automatic import of python-federal

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diff --git a/.gitignore b/.gitignore
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+/Federal-1.0.1.tar.gz
diff --git a/python-federal.spec b/python-federal.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-Federal
+Version:	1.0.1
+Release:	1
+Summary:	A wrapper on to the pandas-datareader package for easier handling of federal reserve (FRED) data
+License:	MIT License
+URL:		https://github.com/Jaseibert/Federal
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/0e/e2/5d1ca569625194e5fd55b880fb38879f3829231786cc9db151e5ccd16ef3/Federal-1.0.1.tar.gz
+BuildArch:	noarch
+
+
+%description
+# Federal Package
+
+This is a simple module built on top of Pandas-Datareader to make it easer to pull in Federal Reserve Data from the Federal Reserve in St. Louis (FRED).
+
+## Installation
+
+`pip install Federal`
+
+## Basic Usage:
+
+```python
+
+# Import the GDP and DateFormatter Modules
+from Federal.Econ import GDP
+from Federal.Formatter import DateFormatter
+
+#Insatiate the GDP and DateFormatter Objects
+g = GDP()
+d = DateFormatter()
+
+#Set your Start & End Dates
+d.start_date(1900,1,1)
+d.end_date(2018,1,1)
+
+# Make the Call
+df = g.metro_gdp(name='Houston')
+df.head()
+```
+
+## Setting Start & End Dates
+
+Once imported, you declare the start date and end date via the `DateFormatter.start_date()` and `DateFormatter.end_date()` functions. These functions define the range of dates for the data that you are for. Once declared these values will be applied to each query unless explicitly changed.
+
+There are several different DateTime format variants that the `DateFormatter.start_date()` and `DateFormatter.end_date()` functions accept:
+
+1. DateTime format: (Year, Month, Day): **Integer**
+```python
+# Import the DateFormatter Modules
+from Federal.Formatter import DateFormatter
+
+#Insatiate the DateFormatter Object
+d = DateFormatter()
+
+d.start_date(1900,1,1)
+d.end_date(2018,1,1)
+```
+
+2. DateTime format: (Day/Month/Year): **String**
+```python
+# Import the DateFormatter Modules
+from Federal.Formatter import DateFormatter
+
+#Insatiate the DateFormatter Object
+d = DateFormatter()
+
+d.start_date('1/1/1900')
+d.end_date('1/1/2018')
+```
+
+3. DateTime format: (Day-Month-Year): **String**
+```python
+# Import the DateFormatter Modules
+from Federal.Formatter import DateFormatter
+
+#Insatiate the DateFormatter Object
+d = DateFormatter()
+
+d.start_date('1-1-1900')
+d.end_date('1-1-2018')
+```
+
+4. DateTime format: (Day.Month.Year): **String**
+```python
+# Import the DateFormatter Modules
+from Federal.Formatter import DateFormatter
+
+#Insatiate the DateFormatter Object
+d = DateFormatter()
+
+d.start_date('1.1.1900')
+d.end_date('1.1.2018')
+```
+
+5. DateTime format: (Month/Day/Year): **String**
+```python
+# Import the DateFormatter Modules
+from Federal.Formatter import DateFormatter
+
+#Insatiate the DateFormatter Object
+d = DateFormatter()
+
+d.start_date('14/1/1900')
+d.end_date('16/1/2018')
+```
+
+### National Gross Domestic Product (GDP)
+
+After instantiating a FRED object, and defining the start and end dates using the `GDP.start_date()` and `GDP.end_date()` functions you can use the function `GDP.national_gdp()` depending on its parameters to return either nominal GDP or real GDP.
+
+```python
+# Import the GDP and DateFormatter Modules
+from Federal.Econ import GDP
+from Federal.Formatter import DateFormatter
+
+#Insatiate the GDP and DateFormatter Objects
+g = GDP()
+d = DateFormatter()
+
+#Set Dates
+d.start_date('1/1/1900')
+d.end_date('1/1/2018')
+
+# Real GDP
+df = g.national_gdp()
+df.head()
+
+# Nominal GDP
+df = g.national_gdp(nominal=True)
+df.head()
+```
+
+### State Gross Domestic Product (GSP)
+
+Similar to the `GDP.national_gdp()` after making the necessary calls you can pull in information around State-Level GDP using the `GDP.state_gdp()` function. It requires one argument, which is the two-character string representing the state of interest. In the case below we pull the state GDP for Indiana.
