 0x7f26553fcaf0>]
+
+
+
+
+
+
+
+
+
+
[19]:
+
+
+
# Normalize for intensity around the Gamma point
+res_norm = res.copy()
+res_norm = res_norm/res_norm.loc[{'kx':slice(-.3, .3), 'ky':slice(-.3, .3)}].sum(axis=(0,1,2))
+
+
+
+
+
[20]:
+
+
+
fig, axs = plt.subplots(4, 1, figsize=(4, 12), constrained_layout=True)
+res_norm.loc[{'energy':slice(-.1, 0)}].sum(axis=(2,3)).T.plot(ax=axs[0])
+res_norm.loc[{'kx':slice(-.2, .2)}].sum(axis=(0,3)).T.plot(ax=axs[1])
+res_norm.loc[{'ky':slice(-.2, .2)}].sum(axis=(1,3)).T.plot(ax=axs[2])
+res_norm.loc[{'kx':slice(-.2, .2), 'ky':slice(-.2, .2), 'energy':slice(-2, 0.5)}].sum(axis=(0,1)).plot(ax=axs[3])
+
+
+
+
+
[20]:
+
+
+
+
+<matplotlib.collections.QuadMesh at 0x7f2655376e00>
+
+
+
+
+
+
+
+
+
+
[21]:
+
+
+
# Lower Hubbard band intensity versus temperature
+plt.figure()
+res_norm.loc[{'kx':slice(-.2, .2), 'ky':slice(-.2, .2), 'energy':slice(-.6, 0.1)}].sum(axis=(0,1,2)).plot()
+
+
+
+
+
[21]:
+
+
+
+
+[<matplotlib.lines.Line2D at 0x7f2655182380>]
+
+
+
+
+
+
+
+
+
+
[ ]:
+
+
+

+
+
+
+
+
+ + +
+ + + + + + + +
+ + + + +
+ + +
+
+ On this page +
+
+ +
+
+

This Page

+ +
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + +
+
+ +
+ +
+ +

+ + © Copyright 2024, OpenCOMPES team. +
+ +

+
+ +
+ +

+ Created using Sphinx 8.1.3. +
+

+
+ +
+ + + +
+ +
+

+ + Built with the PyData Sphinx Theme 0.16.1. +

+ +
+ +
+ +
+ + \ No newline at end of file diff --git a/sed/v1.0.0/tutorial/7_correcting_orthorhombic_symmetry.html b/sed/v1.0.0/tutorial/7_correcting_orthorhombic_symmetry.html new file mode 100644 index 0000000..0344d1a --- /dev/null +++ b/sed/v1.0.0/tutorial/7_correcting_orthorhombic_symmetry.html @@ -0,0 +1,937 @@ + + + + + + + + + + + Distortion correction with orthorhombic symmetry — SED 1.0.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + +
+ + + Ctrl+K +
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+ + +
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+ + + + + +
+ +
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+ + + + + + +
+ + +
+
+ +
+
+ +
+ +
+ + +
+ +
+ + +
+
+ + + + + +
+ +
+

Distortion correction with orthorhombic symmetry#

+

This example showcases how to use the distortion correction workflow with landmarks that are not at symmetry-equivalent positions, such as for orthorhombic systems with different in-plane axis parameters.

+
+
[1]:
+
+
+
%load_ext autoreload
+%autoreload 2
+import numpy as np
+import matplotlib.pyplot as plt
+
+import sed
+from sed.dataset import dataset
+
+%matplotlib widget
+
+
+
+
+

Load Data#

+

For this example, we use the example data from WSe2. Even though the system is hexagonal, we will use it for demonstration.

+
+
[2]:
+
+
+
dataset.get("WSe2") # Put in Path to a storage of at least 20 GByte free space.
+data_path = dataset.dir # This is the path to the data
+scandir, _ = dataset.subdirs # scandir contains the data, _ contains the calibration files
+
+
+
+
+
+
+
+
+INFO - Not downloading WSe2 data as it already exists at "/home/runner/work/sed/sed/docs/tutorial/datasets/WSe2".
+Set 'use_existing' to False if you want to download to a new location.
+INFO - Using existing data path for "WSe2": "/home/runner/work/sed/sed/docs/tutorial/datasets/WSe2"
+INFO - WSe2 data is already present.
+
+
+
+
[3]:
+
+
+
# create sed processor using the config file with time-stamps:
+sp = sed.SedProcessor(folder=scandir, user_config="../src/sed/config/mpes_example_config.yaml", system_config={}, time_stamps=True, verbose=True)
+sp.add_jitter()
+
+
+
+
+
+
+
+
+INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/sed_config.yaml]
+INFO - User config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/mpes_example_config.yaml]
+INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
+WARNING - Entry "KTOF:Lens:Sample:V" for channel "sampleBias" not found. Skipping the channel.
+INFO - add_jitter: Added jitter to columns ['X', 'Y', 't', 'ADC'].
+
+
+

Get slice for momentum calibration

+
+
[4]:
+
+
+
sp.bin_and_load_momentum_calibration(df_partitions=100, plane=203, width=10, apply=True)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

Feature definition:#

+

We will describe the symmetry of the system with a 4-fold symmetry, and select two K points and two M points as symmetry points (as well as the Gamma point).

+
+
[5]:
+
+
+
features = np.array([[252., 355.], [361., 251.], [250., 144.], [156., 247.], [254., 247.]])
+sp.define_features(features=features, rotation_symmetry=4, include_center=True, apply=True)
+# Manual selection: Use a GUI tool to select peaks:
+# sp.define_features(rotation_symmetry=4, include_center=True)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+
+

Spline-warp generation:#

+

For the spline-warp generation, we need to tell the algorithm the difference in length of Gamma-K and Gamma-M. This we can do using the ascale parameter, which can either be a single number (the ratio), or a list of length rotation_symmetry defining the relative length of the respective vectors.

