automatic import of python-guspiopeneuler20.03

3 files changed, 1426 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..ab14e9b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/GusPI-0.0.59.tar.gz
diff --git a/python-guspi.spec b/python-guspi.spec
new file mode 100644
index 0000000..14fcf47
--- /dev/null
+++ b/python-guspi.spec
@@ -0,0 +1,1424 @@
+%global _empty_manifest_terminate_build 0
+Name:		python-GusPI
+Version:	0.0.59
+Release:	1
+Summary:	This open-source python package aims to provide statistical support in supply chain analytics and finance/accounting analytics. We welcome everyone to use this python package for personal or professional projects. Please let us know any feedback you have. We'd love to improve the package and add feature enhancements to benefit researchers.
+License:	MIT License
+URL:		https://github.com/ygeszvain/GusPI
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/dd/ae/761427fd449e583968237358fc0ed1aa8a4109a21424002ab7d247500711/GusPI-0.0.59.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-pandas
+Requires:	python3-matplotlib
+Requires:	python3-seaborn
+Requires:	python3-plotly
+Requires:	python3-numpy
+Requires:	python3-beautifulsoup4
+Requires:	python3-simfin
+Requires:	python3-scipy
+
+%description
+## GusPI
+This open-source python package aims to provide statistical support in supply chain analytics and finance/accounting analytics. We welcome everyone to use this python package for personal or professional projects. Please let us know any feedback you have. We'd love to improve the package and add feature enhancements to benefit researchers.
+
+Report any bugs by opening an issue here: https://github.com/ygeszvain/GusPI/issues
+
+Quick start
+
+```
+$ python3 -m pip install -U GusPI
+```
+
+## Templates
+### Templates for suPY
+
+[SalesData.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/SalesData.csv)
+
+### Templates for fiPY
+
+[income_statement.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/income_statement.csv)
+
+[income_statement_yr.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/income_statement_yr.csv)
+
+[income_statement_m.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/income_statement_m.csv)
+
+[balancesheet.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/balancesheet.csv)
+
+[balance_sheet_yr.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/balance_sheet_yr.csv)
+
+[cashflow.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/cashflow.csv)
+
+## Demo notebook
+
+[demo](https://colab.research.google.com/drive/1qc1ZuvbgWPLCrSP3z-8Umj4FYJSiViq8?usp=sharing)
+
+## GusPI.suPY
+
+```
+from GusPI import suPY
+```
+
+### metrics
+
+This package provides several analytical formulas to support supply chain analytics.
+
+Economic order quantity
+EOQ(demand, mean, STD, C, Ce, Cs, Ct)
+
+Perfect Order Measurement
+POM(TotalOrders, ErrorOrders)
+
+Fill Rate
+FR(TotalItems, ShippedItems)
+
+Inventory Days of Supply
+IDS(InventoryOnHand,AvgDailyUsage)
+
+Freight cost per unit
+FCU(TotalFreightCost,NumberOfItems)
+
+Inventory Turnover
+IT(COGS,AvgInventory)
+
+Days of Supply (DOS)
+DOS(AvgInventory,MonthlyDemand)
+
+Gross Margin Return on Investment (GMROI)
+GMROI(GrossProfit, OpeningStock, ClosingStock)
+
+Inventory Accuracy
+IA(ItemCounts, TotalItemCounts)
+
+Storage Utilization Rate
+SUR(InventoryCube, TotalWarehouseCube)
+
+Total Order Cycle Time
+TOCT(TimeOrderReceivedbyCustomer, TimeOrderPlaced,TotalNumberofOrdersShipped)
+
+Internal Order Cycle Time
+IOCT(TimeOrderShipped, TimeOrderReceived, NumberofOrdersShipped)
+
+Read sales data from csv file and calculate basic safety sock and reorder point.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#safety days: 5
+#leadtime in days: 7
+
+suPy.basicSafetyStock('SalesData.csv','ProductNumber',5,7)
+```
+
+Read sales data from csv file and calculate basic safety sock and reorder point for all products.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#safety days: 5
+#leadtime in days: 7
+
+suPy.basicSafetyStockList('SalesData.csv',5,7)
+```
+
+Read sales data from csv file and calculate safety sock and reorder point.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#service rate: 0.95
+#leadtime in days: 7
+
