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METAR_no_bg.py
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from datetime import datetime
import numpy as np
import cartopy.crs as ccrs
import cartopy.feature as feat
import matplotlib.pyplot as plt
import matplotlib.patheffects as path_effects
import pandas as pd
from metpy.units import units
from siphon.catalog import TDSCatalog
from siphon.ncss import NCSS
from metpy.calc import get_wind_components, reduce_point_density
from metpy.plots.wx_symbols import current_weather, sky_cover, wx_code_map
from metpy.plots import StationPlot
from os.path import expanduser
import os
from synop_read_data import synop_df
# Request METAR data from TDS
# os.system(wget -N http://thredds.ucar.edu/thredds/fileServer/nws/metar/
# ncdecoded/files/Surface_METAR_20171130_0000.nc')
# set up the paths and test for existence
path = expanduser('~') + '/Documents/Metar_plots'
try:
os.listdir(path)
except FileNotFoundError:
os.mkdir(path)
def build_query(west=-58.5, east=32, south=42, north=74):
metar = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/nws/metar/'
'ncdecoded/catalog.xml')
dataset = list(metar.datasets.values())[0]
print(list(dataset.access_urls))
# Access netcdf subset and use siphon to request data
ncss_url = dataset.access_urls['NetcdfSubset']
ncss = NCSS(ncss_url)
print(ncss.variables)
# get current date and time
now = datetime.utcnow()
now = datetime(now.year, now.month, now.day, now.hour)
# build the query
query = ncss.query()
query.lonlat_box(west, east, south, north)
query.time(now)
query.variables('air_temperature', 'dew_point_temperature', 'wind_speed',
'precipitation_amount_hourly', 'hectoPascal_ALTIM',
'air_pressure_at_sea_level', 'wind_from_direction',
'cloud_area_fraction', 'weather', 'report', 'wind_gust')
query.accept('csv')
return ncss, query
def get_data(ncss, query, density=50000.):
attempts = 0
success = False
while attempts <= 5 and not success:
try:
# Get the netcdf dataset
data = ncss.get_data(query)
# convert into pandas dataframe
df = pd.DataFrame(data)
success = True
except ValueError:
attempts += 1
print('Not the right amount of columns, trying for the {} time'
.format(attempts))
df = df.replace(-99999, np.nan)
df = df.dropna(how='any', subset=['wind_from_direction', 'wind_speed',
'dew_point_temperature'])
df['cloud_area_fraction'] = (df['cloud_area_fraction'] * 8)
df['cloud_area_fraction'] = df['cloud_area_fraction'].replace(np.nan, 10) \
.astype(int)
# Get the columns with strings and decode
str_df = df.select_dtypes([np.object])
str_df = str_df.stack().str.decode('utf-8').unstack()
# Replace decoded columns in PlateCarree
for col in str_df:
df[col] = str_df[col]
return df
def reduce_density(df, dens, projection='EU'):
if projection == 'GR':
proj = ccrs.LambertConformal(central_longitude=-35,
central_latitude=65,
standard_parallels=[35])
else:
proj = ccrs.LambertConformal(central_longitude=13, central_latitude=47,
standard_parallels=[35])
# Use the cartopy map projection to transform station locations to the map
# and then refine the number of stations plotted by setting a 300km radius
point_locs = proj.transform_points(ccrs.PlateCarree(),
df['longitude'].values,
df['latitude'].values)
df = df[reduce_point_density(point_locs, dens)]
return proj, point_locs, df
df_synop = synop_df()
df_synop_red = reduce_density(df_synop, 50000, 'GR')
def plot_map_metar_and_synop(proj, point_locs, df_t, df_synop, area='EU', west=-5.5, east=32,
south=42, north=62, fonts=14):
df = df_t
# Map weather strings to WMO codes, which we can use to convert to symbols
# Only use the first symbol if there are multiple
df['weather'] = df['weather'].str.replace('-SG', 'SG')
df['weather'] = df['weather'].str.replace('FZBR', 'FZFG')
df['weather'] = df['weather'].str.replace('-BLSN', 'BLSN')
df['weather'] = df['weather'].str.replace('-DRSN', 'DRSN')
df['weather'] = df['weather'].str.replace('-FZUP', 'FZDZ')
wx = [wx_code_map[s.split()[0] if ' ' in s else s] for s in df['weather']
.fillna('')]
wx2 = df_synop['ww'].fillna(0).astype(int)
wx2 = wx2.values.tolist()
