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plot2d.py
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# coding: utf-8
import datetime, math
import os
import gc
import argparse
import numpy as np
#from Scientific.IO import NetCDF
import netCDF4
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import ast
import toml
plt.rcParams['svg.fonttype'] = 'none'
# do we need viridis?
#print(os.listdir('output/'))
#print(os.listdir('output/barbados/'))
import logging
logger = logging.getLogger(__name__)
def gen_file_list(start, end, path, station, model):
l = []
current = start
while current <= end:
#l.append('output/{}_{}_hysplit-output.nc'.format(current.strftime('%Y%m%d'), station))
#l.append('output/{1}/{0}_{1}_hysplit-output.nc'.format(current.strftime('%Y%m%d'), station))
l.append(path + '/{0}_{1}_{2}-output.nc'.format(current.strftime('%Y%m%d'), station, model))
current = current + datetime.timedelta(days=1)
return l
def plot_landsfc_2d(f, parameter, dt_list, config, savepath, config_dict, model, fcst):
time_list = f.variables["timestamp"][:]
dt_list = [datetime.datetime.fromtimestamp(time) for time in time_list]
height_list = f.variables["range"][:]
no_plots = len(dt_list)
fig, axes = plt.subplots(1, no_plots, sharex=True, sharey=True, figsize=(12, 6))
#fig, axes = plt.subplots(1, no_plots, sharex=True, sharey=True, figsize=(9, 6))
ls_colors = ['lightskyblue', 'darkgreen', 'khaki', 'palegreen', 'red', 'gray', 'tan']
ls_colors = ['lightskyblue', 'seagreen', 'khaki', '#6edd6e', 'darkmagenta', 'gray', 'tan']
for it, dt in enumerate(dt_list):
occ_height = f.variables[parameter][it, :, :]
occ_height = np.ma.masked_less(occ_height, 0)
occ_left = np.cumsum(occ_height, axis=1)
categories = ast.literal_eval(f.variables[parameter].comment)
# fix the typo
if categories[1] == 'forrest':
categories[1] == 'forest'
l1 = axes[it].barh(height_list, occ_height[:, 0].T,
align='center', height=0.35, color=ls_colors[0], edgecolor='none')
l2 = axes[it].barh(height_list, occ_height[:, 1].T, left=occ_left[:, 0].T,
align='center', height=0.35, color=ls_colors[1], edgecolor='none')
l3 = axes[it].barh(height_list, occ_height[:, 2].T, left=occ_left[:, 1].T,
align='center', height=0.35, color=ls_colors[2], edgecolor='none')
l4 = axes[it].barh(height_list, occ_height[:, 3].T, left=occ_left[:, 2].T,
align='center', height=0.35, color=ls_colors[3], edgecolor='none')
l5 = axes[it].barh(height_list, occ_height[:, 4].T, left=occ_left[:, 3].T,
align='center', height=0.35, color=ls_colors[4], edgecolor='none')
l6 = axes[it].barh(height_list, occ_height[:, 5].T, left=occ_left[:, 4].T,
align='center', height=0.35, color=ls_colors[5], edgecolor='none')
l7 = axes[it].barh(height_list, occ_height[:, 6].T, left=occ_left[:, 5].T,
align='center', height=0.35, color=ls_colors[6], edgecolor='none')
#axes[it].set_ylim([0, 12])
axes[it].set_ylim([0, config['height']['plottop']/1000.])
axes[it].tick_params(axis='y', which='major', labelsize=13,
width=1.5, length=3)
axes[it].tick_params(axis='both', which='minor', width=1, length=2)
axes[it].tick_params(axis='both', which='both', right=True, top=True,
direction='in')
axes[it].yaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(1.0))
axes[it].set_xlabel(dt.strftime('%H:%M'), fontsize=13)
axes[it].set_xlim(right=1)
axes[it].xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
dsp_text = 'norm. residence time'
#axes[it].xaxis.set_minor_locator(matplotlib.ticker.FixedLocator([0, xright]))
axes[it].tick_params(axis='x', labeltop=False, labelbottom=True)
param_str = parameter.replace('rt_normed_', '')
axes[0].set_ylabel("Height [km]", fontweight='semibold', fontsize=14)
axes[-1].tick_params(axis='x', labeltop=True, labelbottom=False, labelsize=11)
plt.suptitle("{} {} {}".format(dt.strftime("%Y%m%d"), config_dict['station']["name"], param_str),
fontweight='semibold', fontsize=15)
if 'moving' in config_dict['station'].keys() and config_dict['station']['moving']:
pass
else:
axes[-1].annotate("Endpoint: {:.1f} {:.1f} ".format(config_dict['station']["lat"],
config_dict['station']["lon"]),
xy=(.91, 0.96), xycoords='figure fraction',
horizontalalignment='center', verticalalignment='bottom',
fontsize=12)
axes[-1].annotate("{} ".format(model),
xy=(.91, 0.925), xycoords='figure fraction',
horizontalalignment='center', verticalalignment='bottom',
fontsize=12)
if fcst:
axes[-1].annotate("forecast data ",
xy=(.7, 0.0), xycoords='figure fraction',
horizontalalignment='center', verticalalignment='bottom',
color='red', fontsize=14)
axes[0].annotate('Time UTC', xy=(.5, .0),
xycoords='figure fraction',
horizontalalignment='center', verticalalignment='bottom',
fontsize=14, fontweight='semibold')
axes[-1].annotate(dsp_text, xy=(.90, 0.86),
xycoords='figure fraction',
horizontalalignment='center', verticalalignment='bottom',
fontsize=12, fontweight='semibold')
fig.legend((l1, l2, l3, l4, l5, l6, l7), list(categories.values()),
#bbox_to_anchor=(0.85, 0.952),
bbox_to_anchor=(0.03, 0.947), loc='upper left',
ncol=4, fontsize=11, framealpha=0.8)
#fig.set_tight_layout({'rect': [0, 0, 1, 1], 'pad': 0.1, 'h_pad': 1.5})
#plt.tight_layout(w_pad=0.0002)
plt.tight_layout(rect=[0, 0.02, 1, 0.93])
plt.tight_layout(rect=[0, 0.02, 1, 0.88])
fig.subplots_adjust(wspace=0, top=0.80)
savename = savepath + "/" + dt.strftime("%Y%m%d") + "_{}_{}.png".format(short_name, param_str.replace('_', '-'))
fig.savefig(savename, dpi=400)
savename = savepath + "/" + dt.strftime("%Y%m%d") + "_{}_{}.svg".format(short_name, param_str.replace('_', '-'))
fig.savefig(savename)
plt.close()
def plot_regions_2d(f, parameter, dt_list, config, savepath, config_dict, model, fcst):
with open('geonames_config.toml') as config_file:
geo_config = toml.loads(config_file.read())
geo_names = {int(k): v for k, v in geo_config[config['geonames']]['geo_names'].items()}
#assert geo_names == ast.literal_eval(f.variables[parameter].comment), "geonames not matching {}".format(str(geo_names))
NUM_COLORS = 7
cm = plt.get_cmap('Set2')
cNorm = matplotlib.colors.Normalize(vmin=0, vmax=NUM_COLORS-1)
scalarMap = matplotlib.cm.ScalarMappable(norm=cNorm, cmap=cm)
colors = [scalarMap.to_rgba(i) for i in range(NUM_COLORS)]
colors = [(0.65098039215686276, 0.84705882352941175, 0.32941176470588235, 1.0),
(1.0, 0.85098039215686272, 0.18431372549019609, 1.0),
(0.89803921568627454, 0.7686274509803922, 0.58039215686274515, 1.0),
(0.40000000000000002, 0.76078431372549016, 0.6470588235294118, 1.0),
(0.9882352941176471, 0.55294117647058827, 0.3843137254901961, 1.0),
(0.55294117647058827, 0.62745098039215685, 0.79607843137254897, 1.0),
(0.70196078431372544, 0.70196078431372544, 0.70196078431372544, 1.0)]
time_list = f.variables["timestamp"][:]
dt_list = [datetime.datetime.fromtimestamp(time) for time in time_list]
height_list = f.variables["range"][:]
no_plots = len(dt_list)
fig, axes = plt.subplots(1, no_plots, sharex=True, sharey=True, figsize=(12, 6))
#fig, axes = plt.subplots(1, no_plots, sharex=True, sharey=True, figsize=(9, 6))
for it, dt in enumerate(dt_list):
occ_height = f.variables[parameter][it, :, :]
occ_height = np.ma.masked_less(occ_height, 0)
occ_left = np.cumsum(occ_height, axis=1)
l = []
for i in range(len(geo_names)):
if i == 0:
l.append(axes[it].barh(height_list, occ_height[:, 0].T, left=0,
align='center', height=0.3, color=colors[0], edgecolor='none'))
else:
l.append(axes[it].barh(height_list, occ_height[:, i].T, left=occ_left[:, i-1].T,
align='center', height=0.3, color=colors[i], edgecolor='none'))
axes[it].set_ylim([0, 12])
axes[it].set_ylim([0, config['height']['plottop']/1000.])
axes[it].tick_params(axis='y', which='major', labelsize=14,
width=1.5, length=3)
axes[it].tick_params(axis='both', which='minor', width=1, length=2)
axes[it].tick_params(axis='both', which='both', right=True, top=True,
direction='in')
axes[it].yaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(1.0))
axes[it].set_xlabel(dt.strftime('%H:%M'), fontsize=14)
axes[it].set_xlim(right=1)
axes[it].xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
dsp_text = 'norm. residence time'
#axes[it].xaxis.set_minor_locator(matplotlib.ticker.FixedLocator([0, xright]))
axes[it].tick_params(axis='x', labeltop=False, labelbottom=False)
param_str = parameter.replace('rt_normed_', '')
axes[0].set_ylabel("Height [km]", fontweight='semibold', fontsize=14)
axes[-1].tick_params(axis='x', labeltop=True, labelbottom=False, labelsize=11)
plt.suptitle("{} {} {}".format(dt.strftime("%Y%m%d"), config_dict['station']["name"], param_str),
fontweight='semibold', fontsize=15)
if 'moving' in config_dict['station'].keys() and config_dict['station']['moving']:
pass
else:
axes[-1].annotate("Endpoint: {:.1f} {:.1f} ".format(config_dict['station']["lat"],
config_dict['station']["lon"]),
xy=(.91, 0.96), xycoords='figure fraction',
horizontalalignment='center', verticalalignment='bottom',
fontsize=12)
axes[-1].annotate("{} ".format(model),
xy=(.91, 0.925), xycoords='figure fraction',
horizontalalignment='center', verticalalignment='bottom',
fontsize=12)
if fcst:
axes[-1].annotate("forecast data ",
xy=(.7, 0.0), xycoords='figure fraction',
horizontalalignment='center', verticalalignment='bottom',
color='red', fontsize=14)
axes[0].annotate('Time UTC', xy=(.5, .0),
xycoords='figure fraction',
horizontalalignment='center', verticalalignment='bottom',
fontsize=14, fontweight='semibold')
axes[-1].annotate(dsp_text, xy=(.90, 0.86),
xycoords='figure fraction',
horizontalalignment='center', verticalalignment='bottom',
fontsize=12, fontweight='semibold')
fig.legend(l, list(geo_names.values()),
loc='upper left',
#bbox_to_anchor=(0.01, 0.952),
bbox_to_anchor=(0.01, 0.947),
ncol=5, fontsize=12, framealpha=0.8)
#fig.set_tight_layout({'rect': [0, 0, 1, 1], 'pad': 0.1, 'h_pad': 1.5})
#plt.tight_layout(w_pad=0.0002)
#plt.tight_layout(rect=[0, 0.02, 1, 0.93])
#plt.tight_layout(rect=[0, 0.02, 1, 0.90])
plt.tight_layout(rect=[0, 0.02, 1, 0.88])
fig.subplots_adjust(wspace=0, top=0.80)
savename = savepath + "/" + dt.strftime("%Y%m%d") + "_{}_{}.png".format(short_name, param_str.replace('_', '-'))
fig.savefig(savename, dpi=400)
savename = savepath + "/" + dt.strftime("%Y%m%d") + "_{}_{}.svg".format(short_name, param_str.replace('_', '-'))
fig.savefig(savename)
plt.close()
def plot_lat_2d(f, parameter, dt_list, config, savepath, config_dict, model, fcst):
#geo_names = {int(k): v for k, v in geo_config[config['geonames']]['geo_names'].items()}
#assert geo_names == ast.literal_eval(f.variables[parameter].comment), "geonames not matching {}".format(str(geo_names))
#lat_names = {0: 'N -60', 1: 'S -60'}
lat_names = ast.literal_eval(f.variables[parameter].comment)
NUM_COLORS = f.variables[parameter].shape[-1]
#cm = plt.get_cmap('Set2')
#cNorm = matplotlib.colors.Normalize(vmin=0, vmax=NUM_COLORS-1)
#scalarMap = matplotlib.cm.ScalarMappable(norm=cNorm, cmap=cm)
#colors = [scalarMap.to_rgba(i) for i in range(NUM_COLORS)]
#colors = [matplotlib.cm.get_cmap('Accent')(i) for i in np.linspace(0,1,NUM_COLORS)]
colors = [
(0.2196078431372549, 0.4235294117647059, 0.6901960784313725, 1.0), #blue
(0.7450980392156863, 0.6823529411764706, 0.8313725490196079, 1.0), #violet
(0.9, 0.9, 0.2, 1.0), # yellow
(0.7490196078431373, 0.3568627450980392, 0.09019607843137253, 1.0), #brown
(0.4980392156862745, 0.788235294117647, 0.4980392156862745, 1.0), #green
(0.5, 0.5, 0.5, 1.0) # grey
]
time_list = f.variables["timestamp"][:]
dt_list = [datetime.datetime.fromtimestamp(time) for time in time_list]
height_list = f.variables["range"][:]
no_plots = len(dt_list)
fig, axes = plt.subplots(1, no_plots, sharex=True, sharey=True, figsize=(12, 6))
#fig, axes = plt.subplots(1, no_plots, sharex=True, sharey=True, figsize=(9, 6))
for it, dt in enumerate(dt_list):
occ_height = f.variables[parameter][it, :, :]
occ_height = np.ma.masked_less(occ_height, 0)
occ_left = np.cumsum(occ_height, axis=1)
l = []
for i in range(NUM_COLORS):
if i == 0:
l.append(axes[it].barh(height_list, occ_height[:, 0].T, left=0,
align='center', height=0.3, color=colors[0], edgecolor='none'))
else:
l.append(axes[it].barh(height_list, occ_height[:, i].T, left=occ_left[:, i-1].T,
align='center', height=0.3, color=colors[i], edgecolor='none'))
axes[it].set_ylim([0, 12])
axes[it].set_ylim([0, config['height']['plottop']/1000.])
axes[it].tick_params(axis='y', which='major', labelsize=14,
width=1.5, length=3)
axes[it].tick_params(axis='both', which='minor', width=1, length=2)
axes[it].tick_params(axis='both', which='both', right=True, top=True,
direction='in')
axes[it].yaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(1.0))
axes[it].set_xlabel(dt.strftime('%H:%M'), fontsize=14)
axes[it].set_xlim(right=1.05)
axes[it].xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
dsp_text = 'norm. residence time'
#axes[it].xaxis.set_minor_locator(matplotlib.ticker.FixedLocator([0, xright]))
axes[it].tick_params(axis='x', labeltop=False, labelbottom=False)
param_str = parameter.replace('rt_normed_', '')
axes[0].set_ylabel("Height [km]", fontweight='semibold', fontsize=14)
axes[-1].tick_params(axis='x', labeltop=True, labelbottom=False, labelsize=11)
plt.suptitle("{} {} {}".format(dt.strftime("%Y%m%d"), config_dict['station']["name"], param_str),
fontweight='semibold', fontsize=15)
if 'moving' in config_dict['station'].keys() and config_dict['station']['moving']:
pass
else:
axes[-1].annotate("Endpoint: {:.1f} {:.1f} ".format(config_dict['station']["lat"],
config_dict['station']["lon"]),
xy=(.91, 0.96), xycoords='figure fraction',
horizontalalignment='center', verticalalignment='bottom',
fontsize=12)
axes[-1].annotate("{} ".format(model),
xy=(.91, 0.925), xycoords='figure fraction',
horizontalalignment='center', verticalalignment='bottom',
fontsize=12)
if fcst:
axes[-1].annotate("forecast data ",
xy=(.7, 0.0), xycoords='figure fraction',
horizontalalignment='center', verticalalignment='bottom',
color='red', fontsize=14)
axes[0].annotate('Time UTC', xy=(.5, .0),
xycoords='figure fraction',
horizontalalignment='center', verticalalignment='bottom',
fontsize=14, fontweight='semibold')
axes[-1].annotate(dsp_text, xy=(.90, 0.86),
xycoords='figure fraction',
horizontalalignment='center', verticalalignment='bottom',
fontsize=12, fontweight='semibold')
fig.legend(l, list(lat_names.values()),
loc='upper left',
#bbox_to_anchor=(0.01, 0.952),
bbox_to_anchor=(0.01, 0.947),
ncol=6, fontsize=12, framealpha=0.8)
#fig.set_tight_layout({'rect': [0, 0, 1, 1], 'pad': 0.1, 'h_pad': 1.5})
#plt.tight_layout(w_pad=0.0002)
#plt.tight_layout(rect=[0, 0.02, 1, 0.93])
#plt.tight_layout(rect=[0, 0.02, 1, 0.90])
plt.tight_layout(rect=[0, 0.02, 1, 0.88])
fig.subplots_adjust(wspace=0, top=0.80)
savename = savepath + "/" + dt.strftime("%Y%m%d") + "_{}_{}.png".format(short_name, param_str.replace('_', '-'))
fig.savefig(savename, dpi=400)
savename = savepath + "/" + dt.strftime("%Y%m%d") + "_{}_{}.svg".format(short_name, param_str.replace('_', '-'))
fig.savefig(savename)
plt.close()
def plot_seaice_2d(f, parameter, dt_list, config, savepath, config_dict, model, fcst):
lat_names = ast.literal_eval(f.variables[parameter].comment)
NUM_COLORS = f.variables[parameter].shape[-1]
# "{0: \'outofarea\', 1: \'open ocean\', 2: \'1..30\', 3: \'30..80\', 4: \'>80\', 5: \'mask\'}"
colors = [
(0.41, 0.41, 0.41, 1.0), # grey
(0.0, 0.0, 0.6, 1.0), # darkblue
(0.2, 0.4, 0.8, 1.0), # blue
(0.0, 0.6, 1.0, 1.0), # lightblue
(0.8, 1.0, 1.0, 1.0), # cyan
(0.8, 0.8, 0.8, 1.0), # grey
]
time_list = f.variables["timestamp"][:]
dt_list = [datetime.datetime.fromtimestamp(time) for time in time_list]
height_list = f.variables["range"][:]
no_plots = len(dt_list)
fig, axes = plt.subplots(1, no_plots, sharex=True, sharey=True, figsize=(12, 6))
#fig, axes = plt.subplots(1, no_plots, sharex=True, sharey=True, figsize=(9, 6))
for it, dt in enumerate(dt_list):
occ_height = f.variables[parameter][it, :, :]
occ_height = np.ma.masked_less(occ_height, 0)
occ_left = np.cumsum(occ_height, axis=1)
l = []
for i in range(NUM_COLORS):
if i == 0:
l.append(axes[it].barh(height_list, occ_height[:, 0].T, left=0,
align='center', height=0.3, color=colors[0], edgecolor='none'))
else:
l.append(axes[it].barh(height_list, occ_height[:, i].T, left=occ_left[:, i-1].T,
align='center', height=0.3, color=colors[i], edgecolor='none'))
axes[it].set_ylim([0, 12])
axes[it].set_ylim([0, config['height']['plottop']/1000.])
axes[it].tick_params(axis='y', which='major', labelsize=14,
width=1.5, length=3)
axes[it].tick_params(axis='both', which='minor', width=1, length=2)
axes[it].tick_params(axis='both', which='both', right=True, top=True,
direction='in')
axes[it].yaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(1.0))
axes[it].set_xlabel(dt.strftime('%H:%M'), fontsize=14)
axes[it].set_xlim(right=1.05)
axes[it].xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
dsp_text = 'norm. residence time'
#axes[it].xaxis.set_minor_locator(matplotlib.ticker.FixedLocator([0, xright]))
axes[it].tick_params(axis='x', labeltop=False, labelbottom=False)
param_str = parameter.replace('rt_normed_', '')
axes[0].set_ylabel("Height [km]", fontweight='semibold', fontsize=14)
axes[-1].tick_params(axis='x', labeltop=True, labelbottom=False, labelsize=11)
plt.suptitle("{} {} {}".format(dt.strftime("%Y%m%d"), config_dict['station']["name"], param_str),
fontweight='semibold', fontsize=15)
if 'moving' in config_dict['station'].keys() and config_dict['station']['moving']:
pass
else:
axes[-1].annotate("Endpoint: {:.1f} {:.1f} ".format(config_dict['station']["lat"],
config_dict['station']["lon"]),
xy=(.91, 0.96), xycoords='figure fraction',
horizontalalignment='center', verticalalignment='bottom',
fontsize=12)
axes[-1].annotate("{} ".format(model),
xy=(.91, 0.925), xycoords='figure fraction',
horizontalalignment='center', verticalalignment='bottom',
fontsize=12)
if fcst:
axes[-1].annotate("forecast data ",
xy=(.7, 0.0), xycoords='figure fraction',
horizontalalignment='center', verticalalignment='bottom',
color='red', fontsize=14)
axes[0].annotate('Time UTC', xy=(.5, .0),
xycoords='figure fraction',
horizontalalignment='center', verticalalignment='bottom',
fontsize=14, fontweight='semibold')
axes[-1].annotate(dsp_text, xy=(.90, 0.86),
xycoords='figure fraction',
horizontalalignment='center', verticalalignment='bottom',
fontsize=12, fontweight='semibold')
fig.legend(l, list(lat_names.values()),
loc='upper left',
#bbox_to_anchor=(0.01, 0.952),
bbox_to_anchor=(0.01, 0.947),
ncol=6, fontsize=12, framealpha=0.8)
#fig.set_tight_layout({'rect': [0, 0, 1, 1], 'pad': 0.1, 'h_pad': 1.5})
#plt.tight_layout(w_pad=0.0002)
#plt.tight_layout(rect=[0, 0.02, 1, 0.93])
#plt.tight_layout(rect=[0, 0.02, 1, 0.90])
plt.tight_layout(rect=[0, 0.02, 1, 0.88])
fig.subplots_adjust(wspace=0, top=0.80)
savename = savepath + "/" + dt.strftime("%Y%m%d") + "_{}_{}.png".format(short_name, param_str.replace('_', '-'))
print(savename)
fig.savefig(savename, dpi=400)
savename = savepath + "/" + dt.strftime("%Y%m%d") + "_{}_{}.svg".format(short_name, param_str.replace('_', '-'))
print(savename)
fig.savefig(savename)
plt.close()
def plot_filename(filename, config_dict, model, fcst, config_file='config.toml'):
f = netCDF4.Dataset(filename, 'r')
time_list = f.variables["timestamp"][:]
height_list = f.variables["range"][:]
with open(config_file) as config_file:
config = toml.loads(config_file.read())
#print(time_list)
time_delta = (time_list[1:] - time_list[:-1])[0]
dt_list = [datetime.datetime.fromtimestamp(time) for time in time_list]
#print(dt_list)
#savepath = 'plots/{}/'.format(dt_list[0].strftime('%Y%m%d'))
if model != 'hysplit':
savepath = '{}/{}_{}/'.format(
config['plot_dir'], dt_list[0].strftime('%Y%m%d'), model)
else:
savepath = '{}/{}/'.format(config['plot_dir'], dt_list[0].strftime('%Y%m%d'))
print('savepath ', savepath)
if not os.path.isdir(savepath):
os.makedirs(savepath)
## coordinate arrays for pcmesh
#print(height_list)
height_pcmesh = (height_list - np.mean((height_list[1:] - height_list[:-1])/2.)).tolist()
height_pcmesh.append(height_pcmesh[-1] + np.mean((height_list[1:] - height_list[:-1])))
#print(height_pcmesh)
dt_pcmesh = [dt-datetime.timedelta(seconds=int(time_delta)/2) for dt in dt_list]
dt_pcmesh.append(dt_pcmesh[-1]+datetime.timedelta(seconds=int(time_delta)))
#print(dt_pcmesh)
print(f)
#print(f.variables.keys())
if 'min_height_ag' in f.variables.keys():
min_height_ag = f.variables['min_height_ag'][:]/1000.
fig, ax = plt.subplots(1, figsize=(10, 5.7))
pcmesh = ax.pcolormesh(matplotlib.dates.date2num(dt_pcmesh[:]),
height_pcmesh[:],
np.transpose(min_height_ag),
cmap='gist_rainbow')
cbar = fig.colorbar(pcmesh)
ax.set_xlim([dt_list[0], dt_list[0]+datetime.timedelta(hours=24)])
ax.set_ylim([0, height_list[-1]])
ax.set_xlabel("Time UTC", fontweight='semibold', fontsize=15)
ax.set_ylabel("Height [km]", fontweight='semibold', fontsize=15)
cbar.ax.set_ylabel("Trajectory min Height [km]", fontweight='semibold', fontsize=15)
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%H:%M'))
ax.xaxis.set_major_locator(matplotlib.dates.HourLocator(interval=3))
ax.xaxis.set_minor_locator(matplotlib.dates.MinuteLocator(byminute=[0,30]))
ax.yaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(500))
ax.tick_params(axis='both', which='major', labelsize=14,
width=2, length=4)
ax.tick_params(axis='both', which='minor', width=1.5, length=3)
ax.tick_params(axis='both', which='both', right=True, top=True,
direction='in')
cbar.ax.tick_params(axis='both', which='major', labelsize=14,
width=2, length=4)
savename = savepath + "/" + dt_list[0].strftime("%Y%m%d_%H%M") + "_{}_min_height.png".format(short_name)
fig.savefig(savename, dpi=250)
plt.close()
if 'Tmin_whole' in f.variables.keys():
var = f.variables['Tmin_whole'][:]
fig, ax = plt.subplots(1, figsize=(10, 5.7))
pcmesh = ax.pcolormesh(matplotlib.dates.date2num(dt_pcmesh[:]),
height_pcmesh[:],
np.transpose(var),
vmin=-55, vmax=5,
cmap='gist_rainbow')
cbar = fig.colorbar(pcmesh)
ax.set_xlim([dt_list[0], dt_list[0]+datetime.timedelta(hours=24)])
