%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 * Tue May 30 2023 Python_Bot - 0.6.9-1 - Package Spec generated