+
+
[6]:
+
+
+
gamma_m = np.pi/3.28
+gamma_k = 2/np.sqrt(3)*np.pi/3.28
+# Option 1: Ratio of the two distances:
+#sp.generate_splinewarp(include_center=True, ascale=gamma_k/gamma_m)
+# Option 2: List of distances:
+sp.generate_splinewarp(include_center=True, ascale=[gamma_m, gamma_k, gamma_m, gamma_k])
+
+
+
+
+
+
+
+
+INFO - Calculated thin spline correction based on the following landmarks:
+pouter_ord: [[252. 355.]
+ [361. 251.]
+ [250. 144.]
+ [156. 247.]]
+pcent: (254.0, 247.0)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
[7]:
+
+
+
sp.pose_adjustment(xtrans=4, ytrans=7, angle=1, apply=True)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+INFO - Applied translation with (xtrans=4.0, ytrans=7.0).
+INFO - Applied rotation with angle=1.0.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
[8]:
+
+
+
sp.apply_momentum_correction()
+
+
+
+
+
+
+
+
+INFO - Adding corrected X/Y columns to dataframe:
+Calculating inverse deformation field, this might take a moment...
+INFO - Dask DataFrame Structure:
+                       X        Y        t      ADC timeStamps       Xm       Ym
+npartitions=100
+                 float64  float64  float64  float64    float64  float64  float64
+                     ...      ...      ...      ...        ...      ...      ...
+...                  ...      ...      ...      ...        ...      ...      ...
+                     ...      ...      ...      ...        ...      ...      ...
+                     ...      ...      ...      ...        ...      ...      ...
+Dask Name: apply_dfield, 206 graph layers
+
+
+
+
+

Momentum calibration with orthorhombic axes#

+

For the momentum calibration using symmetry points with non-equal distances, the option equiscale can be used:

+
+
[9]:
+
+
+
point_a = [256, 155]
+point_b = [370, 256]
+sp.calibrate_momentum_axes(point_a=point_a, point_b=point_b, k_coord_a=[0, gamma_m], k_coord_b=[gamma_k, 0], equiscale=False, apply=True)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
[10]:
+
+
+
sp.apply_momentum_calibration()
+
+
+
+
+
+
+
+
+INFO - Adding kx/ky columns to dataframe:
+INFO - Using momentum calibration parameters generated on 03/06/2025, 09:48:38
+INFO - Dask DataFrame Structure:
+                       X        Y        t      ADC timeStamps       Xm       Ym       kx       ky
+npartitions=100
+                 float64  float64  float64  float64    float64  float64  float64  float64  float64
+                     ...      ...      ...      ...        ...      ...      ...      ...      ...
+...                  ...      ...      ...      ...        ...      ...      ...      ...      ...
+                     ...      ...      ...      ...        ...      ...      ...      ...      ...
+                     ...      ...      ...      ...        ...      ...      ...      ...      ...
+Dask Name: assign, 216 graph layers
+
+
+
+
+

Bin the top of the valence band#

+
+
[11]:
+
+
+
axes = ['kx', 'ky']
+bins = [100, 100]
+ranges = [[-2, 2], [-2, 2]]
+res = sp.compute(bins=bins, axes=axes, ranges=ranges, filter=[{"col":"t", "lower_bound": 66100, "upper_bound": 66300}])
+plt.figure()
+res.T.plot()
+
+
+
+
+
+
+
+
+
+
+
[11]:
+
+
+
+
+<matplotlib.collections.QuadMesh at 0x7f876b7932e0>
+
+
+
+
+
+
+
+
+
+
[ ]:
+
+
+

+
+
+
+
+
+ + +
+ + + + + + + +
+ + + + + + + +
+
+ +
+ +
+
+
+ + + + + +
+
+ +
+ +
+ +

+ + © Copyright 2024, OpenCOMPES team. +
+ +

+
+ +
+ +

+ Created using Sphinx 8.1.3. +
+

+
+ +
+ + + +
+ +
+

+ + Built with the PyData Sphinx Theme 0.16.1. +

+ +
+ +
+ +
+ + \ No newline at end of file diff --git a/sed/v1.0.0/tutorial/8_jittering_tutorial.html b/sed/v1.0.0/tutorial/8_jittering_tutorial.html new file mode 100644 index 0000000..6ea5008 --- /dev/null +++ b/sed/v1.0.0/tutorial/8_jittering_tutorial.html @@ -0,0 +1,1166 @@ + + + + + + + + + + + Correct use of Jittering — SED 1.0.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + +
+ + + Ctrl+K +
+ + +
+ +
+ + +
+
+ + + +
+ +
+ + + + + +
+ +
+ +
+ +
+ +
+
+ +
+ +
+
+ +
+
+ +
+ + +
+ +
+ + + +
+ + +
+ +
+ +
+ +
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+ + +
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+ + + + + +
+ +
+ + +
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+ + + + + + +
+ + +
+
+ +
+
+ +
+ +
+ + +
+ +
+ + +
+
+ + + + + +
+ +
+

Correct use of Jittering#

+

This tutorial discusses the background and correct use of the jittering/dithering method implemented in the package.

+
+
[1]:
+
+
+
%load_ext autoreload
+%autoreload 2
+import matplotlib.pyplot as plt
+
+import sed
+from sed.dataset import dataset
+
+%matplotlib widget
+
+
+
+
+

Load Data#

+
+
[2]:
+
+
+
dataset.get("WSe2") # Put in Path to a storage of at least 20 GByte free space.
+data_path = dataset.dir # This is the path to the data
+scandir, _ = dataset.subdirs # scandir contains the data, _ contains the calibration files
+
+
+
+
+
+
+
+
+INFO - Not downloading WSe2 data as it already exists at "/home/runner/work/sed/sed/docs/tutorial/datasets/WSe2".
+Set 'use_existing' to False if you want to download to a new location.
+INFO - Using existing data path for "WSe2": "/home/runner/work/sed/sed/docs/tutorial/datasets/WSe2"
+INFO - WSe2 data is already present.
+
+
+
+
[3]:
+
+
+
# create sed processor using the config file:
+sp = sed.SedProcessor(folder=scandir, config="../src/sed/config/mpes_example_config.yaml", system_config={})
+
+
+
+
+
+
+
+
+INFO - Configuration loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/mpes_example_config.yaml]
+INFO - Folder config loaded from: [/home/runner/work/sed/sed/docs/tutorial/sed_config.yaml]
+INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
+WARNING - Entry "KTOF:Lens:Sample:V" for channel "sampleBias" not found. Skipping the channel.
+
+
+

After loading, the dataframe contains the four columns X, Y, t, ADC, which have all integer values. They originate from a time-to-digital converter, and correspond to digital “bins”.