# Get the wind components, converting from m/s to knots as will
# be appropriate for the station plot.
u, v = get_wind_components(((df['wind_speed'].values)*units('m/s'))
.to('knots'), (df['wind_from_direction'].values)
* units.degree)
u2, v2 = get_wind_components((df_synop['ff'].values) * units('knots'),
(df_synop['dd'].values) * units.degree)
gust = df['wind_gust']*units('m/s').to('km/hour')
cloud_frac = df['cloud_area_fraction']
df_synop['cloud_cover'] = (df_synop['cloud_cover'].replace(np.nan, 10)
.astype(int))
cloud_frac2 = df_synop['cloud_cover']
# Change the DPI of the resulting figure. Higher DPI drastically improves
# look of the text rendering.
plt.rcParams['savefig.dpi'] = 300
# =========================================================================
# Create the figure and an axes set to the projection.
fig = plt.figure(figsize=(20, 16))
ax = fig.add_subplot(1, 1, 1, projection=proj)
# # Set up a cartopy feature for state borders.
state_boundaries = feat.NaturalEarthFeature(category='cultural',
name='admin_0_countries',
scale='10m',
facecolor='#d8dcd6',
alpha=0.5)
ax.coastlines(resolution='10m', zorder=1, color='black')
ax.add_feature(state_boundaries, zorder=1, edgecolor='black')
# ax.add_feature(cartopy.feature.OCEAN, zorder=0)
# Set plot bounds
ax.set_extent((west, east, south, north))
# Start the station plot by specifying the axes to draw on, as well as the
# lon/lat of the stations (with transform). We also the fontsize to 12 pt.
stationplot = StationPlot(ax, df['longitude'].values,
df['latitude'].values, clip_on=True,
transform=ccrs.PlateCarree(), fontsize=fonts)
stationplot2 = StationPlot(ax, df_synop['Longitude'].values, df_synop['Latitude'].values)
# Plot the temperature and dew point to the upper and lower left,
# respectively, of the center point. Each one uses a different color.
Temp = stationplot.plot_parameter('NW', df['air_temperature'],
color='#fd3c06',
fontweight='bold', zorder=2000)
Temp2 = stationplot2.plot_parameter('NW', df_synop['TT'],
color='#fd3c06')
Td = stationplot.plot_parameter('SW', df['dew_point_temperature'],
color='#01ff07')
Td2 = stationplot.plot_parameter('SW', df_synop['TD'],
color='#01ff07')
# fontweight = 'bold'
# More complex ex. uses custom formatter to control how sea-level pressure
# values are plotted. This uses the standard trailing 3-digits of
# the pressure value in tenths of millibars.
p = stationplot.plot_parameter('NE', df['hectoPascal_ALTIM'],
formatter=lambda v:
format(10 * v, '.0f')[-3:],
color="#a2cffe")
p2 = stationplot.plot_parameter('NE', df_synop['SLP'],
formatter=lambda v:
format(10 * v, '.0f')[-3:],
color="#a2cffe")
max_uv = stationplot.plot_parameter('SE', gust, color='#ed0dd9')
for x in [Temp, Temp2, Td, Td2, p, p2, max_uv]:
x.set_path_effects([path_effects.Stroke(linewidth=1.5,
foreground='black'), path_effects.Normal()])
# Add wind barbs
stationplot.plot_barb(u, v, zorder=1000, linewidth=2)
stationplot.plot_barb(u2, v2, zorder=1000, linewidth=2)
# Plot the cloud cover symbols in the center location. This uses the codes
# made above and uses the `sky_cover` mapper to convert these values to
# font codes for the weather symbol font.
stationplot.plot_symbol('C', cloud_frac, sky_cover)
stationplot.plot_symbol('C', cloud_frac2, sky_cover)
# Same this time, but plot current weather to the left of center, using the
# `current_weather` mapper to convert symbols to the right glyphs.
stationplot.plot_symbol('W', wx, current_weather, zorder=2000)
stationplot.plot_symbol('W', wx2, current_weather, zorder=2000)
# Also plot the actual text of the station id. Instead of cardinal
# directions, plot further out by specifying a location of 2 increments
# in x and 0 in y.stationplot.plot_text((2, 0), df['station'])
plt.savefig(path + '/CURR_METAR_'+area+'.png',
bbox_inches='tight', transparent="True", pad_inches=0)
def plot_map_standard(proj, point_locs, df_t, area='EU', west=-5.5, east=32,
south=42, north=62, fonts=14):
df = df_t
# Map weather strings to WMO codes, which we can use to convert to symbols
# Only use the first symbol if there are multiple
df['weather'] = df['weather'].str.replace('-SG', 'SG')
df['weather'] = df['weather'].str.replace('FZBR', 'FZFG')
df['weather'] = df['weather'].str.replace('-BLSN', 'BLSN')
df['weather'] = df['weather'].str.replace('-DRSN', 'DRSN')
df['weather'] = df['weather'].str.replace('-FZUP', 'FZDZ')
wx = [wx_code_map[s.split()[0] if ' ' in s else s] for s in df['weather']
.fillna('')]
