def annotation(ax, text, loc='upper right',fontsize=8):
"""Put a general annotation in the plot."""
at = AnchoredText('%s'% text, prop=dict(size=fontsize), frameon=True, loc=loc)
at.patch.set_boxstyle("round,pad=0.,rounding_size=0.1")
at.zorder = 10
ax.add_artist(at)
return(at)
def annotation_run(ax, time, loc='upper right',fontsize=8):
"""Put annotation of the run obtaining it from the
time array passed to the function."""
at = AnchoredText('Run %s'% time.strftime('%Y%m%d %H UTC'),
prop=dict(size=fontsize), frameon=True, loc=loc)
at.patch.set_boxstyle("round,pad=0.,rounding_size=0.1")
at.zorder = 10
ax.add_artist(at)
return(at)
def annotation_forecast(ax, time, loc='upper left',fontsize=8, local=True):
"""Put annotation of the forecast time."""
if local: # convert to local time
time = convert_timezone(time)
at = AnchoredText('Valid %s' % time.strftime('%A %d %b %Y at %H (Berlin)'),
prop=dict(size=fontsize), frameon=True, loc=loc)
else:
at = AnchoredText('Forecast for %s' % time.strftime('%A %d %b %Y at %H UTC'),
prop=dict(size=fontsize), frameon=True, loc=loc)
at.patch.set_boxstyle("round,pad=0.,rounding_size=0.1")
at.zorder = 10
ax.add_artist(at)
return(at)
def plastic_measurements(plastic_type='count',
output_path=None,
plot_style='plot_tools/plot.mplstyle'):
time_cki, lon_cki, lat_cki, n_plastic_cki, kg_plastic_cki = get_cki_plastic_measurements(
)
time_ci, lon_ci, lat_ci, n_plastic_ci, kg_plastic_ci = get_christmas_plastic_measurements(
)
n_plastic_month_cki = np.zeros(12)
n_months_cki = np.zeros(12)
n_plastic_month_ci = np.zeros(12)
n_months_ci = np.zeros(12)
if plastic_type == 'count':
samples_cki = n_plastic_cki
samples_ci = n_plastic_ci
ylabel = 'Plastic items [#]'
ylim = [0, 140000]
yticks = np.arange(0, 140000, 20000)
ylim_studies = [0, 14]
yticks_studies = np.arange(0, 14, 2)
elif plastic_type == 'mass':
samples_cki = kg_plastic_cki
samples_ci = kg_plastic_ci
ylabel = 'Plastic weight [kg]'
ylim = [0, 4000]
yticks = np.arange(0, 4000, 500)
ylim_studies = [0, 16]
yticks_studies = np.arange(0, 16, 2)
else:
raise ValueError(
f'Unknown plastic type requested: {plastic_type}. Valid values are: count and mass.'
)
for i, t in enumerate(time_cki):
n_months_cki[t.month - 1] += 1
if plastic_type == 'count':
n_plastic_month_cki[t.month - 1] = np.nansum(
[n_plastic_month_cki[t.month - 1], n_plastic_cki[i]])
elif plastic_type == 'mass':
n_plastic_month_cki[t.month - 1] = np.nansum(
[n_plastic_month_cki[t.month - 1], kg_plastic_cki[i]])
for i, t in enumerate(time_ci):
n_months_ci[t.month - 1] += 1
if plastic_type == 'count':
n_plastic_month_ci[t.month - 1] = np.nansum(
[n_plastic_month_ci[t.month - 1], n_plastic_ci[i]])
elif plastic_type == 'mass':
n_plastic_month_ci[t.month - 1] = np.nansum(
[n_plastic_month_ci[t.month - 1], kg_plastic_ci[i]])
(main_colors_cki, main_sizes_cki,
main_edgewidths_cki) = _get_marker_colors_sizes_for_samples(
samples_cki, plastic_type)
(main_colors_ci, main_sizes_ci,
main_edgewidths_ci) = _get_marker_colors_sizes_for_samples(
samples_ci, plastic_type)
plt.style.use(plot_style)
fig = plt.figure(figsize=(8, 6))
months = np.arange(1, 13, 1)
colors = get_months_colors()
color = '#adadad'
land_color = '#cfcfcf'
# (a) seasonal distribution measured plastic CKI
ax1 = plt.subplot(2, 2, 1)
ax1.bar(months - 0.2,
n_plastic_month_cki,
