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Author: osamatarabih

Data_Inerpolate.py

@@ -0,0 +1,89 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Nov  6 11:41:53 2023
+
+@author: osamatarabih
+"""
+import math
+from calendar import monthrange
+import numpy as np
+import pandas as pd
+from scipy.optimize import fsolve
+from scipy import interpolate
+import os
+import datetime
+
+parameter: str = 'RAD' #'RADP'
+units: str = 'n' #'MICROMOLE/m^2/s' #
+station: str = 'm' #'L006'#
+
+os.chdir('C:/LOONE_WQ_Clean/Model_Data_Filled_20082023')
+Data_In = pd.read_csv('./LO_RADT_20082023.csv')
+Data_In = Data_In[Data_In['%s_%s_%s' % (station, parameter, units)]>=0]
+Data_In = Data_In.set_index(['date'])
+Data_In.index = pd.to_datetime(Data_In.index, unit='ns')
+Data_df = Data_In.resample('D').mean()
+Data_df = Data_df.dropna(subset=['%s_%s_%s' % (station, parameter, units)])
+Data_df = Data_df.reset_index()
+Data_df['Yr_M'] = pd.to_datetime(Data_df['date']).dt.to_period('M')
+start_date = Data_df['date'].iloc[0]
+end_date = Data_df['date'].iloc[-1]
+date_rng = pd.date_range(start=start_date, end=end_date, freq='M')
+date_rng = date_rng.union([date_rng[-1] + pd.DateOffset(months=1)])
+Monthly_df = pd.DataFrame(date_rng, columns=['date'])
+Monthly_df['Yr_M'] = pd.to_datetime(Monthly_df['date']).dt.to_period('M')
+New_date = []
+New_data = []
+Days = []
+Days_cum = []
+# Set index for the two dataframes
+Data_df = Data_df.set_index(['Yr_M'])
+Monthly_df = Monthly_df.set_index(['Yr_M'])
+for i in Monthly_df.index:
+    if i in Data_df.index:
+        if type(Data_df.loc[i]['date']) == pd.Timestamp:
+            New_date.append(Data_df.loc[i]['date'])
+            New_data.append(Data_df.loc[i]['%s_%s_%s' % (station, parameter, units)])
+        else:
+            for j in range(len(Data_df.loc[i]['date'])):
+                New_date.append(Data_df.loc[i]['date'][j])
+                New_data.append(Data_df.loc[i]['%s_%s_%s' % (station, parameter, units)][j])
+    elif i not in Data_df.index:
+        New_date.append(datetime.datetime(Monthly_df.loc[i]['date'].year, Monthly_df.loc[i]['date'].month, 1))
+        New_data.append(np.NaN)
+
+New_date = pd.to_datetime(New_date, format='%Y-%m-%d')
+Days = New_date.strftime("%d").astype(float)
+for i in range(len(Days)):
+    if i == 0:
+        Days_cum.append(Days[i])
+    elif New_date[i].month == New_date[i-1].month:
+        Days_cum.append(Days_cum[i-1]+(Days[i]-Days[i-1]))
+    elif New_date[i].month != New_date[i-1].month:
+        Days_cum.append(Days_cum[i-1]+Days[i]+monthrange(New_date[i-1].year, New_date[i-1].month)[1]-Days[i-1])
+Final_df = pd.DataFrame()
+Final_df['date'] = New_date
+Final_df['Data'] = New_data
+Final_df['Days'] = Days
+Final_df['Days_cum'] = Days_cum
+# Final_df.to_csv('C:/Work/Research/LOONE/Nitrogen Module/Interpolated_Data/In-Lake/L008_DO_No_Months_Missing_Trial.csv')  # noqa: E501
+# Remove Negative Data Values
+Final_df = Final_df[Final_df['Data'] >= 0]
+Final_df['date'] = pd.to_datetime(Final_df['date'], format='%Y-%m-%d')
+start_date = Final_df['date'].iloc[0]
+end_date = Final_df['date'].iloc[-1]
+date_rng_TSS_1 = pd.date_range(start=start_date, end=end_date, freq='D')
+# Create a data frame with a date column
+Data_df = pd.DataFrame(date_rng_TSS_1, columns=['date'])
+Data_len = len(Data_df.index)
+Cum_days = np.zeros(Data_len)
+Data_daily = np.zeros(Data_len)
+# Set initial values
+Cum_days[0] = Data_df['date'].iloc[0].day
+Data_daily[0] = Final_df['Data'].iloc[0]
+for i in range(1, Data_len):
+    Cum_days[i] = Cum_days[i-1]+1
+    # Data_daily[i] = interpolate.interp1d(Final_df['Days'], Final_df['TSS'] , kind = 'linear')(Cum_days[i])
+    Data_daily[i] = np.interp(Cum_days[i], Final_df['Days_cum'], Final_df['Data'])
+Data_df['Data_%s'%station] = Data_daily
+Data_df.to_csv('./LO_RADT_20082023.csv', index=False)

