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generate_swmm_import_inp_file.py
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# -*- coding: utf-8 -*-
"""
/***************************************************************************
GenerateSwmmInp
A QGIS plugin
This plugin generates SWMM Input files
Generated by Plugin Builder: http://g-sherman.github.io/Qgis-Plugin-Builder/
-------------------
begin : 2021-07-09
copyright : (C) 2021 by Jannik Schilling
email : [email protected]
***************************************************************************/
/***************************************************************************
* *
* This program is free software; you can redistribute it and/or modify *
* it under the terms of the GNU General Public License as published by *
* the Free Software Foundation; either version 2 of the License, or *
* (at your option) any later version. *
* *
***************************************************************************/
"""
__author__ = 'Jannik Schilling'
__date__ = '2024-01-31'
__copyright__ = '(C) 2021 by Jannik Schilling'
import numpy as np
import os
import pandas as pd
from qgis.core import (
QgsProcessingAlgorithm,
QgsProcessingException,
QgsProcessingParameterBoolean,
QgsProcessingParameterCrs,
QgsProcessingParameterDefinition,
QgsProcessingParameterEnum,
QgsProcessingParameterFile,
QgsProcessingParameterFolderDestination,
QgsProcessingParameterString
)
from qgis.PyQt.QtCore import QCoreApplication
from .g_s_defaults import (
curve_cols_dict,
def_annotation_field,
def_layer_names_dict,
def_ogr_driver_dict,
def_ogr_driver_names,
def_sections_dict,
def_sections_geoms_dict,
def_sections_geoms_list,
def_stylefile_dict,
def_tables_dict,
ImportDataStatus,
pattern_times
)
from .g_s_read_write_data import (
create_layer_from_df,
save_layer_to_file,
layerlist_to_excel
)
from .g_s_import_helpers import (
add_layer_on_completion,
adjust_column_types,
adjust_line_length,
build_df_for_section,
build_df_from_vals_list,
del_kw_from_list,
extract_sections_from_text,
insert_nan_after_kw,
sect_list_import_handler
)
class ImportInpFile (QgsProcessingAlgorithm):
"""
generates geodata and tables from a swmm input file
"""
ADD_Z = 'ADD_Z'
CREATE_EMPTY = 'CREATE_EMPTY'
DATA_CRS = 'DATA_CRS'
GEODATA_DRIVER = 'GEODATA_DRIVER'
INP_FILE = 'INP_FILE'
PREFIX = 'PREFIX'
SAVE_FOLDER = 'SAVE_FOLDER'
TRANSFORM_CRS = 'TRANSFORM_CRS'
def initAlgorithm(self, config):
"""
inputs and outputs of the algorithm
"""
self.addParameter(
QgsProcessingParameterFile(
name=self.INP_FILE,
description=self.tr('SWMM input file to import'),
extension='inp'
)
)
self.addParameter(
QgsProcessingParameterEnum(
self.GEODATA_DRIVER,
self.tr("Which format should be used for geodata"),
def_ogr_driver_names,
defaultValue=[0]
)
)
self.addParameter(
QgsProcessingParameterFolderDestination(
self.SAVE_FOLDER,
self.tr('Folder in which the imported data will be saved.')
