Column Input File Number

Checks to see how many columns there are in the input file and determines what path to take for parsing the data
kjkoeller · Nov 10, 2022 · 98710c9 · 98710c9
1 parent ca7bdad
commit 98710c9
Showing 1 changed file with 113 additions and 61 deletions.
diff --git a/AIJ_Night_Filters.py b/AIJ_Night_Filters.py
@@ -1,7 +1,7 @@
 """
 Author: Kyle Koeller
 Created: 11/11/2020
-Last Updated: 11/03/2022
+Last Updated: 11/10/2022
 
 This program is meant to make the process of collecting the different filters from AIJ Excel spreadsheets faster.
 The user enters however many nights they have and the program goes through and checks those text files for the
@@ -16,13 +16,20 @@
 
 
 def main(c):
+    """
+    Runs the whole program but collects the number of nights that the user wants to parse through.
+
+    :param c: Number of whether the user has used the program before or not. Must be manually changed within the code
+    """
     # warning prompts for the user to read to make sure this program works correctly
     if c == 0:
         # warning prompts for the user to read to make sure this program works correctly
         print()
         print("From each night, yous should have a file that is sort of like this: 2018.09.18.APASS.B_datasubset.dat."
-              "This file has 7 columns and you will only need 6 of them.")
-        print("You may also type the word 'Close' in the next prompt to leave this program and return to the main menu.")
+              "This file has 7 or 4 columns and you will only need 6 or 3 of them respectively.\n")
+        print("All of the nights that you entire MUST have the exact same column number or this program will not work.\n")
+        print(
+            "You may also type the word 'Close' in the next prompt to leave this program and return to the main menu.\n")
         print()
     else:
         print()
@@ -46,11 +53,13 @@ def main(c):
 def get_filters(n):
     """
     Takes a number of nights for a given filter and takes out the HJD, either A_Mag1 or T1_flux, and
-    error for mag or flux
+    error for mag or flux. Determines if the user has entered a file that contains 4 or 7 columns and correctly parses
+    the data based on that.
 
-    :param n: Number of observation nights
+    :param n: NUmber of nights
     :return: the output text files for each night in a given filter
     """
+
     total_hjd = []
     total_amag = []
     total_amag_err = []
@@ -59,9 +68,11 @@ def get_filters(n):
 
     # checks for either the b or v filter as either upper or lowercase will work
     # an example pathway for the files
-    # E:\Research\Data\NSVS_254037\2018.10.12-reduced\Check\V\2018.09.18.APASS.B_datasubset.dat
+    print("Example of a correct file path: "
+          "E:/Research/Data/NSVS_254037/2018.10.12-reduced/Check/V/2018.09.18.APASS.B_datasubset.dat")
+    print()
     print("When entering a file path make sure to use files that are the same filter. So if you are going"
-          " through the Johnson B files, then only use the file paths of the Johnson B and not V or R")
+          " through the Johnson B files, then ONLY use the file paths of the Johnson B and not V or R yet.")
     for i in range(n):
         while True:
             # makes sure the file pathway is real and points to some file
@@ -73,66 +84,107 @@ def get_filters(n):
                 else:
                     continue
             except FileNotFoundError:
-                print("Please enter a correct file path")
+                print("Please enter a correct file path.")
 
