automatic import of python-mixmasta

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diff --git a/.gitignore b/.gitignore
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@@ -0,0 +1 @@
+/mixmasta-0.6.9.tar.gz
diff --git a/python-mixmasta.spec b/python-mixmasta.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-mixmasta
+Version:	0.6.9
+Release:	1
+Summary:	A library for common scientific model transforms
+License:	MIT license
+URL:		https://github.com/jataware/mixmasta
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/42/21/cf29f591d0a0fa76e0f4ad46febc7e7a63dcd84251054513b67d2531c5cc/mixmasta-0.6.9.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-bump2version
+Requires:	python3-Click
+Requires:	python3-coverage
+Requires:	python3-Cython
+Requires:	python3-flake8
+Requires:	python3-fuzzywuzzy
+Requires:	python3-GDAL
+Requires:	python3-geofeather
+Requires:	python3-geopandas
+Requires:	python3-netCDF4
+Requires:	python3-numpy
+Requires:	python3-openpyxl
+Requires:	python3-pip
+Requires:	python3-pydantic
+Requires:	python3-pyproj
+Requires:	python3-Levenshtein
+Requires:	python3-rasterio
+Requires:	python3-Rtree
+Requires:	python3-Shapely
+Requires:	python3-Sphinx
+Requires:	python3-tox
+Requires:	python3-tqdm
+Requires:	python3-twine
+Requires:	python3-watchdog
+Requires:	python3-wheel
+Requires:	python3-xarray
+Requires:	python3-xlrd
+
+%description
+# mixmasta
+[![Python Tests](https://github.com/jataware/mixmasta/actions/workflows/python.yaml/badge.svg)](https://github.com/jataware/mixmasta/actions/workflows/python.yaml)
+
+A library for common scientific model transforms. This library enables fast and intuitive transforms including:
+
+* Converting a `geotiff` to a `csv`
+* Converting a `NetCDF` to a `csv`
+* Geocoding `csv`, `xls`, and `xlsx` data that contains latitude and longitude
+
+
+## Setup
+
+See `docs/docker.md` for instructions on running Mixmasta in Docker (easiest!).
+
+Ensure you have a working installation of [GDAL](https://trac.osgeo.org/gdal/wiki/FAQInstallationAndBuilding#FAQ-InstallationandBuilding)
+
+You also need to ensure that `numpy` is installed prior to `mixmasta` installation. This is an artifact of GDAL, which will build incorrectly if `numpy` is not already configured:
+
+```
+pip install numpy==1.20.1
+pip install mixmasta
+```
+
+> Note: if you had a prior installation of GDAL you may need to run `pip install mixmasta --no-cache-dir` in a clean environment.
+
+You must install the GADM2 and GADM3 data with:
+
+```
+mixmasta download
+```
+
+## Usage
+
+
+Examples can be found in the `input` directory.
+
+Convert a geotiff to a dataframe with:
+
+```
+from mixmasta import mixmasta as mix
+df = mix.raster2df('chirps-v2.0.2021.01.3.tif', feature_name='rainfall', band=1)
+```
+
+Note that you should specify the data band of the geotiff to process if it is multi-band. You may also specify the name of the feature column to produce. You may optionally specify a `date` if the geotiff has an associated date. For example:
+
+Convert a NetCDF to a dataframe with:
+
+```
+from mixmasta import mixmasta as mix
+df = mix.netcdf2df('tos_O1_2001-2002.nc')
+```
+
+Geocode a dataframe:
+
+```
+from mixmasta import mixmasta as mix
+
+# First, load in the geotiff as a dataframe
+df = mix.raster2df('chirps-v2.0.2021.01.3.tif', feature_name='rainfall', band=1)
+
+# next, we can geocode the dataframe to the admin-level desired (`admin2` or `admin3`)
+# by specifying the names of the x and y columns
+# in this case, we will geocode to admin2 where x,y are are 'longitude' and 'latitude', respectively.
+df_g = mix.geocode("admin2", df, x='longitude', y='latitude')
+```
+
+## Running with CLI
+
+After cloning the repository and changing to the `mixmasta` directory, you can run mixmasta via the command line.
+
+Set-up:
+
+While you can point `mixmasta` to any file you would like to transform, the examples below assume your file is in the `inputs` folder; the transformed `.csv` file will be written to the `outputs` folder.
