### Context
* Created: November 2021
* Author: Renier Kramer, renier.kramer@hdsr.nl
* Python version: 3.7 <= x <= 3.11
### Description
A python project that you can use to read the HDSR FEWS-WIS configuration.
It serves as an interface (python objects) to read configuration files (.xmls and .csv).
### Usage (12 examples)
```
--------------------------------------------------------------------------------
# install dependencies (with pip or conda)
pip install hdsr-wis-config-reader
conda install hdsr-wis-config-reader --channel hdsr-mid
# prepare the examples
>>> from hdsr_wis_config_reader import XmlReader, FewsConfigReader, LocationMapper, LocationSetCollection, \
IdMappingCollection, merge_startenddate_github_with_idmap
>>> from hdsr_wis_config_reader.idmappings.columns import IdMapCols
>>> from hdsr_wis_config_reader.utils import PdReadFlexibleCsv, DatesColumns
>>> import pathlib # dependency of hdsr-wis-config-reader
>>> config_dir = pathlib.Path(<path_to_fews_config_dir>) # this points to the FEWS <region_home>/config dir
>>> fews_config = FewsConfigReader(path=config_dir)
>>> location_sets = LocationSetCollection(fews_config=fews_config)
>>> id_mappings = IdMappingCollection(fews_config=fews_config)
--------------------------------------------------------------------------------
# 1. Get the absoluate path to a config file
>>> fews_config.RegionConfigFiles["Filters"]
# ../<path_to_fews_config_dir>/RegionConfigFiles/Filters.xml'
--------------------------------------------------------------------------------
# 2. Get hoofdlocationset names
>>> location_sets.hoofd_loc.name
# 'hoofdlocaties'
>>> location_sets.hoofd_loc.csv_filename
'oppvlwater_hoofdloc'
>>> location_sets.hoofd_loc.fews_name
'OPVLWATER_HOOFDLOC'
--------------------------------------------------------------------------------
# 3. Get a dataframe of parameters
>>> fews_config.get_parameters()
# GROUP DESCRIPTION PARAMETERTYPE UNIT VALUERESOLUTION USESDATUM ID NAME SHORTNAME
# Biomassa instantaneous kg/ha 0.1 B.d Biomassa productie [kg/ha] - dag Biomassa productie [kg/ha] - dag
# Biomassa instantaneous kg/ha 0.1 B.m Biomassa productie [kg/ha] - maand Biomassa productie [kg/ha] - maand
# etc...
--------------------------------------------------------------------------------
# 4. Get a geodataframe with hoofd locations (note that geomeometry height (z) gets -9999 when if Z is nan)
>>> location_sets.hoofd_loc.geo_df_original
# LOC_ID LOC_NAME X Y Z ALLE_TYPES START ... geometry
# KW100110 WIJKERSLOOT_1001-K_WIJKERSLOOT 150501 442988 2.87 krooshek/pompvijzel 19970101 ... POINT Z (150501 442988 2.87)
# KW100120 WIJKERSLOOT_1001-K_WIJKERSLOOT STUW 150439 442885 nan stuw 20120321 ... POINT Z (150439 442885 -9999)
# etc...
# NOTE: this geodataframe cannot be updated
>>> location_sets.sub_loc.geo_df_original.at[0, "LOC_ID"] = "new_value"
# NotImplementedError('.geo_df_original cannot be updated. Please update .geo_df_updated'). Geo_df_updated can be:
>>> location_sets.waterstand_loc.geo_df_updated.at[0, "LOC_ID"] = "new_value"
