Merge pull request #18 from nasa/release/v1.6

release v1.6

Author: teubert

README.md

@@ -3,9 +3,9 @@
 [![GitHub License](https://img.shields.io/badge/License-NOSA-green)](https://github.com/nasa/prog_server/blob/master/license.pdf)
 [![GitHub Releases](https://img.shields.io/github/release/nasa/prog_server.svg)](https://github.com/nasa/prog_server/releases)
 
-The NASA Prognostics As-A-Service (PaaS) Sandbox is a simplified implementation of a Software Oriented Architecture (SOA) for performing prognostics (estimation of time until events and future system states) of engineering systems. The PaaS Sandbox is a wrapper around the [Prognostics Algorithms Package](https://nasa.github.io/progpy/prog_algs_guide.html) and [Prognostics Models Package](https://nasa.github.io/progpy/prog_models_guide.html), allowing one or more users to access the features of these packages through a REST API. The package is intended to be used as a research tool to prototype and benchmark Prognostics As-A-Service (PaaS) architectures and work on the challenges facing such architectures, including Generality, Communication, Security, Environmental Complexity, Utility, and Trust.
+The NASA Prognostics As-A-Service (PaaS) Sandbox is a simplified implementation of a Software Oriented Architecture (SOA) for performing prognostics (estimation of time until events and future system states) of engineering systems. The PaaS Sandbox is a wrapper around the [Prognostics Python Package (ProgPy)](https://nasa.github.io/progpy/), allowing one or more users to access the features of these packages through a REST API. The package is intended to be used as a research tool to prototype and benchmark Prognostics As-A-Service (PaaS) architectures and work on the challenges facing such architectures, including Generality, Communication, Security, Environmental Complexity, Utility, and Trust.
 
-This is designed to be used with the [Prognostics Algorithms Package](https://nasa.github.io/progpy/prog_algs_guide.html) and [Prognostics Models Package](https://nasa.github.io/progpy/prog_models_guide.html).
+This is designed to be used with the [Prognostics Python Package (ProgPy)](https://nasa.github.io/progpy/).
 
 ## Installation
 `pip install prog_server`
@@ -17,19 +17,19 @@ See documentation [here](https://nasa.github.io/progpy/prog_server_guide.html)
 Use the following to cite this repository:
 
 ```
-@misc{2023_nasa_prog_models,
+@misc{2023_nasa_prog_server,
     author    = {Christopher Teubert and Jason Watkins and Katelyn Jarvis},
     title     = {Prognostics As-A-Service (PaaS) Sandbox},
-    month     = May,
+    month     = October,
     year      = 2023,
-    version   = {1.5},
+    version   = {1.6},
     url       = {https://github.com/nasa/prog_server}
     }
 ```
 
 The corresponding reference should look like this:
 
-C. Teubert, J. Watkins, K. Jarvis, Prognostics As-A-Service (PaaS) Sandbox, v1.5, May 2023. URL https://github.com/nasa/prog_server.
+C. Teubert, J. Watkins, K. Jarvis, Prognostics As-A-Service (PaaS) Sandbox, v1.6, Oct 2023. URL https://github.com/nasa/prog_server.
 
 ## Notices
 Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration.  All Rights Reserved.

requirements.txt

@@ -11,7 +11,7 @@
 
 setup(
     name='prog_server',
-    version='1.5.0-pre',
+    version='1.6.0-pre',
     description='The NASA Prognostics As-A-Service (PaaS) Sandbox (a.k.a. prog_server) is a simplified Software Oriented Architecture (SOA) for performing prognostics of engineering systems. The PaaS Sandbox is a wrapper around the Prognostics Algorithms and Models Packages, allowing 1+ users to access these packages features through a REST API. The package is intended to be used as a research tool to prototype and benchmark Prognostics As-A-Service (PaaS) architectures and work on the challenges facing such architectures',
     long_description=long_description,
     long_description_content_type='text/markdown',
@@ -40,7 +40,7 @@
     packages=find_packages(where='src'),
     python_requires='>=3.7, <3.12',
     install_requires=[
-        'prog_algs',
+        'progpy',
         'requests',
         'urllib3',
         'flask'

