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utils_plot.py
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utils_plot.py
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from datetime import datetime
import geopandas as gp
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from osgeo import gdal
def get_tif_array(tif_path):
"""Gets x and y coordinate arrays and data array from a
.tif file.
Args:
tif_path (str): Path to a .tif file.
Returns:
tuple: (X coordinate, Y coordinate, data)
"""
ds = gdal.Open(tif_path)
data = ds.ReadAsArray()
gt = ds.GetGeoTransform()
xres = gt[1]
yres = gt[5]
xmin = gt[0] + xres * 0.5
xmax = gt[0] + (xres * ds.RasterXSize) - xres * 0.5
ymin = gt[3] + (yres * ds.RasterYSize) + yres * 0.5
ymax = gt[3] - yres * 0.5
xx, yy = np.mgrid[xmin:xmax+xres:xres, ymax+yres:ymin:yres]
return xx, yy, data
def plot_ros(ros_file, date, config_plot=None):
"""Generate a figure of a rain or snow field.
Args:
ros_file (str): Path to a .tif file including a ros field.
date (datetime): Date of the data.
config_plot (dict, optional): Parameters of the plot. Defaults to None.
"""
if config_plot is not None:
config = config_plot
else:
config = {}
cmap = plt.cm.colors.ListedColormap(['#f9c3c6', '#f49d9e', '#e47070',
'#e65050', '#c83c3c', '#c5fcbb',
'#b3f8a9', '#78f573', '#50ee4e',
'#3ade3e', '#b5f3fc', '#95cff6',
'#4dafff', '#3c93f1', '#2881ed',
'#2881ed'])
cmap_ra = plt.cm.colors.ListedColormap(['#f9c3c6', '#f49d9e', '#e47070',
'#e65050', '#c83c3c'])
cmap_sl = plt.cm.colors.ListedColormap(['#c5fcbb', '#b3f8a9', '#78f573',
'#50ee4e', '#3ade3e'])
cmap_sn = plt.cm.colors.ListedColormap(['#b5f3fc', '#95cff6', '#4dafff',
'#3c93f1', '#2881ed'])
ros_x, ros_y, ros = get_tif_array(ros_file)
ros[ros < 1] = np.nan
levels = np.arange(1, 17, 1) - 0.5
norm = plt.cm.colors.BoundaryNorm(levels, ncolors=15, clip=False)
fig, ax = plt.subplots()
if 'plt_dem_file' in config.keys():
dem_x, dem_y, dem = get_tif_array(config['plt_dem_file'])
ax.contourf(dem_x, dem_y, dem.T, cmap=plt.cm.get_cmap('Greys'),
alpha=0.7)
ax.pcolormesh(ros_x, ros_y, ros.T, cmap=cmap, norm=norm, rasterized=True,
shading='auto')
cbaxes = fig.add_axes([0.92, 0.60, 0.02, 0.18])
cbaxes.tick_params(axis='both', which='both', labelsize=6)
cb_ra = mpl.colorbar.ColorbarBase(cbaxes, cmap=cmap_ra)
cbaxes = fig.add_axes([0.92, 0.40, 0.02, 0.18])
cbaxes.tick_params(axis='both', which='both', labelsize=6)
cb_sl = mpl.colorbar.ColorbarBase(cbaxes, cmap=cmap_sl)
cbaxes = fig.add_axes([0.92, 0.20, 0.02, 0.18])
cbaxes.tick_params(axis='both', which='both', labelsize=6)
cb_sn = mpl.colorbar.ColorbarBase(cbaxes, cmap=cmap_sn)
cb_ra.set_ticks(np.arange(0.2, 1, 0.2))
cb_sl.set_ticks(np.arange(0.2, 1, 0.2))
cb_sn.set_ticks(np.arange(0.2, 1, 0.2))
cb_ra.set_ticklabels(['5', '10', '15', '25'])
cb_sl.set_ticklabels(['5', '10', '15', '25'])
cb_sn.set_ticklabels(['5', '10', '15', '25'])
cb_ra.ax.set_ylabel('Rain (dBZ)', va='center', labelpad=10, size=6)
cb_sl.ax.set_ylabel('Sleet (dBZ)', va='center', labelpad=10, size=6)
cb_sn.ax.set_ylabel('Snow (dBZ)', va='center', labelpad=10, size=6)
if 'plt_region_shp' in config.keys():
region = gp.read_file(config['plt_region_shp'])
region.plot(ax=ax, facecolor='None', edgecolor='black', linewidth=0.8)
if 'plt_bounds' in config.keys():
b = config['plt_bounds']
ax.set_xlim(b[0], b[2])
ax.set_ylim(b[1], b[3])
ax.set_xticks([])
ax.set_yticks([])
ax.set_aspect('equal')
if 'plt_title' in config.keys():
ax.set_title(config['plt_title']
.format(date=datetime.strftime(date,
'%Y-%m-%d %H:%M UTC')),
size=6, loc='left')
date_str = datetime.strftime(date, '%Y%m%d_%H%M')
plt.figtext(0.80, 0.16, '@enricasellas', ha='left', fontsize=6,
color='black', alpha=0.8)
if 'plt_data_institution' in config.keys():
plt.figtext(0.13, 0.16, config['plt_data_institution'],
fontsize=6)
if 'plt_out_file' in config.keys():
plt.savefig(config['plt_out_file'].format(date=date_str),
bbox_inches='tight', dpi=300)
else:
plt.savefig('/tmp/ros_{}.png'.format(date_str),
bbox_inches='tight', dpi=300)
plt.close('all')