--------------------------------------------------------------------------------
# 5. Get the validation rules for sublocations
>>> location_sets.sub_loc.validation_rules
# [
# {'parameter': 'H.R.', 'extreme_values': {'hmax': 'HR1_HMAX', 'hmin': 'HR1_HMIN'}},
# {'parameter': 'H2.R.', 'extreme_values': {'hmax': 'HR2_HMAX', 'hmin': 'HR2_HMIN'}},
# etc..
# ]
--------------------------------------------------------------------------------
# 6. Get the attribute files for waterstand locations
>>> location_sets.waterstand_loc.attrib_files
# [
# { 'csvFile': 'oppvlwater_langsprofielen',
# 'id': '%LOC_ID%',
# 'attribute': [
# {'number': '%Langsprofiel_Kromme_Rijn%', 'id': 'Langsprofiel_Kromme_Rijn'},
# {'number': '%Langsprofiel_Caspargouwse_Wetering%', 'id': 'Langsprofiel_Caspargouwse_Wetering'},
# etc...
--------------------------------------------------------------------------------
# 7. Get filtered idmapping (you can filter on one or more args: {ex_loc, ex_par, int_loc, int_par}):
>>> df_idmap = id_mappings.idmap_all.get_filtered_df(int_loc="KW761001")
# externalLocation externalParameter internalLocation internalParameter source histtag
# 610 Q1 KW761001 Q.G.0 IdOPVLWATER 610_Q1
# 7610 Q1 KW761001 Q.G.0 IdOPVLWATER 7610_Q1
# 610 Q1 KW761001 Q.G.0 IdOPVLWATER_HYMOS 610_Q1
# To filter only op idmappping opperlvakte water
>>> df_idmap = id_mappings.idmap_opvl_water.get_filtered_df(ex_loc='001')
--------------------------------------------------------------------------------
# 8. Get all external parameters in the groundwater idmapping
>>> id_mappings.idmap_grondwater_caw.get_filtered_column_values(
make_result_unique=True,
target_column=IdMapCols.ex_par,
)
# ['1GW', 'GW1', 'GW2', 'GW3', 'HB1', 'HB2', 'HB3', 'HB4', 'HB5', 'HB6', 'HB7', 'HB8']
--------------------------------------------------------------------------------
# 9. Read an id_mapping xml file into dataframe without a FEWS configuration (filtering also possible)
>>> idmap_file_path = Path(<path_to_fews_config_dir>) / "IdMapFiles" / "IdOPVLWATER.xml"
>>> df_idmap_oppvl = IdMappingCollection.get_idmap_df_via_path(file_path=idmap_file_path)
>>> df_filtered = df_idmap_oppvl.get_filtered_df(int_par="H.G.0")
--------------------------------------------------------------------------------
# 10. Read and merge id_mapping + startendate files (or choose your own startendate file with merge_startenddate_idmap())
>>> df = merge_startenddate_github_with_idmap(df_idmap=id_mappings.idmap_all)
# series start end filename_start filename_end externalLocation externalParameter internalLocation internalParameter idmap_source
# 001_ES1 2012-03-21 15:00:00 2012-06-04 16:10:32 HDSR_CAW_201205091959 HDSR_CAW_201206042000 001 ES1 NaN NaN NaN
# 001_ES2 2012-03-21 15:00:00 2012-05-26 13:29:40 HDSR_CAW_201204262000 HDSR_CAW_201205262000 001 ES2 NaN NaN NaN
# 001_FQ1 2010-04-27 23:59:59 2018-03-27 13:15:00 HDSR_CAW_201204262000 HDSR_CAW_201803271320 001 FQ1 KW100111 F.0 IdOPVLWATER
# 001_FQ1 2010-04-27 23:59:59 2018-03-27 13:15:00 HDSR_CAW_201204262000 HDSR_CAW_201803271320 001 FQ1 KW100111 F.15 IdOPVLWATER_HYMOS
# 001_HB1 2010-04-27 23:59:59 2018-03-27 13:15:00 HDSR_CAW_201204262000 HDSR_CAW_201803271320 001 HB1 OW100102 H.G.0 IdOPVLWATER
--------------------------------------------------------------------------------
11. Related caw_complex with hoofd_locations with sub_locations
>>> mapper = LocationMapper(path_sub_loc_csv=Path(path_sub_loc_csv), path_idmap_xml=Path(path_idmap_xml))
>>> assert mapper.complex_to_sub(caw_complex="2182") == ["KW218221", "KW218231"]
>>> assert mapper.complex_to_sub(caw_complex="4322") == ["KW432211", "KW432212", "KW432221", "KW432222", "KW432223", "KW432224", "KW432225", "KW432226"]
>>> assert mapper.complex_to_sub(caw_complex="4804") == ["KW432211", "KW432212", "KW432221", "KW432222", "KW432223", "KW432224", "KW432225", "KW432226"]
>>> assert mapper.sub_to_hoofd(ex_loc="1811", ex_par="ES2") == "KW108420"
>>> assert mapper.sub_to_complex(ex_loc="1811", ex_par="ES2") == "1084"
>>> assert mapper.complex_to_hoofd(caw_complex="4322") == ["KW432210", "KW432220"]
>>> assert mapper.complex_to_hoofd(caw_complex="4804") == ["KW432210", "KW432220"]
