refactor: Convert string with anomalous backslash into a raw string

deepsource-autofix[bot] · web-flow · commit d4ec14f355f7 · 2023-03-30T13:19:01.000Z
Backslash is present in the literal string but is not a valid escape sequence. If it is intended to be an escape sequence, use the correct escape characters. If it is intended to be a literal backslash, it can either be replaced with with an escaped backslash `\\`, or you can add an `r` prefix to make it a "raw" string.
diff --git a/Auto generated Dataset/GarbageCollector.py b/Auto generated Dataset/GarbageCollector.py
@@ -24,7 +24,7 @@ def find_csv(path_to_dir,suffix=".csv"):
             hold.append(name)
 print(hold)
 
-with open("Auto generated Dataset\Tickers.csv",'r',newline='') as f:
+with open(r"Auto generated Dataset\Tickers.csv",'r',newline='') as f:
     reader=csv.reader(f)
     data=list(reader)
     #print(data)
@@ -53,7 +53,7 @@ def find_csv(path_to_dir,suffix=".csv"):
 #for check in hold:
     #os.remove(check)
 
-with open("Auto generated Dataset\Tickers.csv",'w',newline='') as f:
+with open(r"Auto generated Dataset\Tickers.csv",'w',newline='') as f:
     writer=csv.writer(f)
     writer.writerows(lstoflst)
     
diff --git a/Scipts/CSVGenerator.py b/Scipts/CSVGenerator.py
@@ -8,14 +8,14 @@ def dataset_generator():
     #-------------------------------------------------------------
     #Tickers.csv has the list of symbols of all NSE listed tickers
 
-    comp=csv.reader(open("Prerequisites-Outputs\Tickers.csv"))     
+    comp=csv.reader(open(r"Prerequisites-Outputs\Tickers.csv"))     
     for c in comp:
 
         symbol=c[0]
 
         #Creation of individual CSVs for all listed tickers in Tickers.csv
 
-        history_filename="Auto generated Dataset\{}.csv".format(symbol)  
+        history_filename=r"Auto generated Dataset\{}.csv".format(symbol)  
         f=open(history_filename,'w',newline="")
 
         #---------------------------------------------------------------------------------
diff --git a/Scipts/Cufflinks Demo.py b/Scipts/Cufflinks Demo.py
@@ -11,7 +11,7 @@
 
 
 cf.set_config_file(theme='pearl',sharing='public',offline=True)
-apple_df = pd.read_csv('Auto generated Dataset\ABFRL.NS.csv', index_col=0, parse_dates=True)
+apple_df = pd.read_csv(r'Auto generated Dataset\ABFRL.NS.csv', index_col=0, parse_dates=True)
 
 qf=cf.QuantFig(apple_df,title='ABFRL Quant Figure',legend='top',name='GS')
 qf.add_bollinger_bands()
diff --git a/Scipts/DiscountValuations.py b/Scipts/DiscountValuations.py
@@ -14,7 +14,7 @@
 tickers_vas=[]
 
 def discount_as_per_bv():
-    with open("Auto generated Dataset\Valuations.csv",'r') as mf:
+    with open(r"Auto generated Dataset\Valuations.csv",'r') as mf:
         data=csv.DictReader(mf)
 
         for row in data:
@@ -41,7 +41,7 @@ def store(l1,l2,l3,l4,filename,aim): #aim: Valuation-Book Value or Valuation - S
     list_clubber=[l1,l2,l3,l4]
     export_data_complete=zip_longest(*list_clubber,fillvalue='')
 
-    with open("Auto generated Dataset\{}.csv".format(filename),'w',encoding="ISO-8859-1",newline="") as myfile:
+    with open(r"Auto generated Dataset\{}.csv".format(filename),'w',encoding="ISO-8859-1",newline="") as myfile:
         wr=csv.writer(myfile)
         wr.writerow(("Ticker",aim,"LTP","Discount(%)"))
         wr.writerows(export_data_complete)
@@ -55,7 +55,7 @@ def store(l1,l2,l3,l4,filename,aim): #aim: Valuation-Book Value or Valuation - S
 
 
 def discount_as_per_sales():
-    with open("Auto generated Dataset\Valuations.csv",'r') as mf:
+    with open(r"Auto generated Dataset\Valuations.csv",'r') as mf:
         data=csv.DictReader(mf)
 
         for row in data:
diff --git a/Scipts/Driver.py b/Scipts/Driver.py
@@ -26,7 +26,7 @@ def __init__(self):
         self.geometry(f"{App.WIDTH}x{App.HEIGHT}")
         self.protocol("WM_DELETE_WINDOW", self.on_closing)  # call .on_closing() when app gets closed
 