Define_Dates.py

@@ -0,0 +1,69 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jan 17 13:35:40 2024
+
+@author: osama
+"""
+
+import pandas as pd
+import numpy as np
+from datetime import date, timedelta
+import os
+#Directory where the Script loactes
+os.chdir('C:/Work/Research/Data Analysis/Tools/Python_Scripts')
+from Data_Analyses_Fns import *
+os.chdir('C:/LOONE_WQ/Data/LORS19722023/ts_data')
+
+# Specify the folder path
+folder_path = 'C:/LOONE_WQ/Data/LORS19722023/ts_data'
+
+# Get a list of all CSV files in the folder
+csv_files = [file for file in os.listdir(folder_path) if file.endswith('.csv')]
+
+# Iterate through each CSV file
+for file in csv_files:
+    file_path = os.path.join(folder_path, file)
+    data = pd.read_csv(file_path)
+    
+    # Check if the date in the first row is '1/1/1972'
+    if 'date' in data.columns:
+        data['date'] = pd.to_datetime(data['date'])
+        if data['date'].iloc[0] == pd.to_datetime('1/1/1972'):
+            # Apply your custom function to change the date range
+            data = DF_Date_Range(data, 2007, 1, 1, 2023, 6, 30)
+        elif data['date'].iloc[0] == pd.to_datetime('12/30/1971'):
+            # Apply your custom function to change the date range
+            data = DF_Date_Range(data, 2006, 12, 30, 2023, 6, 30)
+        else:
+            pass
+    else:
+        pass
+        # Save the modified DataFrame to a new CSV file
+    data.to_csv('C:/LOONE_WQ/Data/LORS20072023/ts_data/%s_20072023.csv' % file, index=False)
+    
+
+def rename_files(folder_path, old_part, new_part):
+    # Navigate to the folder containing the files
+    os.chdir('C:/LOONE_WQ/Data/LORS20072023/ts_data')
+
+    # Iterate through all files in the folder
+    for filename in os.listdir(folder_path):
+        # Check if the old_part exists in the filename
+        if old_part in filename:
+            # Create the new filename by replacing old_part with new_part
+            new_filename = filename.replace(old_part, new_part)
+
+            # Construct the full paths
+            old_path = os.path.join(folder_path, filename)
+            new_path = os.path.join(folder_path, new_filename)
+
+            # Rename the file
+            os.rename(old_path, new_path)
+            print(f'Renamed: {filename} to {new_filename}')
+
+# Example usage
+folder_path = 'C:/LOONE_WQ/Data/LORS20072023/ts_data'
+old_part = 'LORS20082023.csv_20072023'
+new_part = 'LORS20072023'
+
+rename_files(folder_path, old_part, new_part)

LOONE_Nut.py

@@ -19,7 +19,7 @@
 from TP_Variables_Regions import TP_Variables
 import TP_Mass_Balance_Functions_Regions as TP_MBFR
 
-def LOONE_Nut(LOONE_Q_Outputs): 
+def LOONE_Nut(): 
     print("LOONE Nut Module is Running!")
     # Based on the defined Start and End year, month, and day on the Pre_defined_Variables File, Startdate and enddate are defined. 
     year, month, day = map(int, Pre_defined_Variables.startdate_entry)
@@ -432,3 +432,5 @@ def LOONE_Nut(LOONE_Q_Outputs):
     #     return[P_Loads_M,P_Lake_M,Smr_Mnth_StL_arr,Smr_Mnth_Cal_arr]
     # else:
     #     return[Smr_Mnth_StL_arr,Smr_Mnth_Cal_arr,P_Lake_df]
+P_NS = LOONE_Nut()
+P_NS.to_csv('./P_NS_June2023_Monthly.csv')