--------------------------------------------------------------------------------
# 12. Load a .csv as pandas dataframe in a flexible way: it tries different separators, encodings, date_columns.
>>> reader = PdReadFlexibleCsv(
path=<csv_path>,
try_separator=",",
expected_columns=[<column1>, <column2>, <column3>, <column4>],
date_columns=[
DatesColumns(column_name=<column2>, date_format="%Y/%m/%d", errors="raise"),
DatesColumns(column_name=<column3>), date_format="%Y-%m-%d", errors="ignore"),
DatesColumns(column_name=<column4>), guess_format=True)
],
)
>>> df = reader.df
```
### License
[MIT][mit]
[mit]: https://github.com/hdsr-mid/hdsr_wis_config_reader/blob/main/LICENSE.txt
### Contributions
All contributions, bug reports, bug fixes, documentation improvements, enhancements
and ideas are welcome on https://github.com/hdsr-mid/hdsr_wis_config_reader/issues
### Test coverage (January 17th 2024)
```
---------- coverage: platform win32, python 3.9.18-final-0 -----------
Name Stmts Miss Cover
-----------------------------------------------------------------------------
hdsr_wis_config_reader\constants.py 19 1 95%
hdsr_wis_config_reader\idmappings\collection.py 79 4 95%
hdsr_wis_config_reader\idmappings\columns.py 94 15 84%
hdsr_wis_config_reader\idmappings\custom_dataframe.py 29 1 97%
hdsr_wis_config_reader\idmappings\files.py 10 1 90%
hdsr_wis_config_reader\idmappings\sections.py 13 1 92%
hdsr_wis_config_reader\idmappings\utils.py 11 0 100%
hdsr_wis_config_reader\location_sets\base.py 110 10 91%
hdsr_wis_config_reader\location_sets\collection.py 59 1 98%
hdsr_wis_config_reader\location_sets\columns.py 19 0 100%
hdsr_wis_config_reader\location_sets\hoofd.py 26 2 92%
hdsr_wis_config_reader\location_sets\location_mapper.py 121 4 97%
hdsr_wis_config_reader\location_sets\msw.py 17 1 94%
hdsr_wis_config_reader\location_sets\ow.py 18 1 94%
hdsr_wis_config_reader\location_sets\ps.py 17 1 94%
hdsr_wis_config_reader\location_sets\sub.py 32 2 94%
hdsr_wis_config_reader\location_sets\wq.py 17 4 76%
hdsr_wis_config_reader\readers\config_reader.py 163 11 93%
hdsr_wis_config_reader\readers\xml_reader.py 48 10 79%
hdsr_wis_config_reader\startenddate.py 114 12 89%
hdsr_wis_config_reader\utils.py 213 51 76%
hdsr_wis_config_reader\validation_rules\files.py 35 10 71%
hdsr_wis_config_reader\validation_rules\logic.py 28 0 100%
-----------------------------------------------------------------------------
TOTAL 1292 143 89%
```
### Conda general tips
#### Build conda environment (on Windows) from any directory using environment.yml:
Note1: prefix is not set in the environment.yml as then conda does not handle it very well
Note2: env_directory can be anywhere, it does not have to be in your code project
```
> conda env create --prefix <env_directory><env_name> --file <path_to_project>/environment.yml
# example: conda env create --prefix C:/Users/xxx/.conda/envs/project_xx --file C:/Users/code_projects/xx/environment.yml
> conda info --envs # verify that <env_name> (project_xx) is in this list
```
#### Start the application from any directory:
```
> conda activate <env_name>
At any location:
> (<env_name>) python <path_to_project>/main.py
```
#### Test the application:
```
> conda activate <env_name>
> cd <path_to_project>
> pytest # make sure pytest is installed (conda install pytest)