+
+
[4]:
+
+
+
sp.dataframe.head()
+
+
+
+
+
[4]:
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
XYtADC
00.00.00.00.0
1365.01002.070101.06317.0
2761.0818.075615.06316.0
3692.0971.066455.06317.0
4671.0712.073026.06317.0
+
+
+

Let’s bin these data along the t dimension within a small range:

+
+
[5]:
+
+
+
axes = ['t']
+bins = [150]
+ranges = [[66000, 67000]]
+res01 = sp.compute(bins=bins, axes=axes, ranges=ranges, df_partitions=20)
+plt.figure()
+res01.plot()
+
+
+
+
+
+
+
+
+
+
+
[5]:
+
+
+
+
+[<matplotlib.lines.Line2D at 0x7f1e6b43ab00>]
+
+
+
+
+
+
+
+
+

We notice some oscillation ontop of the data. These are re-binning artifacts, originating from a non-integer number of machine-bins per bin, as we can verify by binning with a different number of steps:

+
+
[6]:
+
+
+
axes = ['t']
+bins = [100]
+ranges = [[66000, 67000]]
+res02 = sp.compute(bins=bins, axes=axes, ranges=ranges, df_partitions=20)
+plt.figure()
+res01.plot(label="6.66/bin")
+res02.plot(label="10/bin")
+plt.legend()
+
+
+
+
+
+
+
+
+
+
+
[6]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f1e6b3dc460>
+
+
+
+
+
+
+
+
+

If we have a very detailed look, with step-sizes smaller than one, we see the digital nature of the original data behind this issue:

+
+
[7]:
+
+
+
axes = ['t']
+bins = [200]
+ranges = [[66600, 66605]]
+res11 = sp.compute(bins=bins, axes=axes, ranges=ranges, df_partitions=20)
+plt.figure()
+res11.plot()
+
+
+
+
+
+
+
+
+
+
+
[7]:
+
+
+
+
+[<matplotlib.lines.Line2D at 0x7f1e68b3b340>]
+
+
+
+
+
+
+
+
+

To mitigate this problem, we can add some randomness to the data, and re-distribute events into the gaps in-between bins. This is also termed dithering and e.g. known from image manipulation. The important factor is to add the right amount and right type of random distribution, to end up at a quasi-continuous uniform distribution, but not lose information.

+

We can use the add_jitter function for this. We can pass it the columns to add jitter to, and the amplitude of a uniform jitter. Importantly, this step should be taken in the very beginning as first step before any dataframe operations are added.

+

Let’s try with a value of 0.2 for the amplitude:

+
+
[8]:
+
+
+
df_backup = sp.dataframe
+sp.add_jitter(cols=["t"], amps=[0.2])
+
+
+
+
+
+
+
+
+INFO - add_jitter: Added jitter to columns ['t'].
+
+
+

We see that the t column is no longer integer-valued:

+
+
[9]:
+
+
+
sp.dataframe.head()
+
+
+
+
+
[9]:
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
XYtADC
00.00.0-0.0179080.0
1365.01002.070101.1752396317.0
2761.0818.075615.1682486316.0
3692.0971.066454.8905236317.0
4671.0712.073026.0647776317.0
+
+
+
+
[10]:
+
+
+
axes = ['t']
+bins = [200]
+ranges = [[66600, 66605]]
+res12 = sp.compute(bins=bins, axes=axes, ranges=ranges, df_partitions=20)
+plt.figure()
+res11.plot(label="not jittered")
+res12.plot(label="amplitude 0.2")
+plt.legend()
+
+
+
+
+
+
+
+
+
+
+
[10]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f1e72c9bee0>
+
+
+
+
+
+
+
+
+

This is clearly not enough jitter to close the gaps. The ideal (and default) amplitude is 0.5, which exactly fills the gaps:

+
+
[11]:
+
+
+
sp.dataframe = df_backup
+sp.add_jitter(cols=["t"], amps=[0.5])
+
+
+
+
+
+
+
+
+INFO - add_jitter: Added jitter to columns ['t'].
+
+
+
+
[12]:
+
+
+
axes = ['t']
+bins = [200]
+ranges = [[66600, 66605]]
+res13 = sp.compute(bins=bins, axes=axes, ranges=ranges, df_partitions=20)
+plt.figure()
+res11.plot(label="not jittered")
+res12.plot(label="amplitude 0.2")
+res13.plot(label="amplitude 0.5")
+plt.legend()
+
+
+
+
+
+
+
+
+
+
+
[12]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f1e68b20580>
+
+
+
+
+
+
+
+
+

This jittering fills the gaps, and produces a continuous uniform distribution. Let’s check again the longer-range binning that gave us the oscillations initially:

+
+
[13]:
+
+
+
axes = ['t']
+bins = [150]
+ranges = [[66000, 67000]]
+res03 = sp.compute(bins=bins, axes=axes, ranges=ranges, df_partitions=20)
+plt.figure()
+res01.plot(label="not jittered")
+res03.plot(label="jittered")
+plt.legend()
+
+
+
+
+
+
+
+
+
+
+
[13]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f1e68b20550>
+
+
+
+
+
+
+
+
+

Now, the artifacts are absent, and similarly will they be in any dataframe columns derived from a column jittered in such a way. Note that this only applies to data present in digital (i.e. machine-binned) format, and not to data that are intrinsically continuous.

