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==========
User Guide
==========
.. toctree::
:maxdepth: 3
:hidden:
installguide
startingguide
/tutorials/index
upgradeguide
gempak
SUPPORT
citing
media
.. plot::
:include-source: False
:width: 300px
:align: left
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import SkewT
from metpy.units import units
fig = plt.figure(figsize=(4, 4), dpi=150)
skew = SkewT(fig, rotation=45)
col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']
df = pd.read_fwf(get_test_data('may4_sounding.txt', as_file_obj=False),
skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)
# Drop any rows with all NaN values for T, Td, winds
df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed'), how='all'
).reset_index(drop=True)
p = df['pressure'].values * units.hPa
t = df['temperature'].values * units.degC
td = df['dewpoint'].values * units.degC
wind_speed = df['speed'].values * units.knots
wind_dir = df['direction'].values * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_dir)
p_prof, t, td, prof = mpcalc.parcel_profile_with_lcl(p, t, td)
skew.plot(p_prof, t, 'r')
skew.plot(p_prof, td, 'g')
skew.plot(p_prof, prof, 'k') # Plot parcel profile
skew.plot_barbs(p, u, v, y_clip_radius=0.11)
skew.ax.set_title('Sounding for 00Z 4 May 1999')
skew.ax.set_xlim(-10, 35)
skew.ax.set_xlabel(f'Temperature ({t.units:~})')
skew.ax.set_ylim(1000, 250)
skew.ax.set_ylabel(f'Pressure ({p_prof.units:~})')
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
skew.shade_cin(p_prof, t, prof)
skew.shade_cape(p_prof, t, prof)
plt.show()
.. plot::
:include-source: False
:width: 300px
:align: right
import matplotlib.pyplot as plt
import numpy as np
from metpy.cbook import get_test_data
from metpy.io import Level3File
from metpy.plots import colortables
name = get_test_data(f'nids/KOUN_SDUS54_N0QTLX_201305202016', as_file_obj=False)
f = Level3File(name)
datadict = f.sym_block[0][0]
data = f.map_data(datadict['data'])
az = np.array(datadict['start_az'] + [datadict['end_az'][-1]])
rng = np.linspace(0, f.max_range, data.shape[-1] + 1)
xlocs = rng * np.sin(np.deg2rad(az[:, np.newaxis]))
ylocs = rng * np.cos(np.deg2rad(az[:, np.newaxis]))
fig, ax = plt.subplots(1, 1, squeeze=True, figsize=(4, 4), dpi=150)
norm, cmap = colortables.get_with_steps('NWSStormClearReflectivity', -20, 0.5)
ax.pcolormesh(xlocs, ylocs, data, norm=norm, cmap=cmap)
ax.set_aspect('equal', 'datalim')
ax.set_xlim(-40, 20)
ax.set_ylim(-30, 30)
ax.set_title('KTLX Reflectivity at 2013/05/20 2016Z')
plt.show()
.. raw:: html
<div class="clearer"></div>
MetPy's User Guide can help show you how to use MetPy, walking through various parts of
MetPy and show you to other resources. See the :doc:`/api/index` for more information on
a particular function or feature in MetPy.
New to MetPy? Try the :doc:`Getting Started Guide<startingguide>`.
Need some more direct help? Check out our :doc:`support resources<SUPPORT>`.
Used MetPy in your research? We'd love for you to :doc:`cite us<citing>` in your publication.
For more information about MetPy, you can also read our
`article <https://doi.org/10.1175/BAMS-D-21-0125.1>`_ in the October 2022 edition of the
Bulletin of the American Meteorological Society.
.. plot::
:include-source: False
:width: 600px
:align: center
from datetime import datetime, timedelta
import cartopy.crs as ccrs
import pandas as pd
from metpy.cbook import get_test_data
import metpy.plots as mpplots
data = pd.read_csv(get_test_data('SFC_obs.csv', as_file_obj=False))
data['valid'] = pd.to_datetime(data['valid'], format='%Y-%m-%d %H:%M:%S')
obs = mpplots.PlotObs()
obs.data = data
obs.time = datetime(1993, 3, 12, 13)
obs.time_window = timedelta(minutes=15)
obs.level = None
obs.fields = ['tmpf', 'dwpf', 'emsl', 'cloud_cover', 'wxsym']
obs.locations = ['NW', 'SW', 'NE', 'C', 'W']
obs.colors = ['red', 'green', 'black', 'black', 'blue']
obs.formats = [None, None, lambda v: format(10 * v, '.0f')[-3:], 'sky_cover',
'current_weather']
obs.vector_field = ('uwind', 'vwind')
obs.reduce_points = 1.5
panel = mpplots.MapPanel()
panel.layout = (1, 1, 1)
panel.area = 'east'
panel.projection = ccrs.PlateCarree()
panel.layers = ['coastline', 'borders', 'states']
panel.plots = [obs]
panel.title = 'Surface Data Analysis'
pc = mpplots.PanelContainer()
pc.size = (12, 9)
pc.panels = [panel]
pc.show()
|