```
#### List all conda environments on your machine:
```
At any location:
> conda info --envs
```
#### Delete a conda environment:
```
Get directory where environment is located
> conda info --envs
Remove the enviroment
> conda env remove --name <env_name>
Finally, remove the left-over directory by hand
```
#### Write dependencies to environment.yml:
The goal is to keep the .yml as short as possible (not include sub-dependencies), yet make the environment
reproducible. Why? If you do 'conda install matplotlib' you also install sub-dependencies like pyqt, qt
icu, and sip. You should not include these sub-dependencies in your .yml as:
- including sub-dependencies result in an unnecessary strict environment (difficult to solve when conflicting)
- sub-dependencies will be installed when dependencies are being installed
```
> conda activate <conda_env_name>
Recommended:
> conda env export --from-history --no-builds | findstr -v "prefix" > --file <path_to_project>/environment_new.yml
Alternative:
> conda env export --no-builds | findstr -v "prefix" > --file <path_to_project>/environment_new.yml
--from-history:
Only include packages that you have explicitly asked for, as opposed to including every package in the
environment. This flag works regardless how you created the environment (through CMD or Anaconda Navigator).
--no-builds:
By default, the YAML includes platform-specific build constraints. If you transfer across platforms (e.g.
win32 to 64) omit the build info with '--no-builds'.
```
#### Pip and Conda:
If a package is not available on all conda channels, but available as pip package, one can install pip as a dependency.
Note that mixing packages from conda and pip is always a potential problem: conda calls pip, but pip does not know
how to satisfy missing dependencies with packages from Anaconda repositories.
```
> conda activate <env_name>
> conda install pip
> pip install <pip_package>
```
The environment.yml might look like:
```
channels:
- defaults
dependencies:
- <a conda package>=<version>
- pip
- pip:
- <a pip package>==<version>
```
You can also write a requirements.txt file:
```
> pip list --format=freeze > <path_to_project>/requirements.txt
```
Raw data
{
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"home_page": "https://github.com/hdsr-mid/hdsr_wis_config_reader",
"name": "hdsr-wis-config-reader",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": "",
"keywords": "HDSR,FEWS,WIS,config,hdsr_wis_config_reader",
"author": "Renier Kramer",
"author_email": "renier.kramer@hdsr.nl",
"download_url": "https://files.pythonhosted.org/packages/17/85/908192bc71fb2af2bc6a75216aeeeea0ca5633b61486030844d2075ecca5/hdsr_wis_config_reader-1.32.tar.gz",
"platform": null,
"description": "### Context\r\n* Created: November 2021\r\n* Author: Renier Kramer, renier.kramer@hdsr.nl\r\n* Python version: 3.7 <= x <= 3.11\r\n\r\n### Description\r\nA python project that you can use to read the HDSR FEWS-WIS configuration. \r\nIt serves as an interface (python objects) to read configuration files (.xmls and .csv).