import numpy as np import scipy.constants as sconsts import matplotlib.pyplot as plt from matplotlib.mlab import griddata from mpl_toolkits.basemap import Basemap from metpy import (read_mesonet_data, dewpoint, get_wind_components, h_convergence) from metpy import remote_mesonet_data from metpy.constants import C2F from metpy.cbook import append_fields from metpy.vis import station_plot from metpy.tools.oban import gaussian_filter # TODO: Find a way to fix the bad convergence values at the edge of the # masked data data = read_mesonet_data('data/200905082110.mdf', fields=('STID', 'TIME', 'TAIR', 'RELH', 'WSPD', 'WDIR')) #Calculate dewpoint in F from relative humidity and temperature dewpt = C2F(dewpoint(data['TAIR'], data['RELH']/100.)) data = append_fields(data, ('dewpoint',), (dewpt,)) #Convert temperature and dewpoint to Farenheit data['TAIR'] = C2F(data['TAIR']) u,v = get_wind_components(data['WSPD'], data['WDIR']) data = append_fields(data, ('u', 'v'), (u, v)) fig = plt.figure(figsize=(20,12)) ax = fig.add_subplot(1,1,1) m = Basemap(lon_0=-99, lat_0=35, lat_ts=35, resolution='i', projection='stere', urcrnrlat=37., urcrnrlon=-94.25, llcrnrlat=33.7,
import scipy.constants as sconsts import matplotlib.pyplot as plt from matplotlib.mlab import griddata from mpl_toolkits.basemap import Basemap from metpy import (read_mesonet_data, dewpoint, get_wind_components, h_convergence) from metpy import remote_mesonet_data from metpy.constants import C2F from metpy.cbook import append_fields from metpy.vis import station_plot from metpy.tools.oban import gaussian_filter # TODO: Find a way to fix the bad convergence values at the edge of the # masked data data = read_mesonet_data('data/200905082110.mdf', fields=('STID', 'TIME', 'TAIR', 'RELH', 'WSPD', 'WDIR')) #Calculate dewpoint in F from relative humidity and temperature dewpt = C2F(dewpoint(data['TAIR'], data['RELH'] / 100.)) data = append_fields(data, ('dewpoint', ), (dewpt, )) #Convert temperature and dewpoint to Farenheit data['TAIR'] = C2F(data['TAIR']) u, v = get_wind_components(data['WSPD'], data['WDIR']) data = append_fields(data, ('u', 'v'), (u, v)) fig = plt.figure(figsize=(20, 12)) ax = fig.add_subplot(1, 1, 1) m = Basemap(lon_0=-99,
import scipy.constants as sconsts import matplotlib.pyplot as plt from mpl_toolkits.basemap import Basemap from metpy import read_mesonet_data, dewpoint, get_wind_components from metpy.constants import C2F from metpy.cbook import append_fields from metpy.vis import station_plot # stereogrpaphic projection data = read_mesonet_data('data/200811210030.mdf', fields=('STID', 'TIME', 'TAIR', 'RELH', 'WSPD', 'WDIR')) #Calculate dewpoint in F from relative humidity and temperature dewpt = C2F(dewpoint(data['TAIR'], data['RELH']/100.)) data = append_fields(data, ('dewpoint',), (dewpt,)) #Convert temperature and dewpoint to Farenheit data['TAIR'] = C2F(data['TAIR']) #Convert wind speeds to MPH data['WSPD'] *= sconsts.hour / sconsts.mile u,v = get_wind_components(data['WSPD'], data['WDIR']) data = append_fields(data, ('u', 'v'), (u, v)) fig = plt.figure(figsize=(20,12)) ax = fig.add_subplot(1,1,1) m = Basemap(lon_0=-99, lat_0=35, lat_ts=35, resolution='i', projection='stere', urcrnrlat=37., urcrnrlon=-94.25, llcrnrlat=33.7, llcrnrlon=-103., ax=ax) m.bluemarble() station_plot(data, ax=ax, proj=m,