diff --git a/examples/Complex-Sounding-Plot.py b/examples/Complex-Sounding-Plot.py new file mode 100644 index 00000000000..f02bc871798 --- /dev/null +++ b/examples/Complex-Sounding-Plot.py @@ -0,0 +1,354 @@ +# Copyright (c) 2015,2016,2017 MetPy Developers. +# Distributed under the terms of the BSD 3-Clause License. +# SPDX-License-Identifier: BSD-3-Clause + +""" +================================== +Sounding Plot with Complex Layout +================================== + +This example combines simple MetPy plotting functionality, `metpy.calc` +computation functionality, and a few basic tricks to create an +advanced sounding plotter with a clean layout & high readability. +""" + +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 add_metpy_logo, Hodograph, SkewT +from metpy.units import units + +########################################### +# Upper air data can easily be obtained using the siphon package, +# but for this example we will use some of MetPy's sample data. + +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) + +########################################### +# We will pull the data out of the example dataset into +# individual variables and assign units. + +p = df['pressure'].values * units.hPa +z = df['height'].values * units.m +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) + +########################################### +# Now lets make a Skew-T Log-P diagram using some simply +# MetPy functionality + +# Create a new figure. The dimensions here give a good aspect ratio +fig = plt.figure(figsize=(9, 9)) +add_metpy_logo(fig, 430, 30, size='large') + +skew = SkewT(fig, rotation=45, rect=(0.1, 0.1, 0.55, 0.85)) + +# Plot the data using normal plotting functions, in this case using +# log scaling in Y, as dictated by the typical meteorological plot +skew.plot(p, T, 'r') +skew.plot(p, Td, 'g') +skew.plot_barbs(p, u, v) + +# Change to adjust data limits and give it a semblance of what we want +skew.ax.set_adjustable('datalim') +skew.ax.set_ylim(1000, 100) +skew.ax.set_xlim(-20, 30) + +# Add the relevant special lines +skew.plot_dry_adiabats() +skew.plot_moist_adiabats() +skew.plot_mixing_lines() + +# Create a hodograph +ax = plt.axes((0.7, 0.75, 0.2, 0.2)) +h = Hodograph(ax, component_range=60.) +h.add_grid(increment=20) +h.plot(u, v) + +########################################### +# This layout isn't bad, especially for how little code it required, +# but we could add a few simple tricks to greatly increase the +# readability and complexity of our Skew-T/Hodograph layout. Lets +# try another Skew-T with a few more advanced features: + +########################################### +# STEP 1: CREATE THE SKEW-T OBJECT AND MODIFY IT TO CREATE A +# NICE, CLEAN PLOT + +# Create a new figure. The dimensions here give a good aspect ratio +fig = plt.figure(figsize=(18, 12)) +skew = SkewT(fig, rotation=45, rect=(0, 0, 0.50, 0.90)) +# add the metpy logo +add_metpy_logo(fig, 100, 80, size='small') + +# Change to adjust data limits and give it a semblance of what we want +skew.ax.set_adjustable('datalim') +skew.ax.set_ylim(1000, 100) +skew.ax.set_xlim(-20, 30) + +# Set some better labels than the default to increase readability +skew.ax.set_xlabel(str.upper(f'Temperature ({T.units:~P})'), weight='bold') +skew.ax.set_ylabel(str.upper(f'Pressure ({p.units:~P})'), weight='bold') + +# Set the facecolor of the Skew Object and the Figure to white +fig.set_facecolor('#ffffff') +skew.ax.set_facecolor('#ffffff') + +# Here we can use some basic math and Python functionality to make a cool +# shaded isotherm pattern. +x1 = np.linspace(-100, 40, 8) +x2 = np.linspace(-90, 