-        self.bell_image = self.load_image("Prerequisites-Outputs\LOGOn.PNG",130,70)
+        self.bell_image = self.load_image(r"Prerequisites-Outputs\LOGOn.PNG",130,70)
 
         # ============ create two frames ============
 
diff --git a/Scipts/FinancialsExtractor.py b/Scipts/FinancialsExtractor.py
@@ -46,7 +46,7 @@ def financials_fetcher(ticker):
 
 def thread_pool_executor():
 
-    comp=csv.reader(open("Prerequisites-Outputs\Tickers.csv"))
+    comp=csv.reader(open(r"Prerequisites-Outputs\Tickers.csv"))
 
     for c in comp:
         tickers_list.extend(c)
@@ -82,22 +82,22 @@ def thread_pool_executor():
     list_clubber=[ticker_results,tickers_book_value,tickers_evtoebitda,tickers_priceToBook,tickers_marketcap,tickers_priceToSales,tickers_close,tickers_sharesoutstanding,tickers_total_revenue]
     export_data=zip_longest(*list_clubber,fillvalue='')
 
-    with open("Auto generated Dataset\Financials.csv",'a',encoding="ISO-8859-1",newline='') as myfile:
+    with open(r"Auto generated Dataset\Financials.csv",'a',encoding="ISO-8859-1",newline='') as myfile:
         wr=csv.writer(myfile)
         #wr.writerow(("Ticker","Book Value"))
         wr.writerows(export_data)
     myfile.close()
 
     holder=list()
 
-    with open("Auto generated Dataset\Financials.csv",'r') as f:
+    with open(r"Auto generated Dataset\Financials.csv",'r') as f:
         csvreader=csv.reader(f)
         for row in csvreader:
             holder.append(row)
             if not row[1]:
                 holder.remove(row)
 
-    with open("Auto generated Dataset\Financials.csv",'w',newline='') as fw:
+    with open(r"Auto generated Dataset\Financials.csv",'w',newline='') as fw:
         writer=csv.writer(fw)
         writer.writerows(holder) 
     print("Success")
diff --git a/Scipts/FinancialsGarbageCollector.py b/Scipts/FinancialsGarbageCollector.py
@@ -8,9 +8,9 @@
 
 def fgarbage_collector():
 
-   f=open("Auto generated Dataset\FinancialsBunch.csv","w",newline="") 
+   f=open(r"Auto generated Dataset\FinancialsBunch.csv","w",newline="") 
 
-   with open('Auto generated Dataset\Financials.csv',"r+") as mf:
+   with open(r'Auto generated Dataset\Financials.csv',"r+") as mf:
       data=csv.DictReader(mf)
       wr=csv.writer(f)
       wr.writerow(("Ticker","Book Value","EVToEBITDA","PToBV","MarketCap","PToSales","Close","Shares Outstanding","Revenue"))
diff --git a/Scipts/FundamentalScreener.py b/Scipts/FundamentalScreener.py
@@ -46,7 +46,7 @@
 #Filters stocks that are trading 10% discount to their book value as compared to Last Traded Price
 
 def book_value_filter():
-    with open("Auto generated Dataset\Financials.csv",'r') as mf:
+    with open(r"Auto generated Dataset\Financials.csv",'r') as mf:
         data=csv.DictReader(mf)
 
         for row in data:
@@ -67,11 +67,11 @@ def book_value_filter():
     list_clubber_final_50=[tickers_gre_50,tickers_gre_50_bookval,tickers_gre_50_close]
     export_data_complete_50=zip_longest(*list_clubber_final_50,fillvalue='')
 