+
+- Transform geotiff to geocoded csv:
+```
+mixmasta mix --xform=geotiff --input_file=chirps-v2.0.2021.01.3.tif --output_file=geotiffTEST.csv --geo=admin2 --feature_name=rainfall --band=1 --date='5/4/2010' --x=longitude --y=latitude
+```
+
+- Transform geotiff to csv:
+```
+mixmasta mix --xform=geotiff --input_file=maxhop1.tif --output_file=maxhopOUT.csv --geo=admin2 --feature_name=probabilty --band=1 --x=longitude --y=latitude
+```
+
+- Transform netcdf to geocoded csv:
+
+```
+mixmasta mix --xform=netcdf --input_file=tos_O1_2001-2002.nc --output_file=netcdf.csv --geo=admin2 --x=lon --y=lat
+```
+
+- Transform netcdf to csv:
+```
+mixmasta mix --xform=netcdf --input_file=tos_O1_2001-2002.nc --output_file=netcdf.csv
+```
+
+-geocode an existing csv file:
+
+```
+mixmasta mix --xform=geocode --input_file=no_geo.csv --geo=admin3 --output_file=geoed_no_geo.csv --x=longitude --y=latitude
+```
+
+## World Modelers Specific Normalization
+
+For the World Modelers program, it is necessary to convert arbitrary `csv`, `geotiff`, and `netcdf` files into a CauseMos compliant format. This can be accomplished by leveraging a `mapping` annotation file and the `causemosify` command. The output is a `gzipped` `parquet` file. This may be invoked with:
+
+```
+mixmasta causemosify --input_file=chirps-v2.0.2021.01.3.tif --mapper=mapper.json --geo=admin3 --output_file=causemosified_example
+```
+
+This will produce a file called `causemosified_example.parquet.gzip` which can be read using Pandas with:
+
+```
+pd.read_parquet('causemosified_example.parquet.gzip')
+```
+
+## Other Documents
+- Docker Instructions: `docs/docker.md`
+- Geo Entity Resolution Description: `docs/geo-tentity-resolution.md`
+- Package Testing in SpaceTag Env: `docs/spacetag-test.md`
+
+## Credits
+
+This package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the [audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage) project template.
+
+
+# History
+
+## 0.1.0 (2021-02-24)
+
+-   First release on PyPI.
+
+
+
+
+
+%package -n python3-mixmasta
+Summary:	A library for common scientific model transforms
+Provides:	python-mixmasta
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-mixmasta
+# mixmasta
+[![Python Tests](https://github.com/jataware/mixmasta/actions/workflows/python.yaml/badge.svg)](https://github.com/jataware/mixmasta/actions/workflows/python.yaml)
+
+A library for common scientific model transforms. This library enables fast and intuitive transforms including:
+
+* Converting a `geotiff` to a `csv`
+* Converting a `NetCDF` to a `csv`
+* Geocoding `csv`, `xls`, and `xlsx` data that contains latitude and longitude
+
+
+## Setup
+
+See `docs/docker.md` for instructions on running Mixmasta in Docker (easiest!).
+
+Ensure you have a working installation of [GDAL](https://trac.osgeo.org/gdal/wiki/FAQInstallationAndBuilding#FAQ-InstallationandBuilding)
+
+You also need to ensure that `numpy` is installed prior to `mixmasta` installation. This is an artifact of GDAL, which will build incorrectly if `numpy` is not already configured:
+
+```
+pip install numpy==1.20.1
+pip install mixmasta
+```
+
+> Note: if you had a prior installation of GDAL you may need to run `pip install mixmasta --no-cache-dir` in a clean environment.
+
+You must install the GADM2 and GADM3 data with:
+
+```
+mixmasta download
+```
+
+## Usage
+
+
+Examples can be found in the `input` directory.
+
+Convert a geotiff to a dataframe with:
+
+```
+from mixmasta import mixmasta as mix
+df = mix.raster2df('chirps-v2.0.2021.01.3.tif', feature_name='rainfall', band=1)
+```
+
+Note that you should specify the data band of the geotiff to process if it is multi-band. You may also specify the name of the feature column to produce. You may optionally specify a `date` if the geotiff has an associated date. For example:
+
+Convert a NetCDF to a dataframe with:
+
+```
+from mixmasta import mixmasta as mix
+df = mix.netcdf2df('tos_O1_2001-2002.nc')
+```
+
+Geocode a dataframe:
+
+```
+from mixmasta import mixmasta as mix
+
+# First, load in the geotiff as a dataframe
+df = mix.raster2df('chirps-v2.0.2021.01.3.tif', feature_name='rainfall', band=1)
+
+# next, we can geocode the dataframe to the admin-level desired (`admin2` or `admin3`)
+# by specifying the names of the x and y columns
+# in this case, we will geocode to admin2 where x,y are are 'longitude' and 'latitude', respectively.