50, 8) +y = [1100, 50] +for i in range(0, 8): + skew.shade_area(y=y, x1=x1[i], x2=x2[i], color='gray', alpha=0.02, zorder=1) + +########################################### +# STEP 2: PLOT DATA ON THE SKEW-T. TAKE A COUPLE EXTRA STEPS TO +# INCREASE READABILITY + +# Plot the data using normal plotting functions, in this case using +# log scaling in Y, as dictated by the typical meteorological plot +# set the linewidth to 4 for increased readability. +# We will also add the 'label' kew word argument for our legend. +skew.plot(p, T, 'r', lw=4, label='TEMPERATURE') +skew.plot(p, Td, 'g', lw=4, label='DEWPOINT') + +# again we can use some simple python math functionality to 'resample' +# the wind barbs for a cleaner output with increased readability. +# Something like this would work. +interval = np.logspace(2, 3, 40) * units.hPa +idx = mpcalc.resample_nn_1d(p, interval) +skew.plot_barbs(pressure=p[idx], u=u[idx], v=v[idx]) + +# Add the relevant special lines native to the Skew-T Log-P diagram & +# provide basic adjustments to linewidth and alpha to increase readability +# first we add a matplotlib axvline to highlight the 0 degree isotherm +skew.ax.axvline(0 * units.degC, linestyle='--', color='blue', alpha=0.3) +skew.plot_dry_adiabats(lw=1, alpha=0.3) +skew.plot_moist_adiabats(lw=1, alpha=0.3) +skew.plot_mixing_lines(lw=1, alpha=0.3) + +# Calculate LCL height and plot as black dot. Because `p`'s first value is +# ~1000 mb and its last value is ~250 mb, the `0` index is selected for +# `p`, `T`, and `Td` to lift the parcel from the surface. If `p` was inverted, +# i.e. start from low value, 250 mb, to a high value, 1000 mb, the `-1` index +# should be selected. +lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0]) +skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black') + +# Calculate full parcel profile and add to plot as black line +prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC') +skew.plot(p, prof, 'k', linewidth=2, label='SB PARCEL PATH') + +# Shade areas of CAPE and CIN +skew.shade_cin(p, T, prof, Td, alpha=0.2, label='SBCIN') +skew.shade_cape(p, T, prof, alpha=0.2, label='SBCAPE') + +########################################### +# STEP 3: CREATE THE HODOGRAPH INSET. TAKE A FEW EXTRA STEPS TO +# INCREASE READABILITY + +# Create a hodograph object: first we need to add an axis +# then we can create the metpy Hodograph +hodo_ax = plt.axes((0.43, 0.40, 0.5, 0.5)) +h = Hodograph(hodo_ax, component_range=80.) +# Add two separate grid increments for a cooler look. This also +# helps to increase readability +h.add_grid(increment=20, ls='-', lw=1.5, alpha=0.5) +h.add_grid(increment=10, ls='--', lw=1, alpha=0.2) +# The next few steps makes for a clean hodograph inset, removing the +# tick marks, tick labels and axis labels +h.ax.set_box_aspect(1) +h.ax.set_yticklabels([]) +h.ax.set_xticklabels([]) +h.ax.set_xticks([]) +h.ax.set_yticks([]) +h.ax.set_xlabel(' ') +h.ax.set_ylabel(' ') + +# Here we can add a simple python for loop that adds tick marks +# to the inside of the hodograph plot to increase readability! +plt.xticks(np.arange(0, 0, 1)) +plt.yticks(np.arange(0, 0, 1)) +for i in range(10, 120, 10): + h.ax.annotate(str(i), (i, 0), xytext=(0, 2), textcoords='offset pixels', + clip_on=True, fontsize=10, weight='bold', alpha=0.3, zorder=0) +for i in range(10, 120, 10): + h.ax.annotate(str(i), (0, i), xytext=(0, 2), textcoords='offset pixels', + clip_on=True, fontsize=10, weight='bold', alpha=0.3, zorder=0) + +# plot the hodograph itself, using plot_colormapped, colored +# by height +h.plot_colormapped(u, v, c=z, linewidth=6, label='0-12km