+
+```python
+# Import the GDP and DateFormatter Modules
+from Federal.Econ import GDP
+from Federal.Formatter import DateFormatter
+
+#Insatiate the GDP and DateFormatter Objects
+g = GDP()
+d = DateFormatter()
+
+#Set the Dates
+d.start_date('1/1/1900')
+d.end_date('1/1/2018')
+
+# State GDP
+df = g.state_gdp('IN')
+df.head()
+```
+
+### Metropolitan Gross Domestic Product (GMP)
+
+The final variation of GDP that the FRED module pulls in is Metropolitan-Level GDP. The Federal Reserve uses Core-Based Statistical Area (CBSA) Codes to define each metro within their API. Here using the `GDP.metro_gdp()` function you can either pass the CBSA code or a name of a metro area as arguments within the function. Beyond this, similar to national GDP, by passing `GDP.metro_gdp(name='<Any Metro Name>',nominal=True)` with nominal equal to True, the function will return the nominal metro GDP.
+
+
+```python
+# Import the GDP and DateFormatter Modules
+from Federal.Econ import GDP
+from Federal.Formatter import DateFormatter
+
+#Insatiate the GDP and DateFormatter Objects
+g = GDP()
+d = DateFormatter()
+
+#Set the Dates
+d.start_date('1/1/1900')
+d.end_date('1/1/2018')
+
+# Metropolitan GDP - Passing the City Name as an argument
+df = g.metro_gdp(name='Houston')
+df.head()
+
+# Metropolitan GDP - Passing the CBSA code as an argument
+df = g.metro_gdp(cbsa=26420)
+df.head()
+
+# Metropolitan GDP - nominal
+df = g.metro_gdp(cbsa=26420, nominal=True)
+df.head()
+
+# Metropolitan GDP - nominal
+df = g.metro_gdp(name='Houston', nominal=True)
+df.head()
+```
+### National Unemployment
+
+Unemployment is defined by the `Unemployment.national_unemp()` function. This function takes an argument `sa` which returns either seasonally-adjusted non seasonally-adjusted unemployment.
+
+```python
+# Import the GDP and DateFormatter Modules
+from Federal.Econ import Unemployment
+from Federal.Formatter import DateFormatter
+
+#Insatiate the GDP and DateFormatter Objects
+u = Unemployment()
+d = DateFormatter()
+
+#Set the Dates
+d.start_date('1/1/1900')
+d.end_date('1/1/2018')
+
+# Seasonally-Adjusted National Unemployment
+df = u.national_unemp(sa=True)
+df.head()
+
+# Non Seasonally-Adjusted National Unemployment
+df = u.national_unemp(sa=False)
+df.head()
+
+
+
+
+%package -n python3-Federal
+Summary:	A wrapper on to the pandas-datareader package for easier handling of federal reserve (FRED) data
+Provides:	python-Federal
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-Federal
+# Federal Package
+
+This is a simple module built on top of Pandas-Datareader to make it easer to pull in Federal Reserve Data from the Federal Reserve in St. Louis (FRED).
+
+## Installation
+
+`pip install Federal`
+
+## Basic Usage:
+
+```python
+
+# Import the GDP and DateFormatter Modules
+from Federal.Econ import GDP
+from Federal.Formatter import DateFormatter
+
+#Insatiate the GDP and DateFormatter Objects
+g = GDP()
+d = DateFormatter()
+
+#Set your Start & End Dates
+d.start_date(1900,1,1)
+d.end_date(2018,1,1)
+
+# Make the Call
+df = g.metro_gdp(name='Houston')
+df.head()
+```
+
+## Setting Start & End Dates
+
+Once imported, you declare the start date and end date via the `DateFormatter.start_date()` and `DateFormatter.end_date()` functions. These functions define the range of dates for the data that you are for. Once declared these values will be applied to each query unless explicitly changed.