\r\n\r\n### Usage (12 examples)\r\n```\r\n--------------------------------------------------------------------------------\r\n# install dependencies (with pip or conda)\r\npip install hdsr-wis-config-reader\r\nconda install hdsr-wis-config-reader --channel hdsr-mid\r\n\r\n# prepare the examples\r\n>>> from hdsr_wis_config_reader import XmlReader, FewsConfigReader, LocationMapper, LocationSetCollection, \\\r\n IdMappingCollection, merge_startenddate_github_with_idmap\r\n>>> from hdsr_wis_config_reader.idmappings.columns import IdMapCols\r\n>>> from hdsr_wis_config_reader.utils import PdReadFlexibleCsv, DatesColumns\r\n>>> import pathlib # dependency of hdsr-wis-config-reader\r\n>>> config_dir = pathlib.Path(<path_to_fews_config_dir>) # this points to the FEWS <region_home>/config dir\r\n>>> fews_config = FewsConfigReader(path=config_dir)\r\n>>> location_sets = LocationSetCollection(fews_config=fews_config)\r\n>>> id_mappings = IdMappingCollection(fews_config=fews_config)\r\n--------------------------------------------------------------------------------\r\n# 1. Get the absoluate path to a config file\r\n>>> fews_config.RegionConfigFiles[\"Filters\"]\r\n# ../<path_to_fews_config_dir>/RegionConfigFiles/Filters.xml'\r\n--------------------------------------------------------------------------------\r\n# 2. Get hoofdlocationset names\r\n>>> location_sets.hoofd_loc.name\r\n# 'hoofdlocaties'\r\n>>> location_sets.hoofd_loc.csv_filename\r\n'oppvlwater_hoofdloc'\r\n>>> location_sets.hoofd_loc.fews_name\r\n'OPVLWATER_HOOFDLOC'\r\n--------------------------------------------------------------------------------\r\n# 3. Get a dataframe of parameters\r\n>>> fews_config.get_parameters()\r\n# GROUP DESCRIPTION PARAMETERTYPE UNIT VALUERESOLUTION USESDATUM ID NAME SHORTNAME\r\n# Biomassa instantaneous kg/ha 0.1 B.d Biomassa productie [kg/ha] - dag Biomassa productie [kg/ha] - dag\r\n# Biomassa instantaneous kg/ha 0.1 B.m Biomassa productie [kg/ha] - maand Biomassa productie [kg/ha] - maand\r\n# etc...\r\n-------------------------------------------------------------------------------- \r\n# 4. Get a geodataframe with hoofd locations (note that geomeometry height (z) gets -9999 when if Z is nan)\r\n>>> location_sets.hoofd_loc.geo_df_original\r\n# LOC_ID LOC_NAME X Y Z ALLE_TYPES START ... geometry\r\n# KW100110 WIJKERSLOOT_1001-K_WIJKERSLOOT 150501 442988 2.87 krooshek/pompvijzel 19970101 ... POINT Z (150501 442988 2.87)\r\n# KW100120 WIJKERSLOOT_1001-K_WIJKERSLOOT STUW 150439 442885 nan stuw 20120321 ... POINT Z (150439 442885 -9999)\r\n# etc...\r\n\r\n# NOTE: this geodataframe cannot be updated\r\n>>> location_sets.sub_loc.geo_df_original.at[0, \"LOC_ID\"] = \"new_value\"\r\n# NotImplementedError('.geo_df_original cannot be updated. Please update .geo_df_updated'). Geo_df_updated can be:\r\n>>> location_sets.waterstand_loc.geo_df_updated.at[0, \"LOC_ID\"] = \"new_value\"\r\n--------------------------------------------------------------------------------\r\n# 5. Get the validation rules for sublocations\r\n>>> location_sets.sub_loc.validation_rules\r\n# [\r\n# {'parameter': 'H.R.', 'extreme_values': {'hmax': 'HR1_HMAX', 'hmin': 'HR1_HMIN'}}, \r\n# {'parameter': 'H2.R.', 'extreme_values': {'hmax': 'HR2_HMAX', 'hmin': 'HR2_HMIN'}},\r\n# etc..