)
)
self.addParameter(
QgsProcessingParameterString(
self.PREFIX,
self.tr('Prefix for imported data'),
optional=True
)
)
self.addParameter(
QgsProcessingParameterCrs(
self.DATA_CRS,
self.tr('CRS of the SWMM input file'),
defaultValue='epsg:25833'
)
)
add_z_coord_param = QgsProcessingParameterBoolean(
self.ADD_Z,
self.tr('Add z-coordinates to conduits and nodes'),
defaultValue=False,
optional=True
)
add_z_coord_param.setFlags(add_z_coord_param.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
self.addParameter(add_z_coord_param)
empt_param = QgsProcessingParameterBoolean(
self.CREATE_EMPTY,
self.tr('Create Empty?'),
defaultValue=False,
)
# Hide the parameter CREATE_EMPTY , because it´s only for the default data tool
self.addParameter(empt_param)
empt_param.setFlags(empt_param.flags() | QgsProcessingParameterDefinition.FlagHidden)
transform_crs = QgsProcessingParameterString(
self.TRANSFORM_CRS,
self.tr('Transform to Crs'),
defaultValue='NA',
)
# Hide the parameter CREATE_EMPTY , because it´s only for the default data to
self.addParameter(transform_crs)
transform_crs.setFlags(transform_crs.flags() | QgsProcessingParameterDefinition.FlagHidden)
def name(self):
return 'ImportInpFile'
def shortHelpString(self):
return self.tr(""" The tool imports a swmm inp file and saves the data in a folder selected by the user (temporary folders won´t work!).\n
You can add a prefix to the files. Try to aviod characters which could cause trouble with file systems (e.g. '.',',','\','/') \n
The layers (e.g geopackages, shapefiles) are added to the QGIS project.\n
If the tool fails to load the layers, please check the selected CRS and try again.\n
""")
def displayName(self):
return self.tr('3_ImportInpFile')
def group(self):
return self.tr(self.groupId())
def groupId(self):
return ''
def tr(self, string):
return QCoreApplication.translate('Processing', string)
def createInstance(self):
return ImportInpFile()
def processAlgorithm(self, parameters, context, feedback):
pluginPath = os.path.dirname(__file__)
# transfer of parameters
folder_save = self.parameterAsString(parameters, self.SAVE_FOLDER, context)
readfile = self.parameterAsString(parameters, self.INP_FILE, context)
result_prefix = self.parameterAsString(parameters, self.PREFIX, context)
crs_result = self.parameterAsCrs(parameters, self.DATA_CRS, context)
crs_result = str(crs_result.authid())
geodata_driver_num = self.parameterAsEnum(parameters, self.GEODATA_DRIVER, context)
geodata_driver_name = def_ogr_driver_names[geodata_driver_num]
geodata_driver_extension = def_ogr_driver_dict[geodata_driver_name]
create_empty = self.parameterAsBoolean(parameters, self.CREATE_EMPTY, context)
add_z_bool = self.parameterAsBoolean(parameters, self.ADD_Z, context)
transform_crs_string = self.parameterAsString(parameters, self.TRANSFORM_CRS, context)
# parameters shared by many functions
import_parameters_dict = {
'add_z_bool': add_z_bool,
'create_empty': create_empty,
'context': context,
'crs_result': crs_result,
'geodata_driver_num': geodata_driver_num,
'geodata_driver_name': geodata_driver_name,
'geodata_driver_extension': geodata_driver_extension,
'folder_save': folder_save,
'result_prefix': result_prefix,
'pluginPath': pluginPath,
'transform_crs_string': transform_crs_string
}
# check if the selected folder is temporary
if parameters['SAVE_FOLDER'] == 'TEMPORARY_OUTPUT':
raise QgsProcessingException(
'The data set needs to be saved in a directory '
+ '(temporary folders won´t work). Please select a directoy'
)
else:
# check if files are already in folder
for section_name in def_layer_names_dict.keys():
layer_name = def_layer_names_dict[section_name]
if result_prefix != '':
layer_name = (
str(import_parameters_dict['result_prefix'])
+'_'
+ layer_name
)
geodata_driver_name = def_ogr_driver_names[geodata_driver_num]
geodata_driver_extension = def_ogr_driver_dict[geodata_driver_name]
fname = os.path.join(
folder_save,
layer_name+ '.' +geodata_driver_extension
)
if os.path.isfile(fname):
raise QgsProcessingException('File '+fname
+ ' already exists. Please choose another folder.')
for section_name in def_tables_dict.keys():
save_name = def_tables_dict[section_name]['filename']
if result_prefix != '':
save_name = str(result_prefix)+'_'+save_name
ext = '.xlsx' # default setting
save_name_ext = save_name + ext
fname = os.path.join(folder_save, save_name_ext)
if os.path.isfile(fname):
raise QgsProcessingException('File '+fname
+ ' already exists. Please choose another folder.')