setup.py

@@ -2,7 +2,7 @@
 openapi: 3.0.2
 info:
   title: Prognostics As A Service (PaaS) Sandbox (prog_server)
-  description: The PaaS Sandbox (a.k.a., prog_server) exposes a REST API that enables prognostics using the NASA PCoE prog_models and prog_algs packages 
+  description: The PaaS Sandbox (a.k.a., prog_server) exposes a REST API that enables prognostics using the NASA PCoE progpy and progpy packages 
   contact:
     name: Chris Teubert
     email: [email protected] 
@@ -313,7 +313,7 @@ paths:
               schema: 
                 oneOf:
                   - type: object
-                    description: bytestream of the pickled [prog_models.PrognosticsModel](https://nasa.github.io/progpy/api_ref/prog_models/PrognosticModel.html) object
+                    description: bytestream of the pickled [progpy.PrognosticsModel](https://nasa.github.io/progpy/api_ref/progpy/PrognosticModel.html) object
                   - type: object
                     description: JSON representing configuration of model
                     example:
@@ -398,27 +398,27 @@ components:
               "cov": [[0.15, 0.003], [-0.025, 0.27]]
             }
         - type: object
-          description: bytestream of the pickled [prog_models.uncertain_data.UncertainData](https://nasa.github.io/progpy/api_ref/prog_algs/UncertainData.html) object
+          description: bytestream of the pickled [progpy.uncertain_data.UncertainData](https://nasa.github.io/progpy/api_ref/progpy/UncertainData.html) object
     SessionConfiguration:
       type: object
       required:
         - model
       properties:
         model:
           type: string
-          description: Name of the model (from prog_models.models) to use for the session.
+          description: Name of the model (from progpy.models) to use for the session.
         model_cfg:
           type: object
-          description: Configuration JSON for the model. Configuration parameters are specific to the model and can be found in the [prog_models.models documentation](https://nasa.github.io/progpy/api_ref/prog_models/IncludedModels.html).
+          description: Configuration JSON for the model. Configuration parameters are specific to the model and can be found in the [progpy.models documentation](https://nasa.github.io/progpy/api_ref/progpy/IncludedModels.html).
         state_est:
           type: string
-          description: Name of the state estimator (from prog_algs.state_estimators) to use for the session.
+          description: Name of the state estimator (from progpy.state_estimators) to use for the session.
         state_est_cfg:
           type: object
           description: Configuration JSON for the state estimator. Configuration parameters are specific to the state estimator.
         pred:
           type: string
-          description: Name of the predictor (from prog_algs.predictors) to use for the session.
+          description: Name of the predictor (from progpy.predictors) to use for the session.
         pred_cfg:
           type: object
           description: Configuration JSON for the predictor. Configuration parameters are specific to the predictor.
@@ -475,10 +475,10 @@ components:
       properties:
         type:
           type: string
-          description: Name of the model (from prog_models.models) to use for the session.
+          description: Name of the model (from progpy.models) to use for the session.
         cfg:
           type: object
-          description: Configuration JSON for the model. Configuration parameters are specific to the model and can be found in the [prog_models.models documentation](https://nasa.github.io/progpy/api_ref/prog_models/IncludedModels.html).
+          description: Configuration JSON for the model. Configuration parameters are specific to the model and can be found in the [progpy.models documentation](https://nasa.github.io/progpy/api_ref/progpy/IncludedModels.html).
 
     Sessions: 
       type: array

specs/swagger.yaml

@@ -1,5 +1,5 @@
 # Copyright © 2021 United States Government as represented by the Administrator of the
 # National Aeronautics and Space Administration.  All Rights Reserved.
 
-from .session import Session
-__version__ = '1.5.0-pre'
+from prog_client.session import Session
+__version__ = '1.6.0-pre'

src/prog_client/__init__.py

@@ -4,27 +4,27 @@
 import requests, json
 import urllib3
 import pickle
-from prog_algs.uncertain_data import UncertainData
-import prog_models
+from progpy.uncertain_data import UncertainData
+from progpy.utils import containers
 
 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
 
 
 class Session:
     """
-    Create a new Session in `prog_server`.
+    Create a new Session in `prog_server`
 
     Args:
         model (str): The model to use for this session (e.g., batt)
-        host (str, optional): Host address for PaaS Service. Defaults to '127.0.0.1'.
+        host (str, optional): Host address for PaaS Service. Defaults to '127.0.0.1'
         port (int, optional): Port for PaaS Service. Defaults to 5000.
         model_cfg (dict, optional): Configuration for ProgModel.
         x0 (dict, optional): Initial state for ProgModel.
         load_est (str, optional): Load estimator to use.
         load_est_cfg (dict, optional): Configuration for load estimator.
-        state_est (str, optional): State Estimator to use (e.g., ParticleFilter). Class name for state estimator in `prog_algs.state_estimators`
+        state_est (str, optional): State Estimator to use (e.g., ParticleFilter). Class name for state estimator in `progpy.state_estimators`
         state_est_cfg (dict, optional): Configuration for state estimator.
-        pred (str, optional): Prediction algorithm to use (e.g., MonteCarlo). Class name for prediction algorithm in `prog_algs.predictors`
+        pred (str, optional): Prediction algorithm to use (e.g., MonteCarlo). Class name for prediction algorithm in `progpy.predictors`
         pred_cfg (dict, optional): Configuration for prediction algorithm.
 