+

Also note that too large or not well-aligned jittering amplitudes will

+
    +

  • deteriorate your resolution along the jittered axis

    • +

  • will not solve the problem entirely:

    • +
+
+
[14]:
+
+
+
sp.dataframe = df_backup
+sp.add_jitter(cols=["t"], amps=[0.7])
+
+
+
+
+
+
+
+
+INFO - add_jitter: Added jitter to columns ['t'].
+
+
+
+
[15]:
+
+
+
axes = ['t']
+bins = [200]
+ranges = [[66600, 66605]]
+res14 = sp.compute(bins=bins, axes=axes, ranges=ranges, df_partitions=20)
+plt.figure()
+res13.plot(label="Amplitude 0.5")
+res14.plot(label="Amplitude 0.7")
+plt.legend()
+
+
+
+
+
+
+
+
+
+
+
[15]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f1e6b502fb0>
+
+
+
+
+
+
+
+
+

If the step-size of digitization is different from 1, the corresponding stepsize (half the distance between digitized values) can be adjusted as shown above.

+

Also, alternatively also normally distributed noise can be added, which is less sensitive to the exact right amplitude, but will lead to mixing of neighboring voxels, and thus loss of resolution. Also, normally distributed noise is substantially more computation-intensive to generate. It can nevertheless be helpful in situations where e.g. the stepsize is non-uniform.

+
+
[16]:
+
+
+
sp.dataframe = df_backup
+sp.add_jitter(cols=["t"], amps=[0.7], jitter_type="normal")
+
+
+
+
+
+
+
+
+INFO - add_jitter: Added jitter to columns ['t'].
+
+
+
+
[17]:
+
+
+
axes = ['t']
+bins = [200]
+ranges = [[66600, 66605]]
+res15 = sp.compute(bins=bins, axes=axes, ranges=ranges, df_partitions=20)
+plt.figure()
+res14.plot(label="Uniform, Amplitude 0.7")
+res15.plot(label="Normal, Amplitude 0.7")
+plt.legend()
+
+
+
+
+
+
+
+
+
+
+
[17]:
+
+
+
+
+<matplotlib.legend.Legend at 0x7f1e6b6c6f20>
+
+
+
+
+
+
+
+
+
+
[ ]:
+
+
+

+
+
+
+
+
+ + +
+ + + + + + + +
+ + + + +
+ + +
+
+ On this page +
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+

This Page

+ +
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+ + © Copyright 2024, OpenCOMPES team. +
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+
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+ Created using Sphinx 8.1.3. +
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+
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+ +
+

+ + Built with the PyData Sphinx Theme 0.16.1. +

+ +
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+ + \ No newline at end of file diff --git a/sed/v1.0.0/tutorial/9_hextof_workflow_trXPD.html b/sed/v1.0.0/tutorial/9_hextof_workflow_trXPD.html new file mode 100644 index 0000000..1094d78 --- /dev/null +++ b/sed/v1.0.0/tutorial/9_hextof_workflow_trXPD.html @@ -0,0 +1,1378 @@ + + + + + + + + + + + Tutorial for trXPD for the HEXTOF instrument at FLASH with background normalization — SED 1.0.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+

Tutorial for trXPD for the HEXTOF instrument at FLASH with background normalization#

+
+

Preparation#

+
+

Import necessary libraries#

+
+
[1]:
+
+
+
%load_ext autoreload
+%autoreload 2
+
+from pathlib import Path
+import os
+
+from sed import SedProcessor
+from sed.dataset import dataset
+import xarray as xr
+
+%matplotlib widget
+import matplotlib.pyplot as plt
+from scipy.ndimage import gaussian_filter
+
+
+
+
+
+

Get data paths#

+

If it is your beamtime, you can access both read the raw data and write to processed directory. For the public data, you can not write to processed directory.

+

The paths are such that if you are on Maxwell, it uses those. Otherwise data is downloaded in current directory from Zenodo: https://zenodo.org/records/12609441

+
+
[2]:
+
+
+
beamtime_dir = "/asap3/flash/gpfs/pg2/2023/data/11019101" # on Maxwell
+if os.path.exists(beamtime_dir) and os.access(beamtime_dir, os.R_OK):
+    path = beamtime_dir + "/raw/hdf/offline/fl1user3"
+    buffer_path = beamtime_dir + "/processed/tutorial/"
+else:
+    # data_path can be defined and used to store the data in a specific location
+    dataset.get("W110") # Put in Path to a storage of at least 10 GByte free space.
+    path = dataset.dir
+    buffer_path = path + "/processed/"
+
+
+
+
+
+
+
+
+INFO - Not downloading W110 data as it already exists at "/home/runner/work/sed/sed/docs/tutorial/datasets/W110".
+Set 'use_existing' to False if you want to download to a new location.
+INFO - Using existing data path for "W110": "/home/runner/work/sed/sed/docs/tutorial/datasets/W110"
+INFO - W110 data is already present.
+
+
+
+
+

Config setup#

+

Here we get the path to the config file and setup the relevant directories. This can also be done directly in the config file.

+
+
[3]:
+
+
+
# pick the default configuration file for hextof@FLASH
+config_file = Path('../src/sed/config/flash_example_config.yaml')
+assert config_file.exists()
+
+
+
+
+
[4]:
+
+
+
# here we setup a dictionary that will be used to override the path configuration
+config_override = {
+    "core": {
+        "beamtime_id": 11019101,
+        "paths": {
+            "raw": path,
+            "processed": buffer_path
+        },
+    },
+}
+
+
+
+
+
+

Prepare Energy Calibration#

+

Instead of making completely new energy calibration we can take existing values from the calibration made in the previous tutorial. This allows us to calibrate the conversion between the digital values of the dld and the energy.

+

For this we need to add all those parameters as a dictionary and use them during creation of the processor object.

