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OHW_ML_project_update.py
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OHW_ML_project_update.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
from argopy import DataFetcher as ArgoDataFetcher
import numpy as np
import matplotlib.pyplot as plt
import csv
import pandas as pd
import xarray
from netCDF4 import Dataset
import scipy as sp
# In[2]:
final_table = pd.DataFrame()
for i in range(11):
print(i)
month_str_end = f'{str(i+2).zfill(2)}' # Ensure month is formatted with leading zero
month_str_start = f'{str(i+1).zfill(2)}' # Ensure month is formatted with leading zero
start_date = f'2021-{month_str_start}-01'
end_date = f'2021-{month_str_end}-01' # You need to specify the end date accordingly
region = [-50, -20, 30, 50, 0, 2000, start_date, end_date]
argo_loader = ArgoDataFetcher()
ds = argo_loader.region(region).to_xarray()
lon = np.array(ds.variables['LONGITUDE'])
lat = np.array(ds.variables['LATITUDE'])
temp = np.array(ds.variables['TEMP'])
time = np.array(ds.variables['TIME'])
depth = np.array(ds.variables['PRES'])
salinity = np.array(ds.variables['PSAL'])
# df = pd.DataFrame({'Longitude': lon, 'Latitude': lat, 'Depth': depth, 'Temperature': temp, 'Time': time})
df = pd.DataFrame({'Longitude': lon, 'Latitude': lat, 'Depth': depth, 'salinity': salinity, 'Time': time})
df_new = df.copy()
df_new.Depth = df_new.Depth.round()
table = pd.pivot_table(df_new, values='salinity', index=['Longitude', 'Latitude', 'Time'], columns='Depth')
if final_table.shape[0] == 0:
final_table = table
else:
final_table = pd.concat([final_table, table])
# In[3]:
final_table_2 = pd.DataFrame()
region = [-50, -20, 30, 50, 0, 2000, '2022-12-1', '2022-12-31']
argo_loader = ArgoDataFetcher()
ds = argo_loader.region(region).to_xarray()
lon = np.array(ds.variables['LONGITUDE'])
lat = np.array(ds.variables['LATITUDE'])
#temp = np.array(ds.variables['TEMP'])
time = np.array(ds.variables['TIME'])
depth = np.array(ds.variables['PRES'])
salinity = np.array(ds.variables['PSAL'])
#df = pd.DataFrame({'Longitude': lon, 'Latitude': lat, 'Depth': depth, 'Temperature': temp, 'Time': time})
df = pd.DataFrame({'Longitude': lon, 'Latitude': lat, 'Depth': depth, 'salinity': salinity, 'Time': time})
df_new = df.copy()
df_new.Depth = df_new.Depth.round()
final_table_2 = pd.pivot_table(df_new, values='salinity', index=['Longitude', 'Latitude', 'Time'], columns='Depth')
final_table = pd.concat([final_table, final_table_2])
# In[4]:
table_intrp = final_table.interpolate(axis=1)
# In[5]:
table_intrp=table_intrp.iloc[:,10:].dropna()
# In[6]:
table_intrp.to_csv('salin.csv')
# In[8]:
final_table
# In[ ]: