Use case: create sounding graphs on demand for any location¶

# Plotting a Skew-T Log-P diagram using the Metpy library.

# Prevent matplotlib from showing whatever it wants.
plt.ioff()

# We start by creating the graph and adding the lines.
fig = plt.figure(figsize=(10, 8), tight_layout=True)
skew = SkewT(fig, rotation=45)
skew.plot(p[:len(t)], t, 'r', label='Temperature')
skew.plot(p[:len(td)], td, 'g', label='Dewpoint')
skew.plot_barbs(p[:len(ws)], u, v)
skew.ax.set_ylim(1050, 100)
skew.ax.set_xlim(-40, 60)
skew.ax.xaxis.set_label_text('Temperature (°C)')
skew.ax.yaxis.set_label_text('Pressure (hPa|mbar)')

# Add a secondary axis that automatically converts between pressure and height
# assuming a standard atmosphere. The value of -0.12 puts the secondary axis
# 0.12 normalized (0 to 1) coordinates left of the original axis.
secax = skew.ax.secondary_yaxis(
    -0.12,
    functions=(
        lambda p: mpcalc.pressure_to_height_std(units.Quantity(p, 'hPa')).m_as('km'),
        lambda h: mpcalc.height_to_pressure_std(units.Quantity(h, 'km')).m
    )
)
# Show 0, 1, 3, 6, 9, 12, 15 km marks.
secax.yaxis.set_major_locator(plt.FixedLocator([0, 1, 3, 6, 9, 12, 15]))
secax.yaxis.set_minor_locator(plt.NullLocator())
secax.yaxis.set_major_formatter(plt.ScalarFormatter())
secax.set_ylabel('Height (km)')

# 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='Parcel profile*')

# Shade areas of CAPE and CIN.
skew.shade_cin(p, t, prof, td, label='CIN*')
skew.shade_cape(p, t, prof, label='CAPE*')

# Slanted line at 0 isotherm.
skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)

# Add the relevant special lines.
wet_bulb = mpcalc.wet_bulb_temperature(p, t, td)
skew.plot(p, wet_bulb, 'b', linewidth=1, label='Wet Bulb*')
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()

# Add title with all the data that was used for the plot.
skew.ax.title.set_text(f'{model} Sounding at (' +
                       f'{str(lat) + DEG_SYMBOL + "N" if lat >= 0 else str(lat*-1) + DEG_SYMBOL + "S"}, ' +
                       f'{str(lon) + DEG_SYMBOL + "E" if lon >= 0 else str(lon*-1) + DEG_SYMBOL + "W"}) ' + '\n' +
                       f'Model Run: {model_run} ' + '\n' +
                       f'Forecast Hour: {forecast_hour[0:5]}{datetime.datetime.strptime(forecast_hour[5:], "%Y-%m-%dT%H:%M:%SZ")}'
                       )
skew.ax.legend()

# Use plt.show() or plt.savefig('name.png') outside of Jupyter Notebook.
display(fig)

# Plotting a Hodograph using the Metpy library.

fig = plt.figure(figsize=(7, 7), tight_layout=True)
ax = fig.add_subplot(1, 1, 1)

# Pressure level values to display on the Hodograph if present.
# You may add more or remove some as you please.
boundaries_default = [950, 850, 700, 500, 300, 250]

# Always show first pressure level.
boundaries_p = [p[0].magnitude]

# Also show all the ones from the list above, but only if they're over
# the first pressure level and are at least 50mbar over first pressure level.
# i.e. if first pressure level is at 855mbar, don't show 950 nor 850.
boundaries_p.extend([bd for bd in boundaries_default if ((boundaries_p[0]-bd)>=50 and bd>=p[-1].magnitude)])
boundaries_p *= units.hPa
u_pts, v_pts = mpinterpolate.interpolate_1d(boundaries_p, p, u, v)
boundaries_p[0] = round(boundaries_p[0])

# Add the points on the Hodograph for the chosen pressure levels.
ax.scatter(u_pts, v_pts, c="black", zorder=10)
texts = []
for up, vp, z in zip(u_pts, v_pts, boundaries_p):
    z_str = int(z.magnitude)
    texts.append(ax.text(up, vp, z_str, ha='center', fontsize=10,
                    path_effects=[mpatheffects.withStroke(foreground='white', linewidth=2)],
                    zorder=12)
                )