# reading input text file
feedback.setProgressText(self.tr('reading inp ...'))
feedback.setProgress(3)
encodings = ['utf-8', 'windows-1250', 'windows-1252'] # add more
for e in encodings:
try:
with open(readfile, 'r', encoding=e) as f:
inp_text = f.readlines()
except UnicodeDecodeError:
feedback.setProgressText('got unicode error with %s , trying different encoding' % e)
else:
feedback.setProgressText('opening the file with encoding: %s ' % e)
break
# delete unused lines
inp_text = [x for x in inp_text if x != '\n']
inp_text = [x for x in inp_text if x != '\r']
inp_text = [x for x in inp_text if not x.startswith(';;')]
inp_text = [x.replace('\n', '') for x in inp_text]
inp_text = [x.strip() for x in inp_text]
# SWMM sections in the text file
inp_text_sections = [i for i in inp_text if i.startswith('[') and i.endswith(']')]
pos_start_list = [inp_text.index(sect) for sect in inp_text_sections]
pos_end_list = pos_start_list[1:]+[len(inp_text)]
# make a dict of sections to extract
dict_search = {
s[1:-1].upper(): [
pos_start_list[i],
pos_end_list[i]
] for i, s in enumerate(inp_text_sections) if s[1:-1].upper() in def_sections_dict.keys()
}
# sections which are not available
unknown_sections = [s for s in inp_text_sections if not s[1:-1].upper() in def_sections_dict.keys()]
if len(unknown_sections) > 0:
feedback.pushWarning(
'Warning: unknown sections in input file: '
+ (' ,').join(unknown_sections)
+ 'These sections will be ignored'
)
# dict for raw values for every section
dict_all_vals = {
k: extract_sections_from_text(
inp_text,
dict_search[k],
k
) for k in dict_search.keys()
}
# sections which will be converted into tables
# --------------------------------------------
dict_res_table = {}
# options section
if 'OPTIONS' in dict_all_vals.keys():
section_name = 'OPTIONS'
feedback.setProgressText('Preparing section \"'+section_name+'\"')
feedback.setProgress(5)
from .g_s_options import convert_options_format_for_import
df_options = build_df_for_section(
'OPTIONS',
dict_all_vals
)
df_options_converted = convert_options_format_for_import(
df_options,
import_parameters_dict,
feedback
)
if import_parameters_dict['add_z_bool']:
if not 'LINK_OFFSETS' in df_options_converted['Option'].tolist():
import_parameters_dict['link_offsets'] = 'elevation' # assume absolute elevation if no info
feedback.pushWarning(
'OPTIONS: no value for LINK_OFFSETS in inp file. LINK_OFFSETS = ELEVATION is assumed for z-coordinates'
)
else:
link_offset_value = df_options_converted.loc[
df_options_converted['Option'] == 'LINK_OFFSETS',
'Value'
].tolist()[0]
if link_offset_value == 'DEPTH':
import_parameters_dict['link_offsets'] = 'depth' # relative
elif link_offset_value == 'ELEVATION':
import_parameters_dict['link_offsets'] = 'elevation' # absolute
else:
import_parameters_dict['link_offsets'] = 'elevation' # absolute
feedback.pushWarning(
'OPTIONS: value \"'
+ link_offset_value
+ '\" for LINK_OFFSETS in inp file is not valid. LINK_OFFSETS = ELEVATION is assumed for z-coordinates'
)
dict_res_table['OPTIONS'] = {
'OPTIONS': df_options_converted
}
# inflows section
section_name = 'INFLOWS'