         # noinspection PyUnboundLocalVariable
         df = pd.read_csv(file, delimiter="\t")
 
-        # set parameters to lists from the file by the column header
-        hjd = []
-        amag = []
-        amag_err = []
-        flux = []
-        flux_err = []
-        try:
-            hjd = list(df["HJD"])
-            amag = list(df["Source_AMag_T1"])
-            amag_err = list(df["Source_AMag_Err_T1"])
-            flux = list(df["rel_flux_T1"])
-            flux_err = list(df["rel_flux_err_T1"])
-        except KeyError:
-            print("The file you entered does not have the columns of HJD, Source_AMag_T1, or Source_AMag_Err_T1. "
-                  "Please re-enter the file path and make sure its the correct file.")
-            c = 1
-            main(c)
-
-        total_hjd.append(hjd)
-        total_amag.append(amag)
-        total_amag_err.append(amag_err)
-        total_flux.append(flux)
-        total_flux_err.append(flux_err)
-
-    # converts the Dataframe embedded lists into a normal flat list
-    new_hjd = [item for elem in total_hjd for item in elem]
-    new_amag = [item for elem in total_amag for item in elem]
-    new_amag_err = [item for elem in total_amag_err for item in elem]
-    new_flux = [item for elem in total_flux for item in elem]
-    new_flux_err = [item for elem in total_flux_err for item in elem]
-
-    # outputs the new file to dataframe and then into a text file for use in Peranso or PHOEBE
-    data1 = pd.DataFrame({
-        "HJD": new_hjd,
-        "rel flux": new_flux,
-        "rel flux error": new_flux_err
-    })
-
-    data2 = pd.DataFrame({
-        "HJD":new_hjd,
-        "AMag": new_amag,
-        "AMag Error": new_amag_err
-    })
-
-    print("")
-    output = input("What is the file output name (WITHOUT any file extension): ")
-
-    # output both text files with a designation of magnitude or flux
-    data1.to_csv(output+"_magnitudes.txt", index=False, header=False, sep="\t")
-    data2.to_csv(output+"_flux.txt", index=False, header=False, sep="\t")
-    print("")
+        # if statement checks whether the user has entered a file with 4 or 7 columns and correctly parses the data
+        # into specified output files
+        if len(df.columns) == 7:
+            # set parameters to lists from the file by the column header
+            hjd = []
+            amag = []
+            amag_err = []
+            flux = []
+            flux_err = []
+            try:
+                hjd = list(df["HJD"])
+                amag = list(df["Source_AMag_T1"])
+                amag_err = list(df["Source_AMag_Err_T1"])
+                flux = list(df["rel_flux_T1"])
+                flux_err = list(df["rel_flux_err_T1"])
+            except KeyError:
+                print("The file you entered does not have the columns of HJD, Source_AMag_T1, or Source_AMag_Err_T1. "
+                      "Please re-enter the file path and make sure its the correct file.")
+                c = 1
+                main(c)
+
+            total_hjd.append(hjd)
+            total_amag.append(amag)
+            total_amag_err.append(amag_err)
+            total_flux.append(flux)
+            total_flux_err.append(flux_err)
+            # converts the Dataframe embedded lists into a normal flat list
+            new_hjd = [item for elem in total_hjd for item in elem]
+            new_amag = [item for elem in total_amag for item in elem]
+            new_amag_err = [item for elem in total_amag_err for item in elem]
+            new_flux = [item for elem in total_flux for item in elem]
+            new_flux_err = [item for elem in total_flux_err for item in elem]
+
+            # outputs the new file to dataframe and then into a text file for use in Peranso or PHOEBE
+            data1 = pd.DataFrame({
+                "HJD": new_hjd,
+                "rel flux": new_flux,
+                "rel flux error": new_flux_err
+            })
+
+            data2 = pd.DataFrame({
+                "HJD": new_hjd,
+                "AMag": new_amag,
+                "AMag Error": new_amag_err
+            })
+
+            print("")
+            output = input("What is the file output name (WITHOUT any file extension): ")
+
+            # output both text files with a designation of magnitude or flux
+            data1.to_csv(output + "_magnitudes.txt", index=False, header=False, sep="\t")
+            data2.to_csv(output + "_flux.txt", index=False, header=False, sep="\t")
+            print("")
+        elif df.columns == 4:
+            # set parameters to lists from the file by the column header
+            hjd = []
+            amag = []
+            amag_err = []
+            try:
+                hjd = list(df["HJD"])
+                amag = list(df["Source_AMag_T1"])
+                amag_err = list(df["Source_AMag_Err_T1"])
+            except KeyError:
+                print("The file you entered does not have the columns of HJD, Source_AMag_T1, or Source_AMag_Err_T1. "
+                      "Please re-enter the file path and make sure its the correct file.")
+                c = 1
+                main(c)
+
+            total_hjd.append(hjd)
+            total_amag.append(amag)
+            total_amag_err.append(amag_err)
+
+            # converts the Dataframe embedded lists into a normal flat list
+            new_hjd = [item for elem in total_hjd for item in elem]
+            new_amag = [item for elem in total_amag for item in elem]
+            new_amag_err = [item for elem in total_amag_err for item in elem]
+
+            # outputs the new file to dataframe and then into a text file for use in Peranso or PHOEBE
+            data2 = pd.DataFrame({
+                "HJD": new_hjd,
+                "AMag": new_amag,
+                "AMag Error": new_amag_err
+            })
+
+            print("")
+            output = input("What is the file output name (WITHOUT any file extension): ")
+
+            # output both text files with a designation of magnitude or flux
+            data2.to_csv(output + "_magnitudes.txt", index=False, header=False, sep="\t")
+            print("")
+        else:
+            print("The file you entered does not have the correct amount of columns.")
+
     print("Fished saving the file to the same location as this program.")
 
 
 # Press the green button in the gutter to run the script.
 if __name__ == '__main__':
-    count = 0
-    main(count)
+    main(0)