1.Unidata Python Gallery
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页面链接:https://unidata.github.io/python-gallery/examples/index.html
This is a list of useful and/or new Python tools that the Unidata Python Team and community are keeping an eye on or using.
3.实例
from datetime import datetime
import matplotlib.pyplot as plt
import metpy.calc as mpcalc
from metpy.units import units
import numpy as np
from pyproj import Geod
from scipy.interpolate import griddata
from scipy.ndimage import gaussian_filter
from siphon.simplewebservice.wyoming import WyomingUpperAir
def vertical_interpolate(vcoord_data, interp_var, interp_levels):
"""A function to interpolate sounding data from each station to
every millibar. Assumes a log-linear relationship.
Input
-----
vcoord_data : A 1D array of vertical level values (e.g., pressure from a radiosonde)
interp_var : A 1D array of the variable to be interpolated to all pressure levels
vcoord_interp_levels : A 1D array containing veritcal levels to interpolate to
Return
------
interp_data : A 1D array that contains the interpolated variable on the interp_levels
"""
# Make veritcal coordinate data and grid level log variables
lnp = np.log(vcoord_data)
lnp_intervals = np.log(interp_levels)
# Use numpy to interpolate from observed levels to grid levels
interp_data = np.interp(lnp_intervals[::-1], lnp[::-1], interp_var[::-1])[::-1]
# Mask for missing data (generally only near the surface)
mask_low = interp_levels > vcoord_data[0]
mask_high = interp_levels < vcoord_data[-1]
interp_data[mask_low] = interp_var[0]
interp_data[mask_high] = interp_var[-1]
return interp_data
def radisonde_cross_section(stns, data, start=1000, end=100, step=10):
"""This function takes a list of radiosonde observation sites with a
dictionary of Pandas Dataframes with the requesite data for each station.
Input
-----
stns : List of statition three-letter identifiers
data : A dictionary of Pandas Dataframes containing the radiosonde observations
for the stations
start : interpolation start value, optional (default = 1000 hPa)
end : Interpolation end value, optional (default = 100 hPa)
step : Interpolation interval, option (default = 10 hPa)
Return
------
cross_section : A dictionary that contains the following variables
grid_data : An interpolated grid with 100 points between the first and last station,
with the corresponding number of vertical points based on start, end, and interval
(default is 90)
obs_distance : An array of distances between each radiosonde observation location
x_grid : A 2D array of horizontal direction grid points
p_grid : A 2D array of vertical pressure levels
ground_elevation : A representation of the terrain between radiosonde observation sites
based on the elevation of each station converted to pressure using the standard
atmosphere
"""
# Set up vertical grid, largest value first (high pressure nearest surface)
vertical_levels = np.arange(start, end-1, -step)
# Number of vertical levels and stations
plevs = len(vertical_levels)
nstns = len(stns)
# Create dictionary of interpolated values and include neccsary attribute data
# including lat, lon, and elevation of each station
lats = []
lons = []
elev = []
keys = data[stns[0]].keys()[:8]
tmp_grid = dict.fromkeys(keys)
# Interpolate all variables for each radiosonde observation
# Temperature, Dewpoint, U-wind, V-wind
for key in tmp_grid.keys():
tmp_grid[key] = np.empty((nstns, plevs))
for station, loc in zip(stns, range(nstns)):
if key == 'pressure':
lats.append(data[station].latitude[0])
lons.append(data[station].longitude[0])
