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plots_dataset_version_comparison.py
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plots_dataset_version_comparison.py
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# -*- coding: utf-8 -*-
"""
@author: bav@geus.dk
tip list:
%matplotlib inline
%matplotlib qt
import pdb; pdb.set_trace()
"""
import matplotlib.pyplot as plt
import pandas as pd
import os
import matplotlib
matplotlib.use('Agg')
import tocgen
new_version = 'aws-l3-dev'
old_version = 'V19'
if old_version == 'aws-l3':
path_old = '../aws-l3/level_3/'
else:
path_old = 'C:/Users/bav/Downloads/'+old_version+'/hour/'
if 'thredds' in new_version:
path_new = 'C:/Users/bav/GitHub/PROMICE data/thredds-dev/level_3_sites/'
df_meta = pd.read_csv(path_new+'../AWS_latest_locations.csv')
df_meta2 = pd.read_csv(path_new+'../AWS_stations_metadata.csv')
elif 'dev' in new_version:
path_new = 'C:/Users/bav/GitHub/PROMICE data/aws-l3-dev/stations/'
df_meta = pd.read_csv(path_new+'../AWS_latest_locations.csv')
df_meta2 = pd.read_csv(path_new+'../AWS_stations_metadata.csv')
else:
path_new = '../aws-l3/'
df_meta = pd.read_csv(path_new+'/AWS_latest_locations.csv')
df_meta2 = pd.read_csv(path_new+'/AWS_metadata.csv')
path_new = '../aws-l3/level_3/'
# path_new = 'C:/Users/bav/Downloads/V15/hour/'
# path_new = 'https://thredds.geus.dk/thredds/fileServer/aws_l3_station_csv/level_3/'
# path_new = 'https://thredds.geus.dk/thredds/fileServer/aws_l3_time_csv/level_3/hour/'
# path_new = 'https://thredds.geus.dk/thredds/dodsC/aws_l3_time_netcdf/level_3/hour/'
from datetime import date
today = date.today().strftime("%Y%m%d")
filename = 'plot_compilations/'+old_version+'_versus_'+new_version+'.md' #'_'+today+'.md'
figure_folder='figures/'+old_version+'_versus_'+new_version #+'_'+today
try:
os.mkdir(figure_folder)
except:
pass
f = open(filename, "w")
def Msg(txt):
f = open(filename, "a")
print(txt)
f.write(txt + "\n")
Msg('# Comparison of data '+new_version+' to '+old_version+' (old).')
plt.close('all')
#%%
import toml
import xarray as xr
import numpy as np
# for station in ['QAS_M']: #
for station in np.unique(pd.concat((df_meta.stid,df_meta2.station_id))):
Msg('## '+station)
# if path_new == 'aws-l3-dev':
# config_path = '../aws-l0/metadata/station_configurations/'+station+'.toml'
# with open(config_path, 'r') as file:
# data = toml.load(file) # Load the TOML file
# station_save=station
# station = data.get("station_site") # Get the station site
file = path_new+station+'_hour.csv'
try:
df_new = pd.read_csv(file, index_col=0, parse_dates=True)
except:
file = path_new+station+'/'+station+'_hour.csv'
if os.path.isfile(file):
df_new = pd.read_csv(file, index_col=0, parse_dates=True)
else:
Msg('No new file for this station')
continue
# df_new = pd.read_csv('../aws-l3/'+station+'_hour.csv', index_col=0, parse_dates=True)
# df_new = pd.read_csv(
# 'https://thredds.geus.dk/thredds/fileServer/aws_l3_station_csv/level_3/'+station+'/'+station+'_hour.csv',
# index_col=0, parse_dates=True)
# if not os.path.isfile(path_old+'/'+station+'_hour.csv'):
# Msg(path_old+'/'+station+'_hour.csv cannot be found in old data')
# continue
# if path_l3 == 'aws-l3-dev':
# station = station_save
df_old = pd.DataFrame()
df_old['time'] = df_new.index.values
file = path_old+station+'/'+station+'_hour.csv'
if not os.path.isfile(file):
file = path_old+station+'_hour.csv'
if not os.path.isfile(file):
Msg('cannot find old file for '+station)
if os.path.isfile(file):
print(file)
df_old = pd.read_csv(file)
df_old.time = pd.to_datetime(df_old.time, utc=True)
df_old = df_old.set_index('time')
Msg('Variables in new file:\n'+ ', '.join(df_new.columns.values))
Msg('\nNew variables not in old files:\n'+ ', '.join(
[v for v in df_new.columns if v not in df_old.columns]
))