+
+
[5]:
+
+
+
energy_cal = {
+    "energy": {
+        "calibration": {
+            "E0": -132.47100427179566,
+            "creation_date": '2024-11-30T20:47:03.305244',
+            "d": 0.8096677238144319,
+            "energy_scale": "kinetic",
+            "t0": 4.0148196706891397e-07,
+        },
+        "offsets":{
+            "constant": 1,
+            "creation_date": '2024-11-30T21:17:07.762199',
+            "columns": {
+                "monochromatorPhotonEnergy": {
+                    "preserve_mean": True,
+                    "weight": -1,
+                },
+                "tofVoltage": {
+                    "preserve_mean": True,
+                    "weight": -1,
+                },
+            },
+        },
+    },
+}
+
+
+
+
+
+
+

Read data#

+

Now we can use those parameters and load our trXPD data using additional config file

+
+
[6]:
+
+
+
run_number = 44498
+sp_44498 = SedProcessor(runs=[run_number], folder_config=energy_cal, config=config_override, system_config=config_file, verbose=True)
+sp_44498.add_jitter()
+
+
+
+
+
+
+
+
+INFO - System config loaded from: [/home/runner/work/sed/sed/docs/src/sed/config/flash_example_config.yaml]
+INFO - Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/sed/config/default.yaml]
+INFO - Reading files: 0 new files of 14 total.
+loading complete in  0.13 s
+INFO - add_jitter: Added jitter to columns ['dldPosX', 'dldPosY', 'dldTimeSteps'].
+
+
+

We can inspect dataframe right after data readout

+
+
[7]:
+
+
+
sp_44498.dataframe.head()
+
+
+
+
+
[7]:
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
trainIdpulseIdelectronIddldPosXdldPosYdldTimeStepspulserSignAdcbamtimeStampmonochromatorPhotonEnergygmdBdadelayStagesampleBiastofVoltageextractorVoltageextractorCurrentcryoTemperaturesampleTemperaturedldTimeBinSizedldSectorID
0162802283010651.154674895.1546744595.15467432919.0-6187.968751.677563e+09116.8582992.5214571448.60205172.99590319.9953566029.299805-0.07385749.20878.9899980.0205763
1162802283011650.795029887.7950294595.79502932919.0-6187.968751.677563e+09116.8582992.5214571448.60205172.99590319.9953566029.299805-0.07385749.20878.9899980.0205760
2162802283050681.824157671.8241574422.82415732914.0-6170.156251.677563e+09116.8582992.5214571448.60205172.99590319.9953566029.299805-0.07385749.20878.9899980.0205766
3162802283051684.877689657.8776894424.87768932914.0-6170.156251.677563e+09116.8582992.5214571448.60205172.99590319.9953566029.299805-0.07385749.20878.9899980.0205763
4162802283052669.901034686.9010344423.90103432914.0-6170.156251.677563e+09116.8582992.5214571448.60205172.99590319.9953566029.299805-0.07385749.20878.9899980.0205765
+
+
+

Now we will do energy calibration, add energy offset, jittering and dld sectors alignment

+
+
[8]:
+
+
+
sp_44498.align_dld_sectors()
+sp_44498.append_energy_axis()
+sp_44498.add_energy_offset()
+
+
+
+
+
+
+
+
+INFO - Aligning 8s sectors of dataframe
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID
+npartitions=14
+                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...
+Dask Name: assign, 16 graph layers
+INFO - Adding energy column to dataframe:
+INFO - Using energy calibration parameters generated on 11/30/2024, 20:47:03
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
+npartitions=14
+                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+Dask Name: assign, 31 graph layers
+INFO - Adding energy offset to dataframe:
+INFO - Using energy offset parameters generated on 11/30/2024, 21:17:07
+INFO - Energy offset parameters:
+   Constant: 1.0
+   Column[monochromatorPhotonEnergy]: Weight=-1.0, Preserve Mean: True, Reductions: None.
+   Column[tofVoltage]: Weight=-1.0, Preserve Mean: True, Reductions: None.
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
+npartitions=14
+                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float32    float32    float32          float32          float32         float32           float32        float32        int8  float64
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+Dask Name: assign, 64 graph layers
+
+
+
+
[9]:
+
+
+
sp_44498.attributes.metadata['energy_calibration']
+
+
+
+
+
[9]:
+
+
+
+
+{'applied': True,
+ 'calibration': {'creation_date': datetime.datetime(2024, 11, 30, 20, 47, 3, 305244),
+  'd': 0.8096677238144319,
+  't0': 4.0148196706891397e-07,
+  'E0': -132.47100427179566,
+  'energy_scale': 'kinetic',
+  'calib_type': 'fit',
+  'fit_function': '(a0/(x0-a1))**2 + a2',
+  'coefficients': array([ 8.09667724e-01,  4.01481967e-07, -1.32471004e+02]),
+  'axis': 0.0},
+ 'tof': 0.0}
+
+
+

We can do the SASE jitter correction, using information from the bam column and do calibration of the pump-probe delay axis, we need to shift the delay stage values to center the pump-probe-time overlap time zero.

+
+
[10]:
+
+
+
sp_44498.add_delay_offset(
+    constant=-1448, # this is time zero position determined from side band fit
+    flip_delay_axis=True, # invert the direction of the delay axis
+    columns=['bam'], # use the bam to offset the values
+    weights=[-0.001], # bam is in fs, delay in ps
+    preserve_mean=True # preserve the mean of the delay axis to keep t0 position
+)
+
+
+
+
+
+
+
+
+INFO - Adding delay offset to dataframe:
+INFO - Delay offset parameters:
+   Column[bam]: Weight=-0.001, Preserve Mean: True, Reductions: None.
+   Constant: -1448
+   Flip delay axis: True
+INFO - Dask DataFrame Structure:
+               trainId pulseId electronId  dldPosX  dldPosY dldTimeSteps pulserSignAdc      bam timeStamp monochromatorPhotonEnergy   gmdBda delayStage sampleBias tofVoltage extractorVoltage extractorCurrent cryoTemperature sampleTemperature dldTimeBinSize dldSectorID   energy
+npartitions=14
+                uint32   int64      int64  float64  float64      float32       float32  float32   float64                   float32  float32    float64    float32    float32          float32          float32         float32           float32        float32        int8  float64
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+...                ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+                   ...     ...        ...      ...      ...          ...           ...      ...       ...                       ...      ...        ...        ...        ...              ...              ...             ...               ...            ...         ...      ...
+Dask Name: assign, 84 graph layers
+
+
+
+