elev.append(data[station].elevation[0])
tmp_grid[key][loc, :] = vertical_levels
else:
tmp_grid[key][loc, :] = vertical_interpolate(
data[station]['pressure'].values, data[station][key].values,
vertical_levels)
# Compute distance between each station using Pyproj
g = Geod(ellps='sphere')
_, _, dist = g.inv(nstns*[lons[0]], nstns*[lats[0]], lons[:], lats[:])
# Compute sudo ground elevation in pressure from standard atmsophere and the elevation
# of each station
ground_elevation = mpcalc.height_to_pressure_std(np.array(elev) * units('meters'))
# Set up grid for 2D interpolation
grid = dict.fromkeys(keys)
x = np.linspace(dist[0], dist[-1], 100)
nx = len(x)
pp, xx = np.meshgrid(vertical_levels, x)
pdist, ddist = np.meshgrid(vertical_levels, dist)
# Interpolate to 2D grid using scipy.interpolate.griddata
for key in grid.keys():
grid[key] = np.empty((nx, plevs))
grid[key][:] = griddata((ddist.flatten(), pdist.flatten()),
tmp_grid[key][:].flatten(),
(xx, pp),
method='cubic')
# Gather needed data in dictionary for return
cross_section = {'grid_data': grid, 'obs_distance': dist,
'x_grid': xx, 'p_grid': pp, 'elevation': ground_elevation}
return cross_section
# A roughly east-west cross section
stn_list = ['DNR', 'LBF', 'OAX', 'DVN', 'DTX', 'BUF']
# Set a date and hour of your choosing
date = datetime(2019, 12, 20, 0)
df = {}
# Loop over stations to get data and put into dictionary
for station in stn_list:
df[station] = WyomingUpperAir.request_data(date, station)
xsect = radisonde_cross_section(stn_list, df)
potemp = mpcalc.potential_temperature(
xsect['p_grid'] * units('hPa'), xsect['grid_data']['temperature'] * units('degC'))
relhum = mpcalc.relative_humidity_from_dewpoint(
xsect['grid_data']['temperature'] * units('degC'),
xsect['grid_data']['dewpoint'] * units('degC'))
mixrat = mpcalc.mixing_ratio_from_relative_humidity(relhum,
xsect['grid_data']['temperature'] *
units('degC'),
xsect['p_grid'] * units('hPa'))
fig = plt.figure(figsize=(17, 11))
# Specify plotting axis (single panel)
ax = plt.subplot(111)
# Set y-scale to be log since pressure decreases exponentially with height
ax.set_yscale('log')
# Set limits, tickmarks, and ticklabels for y-axis
ax.set_ylim([1030, 101])
ax.set_yticks(range(1000, 101, -100))
ax.set_yticklabels(range(1000, 101, -100))
# Invert the y-axis since pressure decreases with increasing height
ax.invert_yaxis()
# Plot the sudo elevation on the cross section
ax.fill_between(xsect['obs_distance'], xsect['elevation'].m, 1030,
where=xsect['elevation'].m <= 1030, facecolor='lightgrey',
interpolate=True, zorder=10)
# Don't plot xticks
plt.xticks([], [])
# Plot wind barbs for each sounding location
for stn, stn_name in zip(range(len(stn_list)), stn_list):
ax.axvline(xsect['obs_distance'][stn], ymin=0, ymax=1,
linewidth=2, color='blue', zorder=11)
ax.text(xsect['obs_distance'][stn], 1100, stn_name, ha='center', color='blue')
ax.barbs(xsect['obs_distance'][stn], df[stn_name]['pressure'][::2],
df[stn_name]['u_wind'][::2, None],
df[stn_name]['v_wind'][::2, None], zorder=15)
# Plot smoothed potential temperature grid (K)
cs = ax.contour(xsect['x_grid'], xsect['p_grid'], gaussian_filter(
potemp, sigma=1.0), range(0, 500, 5), colors='red')
ax.clabel(cs, fmt='%i')
# Plot smoothed mixing ratio grid (g/kg)
cs = ax.contour(xsect['x_grid'], xsect['p_grid'], gaussian_filter(
mixrat*1000, sigma=2.0), range(0, 41, 2), colors='tab:green')
ax.clabel(cs, fmt='%i')
# Add some informative titles
plt.title('Cross-Section from DNR to BUF Potential Temp. '
'(K; red) and Mix. Rat. (g/kg; green)', loc='left')
plt.title(date, loc='right')
plt.show()