+suPy.safetyStockwtServiceRate('SalesData.csv','ProductNumber',0.95,7)
+```
+
+Read sales data from csv file and calculate basic safety sock and reorder point for all products.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#service rate: 0.95
+#leadtime in days: 7
+
+suPy.safetyStockwtServiceRateList('SalesData.csv',0.95,7)
+```
+
+Read sales data from csv file and calculate coefficient of variation of a product.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#CV is non-negative and higher CV indicates higher volatility
+
+suPy.cvPerProduct('SalesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and calculate 'Intercept', 'Slope', 'Mean Absolute Error', 'Mean Squared Error', 'Root Mean Squared Error' of a product.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+
+suPy.linearRegressionPerProduct('SalesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and calculate EOQ of a product.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#Setup cost: 2000
+#Holding cost: 1000
+
+suPy.eoqPerProduct('SalesData.csv','ProductNumber',2000,1000)
+```
+
+Read sales data from csv file and create a list of average quantity sold per year for products.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+
+suPy.avgQtySoldList('SalesData.csv')
+```
+
+Read sales data from csv file and calculate the seasonality index of a product for a given year.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#year: 2018
+
+suPy.seasonalityIndexPerProduct('SalesData.csv','ProductNumber',2018)
+```
+
+### graphs
+
+Read sales data from csv file and print out a line plot of a product quantity sold.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+
+#print the line plot
+suPy.line plotQtyByMonth('salesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and print out a line plot of a product's total cost sold.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+
+#print the line plot
+suPy.line plotTotalCostByMonth('salesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and print out a line plot of a product's total sales.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+
+#print the line plot
+suPy.line plotTotalSalesByMonth('salesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and print out a line plot of a product's average cost.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+
+#print the line plot
+suPy.line plotAverageCostByMonth('salesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and print out a line plot of a product's average sales.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+
+#print the line plot
+suPy.line plotAverageSalesPriceByMonth('salesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and print out sales forecast for a product.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#length in month for the prediction: 12
+
+#print the metrics and line plot
+suPy.forecastQtyMonthlySales('SalesData.csv','ProductNumber',12)
+```
+
+Read sales data from csv file and print out pricing forecast for a product.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#length in month for the prediction: 12
+
+#print the metrics and line plot
+suPy.forecastMonthlyPrice('SalesData.csv','ProductNumber',12)
+```
+
+Read sales data from csv file and print out cost forecast for a product.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#length in month for the prediction: 12
+
+#print the metrics and line plot
+suPy.forecastMonthlyCost('SalesData.csv','ProductNumber',12)
+```
+
+## GusPI.finPy
+
+```
+from GusPI import finPy
+```
+
+Get public financial data with simfin - annual income statements.
+
+```
+#Example
+
+#country: us
+
+#get annual income statements and return a dataframe
+finPy.get_annual_finData_income(country)
+```
+
+Get public financial data with simfin - annual balancesheet.
+
+```
+#Example
+
+#country: us
+
+#get annual balancesheet and return a dataframe
+finPy.get_annual_finData_balance(country)
+```
+
+Get public financial data with simfin - annual cashflow statements.
+
+```
+#Example
+
+#country: us
+
+#get annual cashflow statements and return a dataframe
+finPy.get_annual_finData_cashflow(country)
+```
+
+Get public financial data with simfin - annual cashflow statements.
+
+```
+#Example
+
+#category: income, balancesheet, or cashflow
+#symbol: 'MSFT', 'AAPL'...