+
+There are several different DateTime format variants that the `DateFormatter.start_date()` and `DateFormatter.end_date()` functions accept:
+
+1. DateTime format: (Year, Month, Day): **Integer**
+```python
+# Import the DateFormatter Modules
+from Federal.Formatter import DateFormatter
+
+#Insatiate the DateFormatter Object
+d = DateFormatter()
+
+d.start_date(1900,1,1)
+d.end_date(2018,1,1)
+```
+
+2. DateTime format: (Day/Month/Year): **String**
+```python
+# Import the DateFormatter Modules
+from Federal.Formatter import DateFormatter
+
+#Insatiate the DateFormatter Object
+d = DateFormatter()
+
+d.start_date('1/1/1900')
+d.end_date('1/1/2018')
+```
+
+3. DateTime format: (Day-Month-Year): **String**
+```python
+# Import the DateFormatter Modules
+from Federal.Formatter import DateFormatter
+
+#Insatiate the DateFormatter Object
+d = DateFormatter()
+
+d.start_date('1-1-1900')
+d.end_date('1-1-2018')
+```
+
+4. DateTime format: (Day.Month.Year): **String**
+```python
+# Import the DateFormatter Modules
+from Federal.Formatter import DateFormatter
+
+#Insatiate the DateFormatter Object
+d = DateFormatter()
+
+d.start_date('1.1.1900')
+d.end_date('1.1.2018')
+```
+
+5. DateTime format: (Month/Day/Year): **String**
+```python
+# Import the DateFormatter Modules
+from Federal.Formatter import DateFormatter
+
+#Insatiate the DateFormatter Object
+d = DateFormatter()
+
+d.start_date('14/1/1900')
+d.end_date('16/1/2018')
+```
+
+### National Gross Domestic Product (GDP)
+
+After instantiating a FRED object, and defining the start and end dates using the `GDP.start_date()` and `GDP.end_date()` functions you can use the function `GDP.national_gdp()` depending on its parameters to return either nominal GDP or real GDP.
+
+```python
+# Import the GDP and DateFormatter Modules
+from Federal.Econ import GDP
+from Federal.Formatter import DateFormatter
+
+#Insatiate the GDP and DateFormatter Objects
+g = GDP()
+d = DateFormatter()
+
+#Set Dates
+d.start_date('1/1/1900')
+d.end_date('1/1/2018')
+
+# Real GDP
+df = g.national_gdp()
+df.head()
+
+# Nominal GDP
+df = g.national_gdp(nominal=True)
+df.head()
+```
+
+### State Gross Domestic Product (GSP)
+
+Similar to the `GDP.national_gdp()` after making the necessary calls you can pull in information around State-Level GDP using the `GDP.state_gdp()` function. It requires one argument, which is the two-character string representing the state of interest. In the case below we pull the state GDP for Indiana.
+
+```python
+# Import the GDP and DateFormatter Modules
+from Federal.Econ import GDP
+from Federal.Formatter import DateFormatter
+
+#Insatiate the GDP and DateFormatter Objects
+g = GDP()
+d = DateFormatter()
+
+#Set the Dates
+d.start_date('1/1/1900')
+d.end_date('1/1/2018')
+
+# State GDP
+df = g.state_gdp('IN')
+df.head()
+```
+
+### Metropolitan Gross Domestic Product (GMP)
+
+The final variation of GDP that the FRED module pulls in is Metropolitan-Level GDP. The Federal Reserve uses Core-Based Statistical Area (CBSA) Codes to define each metro within their API. Here using the `GDP.metro_gdp()` function you can either pass the CBSA code or a name of a metro area as arguments within the function. Beyond this, similar to national GDP, by passing `GDP.metro_gdp(name='<Any Metro Name>',nominal=True)` with nominal equal to True, the function will return the nominal metro GDP.
+
+
+```python
+# Import the GDP and DateFormatter Modules
+from Federal.Econ import GDP
+from Federal.Formatter import DateFormatter
+
+#Insatiate the GDP and DateFormatter Objects
+g = GDP()
+d = DateFormatter()
+
+#Set the Dates
+d.start_date('1/1/1900')
+d.end_date('1/1/2018')
+
+# Metropolitan GDP - Passing the City Name as an argument
+df = g.metro_gdp(name='Houston')
+df.head()
+
+# Metropolitan GDP - Passing the CBSA code as an argument
+df = g.metro_gdp(cbsa=26420)
+df.head()
+
+# Metropolitan GDP - nominal
+df = g.metro_gdp(cbsa=26420, nominal=True)
+df.head()
+
+# Metropolitan GDP - nominal
+df = g.metro_gdp(name='Houston', nominal=True)
+df.head()
+```
+### National Unemployment
+
+Unemployment is defined by the `Unemployment.national_unemp()` function. This function takes an argument `sa` which returns either seasonally-adjusted non seasonally-adjusted unemployment.
+
+```python
+# Import the GDP and DateFormatter Modules
+from Federal.Econ import Unemployment
+from Federal.Formatter import DateFormatter
+
+#Insatiate the GDP and DateFormatter Objects
+u = Unemployment()
+d = DateFormatter()
+
+#Set the Dates
+d.start_date('1/1/1900')
+d.end_date('1/1/2018')
+
+# Seasonally-Adjusted National Unemployment
+df = u.national_unemp(sa=True)
+df.head()
+
+# Non Seasonally-Adjusted National Unemployment
+df = u.national_unemp(sa=False)
+df.head()
+
+
+
+
+%package help
+Summary:	Development documents and examples for Federal
+Provides:	python3-Federal-doc
+%description help
+# Federal Package
+
+This is a simple module built on top of Pandas-Datareader to make it easer to pull in Federal Reserve Data from the Federal Reserve in St. Louis (FRED).
+
+## Installation
+
+`pip install Federal`
+
+## Basic Usage:
+
+```python
+
+# Import the GDP and DateFormatter Modules
+from Federal.Econ import GDP
+from Federal.Formatter import DateFormatter
+
+#Insatiate the GDP and DateFormatter Objects
+g = GDP()
+d = DateFormatter()
+
+#Set your Start & End Dates
+d.start_date(1900,1,1)
+d.end_date(2018,1,1)
+
+# Make the Call
+df = g.metro_gdp(name='Houston')
+df.head()
+```
+
+## Setting Start & End Dates
+
+Once imported, you declare the start date and end date via the `DateFormatter.start_date()` and `DateFormatter.end_date()` functions. These functions define the range of dates for the data that you are for. Once declared these values will be applied to each query unless explicitly changed.
+
+There are several different DateTime format variants that the `DateFormatter.start_date()` and `DateFormatter.end_date()` functions accept:
+
+1. DateTime format: (Year, Month, Day): **Integer**
+```python
+# Import the DateFormatter Modules
+from Federal.Formatter import DateFormatter
+
+#Insatiate the DateFormatter Object
+d = DateFormatter()
+
+d.start_date(1900,1,1)
+d.end_date(2018,1,1)
+```
+
+2. DateTime format: (Day/Month/Year): **String**
+```python
+# Import the DateFormatter Modules
+from Federal.Formatter import DateFormatter
+
+#Insatiate the DateFormatter Object
+d = DateFormatter()
+
+d.start_date('1/1/1900')
+d.end_date('1/1/2018')
+```
+
+3. DateTime format: (Day-Month-Year): **String**
+```python
+# Import the DateFormatter Modules
+from Federal.Formatter import DateFormatter
+
+#Insatiate the DateFormatter Object
+d = DateFormatter()
+
+d.start_date('1-1-1900')
+d.end_date('1-1-2018')
+```
+
+4. DateTime format: (Day.Month.Year): **String**
+```python
+# Import the DateFormatter Modules
+from Federal.Formatter import DateFormatter
+
+#Insatiate the DateFormatter Object
+d = DateFormatter()
+
+d.start_date('1.1.1900')
+d.end_date('1.1.2018')
+```
+
+5. DateTime format: (Month/Day/Year): **String**
+```python
+# Import the DateFormatter Modules
+from Federal.Formatter import DateFormatter
+
+#Insatiate the DateFormatter Object
+d = DateFormatter()
+
+d.start_date('14/1/1900')
+d.end_date('16/1/2018')
+```
+
+### National Gross Domestic Product (GDP)
+
+After instantiating a FRED object, and defining the start and end dates using the `GDP.start_date()` and `GDP.end_date()` functions you can use the function `GDP.national_gdp()` depending on its parameters to return either nominal GDP or real GDP.