feedback.setProgressText('Preparing section \"'+section_name+'\"')
feedback.setProgress(8)
if 'INFLOWS' in dict_all_vals.keys():
df_inflows = build_df_for_section(
'INFLOWS',
dict_all_vals
)
else:
df_inflows = build_df_from_vals_list(
[],
def_sections_dict['INFLOWS']
)
if 'DWF' in dict_all_vals.keys():
df_dry_weather = build_df_for_section(
'DWF',
dict_all_vals
)
else:
df_dry_weather = build_df_from_vals_list(
[],
def_sections_dict['DWF']
)
if 'HYDROGRAPHS' in dict_all_vals.keys():
from .g_s_nodes import get_hydrogrphs
df_hydrographs_raw = build_df_for_section(
'HYDROGRAPHS',
dict_all_vals
)
hg_name_list = np.unique(df_hydrographs_raw['Name'])
df_hydrographs = pd.DataFrame()
for hg_name in hg_name_list:
df_hydrographs = pd.concat([df_hydrographs, get_hydrogrphs(hg_name, df_hydrographs_raw)])
df_hydrographs = df_hydrographs.reset_index(drop=True)
else:
df_hydrographs = build_df_from_vals_list(
[],
list(def_tables_dict['INFLOWS']['tables']['Hydrographs'].keys())
)
if 'RDII' in dict_all_vals.keys():
df_rdii = build_df_for_section(
'RDII',
dict_all_vals
)
else:
df_rdii = build_df_from_vals_list(
[],
def_sections_dict['RDII']
)
dict_inflows = {
'Direct': df_inflows,
'Dry_Weather': df_dry_weather,
'Hydrographs': df_hydrographs,
'RDII': df_rdii
}
dict_res_table['INFLOWS'] = dict_inflows
pattern_types = list(def_tables_dict['PATTERNS']['tables'].keys())
pattern_cols = {k: list(v.keys())for k, v in def_tables_dict['PATTERNS']['tables'].items()}
if 'PATTERNS' in dict_all_vals.keys():
section_name = 'PATTERNS'
feedback.setProgressText('Preparing section \"'+section_name+'\"')
feedback.setProgress(12)
all_patterns = build_df_for_section('PATTERNS', dict_all_vals)
if len(all_patterns) == 0:
all_patterns = dict()
else:
occuring_patterns_types = all_patterns.loc[all_patterns[1].isin(pattern_types), [0, 1]].set_index(0)
occuring_patterns_types.columns = ["PatternType"]
all_patterns = all_patterns.fillna(np.nan)
all_patterns = all_patterns.replace({
'HOURLY': np.nan,
'DAILY': np.nan,
'MONTHLY': np.nan,
'WEEKEND': np.nan
})
def adjust_patterns_df(pattern_row):
"""
reorders a list of the patterns section for the input file
:param list pattern_row
:return: pd.DataFrame
"""
pattern_adjusted = [[pattern_row[0], i] for i in pattern_row[1:] if pd.notna(i)]
return (pd.DataFrame(pattern_adjusted, columns=['Name', 'Factor']))
all_patterns = pd.concat([adjust_patterns_df(all_patterns.loc[i, :]) for i in all_patterns.index])
all_patterns = all_patterns.join(
occuring_patterns_types,
on='Name'
)
all_patterns = {k: v.iloc[:, :-1] for k, v in all_patterns.groupby("PatternType")}
else:
all_patterns = dict()
def add_pattern_timesteps(pattern_type):
"""
adds time strings from the pattern_times dict
:param str pattern_row
:return: list
"""
count_patterns = int(len(all_patterns[pattern_type])/len(pattern_times[pattern_type]))
new_col = pattern_times[pattern_type]*count_patterns
return new_col
for pattern_type in pattern_cols.keys():
if pattern_type in all_patterns.keys():
all_patterns[pattern_type]['Time'] = add_pattern_timesteps(pattern_type)
all_patterns[pattern_type] = all_patterns[pattern_type][['Name', 'Time', 'Factor']]
if pattern_type == 'DAILY':