bin in the calibrated energy and corrected delay axis#

+

Visualize trXPS data

+
+
[11]:
+
+
+
axes = ['energy', 'delayStage']
+ranges = [[-37.5,-27.5], [-1.5,1.5]]
+bins = [200,60]
+res_corr = sp_44498.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time="delayStage")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+INFO - Calculate normalization histogram for axis 'delayStage'...
+
+
+
+
+
+
+
+
+
+
[12]:
+
+
+
fig,ax = plt.subplots(1,2,figsize=(6,2.25), layout='constrained')
+fig.suptitle(f"Run {run_number}: W 4f, side bands")
+res_corr.plot(robust=True, ax=ax[0], cmap='terrain')
+ax[0].set_title('raw')
+bg = res_corr.sel(delayStage=slice(-1.3,-1.0)).mean('delayStage')
+(res_corr-bg).plot(robust=True, ax=ax[1])
+ax[1].set_title('difference')
+
+
+
+
+
[12]:
+
+
+
+
+Text(0.5, 1.0, 'difference')
+
+
+
+
+
+
+
+
+
+
+
+

XPD from W4f core level#

+

Now we can bin not only in energy but also in both momentum directions to get XPD patterns of different core level line of tungsten.

+
+
[13]:
+
+
+
axes = ['energy', 'dldPosX', 'dldPosY']
+ranges = [[-38,-28], [420,900], [420,900]]
+bins = [100,240,240]
+res_kx_ky = sp_44498.compute(bins=bins, axes=axes, ranges=ranges)
+
+
+
+
+
+
+
+
+
+
+
[14]:
+
+
+
## EDC and integration region for XPD
+plt.figure()
+res_kx_ky.mean(('dldPosX', 'dldPosY')).plot()
+plt.vlines([-30.3,-29.9], 0, 2.4, color='r', linestyles='dashed')
+plt.vlines([-31.4,-31.2], 0, 2.4, color='orange', linestyles='dashed')
+plt.vlines([-33.6,-33.4], 0, 2.4, color='g', linestyles='dashed')
+plt.vlines([-37.0,-36.0], 0, 2.4, color='b', linestyles='dashed')
+plt.title('EDC and integration regions for XPD')
+plt.show()
+
+## XPD plots
+fig,ax = plt.subplots(2,2,figsize=(6,4.7), layout='constrained')
+res_kx_ky.sel(energy=slice(-30.3,-29.9)).mean('energy').plot(robust=True, ax=ax[0,0], cmap='terrain')
+ax[0,0].set_title("XPD of $1^{st}$ order sidebands")
+res_kx_ky.sel(energy=slice(-31.4,-31.2)).mean('energy').plot(robust=True, ax=ax[0,1], cmap='terrain')
+ax[0,1].set_title("XPD of W4f 7/2 peak")
+res_kx_ky.sel(energy=slice(-33.6,-33.4)).mean('energy').plot(robust=True, ax=ax[1,0], cmap='terrain')
+ax[1,0].set_title("XPD of W4f 5/2 peak")
+res_kx_ky.sel(energy=slice(-37.0,-36.0)).mean('energy').plot(robust=True, ax=ax[1,1], cmap='terrain')
+ax[1,1].set_title("XPD of W5p 3/2 peak")
+
+
+
+
+
+
+
+
+
+
+
[14]:
+
+
+
+
+Text(0.5, 1.0, 'XPD of W5p 3/2 peak')
+
+
+
+
+
+
+
+
+

As we can see there is some structure visible, but it looks very similar to each other. We probably have to do some normalization to remove the detector structure/artefacts. The best option is to divide by a flat-field image. The flat-field image can be obtained from a sample that shows no structure under identical measurement conditions. Unfortunately, we don’t have such a flat-field image.

+

In this case, we can make a flat-field image from the actual dataset using several different approaches.

+

As a first option, we can integrate in energy over the whole region and use this image as a background. Additionally, we introduce the Gaussian Blur for comparison.

