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make_maps.py
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make_maps.py
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import argparse, os
import shutil
import numpy.ma
from netCDF4 import Dataset
from matplotlib import pyplot as plt
from matplotlib.colors import LogNorm
import matplotlib.ticker as mticker
import cartopy.crs as ccrs
import cartopy
from datetime import datetime as dt
import numpy as np
import matplotlib as mpl
import matplotlib.colors as colors
from matplotlib.colors import ListedColormap
parser = argparse.ArgumentParser(description="Make maps launcher")
parser.add_argument("-v", "--verbose", help="Verbose mode.", action="store_true")
parser.add_argument("-i", "--input_path", help="Input path", required=True)
parser.add_argument("-o", "--output_path", help="Output path")
args = parser.parse_args()
def compute_diff():
from datetime import datetime as dt
from datetime import timedelta
dir_base_new = '/store3/OC/CCI_v2017/daily_v202411'
format_name = 'M$DATE1$.0000.bal.all_products.CCI.$DATE2$0000.v0.$DATE1$0000.data_BAL202411.nc'
dir_base_old = '/store3/OC/CCI_v2017/daily_v202207'
dir_base_dif = '/store3/OC/CCI_v2017/daily_diff'
date_here = dt(2018, 6, 1)
end_date = dt(2018, 9, 30)
while date_here <= end_date:
print('-------------------------------------------------------------------')
print(date_here)
date1 = date_here.strftime('%Y%j')
date2 = date_here.strftime('%d%b%y')
name = format_name.replace('$DATE1$', date1)
name = name.replace('$DATE2$', date2)
file_new = os.path.join(dir_base_new, name)
dir_old = os.path.join(dir_base_old, date_here.strftime('%Y'), date_here.strftime('%j'))
file_old = os.path.join(dir_old, f'C{date1}-chl-bal-hr.nc')
if not os.path.exists(file_old) or not os.path.exists(file_new):
date_here = date_here + timedelta(hours=24)
continue
print(file_new)
print(file_old)
dataset_old = Dataset(file_old)
chl_old = dataset_old.variables['CHL'][:]
chl_old = chl_old.squeeze()
chl_old = np.flipud(chl_old)
dataset_old.close()
dataset_new = Dataset(file_new)
chl_new = dataset_new.variables['CHL'][:]
lat_array = dataset_new.variables['latitude'][:]
lon_array = dataset_new.variables['longitude'][:]
##getting cdf_flag_multiple and weights
cdf_mlp3b = dataset_new.variables['CDF_MLP3B'][:]
cdf_mlp4b = dataset_new.variables['CDF_MLP4B'][:]
cdf_mlp5b = dataset_new.variables['CDF_MLP5B'][:]
cdf_mask_mlp3b = np.ma.where(cdf_mlp3b >= 0.001, 2, 0)
cdf_mask_mlp4b = np.ma.where(cdf_mlp4b >= 0.001, 4, 0)
cdf_mask_mlp5b = np.ma.where(cdf_mlp5b >= 0.001, 8, 0)
cdf_flag_multiple = np.ma.filled(cdf_mask_mlp3b, 0) + np.ma.filled(cdf_mask_mlp4b, 0) + np.ma.filled(
cdf_mask_mlp5b, 0)
cdf_flag_multiple[cdf_flag_multiple == 0] = 1
cdf_flag_multiple = np.ma.array(cdf_flag_multiple,
mask=(cdf_mlp3b.mask * cdf_mask_mlp4b.mask * cdf_mask_mlp5b.mask))
cdf_mlp3b = np.ma.masked_less(cdf_mlp3b, 0.001)
cdf_mlp4b = np.ma.masked_less(cdf_mlp4b, 0.001)
cdf_mlp5b = np.ma.masked_less(cdf_mlp5b, 0.001)
cdf_sum = np.ma.filled(cdf_mlp3b, 0) + np.ma.filled(cdf_mlp4b, 0) + np.ma.filled(cdf_mlp5b, 0)
weight_mlp3b = np.ma.divide(cdf_mlp3b, cdf_sum)
weight_mlp4b = np.ma.divide(cdf_mlp4b, cdf_sum)
weight_mlp5b = np.ma.divide(cdf_mlp5b, cdf_sum)
##chl dif
chl_dif = chl_old - chl_new
##chl percent difference
chl_percent_diff = 100 * ((chl_new - chl_old) / chl_old)
chl_abs_percent_diff = np.ma.abs(chl_percent_diff)
##coverage
coverage = np.zeros(chl_old.shape)
coverage[~chl_old.mask] = coverage[~chl_old.mask] + 1
coverage[~chl_new.mask] = coverage[~chl_new.mask] + 2
file_out = os.path.join(dir_base_dif, f'Diff_Completed_{date1}.nc')
nc_out = Dataset(file_out, 'w')
# copy dimensions
for name, dimension in dataset_new.dimensions.items():
nc_out.createDimension(
name, (len(dimension) if not dimension.isunlimited() else None))
nc_out.createVariable('latitude', 'f4', ('lat',), fill_value=-999.0, zlib=True, complevel=6)
nc_out['latitude'][:] = lat_array
nc_out.createVariable('longitude', 'f4', ('lon',), fill_value=-999.0, zlib=True, complevel=6)
nc_out['longitude'][:] = lon_array
nc_out.createVariable('chl_old', 'f4', ('lat', 'lon'), fill_value=-999.0, zlib=True, complevel=6)
nc_out['chl_old'][:] = chl_old
nc_out.createVariable('chl_new', 'f4', ('lat', 'lon'), fill_value=-999.0, zlib=True, complevel=6)
nc_out['chl_new'][:] = chl_new
nc_out.createVariable('chl_dif', 'f4', ('lat', 'lon'), fill_value=-999.0, zlib=True, complevel=6)
nc_out['chl_dif'][:] = chl_dif
nc_out.createVariable('coverage', 'i4', ('lat', 'lon'), fill_value=-999.0, zlib=True, complevel=6)
nc_out['coverage'][:] = coverage
nc_out.createVariable('cdf_flag_multiple', 'i4', ('lat', 'lon'), fill_value=-999.0, zlib=True, complevel=6)
nc_out['cdf_flag_multiple'][:] = cdf_flag_multiple
nc_out.createVariable('weight_mlp_3b', 'f4', ('lat', 'lon'), fill_value=-999.0, zlib=True, complevel=6)
nc_out['weight_mlp_3b'][:] = weight_mlp3b
nc_out.createVariable('weight_mlp_4b', 'f4', ('lat', 'lon'), fill_value=-999.0, zlib=True, complevel=6)
nc_out['weight_mlp_4b'][:] = weight_mlp4b
nc_out.createVariable('weight_mlp_5b', 'f4', ('lat', 'lon'), fill_value=-999.0, zlib=True, complevel=6)
nc_out['weight_mlp_5b'][:] = weight_mlp5b
nc_out.createVariable('chl_rpd', 'f4', ('lat', 'lon'), fill_value=-999.0, zlib=True, complevel=6)
nc_out['chl_rpd'][:] = chl_percent_diff
nc_out.createVariable('chl_apd', 'f4', ('lat', 'lon'), fill_value=-999.0, zlib=True, complevel=6)
nc_out['chl_apd'][:] = chl_abs_percent_diff
nc_out.close()
dataset_new.close()
date_here = date_here + timedelta(hours=24)
return True
##using complete diff files, local
def compare_old_new_v2():
from datetime import datetime as dt
from datetime import timedelta
dir_daily_diff = '/mnt/c/DATA_LUIS/OCTAC_WORK/MATCH-UPS_ANALYSIS_2024/BAL/CODE_DAVIDE_2024/daily_diff'
file_out = '/mnt/c/DATA_LUIS/OCTAC_WORK/MATCH-UPS_ANALYSIS_2024/BAL/CODE_DAVIDE_2024/CoverageNew_v2.nc'
date_here = dt(2018, 6, 1)
end_date = dt(2018, 9, 30)
sum_rpd_cdf = np.ma.zeros((1147, 1185))
sum_apd_cdf = np.ma.zeros((1147, 1185))
n_cdf_total = np.ma.zeros((1147, 1185))
sum_rpd_nocdf = np.ma.zeros((1147, 1185))
sum_apd_nocdf = np.ma.zeros((1147, 1185))
n_nocdf_total = np.ma.zeros((1147, 1185))
sum_rpd = np.ma.zeros((1147, 1185))
sum_apd = np.ma.zeros((1147, 1185))
n_total = np.ma.zeros((1147, 1185))
sum_rpd_cdf_complete = np.ma.zeros((1147, 1185))
sum_apd_cdf_complete = np.ma.zeros((1147, 1185))
n_cdf_complete_total = np.ma.zeros((1147, 1185))
sum_weight_mpl3 = np.ma.zeros((1147, 1185))
sum_weight_mpl4 = np.ma.zeros((1147, 1185))
sum_weight_mpl5 = np.ma.zeros((1147, 1185))
nc_out = None
while date_here <= end_date:
date = date_here.strftime('%Y%j')
file_diff = os.path.join(dir_daily_diff, f'Diff_Completed_{date}.nc')
# print(file_diff)
if not os.path.exists(file_diff):
date_here = date_here + timedelta(hours=24)
continue
print('-------------------------------------------------------------------')
print(date_here)
dataset = Dataset(file_diff)
if nc_out is None:
print('Starting nc_out')
nc_out = Dataset(file_out, 'w')
# copy dimensions
for name, dimension in dataset.dimensions.items():
nc_out.createDimension(name, (len(dimension) if not dimension.isunlimited() else None))
lat_array = dataset.variables['latitude'][:]
lon_array = dataset.variables['longitude'][:]
nc_out.createVariable('latitude', 'f4', ('lat',), fill_value=-999.0, zlib=True, complevel=6)
nc_out['latitude'][:] = lat_array
nc_out.createVariable('longitude', 'f4', ('lon',), fill_value=-999.0, zlib=True, complevel=6)
nc_out['longitude'][:] = lon_array
flag_multiple = dataset.variables['cdf_flag_multiple'][:]
chl_rpd = dataset.variables['chl_rpd'][:]
chl_apd = dataset.variables['chl_apd'][:]
sum_rpd_cdf[flag_multiple >= 2] = sum_rpd_cdf[flag_multiple >= 2] + chl_rpd[flag_multiple >= 2]
sum_apd_cdf[flag_multiple >= 2] = sum_apd_cdf[flag_multiple >= 2] + chl_apd[flag_multiple >= 2]
n_cdf_total[flag_multiple >= 2] = n_cdf_total[flag_multiple >= 2] + 1
sum_rpd_nocdf[flag_multiple == 1] = sum_rpd_nocdf[flag_multiple == 1] + chl_rpd[flag_multiple == 1]
sum_apd_nocdf[flag_multiple == 1] = sum_apd_nocdf[flag_multiple == 1] + chl_apd[flag_multiple == 1]
n_nocdf_total[flag_multiple == 1] = n_nocdf_total[flag_multiple == 1] + 1
sum_rpd[flag_multiple >= 1] = sum_rpd[flag_multiple >= 1] + chl_rpd[flag_multiple >= 1]
sum_apd[flag_multiple >= 1] = sum_apd[flag_multiple >= 1] + chl_apd[flag_multiple >= 1]
n_total[flag_multiple >= 1] = n_total[flag_multiple >= 1] + 1
sum_rpd_cdf_complete[flag_multiple == 14] = sum_rpd_cdf_complete[flag_multiple == 14] + chl_rpd[
flag_multiple == 14]
sum_apd_cdf_complete[flag_multiple == 14] = sum_apd_cdf_complete[flag_multiple == 14] + chl_apd[
flag_multiple == 14]
n_cdf_complete_total[flag_multiple == 14] = n_cdf_complete_total[flag_multiple == 14] + 1
weight_mpl3 = dataset.variables['weight_mlp_3b'][:]
sum_weight_mpl3 = sum_weight_mpl3 + np.ma.filled(weight_mpl3, 0)
weight_mpl4 = dataset.variables['weight_mlp_4b'][:]
sum_weight_mpl4 = sum_weight_mpl4 + np.ma.filled(weight_mpl4, 0)
weight_mpl5 = dataset.variables['weight_mlp_5b'][:]
sum_weight_mpl5 = sum_weight_mpl5 + np.ma.filled(weight_mpl5, 0)
dataset.close()
date_here = date_here + timedelta(hours=24)
n_cdf_total = np.ma.masked_equal(n_cdf_total, 0)
n_nocdf_total = np.ma.masked_equal(n_nocdf_total, 0)
n_total = np.ma.masked_equal(n_total, 0)
n_cdf_complete_total = np.ma.masked_equal(n_cdf_complete_total, 0)
coverage_cdf = (n_cdf_total / n_total) * 100
coverage_no_cdf = (n_nocdf_total / n_total) * 100
mrpd_cdf = sum_rpd_cdf / n_cdf_total
mapd_cdf = sum_apd_cdf / n_cdf_total
mrpd_nocdf = sum_rpd_nocdf / n_nocdf_total
mapd_nocdf = sum_apd_nocdf / n_nocdf_total
mrpd = sum_rpd / n_total
mapd = sum_apd / n_total
mrpd_cdf_complete = sum_rpd_cdf_complete / n_cdf_complete_total
mapd_cdf_complete = sum_apd_cdf_complete / n_cdf_complete_total
coverage_mlp3 = (sum_weight_mpl3 / n_cdf_total) * 100
coverage_mlp4 = (sum_weight_mpl4 / n_cdf_total) * 100
coverage_mlp5 = (sum_weight_mpl5 / n_cdf_total) * 100
variable_list = {
'n_total': n_total,
'n_cdf_total': n_cdf_total,
'n_nocdf_total': n_nocdf_total,
'coverage_cdf': coverage_cdf,
'coverage_nocdf': coverage_no_cdf,
'mrpd_cdf': mrpd_cdf,
'mapd_cdf': mapd_cdf,
'mrpd_nocdf': mrpd_nocdf,
'mapd_nocdf': mapd_nocdf,
'mrpd': mrpd,
'mapd': mapd,
'mrpd_cdf_complete': mrpd_cdf_complete,
'mapd_cdf_complete': mapd_cdf_complete,
'coverage_mlp3': coverage_mlp3,
'coverage_mlp4': coverage_mlp4,
'coverage_mlp5': coverage_mlp5,
}
for name_var in variable_list.keys():
array = variable_list[name_var]
nc_out.createVariable(name_var, 'f4', ('lat', 'lon'), fill_value=-999.0, zlib=True, complevel=6)
nc_out[name_var][:] = array
nc_out.close()
return True
##using incomplete diff files
def compare_old_new_v1():
from datetime import datetime as dt
from datetime import timedelta
dir_base_new = '/store3/OC/CCI_v2017/daily_v202411'
format_name = 'M$DATE1$.0000.bal.all_products.CCI.$DATE2$0000.v0.$DATE1$0000.data_BAL202411.nc'
dir_base_old = '/store3/OC/CCI_v2017/daily_diff'
file_comparison_old_new = '/store3/OC/CCI_v2017/daily_v202411/Comparison_old_new.csv'
fw = open(file_comparison_old_new, 'w')
fw.write('Date;Chl-aOld;Chl-aNew;FlagMultiple')
date_here = dt(2018, 6, 1)
end_date = dt(2018, 9, 30)
weight_mlp3b_total = np.ma.zeros((1147, 1185))
weight_mlp4b_total = np.ma.zeros((1147, 1185))
weight_mlp5b_total = np.ma.zeros((1147, 1185))
n_cdf_total = np.ma.zeros((1147, 1185))
n_nocdf_total = np.ma.zeros((1147, 1185))
file_out = '/store3/OC/CCI_v2017/daily_v202411/CoverageNew.nc'
nc_out = None
while date_here <= end_date:
print('-------------------------------------------------------------------')
print(date_here)
date1 = date_here.strftime('%Y%j')
date2 = date_here.strftime('%d%b%y')
name = format_name.replace('$DATE1$', date1)
name = name.replace('$DATE2$', date2)
file_new = os.path.join(dir_base_new, name)
file_old = os.path.join(dir_base_old, f'Diff_{date1}.nc')
if os.path.exists(file_new) and os.path.exists(file_old):
dataset_old = Dataset(file_old)
chla_new = dataset_old.variables['chl_new'][:]
chla_old = dataset_old.variables['chl_old'][:]
coverage = dataset_old.variables['coverage'][:]
dataset_old.close()
dataset = Dataset(file_new)
cdf_mlp3b = dataset.variables['CDF_MLP3B'][:]
cdf_mlp4b = dataset.variables['CDF_MLP4B'][:]
cdf_mlp5b = dataset.variables['CDF_MLP5B'][:]
if nc_out is None:
nc_out = Dataset(file_out, 'w')
# copy dimensions
for name, dimension in dataset.dimensions.items():
nc_out.createDimension(name, (len(dimension) if not dimension.isunlimited() else None))
lat_array = dataset.variables['latitude'][:]
lon_array = dataset.variables['longitude'][:]
nc_out.createVariable('latitude', 'f4', ('lat',), fill_value=-999.0, zlib=True, complevel=6)
nc_out['latitude'][:] = lat_array
nc_out.createVariable('longitude', 'f4', ('lon',), fill_value=-999.0, zlib=True, complevel=6)
nc_out['longitude'][:] = lon_array
dataset.close()
cdf_mask_mlp3b = np.ma.where(cdf_mlp3b >= 0.001, 2, 0)
cdf_mask_mlp4b = np.ma.where(cdf_mlp4b >= 0.001, 4, 0)
cdf_mask_mlp5b = np.ma.where(cdf_mlp5b >= 0.001, 8, 0)
cdf_flag_multiple = cdf_mask_mlp3b + cdf_mask_mlp4b + cdf_mask_mlp5b
cdf_flag_multiple[cdf_flag_multiple == 0] = 1
cdf_mlp3b = np.ma.masked_less(cdf_mlp3b, 0.001)
cdf_mlp4b = np.ma.masked_less(cdf_mlp4b, 0.001)
cdf_mlp5b = np.ma.masked_less(cdf_mlp5b, 0.001)
cdf_mlp3b[np.logical_and(cdf_mlp3b.mask, cdf_flag_multiple >= 2)] = 0
cdf_mlp4b[np.logical_and(cdf_mlp4b.mask, cdf_flag_multiple >= 2)] = 0
cdf_mlp5b[np.logical_and(cdf_mlp5b.mask, cdf_flag_multiple >= 2)] = 0
cdf_sum = cdf_mlp3b + cdf_mlp4b + cdf_mlp5b
weight_mlp3b = np.ma.divide(cdf_mlp3b, cdf_sum)
weight_mlp4b = np.ma.divide(cdf_mlp4b, cdf_sum)
weight_mlp5b = np.ma.divide(cdf_mlp5b, cdf_sum)
weight_mlp3b_total[weight_mlp3b > 0] = weight_mlp3b_total[weight_mlp3b > 0] + weight_mlp3b[weight_mlp3b > 0]
weight_mlp4b_total[weight_mlp4b > 0] = weight_mlp4b_total[weight_mlp4b > 0] + weight_mlp4b[weight_mlp4b > 0]
weight_mlp5b_total[weight_mlp5b > 0] = weight_mlp5b_total[weight_mlp5b > 0] + weight_mlp4b[weight_mlp5b > 0]
n_cdf_total[cdf_flag_multiple >= 2] = n_cdf_total[cdf_flag_multiple >= 2] + 1
n_nocdf_total[cdf_flag_multiple == 1] = n_nocdf_total[cdf_flag_multiple == 1] + 1
values_new = chla_new[coverage == 3].flatten()
values_old = chla_old[coverage == 3].flatten()
flag_multiple = cdf_flag_multiple[coverage == 3].flatten()
if len(values_new) > 0:
for idx in range(len(values_new)):
fw.write('\n')
fw.write(
f'{date_here.strftime("%Y-%m-%d")};{values_old[idx]};{values_new[idx]};{flag_multiple[idx]}')
date_here = date_here + timedelta(hours=24)
fw.close()
##coverage file
weight_mlp3b_total = np.ma.masked_equal(weight_mlp3b_total, 0)
weight_mlp4b_total = np.ma.masked_equal(weight_mlp4b_total, 0)
weight_mlp5b_total = np.ma.masked_equal(weight_mlp5b_total, 0)
n_total = n_cdf_total + n_nocdf_total
n_cdf_total = np.ma.masked_equal(n_cdf_total, 0)
n_nocdf_total = np.ma.masked_equal(n_nocdf_total, 0)
n_total = np.ma.masked_equal(n_total, 0)
coverage_cdf = (n_cdf_total / n_total) * 100
coverage_no_cdf = (n_nocdf_total / n_total) * 100
coverage_cdf_mlp3 = (weight_mlp3b_total / n_cdf_total) * 100
coverage_cdf_mlp4 = (weight_mlp4b_total / n_cdf_total) * 100
coverage_cdf_mlp5 = (weight_mlp5b_total / n_cdf_total) * 100
coverage_total_mlp3 = ((weight_mlp3b_total + n_nocdf_total) / n_total) * 100
variable_list = {
'weight_mlp3b_total': weight_mlp3b_total,
'weight_mlp4b_total': weight_mlp4b_total,
'weight_mlp5b_total': weight_mlp5b_total,
'n_total': n_total,
'n_cdf_total': n_cdf_total,
'n_nocdf_total': n_nocdf_total,
'coverage_cdf': coverage_cdf,
'coverage_nocdf': coverage_no_cdf,
'coverage_cdf_mlp3': coverage_cdf_mlp3,
'coverage_cdf_mlp4': coverage_cdf_mlp4,
'coverage_cdf_mlp5': coverage_cdf_mlp5,
'coverage_total_mlp3': coverage_total_mlp3,
}
for name_var in variable_list.keys():
array = variable_list[name_var]
nc_out.createVariable(name_var, 'f4', ('lat', 'lon'), fill_value=-999.0, zlib=True, complevel=6)
nc_out[name_var][:] = array
nc_out.close()
return True
def plot_coverage():
file_coverage = '/mnt/c/DATA_LUIS/OCTAC_WORK/MATCH-UPS_ANALYSIS_2024/BAL/CODE_DAVIDE_2024/CoverageNew_v2.nc'
dataset = Dataset(file_coverage)
lat_array = dataset.variables['latitude'][:]
lon_array = dataset.variables['longitude'][:]
coverage = dataset.variables['coverage_cdf'][:]
fig, ax = start_full_figure()
h = ax.pcolormesh(lon_array, lat_array, coverage, vmin=0, vmax=100, cmap=mpl.colormaps['jet'])
cbar = fig.colorbar(h, cax=None, ax=ax, use_gridspec=True, fraction=0.03, format="$%.2f$")
cbar.ax.tick_params(labelsize=15)
cbar.set_label(label=f'Coverage(%)', size=15)
title = f'CDF Coverage'
ax.set_title(title)
file_out = os.path.join(os.path.dirname(file_coverage), f'CDFCoverage.png')
fig.savefig(file_out, dpi=300, bbox_inches='tight')
plt.close(fig)
coverage = dataset.variables['coverage_nocdf'][:]
fig, ax = start_full_figure()
h = ax.pcolormesh(lon_array, lat_array, coverage, vmin=0, vmax=100, cmap=mpl.colormaps['jet'])
cbar = fig.colorbar(h, cax=None, ax=ax, use_gridspec=True, fraction=0.03, format="$%.2f$")
cbar.ax.tick_params(labelsize=15)
cbar.set_label(label=f'Coverage(%)', size=15)
title = f'NO CDF Coverage'
ax.set_title(title)
file_out = os.path.join(os.path.dirname(file_coverage), f'NOCDFCoverage.png')
fig.savefig(file_out, dpi=300, bbox_inches='tight')
plt.close(fig)
# coverage_variables = ['coverage_mlp3','coverage_mlp4','coverage_mlp5']
# titles = ['Coverage MLP 3 bands','Coverage MLP 4 bands','Coverage MLP 5 bands']
# for variable,title in zip(coverage_variables,titles):
# coverage = dataset.variables[variable][:]
# fig, ax = start_full_figure()
# h = ax.pcolormesh(lon_array, lat_array, coverage, vmin=0, vmax=100, cmap=mpl.colormaps['jet'])
# cbar = fig.colorbar(h, cax=None, ax=ax, use_gridspec=True, fraction=0.03, format="$%.2f$")
# cbar.ax.tick_params(labelsize=15)
# cbar.set_label(label=f'Coverage(%)', size=15)
# ax.set_title(title)
# file_out = os.path.join(os.path.dirname(file_coverage), f'{variable.upper()}.png')
# fig.savefig(file_out, dpi=300, bbox_inches='tight')
# plt.close(fig)
variables = ['mrpd_cdf', 'mrpd_nocdf', 'mrpd', 'mrpd_cdf_complete']
titles = [f'Mean RPD (%) - Only CDF', f'Mean RPD (%) - Only NO CDF', f'Mean RPD (%) - All data',
f'Mean RPD(%) - Only CDF Complete']
for variable, title in zip(variables, titles):
array = dataset.variables[variable][:]
# ranged color maps
bounds = [-1000, -50, -40, -30, -20, -10, -5, 0, 5, 10, 20, 30, 40, 50, 1000]
norm = colors.BoundaryNorm(boundaries=bounds, ncolors=len(bounds) + 1)
cmap_r = mpl.colormaps['jet'].resampled(len(bounds) + 1)
# continous color maps
cmap_c = mpl.colormaps['jet']
fig, ax = start_full_figure()
# continued
# h = ax.pcolormesh(lon_array, lat_array, array, vmin=-50, vmax=50, cmap=cmap_c)
# range
h = ax.pcolormesh(lon_array, lat_array, array, norm=norm, cmap=cmap_r)
cbar = fig.colorbar(h, cax=None, ax=ax, use_gridspec=True, fraction=0.03, format="$%.2f$")
cbar.ax.tick_params(labelsize=15)
cbar.set_label(label=f'RPD(%)', size=15)
ax.set_title(title)
file_out = os.path.join(os.path.dirname(file_coverage), f'{variable.upper()}.png')
fig.savefig(file_out, dpi=300, bbox_inches='tight')
plt.close(fig)
variables = ['mapd_cdf', 'mapd_nocdf', 'mapd', 'mapd_cdf_complete']
titles = [f'Mean APD (%) - Only CDF', f'Mean APD (%) - Only NO CDF', f'Mean APD (%) - All data',
f'Mean APD(%) - Only CDF Complete']
for variable, title in zip(variables, titles):
array = dataset.variables[variable][:]
# ranged color maps
bounds = [0, 5, 10, 20, 30, 40, 50, 1000]
norm = colors.BoundaryNorm(boundaries=bounds, ncolors=len(bounds) + 1)
cmap_r = mpl.colormaps['jet'].resampled(len(bounds) + 1)
# continous color maps
cmap_c = mpl.colormaps['jet']
fig, ax = start_full_figure()
# continued
# h = ax.pcolormesh(lon_array, lat_array, array, vmin=-50, vmax=50, cmap=cmap_c)
# range
h = ax.pcolormesh(lon_array, lat_array, array, norm=norm, cmap=cmap_r)
cbar = fig.colorbar(h, cax=None, ax=ax, use_gridspec=True, fraction=0.03, format="$%.2f$")
cbar.ax.tick_params(labelsize=15)
cbar.set_label(label=f'APD(%)', size=15)
ax.set_title(title)
file_out = os.path.join(os.path.dirname(file_coverage), f'{variable.upper()}.png')
fig.savefig(file_out, dpi=300, bbox_inches='tight')
plt.close(fig)
dataset.close()
return True
def plot_map_general(file_nc, file_out, variable, title, label, vmin, vmax):
dataset = Dataset(file_nc)
lat_array = dataset.variables['lat'][:]
lon_array = dataset.variables['lon'][:]
data = dataset.variables[variable][:]
print(file_out, np.ma.min(data), np.ma.max(data))
dataset.close()
fig, ax = start_full_figure()
if vmin is not None and vmax is not None:
h = ax.pcolormesh(lon_array, lat_array, data, vmin=vmin, vmax=vmax, cmap=mpl.colormaps['jet'])
else:
h = ax.pcolormesh(lon_array, lat_array, data, cmap=mpl.colormaps['jet'])
cbar = fig.colorbar(h, cax=None, ax=ax, use_gridspec=True, fraction=0.03, format="$%.2f$")
cbar.ax.tick_params(labelsize=15)
cbar.set_label(label=label, size=15)
ax.set_title(title)
fig.savefig(file_out, dpi=300, bbox_inches='tight')
plt.close(fig)
def compute_year_coverage_cci(year):
print(f'COMPUTING COVERAGE FOR YEAR....')
from datetime import timedelta
dir_base = '/store3/OC/CCI_v2017/daily_v202411'
file_out = os.path.join(dir_base, f'COVERAGE_ENSCDF_{year}.nc')
file_in_format = 'MDATE1.0000.bal.all_products.CCI.DATE20000.v0.DATE10000.data_BAL202411.nc'
format_date1 = '%Y%j'
format_date2 = '%d%b%y'
nlat = 1147
nlon = 1185
ntime = 13
date_here = dt(year, 1, 1)
date_end = dt(year, 12, 31)
##arrays
n_total = np.zeros((ntime, nlat, nlon))
n_cdf_total = np.zeros((ntime, nlat, nlon))
n_nocdf_total = np.zeros((ntime, nlat, nlon))
weight_mlp3b_cdf = np.zeros((ntime, nlat, nlon))
weight_mlp4b_cdf = np.zeros((ntime, nlat, nlon))
weight_mlp5b_cdf = np.zeros((ntime, nlat, nlon))
coverage_cdf = np.ma.masked_all((ntime, nlat, nlon))
coverage_no_cdf = np.ma.masked_all((ntime, nlat, nlon))
coverage_cdf_mlp3 = np.ma.masked_all((ntime, nlat, nlon))
coverage_cdf_mlp4 = np.ma.masked_all((ntime, nlat, nlon))
coverage_cdf_mlp5 = np.ma.masked_all((ntime, nlat, nlon))
coverage_total_mlp3 = np.ma.masked_all((ntime, nlat, nlon))
coverage_total_mlp4 = np.ma.masked_all((ntime, nlat, nlon))
coverage_total_mlp5 =np.ma.masked_all((ntime, nlat, nlon))
lat = None
lon = None
while date_here <= date_end:
yyyy = date_here.strftime('%Y')
jjj = date_here.strftime('%j')
date1_str = date_here.strftime(format_date1)
date2_str = date_here.strftime(format_date2)
name_file = file_in_format.replace('DATE1', date1_str)
name_file = name_file.replace('DATE2', date2_str)
file_in = os.path.join(dir_base, yyyy, jjj, name_file)
index_month = int(date_here.month)
if not os.path.exists(file_in):
print(f'[WARNING] Input file {file_in} does not exist. Skipping...')
date_here = date_here + timedelta(hours=24)
continue
print(f'[INFO] Date: {date_here}')
input_dataset = Dataset(file_in)
if lat is None and lon is None:
lat = input_dataset.variables['lat'][:]
lon = input_dataset.variables['lon'][:]
weight_mlp3 = input_dataset.variables['WEIGHT_MLP3B'][:]
weight_mlp4 = input_dataset.variables['WEIGHT_MLP4B'][:]
weight_mlp5 = input_dataset.variables['WEIGHT_MLP5B'][:]
cdf_flag = input_dataset.variables['CDF_FLAG_MULTIPLE'][:]
row,col = np.where(cdf_flag>=1)
n_total[0, row, col] = n_total[0, row, col] + 1
n_total[index_month, row, col] = n_total[index_month, row, col] + 1
row, col = np.where(cdf_flag >= 2)
n_cdf_total[0, row, col] = n_cdf_total[0, row, col] + 1
n_cdf_total[index_month, row, col] = n_cdf_total[index_month, row, col] + 1
row, col = np.where(cdf_flag == 1)
n_nocdf_total[0, row, col] = n_nocdf_total[0, row, col] + 1
n_nocdf_total[index_month, row, col] = n_nocdf_total[index_month, row, col] + 1
row, col = np.where(cdf_flag == 2)
weight_mlp3b_cdf[0, row,col] = weight_mlp3b_cdf[0,row,col] + weight_mlp3[row,col]
weight_mlp3b_cdf[index_month, row, col] = weight_mlp3b_cdf[index_month, row, col] + weight_mlp3[row, col]
row, col = np.where(cdf_flag == 4)
weight_mlp4b_cdf[0, row, col] = weight_mlp4b_cdf[0, row, col] + weight_mlp4[row, col]
weight_mlp4b_cdf[index_month, row, col] = weight_mlp4b_cdf[index_month, row, col] + weight_mlp4[row, col]
row, col = np.where(cdf_flag == 8)
weight_mlp5b_cdf[0, row, col] = weight_mlp5b_cdf[0, row, col] + weight_mlp5[row, col]
weight_mlp5b_cdf[index_month, row, col] = weight_mlp5b_cdf[index_month, row, col] + weight_mlp5[row, col]
row, col = np.where(cdf_flag == 6)
weight_mlp3b_cdf[0, row, col] = weight_mlp3b_cdf[0, row, col] + weight_mlp3[row, col]
weight_mlp3b_cdf[index_month, row, col] = weight_mlp3b_cdf[index_month, row, col] + weight_mlp3[row, col]
weight_mlp4b_cdf[0, row, col] = weight_mlp4b_cdf[0, row, col] + weight_mlp4[row, col]
weight_mlp4b_cdf[index_month, row, col] = weight_mlp4b_cdf[index_month, row, col] + weight_mlp4[row, col]
row, col = np.where(cdf_flag == 10)
weight_mlp3b_cdf[0, row, col] = weight_mlp3b_cdf[0, row, col] + weight_mlp3[row, col]
weight_mlp3b_cdf[index_month, row, col] = weight_mlp3b_cdf[index_month, row, col] + weight_mlp3[row, col]
weight_mlp5b_cdf[0, row, col] = weight_mlp5b_cdf[0, row, col] + weight_mlp5[row, col]
weight_mlp5b_cdf[index_month, row, col] = weight_mlp5b_cdf[index_month, row, col] + weight_mlp5[row, col]
row, col = np.where(cdf_flag == 12)
weight_mlp4b_cdf[0, row, col] = weight_mlp4b_cdf[0, row, col] + weight_mlp4[row, col]
weight_mlp4b_cdf[index_month, row, col] = weight_mlp4b_cdf[index_month, row, col] + weight_mlp4[row, col]
weight_mlp5b_cdf[0, row, col] = weight_mlp5b_cdf[0, row, col] + weight_mlp5[row, col]
weight_mlp5b_cdf[index_month, row, col] = weight_mlp5b_cdf[index_month, row, col] + weight_mlp5[row, col]
row, col = np.where(cdf_flag == 14)
weight_mlp3b_cdf[0, row, col] = weight_mlp3b_cdf[0, row, col] + weight_mlp3[row, col]
weight_mlp3b_cdf[index_month, row, col] = weight_mlp3b_cdf[index_month, row, col] + weight_mlp3[row, col]
weight_mlp4b_cdf[0, row, col] = weight_mlp4b_cdf[0, row, col] + weight_mlp4[row, col]
weight_mlp4b_cdf[index_month, row, col] = weight_mlp4b_cdf[index_month, row, col] + weight_mlp4[row, col]
weight_mlp5b_cdf[0, row, col] = weight_mlp5b_cdf[0, row, col] + weight_mlp5[row, col]
weight_mlp5b_cdf[index_month, row, col] = weight_mlp5b_cdf[index_month, row, col] + weight_mlp5[row, col]
input_dataset.close()
date_here = date_here + timedelta(hours=24)
print('[INFO] Output shapes: ')
print(f'--> n_total: {n_total.shape}')
print(f'--> n_cdf_total: {n_cdf_total.shape}')
print(f'--> n_nocdf_total: {n_nocdf_total.shape}')
print(f'--> weight_mlp_3b: {weight_mlp3b_cdf.shape}')
print(f'--> weight_mlp_4b: {weight_mlp4b_cdf.shape}')
print(f'--> weight_mlp_5b: {weight_mlp5b_cdf.shape}')
coverage_cdf[np.where(n_total>0)] = n_cdf_total[np.where(n_total>0)]/n_total[np.where(n_total>0)]
coverage_no_cdf[np.where(n_total>0)] = n_nocdf_total[np.where(n_total>0)]/n_total[np.where(n_total>0)]
print(f'--> coverage_cdf: {coverage_cdf.shape}')
print(f'--> coverage_no_cdf: {coverage_no_cdf.shape}')
coverage_cdf_mlp3[np.where(n_cdf_total > 0)] = weight_mlp3b_cdf[np.where(n_cdf_total > 0)]/ n_cdf_total[np.where(n_cdf_total > 0)]
coverage_cdf_mlp4[np.where(n_cdf_total > 0)] = weight_mlp4b_cdf[np.where(n_cdf_total > 0)]/ n_cdf_total[np.where(n_cdf_total > 0)]
coverage_cdf_mlp5[np.where(n_cdf_total > 0)] = weight_mlp5b_cdf[np.where(n_cdf_total > 0)]/ n_cdf_total[np.where(n_cdf_total > 0)]
print(f'--> coverage_cdf_mlp3: {coverage_cdf_mlp3.shape}')
print(f'--> coverage_cdf_mlp4: {coverage_cdf_mlp4.shape}')
print(f'--> coverage_cdf_mlp5: {coverage_cdf_mlp5.shape}')
weight_mlp3b_total = n_nocdf_total.copy()
print(f'--> weight_mlp3b_total (start): {weight_mlp3b_total.shape}')
weight_mlp3b_total[np.where(n_nocdf_total == 0)] = weight_mlp3b_cdf[np.where(n_nocdf_total==0)]
weight_mlp3b_total[np.where(n_nocdf_total > 0)] = weight_mlp3b_cdf[np.where(n_nocdf_total > 0)]+ weight_mlp3b_total[np.where(n_nocdf_total > 0)]
print(f'--> weight_mlp3b_total (end): {weight_mlp3b_total.shape}')
coverage_total_mlp3[np.where(n_total > 0)] = weight_mlp3b_total[np.where(n_total > 0)]/ n_total[np.where(n_total > 0)]
coverage_total_mlp4[np.where(n_total > 0)] = weight_mlp4b_cdf[np.where(n_total > 0)]/ n_total[np.where(n_total > 0)]
coverage_total_mlp5[np.where(n_total > 0)] = weight_mlp5b_cdf[np.where(n_total > 0)]/ n_total[np.where(n_total > 0)]
print(f'--> coverage_total_mlp3: {coverage_total_mlp3.shape}')
print(f'--> coverage_total_mlp4: {coverage_total_mlp4.shape}')
print(f'--> coverage_total_mlp5: {coverage_total_mlp5.shape}')
n_total = np.ma.masked_equal(n_total,0)
n_cdf_total = np.ma.masked_equal(n_cdf_total,0)
n_nocdf_total = np.ma.masked_equal(n_nocdf_total,0)
weight_mlp3b_cdf = np.ma.masked_equal(weight_mlp3b_cdf,0)
weight_mlp4b_cdf = np.ma.masked_equal(weight_mlp4b_cdf,0)
weight_mlp5b_cdf = np.ma.masked_equal(weight_mlp5b_cdf,0)
weight_mlp3b_total = np.ma.masked_equal(weight_mlp3b_total, 0)
print('[INFO] Creating output file: ')
nc_out = Dataset(file_out,'w')
nc_out.createDimension('lat',nlat)
nc_out.createDimension('lon',nlon)
nc_out.createDimension('time',13)
var_lat = nc_out.createVariable('lat','f4',('lat',),zlib=True,complevel=6,fill_value=-999.0)
var_lat[:] = lat[:]
var_lon = nc_out.createVariable('lon', 'f4', ('lon',), zlib=True, complevel=6, fill_value=-999.0)
var_lon[:] = lon[:]
var_time = nc_out.createVariable('time', 'i4', ('time',), zlib=True, complevel=6, fill_value=-999.0)
var_time[:] = np.arange(13).astype(np.int32)
var_time.units = f'0 is all the year, 1-12 for each month. Year: {year}'
list_variables = {
'n_total': n_total,
'n_cdf_total': n_cdf_total,
'n_nocdf_total': n_nocdf_total,
'weight_mlp3b_cdf' : weight_mlp3b_cdf,
'weight_mlp4b_cdf' : weight_mlp4b_cdf,
'weight_mlp5b_cdf' : weight_mlp5b_cdf,
'weight_mlp3b_total' : weight_mlp3b_total,
'coverage_cdf': coverage_cdf,
'coverage_no_cdf': coverage_no_cdf,
'coverage_cdf_mlp3': coverage_cdf_mlp3,
'coverage_cdf_mlp4' : coverage_cdf_mlp4,
'coverage_cdf_mlp5' :coverage_cdf_mlp5,
'coverage_total_mlp3' :coverage_total_mlp3,
'coverage_total_mlp4' :coverage_total_mlp4,
'coverage_total_mlp5' :coverage_total_mlp5
}
for name_var in list_variables:
array = list_variables[name_var]
var = nc_out.createVariable(name_var,'f4',('time','lat','lon'),zlib=True,complevel=6,fill_value=-999.0)
var[:] = array[:]
nc_out.close()
return True
def compute_year_cyano(year):
print(f'YEAR: {year}')
dir_base = '/mnt/c/DATA_LUIS/OCTAC_WORK/BAL_EVOLUTION_202411/CYANOBLOOM_EVOLUTION'
file_out = os.path.join(dir_base, f'CYANOBLOOM_COVERAGE_{year}.nc')