     Use:
@@ -103,7 +103,7 @@ def set_state(self, x):
         if isinstance(x, UncertainData):
             x = pickle.dumps(x)
             input_format = 'uncertain_data'
-        elif isinstance(x, prog_models.utils.containers.DictLikeMatrixWrapper):
+        elif isinstance(x, containers.DictLikeMatrixWrapper):
             x = pickle.dumps(x)
             input_format = 'state_container'
         elif isinstance(x, dict):

src/prog_client/session.py

@@ -2,8 +2,7 @@
 # National Aeronautics and Space Administration.  All Rights Reserved.
 
 from .models.prog_server import server
-
-__version__ = '1.5.0-pre'
+__version__ = '1.6.0-pre'
 
 def run(**kwargs):
     """

src/prog_server/__init__.py

@@ -1,7 +1,7 @@
 # Copyright © 2021 United States Government as represented by the Administrator of the
 # National Aeronautics and Space Administration.  All Rights Reserved.
 
-from .controllers import *
+from prog_server.controllers import *
 from flask import Flask
 
 app = Flask("prog_server")

src/prog_server/app.py

@@ -1,11 +1,11 @@
 # Copyright © 2021 United States Government as represented by the Administrator of the
 # National Aeronautics and Space Administration.  All Rights Reserved.
 
-from .models.session import Session
-from .models.load_ests import update_moving_avg
-from prog_models.sim_result import SimResult, LazySimResult
-from prog_algs.uncertain_data import UnweightedSamples
-from prog_algs.predictors import Prediction, UnweightedSamplesPrediction
+from prog_server.models.session import Session
+from prog_server.models.load_ests import update_moving_avg
+from progpy.sim_result import SimResult, LazySimResult
+from progpy.uncertain_data import UnweightedSamples
+from progpy.predictors import Prediction, UnweightedSamplesPrediction
 from flask import request, abort, jsonify
 from flask import current_app as app
 import json

src/prog_server/controllers.py

@@ -1,12 +1,12 @@
 # Copyright © 2021 United States Government as represented by the Administrator of the
 # National Aeronautics and Space Administration.  All Rights Reserved.
 
-from statistics import mean
-from numpy.random import normal
 from flask import abort
 from functools import partial
+from numpy.random import normal
+from statistics import mean
 
-def Variable(t, x = None, session = None, cfg = None):
+def Variable(t, x=None, session=None, cfg=None):
     """Variable (i.e. piecewise) load estimator. The piecewise load function is defined in the load_est_cfg as ordered dictionary starting_time: load. 
 
     cfg: ordered dictionary starting_time: load. First key should always be 0
@@ -19,15 +19,15 @@ def Variable(t, x = None, session = None, cfg = None):
             return cfg[time]
     return cfg[keys[-1]]
 
-def Const(t, x = None, session = None, cfg = None):
+def Const(t, x=None, session=None, cfg=None):
     """Constant load estimator. Load is assumed to be constant over time. 
 
     cfg: dictionary with one key (load) where value is the constant load (dict)
         e.g., {'load': {'u1': 0.1}}
     """
     return cfg['load']
 
-def MovingAverage(t, x=None, session = None, cfg = None):
+def MovingAverage(t, x=None, session=None, cfg=None):
     """Moving average load estimator. Load is estimated as the mean of the last `window_size` samples. Noise can be added using the following optional configuration parameters:
 
         * base_std: standard deviation of noise
@@ -40,7 +40,7 @@ def MovingAverage(t, x=None, session = None, cfg = None):
     load = {key : mean(session.moving_avg_loads[key]) for key in session.model.inputs} 
     return {key : normal(load[key], std) for key in load.keys()}
 
-def update_moving_avg(u, session = None, cfg = {}):
+def update_moving_avg(u, session=None, cfg={}):
     for key in session.model.inputs:
         session.moving_avg_loads[key].append(u[key])
         if len(session.moving_avg_loads[key]) > cfg.get('window_size', 10):
@@ -51,5 +51,5 @@ def build_load_est(name, cfg, session):
         abort(400, f"{name} is not a valid load estimation method")
     load_est_fcn = globals()[name]
     return partial(load_est_fcn,
-        cfg = cfg,
-        session = session)
+        cfg=cfg,
+        session=session)