import xarray as xr
import numpy as np
import json
from matplotlib import pyplot as plt
import matplotlib as mpl
from matplotlib import patches
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
from PIL import Image
from datetime import datetime
def get_kitweather_rain_cm(rain_cm_file):
rgb_colors = []
with open(rain_cm_file, 'r') as f:
lines = f.readlines()
for i, line in enumerate(lines):
rgb_colors.append([float(line[0:3])/255, float(line[4:7])/255, float(line[8:11])/255, 1])
rgb_colors = [[1, 1, 1, 0]] + rgb_colors + [[0.35, 0, 0.4, 1]]
cmap = mpl.colors.ListedColormap(rgb_colors[1:-1]) # , name=colorpalette
cmap = cmap.with_extremes(bad='white', under=rgb_colors[0], over=rgb_colors[-1])
levels = [0.1,0.2,0.3,0.5,1,2,3,5,10,20,30,50]
norm = mpl.colors.BoundaryNorm(levels, cmap.N)
return levels, cmap, norm
def pb_str_to_datetime(time_str):
return datetime.strptime(time_str, '%Y-%m-%dT%H:%M:%S')
def get_track_wts_of_time(dt, all_nodes):
nodes = [n for n in all_nodes if pb_str_to_datetime(n.time) == dt]
return nodes
def crop_top_bottom_whitespace(path):
# pixels from image left where a vertical column is scanned from top and bottom for non-white pixels
x_scan_position = 450
add_bottom_delta = 20
im = Image.open(path)
image_array_y = np.where(np.asarray(im.convert('L')) < 255, 1, 0)[:, x_scan_position]
vmargins = [np.where(image_array_y[2:] == 1)[0][0] + 2 + 1,
image_array_y[:-2].shape[0] - np.where(image_array_y[:-2] == 1)[0][-1] + 2]
im_cropped = Image.new('RGBA',(im.size[0], im.size[1] - vmargins[0] - vmargins[1] + add_bottom_delta), (0, 0, 0, 0))
im_cropped.paste(im.crop((0, vmargins[0], im.size[0], im.size[1] - vmargins[1] + add_bottom_delta)), (0, 0))
im.close()
im_cropped.save(path, 'png')
im_cropped.close()
return
def plot_kw_style(dataset, dataset_desc, config, lev=700):
set_ = dataset_desc.sets[0]
lv = lev
all_nodes = [e.parent for track in set_.tracks for e in track.edges]
for tidx, t in enumerate(dataset.time.data):
time_dt = datetime.utcfromtimestamp(t.astype(datetime) / 1e9)
fig_name = time_dt.strftime(str(lv) + "_%Y%m%dT%H.png")
# get WTs which are part of tracks for current ts
wts = get_track_wts_of_time(time_dt, all_nodes)
print(wts)
ds_t = dataset.sel(time=t)
resolution = 1600
cbar_space_px = 80
subplotparameters = mpl.figure.SubplotParams(left=0, bottom=0, right=1 - cbar_space_px / resolution, top=1,
wspace=0, hspace=0)
fig, ax = plt.subplots(figsize=(resolution / 100, resolution / 100),
dpi=100,
subplotpars=subplotparameters,
subplot_kw=dict(projection=ccrs.PlateCarree()))
extent = [-75, 45, -10, 40]
levels_rain, rain_cm, norm = get_kitweather_rain_cm(config.kw_rain_cm_file)
distance_plot_to_cbar = 0.010
axins = ax.inset_axes([1 + distance_plot_to_cbar, 0.05, 0.015, 0.93],
transform=ax.transAxes)
ticks_list = levels_rain
cbar = fig.colorbar(mpl.cm.ScalarMappable(cmap=rain_cm, norm=norm),
cax=axins, extend='both', extendfrac=0.03,
ticks=ticks_list)
unit_text = 'mm/hr'
y_off = -0.06
axins.text(0.25, y_off, unit_text, transform=axins.transAxes,
horizontalalignment='left', verticalalignment='center')
# field contour
levels = np.linspace(0, 1e-4, 50)
ds_t[config.field].sel(level=lv).plot.contourf(levels=levels, cmap='Blues', subplot_kws={'transform_first': True})
"""
ds_t.sel(level=700).plot.streamplot(x='longitude', y='latitude',
u=config.u_dim, v=config.v_dim,
linewidth=0.6,
arrowsize=0.5,
density=6,
color='blue') # , transform_first=True not working, or is already implemented. still slow.
"""
ds_t.sel(level=lv).plot.streamplot(x='longitude', y='latitude',
u=config.u_dim, v=config.v_dim,
linewidth=0.3,
arrowsize=0.3,
density=8,
color='red')
# generate plot per pressure level, per time step
for obj_idx, node in enumerate(wts):
line_pts = node.object.properties.linePts
line = patches.Path([[p.lon, p.lat] for p in line_pts])
if node.object.id == -1:
patch = patches.PathPatch(line, linewidth=3, facecolor='none', edgecolor='orange') # cmap(time_weight)
else:
patch = patches.PathPatch(line, linewidth=3, facecolor='none', edgecolor='green') # cmap(time_weight)
ax.add_patch(patch)
# plot vortices
# ds_t.sel(level=700).vortices.plot.contourf(levels=[-0.5,0.5,99], colors=('#00000000', 'blue'), subplot_kws={'transform_first': True}, add_colorbar=False)
ds_t.sel(level=lv).saddle.plot.contourf(levels=[-0.5,0.5,99], colors=('#00000000', 'orange'), subplot_kws={'transform_first': True}, add_colorbar=False)
ds_t.sel(level=lv).foci_c.plot.contourf(levels=[-0.5, 0.5, 99], colors=('#00000000', 'orange'),
subplot_kws={'transform_first': True}, add_colorbar=False) #### TODO all points seem to end up here!!
# ds_t.prec_rate_rea.plot.contourf(levels=levels_rain, extend='max', subplot_kws={'transform_first': True},
# cmap=rain_cm, norm=norm, add_colorbar=False)
# ax.coastlines()
ax.add_feature(cfeature.BORDERS.with_scale('50m'), linewidth=0.3)
ax.add_feature(cfeature.COASTLINE.with_scale('50m'), linewidth=0.3)
ax.set_extent(extent, crs=ccrs.PlateCarree())
ax.add_feature(cfeature.LAND.with_scale('50m'), facecolor=list(np.array([255, 225, 171]) / 255))
ax.get_xaxis().set_ticklabels([])
ax.get_yaxis().set_ticklabels([])
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True, linewidth=0.5, color='gray', alpha=0.5, linestyle='--')
gl.top_labels = False
gl.right_labels = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
ax.set_title("")
fig.tight_layout()
plt.savefig(config.fig_dir + fig_name, format='png', backend='agg')
plt.close(fig)
crop_top_bottom_whitespace(config.fig_dir + fig_name)
print("Saved to " + fig_name)
# for each timestep...