# file_nc = os.path.join(dir_base, f'CYANOBLOOM_{year}.nc')
# dataset = Dataset(file_nc)
# cbloom = dataset.variables['CYANOBLOOM']
#
# coverage_valid = np.zeros((cbloom.shape[1], cbloom.shape[2]))
# coverage_sub = np.zeros((cbloom.shape[1], cbloom.shape[2]))
# coverage_surface = np.zeros((cbloom.shape[1], cbloom.shape[2]))
# coverage_both = np.zeros((cbloom.shape[1], cbloom.shape[2]))
# coverage_any = np.zeros((cbloom.shape[1], cbloom.shape[2]))
# p_coverage_sub = np.zeros((cbloom.shape[1], cbloom.shape[2]))
# p_coverage_surface = np.zeros((cbloom.shape[1], cbloom.shape[2]))
# p_coverage_both = np.zeros((cbloom.shape[1], cbloom.shape[2]))
# p_coverage_any = np.zeros((cbloom.shape[1], cbloom.shape[2]))
# first_day = np.zeros((cbloom.shape[1], cbloom.shape[2]))
# last_day = np.zeros((cbloom.shape[1], cbloom.shape[2]))
# ndays = cbloom.shape[0]
# for iday in range(ndays):
# jday = iday + 1
# # for jday in range(161,271,1):
# #iday = jday - 1
#
# cbloom_jday = np.ma.squeeze(cbloom[iday, :, :])
# cbloom_jday = np.ma.filled(cbloom_jday, -999)
# coverage_valid[cbloom_jday >= 0] = coverage_valid[cbloom_jday >= 0] + 1
# coverage_sub[cbloom_jday == 1] = coverage_sub[cbloom_jday == 1] + 1
# coverage_surface[cbloom_jday == 2] = coverage_surface[cbloom_jday == 2] + 1
# coverage_both[cbloom_jday == 3] = coverage_both[cbloom_jday == 3] + 1
# coverage_any[cbloom_jday >= 1] = coverage_any[cbloom_jday >= 1] + 1
# first_day[np.logical_and(cbloom_jday >= 1, first_day == 0)] = jday
# last_day[cbloom_jday >= 1] = jday
# dataset.close()
#
# coverage_valid[coverage_valid==0] = -999
# coverage_sub[coverage_valid==-999]=-999
# coverage_surface[coverage_valid == -999] = -999
# coverage_both[coverage_valid == -999] = -999
# coverage_any[coverage_valid == -999] = -999
# first_day[coverage_valid == -999] = -999
# last_day[coverage_valid == -999] = -999
#
# p_coverage_sub[coverage_valid != -999] = (coverage_sub[coverage_valid != -999] / coverage_valid[
# coverage_valid != -999]) * 100
# p_coverage_surface[coverage_valid != -999] = (coverage_surface[coverage_valid != -999] / coverage_valid[
# coverage_valid != -999]) * 100
# p_coverage_both[coverage_valid != -999] = (coverage_both[coverage_valid != -999] / coverage_valid[
# coverage_valid != -999]) * 100
# p_coverage_any[coverage_valid != -999] = (coverage_any[coverage_valid != -999] / coverage_valid[
# coverage_valid != -999]) * 100
# p_coverage_sub[coverage_valid == -999] = -999
# p_coverage_surface[coverage_valid == -999] = -999
# p_coverage_both[coverage_valid == -999] = -999
# p_coverage_any[coverage_valid == -999] = -999
#
#
# new_variables = {
# 'COVERAGE_VALID': coverage_valid,
# 'COVERAGE_SUB': coverage_sub,
# 'COVERAGE_SURFACE': coverage_surface,
# 'COVERAGE_BOTH': coverage_both,
# 'COVERAGE_ANY': coverage_any,
# 'P_COVERAGE_SUB': p_coverage_sub,
# 'P_COVERAGE_SURFACE': p_coverage_surface,
# 'P_COVERAGE_BOTH': p_coverage_both,
# 'P_COVERAGE_ANY': p_coverage_any,
# 'FIRST_DAY': first_day,
# 'LAST_DAY': last_day
# }
#
# line_start ='Year;CoverageValid;CoverageSub;CoverageSurface;CoverageBoth;CoverageAny;P_CoverageSub;P_Coverage_Surface;P_Coverage_Both;P_Coverage_Any;FirstDay;LastDay'
# line = f'{year}'
# shutil.copy(file_nc, file_out)
# ncout = Dataset(file_out, 'a')
# for var_name in new_variables:
# var = ncout.createVariable(var_name, 'f4', ('lat', 'lon'), fill_value=-999.0, zlib=True, complevel=6)
# data = new_variables[var_name]
# var[:] = data
# if var_name.startswith('COVERAGE'):
# line = f'{line};{np.sum(data[data!=-999])}'
# else:
# line = f'{line};{np.median(data[data != -999])}'
# ncout.close()
line_start = 'Year;CoverageValid;CoverageSub;CoverageSurface;CoverageBoth;CoverageAny;P_CoverageSub;P_Coverage_Surface;P_Coverage_Both;P_Coverage_Any;FirstDay;LastDay'
line = f'{year}'
ncout = Dataset(file_out)
for var_name in ncout.variables:
if var_name == 'lat' or var_name == 'lon' or var_name == 'time' or var_name == 'CYANOBLOOM':
continue
data = ncout.variables[var_name][:]
# data = np.ma.filled(data, -999)
if var_name.startswith('COVERAGE'):
line = f'{line};{np.ma.sum(data)}'
else:
line = f'{line};{np.ma.median(data)}'
ncout.close()
return line_start, line
def finisce():
return True
def main():
# if plot_coverage():
# return
# if compare_old_new_v2():
# return
# if compute_diff():
# return
# fcsv_out = '/mnt/c/DATA_LUIS/OCTAC_WORK/BAL_EVOLUTION_202411/CYANOBLOOM_EVOLUTION/CyanoEvolution.csv'
# fw = open(fcsv_out,'w')
# started = False
# for year in range(1997,2024):
# line_start,line = compute_year_cyano(year)
# if not started:
# fw.write(line_start)
# fw.write('\n')
# fw.write(line)
# started = True
# else:
# fw.write('\n')
# fw.write(line)
# fw.close()
##maps
# variable = 'FIRST_DAY'
# label = 'Day'
# vmin = 1
# vmax = 366
# for year in range(1997, 2024):
# dir_base = '/mnt/c/DATA_LUIS/OCTAC_WORK/BAL_EVOLUTION_202411/CYANOBLOOM_EVOLUTION'
# file_nc = os.path.join(dir_base, f'CYANOBLOOM_COVERAGE_{year}.nc')
# title = f'{variable}-{year}'
# file_out = os.path.join(os.path.dirname(file_nc), f'{variable}_{year}.tif')
# plot_map_general(file_nc,file_out,variable,title,label,vmin,vmax)
for year in range(1997,2024):
