automatic import of python-strato-query

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diff --git a/.gitignore b/.gitignore
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+/strato_query-3.10.2.tar.gz
diff --git a/python-strato-query.spec b/python-strato-query.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-strato-query
+Version:	3.10.2
+Release:	1
+Summary:	StratoDem Analytics API tools
+License:	MIT
+URL:		https://github.com/StratoDem/strato-query
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/a9/a3/4c3071318db948b26065f203152c1cc651bbefd3c945e1a9d439ebcbc59e/strato_query-3.10.2.tar.gz
+BuildArch:	noarch
+
+
+%description
+# Strato-Query
+tools to help create queries to StratoDem's API
+
+## Installation and usage
+#### Python:
+```
+$ pip install strato-query
+```
+
+#### R:
+```
+library(devtools)
+devtools::install_github('StratoDem/strato-query')
+```
+
+### Authentication
+`strato_query` looks for an `API_TOKEN` environment variable.
+```bash
+# Example passing a StratoDem Analytics API token to a Python file using the API
+$ API_TOKEN=my-api-token-here python examples/examples.py
+```
+
+### Median household income for 80+ households across the US, by year
+#### Python:
+```python
+from strato_query.base_API_query import *
+from strato_query.standard_filters import *
+
+
+# Finds median household income in the US for those 80+ from 2010 to 2013
+df = BaseAPIQuery.query_api_df(
+    query_params=APIMedianQueryParams(
+        query_type='MEDIAN',
+        table='incomeforecast_us_annual_income_group_age',
+        data_fields=('year', {'median_value': 'median_income'}),
+        median_variable_name='income_g',
+        data_filters=(
+            GtrThanOrEqFilter(var='age_g', val=17).to_dict(),
+            BetweenFilter(var='year', val=[2010, 2013]).to_dict(),
+        ),
+        groupby=('year',),
+        order=('year',),
+        aggregations=(),
+    )
+)
+
+print('Median US household income 80+:')
+print(df.head())
+```
+#### R:
+```R
+library(stRatoquery)
+
+
+# Finds median household income in the US for those 80+ from 2010 to 2013
+df = submit_api_query(
+  query = median_query_params(
+    table = 'incomeforecast_us_annual_income_group_age',
+    data_fields = api_fields(fields_list = list('year', 'geoid2', list(median_value = 'median_hhi'))),
+    data_filters = list(
+        ge_filter(filter_variable = 'age_g', filter_value = 17),
+        between_filter(filter_variable = 'year', filter_value = c(2010, 2013))
+    ),
+    groupby=c('year'),
+    median_variable_name='income_g',
+    aggregations=list()
+  ),
+  apiToken = 'my-api-token-here')
+
+print('Median US household income 80+:')
+print(head(df))
+```
+
+Output:
+```
+Median US household income 80+:
+   MEDIAN_VALUE  YEAR
+0         27645  2010
+1         29269  2011
+2         30474  2012
+3         30712  2013
+```
+
+### Population density in the Boston MSA
+#### Python:
+```python
+from strato_query.base_API_query import *
+from strato_query.standard_filters import *
+
+
+df = BaseAPIQuery.query_api_df(
+    query_params=APIQueryParams(
+        query_type='COUNT',
+        table='populationforecast_metro_annual_population',
+        data_fields=('year', 'cbsa', {'population': 'population'}),
+        data_filters=(
+            LessThanFilter(var='year', val=2015).to_dict(),
+            EqFilter(var='cbsa', val=14454).to_dict(),
+        ),
+        aggregations=(dict(aggregation_func='sum', variable_name='population'),),
+        groupby=('cbsa', 'year'),
+        order=('year',),
