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script_07_mask_example.py
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script_07_mask_example.py
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#-----------------------------------------------------------------------------------------------------------
# INPE / CPTEC Training: NWP Data Processing With Python - Script 7 + Masking Regions
# Author: Diego Souza
#-----------------------------------------------------------------------------------------------------------
import pygrib # Provides a high-level interface to the ECWMF ECCODES C library for reading GRIB files
import matplotlib.pyplot as plt # Plotting library
import cartopy, cartopy.crs as ccrs # Plot maps
import cartopy.io.shapereader as shpreader # Import shapefiles
import numpy as np # Scientific computing with Python
import matplotlib # Comprehensive library for creating static, animated, and interactive visualizations in Python
#-----------------------------------------------------------------------------------------------------------
# Open the GRIB file
grib = pygrib.open("Samples/gfs.t00z.pgrb2.0p50.f000")
# Select the variable
grb = grib.select(name='2 metre temperature')[0]
# Get information from the file
init = str(grb.analDate) # Init date / time
run = str(grb.hour).zfill(2) # Run
ftime = str(grb.forecastTime) # Forecast hour
valid = str(grb.validDate) # Valid date / time
print('Init: ' + init + ' UTC')
print('Run: ' + run + 'Z')
print('Forecast: +' + ftime)
print('Valid: ' + valid + ' UTC')
# Select the extent [min. lon, min. lat, max. lon, max. lat]
extent = [-93.0, -60.00, -25.00, 18.00]
# Read the data for a specific region
tmtmp, lats, lons = grb.data(lat1=extent[1],lat2=extent[3],lon1=extent[0]+360,lon2=extent[2]+360)
#-----------------------------------------------------------------------------------------------------------
# Convert from K to °C
tmtmp = tmtmp - 273.15
#-----------------------------------------------------------------------------------------------------------
# Choose the plot size (width x height, in inches)
plt.figure(figsize=(13,13))
# Use the Cilindrical Equidistant projection in cartopy
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent([extent[0], extent[2], extent[1], extent[3]], ccrs.PlateCarree())
# Define the image extent
img_extent = [extent[0], extent[2], extent[1], extent[3]]
# Add a shapefile
# https://geoftp.ibge.gov.br/organizacao_do_territorio/malhas_territoriais/malhas_municipais/municipio_2019/Brasil/BR/br_unidades_da_federacao.zip
shapefile = list(shpreader.Reader('BR_UF_2019.shp').geometries())
ax.add_geometries(shapefile, ccrs.PlateCarree(), edgecolor='gray',facecolor='none', linewidth=0.3)
# Add coastlines, borders and gridlines
ax.coastlines(resolution='10m', color='black', linewidth=0.8)
ax.add_feature(cartopy.feature.BORDERS, edgecolor='black', linewidth=0.5)
gl = ax.gridlines(crs=ccrs.PlateCarree(), color='gray', alpha=1.0, linestyle='--', linewidth=0.25, xlocs=np.arange(-180, 180, 5), ylocs=np.arange(-90, 90, 5), draw_labels=True)
gl.top_labels = False
gl.right_labels = False
# Define de contour interval
data_min = -20
data_max = 48
interval = 2
levels = np.arange(data_min,data_max,interval)
# Create a custom color palette
# The reference color palette may be found at the following page: http://wxmaps.org/pix/temp8
# You may get the hex files st the following page: https://imagecolorpicker.com/
colors = ["#d3d2d2", "#bcbcbc", "#969696", "#1464d2", "#1e6eeb", "#2882f0",
"#3c96f5", "#50a5f5", "#78b9fa", "#96d2fa", "#b4f0fa", "#1eb41e", "#37d23c",
"#50f050", "#78f573", "#96f58c", "#b4faaa", "#c8ffbe", "#ffe878", "#ffc03c",
"#ffa000", "#ff6000", "#ff3200", "#e11400", "#c00000", "#a50000", "#785046",
"#8c6359", "#b48b82", "#e1beb4"]
cmap = matplotlib.colors.ListedColormap(colors)
cmap.set_over('#fadad5')
cmap.set_under('#e5e5e5')
# Masking a Region from a Shapefile --------------------------------------------
import regionmask
import geopandas as gp
import pandas as pd
# Read the shapefile with Geopandas
states = gp.read_file('BR_UF_2019.shp')
#display(states)
# Read the subregions from the shapefile
states_regions = regionmask.from_geopandas(states, names="SIGLA_UF", abbrevs="_from_name", name="sigla_uf")
#print(states_regions.names)
# Creat the mask with a lat lon array
mask = states_regions.mask(lons, lats)
# Get the index for a given subregion
index = states_regions.names.index("MG")
# Mask the data for a given subregion
tmtmp = np.where(mask==index, tmtmp, np.nan)
#-------------------------------------------------------------------------------
# Add a background image
import cartopy.feature as cfeature
land = ax.add_feature(cfeature.LAND)
ocean = ax.add_feature(cfeature.OCEAN)
# Plot the contours
img1 = ax.contourf(lons, lats, tmtmp, cmap=cmap, levels=levels, extend='both')
img2 = ax.contour(lons, lats, tmtmp, colors='white', linewidths=0.3, levels=levels)
#ax.clabel(img2, inline=1, inline_spacing=0, fontsize='10',fmt = '%1.0f', colors= 'black')
# Add a colorbar
plt.colorbar(img1, label='2 m Temperature (°C)', orientation='vertical', pad=0.05, fraction=0.05)
# Add a title
plt.title('GFS: 2 m Temperature' , fontweight='bold', fontsize=10, loc='left')
plt.title('Valid: ' + valid, fontsize=10, loc='right')
#-----------------------------------------------------------------------------------------------------------
# Save the image
plt.savefig('image_7_Mask.png')
# Show the image
plt.show()