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Create Advanced_Sounding_With_Complex_Layout.py #3204

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Oct 10, 2023
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Create Advanced_Sounding_With_Complex_Layout.py #3204

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160 changes: 71 additions & 89 deletions examples/Complex-Sounding-Plot.py → .../Advanced_Sounding_With_Complex_Layout.py
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Expand Up @@ -2,16 +2,18 @@
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause


"""
==================================
Sounding Plot with Complex Layout
==================================
==========================================
Advanced Sounding Plot with Complex Layout
==========================================

This example combines simple MetPy plotting functionality, `metpy.calc`
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computation functionality, and a few basic tricks to create an
advanced sounding plotter with a clean layout & high readability.
computation functionality, and a few basic Matplotlib tricks to create
an advanced sounding plot with a complex layout & high readability.
"""

# First let's start with some simple imports
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
Expand All @@ -24,36 +26,29 @@
###########################################
# 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
# Now let's 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')

add_metpy_logo(fig, 90, 80, size='small')
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
Expand All @@ -77,22 +72,20 @@
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,
# This layout isn't bad, especially for how little code it requires,
# but we could add a few simple tricks to greatly increase the
# readability and complexity of our Skew-T/Hodograph layout. Lets
# readability and complexity of our Skew-T/Hodograph layout. Let's
# try another Skew-T with a few more advanced features:
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###########################################
# 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')
skew = SkewT(fig, rotation=45, rect=(0.05, 0.05, 0.50, 0.90))

# add the Metpy logo
add_metpy_logo(fig, 105, 85, size='small')

# Change to adjust data limits and give it a semblance of what we want
skew.ax.set_adjustable('datalim')
Expand All @@ -103,7 +96,7 @@
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
# Set the facecolor of the skew-t object and the figure to white
fig.set_facecolor('#ffffff')
skew.ax.set_facecolor('#ffffff')

Expand All @@ -115,18 +108,16 @@
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.
# Set the linewidth to 4 for increased readability.
# We will also add the 'label' keyword 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'
# 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
Expand All @@ -135,20 +126,19 @@

# 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
# 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
# Calculate LCL height and plot as a 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
# i.e. start from a 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')
Expand All @@ -157,20 +147,20 @@
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))
# then we can create the Metpy Hodograph
hodo_ax = plt.axes((0.48, 0.45, 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
# tick marks, tick labels, and axis labels
h.ax.set_box_aspect(1)
h.ax.set_yticklabels([])
h.ax.set_xticklabels([])
Expand All @@ -179,7 +169,7 @@
h.ax.set_xlabel(' ')
h.ax.set_ylabel(' ')

# Here we can add a simple python for loop that adds tick marks
# 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))
Expand All @@ -205,22 +195,19 @@
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'
# To do this, let's 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,
fig.patches.extend([plt.Rectangle((0.563, 0.05), 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
# Now let's take a moment to calculate some simple severe-weather parameters using
# metpy's calculations
# here are some classic severe parameters!
# Here are some classic severe parameters!
kindex = mpcalc.k_index(p, T, Td)
total_totals = mpcalc.total_totals_index(p, T, Td)

Expand All @@ -240,7 +227,6 @@

# 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,
Expand All @@ -265,89 +251,85 @@
# 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
# There is a lot we can do with this data operationally, so let's 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,
# First lets plot some thermodynamic parameters
plt.figtext(0.58, 0.37, 'SBCAPE: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.66, 0.32, f'{int(sbcape.m)} J/kg', weight='bold',
plt.figtext(0.71, 0.37, f'{int(sbcape.m)} J/kg', weight='bold',
fontsize=15, color='orangered', ha='right')
plt.figtext(0.53, 0.29, 'SBCIN: ', weight='bold',
plt.figtext(0.58, 0.34, 'SBCIN: ', weight='bold',
fontsize=15, color='black', ha='left')
plt.figtext(0.66, 0.29, f'{int(sbcin.m)} J/kg', weight='bold',
plt.figtext(0.71, 0.34, 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,
plt.figtext(0.58, 0.29, 'MLCAPE: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.66, 0.24, f'{int(mlcape.m)} J/kg', weight='bold',
plt.figtext(0.71, 0.29, 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,
plt.figtext(0.58, 0.26, 'MLCIN: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.66, 0.21, f'{int(mlcin.m)} J/kg', weight='bold',
plt.figtext(0.71, 0.26, 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,
plt.figtext(0.58, 0.21, 'MUCAPE: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.66, 0.16, f'{int(mucape.m)} J/kg', weight='bold',
plt.figtext(0.71, 0.21, 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,
plt.figtext(0.58, 0.18, 'MUCIN: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.66, 0.13, f'{int(mucin.m)} J/kg', weight='bold',
plt.figtext(0.71, 0.18, 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,
plt.figtext(0.58, 0.13, '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',
plt.figtext(0.71, 0.13, 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,
plt.figtext(0.58, 0.10, 'K-INDEX: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.66, 0.05, f'{int(kindex.m)} °C', weight='bold',
plt.figtext(0.71, 0.10, 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,
plt.figtext(0.73, 0.37, '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}',
plt.figtext(0.88, 0.37, 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,
plt.figtext(0.73, 0.34, '0-1km SHEAR: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.83, 0.29, f'{int(bshear1.m)} kts', weight='bold',
plt.figtext(0.88, 0.34, 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,
plt.figtext(0.73, 0.29, '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}',
plt.figtext(0.88, 0.29, 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,
plt.figtext(0.73, 0.26, '0-3km SHEAR: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.83, 0.21, f'{int(bshear3.m)} kts', weight='bold',
plt.figtext(0.88, 0.26, 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,
plt.figtext(0.73, 0.21, '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}',
plt.figtext(0.88, 0.21, 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,
plt.figtext(0.73, 0.18, '0-6km SHEAR: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.83, 0.13, f'{int(bshear6.m)} kts', weight='bold',
plt.figtext(0.88, 0.18, 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,
plt.figtext(0.73, 0.13, '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,
plt.figtext(0.88, 0.13, f'{int(sig_tor[0].m)}', weight='bold', fontsize=15,
color='orangered', ha='right')
plt.figtext(0.68, 0.05, 'SUPERCELL COMP: ', weight='bold', fontsize=15,
plt.figtext(0.73, 0.10, '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,
plt.figtext(0.88, 0.10, f'{int(super_comp[0].m)}', weight='bold', fontsize=15,
color='orangered', ha='right')

# add legends to the skew and hodo
# 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',
# add a quick plot title, this could be automated by
# declaring a station and datetime variable when using
# realtime observation data from Siphon.
plt.figtext(0.45, 0.97, 'OUN | MAY 4TH 1999 - 00Z VERTICAL PROFILE',
weight='bold', fontsize=20, ha='center')

# Show the plot
Expand Down