WIND') +# compute Bunkers storm motion so we can plot it on the hodograph! +RM, LM, MW = mpcalc.bunkers_storm_motion(p, u, v, z) +h.ax.text((RM[0].m + 0.5), (RM[1].m - 0.5), 'RM', weight='bold', ha='left', + fontsize=13, alpha=0.6) +h.ax.text((LM[0].m + 0.5), (LM[1].m - 0.5), 'LM', weight='bold', ha='left', + fontsize=13, alpha=0.6) +h.ax.text((MW[0].m + 0.5), (MW[1].m - 0.5), 'MW', weight='bold', ha='left', + fontsize=13, alpha=0.6) +h.ax.arrow(0, 0, RM[0].m - 0.3, RM[1].m - 0.3, linewidth=2, color='black', + alpha=0.2, label='Bunkers RM Vector', + length_includes_head=True, head_width=2) + +########################################### +# STEP 4: ADD A FEW EXTRA ELEMENTS TO REALLY MAKE A NEAT PLOT + + +# First we want to actually add values of data to the plot for easy viewing +# to do this, lets first add a simple rectangle using matplotlib's 'patches' +# functionality to add some simple layout for plotting calculated parameters +# xloc yloc xsize ysize +fig.patches.extend([plt.Rectangle((0.513, 0.00), 0.334, 0.37, + edgecolor='black', facecolor='white', + linewidth=1, alpha=1, transform=fig.transFigure, + figure=fig)]) + +# now lets take a moment to calculate some simple severe-weather parameters using +# metpy's calculations +# here are some classic severe parameters! +kindex = mpcalc.k_index(p, T, Td) +total_totals = mpcalc.total_totals_index(p, T, Td) + +# mixed layer parcel properties! +ml_t, ml_td = mpcalc.mixed_layer(p, T, Td, depth=50 * units.hPa) +ml_p, _, _ = mpcalc.mixed_parcel(p, T, Td, depth=50 * units.hPa) +mlcape, mlcin = mpcalc.mixed_layer_cape_cin(p, T, prof, depth=50 * units.hPa) + +# most unstable parcel properties! +mu_p, mu_t, mu_td, _ = mpcalc.most_unstable_parcel(p, T, Td, depth=50 * units.hPa) +mucape, mucin = mpcalc.most_unstable_cape_cin(p, T, Td, depth=50 * units.hPa) + +# Estimate height of LCL in meters from hydrostatic thickness (for sig_tor) +new_p = np.append(p[p > lcl_pressure], lcl_pressure) +new_t = np.append(T[p > lcl_pressure], lcl_temperature) +lcl_height = mpcalc.thickness_hydrostatic(new_p, new_t) + +# Compute Surface-based CAPE +sbcape, sbcin = mpcalc.surface_based_cape_cin(p, T, Td) + +# Compute SRH +(u_storm, v_storm), *_ = mpcalc.bunkers_storm_motion(p, u, v, z) +*_, total_helicity1 = mpcalc.storm_relative_helicity(z, u, v, depth=1 * units.km, + storm_u=u_storm, storm_v=v_storm) +*_, total_helicity3 = mpcalc.storm_relative_helicity(z, u, v, depth=3 * units.km, + storm_u=u_storm, storm_v=v_storm) +*_, total_helicity6 = mpcalc.storm_relative_helicity(z, u, v, depth=6 * units.km, + storm_u=u_storm, storm_v=v_storm) + +# Copmute Bulk Shear components and then magnitude +ubshr1, vbshr1 = mpcalc.bulk_shear(p, u, v, height=z, depth=1 * units.km) +bshear1 = mpcalc.wind_speed(ubshr1, vbshr1) +ubshr3, vbshr3 = mpcalc.bulk_shear(p, u, v, height=z, depth=3 * units.km) +bshear3 = mpcalc.wind_speed(ubshr3, vbshr3) +ubshr6, vbshr6 = mpcalc.bulk_shear(p, u, v, height=z, depth=6 * units.km) +bshear6 = mpcalc.wind_speed(ubshr6, vbshr6) + +# Use all computed pieces to calculate the Significant Tornado parameter +sig_tor = mpcalc.significant_tornado(sbcape, lcl_height, + total_helicity3, bshear3).to_base_units() + +# Perform the calculation of supercell composite if an effective layer exists +super_comp = mpcalc.supercell_composite(mucape, total_helicity3, bshear3) + +# there is a lot we can do with this data operationally, so lets plot some of +# these values right on the plot, in the box we made +# first lets plot some thermodynamic parameters +plt.figtext(0.53, 0.32, 'SBCAPE: ', weight='bold', fontsize=15, + color='black', ha='left') +plt.figtext(0.66, 