-    with open("Auto generated Dataset\Filtered_50_percent.csv",'w',encoding="ISO-8859-1",newline="") as myfile:
+    with open(r"Auto generated Dataset\Filtered_50_percent.csv",'w',encoding="ISO-8859-1",newline="") as myfile:
         wr=csv.writer(myfile)
         wr.writerow(("Ticker","Book Value","LTP"))
         wr.writerows(export_data_complete_50)
                 
-    with open("Auto generated Dataset\Filtered_50_percent.csv",'r') as f,open('Filtered_50_percentt.csv','w') as out_file:
+    with open(r"Auto generated Dataset\Filtered_50_percent.csv",'r') as f,open('Filtered_50_percentt.csv','w') as out_file:
         out_file.writelines(unique_everseen(f))
 
diff --git a/Scipts/Fundamental_Screener3.py b/Scipts/Fundamental_Screener3.py
@@ -16,7 +16,7 @@
 #Filters stocks with attractive P/S of below 1.25 value
 
 def pts_filter():
-    with open("Auto generated Dataset\FinancialsBunch.csv",'r') as mf:
+    with open(r"Auto generated Dataset\FinancialsBunch.csv",'r') as mf:
         data=csv.DictReader(mf)
 
         for row in data:
@@ -41,7 +41,7 @@ def pts_filter():
     list_clubber=[tickers_name,tickers_MCap,tickers_PTS,tickers_close,tickers_bookval]
     export_data_complete=zip_longest(*list_clubber,fillvalue='')
 
-    with open("Auto generated Dataset\MCapPTS.csv",'w',encoding="ISO-8859-1",newline="") as myfile:
+    with open(r"Auto generated Dataset\MCapPTS.csv",'w',encoding="ISO-8859-1",newline="") as myfile:
         wr=csv.writer(myfile)
         wr.writerow(("Ticker","Market Cap","P/S","LTP","Book Value"))
         wr.writerows(export_data_complete)
diff --git a/Scipts/GarbageCollector.py b/Scipts/GarbageCollector.py
@@ -35,7 +35,7 @@ def garbage_collector():
             if(len(csv_dict)<2):
                 hold.append(name)
 
-    with open("Prerequisites-Outputs\Tickers.csv",'r',newline='') as f:
+    with open(r"Prerequisites-Outputs\Tickers.csv",'r',newline='') as f:
         reader=csv.reader(f)
         data=list(reader)
         #print(data)
@@ -66,6 +66,6 @@ def garbage_collector():
     #for check in hold:
         #os.remove(check)
 
-    with open("Prerequisites-Outputs\Tickers.csv",'w',newline='') as f:
+    with open(r"Prerequisites-Outputs\Tickers.csv",'w',newline='') as f:
         writer=csv.writer(f)
         writer.writerows(lstoflst)
diff --git a/Scipts/Mailer.py b/Scipts/Mailer.py
@@ -17,7 +17,7 @@
 def mailer(emailto,fileToSend,username,password):
     emailfrom = '"DEV_FINWIZ"'
     emailto = "receiver's email"
-    fileToSend = "Auto generated Dataset\Discount_Sales.csv"
+    fileToSend = r"Auto generated Dataset\Discount_Sales.csv"
     username = "sender's email id"
     password = "sender's password here"
 
diff --git a/Scipts/PatternRecognition.py b/Scipts/PatternRecognition.py
@@ -68,7 +68,7 @@ def pattern_recognition_initializer(candles,index):
     
 
 def pattern_recognition():
-    symbols_file=open("Prerequisites-Outputs\Tickers.csv")
+    symbols_file=open(r"Prerequisites-Outputs\Tickers.csv")
     tickers=csv.reader(symbols_file)
 
     
@@ -77,7 +77,7 @@ def pattern_recognition():
 
         ticker=company[0]
         try:
-            history_file=open("Auto generated Dataset\{}.csv".format(ticker))
+            history_file=open(r"Auto generated Dataset\{}.csv".format(ticker))
         except:
             continue
         
@@ -110,23 +110,23 @@ def pattern_recognition():
     list_clubber=[bullishengulf,datee]
     export_data_complete=zip_longest(*list_clubber,fillvalue='')
 