+df_g = mix.geocode("admin2", df, x='longitude', y='latitude')
+```
+
+## Running with CLI
+
+After cloning the repository and changing to the `mixmasta` directory, you can run mixmasta via the command line.
+
+Set-up:
+
+While you can point `mixmasta` to any file you would like to transform, the examples below assume your file is in the `inputs` folder; the transformed `.csv` file will be written to the `outputs` folder.
+
+- Transform geotiff to geocoded csv:
+```
+mixmasta mix --xform=geotiff --input_file=chirps-v2.0.2021.01.3.tif --output_file=geotiffTEST.csv --geo=admin2 --feature_name=rainfall --band=1 --date='5/4/2010' --x=longitude --y=latitude
+```
+
+- Transform geotiff to csv:
+```
+mixmasta mix --xform=geotiff --input_file=maxhop1.tif --output_file=maxhopOUT.csv --geo=admin2 --feature_name=probabilty --band=1 --x=longitude --y=latitude
+```
+
+- Transform netcdf to geocoded csv:
+
+```
+mixmasta mix --xform=netcdf --input_file=tos_O1_2001-2002.nc --output_file=netcdf.csv --geo=admin2 --x=lon --y=lat
+```
+
+- Transform netcdf to csv:
+```
+mixmasta mix --xform=netcdf --input_file=tos_O1_2001-2002.nc --output_file=netcdf.csv
+```
+
+-geocode an existing csv file:
+
+```
+mixmasta mix --xform=geocode --input_file=no_geo.csv --geo=admin3 --output_file=geoed_no_geo.csv --x=longitude --y=latitude
+```
+
+## World Modelers Specific Normalization
+
+For the World Modelers program, it is necessary to convert arbitrary `csv`, `geotiff`, and `netcdf` files into a CauseMos compliant format. This can be accomplished by leveraging a `mapping` annotation file and the `causemosify` command. The output is a `gzipped` `parquet` file. This may be invoked with:
+
+```
+mixmasta causemosify --input_file=chirps-v2.0.2021.01.3.tif --mapper=mapper.json --geo=admin3 --output_file=causemosified_example
+```
+
+This will produce a file called `causemosified_example.parquet.gzip` which can be read using Pandas with:
+
+```
+pd.read_parquet('causemosified_example.parquet.gzip')
+```
+
+## Other Documents
+- Docker Instructions: `docs/docker.md`
+- Geo Entity Resolution Description: `docs/geo-tentity-resolution.md`
+- Package Testing in SpaceTag Env: `docs/spacetag-test.md`
+
+## Credits
+
+This package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the [audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage) project template.
+
+
+# History
+
+## 0.1.0 (2021-02-24)
+
+-   First release on PyPI.
+
+
+
+
+
+%package help
+Summary:	Development documents and examples for mixmasta
+Provides:	python3-mixmasta-doc
+%description help
+# mixmasta
+[![Python Tests](https://github.com/jataware/mixmasta/actions/workflows/python.yaml/badge.svg)](https://github.com/jataware/mixmasta/actions/workflows/python.yaml)
+
+A library for common scientific model transforms. This library enables fast and intuitive transforms including:
+
+* Converting a `geotiff` to a `csv`
+* Converting a `NetCDF` to a `csv`
+* Geocoding `csv`, `xls`, and `xlsx` data that contains latitude and longitude
+
+
+## Setup
+
+See `docs/docker.md` for instructions on running Mixmasta in Docker (easiest!).
+
+Ensure you have a working installation of [GDAL](https://trac.osgeo.org/gdal/wiki/FAQInstallationAndBuilding#FAQ-InstallationandBuilding)
+
+You also need to ensure that `numpy` is installed prior to `mixmasta` installation. This is an artifact of GDAL, which will build incorrectly if `numpy` is not already configured:
+
+```
+pip install numpy==1.20.1
+pip install mixmasta
+```
+
+> Note: if you had a prior installation of GDAL you may need to run `pip install mixmasta --no-cache-dir` in a clean environment.