+
+
[15]:
+
+
+
## Background image
+bgd = res_kx_ky.mean(('energy'))
+
+## Apply Gaussian Blur to background image
+bgd_blur = xr.apply_ufunc(gaussian_filter, bgd, 15)
+
+fig,ax = plt.subplots(1,2,figsize=(6,2.7), layout='constrained')
+bgd.plot(robust=True, cmap='terrain', ax=ax[0])
+ax[0].set_title('Background image')
+bgd_blur.plot(cmap='terrain', ax=ax[1])
+ax[1].set_title('Gaussian Blur of background image')
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
[16]:
+
+
+
## XPD normalized by background image
+fig,ax = plt.subplots(2,2,figsize=(6,4.7), layout='constrained')
+(res_kx_ky/bgd).sel(energy=slice(-30.3,-29.9)).mean('energy').plot(robust=True, ax=ax[0,0], cmap='terrain')
+(res_kx_ky/bgd).sel(energy=slice(-31.4,-31.2)).mean('energy').plot(robust=True, ax=ax[0,1], cmap='terrain')
+(res_kx_ky/bgd).sel(energy=slice(-33.6,-33.4)).mean('energy').plot(robust=True, ax=ax[1,0], cmap='terrain')
+(res_kx_ky/bgd).sel(energy=slice(-37.0,-36.0)).mean('energy').plot(robust=True, ax=ax[1,1], cmap='terrain')
+fig.suptitle(f'Run {run_number}: XPD patterns after background normalization',fontsize='11')
+
+## XPD normalized by Gaussian-blurred background image
+fig,ax = plt.subplots(2,2,figsize=(6,4.7), layout='constrained')
+(res_kx_ky/bgd_blur).sel(energy=slice(-30.3,-29.9)).mean('energy').plot(robust=True, ax=ax[0,0], cmap='terrain')
+(res_kx_ky/bgd_blur).sel(energy=slice(-31.4,-31.2)).mean('energy').plot(robust=True, ax=ax[0,1], cmap='terrain')
+(res_kx_ky/bgd_blur).sel(energy=slice(-33.6,-33.4)).mean('energy').plot(robust=True, ax=ax[1,0], cmap='terrain')
+(res_kx_ky/bgd_blur).sel(energy=slice(-37.0,-36.0)).mean('energy').plot(robust=True, ax=ax[1,1], cmap='terrain')
+fig.suptitle(f'Run {run_number}: XPD patterns after Gaussian-blurred background normalization',fontsize='11')
+
+## XPD normalized by Gaussian-blurred background image and blurred to improve contrast
+fig,ax = plt.subplots(2,2,figsize=(6,4.7), layout='constrained')
+(xr.apply_ufunc(gaussian_filter, res_kx_ky/bgd_blur, 1)).sel(energy=slice(-30.3,-29.9)).mean('energy').plot(robust=True, ax=ax[0,0], cmap='terrain')
+(xr.apply_ufunc(gaussian_filter, res_kx_ky/bgd_blur, 1)).sel(energy=slice(-31.4,-31.2)).mean('energy').plot(robust=True, ax=ax[0,1], cmap='terrain')
+(xr.apply_ufunc(gaussian_filter, res_kx_ky/bgd_blur, 1)).sel(energy=slice(-33.6,-33.4)).mean('energy').plot(robust=True, ax=ax[1,0], cmap='terrain')
+(xr.apply_ufunc(gaussian_filter, res_kx_ky/bgd_blur, 1)).sel(energy=slice(-37.0,-36.0)).mean('energy').plot(robust=True, ax=ax[1,1], cmap='terrain')
+fig.suptitle(f'Run {run_number}: resulting Gaussian-blurred XPD patterns',fontsize='11')
+
+
+
+
+
[16]:
+
+
+
+
+Text(0.5, 0.98, 'Run 44498: resulting Gaussian-blurred XPD patterns')
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

Sometimes, after this division, you may not be happy with intensity distribution. Thus, other option for background correction is to duplicate the XPD pattern, apply large Gaussian blurring that eliminates the fine structures in the XPD pattern. Then divide the XPD pattern by its blurred version. This process sometimes enhances the visibility of the fine structures a lot.

+
+
[17]:
+
+
+
## XPD normalized by Gaussian-blurred background image
+
+### Define integration regions for XPD
+SB = res_kx_ky.sel(energy=slice(-30.3,-29.9)).mean('energy')
+W_4f_7 = res_kx_ky.sel(energy=slice(-31.4,-31.2)).mean('energy')
+W_4f_5 = res_kx_ky.sel(energy=slice(-33.6,-33.4)).mean('energy')
+W_5p = res_kx_ky.sel(energy=slice(-37.0,-36.0)).mean('energy')
+
+### Make corresponding Gaussian Blur background
+SB_blur = xr.apply_ufunc(gaussian_filter, SB, 15)
+W_4f_7_blur = xr.apply_ufunc(gaussian_filter, W_4f_7, 15)
+W_4f_5_blur = xr.apply_ufunc(gaussian_filter, W_4f_5, 15)
+W_5p_blur = xr.apply_ufunc(gaussian_filter, W_5p, 15)
+
+### Visualize results
+fig,ax = plt.subplots(2,2,figsize=(6,4.7), layout='constrained')
+(SB/SB_blur).plot(robust=True, ax=ax[0,0], cmap='terrain')
+(W_4f_7/W_4f_7_blur).plot(robust=True, ax=ax[0,1], cmap='terrain')
+(W_4f_5/W_4f_5_blur).plot(robust=True, ax=ax[1,0], cmap='terrain')
+(W_5p/W_5p_blur).plot(robust=True, ax=ax[1,1], cmap='terrain')
+fig.suptitle(f'Run {run_number}: XPD patterns after Gaussian Blur normalization',fontsize='11')
+
+### Apply Gaussian Blur to resulted images to improve contrast
+SB_norm = xr.apply_ufunc(gaussian_filter, SB/SB_blur, 1)
+W_4f_7_norm = xr.apply_ufunc(gaussian_filter, W_4f_7/W_4f_7_blur, 1)
+W_4f_5_norm = xr.apply_ufunc(gaussian_filter, W_4f_5/W_4f_5_blur, 1)
+W_5p_norm = xr.apply_ufunc(gaussian_filter, W_5p/W_5p_blur, 1)
+
+### Visualize results
+fig,ax = plt.subplots(2,2,figsize=(6,4.7), layout='constrained')
+SB_norm.plot(robust=True, ax=ax[0,0], cmap='terrain')
+W_4f_7_norm.plot(robust=True, ax=ax[0,1], cmap='terrain')
+W_4f_5_norm.plot(robust=True, ax=ax[1,0], cmap='terrain')
+W_5p_norm.plot(robust=True, ax=ax[1,1], cmap='terrain')
+fig.suptitle(f'Run {run_number}: XPD patterns after Gauss Blur normalization',fontsize='11')
+
+
+
+
+
[17]:
+
+
+
+
+Text(0.5, 0.98, 'Run 44498: XPD patterns after Gauss Blur normalization')
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

Third option for background normalization is to use the simultaneously acquired pre-core level region. As an example for W4f 7/2 peak, we define a region on the high energy side of it and integrate in energy to use as a background