-    with open("Prerequisites-Outputs\BullishEngulfing.csv",'w',encoding="ISO-8859-1",newline="") as myfile:
+    with open(r"Prerequisites-Outputs\BullishEngulfing.csv",'w',encoding="ISO-8859-1",newline="") as myfile:
         wr=csv.writer(myfile)
         wr.writerow(("Ticker","Date Of Formation"))
         wr.writerows(export_data_complete)
 
     list_clubberr=[bearishengulf,dateee]
     export_data_completee=zip_longest(*list_clubberr,fillvalue='')
 
-    with open("Prerequisites-Outputs\BearishEngulfing.csv",'w',encoding="ISO-8859-1",newline="") as myfile:
+    with open(r"Prerequisites-Outputs\BearishEngulfing.csv",'w',encoding="ISO-8859-1",newline="") as myfile:
         wr=csv.writer(myfile)
         wr.writerow(("Ticker","Date Of Formation"))
         wr.writerows(export_data_completee)
 
     list_clubberrr=[gravestone,dateeee]
     export_data_completeee=zip_longest(*list_clubberrr,fillvalue='')
 
-    with open("Prerequisites-Outputs\Gravestone.csv",'w',encoding="ISO-8859-1",newline="") as myfile:
+    with open(r"Prerequisites-Outputs\Gravestone.csv",'w',encoding="ISO-8859-1",newline="") as myfile:
         wr=csv.writer(myfile)
         wr.writerow(("Ticker","Date Of Formation"))
         wr.writerows(export_data_completeee)
diff --git a/Scipts/Valuation.py b/Scipts/Valuation.py
@@ -15,7 +15,7 @@
 #Computes fair value for all tickers as per their book value and as per their annual sales
 
 def valuation_computer():
-    with open("Auto generated Dataset\FinancialsBunch.csv",'r') as mf:
+    with open(r"Auto generated Dataset\FinancialsBunch.csv",'r') as mf:
         data=csv.DictReader(mf)
 
         for row in data:
@@ -50,7 +50,7 @@ def valuation_computer():
     list_clubber=[tickers,val_as_per_bv,val_as_per_pts,latest_close]
     export_data_complete=zip_longest(*list_clubber,fillvalue='')
 
-    with open("Auto generated Dataset\Valuations.csv",'w',encoding="ISO-8859-1",newline="") as myfile:
+    with open(r"Auto generated Dataset\Valuations.csv",'w',encoding="ISO-8859-1",newline="") as myfile:
         wr=csv.writer(myfile)
         wr.writerow(("Ticker","Valuation As Per Book Value","Valuation As Per Sales","LTP"))
         wr.writerows(export_data_complete)
diff --git a/Scipts/evebitda_screening.py b/Scipts/evebitda_screening.py
@@ -16,7 +16,7 @@
 #Filters stocks with EV/EBITDA ratio in range 0-10, stores the resultant dataset in EVToEbitda_Output.csv
 
 def ev_ebitda_filter():
-    with open("Auto generated Dataset\FinancialsBunch.csv",'r') as mf:
+    with open(r"Auto generated Dataset\FinancialsBunch.csv",'r') as mf:
         data=csv.DictReader(mf)
 
         for row in data:
@@ -41,7 +41,7 @@ def ev_ebitda_filter():
     list_clubber=[tickers_name,tickers_EV,tickers_PTS,tickers_close,tickers_bookval]
     export_data_complete=zip_longest(*list_clubber,fillvalue='')
 
-    with open("Prerequisites-Outputs\EVToEbitda_Output.csv",'w',encoding="ISO-8859-1",newline="") as myfile:
+    with open(r"Prerequisites-Outputs\EVToEbitda_Output.csv",'w',encoding="ISO-8859-1",newline="") as myfile:
         wr=csv.writer(myfile)
         wr.writerow(("Ticker","EVToEBITDA","P/S","LTP","Book Value"))
         wr.writerows(export_data_complete)
diff --git a/Scipts/talibtest.py b/Scipts/talibtest.py
@@ -4,7 +4,7 @@
 import numpy
 import csv
     
-symbols_file=open("Auto generated Dataset\Tickers.csv",'r')
+symbols_file=open(r"Auto generated Dataset\Tickers.csv",'r')
 tickers=csv.reader(symbols_file)
 
 def can_strat(openn,high,low,close):