\r\n# ]\r\n--------------------------------------------------------------------------------\r\n# 6. Get the attribute files for waterstand locations \r\n>>> location_sets.waterstand_loc.attrib_files\r\n# [\r\n# { 'csvFile': 'oppvlwater_langsprofielen', \r\n# 'id': '%LOC_ID%', \r\n# 'attribute': [\r\n# {'number': '%Langsprofiel_Kromme_Rijn%', 'id': 'Langsprofiel_Kromme_Rijn'}, \r\n# {'number': '%Langsprofiel_Caspargouwse_Wetering%', 'id': 'Langsprofiel_Caspargouwse_Wetering'},\r\n# etc...\r\n--------------------------------------------------------------------------------\r\n# 7. Get filtered idmapping (you can filter on one or more args: {ex_loc, ex_par, int_loc, int_par}): \r\n>>> df_idmap = id_mappings.idmap_all.get_filtered_df(int_loc=\"KW761001\")\r\n# externalLocation externalParameter internalLocation internalParameter source histtag\r\n# 610 Q1 KW761001 Q.G.0 IdOPVLWATER 610_Q1\r\n# 7610 Q1 KW761001 Q.G.0 IdOPVLWATER 7610_Q1\r\n# 610 Q1 KW761001 Q.G.0 IdOPVLWATER_HYMOS 610_Q1\r\n# To filter only op idmappping opperlvakte water\r\n>>> df_idmap = id_mappings.idmap_opvl_water.get_filtered_df(ex_loc='001')\r\n--------------------------------------------------------------------------------\r\n# 8. Get all external parameters in the groundwater idmapping\r\n>>> id_mappings.idmap_grondwater_caw.get_filtered_column_values(\r\n make_result_unique=True,\r\n target_column=IdMapCols.ex_par,\r\n ) \r\n# ['1GW', 'GW1', 'GW2', 'GW3', 'HB1', 'HB2', 'HB3', 'HB4', 'HB5', 'HB6', 'HB7', 'HB8']\r\n--------------------------------------------------------------------------------\r\n# 9. Read an id_mapping xml file into dataframe without a FEWS configuration (filtering also possible)\r\n>>> idmap_file_path = Path(<path_to_fews_config_dir>) / \"IdMapFiles\" / \"IdOPVLWATER.xml\"\r\n>>> df_idmap_oppvl = IdMappingCollection.get_idmap_df_via_path(file_path=idmap_file_path)\r\n>>> df_filtered = df_idmap_oppvl.get_filtered_df(int_par=\"H.G.0\")\r\n--------------------------------------------------------------------------------\r\n# 10. Read and merge id_mapping + startendate files (or choose your own startendate file with merge_startenddate_idmap())\r\n>>> df = merge_startenddate_github_with_idmap(df_idmap=id_mappings.idmap_all)\r\n# series start end filename_start filename_end externalLocation externalParameter internalLocation internalParameter idmap_source\r\n# 001_ES1 2012-03-21 15:00:00 2012-06-04 16:10:32 HDSR_CAW_201205091959 HDSR_CAW_201206042000 001 ES1 NaN NaN NaN \r\n# 001_ES2 2012-03-21 15:00:00 2012-05-26 13:29:40 HDSR_CAW_201204262000 HDSR_CAW_201205262000 001 ES2 NaN NaN NaN \r\n# 001_FQ1 2010-04-27 23:59:59 2018-03-27 13:15:00 HDSR_CAW_201204262000 HDSR_CAW_201803271320 001 FQ1 KW100111 F.0 IdOPVLWATER \r\n# 001_FQ1 2010-04-27 23:59:59 2018-03-27 13:15:00 HDSR_CAW_201204262000 HDSR_CAW_201803271320 001 FQ1 KW100111 F.15 IdOPVLWATER_HYMOS \r\n# 001_HB1 2010-04-27 23:59:59 2018-03-27 13:15:00 HDSR_CAW_201204262000 HDSR_CAW_201803271320 001 HB1 OW100102 H.G.0 IdOPVLWATER \r\n--------------------------------------------------------------------------------\r\n11. Related caw_complex with hoofd_locations with sub_locations\r\n>>> mapper = LocationMapper(path_sub_loc_csv=Path(path_sub_loc_csv), path_idmap_xml=Path(path_idmap_xml))\r\n>>> assert mapper.complex_to_sub(caw_complex=\"2182\") == [\"KW218221\", \"KW218231\"]\r\n>>> assert mapper.complex_to_sub(caw_complex=\"4322\") == [\"KW432211\", \"KW432212\", \"KW432221\", \"KW432222\", \"KW432223\", \"KW432224\", \"KW432225\", \"KW432226\"]\r\n>>> assert