all_patterns[pattern_type] = all_patterns[pattern_type].rename({'Time': 'Day'})
if pattern_type == 'MONTHLY':
all_patterns[pattern_type] = all_patterns[pattern_type].rename({'Time': 'Month'})
all_patterns[pattern_type]['Factor'] = [float(x) for x in all_patterns[pattern_type]['Factor']]
all_patterns[pattern_type].columns = pattern_cols[pattern_type]
else:
all_patterns[pattern_type] = build_df_from_vals_list([], pattern_cols[pattern_type])
dict_res_table['PATTERNS'] = all_patterns
# curves section
if 'CURVES' in dict_all_vals.keys():
section_name = 'CURVES'
feedback.setProgressText('Preparing section \"'+section_name+'\"')
feedback.setProgress(16)
curve_type_dict = {x[0]: x[1] for x in dict_all_vals['CURVES']['data'] if x[1].capitalize() in curve_cols_dict.keys()}
occuring_curve_types = list(set(curve_type_dict.values()))
all_curves = [del_kw_from_list(x, occuring_curve_types, 1) for x in dict_all_vals['CURVES']['data'].copy()]
all_curves = build_df_from_vals_list(
all_curves,
def_sections_dict['CURVES']
)
all_curves['CurveType'] = [curve_type_dict[i].capitalize() for i in all_curves['Name']] # capitalize as in curve_cols_dict
all_curves['XVal'] = [float(x) for x in all_curves['XVal']]
all_curves['YVal'] = [float(x) for x in all_curves['YVal']]
all_curves = {k: v[['Name', 'XVal', 'YVal']] for k, v in all_curves.groupby('CurveType')}
else:
all_curves = dict()
for curve_type in curve_cols_dict.keys():
if curve_type in all_curves.keys():
all_curves[curve_type].columns = curve_cols_dict[curve_type]
else:
all_curves[curve_type] = build_df_from_vals_list([], curve_cols_dict[curve_type])
dict_res_table['CURVES'] = all_curves
# quality section
feedback.setProgressText('Preparing QUALITY parameters')
feedback.setProgress(20)
quality_cols_dict = {
k: def_sections_dict[k] for k in [
'POLLUTANTS',
'LANDUSES',
'COVERAGES',
'LOADINGS',
'BUILDUP',
'WASHOFF'
]
}
all_quality = {k: build_df_for_section(k, dict_all_vals) for k in quality_cols_dict.keys()}
if len(all_quality['BUILDUP']) == 0: # fill with np.nan in order to facilitate join below
if len(all_quality['LANDUSES']) > 0:
landuse_names = all_quality['LANDUSES']['Name']
landuse_count = len(landuse_names)
all_quality['BUILDUP'].loc[0:landuse_count, :] = np.nan
all_quality['BUILDUP']['Name'] = landuse_names
landuses = all_quality['BUILDUP'].copy().join(all_quality['LANDUSES'].copy().set_index('Name'), on='Name')
col_names = all_quality['LANDUSES'].columns.tolist()
col_names.extend(all_quality['BUILDUP'].columns.tolist()[1:])
landuses = landuses[col_names]
landuses['join_name'] = landuses['Name']+landuses['Pollutant']
all_quality['WASHOFF']['join_name'] = all_quality['WASHOFF']['Name'] + all_quality['WASHOFF']['Pollutant']
all_quality['WASHOFF'] = all_quality['WASHOFF'].drop(columns=['Name', 'Pollutant'])
landuses = landuses.join(all_quality['WASHOFF'].set_index('join_name'), on='join_name')
landuses = landuses.drop(columns=['join_name'])
all_quality['LANDUSES'] = landuses
del all_quality['BUILDUP']
del all_quality['WASHOFF']
all_quality = {
k: adjust_column_types(
v, def_tables_dict['QUALITY']['tables'][k]
) for k, v in all_quality.items()