# Get the wind components, converting from m/s to knots as will
# be appropriate for the station plot.
u, v = get_wind_components(((df['wind_speed'].values)*units('m/s'))
.to('knots'), (df['wind_from_direction'].values)
* units.degree)
gust = df['wind_gust']*units('m/s').to('km/hour')
cloud_frac = df['cloud_area_fraction']
# Change the DPI of the resulting figure. Higher DPI drastically improves
# look of the text rendering.
plt.rcParams['savefig.dpi'] = 300
# =========================================================================
# Create the figure and an axes set to the projection.
fig = plt.figure(figsize=(20, 16))
ax = fig.add_subplot(1, 1, 1, projection=proj)
# # Set up a cartopy feature for state borders.
state_boundaries = feat.NaturalEarthFeature(category='cultural',
name='admin_0_countries',
scale='10m',
facecolor='#d8dcd6',
alpha=0.5)
ax.coastlines(resolution='10m', zorder=1, color='black')
ax.add_feature(state_boundaries, zorder=1, edgecolor='black')
# ax.add_feature(cartopy.feature.OCEAN, zorder=0)
# Set plot bounds
ax.set_extent((west, east, south, north))
# Start the station plot by specifying the axes to draw on, as well as the
# lon/lat of the stations (with transform). We also the fontsize to 12 pt.
stationplot = StationPlot(ax, df['longitude'].values,
df['latitude'].values, clip_on=True,
transform=ccrs.PlateCarree(), fontsize=fonts)
# Plot the temperature and dew point to the upper and lower left,
# respectively, of the center point. Each one uses a different color.
Temp = stationplot.plot_parameter('NW', df['air_temperature'],
color='#fd3c06',
fontweight='bold', zorder=2000)
Td = stationplot.plot_parameter('SW', df['dew_point_temperature'],
color='#01ff07')
# fontweight = 'bold'
# More complex ex. uses custom formatter to control how sea-level pressure
# values are plotted. This uses the standard trailing 3-digits of
# the pressure value in tenths of millibars.
p = stationplot.plot_parameter('NE', df['hectoPascal_ALTIM'],
formatter=lambda v:
format(10 * v, '.0f')[-3:],
color="#a2cffe")
max_uv = stationplot.plot_parameter('SE', gust, color='#ed0dd9')
for x in [Temp, Td, p, max_uv]:
x.set_path_effects([path_effects.Stroke(linewidth=1.5,
foreground='black'), path_effects.Normal()])
# Add wind barbs
stationplot.plot_barb(u, v, zorder=1000, linewidth=2)
# Plot the cloud cover symbols in the center location. This uses the codes
# made above and uses the `sky_cover` mapper to convert these values to
# font codes for the weather symbol font.
stationplot.plot_symbol('C', cloud_frac, sky_cover)
# Same this time, but plot current weather to the left of center, using the
# `current_weather` mapper to convert symbols to the right glyphs.
stationplot.plot_symbol('W', wx, current_weather, zorder=2000)
# Also plot the actual text of the station id. Instead of cardinal
# directions, plot further out by specifying a location of 2 increments
# in x and 0 in y.stationplot.plot_text((2, 0), df['station'])
plt.savefig(path + '/CURR_METAR_'+area+'.png',
bbox_inches='tight', transparent="True", pad_inches=0)
if __name__ == '__main__':
# First try of synop and metar plot
ncss, query = build_query()
df_tot = get_data(ncss, query)
df_synop = synop_df()
proj2, point_locs2, df_synop_red = reduce_density(df_synop, 180000)
proj, point_locs, df = reduce_density(df_tot, 180000)
plot_map_metar_and_synop(proj, point_locs, df, df_synop_red, area='EU', fonts=15)
ncss, query = build_query()
df_tot = get_data(ncss, query)
proj, point_locs, df = reduce_density(df_tot, 180000)
plot_map_standard(proj, point_locs, df, area='EU', fonts=15)
proj, point_locs, df = reduce_density(df_tot, 20000)
plot_map_standard(proj, point_locs, df, area='AT', west=8.9, east=17.42,
south=45.9, north=49.4)
proj, point_locs, df = reduce_density(df_tot, 90000)
plot_map_standard(proj, point_locs, df, area='UK', west=-10.1, east=9.4,
south=48.64, north=58.4, fonts=16)
proj, point_locs, df = reduce_density(df_tot, 80000)
plot_map_standard(proj, point_locs, df, area='SCANDI_S', west=1, east=32.7,
south=54, north=64.5, fonts=16)
proj, point_locs, df = reduce_density(df_tot, 70000)
plot_map_standard(proj, point_locs, df, area='SCANDI_N', west=8, east=39.7,
south=64, north=72)
proj, point_locs, df = reduce_density(df_tot, 50000, 'GR')
plot_map_standard(proj, point_locs, df, area='GR_ICE', west=-58, east=-12,
south=57, north=70.5, fonts=22)
plt.close('all')