+
+```python
+# Import the GDP and DateFormatter Modules
+from Federal.Econ import GDP
+from Federal.Formatter import DateFormatter
+
+#Insatiate the GDP and DateFormatter Objects
+g = GDP()
+d = DateFormatter()
+
+#Set Dates
+d.start_date('1/1/1900')
+d.end_date('1/1/2018')
+
+# Real GDP
+df = g.national_gdp()
+df.head()
+
+# Nominal GDP
+df = g.national_gdp(nominal=True)
+df.head()
+```
+
+### State Gross Domestic Product (GSP)
+
+Similar to the `GDP.national_gdp()` after making the necessary calls you can pull in information around State-Level GDP using the `GDP.state_gdp()` function. It requires one argument, which is the two-character string representing the state of interest. In the case below we pull the state GDP for Indiana.
+
+```python
+# Import the GDP and DateFormatter Modules
+from Federal.Econ import GDP
+from Federal.Formatter import DateFormatter
+
+#Insatiate the GDP and DateFormatter Objects
+g = GDP()
+d = DateFormatter()
+
+#Set the Dates
+d.start_date('1/1/1900')
+d.end_date('1/1/2018')
+
+# State GDP
+df = g.state_gdp('IN')
+df.head()
+```
+
+### Metropolitan Gross Domestic Product (GMP)
+
+The final variation of GDP that the FRED module pulls in is Metropolitan-Level GDP. The Federal Reserve uses Core-Based Statistical Area (CBSA) Codes to define each metro within their API. Here using the `GDP.metro_gdp()` function you can either pass the CBSA code or a name of a metro area as arguments within the function. Beyond this, similar to national GDP, by passing `GDP.metro_gdp(name='<Any Metro Name>',nominal=True)` with nominal equal to True, the function will return the nominal metro GDP.
+
+
+```python
+# Import the GDP and DateFormatter Modules
+from Federal.Econ import GDP
+from Federal.Formatter import DateFormatter
+
+#Insatiate the GDP and DateFormatter Objects
+g = GDP()
+d = DateFormatter()
+
+#Set the Dates
+d.start_date('1/1/1900')
+d.end_date('1/1/2018')
+
+# Metropolitan GDP - Passing the City Name as an argument
+df = g.metro_gdp(name='Houston')
+df.head()
+
+# Metropolitan GDP - Passing the CBSA code as an argument
+df = g.metro_gdp(cbsa=26420)
+df.head()
+
+# Metropolitan GDP - nominal
+df = g.metro_gdp(cbsa=26420, nominal=True)
+df.head()
+
+# Metropolitan GDP - nominal
+df = g.metro_gdp(name='Houston', nominal=True)
+df.head()
+```
+### National Unemployment
+
+Unemployment is defined by the `Unemployment.national_unemp()` function. This function takes an argument `sa` which returns either seasonally-adjusted non seasonally-adjusted unemployment.
+
+```python
+# Import the GDP and DateFormatter Modules
+from Federal.Econ import Unemployment
+from Federal.Formatter import DateFormatter
+
+#Insatiate the GDP and DateFormatter Objects
+u = Unemployment()
+d = DateFormatter()
+
+#Set the Dates
+d.start_date('1/1/1900')
+d.end_date('1/1/2018')
+
+# Seasonally-Adjusted National Unemployment
+df = u.national_unemp(sa=True)
+df.head()
+
+# Non Seasonally-Adjusted National Unemployment
+df = u.national_unemp(sa=False)
+df.head()
+
+
+
+
+%prep
+%autosetup -n Federal-1.0.1
+
+%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-Federal -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..632e7de
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+1f1ac68de15ac8cb6aab4a96c1e8b6b3  Federal-1.0.1.tar.gz