def min_temp_day():
df_out_min = p.DataFrame()
df_out_min5 = p.DataFrame()
for file in files.lat_lon_files():
df = p.read_csv (file)
if len(df) > 0:
cols = files.lat_lon_cols(df)
lat_lon_5 = cols[0]
# make year associated with winter
df['year2'] = df['year'].shift(190)
df['doy2'] = df['day_of_year']
# make doy be part of previous year in the winter
df.loc[(df.doy2 < 190), 'doy2'] = df.doy2 + 365
# lop off the beginning which isn't useful
df2 = df.iloc[190:].copy()
df = df.set_index('doy2')
dfg = df.groupby('year2')
for col in cols:
df_out_min[col] = dfg[col].idxmin()
df_out_min5[lat_lon_5] = dfg['mean'].idxmin()
print ('processed ' + lat_lon_5)
df_out_min.to_csv(out_dir + 'min_lat_lon.csv')
decades(df_out_min).to_csv(out_dir + 'min_lat_lon_dec.csv')
df_out_min5.to_csv(out_dir + 'min_lat_lon5.csv')
decades(df_out_min5).to_csv(out_dir + 'min_lat_lon5_dec.csv')
def leap_year_delta():
df_out_delta = p.DataFrame()
for file in files.lat_lon_files(DIR, SHORT_NAME):
df = p.read_csv(file)
years = find_full_years(df)
if len(years) > 0:
cols = files.lat_lon_cols(df)
lat_lon = cols[0]
df_out_delta[lat_lon] = calc_leap_delta(df, years)['temp']
# print('processed ' + lat_lon)
df_out_delta.reset_index(inplace=True)
df_out_delta.to_csv(OUT_DIR + 'leap_deltas.csv')
out = []
for loc in df_out_delta.columns.to_list().remove('year'):
delta = df_out_delta[loc]
delta = delta[delta != 0]
out.append({'loc': loc, 'mean': delta.mean(), 'max': delta.max()})
return p.DataFrame(out)
def max_temp_day():
df_out_max = p.DataFrame()
df_out_max5 = p.DataFrame()
for file in files.lat_lon_files():
df = p.read_csv (file)
if len(df) > 0:
cols = files.lat_lon_cols(df)
lat_lon_5 = cols[0]
df = df.set_index('day_of_year')
dfg = df.groupby('year')
for col in cols:
df_out_max[col] = dfg[col].idxmax()
df_out_max5[lat_lon_5] = dfg['mean'].idxmax()
print ('processed ' + lat_lon_5)
df_out_max.to_csv(out_dir + 'max_lat_lon.csv')
decades(df_out_max).to_csv(out_dir + 'max_lat_lon_dec.csv')
df_out_max5.to_csv(out_dir + 'max_lat_lon5.csv')
decades(df_out_max5).to_csv(out_dir + 'max_lat_lon5_dec.csv')
def process():
# df_out_norm_mo = p.DataFrame()
# df_out_new_leap_mo = p.DataFrame()
df_out_norm_yr = p.DataFrame()
df_out_new_leap_yr = p.DataFrame()
df_out_tropical_yr = p.DataFrame()
# = p.DataFrame()
for file in files.lat_lon_files(DIR, SHORT_NAME):
df = p.read_csv(file)
years = find_full_years(df)
if len(years) > 0:
cols = files.lat_lon_cols(df)
# only use the mean of the col
# use the first lat_lon as the name for the new column
lat_lon = cols[0]
# df_out_norm_mo[lat_lon] = calc_normal_leap_mo(df, years)['temp']
# df_out_new_leap_mo[lat_lon] = calc_new_leap_mo(df, years)['temp']
df_out_norm_yr[lat_lon] = calc_normal_leap_yr(df, years)['temp']
df_out_new_leap_yr[lat_lon] = calc_new_leap_yr(df, years)['temp']
df_out_tropical_yr[lat_lon] = calc_tropical_yr(df, years)['temp']
# df_out_no_leap_mo[lat_lon] = no_leap(df, years)['temp']
print('processed ' + lat_lon)
# df_out_norm_mo.reset_index(inplace=True)
# df_out_new_leap_mo.reset_index(inplace=True)
df_out_norm_yr.reset_index(inplace=True)
df_out_new_leap_yr.reset_index(inplace=True)
df_out_tropical_yr.reset_index(inplace=True)
# df_out_no_leap_mo.reset_index(inplace=True)
# df_out_norm_mo.to_csv(OUT_DIR + 'normal_mo.csv')
# df_out_new_leap_mo.to_csv(OUT_DIR + 'new_leap_mo.csv')
# df_out_no_leap_mo.to_csv(OUT_DIR + 'no_leap_mo.csv')
df_out_norm_yr.to_csv(OUT_DIR + 'normal_yr.csv')
df_out_new_leap_yr.to_csv(OUT_DIR + 'new_leap_yr.csv')
df_out_tropical_yr.to_csv(OUT_DIR + 'tropical_yr.csv')
# df_out_no_leap_yr = df_out_no_leap_mo.groupby('year').mean()
# df_out_no_leap_yr.to_csv(OUT_DIR + 'no_leap_yr.csv')
return True