+        join=APIQueryParams(
+            query_type='AREA',
+            table='geocookbook_metro_na_shapes_full',
+            data_fields=('cbsa', 'area', 'name'),
+            data_filters=(),
+            groupby=('cbsa', 'name'),
+            aggregations=(),
+            on=dict(left=('cbsa',), right=('cbsa',)),
+        )
+    )
+)
+
+df['POP_PER_SQ_MI'] = df['POPULATION'].div(df['AREA'])
+df_final = df[['YEAR', 'NAME', 'POP_PER_SQ_MI']]
+
+print('Population density in the Boston MSA up to 2015:')
+print(df_final.head())
+print('Results truncated')
+```
+
+#### R:
+```R
+library(stRatoquery)
+
+df = submit_api_query(
+  query = api_query_params(
+    table = 'populationforecast_metro_annual_population',
+    data_fields = api_fields(fields_list = list('year', 'cbsa', list(population = 'population'))),
+    data_filters = list(
+        lt_filter(filter_variable = 'year', filter_value = 2015),
+        eq_filter(filter_variable = 'cbsa', filter_value = 14454)
+    ),
+    groupby=c('year'),
+    aggregations = list(sum_aggregation(variable_name = 'population')),
+    join = api_query_params(
+        table = 'geocookbook_metro_na_shapes_full',
+        query_type = 'AREA',
+        data_fields = api_fields(fields_list = list('cbsa', 'area', 'name')),
+        data_filters = list(),
+        groupby = c('cbsa', 'name'),
+        aggregations = list(),
+        on = list(left = c('cbsa'), right = c('cbsa'))
+    )
+  ),
+  apiToken = 'my-api-token-here')
+```
+
+Output:
+```
+Population density in the Boston MSA up to 2015:
+   YEAR        NAME  POP_PER_SQ_MI
+0  2000  Boston, MA    1139.046639
+1  2001  Boston, MA    1149.129937
+2  2002  Boston, MA    1153.094740
+3  2003  Boston, MA    1152.352351
+4  2004  Boston, MA    1149.932307
+Results truncated
+```
+
+### Example use of query base class with API call and example filter
+```python
+from strato_query.base_API_query import *
+from strato_query.standard_filters import *
+
+
+class ExampleAPIQuery(BaseAPIQuery):
+    @classmethod
+    def get_df_from_API_call(cls, **kwargs):
+        # This API call will return the population 65+ in 2018 within 5 miles of the lat/long pair
+        age_filter = GtrThanOrEqFilter(
+            var='age_g',
+            val=14).to_dict()
+
+        year_filter = EqFilter(
+            var='year',
+            val=2018).to_dict()
+
+        mile_radius_filter = dict(
+            filter_type='mile_radius',
+            filter_value=dict(
+                latitude=26.606484,
+                longitude=-81.851531,
+                miles=5),
+            filter_variable='')
+
+        df = cls.query_api_df(
+            query_params=APIQueryParams(
+                table='populationforecast_tract_annual_population_age',
+                data_fields=('POPULATION',),
+                data_filters=(age_filter, year_filter, mile_radius_filter),
+                query_type='COUNT',
+                aggregations=(),
+                groupby=()
+            )
+        )
+
+        return df
+```
+
+
+%package -n python3-strato-query
+Summary:	StratoDem Analytics API tools
+Provides:	python-strato-query
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-strato-query
+# Strato-Query
+tools to help create queries to StratoDem's API
+
+## Installation and usage
+#### Python:
+```
+$ pip install strato-query
+```
+
+#### R:
+```
+library(devtools)
+devtools::install_github('StratoDem/strato-query')
+```
+
+### Authentication
+`strato_query` looks for an `API_TOKEN` environment variable.