}
dict_res_table['QUALITY'] = all_quality
# timeseries section
ts_cols_dict = def_tables_dict['TIMESERIES']['tables']['TIMESERIES']
if 'TIMESERIES' in dict_all_vals.keys():
all_time_series = [adjust_line_length(x, 1, 4) for x in dict_all_vals['TIMESERIES']['data'].copy()]
# for external File
all_time_series = [insert_nan_after_kw(x, 2, 'FILE', [3, 4]) for x in all_time_series]
all_time_series = [del_kw_from_list(x, 'FILE', 2) for x in all_time_series]
all_time_series = build_df_from_vals_list(
all_time_series,
def_sections_dict['TIMESERIES']
)
else:
all_time_series = build_df_from_vals_list([], list(ts_cols_dict.keys()))
all_time_series = adjust_column_types(all_time_series, ts_cols_dict)
dict_res_table['TIMESERIES'] = {'TIMESERIES': all_time_series}
# streets and inlets section
if 'STREETS' in dict_all_vals.keys() or 'INLETS' in dict_all_vals.keys():
section_name = 'STREETS'
feedback.setProgressText('Preparing section \"'+section_name+'\"')
feedback.setProgress(25)
street_data = {}
street_data['STREETS'] = build_df_for_section('STREETS', dict_all_vals)
if 'INLETS' in dict_all_vals.keys():
from .g_s_links import get_inlet_from_inp
inl_list = [get_inlet_from_inp(inl_line) for inl_line in dict_all_vals['INLETS']['data']]
street_data['INLETS'] = build_df_from_vals_list(inl_list, def_sections_dict['INLETS'])
else:
street_data['INLETS'] = build_df_for_section('INLETS', dict_all_vals)
street_data['INLET_USAGE'] = build_df_for_section('INLET_USAGE', dict_all_vals)
dict_res_table['STREETS'] = street_data
# transects in hec2 format
if 'TRANSECTS' in dict_all_vals.keys():
feedback.setProgress(1)
transects_columns = [
'TransectName',
'RoughnessLeftBank',
'RoughnessRightBank',
'RoughnessChannel',
'BankStationLeft',
'BankStationRight',
'ModifierMeander',
'ModifierStations',
'ModifierElevations'
]
section_name = 'TRANSECTS'
feedback.setProgressText('Preparing section \"'+section_name+'\"')
transects_list = dict_all_vals['TRANSECTS']['data'].copy()
tr_startp = [i for i, x in enumerate(transects_list) if x[0] == 'NC']
n_trans = len(tr_startp)
tr_endp = tr_startp[1:]+[len(transects_list)]
def get_transects_data2(tr_i):
tr_roughness = [float(x) for x in tr_i[0][1:]]
tr_name = tr_i[1][1]
tr_count = tr_i[1][2]
tr_bankstat_left = float(tr_i[1][3])
tr_bankstat_right = float(tr_i[1][4])
tr_modifier = [float(x) for x in tr_i[1][7:10]]
tr_data = [tr_name]+tr_roughness+[tr_bankstat_left]+[tr_bankstat_right]+tr_modifier
tr_values = [del_kw_from_list(x, 'GR', 0) for x in tr_i[2:]]
tr_values = [x for sublist in tr_values for x in sublist]
tr_values_splitted = [[
tr_name,
float(tr_values[x*2]), # split into list of lists of len 2
float(tr_values[(x*2)+1])
] for x in range(int(tr_count))]
return tr_values_splitted, tr_data
all_tr_vals = []
all_tr_dats = []
for i, x in enumerate(zip(tr_startp, tr_endp)):
if feedback.isCanceled():
break
val, dat = get_transects_data2(transects_list[x[0]:x[1]])
all_tr_vals = all_tr_vals + val
all_tr_dats = all_tr_dats + [dat]
feedback.setProgress(((i+1)/n_trans)*90)
all_tr_vals_df = build_df_from_vals_list(
all_tr_vals,
['TransectName', 'Elevation', 'Station']
)
