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val_lib.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Aug 11 13:23:03 2020
@author: bav
"""
import pandas as pd
import numpy as np
import bav_lib as bl
import rasterio as rio
import os
import matplotlib.pyplot as plt
#%%
def load_cook_data():
print('loading Cook\'s field measurements')
data_cook = pd.concat((pd.read_csv('data/Cook_data/Hbio_albedo_JB.csv'),
pd.read_csv('data/Cook_data/Lbio_albedo_JB.csv')),
axis=1)
data_cook=data_cook.drop( columns='Unnamed: 0' )
# creating measurements' labels from column names
col = data_cook.columns.values.astype('U')
tmp = pd.DataFrame(np.char.split(col,sep="_"))
labels = pd.DataFrame()
labels[['bio_load', 'day', 'month', 'sitename']] = pd.DataFrame(tmp[0].tolist(), index= tmp.index)
labels['samplename'] = labels['bio_load'] +'_'+labels['day'] +'_'+labels['month'] + '_17_'++labels['sitename']
WL = pd.read_csv('data/Cook_data/Wavlengths.csv',header=None).values
lambda_names = ['lambda_' + str(x[0]) for x in WL]
data_cook=data_cook.transpose()
data_cook.columns = lambda_names
data_cook['samplename'] = labels['samplename'].values
# data_cook['bio_load'] = labels['bio_load'].values
# data_cook['day'] = labels['day'].values
# data_cook['month'] = labels['month'].values
# data_cook['sitename'] = labels['sitename'].values
data_cook=data_cook.reset_index()
data_cook=data_cook.set_index(['samplename'])
data_cook=data_cook.drop(columns='index')
print('Loading Cook\'s metadata')
from openpyxl import load_workbook
wb = load_workbook(filename='data/Cook_data/metadata_JB.xlsx',
read_only=True)
ws = wb['Sheet1']
# Read the cell values into a list of lists
data_rows = []
for row in ws['A2':'H18']:
data_cols = []
for cell in row:
data_cols.append(cell.value)
data_rows.append(data_cols)
# Transform into dataframe
df = pd.DataFrame(data_rows)
df.columns=df.iloc[0,:].values
df=df.drop(index=0,axis=1)
# df[['day','month','year',"sitename"]]= df["Site Name"].str.split("_", n = 4, expand = True)
df['bio_load']= 'HA'
data_rows = []
for row in ws['A21':'H34']:
data_cols = []
for cell in row:
data_cols.append(cell.value)
data_rows.append(data_cols)
# Transform into dataframe
df2 = pd.DataFrame(data_rows)
df2.columns=df2.iloc[0,:].values
df2=df2.drop(index=0,axis=1)
# df2[['day','month','year',"sitename"]]= df2["Site Name"].str.split("_", n = 4, expand = True)
df2['bio_load']= 'LA'
metadata = pd.concat((df,df2))
metadata['samplename']= metadata['bio_load']+'_'+metadata['Site Name']
metadata=metadata.set_index(['samplename'])
metadata=metadata.drop(columns=['Site Name','bio_load'])
metadata= metadata.drop('HA_14_7_17_SB7',axis=0)
metadata= metadata.loc[metadata['Cloud Cover'].str.extract('(\d+)').astype(int).values<=3]
return(data_cook, metadata, WL)
#%%
def plot_sice_output(InputFolder, var_list = ('albedo_bb_planar_sw','albedo_bb_spherical_sw')):
#% Plotting output
try:
os.mkdir(InputFolder+'plots')
except:
print('folder exist')
fig,ax=bl.heatmap_discrete(rio.open(InputFolder+'diagnostic_retrieval.tif').read(1),
'diagnostic_retrieval ')
ax.set_title(InputFolder)
fig.savefig(InputFolder+'plots/diagnostic_retrieval.png',bbox_inches='tight')
for i in range(len(var_list)):
var_1 = rio.open(InputFolder+var_list[i]+'.tif').read(1)
plt.figure(figsize=(10,15))
bl.heatmap(var_1,var_list[i], col_lim=(0, 1) ,cmap_in='jet')
plt.title(InputFolder)
plt.savefig(InputFolder+'plots/'+var_list[i]+'.png',bbox_inches='tight')
plt.close()
var_list = ('O3_SICE', 'grain_diameter', 'snow_specific_area',
'al','conc','r0')
for i in range(len(var_list)):
var_1 = rio.open(InputFolder+var_list[i]+'.tif').read(1)
plt.figure(figsize=(10,15))
bl.heatmap(var_1,var_list[i],cmap_in='jet')
plt.title(InputFolder)
plt.savefig(InputFolder+'plots/'+var_list[i]+'.png',bbox_inches='tight')
plt.close()
for i in np.arange(21):
var_name = 'albedo_spectral_spherical_'+ str(i+1).zfill(2)
try:
var_1 = rio.open(InputFolder+var_name+'.tif').read(1)
except:
continue
plt.figure(figsize=(10,15))
bl.heatmap(var_1,var_name, col_lim=(0, 1) ,cmap_in='jet')
plt.title(InputFolder)
plt.savefig(InputFolder+'plots/'+var_name+'.png',bbox_inches='tight')
plt.close()
var_name = 'albedo_spectral_planar_'+ str(i+1).zfill(2)
var_1 = rio.open(InputFolder+var_name+'.tif').read(1)
plt.figure(figsize=(10,15))
bl.heatmap(var_1,var_name, col_lim=(0, 1) ,cmap_in='jet')
plt.title(InputFolder)
plt.savefig(InputFolder+'plots/'+var_name+'.png',bbox_inches='tight')
plt.close()
var_name = 'rBRR_'+ str(i+1).zfill(2)
var_1 = rio.open(InputFolder+var_name+'.tif').read(1)
plt.figure(figsize=(10,15))
bl.heatmap(var_1,var_name, col_lim=(0, 1) ,cmap_in='jet')
plt.title(InputFolder)
plt.savefig(InputFolder+'plots/'+var_name+'.png',bbox_inches='tight')
plt.close()
var_name = 'r_TOA_'+ str(i+1).zfill(2)
var_1 = rio.open(InputFolder+var_name+'.tif').read(1)
plt.figure(figsize=(10,15))
bl.heatmap(var_1,var_name, col_lim=(0, 1) ,cmap_in='jet')
plt.title(InputFolder)
plt.savefig(InputFolder+'plots/'+var_name+'.png',bbox_inches='tight')
plt.close()