Msg('\nOld variables removed from new files:\n'+ ', '.join(
[v for v in df_old.columns if v not in df_new.columns]
))
Msg(' ')
var_list = df_new.columns.values
var_list_list = [var_list[i:i+5] for i in range(0, len(var_list), 5)]
# var_list_list = [ ['gps_lat','gps_lon','gps_alt']]
# var_list_list = [ ['t_i','p_i','rh_i_cor']]
# ['dlr','ulr','t_rad'],
# ['dsr','usr'],
# ['rh_u','rh_l','rh_u_cor','rh_l_cor']
# ]
for k, var_list in enumerate(var_list_list):
fig, ax_list = plt.subplots(len(var_list),1,sharex=True, figsize=(13,13))
if len(var_list)==1:
ax_list = [ax_list]
for var, ax in zip(var_list, ax_list):
ax.set_ylabel(var)
try:
ax.plot(df_old[var].index, df_old[var].values,
marker='^',linestyle='None', label=old_version,
alpha=0.7, color='tab:blue')
except:
print(var,'not in old data')
ax.plot(df_new[var].index, df_new[var].values,
marker='.',markeredgecolor='None', linestyle='None',
label=new_version, alpha=0.7,
color='tab:orange')
ax.legend()
ax.grid()
plt.suptitle('%s %i/%i'%(station, k+1, len(var_list_list)))
fig.savefig(figure_folder+'/%s_%i.png'%(station,k), dpi =120)
Msg(''%(station, figure_folder, station,k))
Msg(' ')
tocgen.processFile(filename, filename[:-3]+"_toc.md")
f.close()
# os.remove(filename)
# os.rename(filename[:-3]+"_toc.md", filename)
# if __name__ == '__main__':
# main()
# %% scatter version
figure_folder='figures/'+old_version+'_versus_'+new_version +'_scatter'
filename = 'plot_compilations/'+old_version+'_versus_'+new_version+'_scatter.md' #'_'+today+'.md'
try:
os.mkdir(figure_folder)
except:
pass
f = open(filename, "w")
def Msg(txt):
f = open(filename, "a")
print(txt)
f.write(txt + "\n")
import xarray as xr
import numpy as np
# for station in ['NAE']: #
for station in np.unique(pd.concat((df_meta.stid,df_meta2.station_id))):
Msg('## '+station)
file = path_new+station+'_hour.csv'
try:
df_new = pd.read_csv(file, index_col=0, parse_dates=True)
except:
file = path_new+station+'/'+station+'_hour.csv'
if os.path.isfile(file):
df_new = pd.read_csv(file, index_col=0, parse_dates=True)
else:
Msg('No new file for this station')
continue
try:
file = path_old+station+'/'+station+'_hour.csv'
df_old = pd.read_csv(file)
except:
file = path_old+station+'_hour.csv'
if os.path.isfile(file):
df_old = pd.read_csv(file, index_col=0, parse_dates=True)
else:
Msg('No old file for this station')
continue
Msg('Variables in new file:\n'+ ', '.join(df_new.columns.values))
Msg('\nNew variables not in old files:\n'+ ', '.join(
[v for v in df_new.columns if v not in df_old.columns]
))
Msg('\nOld variables removed from new files:\n'+ ', '.join(
[v for v in df_old.columns if v not in df_new.columns]
))
Msg(' ')
var_list = df_new.columns.intersection(df_old.columns)
var_list = [v for v in var_list if df_new[v].notnull().any() and df_old[v].notnull().any()]
var_list_list = [var_list[i:i+9] for i in range(0, len(var_list), 9)]
# var_list_list = [['gps_lat','gps_lon','gps_alt'],
# ['dlr','ulr','t_rad'],
# ['rh_u','rh_l','rh_u_cor','rh_l_cor']]
for k, var_list in enumerate(var_list_list):
fig, ax_list = plt.subplots(3,3, figsize=(15,15))
ax_list = ax_list.flatten()
if len(var_list)==1:
fig, ax_list = plt.subplots(1,1, figsize=(15,15))
ax_list = [ax_list]
for var, ax in zip(var_list, ax_list):
ax.set_xlabel(var +' old')
ax.set_ylabel(var +' new')
try:
msk = df_old[var].index.intersection(df_new[var].index)
ax.plot(df_old.loc[msk, var].values, df_new.loc[msk,var].values,
marker='.',markeredgecolor='None', linestyle='None',
label='_nolegend_', alpha=0.7, c='k')
except:
print('error',var)
ax.grid()
plt.suptitle('%s %i/%i'%(station, k+1, len(var_list_list)))
fig.savefig(figure_folder+'/%s_%i.png'%(station,k), dpi =120)
Msg(''%(station, figure_folder, station,k))
Msg(' ')
tocgen.processFile(filename, filename[:-3]+"_toc.md")
f.close()