width=0.4,
color=colors,
edgecolor='k',
zorder=5)
ax1.set_xticks(months)
ax1.set_xticklabels([
'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct',
'Nov', 'Dec'
])
ax1.set_ylim(ylim)
ax1.set_yticks(yticks)
ax1.set_ylabel(ylabel)
anchored_text1 = AnchoredText(
f'(a) Plastic samples from Cocos Keeling Islands (CKI)',
loc='upper left',
borderpad=0.0)
ax1.add_artist(anchored_text1)
# number of sampling studies per month
ax2 = ax1.twinx()
ax2.grid(False)
ax2.bar(months + 0.2,
n_months_cki,
width=0.4,
color=color,
edgecolor='k',
zorder=6)
ax2.set_ylim(ylim_studies)
ax2.set_yticks(yticks_studies)
ax2.set_ylabel('Sampling studies [#]')
ax2.spines['right'].set_color(color)
ax2.tick_params(axis='y', colors=color)
ax2.yaxis.label.set_color(color)
# (b) seasonal distribution measured plastic CI
ax3 = plt.subplot(2, 2, 3)
ax3.bar(months - 0.2,
n_plastic_month_ci,
width=0.4,
color=colors,
edgecolor='k',
zorder=5)
ax3.set_xticks(months)
ax3.set_xticklabels([
'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct',
'Nov', 'Dec'
])
ax3.set_ylim(ylim)
ax3.set_yticks(yticks)
ax3.set_ylabel(ylabel)
anchored_text2 = AnchoredText(
f'(b) Plastic samples from Christmas Island (CI)',
loc='upper left',
borderpad=0.0)
ax3.add_artist(anchored_text2)
# number of sampling studies per month
ax4 = ax3.twinx()
ax4.grid(False)
ax4.bar(months + 0.2,
n_months_ci,
width=0.4,
color=color,
edgecolor='k',
zorder=6)
ax4.set_ylim(ylim_studies)
ax4.set_yticks(yticks_studies)
ax4.set_ylabel('Sampling studies [#]')
ax4.spines['right'].set_color(color)
ax4.tick_params(axis='y', colors=color)
ax4.yaxis.label.set_color(color)
# (c) map of measured plastic CKI
ax5 = plt.subplot(2, 2, 2, projection=ccrs.PlateCarree())
lon_range_cki, lat_range_cki = get_cki_box_lon_lat_range()
mplot1 = _iot_basic_map(ax5,
lon_range=lon_range_cki,
lat_range=lat_range_cki,
dlon=0.2,
dlat=0.1)
l_nonan_cki = ~np.isnan(samples_cki)
mplot1.points(lon_cki[l_nonan_cki],
lat_cki[l_nonan_cki],
facecolor=main_colors_cki,
edgecolor='k',
marker='o',
markersize=np.array(main_sizes_cki) * 5,
edgewidth=main_edgewidths_cki)
anchored_text3 = AnchoredText(f'(c) Sampled plastics on CKI',
loc='upper left',
borderpad=0.0)
anchored_text3.zorder = 8
ax5.add_artist(anchored_text3)
# (d) map of measured plastic CI
ax6 = plt.subplot(2, 2, 4, projection=ccrs.PlateCarree())
lon_range_ci, lat_range_ci = get_christmas_box_lon_lat_range()
mplot2 = _iot_basic_map(ax6,
lon_range=lon_range_ci,
lat_range=lat_range_ci,
dlon=0.2,
dlat=0.1)
l_nonan_ci = ~np.isnan(samples_ci)
mplot2.points(lon_ci[l_nonan_ci],
lat_ci[l_nonan_ci],
facecolor=main_colors_ci,
edgecolor='k',
marker='o',
markersize=np.array(main_sizes_ci) * 5,
edgewidth=main_edgewidths_ci)
anchored_text4 = AnchoredText(f'(d) Sampled plastics on CI',
loc='upper left',
borderpad=0.0)
ax6.add_artist(anchored_text4)
# legend
legend_entries = _get_legend_entries_samples(plastic_type)
ax5.legend(handles=legend_entries,
loc='upper left',
title=ylabel,
bbox_to_anchor=(1.1, 1.0))
# save
if output_path:
plt.savefig(output_path, bbox_inches='tight', dpi=300)
plt.show()
def _spectrogram(
tr,
starttime,
endtime,
is_infrasound,
rescale,
spec_win_dur,
db_lim,
freq_lim,
log,
is_local_time,
resolution,
):
"""
Make a combination waveform and spectrogram plot for an infrasound or
seismic signal.
Args:
tr (:class:`~obspy.core.trace.Trace`): Input data, usually starts
before `starttime` and ends after `endtime` (this function expects
the response to be removed!)
starttime (:class:`~obspy.core.utcdatetime.UTCDateTime`): Start time
endtime (:class:`~obspy.core.utcdatetime.UTCDateTime`): End time
is_infrasound (bool): `True` if infrasound, `False` if seismic
rescale (int or float): Scale waveforms by this factor for plotting
spec_win_dur (int or float): See docstring for :func:`~sonify.sonify`
db_lim (tuple or str): See docstring for :func:`~sonify.sonify`
freq_lim (tuple): Tuple defining frequency limits for spectrogram plot
log (bool): See docstring for :func:`~sonify.sonify`
is_local_time (bool): Passed to :class:`_UTCDateFormatter`
resolution (str): See docstring for :func:`~sonify.sonify`
Returns:
Tuple of (`fig`, `spec_line`, `wf_line`, `time_box`, `wf_progress`)
"""
if is_infrasound:
ylab = 'Pressure (Pa)'
clab = f'Power (dB rel. [{REFERENCE_PRESSURE * 1e6:g} µPa]$^2$ Hz$^{{-1}}$)'
ref_val = REFERENCE_PRESSURE
else:
ylab = 'Velocity (µm s$^{-1}$)'
if REFERENCE_VELOCITY == 1:
clab = (
f'Power (dB rel. {REFERENCE_VELOCITY:g} [m s$^{{-1}}$]$^2$ Hz$^{{-1}}$)'
)
else:
clab = (
f'Power (dB rel. [{REFERENCE_VELOCITY:g} m s$^{{-1}}$]$^2$ Hz$^{{-1}}$)'
)
ref_val = REFERENCE_VELOCITY
fs = tr.stats.sampling_rate
nperseg = int(spec_win_dur * fs) # Samples
nfft = np.power(2, int(np.ceil(np.log2(nperseg))) + 1) # Pad fft with zeroes
f, t, sxx = signal.spectrogram(
tr.data, fs, window='hann', nperseg=nperseg, noverlap=nperseg // 2, nfft=nfft
)
# [dB rel. (ref_val <ref_val_unit>)^2 Hz^-1]
sxx_db = 10 * np.log10(sxx / (ref_val**2))
t_mpl = tr.stats.starttime.matplotlib_date + (t / mdates.SEC_PER_DAY)
# Ensure a 16:9 aspect ratio
fig = Figure(figsize=(FIGURE_WIDTH, (9 / 16) * FIGURE_WIDTH))
# width_ratios effectively controls the colorbar width
gs = GridSpec(2, 2, figure=fig, height_ratios=[2, 1], width_ratios=[40, 1])
spec_ax = fig.add_subplot(gs[0, 0])
wf_ax = fig.add_subplot(gs[1, 0], sharex=spec_ax) # Share x-axis with spec
cax = fig.add_subplot(gs[0, 1])
wf_lw = 0.5
wf_ax.plot(tr.times('matplotlib'), tr.data * rescale, '#b0b0b0', linewidth=wf_lw)
wf_progress = wf_ax.plot(np.nan, np.nan, 'black', linewidth=wf_lw)[0]
wf_ax.set_ylabel(ylab)
wf_ax.grid(linestyle=':')
max_value = np.abs(tr.copy().trim(starttime, endtime).data).max() * rescale
wf_ax.set_ylim(-max_value, max_value)
im = spec_ax.pcolormesh(
t_mpl, f, sxx_db, cmap='inferno', shading='nearest', rasterized=True
)
spec_ax.set_ylabel('Frequency (Hz)')
spec_ax.grid(linestyle=':')
spec_ax.set_ylim(freq_lim)
if log:
spec_ax.set_yscale('log')
# Tick locating and formatting
locator = mdates.AutoDateLocator()
wf_ax.xaxis.set_major_locator(locator)
wf_ax.xaxis.set_major_formatter(_UTCDateFormatter(locator, is_local_time))
fig.autofmt_xdate()