+#country: us
+
+#get annual cashflow statements and return a dataframe
+finPy.getannual_finData_by_symbol(category,symbol,country)
+```
+
+Read financial statements from csv files and provide a line chart for analysis.
+
+```
+#Example
+
+#dataframe for statements
+#category from the dataframe such as revenue
+
+#print line plots
+finPy.lineplot(dataframe, '3 year BalanceSheet Graph')
+```
+
+Read financial statements from csv files and provide multiple line charts for analysis.
+
+```
+#Example
+
+#dataframe for statements
+
+#print multiple line plots
+finPy.multiLineplot(dataframe, '3 year BalanceSheet Graph')
+```
+
+Read financial statements from csv files and provide financial metrics for analysis.
+
+```
+#Example
+
+#dataframe from a csv file: balance_sheet_yr.csv
+#dataframe from a csv file: income_statement_3yr.csv
+
+#print financial metrics
+finPy.calculateMetrics(df_balancesheet,df_income)
+```
+
+Get financial statements for a list of company symbols and provide financial metrics for analysis.
+
+```
+#Example
+
+#symbols = ['AAPL', 'MSFT', 'FIS']
+#mass = calculate_ratio_mass(symbols)
+
+#get financial matrics for multiple companies
+finPy.calculate_ratio_mass(symbols)
+```
+
+Read financial statements from csv files and provide horizontal analysis for the last two periods.
+
+```
+#Example
+
+#dataframe from a csv file: balance_sheet_yr.csv
+
+#print financial metrics
+finPy.horizontalAnalysisLastTwo(dataframe)
+```
+
+## GusPI.statsPy
+
+```
+#Example
+
+#perform Benford's Law anamoly detection
+#dataframe from a csv file: GLACCT_sample.csv
+
+df = pd.read_csv("GLACCT_sample.csv")
+# (dataframe,colname to perform detection,target_colname,target_value)
+value_arr = statsPy.init_benfordlaw(df, 'TotalAmount', 'GLACCT', '11111')
+result = statsPy.process_benfordlaw(value_arr, alpha=0.3)
+statsPy.plot_benfordlaw(result)
+```
+
+## GusPI.scraper
+
+The scrape package provides an easy way to scrape Yelp business info and Yelp reviews for a specific business.
+
+```
+from GusPI import scraper
+```
+
+YelpBizInfo
+The function collects business info and save it into a csv file.
+
+```
+#Example
+
+#declare a list: https://www.yelp.com/biz/`artisan-ramen-milwaukee`
+CUISINES = ['artisan-ramen-milwaukee','red-light-ramen-milwaukee-5']
+
+#scrape the business info
+scraper.YelpBizInfo(CUISINES)
+```
+
+YelpReview
+The function collects reviews for respective business and save them into separate files by business names.
+```
+#Example
+
+#declare a list: https://www.yelp.com/biz/`artisan-ramen-milwaukee`
+CUISINES = ['artisan-ramen-milwaukee','red-light-ramen-milwaukee-5']
+
+#scrape the business info
+scraper.YelpReview(CUISINES)
+```
+
+
+
+
+%package -n python3-GusPI
+Summary:	This open-source python package aims to provide statistical support in supply chain analytics and finance/accounting analytics. We welcome everyone to use this python package for personal or professional projects. Please let us know any feedback you have. We'd love to improve the package and add feature enhancements to benefit researchers.
+Provides:	python-GusPI
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-GusPI
+## GusPI
+This open-source python package aims to provide statistical support in supply chain analytics and finance/accounting analytics. We welcome everyone to use this python package for personal or professional projects. Please let us know any feedback you have. We'd love to improve the package and add feature enhancements to benefit researchers.