+```bash
+# Example passing a StratoDem Analytics API token to a Python file using the API
+$ API_TOKEN=my-api-token-here python examples/examples.py
+```
+
+### Median household income for 80+ households across the US, by year
+#### Python:
+```python
+from strato_query.base_API_query import *
+from strato_query.standard_filters import *
+
+
+# Finds median household income in the US for those 80+ from 2010 to 2013
+df = BaseAPIQuery.query_api_df(
+    query_params=APIMedianQueryParams(
+        query_type='MEDIAN',
+        table='incomeforecast_us_annual_income_group_age',
+        data_fields=('year', {'median_value': 'median_income'}),
+        median_variable_name='income_g',
+        data_filters=(
+            GtrThanOrEqFilter(var='age_g', val=17).to_dict(),
+            BetweenFilter(var='year', val=[2010, 2013]).to_dict(),
+        ),
+        groupby=('year',),
+        order=('year',),
+        aggregations=(),
+    )
+)
+
+print('Median US household income 80+:')
+print(df.head())
+```
+#### R:
+```R
+library(stRatoquery)
+
+
+# Finds median household income in the US for those 80+ from 2010 to 2013
+df = submit_api_query(
+  query = median_query_params(
+    table = 'incomeforecast_us_annual_income_group_age',
+    data_fields = api_fields(fields_list = list('year', 'geoid2', list(median_value = 'median_hhi'))),
+    data_filters = list(
+        ge_filter(filter_variable = 'age_g', filter_value = 17),
+        between_filter(filter_variable = 'year', filter_value = c(2010, 2013))
+    ),
+    groupby=c('year'),
+    median_variable_name='income_g',
+    aggregations=list()
+  ),
+  apiToken = 'my-api-token-here')
+
+print('Median US household income 80+:')
+print(head(df))
+```
+
+Output:
+```
+Median US household income 80+:
+   MEDIAN_VALUE  YEAR
+0         27645  2010
+1         29269  2011
+2         30474  2012
+3         30712  2013
+```
+
+### Population density in the Boston MSA
+#### Python:
+```python
+from strato_query.base_API_query import *
+from strato_query.standard_filters import *
+
+
+df = BaseAPIQuery.query_api_df(
+    query_params=APIQueryParams(
+        query_type='COUNT',
+        table='populationforecast_metro_annual_population',
+        data_fields=('year', 'cbsa', {'population': 'population'}),
+        data_filters=(
+            LessThanFilter(var='year', val=2015).to_dict(),
+            EqFilter(var='cbsa', val=14454).to_dict(),
+        ),
+        aggregations=(dict(aggregation_func='sum', variable_name='population'),),
+        groupby=('cbsa', 'year'),
+        order=('year',),
+        join=APIQueryParams(
+            query_type='AREA',
+            table='geocookbook_metro_na_shapes_full',
+            data_fields=('cbsa', 'area', 'name'),
+            data_filters=(),
+            groupby=('cbsa', 'name'),
+            aggregations=(),
+            on=dict(left=('cbsa',), right=('cbsa',)),
+        )
+    )
+)
+
+df['POP_PER_SQ_MI'] = df['POPULATION'].div(df['AREA'])
+df_final = df[['YEAR', 'NAME', 'POP_PER_SQ_MI']]
+
+print('Population density in the Boston MSA up to 2015:')
+print(df_final.head())
+print('Results truncated')
+```
+
+#### R:
+```R
+library(stRatoquery)
+
+df = submit_api_query(
+  query = api_query_params(
+    table = 'populationforecast_metro_annual_population',
+    data_fields = api_fields(fields_list = list('year', 'cbsa', list(population = 'population'))),
+    data_filters = list(
+        lt_filter(filter_variable = 'year', filter_value = 2015),
+        eq_filter(filter_variable = 'cbsa', filter_value = 14454)
+    ),
+    groupby=c('year'),
+    aggregations = list(sum_aggregation(variable_name = 'population')),
+    join = api_query_params(
+        table = 'geocookbook_metro_na_shapes_full',
+        query_type = 'AREA',
+        data_fields = api_fields(fields_list = list('cbsa', 'area', 'name')),
+        data_filters = list(),
+        groupby = c('cbsa', 'name'),
+        aggregations = list(),
+        on = list(left = c('cbsa'), right = c('cbsa'))