mapper.complex_to_sub(caw_complex=\"4804\") == [\"KW432211\", \"KW432212\", \"KW432221\", \"KW432222\", \"KW432223\", \"KW432224\", \"KW432225\", \"KW432226\"]\r\n>>> assert mapper.sub_to_hoofd(ex_loc=\"1811\", ex_par=\"ES2\") == \"KW108420\"\r\n>>> assert mapper.sub_to_complex(ex_loc=\"1811\", ex_par=\"ES2\") == \"1084\"\r\n>>> assert mapper.complex_to_hoofd(caw_complex=\"4322\") == [\"KW432210\", \"KW432220\"]\r\n>>> assert mapper.complex_to_hoofd(caw_complex=\"4804\") == [\"KW432210\", \"KW432220\"]\r\n--------------------------------------------------------------------------------\r\n# 12. Load a .csv as pandas dataframe in a flexible way: it tries different separators, encodings, date_columns.\r\n>>> reader = PdReadFlexibleCsv(\r\n path=<csv_path>,\r\n try_separator=\",\",\r\n expected_columns=[<column1>, <column2>, <column3>, <column4>],\r\n date_columns=[\r\n DatesColumns(column_name=<column2>, date_format=\"%Y/%m/%d\", errors=\"raise\"),\r\n DatesColumns(column_name=<column3>), date_format=\"%Y-%m-%d\", errors=\"ignore\"),\r\n DatesColumns(column_name=<column4>), guess_format=True)\r\n ],\r\n )\r\n>>> df = reader.df\r\n```\r\n\r\n### License \r\n[MIT][mit]\r\n\r\n[mit]: https://github.com/hdsr-mid/hdsr_wis_config_reader/blob/main/LICENSE.txt\r\n\r\n### Contributions\r\nAll contributions, bug reports, bug fixes, documentation improvements, enhancements \r\nand ideas are welcome on https://github.com/hdsr-mid/hdsr_wis_config_reader/issues\r\n\r\n### Test coverage (January 17th 2024)\r\n```\r\n---------- coverage: platform win32, python 3.9.18-final-0 -----------\r\nName Stmts Miss Cover\r\n-----------------------------------------------------------------------------\r\nhdsr_wis_config_reader\\constants.py 19 1 95%\r\nhdsr_wis_config_reader\\idmappings\\collection.py 79 4 95%\r\nhdsr_wis_config_reader\\idmappings\\columns.py 94 15 84%\r\nhdsr_wis_config_reader\\idmappings\\custom_dataframe.py 29 1 97%\r\nhdsr_wis_config_reader\\idmappings\\files.py 10 1 90%\r\nhdsr_wis_config_reader\\idmappings\\sections.py 13 1 92%\r\nhdsr_wis_config_reader\\idmappings\\utils.py 11 0 100%\r\nhdsr_wis_config_reader\\location_sets\\base.py 110 10 91%\r\nhdsr_wis_config_reader\\location_sets\\collection.py 59 1 98%\r\nhdsr_wis_config_reader\\location_sets\\columns.py 19 0 100%\r\nhdsr_wis_config_reader\\location_sets\\hoofd.py 26 2 92%\r\nhdsr_wis_config_reader\\location_sets\\location_mapper.py 121 4 97%\r\nhdsr_wis_config_reader\\location_sets\\msw.py 17 1 94%\r\nhdsr_wis_config_reader\\location_sets\\ow.py 18 1 94%\r\nhdsr_wis_config_reader\\location_sets\\ps.py 17 1 94%\r\nhdsr_wis_config_reader\\location_sets\\sub.py 32 2 94%\r\nhdsr_wis_config_reader\\location_sets\\wq.py 17 4 76%\r\nhdsr_wis_config_reader\\readers\\config_reader.py 163 11 93%\r\nhdsr_wis_config_reader\\readers\\xml_reader.py 48 10 79%\r\nhdsr_wis_config_reader\\startenddate.py 114 12 89%\r\nhdsr_wis_config_reader\\utils.py 213 51 76%\r\nhdsr_wis_config_reader\\validation_rules\\files.py 35 10 71%\r\nhdsr_wis_config_reader\\validation_rules\\logic.py 28 0 100%\r\n-----------------------------------------------------------------------------\r\nTOTAL 1292 143 89%\r\n```\r\n\r\n### Conda general tips\r\n#### Build conda environment (on Windows) from any directory