+
+You must install the GADM2 and GADM3 data with:
+
+```
+mixmasta download
+```
+
+## Usage
+
+
+Examples can be found in the `input` directory.
+
+Convert a geotiff to a dataframe with:
+
+```
+from mixmasta import mixmasta as mix
+df = mix.raster2df('chirps-v2.0.2021.01.3.tif', feature_name='rainfall', band=1)
+```
+
+Note that you should specify the data band of the geotiff to process if it is multi-band. You may also specify the name of the feature column to produce. You may optionally specify a `date` if the geotiff has an associated date. For example:
+
+Convert a NetCDF to a dataframe with:
+
+```
+from mixmasta import mixmasta as mix
+df = mix.netcdf2df('tos_O1_2001-2002.nc')
+```
+
+Geocode a dataframe:
+
+```
+from mixmasta import mixmasta as mix
+
+# First, load in the geotiff as a dataframe
+df = mix.raster2df('chirps-v2.0.2021.01.3.tif', feature_name='rainfall', band=1)
+
+# next, we can geocode the dataframe to the admin-level desired (`admin2` or `admin3`)
+# by specifying the names of the x and y columns
+# in this case, we will geocode to admin2 where x,y are are 'longitude' and 'latitude', respectively.
+df_g = mix.geocode("admin2", df, x='longitude', y='latitude')
+```
+
+## Running with CLI
+
+After cloning the repository and changing to the `mixmasta` directory, you can run mixmasta via the command line.
+
+Set-up:
+
+While you can point `mixmasta` to any file you would like to transform, the examples below assume your file is in the `inputs` folder; the transformed `.csv` file will be written to the `outputs` folder.
+
+- Transform geotiff to geocoded csv:
+```
+mixmasta mix --xform=geotiff --input_file=chirps-v2.0.2021.01.3.tif --output_file=geotiffTEST.csv --geo=admin2 --feature_name=rainfall --band=1 --date='5/4/2010' --x=longitude --y=latitude
+```
+
+- Transform geotiff to csv:
+```
+mixmasta mix --xform=geotiff --input_file=maxhop1.tif --output_file=maxhopOUT.csv --geo=admin2 --feature_name=probabilty --band=1 --x=longitude --y=latitude
+```
+
+- Transform netcdf to geocoded csv:
+
+```
+mixmasta mix --xform=netcdf --input_file=tos_O1_2001-2002.nc --output_file=netcdf.csv --geo=admin2 --x=lon --y=lat
+```
+
+- Transform netcdf to csv:
+```
+mixmasta mix --xform=netcdf --input_file=tos_O1_2001-2002.nc --output_file=netcdf.csv
+```
+
+-geocode an existing csv file:
+
+```
+mixmasta mix --xform=geocode --input_file=no_geo.csv --geo=admin3 --output_file=geoed_no_geo.csv --x=longitude --y=latitude
+```
+
+## World Modelers Specific Normalization
+
+For the World Modelers program, it is necessary to convert arbitrary `csv`, `geotiff`, and `netcdf` files into a CauseMos compliant format. This can be accomplished by leveraging a `mapping` annotation file and the `causemosify` command. The output is a `gzipped` `parquet` file. This may be invoked with:
+
+```
+mixmasta causemosify --input_file=chirps-v2.0.2021.01.3.tif --mapper=mapper.json --geo=admin3 --output_file=causemosified_example
+```
+
+This will produce a file called `causemosified_example.parquet.gzip` which can be read using Pandas with:
+
+```
+pd.read_parquet('causemosified_example.parquet.gzip')
+```
+
+## Other Documents
+- Docker Instructions: `docs/docker.md`
+- Geo Entity Resolution Description: `docs/geo-tentity-resolution.md`
+- Package Testing in SpaceTag Env: `docs/spacetag-test.md`
+
+## Credits
+
+This package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the [audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage) project template.
+
+
+# History
+
+## 0.1.0 (2021-02-24)
+
+-   First release on PyPI.
+
+
+
+
+
+%prep
+%autosetup -n mixmasta-0.6.9
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-mixmasta -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.9-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..2bdcf3b
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+39c698c50ad9db243e3b0e7216ffdedf  mixmasta-0.6.9.tar.gz