ax.set_ylim([0, height_list[-1]])
ax.set_xlabel("Time UTC", fontweight='semibold', fontsize=15)
ax.set_ylabel("Height [km]", fontweight='semibold', fontsize=15)
cbar.ax.set_ylabel("Trajectory min T [C]", fontweight='semibold', fontsize=15)
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%H:%M'))
ax.xaxis.set_major_locator(matplotlib.dates.HourLocator(interval=3))
ax.xaxis.set_minor_locator(matplotlib.dates.MinuteLocator(byminute=[0,30]))
ax.yaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(500))
ax.tick_params(axis='both', which='major', labelsize=14,
width=2, length=4)
ax.tick_params(axis='both', which='minor', width=1.5, length=3)
ax.tick_params(axis='both', which='both', right=True, top=True,
direction='in')
cbar.ax.tick_params(axis='both', which='major', labelsize=14,
width=2, length=4)
savename = savepath + "/" + dt_list[0].strftime("%Y%m%d_%H%M")+ "_{}_min_T_whole.png".format(short_name)
fig.savefig(savename, dpi=250)
plt.close()
if 'Tmin_24h' in f.variables.keys():
var = f.variables['Tmin_24h'][:]
fig, ax = plt.subplots(1, figsize=(10, 5.7))
pcmesh = ax.pcolormesh(matplotlib.dates.date2num(dt_pcmesh[:]),
height_pcmesh[:],
np.transpose(var),
vmin=-55, vmax=5,
cmap='gist_rainbow')
cbar = fig.colorbar(pcmesh)
ax.set_xlim([dt_list[0], dt_list[0]+datetime.timedelta(hours=24)])
ax.set_ylim([0, height_list[-1]])
ax.set_xlabel("Time UTC", fontweight='semibold', fontsize=15)
ax.set_ylabel("Height [km]", fontweight='semibold', fontsize=15)
cbar.ax.set_ylabel("Trajectory min T 24h [C]", fontweight='semibold', fontsize=15)
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%H:%M'))
ax.xaxis.set_major_locator(matplotlib.dates.HourLocator(interval=3))
ax.xaxis.set_minor_locator(matplotlib.dates.MinuteLocator(byminute=[0,30]))
ax.yaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(500))
ax.tick_params(axis='both', which='major', labelsize=14,
width=2, length=4)
ax.tick_params(axis='both', which='minor', width=1.5, length=3)
ax.tick_params(axis='both', which='both', right=True, top=True,
direction='in')
cbar.ax.tick_params(axis='both', which='major', labelsize=14,
width=2, length=4)
savename = savepath + "/" + dt_list[0].strftime("%Y%m%d_%H%M") + "_{}_mint_T_24h.png".format(short_name)
fig.savefig(savename, dpi=250)
plt.close()
if 'mean_bearing_from_endpoint' in f.variables.keys():
var = f.variables['mean_bearing_from_endpoint'][:]
fig, ax = plt.subplots(1, figsize=(10, 5.7))
pcmesh = ax.pcolormesh(matplotlib.dates.date2num(dt_pcmesh[:]),
height_pcmesh[:],
np.transpose(var),
vmin=0, vmax=360,
cmap='gist_rainbow')
cbar = fig.colorbar(pcmesh)
ax.set_xlim([dt_list[0], dt_list[0]+datetime.timedelta(hours=24)])
ax.set_ylim([0, height_list[-1]])
ax.set_xlabel("Time UTC", fontweight='semibold', fontsize=15)
ax.set_ylabel("Height [km]", fontweight='semibold', fontsize=15)
cbar.ax.set_ylabel("Bearing of Trajectory (mean)", fontweight='semibold', fontsize=15)
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%H:%M'))
ax.xaxis.set_major_locator(matplotlib.dates.HourLocator(interval=3))
ax.xaxis.set_minor_locator(matplotlib.dates.MinuteLocator(byminute=[0,30]))
ax.yaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(500))
ax.tick_params(axis='both', which='major', labelsize=14,
width=2, length=4)
ax.tick_params(axis='both', which='minor', width=1.5, length=3)
ax.tick_params(axis='both', which='both', right=True, top=True,
direction='in')
cbar.ax.tick_params(axis='both', which='major', labelsize=14,
width=2, length=4)
savename = savepath + "/" + dt_list[0].strftime("%Y%m%d_%H%M")+ "_{}_mean_bearing.png".format(short_name)