feedback.setProgress(92)
all_tr_vals_df = all_tr_vals_df[[
'TransectName',
'Station',
'Elevation'
]] # order of columns according to swmm interface
feedback.setProgress(93)
all_tr_dats_df = build_df_from_vals_list(all_tr_dats, transects_columns)
feedback.setProgress(94)
all_tr_dats_df = all_tr_dats_df[[
'TransectName',
'RoughnessLeftBank',
'RoughnessRightBank',
'RoughnessChannel',
'BankStationLeft',
'BankStationRight',
'ModifierStations',
'ModifierElevations',
'ModifierMeander'
]] # order of columns according to swmm interface
transects_dict = {
'Data': all_tr_dats_df,
'XSections': all_tr_vals_df
}
feedback.setProgress(95)
dict_res_table['TRANSECTS'] = transects_dict
feedback.setProgress(100)
# writing tables:
feedback.setProgressText('Writing tables ...')
n_itms = len(dict_res_table)
for i, it in enumerate(dict_res_table.items()):
if feedback.isCanceled():
break
layer_list = []
section_name = it[0]
for sheet_name, df in it[1].items():
data_dict = {
'data': df,
'layer_name': sheet_name
}
created_layer = create_layer_from_df(
data_dict,
section_name,
feedback=feedback,
**import_parameters_dict
)
layer_list = layer_list+[created_layer]
layerlist_to_excel(
layer_list,
section_name,
feedback = feedback,
**import_parameters_dict
)
del layer_list
# sections with geometries, which will be added as layers
#------------------------------
# prepare
feedback.setProgress(0)
for section_name in def_sections_geoms_list: # the list is used to keep the order
if feedback.isCanceled():
break
if section_name in dict_all_vals.keys():
sect_list_import_handler(
section_name,
dict_all_vals,
'geodata',
feedback,
import_parameters_dict
)
# make layers
n_itms = len(def_sections_geoms_dict.keys())
for n, section_name in enumerate(def_sections_geoms_dict.keys()):
if feedback.isCanceled():
break
if section_name in dict_all_vals.keys():
if dict_all_vals[section_name]['status'] == ImportDataStatus.GEOM_READY:
data_dict = dict_all_vals[section_name]
if result_prefix != '':
layer_name = (
str(import_parameters_dict['result_prefix'])
+'_'
+ def_layer_names_dict[section_name]
)
else:
layer_name = def_layer_names_dict[section_name]
data_dict['layer_name'] = layer_name
created_layer = create_layer_from_df(
data_dict,
section_name,
feedback=feedback,
custom_fields=def_annotation_field,
**import_parameters_dict
)
save_layer_to_file(
created_layer,
layer_name,
**import_parameters_dict
)
dict_all_vals[section_name]['status'] = ImportDataStatus.FILE_READY
feedback.setProgress((n+1)/n_itms*100)
# add layers to canvas
feedback.setProgressText(
self.tr('Adding layers to canvas')
)
n_itms = len(def_sections_geoms_dict.keys())
for n, section_name in enumerate(def_sections_geoms_dict.keys()):
if feedback.isCanceled():
break
if section_name in dict_all_vals.keys():
if dict_all_vals[section_name]['status'] == ImportDataStatus.FILE_READY:
data_dict = dict_all_vals[section_name]
add_layer_on_completion(
data_dict['layer_name'],
def_stylefile_dict[section_name],
**import_parameters_dict
)
dict_all_vals[section_name]['status'] == ImportDataStatus.DONE
feedback.setProgressText(
self.tr('all data was saved in '+str(folder_save))
)
return {}