if year==2008:
continue
compute_year_coverage_cci(year)
if finisce():
return
input_path = args.input_path
if os.path.isdir(input_path):
print(f'[INFO] Input path directory: {input_path}')
launch_multiple_maps(input_path)
else:
try:
print(f'[INFO] Input path: {input_path}')
dataset = Dataset(input_path)
except:
print(f'[ERROR] Input path is not valid')
return
if args.output_path:
output_path = args.output_path
else:
output_path = input_path.replace('.nc', '.png')
dateherestr, type = get_date_here_from_file_name(input_path)
if type == 'diff': ##Daily diff
name = os.path.basename(input_path)
datestr = name[5:12]
dateherestr = dt.strptime(datestr, '%Y%j').strftime('%Y%m%d')
launch_single_map_daily_diff(dataset, output_path, dateherestr)
elif type == 'cci':
launch_single_map_cci(dataset, output_path, dateherestr)
elif type == 'olci':
launch_single_map_olci(dataset, output_path, dateherestr)
def launch_multiple_maps(input_dir):
for name in os.listdir(input_dir):
if not name.endswith('.nc') or name.startswith('Coverage'):
continue
input_path = os.path.join(input_dir, name)
date_here_str, type = get_date_here_from_file_name(input_path)
if type == 'cci':
try:
print(f'[INFO] Input path: {input_path}')
dataset = Dataset(input_path)
except:
continue
output_path = input_path.replace('.nc', '.png')
launch_single_map_cci(dataset, output_path, date_here_str)
elif type == 'olci':
try:
print(f'[INFO] Input path: {input_path}')
dataset = Dataset(input_path)
except:
continue
output_path = input_path.replace('.nc', '.png')
launch_single_map_olci(dataset, output_path, date_here_str)
elif type == 'diff':
if not name.endswith('.nc'):
continue
input_path = os.path.join(input_dir, name)
try:
print(f'[INFO] Input path: {input_path}')
dataset = Dataset(input_path)
except:
continue
datestr = name[5:12]
dateherestr = dt.strptime(datestr, '%Y%j').strftime('%Y%m%d')
output_path = input_path.replace('.nc', '.png')
# print(output_path)
launch_single_map_daily_diff(dataset, output_path, dateherestr)
def launch_single_map_daily_diff(dataset, output_path, dateherestr):
print(f'[INFO] Output path: {output_path}')
lat_array = dataset.variables['latitude'][:]
lon_array = dataset.variables['longitude'][:]
chl_old = dataset.variables['chl_old'][:]
fig, ax = start_full_figure()
h = ax.pcolormesh(lon_array, lat_array, chl_old, norm=LogNorm(vmin=0.1, vmax=100))
cbar = fig.colorbar(h, cax=None, ax=ax, use_gridspec=True, fraction=0.03, format="$%.2f$")
cbar.ax.tick_params(labelsize=15)
units = r'mg m$^-$$^3$'
cbar.set_label(label=f'CHL ({units})', size=15)
title = f'Chlorophyll a concentration - OLD ({units})'
if dateherestr is not None:
title = f'{title} - {dateherestr}'
ax.set_title(title, fontsize=20)
file_chla_old = os.path.join(os.path.dirname(output_path), f'Img_Chla_OLD_{dateherestr}.png')
fig.savefig(file_chla_old, dpi=300, bbox_inches='tight')
plt.close(fig)
chl_new = dataset.variables['chl_new'][:]
fig, ax = start_full_figure()
h = ax.pcolormesh(lon_array, lat_array, chl_new, norm=LogNorm(vmin=0.1, vmax=100))
cbar = fig.colorbar(h, cax=None, ax=ax, use_gridspec=True, fraction=0.03, format="$%.2f$")
cbar.ax.tick_params(labelsize=15)
units = r'mg m$^-$$^3$'
cbar.set_label(label=f'CHL ({units})', size=15)
title = f'Chlorophyll a concentration - NEW ({units})'
if dateherestr is not None:
title = f'{title} - {dateherestr}'
ax.set_title(title, fontsize=20)
file_chla_new = os.path.join(os.path.dirname(output_path), f'Img_Chla_NEW_{dateherestr}.png')
fig.savefig(file_chla_new, dpi=300, bbox_inches='tight')
plt.close(fig)
chl_dif = dataset.variables['chl_dif'][:]
fig, ax = start_full_figure()
h = ax.pcolormesh(lon_array, lat_array, chl_dif, cmap=mpl.colormaps['jet'])
cbar = fig.colorbar(h, cax=None, ax=ax, use_gridspec=True, fraction=0.03, format="$%.2f$")
cbar.ax.tick_params(labelsize=15)
units = r'mg m$^-$$^3$'
cbar.set_label(label=f'CHL-Diff', size=15)
title = f'Chl-a OLD - Chl-a NEW ({units})'
if dateherestr is not None:
title = f'{title} - {dateherestr}'
ax.set_title(title, fontsize=20)
file_diff = os.path.join(os.path.dirname(output_path), f'Img_Chla_DIFF_{dateherestr}.png')
fig.savefig(file_diff, dpi=300, bbox_inches='tight')
plt.close(fig)
# coverage
coverage = dataset.variables['coverage'][:]
coverage = numpy.ma.masked_equal(coverage, 0)
fig, ax = start_full_figure()
bounds = [0, 1, 2, 3, 4]
norm = colors.BoundaryNorm(boundaries=bounds, ncolors=5)
newcolors = [
[0, 0, 0, 1],
[1, 0, 0, 1],
[0, 0, 1, 1],
[0, 1, 0, 1]
]
newcmp = ListedColormap(newcolors)
h = ax.pcolormesh(lon_array, lat_array, coverage, norm=norm, cmap=newcmp)
cbar = fig.colorbar(h, cax=None, ax=ax, use_gridspec=True, fraction=0.03, format="$%.2f$")
cbar.ax.tick_params(labelsize=15)