+
+Report any bugs by opening an issue here: https://github.com/ygeszvain/GusPI/issues
+
+Quick start
+
+```
+$ python3 -m pip install -U GusPI
+```
+
+## Templates
+### Templates for suPY
+
+[SalesData.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/SalesData.csv)
+
+### Templates for fiPY
+
+[income_statement.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/income_statement.csv)
+
+[income_statement_yr.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/income_statement_yr.csv)
+
+[income_statement_m.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/income_statement_m.csv)
+
+[balancesheet.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/balancesheet.csv)
+
+[balance_sheet_yr.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/balance_sheet_yr.csv)
+
+[cashflow.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/cashflow.csv)
+
+## Demo notebook
+
+[demo](https://colab.research.google.com/drive/1qc1ZuvbgWPLCrSP3z-8Umj4FYJSiViq8?usp=sharing)
+
+## GusPI.suPY
+
+```
+from GusPI import suPY
+```
+
+### metrics
+
+This package provides several analytical formulas to support supply chain analytics.
+
+Economic order quantity
+EOQ(demand, mean, STD, C, Ce, Cs, Ct)
+
+Perfect Order Measurement
+POM(TotalOrders, ErrorOrders)
+
+Fill Rate
+FR(TotalItems, ShippedItems)
+
+Inventory Days of Supply
+IDS(InventoryOnHand,AvgDailyUsage)
+
+Freight cost per unit
+FCU(TotalFreightCost,NumberOfItems)
+
+Inventory Turnover
+IT(COGS,AvgInventory)
+
+Days of Supply (DOS)
+DOS(AvgInventory,MonthlyDemand)
+
+Gross Margin Return on Investment (GMROI)
+GMROI(GrossProfit, OpeningStock, ClosingStock)
+
+Inventory Accuracy
+IA(ItemCounts, TotalItemCounts)
+
+Storage Utilization Rate
+SUR(InventoryCube, TotalWarehouseCube)
+
+Total Order Cycle Time
+TOCT(TimeOrderReceivedbyCustomer, TimeOrderPlaced,TotalNumberofOrdersShipped)
+
+Internal Order Cycle Time
+IOCT(TimeOrderShipped, TimeOrderReceived, NumberofOrdersShipped)
+
+Read sales data from csv file and calculate basic safety sock and reorder point.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#safety days: 5
+#leadtime in days: 7
+
+suPy.basicSafetyStock('SalesData.csv','ProductNumber',5,7)
+```
+
+Read sales data from csv file and calculate basic safety sock and reorder point for all products.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#safety days: 5
+#leadtime in days: 7
+
+suPy.basicSafetyStockList('SalesData.csv',5,7)
+```
+
+Read sales data from csv file and calculate safety sock and reorder point.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#service rate: 0.95
+#leadtime in days: 7
+
+suPy.safetyStockwtServiceRate('SalesData.csv','ProductNumber',0.95,7)
+```
+
+Read sales data from csv file and calculate basic safety sock and reorder point for all products.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#service rate: 0.95
+#leadtime in days: 7
+
+suPy.safetyStockwtServiceRateList('SalesData.csv',0.95,7)
+```
+
+Read sales data from csv file and calculate coefficient of variation of a product.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#CV is non-negative and higher CV indicates higher volatility
+
+suPy.cvPerProduct('SalesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and calculate 'Intercept', 'Slope', 'Mean Absolute Error', 'Mean Squared Error', 'Root Mean Squared Error' of a product.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+
+suPy.linearRegressionPerProduct('SalesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and calculate EOQ of a product.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#Setup cost: 2000
+#Holding cost: 1000
+
+suPy.eoqPerProduct('SalesData.csv','ProductNumber',2000,1000)
+```
+
+Read sales data from csv file and create a list of average quantity sold per year for products.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+
+suPy.avgQtySoldList('SalesData.csv')
+```
+
+Read sales data from csv file and calculate the seasonality index of a product for a given year.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#year: 2018
+
+suPy.seasonalityIndexPerProduct('SalesData.csv','ProductNumber',2018)
+```
+
+### graphs
+
+Read sales data from csv file and print out a line plot of a product quantity sold.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+
+#print the line plot
+suPy.line plotQtyByMonth('salesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and print out a line plot of a product's total cost sold.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+
+#print the line plot
+suPy.line plotTotalCostByMonth('salesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and print out a line plot of a product's total sales.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+
+#print the line plot
+suPy.line plotTotalSalesByMonth('salesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and print out a line plot of a product's average cost.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+
+#print the line plot
+suPy.line plotAverageCostByMonth('salesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and print out a line plot of a product's average sales.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+
+#print the line plot
+suPy.line plotAverageSalesPriceByMonth('salesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and print out sales forecast for a product.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#length in month for the prediction: 12
+
+#print the metrics and line plot
+suPy.forecastQtyMonthlySales('SalesData.csv','ProductNumber',12)
+```
+
+Read sales data from csv file and print out pricing forecast for a product.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#length in month for the prediction: 12
+
+#print the metrics and line plot
+suPy.forecastMonthlyPrice('SalesData.csv','ProductNumber',12)
+```
+
+Read sales data from csv file and print out cost forecast for a product.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#length in month for the prediction: 12
+
+#print the metrics and line plot
+suPy.forecastMonthlyCost('SalesData.csv','ProductNumber',12)
+```
+
+## GusPI.finPy
+
+```
+from GusPI import finPy
+```
+
+Get public financial data with simfin - annual income statements.
+
+```
+#Example
+
+#country: us
+
+#get annual income statements and return a dataframe
+finPy.get_annual_finData_income(country)
+```
+
+Get public financial data with simfin - annual balancesheet.
+
+```
+#Example
+
+#country: us
+
+#get annual balancesheet and return a dataframe
+finPy.get_annual_finData_balance(country)
+```
+
+Get public financial data with simfin - annual cashflow statements.
+
+```
+#Example
+
+#country: us
+
+#get annual cashflow statements and return a dataframe
+finPy.get_annual_finData_cashflow(country)
+```
+
+Get public financial data with simfin - annual cashflow statements.
+
+```
+#Example
+
+#category: income, balancesheet, or cashflow
+#symbol: 'MSFT', 'AAPL'...
+#country: us
+
+#get annual cashflow statements and return a dataframe
+finPy.getannual_finData_by_symbol(category,symbol,country)
+```
+
+Read financial statements from csv files and provide a line chart for analysis.
+
+```
+#Example
+
+#dataframe for statements
+#category from the dataframe such as revenue
+
+#print line plots
+finPy.lineplot(dataframe, '3 year BalanceSheet Graph')
+```
+
+Read financial statements from csv files and provide multiple line charts for analysis.
+
+```
+#Example
+
+#dataframe for statements
+
+#print multiple line plots
+finPy.multiLineplot(dataframe, '3 year BalanceSheet Graph')
+```
+
+Read financial statements from csv files and provide financial metrics for analysis.
+
+```
+#Example
+
+#dataframe from a csv file: balance_sheet_yr.csv
+#dataframe from a csv file: income_statement_3yr.csv
+
+#print financial metrics
+finPy.calculateMetrics(df_balancesheet,df_income)
+```
+
+Get financial statements for a list of company symbols and provide financial metrics for analysis.
+
+```
+#Example
+
+#symbols = ['AAPL', 'MSFT', 'FIS']
+#mass = calculate_ratio_mass(symbols)
+
+#get financial matrics for multiple companies
+finPy.calculate_ratio_mass(symbols)
+```
+
+Read financial statements from csv files and provide horizontal analysis for the last two periods.