fig.savefig(savename, dpi=250)
plt.close()
if 'traj_distance_total' in f.variables.keys():
var = f.variables['traj_distance_total'][:]
fig, ax = plt.subplots(1, figsize=(10, 5.7))
pcmesh = ax.pcolormesh(matplotlib.dates.date2num(dt_pcmesh[:]),
height_pcmesh[:],
np.transpose(var),
vmin=2500, vmax=18000,
cmap='gist_rainbow')
cbar = fig.colorbar(pcmesh)
ax.set_xlim([dt_list[0], dt_list[0]+datetime.timedelta(hours=24)])
ax.set_ylim([0, height_list[-1]])
ax.set_xlabel("Time UTC", fontweight='semibold', fontsize=15)
ax.set_ylabel("Height [km]", fontweight='semibold', fontsize=15)
cbar.ax.set_ylabel("Total length [km]", fontweight='semibold', fontsize=15)
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%H:%M'))
ax.xaxis.set_major_locator(matplotlib.dates.HourLocator(interval=3))
ax.xaxis.set_minor_locator(matplotlib.dates.MinuteLocator(byminute=[0,30]))
ax.yaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(500))
ax.tick_params(axis='both', which='major', labelsize=14,
width=2, length=4)
ax.tick_params(axis='both', which='minor', width=1.5, length=3)
ax.tick_params(axis='both', which='both', right=True, top=True,
direction='in')
cbar.ax.tick_params(axis='both', which='major', labelsize=14,
width=2, length=4)
savename = savepath + "/" + dt_list[0].strftime("%Y%m%d_%H%M") + "_{}_length_tot.png".format(short_name)
fig.savefig(savename, dpi=250)
plt.close()
for rh in config['height']['reception']:
if rh != 'md':
rh_string = rh + 'km'
else:
rh_string = rh
if 'rt_normed_lat_below'+rh_string in f.variables:
plot_lat_2d(f, 'rt_normed_lat_below'+rh_string, dt_list, config, savepath, config_dict, model, fcst)
plot_regions_2d(f, 'rt_normed_region_below'+rh_string, dt_list, config, savepath, config_dict, model, fcst)
plot_landsfc_2d(f, 'rt_normed_landsfc_below'+rh_string, dt_list, config, savepath, config_dict, model, fcst)
if 'rt_normed_seaice_below'+rh_string in f.variables:
plot_seaice_2d(f, 'rt_normed_seaice_below'+rh_string, dt_list, config, savepath, config_dict, model, fcst)
gc.collect()
parser = argparse.ArgumentParser(description='Plot trace output')
parser.add_argument('--station', help='station name like limassol, barbados or mcmurdo', required=True)
parser.add_argument('--date', help='date in the format YYYYMMDD')
parser.add_argument('--daterange', help='date range in the format YYYYMMDD-YYYMMDD')
parser.add_argument('--model', help='model to plot, default hysplit', default='hysplit')
parser.add_argument('--fcst', default='false', help='add a flag indicating forecast data')
args = parser.parse_args()
if args.fcst == 'false':
fcst = False
elif args.fcst == 'true':
fcst = True
else:
raise ValueError
if args.date is not None:
begin = datetime.datetime.strptime(args.date, '%Y%m%d')
end = begin
if args.daterange is not None:
begin = datetime.datetime.strptime(args.daterange.split('-')[0], '%Y%m%d')
end = datetime.datetime.strptime(args.daterange.split('-')[1], '%Y%m%d')
station = args.station
if args.station is not None:
config = 'config_{}.toml'.format(args.station)
config_dict = toml.load(config)
short_name = config_dict['station']['short_name']
#filelist = gen_file_list(datetime.date(2016, 1, 22), datetime.date(2016, 1, 24), station)
filelist = gen_file_list(begin, end, config_dict['output_dir'], short_name, args.model)
for filename in filelist:
print('=============================================================================')
print('plotting file ', filename)
plot_filename(filename, config_dict, args.model, fcst, config_file=config)
logger.warning('DeprecationWarning: New version of plot2d.py. For old version see plot2d_legacy.py')