+
+
[18]:
+
+
+
### Define peak and background region on the high energy side of the peak
+W_4f_7 = res_kx_ky.sel(energy=slice(-31.4,-31.2)).mean('energy')
+W_4f_7_bgd = res_kx_ky.sel(energy=slice(-32.0,-31.8)).mean('energy')
+
+### Make normalization by background, add Gaussian Blur to the resulting image
+W_4f_7_nrm1 = W_4f_7/(W_4f_7_bgd+W_4f_7_bgd.max()*0.00001)
+W_4f_7_nrm1_blur = xr.apply_ufunc(gaussian_filter, W_4f_7_nrm1, 1)
+
+### Add Gaussian Blur to the background image, normalize by it and add Gaussian Blur to the resulting image
+W_4f_7_bgd_blur = xr.apply_ufunc(gaussian_filter, W_4f_7_bgd, 15)
+W_4f_7_nrm2 = W_4f_7/W_4f_7_bgd_blur
+W_4f_7_nrm2_blur = xr.apply_ufunc(gaussian_filter, W_4f_7_nrm2, 1)
+
+### Visualize all steps
+fig,ax = plt.subplots(4,2,figsize=(6,8), layout='constrained')
+W_4f_7.plot(robust=True, ax=ax[0,0], cmap='terrain')
+W_4f_7_bgd.plot(robust=True, ax=ax[0,1], cmap='terrain')
+W_4f_7_nrm1.plot(robust=True, ax=ax[1,0], cmap='terrain')
+W_4f_7_nrm1_blur.plot(robust=True, ax=ax[1,1], cmap='terrain')
+W_4f_7_bgd_blur.plot(robust=True, ax=ax[2,0], cmap='terrain')
+W_4f_7_nrm2.plot(robust=True, ax=ax[2,1], cmap='terrain')
+W_4f_7_nrm2_blur.plot(robust=True, ax=ax[3,0], cmap='terrain')
+fig.suptitle(f'Run {run_number}: XPD patterns of W4f7/2 with pre-core level normalization',fontsize='11')
+
+
+
+
+
[18]:
+
+
+
+
+Text(0.5, 0.98, 'Run 44498: XPD patterns of W4f7/2 with pre-core level normalization')
+
+
+
+
+
+
+
+
+
+
[19]:
+
+
+
fig,ax = plt.subplots(1,3,figsize=(6,2), layout='constrained')
+(xr.apply_ufunc(gaussian_filter, res_kx_ky/bgd_blur, 1)).sel(energy=slice(-31.4,-31.2)).mean('energy').plot(robust=True, ax=ax[0], cmap='terrain')
+W_4f_7_norm.plot(robust=True, ax=ax[1], cmap='terrain')
+W_4f_7_nrm2_blur.plot(robust=True, ax=ax[2], cmap='terrain')
+fig.suptitle(f'Run {run_number}: comparison of different normalizations\nof XPD pattern for W4f 7/2 peak with Gaussian Blur',fontsize='11')
+
+
+
+
+
[19]:
+
+
+
+
+Text(0.5, 0.98, 'Run 44498: comparison of different normalizations\nof XPD pattern for W4f 7/2 peak with Gaussian Blur')
+
+
+
+
+
+
+
+
+
+
[ ]:
+
+
+

+
+
+
+
+
+ + +
+ + + + + + + +
+ + + + + + + +
+
+ +
+ +
+
+
+ + + + + +
+
+ +
+ +
+ +

+ + © Copyright 2024, OpenCOMPES team. +
+ +

+
+ +
+ +

+ Created using Sphinx 8.1.3. +
+

+
+ +
+ + + +
+ +
+

+ + Built with the PyData Sphinx Theme 0.16.1. +

+ +
+ +
+ +
+ + \ No newline at end of file diff --git a/sed/latest/user_guide/config.html b/sed/v1.0.0/user_guide/config.html similarity index 99% rename from sed/latest/user_guide/config.html rename to sed/v1.0.0/user_guide/config.html index 185d0fa..7b6899b 100644 --- a/sed/latest/user_guide/config.html +++ b/sed/v1.0.0/user_guide/config.html @@ -8,7 +8,7 @@ - Configuration — SED 1.0.0a1.dev19+gf1bb527 documentation + Configuration — SED 1.0.0 documentation @@ -38,7 +38,7 @@ - + @@ -47,7 +47,7 @@ @@ -57,7 +57,7 @@ - + @@ -119,7 +119,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

diff --git a/sed/latest/user_guide/index.html b/sed/v1.0.0/user_guide/index.html similarity index 98% rename from sed/latest/user_guide/index.html rename to sed/v1.0.0/user_guide/index.html index 7b109d5..b9476fe 100644 --- a/sed/latest/user_guide/index.html +++ b/sed/v1.0.0/user_guide/index.html @@ -9,7 +9,7 @@ - User Guide — SED 1.0.0a1.dev19+gf1bb527 documentation + User Guide — SED 1.0.0 documentation @@ -39,7 +39,7 @@ - + @@ -50,7 +50,7 @@ @@ -60,7 +60,7 @@ - + @@ -122,7 +122,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

diff --git a/sed/latest/user_guide/installation.html b/sed/v1.0.0/user_guide/installation.html similarity index 98% rename from sed/latest/user_guide/installation.html rename to sed/v1.0.0/user_guide/installation.html index bcf8661..ae0d254 100644 --- a/sed/latest/user_guide/installation.html +++ b/sed/v1.0.0/user_guide/installation.html @@ -8,7 +8,7 @@ - Installation — SED 1.0.0a1.dev19+gf1bb527 documentation + Installation — SED 1.0.0 documentation @@ -38,7 +38,7 @@ - + @@ -47,7 +47,7 @@ @@ -57,7 +57,7 @@ - + @@ -119,7 +119,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation

diff --git a/sed/latest/workflows/index.html b/sed/v1.0.0/workflows/index.html similarity index 98% rename from sed/latest/workflows/index.html rename to sed/v1.0.0/workflows/index.html index ca95ee8..2b436aa 100644 --- a/sed/latest/workflows/index.html +++ b/sed/v1.0.0/workflows/index.html @@ -9,7 +9,7 @@ - Workflows — SED 1.0.0a1.dev19+gf1bb527 documentation + Workflows — SED 1.0.0 documentation @@ -39,7 +39,7 @@ - + @@ -50,7 +50,7 @@ @@ -60,7 +60,7 @@ - + @@ -122,7 +122,7 @@ -

SED 1.0.0a1.dev19+gf1bb527 documentation

+

SED 1.0.0 documentation