+
+```
+#Example
+
+#dataframe from a csv file: balance_sheet_yr.csv
+
+#print financial metrics
+finPy.horizontalAnalysisLastTwo(dataframe)
+```
+
+## GusPI.statsPy
+
+```
+#Example
+
+#perform Benford's Law anamoly detection
+#dataframe from a csv file: GLACCT_sample.csv
+
+df = pd.read_csv("GLACCT_sample.csv")
+# (dataframe,colname to perform detection,target_colname,target_value)
+value_arr = statsPy.init_benfordlaw(df, 'TotalAmount', 'GLACCT', '11111')
+result = statsPy.process_benfordlaw(value_arr, alpha=0.3)
+statsPy.plot_benfordlaw(result)
+```
+
+## GusPI.scraper
+
+The scrape package provides an easy way to scrape Yelp business info and Yelp reviews for a specific business.
+
+```
+from GusPI import scraper
+```
+
+YelpBizInfo
+The function collects business info and save it into a csv file.
+
+```
+#Example
+
+#declare a list: https://www.yelp.com/biz/`artisan-ramen-milwaukee`
+CUISINES = ['artisan-ramen-milwaukee','red-light-ramen-milwaukee-5']
+
+#scrape the business info
+scraper.YelpBizInfo(CUISINES)
+```
+
+YelpReview
+The function collects reviews for respective business and save them into separate files by business names.
+```
+#Example
+
+#declare a list: https://www.yelp.com/biz/`artisan-ramen-milwaukee`
+CUISINES = ['artisan-ramen-milwaukee','red-light-ramen-milwaukee-5']
+
+#scrape the business info
+scraper.YelpReview(CUISINES)
+```
+
+
+
+
+%package help
+Summary:	Development documents and examples for GusPI
+Provides:	python3-GusPI-doc
+%description help
+## GusPI
+This open-source python package aims to provide statistical support in supply chain analytics and finance/accounting analytics. We welcome everyone to use this python package for personal or professional projects. Please let us know any feedback you have. We'd love to improve the package and add feature enhancements to benefit researchers.
+
+Report any bugs by opening an issue here: https://github.com/ygeszvain/GusPI/issues
+
+Quick start
+
+```
+$ python3 -m pip install -U GusPI
+```
+
+## Templates
+### Templates for suPY
+
+[SalesData.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/SalesData.csv)
+
+### Templates for fiPY
+
+[income_statement.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/income_statement.csv)
+
+[income_statement_yr.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/income_statement_yr.csv)
+
+[income_statement_m.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/income_statement_m.csv)
+
+[balancesheet.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/balancesheet.csv)
+
+[balance_sheet_yr.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/balance_sheet_yr.csv)
+
+[cashflow.csv](https://github.com/ygeszvain/GusPI/blob/master/sampleFiles/cashflow.csv)
+
+## Demo notebook
+
+[demo](https://colab.research.google.com/drive/1qc1ZuvbgWPLCrSP3z-8Umj4FYJSiViq8?usp=sharing)
+
+## GusPI.suPY
+
+```
+from GusPI import suPY
+```
+
+### metrics
+
+This package provides several analytical formulas to support supply chain analytics.