0.32, f'{int(sbcape.m)} J/kg', weight='bold', + fontsize=15, color='orangered', ha='right') +plt.figtext(0.53, 0.29, 'SBCIN: ', weight='bold', + fontsize=15, color='black', ha='left') +plt.figtext(0.66, 0.29, f'{int(sbcin.m)} J/kg', weight='bold', + fontsize=15, color='lightblue', ha='right') + +plt.figtext(0.53, 0.24, 'MLCAPE: ', weight='bold', fontsize=15, + color='black', ha='left') +plt.figtext(0.66, 0.24, f'{int(mlcape.m)} J/kg', weight='bold', + fontsize=15, color='orangered', ha='right') +plt.figtext(0.53, 0.21, 'MLCIN: ', weight='bold', fontsize=15, + color='black', ha='left') +plt.figtext(0.66, 0.21, f'{int(mlcin.m)} J/kg', weight='bold', + fontsize=15, color='lightblue', ha='right') + +plt.figtext(0.53, 0.16, 'MUCAPE: ', weight='bold', fontsize=15, + color='black', ha='left') +plt.figtext(0.66, 0.16, f'{int(mucape.m)} J/kg', weight='bold', + fontsize=15, color='orangered', ha='right') +plt.figtext(0.53, 0.13, 'MUCIN: ', weight='bold', fontsize=15, + color='black', ha='left') +plt.figtext(0.66, 0.13, f'{int(mucin.m)} J/kg', weight='bold', + fontsize=15, color='lightblue', ha='right') + +plt.figtext(0.53, 0.08, 'TT-INDEX: ', weight='bold', fontsize=15, + color='black', ha='left') +plt.figtext(0.66, 0.08, f'{int(total_totals.m)} Δ°C', weight='bold', + fontsize=15, color='orangered', ha='right') +plt.figtext(0.53, 0.05, 'K-INDEX: ', weight='bold', fontsize=15, + color='black', ha='left') +plt.figtext(0.66, 0.05, f'{int(kindex.m)} °C', weight='bold', + fontsize=15, color='orangered', ha='right') + +# now some kinematic parameters +met_per_sec = (units.m * units.m) / (units.sec * units.sec) +plt.figtext(0.68, 0.32, '0-1km SRH: ', weight='bold', fontsize=15, + color='black', ha='left') +plt.figtext(0.83, 0.32, f'{int(total_helicity1.m)* met_per_sec:~P}', + weight='bold', fontsize=15, color='navy', ha='right') +plt.figtext(0.68, 0.29, '0-1km SHEAR: ', weight='bold', fontsize=15, + color='black', ha='left') +plt.figtext(0.83, 0.29, f'{int(bshear1.m)} kts', weight='bold', + fontsize=15, color='blue', ha='right') + +plt.figtext(0.68, 0.24, '0-3km SRH: ', weight='bold', fontsize=15, + color='black', ha='left') +plt.figtext(0.83, 0.24, f'{int(total_helicity3.m)* met_per_sec:~P}', + weight='bold', fontsize=15, color='navy', ha='right') +plt.figtext(0.68, 0.21, '0-3km SHEAR: ', weight='bold', fontsize=15, + color='black', ha='left') +plt.figtext(0.83, 0.21, f'{int(bshear3.m)} kts', weight='bold', + fontsize=15, color='blue', ha='right') + +plt.figtext(0.68, 0.16, '0-6km SRH: ', weight='bold', fontsize=15, + color='black', ha='left') +plt.figtext(0.83, 0.16, f'{int(total_helicity6.m)* met_per_sec:~P}', + weight='bold', fontsize=15, color='navy', ha='right') +plt.figtext(0.68, 0.13, '0-6km SHEAR: ', weight='bold', fontsize=15, + color='black', ha='left') +plt.figtext(0.83, 0.13, f'{int(bshear6.m)} kts', weight='bold', + fontsize=15, color='blue', ha='right') + +plt.figtext(0.68, 0.08, 'SIG TORNADO: ', weight='bold', fontsize=15, + color='black', ha='left') +plt.figtext(0.83, 0.08, f'{int(sig_tor.m)}', weight='bold', fontsize=15, + color='orangered', ha='right') +plt.figtext(0.68, 0.05, 'SUPERCELL COMP: ', weight='bold', fontsize=15, + color='black', ha='left') +plt.figtext(0.83, 0.05, f'{int(super_comp.m)}', weight='bold', fontsize=15, + color='orangered', ha='right') + +# add legends to the skew and hodo +skewleg = skew.ax.legend(loc='upper left') +hodoleg = h.ax.legend(loc='upper left') + +# add a plot title +plt.figtext(0.40, 0.92, 'OUN | MAY 4TH 1999 - 00Z VERTICAL PROFILE', + weight='bold', fontsize=20, ha='center') + +# Show the plot +plt.show()