from datetime import datetime, timedelta

import numpy as np

import os

import cartopy

import cartopy.crs as ccrs

import cartopy.feature as feat

from cartopy.util import add_cyclic_point

import matplotlib.pyplot as plt

import pandas as pd

from matplotlib.dates import AutoDateLocator, DateFormatter

import matplotlib.pyplot as plt

import metpy.calc as mpcalc

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.cbook import get_test_data

from metpy.plots import add_metpy_logo, StationPlot

from metpy.plots.wx_symbols import current_weather, sky_cover, wx_code_map

from metpy.units import units

from owslib.wmts import WebMapTileService

os.environ["CARTOPY_USER_BACKGROUNDS"] = "/home/sh16450/Documents/repos/etc/cartopy/BG"

# =============================================================================

# Setinng up the MODIS image background

# =============================================================================

# URL of NASA GIBS

URL = 'http://gibs.earthdata.nasa.gov/wmts/epsg4326/best/wmts.cgi'

wmts = WebMapTileService(URL)

# Layers for MODIS true color and snow RGB

layers = ['MODIS_Terra_SurfaceReflectance_Bands143']

#'MODIS_Terra_CorrectedReflectance_Bands367'

date_str = '2017-12-01'

# Plot setup

plot_CRS = ccrs.LambertConformal(central_longitude=13, central_latitude=47,

standard_parallels=[35])

geodetic_CRS = ccrs.Geodetic()

x0, y0 = plot_CRS.transform_point(-5.5, 42.1, geodetic_CRS)

x1, y1 = plot_CRS.transform_point(32.5, 52.4, geodetic_CRS)

fig = plt.figure(figsize=(20,14), dpi=100)

ax = plt.axes(projection=plot_CRS)

ax.set_xlim((x0, x1))

ax.set_ylim((y0, y1))

ax.add_wmts(wmts, layers[0], wmts_kwargs={'time': date_str})

# Request METAR data from TDS

# os.system(wget -N http://thredds.ucar.edu/thredds/fileServer/nws/metar/ncdecoded/files/Surface_METAR_20171130_0000.nc

# )

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)

# define time range you want the data for

start = now - timedelta(days=1)

end = now

# build the query

query = ncss.query()

query.lonlat_box(-5.5,31.8,42.7,52.5)

query.time(now)

query.variables('air_temperature', 'dew_point_temperature', 'wind_speed',

'precipitation_amount_hourly', 'inches_ALTIM',

'air_pressure_at_sea_level', 'wind_from_direction','cloud_area_fraction','weather','report')

query.accept('csv')

# Get the netcdf dataset

data = ncss.get_data(query)

# convert into pandas dataframe

df = pd.DataFrame(data)

df = df.replace(-99999,np.nan)

df= df.dropna(how='any', subset=['wind_from_direction', 'wind_speed'])

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]

# Set up the map projection

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, 1000.)]

# 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'].replace('-SG','SG')

df['weather'] = df['weather'].replace('FZBR','FZFG')

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)

cloud_frac = df['cloud_area_fraction']

# Change the DPI of the resulting figure. Higher DPI drastically improves the

# look of the text rendering.

# plt.rcParams['savefig.dpi'] = 100

# ============================================================================

# Create the figure and an axes set to the projection.

# fig = plt.figure(figsize=(20, 8))

# 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='none')

#

# # Add some various map elements to the plot to make it recognizable.

# ax.add_feature(feat.LAND, zorder=-1)

# ax.add_feature(feat.OCEAN, zorder=-1)

# ax.add_feature(feat.LAKES, zorder=-1)

ax.coastlines(resolution='10m', zorder=2, color='black')

ax.add_feature(state_boundaries, zorder=2, edgecolor='black')

# ax.background_img(name='BM',resolution='high')

# Set plot bounds

ax.set_extent((-5.8, 31.8, 41, 53))

# 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=16)

# Plot the temperature and dew point to the upper and lower left, respectively, of

# the center point. Each one uses a different color.

stationplot.plot_parameter('NW', df['air_temperature'],color='red',fontweight='bold')

stationplot.plot_parameter('SW', df['dew_point_temperature'],

color='darkgreen',fontweight='bold')

# A more complex example uses a custom formatter to control how the sea-level pressure

# values are plotted. This uses the standard trailing 3-digits of the pressure value

# in tenths of millibars.

stationplot.plot_parameter('NE', df['air_pressure_at_sea_level'], formatter=lambda v: format(10 * v, '.0f')[-3:])

# 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)

# Add wind barbs

stationplot.plot_barb(u, v)

# 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.show()