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solarPOT/Solar/deprecated/make_yearly_means.py
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make_yearly_means.py
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# coding: utf-8
# In[1]:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import pandas as pd
import xarray as xr
import os
import time
import util
from meteo_data import Meteo_Reader
# In[2]:
loc_path = os.path.abspath("/mnt/sda1/hyenergy/data/meteo")
data_path = os.path.join(loc_path, "raw_data")
# In[3]:
data1 = Meteo_Reader(data_path)
# In[4]:
start_range = pd.date_range('20040101', '20151231', freq = 'MS')
end_range = pd.date_range('20040101', '20151231', freq = 'M')
# In[5]:
start = []
end = []
for month in range(12):
start.append(start_range[month::12].strftime('%Y%m%d'))
end.append(end_range[month::12].strftime('%Y%m%d'))
# ## Cycle through all months
# In[6]:
for month in range(6,12):
tt = util.Timer()
data1.read_data(start[month][0], end[month][0], reset = True)
for i in range(1,12):
data1.read_data(start[month][i], end[month][i])
table = data1.data.to_dataframe().dropna(axis = 0, subset = ['SIS']).reset_index()
table['day'] = pd.to_datetime(table['date'].values).day
table['year'] = pd.to_datetime(table['date'].values).year
table = table.groupby(['lat', 'lon', 'hour', 'day']).mean().reset_index()
table['date'] = pd.to_datetime('2001-%d-1' %(month+1)) + pd.to_timedelta(table['day']-1, unit = 'd')
table.set_index(['date','lon', 'lat', 'hour'], inplace = True)
xarr = table.to_xarray().drop('day')
if month == 1:
xarr = xarr.sel(date = slice('20010201', '20010228'))
xarr.to_netcdf(os.path.join(data_path,('2001-%02d.nc' %(month+1))))
tt.stop()
print('\nFINISHED MONTH %02d\n\n\n' %(month+1))
# ## Read all months and merge them to a yearly file
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