+
+Economic order quantity
+EOQ(demand, mean, STD, C, Ce, Cs, Ct)
+
+Perfect Order Measurement
+POM(TotalOrders, ErrorOrders)
+
+Fill Rate
+FR(TotalItems, ShippedItems)
+
+Inventory Days of Supply
+IDS(InventoryOnHand,AvgDailyUsage)
+
+Freight cost per unit
+FCU(TotalFreightCost,NumberOfItems)
+
+Inventory Turnover
+IT(COGS,AvgInventory)
+
+Days of Supply (DOS)
+DOS(AvgInventory,MonthlyDemand)
+
+Gross Margin Return on Investment (GMROI)
+GMROI(GrossProfit, OpeningStock, ClosingStock)
+
+Inventory Accuracy
+IA(ItemCounts, TotalItemCounts)
+
+Storage Utilization Rate
+SUR(InventoryCube, TotalWarehouseCube)
+
+Total Order Cycle Time
+TOCT(TimeOrderReceivedbyCustomer, TimeOrderPlaced,TotalNumberofOrdersShipped)
+
+Internal Order Cycle Time
+IOCT(TimeOrderShipped, TimeOrderReceived, NumberofOrdersShipped)
+
+Read sales data from csv file and calculate basic safety sock and reorder point.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#safety days: 5
+#leadtime in days: 7
+
+suPy.basicSafetyStock('SalesData.csv','ProductNumber',5,7)
+```
+
+Read sales data from csv file and calculate basic safety sock and reorder point for all products.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#safety days: 5
+#leadtime in days: 7
+
+suPy.basicSafetyStockList('SalesData.csv',5,7)
+```
+
+Read sales data from csv file and calculate safety sock and reorder point.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#service rate: 0.95
+#leadtime in days: 7
+
+suPy.safetyStockwtServiceRate('SalesData.csv','ProductNumber',0.95,7)
+```
+
+Read sales data from csv file and calculate basic safety sock and reorder point for all products.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#service rate: 0.95
+#leadtime in days: 7
+
+suPy.safetyStockwtServiceRateList('SalesData.csv',0.95,7)
+```
+
+Read sales data from csv file and calculate coefficient of variation of a product.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#CV is non-negative and higher CV indicates higher volatility
+
+suPy.cvPerProduct('SalesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and calculate 'Intercept', 'Slope', 'Mean Absolute Error', 'Mean Squared Error', 'Root Mean Squared Error' of a product.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+
+suPy.linearRegressionPerProduct('SalesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and calculate EOQ of a product.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#Setup cost: 2000
+#Holding cost: 1000
+
+suPy.eoqPerProduct('SalesData.csv','ProductNumber',2000,1000)
+```
+
+Read sales data from csv file and create a list of average quantity sold per year for products.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+
+suPy.avgQtySoldList('SalesData.csv')
+```
+
+Read sales data from csv file and calculate the seasonality index of a product for a given year.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#year: 2018
+
+suPy.seasonalityIndexPerProduct('SalesData.csv','ProductNumber',2018)
+```
+
+### graphs
+
+Read sales data from csv file and print out a line plot of a product quantity sold.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+
+#print the line plot
+suPy.line plotQtyByMonth('salesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and print out a line plot of a product's total cost sold.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+
+#print the line plot
+suPy.line plotTotalCostByMonth('salesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and print out a line plot of a product's total sales.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+
+#print the line plot
+suPy.line plotTotalSalesByMonth('salesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and print out a line plot of a product's average cost.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+
+#print the line plot
+suPy.line plotAverageCostByMonth('salesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and print out a line plot of a product's average sales.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+
+#print the line plot
+suPy.line plotAverageSalesPriceByMonth('salesData.csv','ProductNumber')
+```
+
+Read sales data from csv file and print out sales forecast for a product.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#length in month for the prediction: 12
+
+#print the metrics and line plot
+suPy.forecastQtyMonthlySales('SalesData.csv','ProductNumber',12)
+```
+
+Read sales data from csv file and print out pricing forecast for a product.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#length in month for the prediction: 12
+
+#print the metrics and line plot
+suPy.forecastMonthlyPrice('SalesData.csv','ProductNumber',12)
+```
+
+Read sales data from csv file and print out cost forecast for a product.
+
+```
+#Example
+
+#sales data from a csv file: salesData.csv
+#product number to perform analysis on: ProductNumber
+#length in month for the prediction: 12
+
+#print the metrics and line plot
+suPy.forecastMonthlyCost('SalesData.csv','ProductNumber',12)
+```
+
+## GusPI.finPy
+
+```
+from GusPI import finPy
+```
+
+Get public financial data with simfin - annual income statements.
+
+```
+#Example
+
+#country: us
+
+#get annual income statements and return a dataframe
+finPy.get_annual_finData_income(country)
+```
+
+Get public financial data with simfin - annual balancesheet.
+
+```
+#Example
+
+#country: us
+
+#get annual balancesheet and return a dataframe
+finPy.get_annual_finData_balance(country)
+```
+
+Get public financial data with simfin - annual cashflow statements.
+
+```
+#Example
+
+#country: us
+
+#get annual cashflow statements and return a dataframe
+finPy.get_annual_finData_cashflow(country)
+```
+
+Get public financial data with simfin - annual cashflow statements.
+
+```
+#Example
+
+#category: income, balancesheet, or cashflow
+#symbol: 'MSFT', 'AAPL'...
+#country: us
+
+#get annual cashflow statements and return a dataframe
+finPy.getannual_finData_by_symbol(category,symbol,country)
+```
+
+Read financial statements from csv files and provide a line chart for analysis.
+
+```
+#Example
+
+#dataframe for statements
+#category from the dataframe such as revenue
+
+#print line plots
+finPy.lineplot(dataframe, '3 year BalanceSheet Graph')
+```
+
+Read financial statements from csv files and provide multiple line charts for analysis.
+
+```
+#Example
+
+#dataframe for statements
+
+#print multiple line plots
+finPy.multiLineplot(dataframe, '3 year BalanceSheet Graph')
+```
+
+Read financial statements from csv files and provide financial metrics for analysis.
+
+```
+#Example
+
+#dataframe from a csv file: balance_sheet_yr.csv
+#dataframe from a csv file: income_statement_3yr.csv
+
+#print financial metrics
+finPy.calculateMetrics(df_balancesheet,df_income)
+```
+
+Get financial statements for a list of company symbols and provide financial metrics for analysis.
+
+```
+#Example
+
+#symbols = ['AAPL', 'MSFT', 'FIS']
+#mass = calculate_ratio_mass(symbols)
+
+#get financial matrics for multiple companies
+finPy.calculate_ratio_mass(symbols)
+```
+
+Read financial statements from csv files and provide horizontal analysis for the last two periods.
+
+```
+#Example
+
+#dataframe from a csv file: balance_sheet_yr.csv
+
+#print financial metrics
+finPy.horizontalAnalysisLastTwo(dataframe)
+```
+
+## GusPI.statsPy
+
+```
+#Example
+
+#perform Benford's Law anamoly detection
+#dataframe from a csv file: GLACCT_sample.csv
+
+df = pd.read_csv("GLACCT_sample.csv")
+# (dataframe,colname to perform detection,target_colname,target_value)
+value_arr = statsPy.init_benfordlaw(df, 'TotalAmount', 'GLACCT', '11111')
+result = statsPy.process_benfordlaw(value_arr, alpha=0.3)
+statsPy.plot_benfordlaw(result)
+```
+
+## GusPI.scraper
+
+The scrape package provides an easy way to scrape Yelp business info and Yelp reviews for a specific business.
+
+```
+from GusPI import scraper
+```
+
+YelpBizInfo
+The function collects business info and save it into a csv file.
+
+```
+#Example
+
+#declare a list: https://www.yelp.com/biz/`artisan-ramen-milwaukee`
+CUISINES = ['artisan-ramen-milwaukee','red-light-ramen-milwaukee-5']
+
+#scrape the business info
+scraper.YelpBizInfo(CUISINES)
+```
+
+YelpReview
+The function collects reviews for respective business and save them into separate files by business names.
+```
+#Example
+
+#declare a list: https://www.yelp.com/biz/`artisan-ramen-milwaukee`
+CUISINES = ['artisan-ramen-milwaukee','red-light-ramen-milwaukee-5']
+
+#scrape the business info
+scraper.YelpReview(CUISINES)
+```
+
+
+
+
+%prep
+%autosetup -n GusPI-0.0.59
+
+%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-GusPI -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.59-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..44c5b7b
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+3c58e7cfc6e2dc1eef5c430ef6e393bc  GusPI-0.0.59.tar.gz