# Scale Hodograph according to strongest wind value.
max_range = math.ceil(max(ws).magnitude) + 1
h = Hodograph(ax, component_range=max_range)

if max_range < 50:
    inc = math.ceil(max_range / 10)
else:
    inc = 5 * math.ceil(max_range / 100)

# Put axes in the middle of the graph.
ax.spines['left'].set_position('zero')
ax.spines['bottom'].set_position('zero')
ax.patch.set_edgecolor('black')
ax.patch.set_linewidth(0.5)

# Add color that changes with pressure levels.
h.plot_colormapped(u, v, p)

# Rescale Hodograph to zoom where the values are.
# Comment h.ax.autoscale() if you want the entire Hodograph instead.
h.ax.autoscale()
bottom, top = h.ax.get_ylim()
left, right = h.ax.get_xlim()

# Add the grid lines for the wind speeds.
h.add_grid(increment=inc)

# Make sure axes stay inside the plot if the winds are all super far in one direction.
if left > 0:
    left = -1
if bottom > 0:
    bottom = -1
if right < 0:
    right = 1
if top < 0:
    top = 1

# Resize the Hodograph to zoom in on the plot, but only show values on the longest axis
# and add a little buffer to make sure the entire plot always fits inside.
norm_bottom = abs(bottom)
norm_left = abs(left)
whole, remainder = divmod(max_range, inc)
highest = max(norm_bottom, top, norm_left, right)
if highest in (norm_bottom, top):
    if highest == norm_bottom:
        norm_bottom = (whole + math.ceil(remainder/inc)) * inc
    else:
        top = (whole + math.ceil(remainder/inc)) * inc
    ax.set_ylim(bottom=-norm_bottom, top=top)
    ax.set_xlim(left=-norm_left, right=right)
    
    ax.yaxis.set_major_locator(mticker.MultipleLocator(inc))
    ticks_loc_y = ax.get_yticks().tolist()
    ax.yaxis.set_major_locator(mticker.FixedLocator(ticks_loc_y))
    ax.set_yticklabels([f'{abs(int(y))}' for y in ticks_loc_y])
    ax.set_xticks([])
else:
    if highest == norm_left:
        norm_left = (whole + math.ceil(remainder/inc)) * inc
    else:
        right = (whole + math.ceil(remainder/inc)) * inc
    ax.set_ylim(bottom=-norm_bottom, top=top)
    ax.set_xlim(left=-norm_left, right=right)
    
    ax.xaxis.set_major_locator(mticker.MultipleLocator(inc))
    ticks_loc_x = ax.get_xticks().tolist()
    ax.xaxis.set_major_locator(mticker.FixedLocator(ticks_loc_x))
    ax.set_xticklabels([f'{abs(int(x))}' for x in ticks_loc_x])
    ax.set_yticks([])

# Remove axes labels since the axes are in the middle of the plot.
ax.set_xlabel('')
ax.set_ylabel('')

h.ax.title.set_text('Hodograph, Wind Speed in knots, Height in hPa')

# Make sure Hodograph points' text don't overlap.
adjust_text(texts, arrowprops=dict(arrowstyle='->', color='r', lw=0.5), expand_text=(1.8, 1.8))

# Use plt.show() or plt.savefig('name.png') outside of Jupyter Notebook.
display(fig)

# Plotting Convection Indices table.

fig = plt.figure(figsize=(12, 2), tight_layout=True)
table_ax = fig.add_subplot(1, 1, 1)
table_ax.axis('tight')
table_ax.axis('off')

# Table's column and row names.
col_names = ['CAPE', 'CIN', 'LCL', 'LI', 'LFC', 'EL']
row_names = ['SFC', 'ML', 'MU', 'Units']
tab = table_ax.table(cellText=conv_indices, colLabels=col_names, rowLabels=row_names, loc='center', cellLoc='center')

# Change table font size and scale to look better.
tab.auto_set_font_size(False)
tab.set_fontsize(12)
tab.scale(1.5, 1.5)

# Populate the table.
for (row, col), cell in tab.get_celld().items():
    # Put row and column titles bold.
    if (row == 0) or (col == -1):
        cell.set_text_props(fontproperties=FontProperties(size=12, weight='bold'))

# Use plt.show() or plt.savefig('name.png') outside of Jupyter Notebook.
display(fig)