+    )
+  ),
+  apiToken = 'my-api-token-here')
+```
+
+Output:
+```
+Population density in the Boston MSA up to 2015:
+   YEAR        NAME  POP_PER_SQ_MI
+0  2000  Boston, MA    1139.046639
+1  2001  Boston, MA    1149.129937
+2  2002  Boston, MA    1153.094740
+3  2003  Boston, MA    1152.352351
+4  2004  Boston, MA    1149.932307
+Results truncated
+```
+
+### Example use of query base class with API call and example filter
+```python
+from strato_query.base_API_query import *
+from strato_query.standard_filters import *
+
+
+class ExampleAPIQuery(BaseAPIQuery):
+    @classmethod
+    def get_df_from_API_call(cls, **kwargs):
+        # This API call will return the population 65+ in 2018 within 5 miles of the lat/long pair
+        age_filter = GtrThanOrEqFilter(
+            var='age_g',
+            val=14).to_dict()
+
+        year_filter = EqFilter(
+            var='year',
+            val=2018).to_dict()
+
+        mile_radius_filter = dict(
+            filter_type='mile_radius',
+            filter_value=dict(
+                latitude=26.606484,
+                longitude=-81.851531,
+                miles=5),
+            filter_variable='')
+
+        df = cls.query_api_df(
+            query_params=APIQueryParams(
+                table='populationforecast_tract_annual_population_age',
+                data_fields=('POPULATION',),
+                data_filters=(age_filter, year_filter, mile_radius_filter),
+                query_type='COUNT',
+                aggregations=(),
+                groupby=()
+            )
+        )
+
+        return df
+```
+
+
+%package help
+Summary:	Development documents and examples for strato-query
+Provides:	python3-strato-query-doc
+%description help
+# Strato-Query
+tools to help create queries to StratoDem's API
+
+## Installation and usage
+#### Python:
+```
+$ pip install strato-query
+```
+
+#### R:
+```
+library(devtools)
+devtools::install_github('StratoDem/strato-query')
+```
+
+### Authentication
+`strato_query` looks for an `API_TOKEN` environment variable.
+```bash
+# Example passing a StratoDem Analytics API token to a Python file using the API
+$ API_TOKEN=my-api-token-here python examples/examples.py
+```
+
+### Median household income for 80+ households across the US, by year
+#### Python:
+```python
+from strato_query.base_API_query import *
+from strato_query.standard_filters import *
+
+
+# Finds median household income in the US for those 80+ from 2010 to 2013
+df = BaseAPIQuery.query_api_df(
+    query_params=APIMedianQueryParams(
+        query_type='MEDIAN',
+        table='incomeforecast_us_annual_income_group_age',
+        data_fields=('year', {'median_value': 'median_income'}),
+        median_variable_name='income_g',
+        data_filters=(
+            GtrThanOrEqFilter(var='age_g', val=17).to_dict(),
+            BetweenFilter(var='year', val=[2010, 2013]).to_dict(),
+        ),
+        groupby=('year',),
+        order=('year',),
+        aggregations=(),
+    )
+)
+
+print('Median US household income 80+:')
+print(df.head())
+```
+#### R:
+```R
+library(stRatoquery)
+
+
+# Finds median household income in the US for those 80+ from 2010 to 2013
+df = submit_api_query(
+  query = median_query_params(
+    table = 'incomeforecast_us_annual_income_group_age',
+    data_fields = api_fields(fields_list = list('year', 'geoid2', list(median_value = 'median_hhi'))),
+    data_filters = list(
+        ge_filter(filter_variable = 'age_g', filter_value = 17),
+        between_filter(filter_variable = 'year', filter_value = c(2010, 2013))
+    ),
+    groupby=c('year'),
+    median_variable_name='income_g',
+    aggregations=list()
+  ),
+  apiToken = 'my-api-token-here')
+
+print('Median US household income 80+:')
+print(head(df))
+```
+
+Output:
+```
+Median US household income 80+:
+   MEDIAN_VALUE  YEAR
+0         27645  2010
+1         29269  2011
+2         30474  2012
+3         30712  2013
+```
+
+### Population density in the Boston MSA
+#### Python:
+```python
+from strato_query.base_API_query import *
+from strato_query.standard_filters import *
+
+
+df = BaseAPIQuery.query_api_df(
+    query_params=APIQueryParams(
+        query_type='COUNT',
+        table='populationforecast_metro_annual_population',
+        data_fields=('year', 'cbsa', {'population': 'population'}),
+        data_filters=(
+            LessThanFilter(var='year', val=2015).to_dict(),
+            EqFilter(var='cbsa', val=14454).to_dict(),
+        ),
+        aggregations=(dict(aggregation_func='sum', variable_name='population'),),
+        groupby=('cbsa', 'year'),
+        order=('year',),
+        join=APIQueryParams(
+            query_type='AREA',
+            table='geocookbook_metro_na_shapes_full',
+            data_fields=('cbsa', 'area', 'name'),
+            data_filters=(),
+            groupby=('cbsa', 'name'),
+            aggregations=(),
+            on=dict(left=('cbsa',), right=('cbsa',)),
+        )
+    )
+)
+
+df['POP_PER_SQ_MI'] = df['POPULATION'].div(df['AREA'])
+df_final = df[['YEAR', 'NAME', 'POP_PER_SQ_MI']]
+
+print('Population density in the Boston MSA up to 2015:')
+print(df_final.head())
+print('Results truncated')
+```
+
+#### R:
+```R
+library(stRatoquery)
+
+df = submit_api_query(
+  query = api_query_params(
+    table = 'populationforecast_metro_annual_population',
+    data_fields = api_fields(fields_list = list('year', 'cbsa', list(population = 'population'))),
+    data_filters = list(
+        lt_filter(filter_variable = 'year', filter_value = 2015),
+        eq_filter(filter_variable = 'cbsa', filter_value = 14454)
+    ),
+    groupby=c('year'),
+    aggregations = list(sum_aggregation(variable_name = 'population')),
+    join = api_query_params(
+        table = 'geocookbook_metro_na_shapes_full',
+        query_type = 'AREA',
+        data_fields = api_fields(fields_list = list('cbsa', 'area', 'name')),
+        data_filters = list(),
+        groupby = c('cbsa', 'name'),
+        aggregations = list(),
+        on = list(left = c('cbsa'), right = c('cbsa'))
+    )
+  ),
+  apiToken = 'my-api-token-here')
+```
+
+Output:
+```
+Population density in the Boston MSA up to 2015:
+   YEAR        NAME  POP_PER_SQ_MI
+0  2000  Boston, MA    1139.046639
+1  2001  Boston, MA    1149.129937
+2  2002  Boston, MA    1153.094740
+3  2003  Boston, MA    1152.352351
+4  2004  Boston, MA    1149.932307
+Results truncated
+```
+
+### Example use of query base class with API call and example filter
+```python
+from strato_query.base_API_query import *
+from strato_query.standard_filters import *
+
+
+class ExampleAPIQuery(BaseAPIQuery):
+    @classmethod
+    def get_df_from_API_call(cls, **kwargs):
+        # This API call will return the population 65+ in 2018 within 5 miles of the lat/long pair
+        age_filter = GtrThanOrEqFilter(
+            var='age_g',
+            val=14).to_dict()
+
+        year_filter = EqFilter(
+            var='year',
+            val=2018).to_dict()
+
+        mile_radius_filter = dict(
+            filter_type='mile_radius',
+            filter_value=dict(
+                latitude=26.606484,
+                longitude=-81.851531,
+                miles=5),
+            filter_variable='')
+
+        df = cls.query_api_df(
+            query_params=APIQueryParams(
+                table='populationforecast_tract_annual_population_age',
+                data_fields=('POPULATION',),
+                data_filters=(age_filter, year_filter, mile_radius_filter),
+                query_type='COUNT',
+                aggregations=(),
+                groupby=()
+            )
+        )
+
+        return df
+```
+
+
+%prep
+%autosetup -n strato-query-3.10.2
+
+%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-strato-query -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 3.10.2-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..30aae78
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+02e416ab98720f3a2f4beae07ff5b1ef  strato_query-3.10.2.tar.gz