using environment.yml:\r\nNote1: prefix is not set in the environment.yml as then conda does not handle it very well\r\nNote2: env_directory can be anywhere, it does not have to be in your code project\r\n```\r\n> conda env create --prefix <env_directory><env_name> --file <path_to_project>/environment.yml\r\n# example: conda env create --prefix C:/Users/xxx/.conda/envs/project_xx --file C:/Users/code_projects/xx/environment.yml\r\n> conda info --envs # verify that <env_name> (project_xx) is in this list \r\n```\r\n#### Start the application from any directory:\r\n```\r\n> conda activate <env_name>\r\nAt any location:\r\n> (<env_name>) python <path_to_project>/main.py\r\n```\r\n#### Test the application:\r\n```\r\n> conda activate <env_name>\r\n> cd <path_to_project>\r\n> pytest # make sure pytest is installed (conda install pytest)\r\n```\r\n#### List all conda environments on your machine:\r\n```\r\nAt any location:\r\n> conda info --envs\r\n```\r\n#### Delete a conda environment:\r\n```\r\nGet directory where environment is located \r\n> conda info --envs\r\nRemove the enviroment\r\n> conda env remove --name <env_name>\r\nFinally, remove the left-over directory by hand\r\n```\r\n#### Write dependencies to environment.yml:\r\nThe goal is to keep the .yml as short as possible (not include sub-dependencies), yet make the environment \r\nreproducible. Why? If you do 'conda install matplotlib' you also install sub-dependencies like pyqt, qt \r\nicu, and sip. You should not include these sub-dependencies in your .yml as:\r\n- including sub-dependencies result in an unnecessary strict environment (difficult to solve when conflicting)\r\n- sub-dependencies will be installed when dependencies are being installed\r\n```\r\n> conda activate <conda_env_name>\r\n\r\nRecommended:\r\n> conda env export --from-history --no-builds | findstr -v \"prefix\" > --file <path_to_project>/environment_new.yml \r\n\r\nAlternative:\r\n> conda env export --no-builds | findstr -v \"prefix\" > --file <path_to_project>/environment_new.yml \r\n\r\n--from-history: \r\n Only include packages that you have explicitly asked for, as opposed to including every package in the \r\n environment. This flag works regardless how you created the environment (through CMD or Anaconda Navigator).\r\n--no-builds:\r\n By default, the YAML includes platform-specific build constraints. If you transfer across platforms (e.g. \r\n win32 to 64) omit the build info with '--no-builds'.\r\n```\r\n#### Pip and Conda:\r\nIf a package is not available on all conda channels, but available as pip package, one can install pip as a dependency.\r\nNote that mixing packages from conda and pip is always a potential problem: conda calls pip, but pip does not know \r\nhow to satisfy missing dependencies with packages from Anaconda repositories. \r\n```\r\n> conda activate <env_name>\r\n> conda install pip\r\n> pip install <pip_package>\r\n```\r\nThe environment.yml might look like:\r\n```\r\nchannels:\r\n - defaults\r\ndependencies:\r\n - <a conda package>=<version>\r\n - pip\r\n - pip:\r\n - <a pip package>==<version>\r\n```\r\nYou can also write a requirements.txt file:\r\n```\r\n> pip list --format=freeze > <path_to_project>/requirements.txt\r\n```\r\n",
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