# "Crop" x-axis!
wf_ax.set_xlim(starttime.matplotlib_date, endtime.matplotlib_date)
# Initialize animated stuff
line_kwargs = dict(x=starttime.matplotlib_date, color='forestgreen', linewidth=1)
spec_line = spec_ax.axvline(**line_kwargs)
wf_line = wf_ax.axvline(ymin=0.01, clip_on=False, zorder=10, **line_kwargs)
time_box = AnchoredText(
s=starttime.strftime('%H:%M:%S'),
pad=0.2,
loc='lower right',
bbox_to_anchor=[1, 1],
bbox_transform=wf_ax.transAxes,
borderpad=0,
prop=dict(color='forestgreen'),
)
offset_px = -0.0025 * RESOLUTIONS[resolution][1] # Resolution-independent!
time_box.txt._text.set_y(offset_px) # [pixels] Vertically center text
time_box.zorder = 12 # This should place it on the very top; see below
time_box.patch.set_linewidth(matplotlib.rcParams['axes.linewidth'])
wf_ax.add_artist(time_box)
# Adjustments to ensure time marker line is zordered properly
# 9 is below marker; 11 is above marker
spec_ax.spines['bottom'].set_zorder(9)
wf_ax.spines['top'].set_zorder(9)
for side in 'bottom', 'left', 'right':
wf_ax.spines[side].set_zorder(11)
# Pick smart limits rounded to nearest 10
if db_lim == 'smart':
db_min = np.percentile(sxx_db, 20)
db_max = sxx_db.max()
db_lim = (np.ceil(db_min / 10) * 10, np.floor(db_max / 10) * 10)
# Clip image to db_lim if provided (doesn't clip if db_lim=None)
im.set_clim(db_lim)
# Automatically determine whether to show triangle extensions on colorbar
# (kind of adopted from xarray)
if db_lim:
min_extend = sxx_db.min() < db_lim[0]
max_extend = sxx_db.max() > db_lim[1]
else:
min_extend = False
max_extend = False
if min_extend and max_extend:
extend = 'both'
elif min_extend:
extend = 'min'
elif max_extend:
extend = 'max'
else:
extend = 'neither'
fig.colorbar(im, cax, extend=extend, extendfrac=EXTENDFRAC, label=clab)
spec_ax.set_title(tr.id)
fig.tight_layout()
fig.subplots_adjust(hspace=0, wspace=0.05)
# Finnicky formatting to get extension triangles (if they exist) to extend
# above and below the vertical extent of the spectrogram axes
pos = cax.get_position()
triangle_height = EXTENDFRAC * pos.height
ymin = pos.ymin
height = pos.height
if min_extend and max_extend:
ymin -= triangle_height
height += 2 * triangle_height
elif min_extend and not max_extend:
ymin -= triangle_height
height += triangle_height
elif max_extend and not min_extend:
height += triangle_height
else:
pass
cax.set_position([pos.xmin, ymin, pos.width, height])
# Move offset text around and format it more nicely, see
# https://github.com/matplotlib/matplotlib/blob/710fce3df95e22701bd68bf6af2c8adbc9d67a79/lib/matplotlib/ticker.py#L677
magnitude = wf_ax.yaxis.get_major_formatter().orderOfMagnitude
if magnitude: # I.e., if offset text is present
wf_ax.yaxis.get_offset_text().set_visible(False) # Remove original text
sf = ScalarFormatter(useMathText=True)
sf.orderOfMagnitude = magnitude # Formatter needs to know this!
sf.locs = [47] # Can't be an empty list
wf_ax.text(
0.002,
0.95,
sf.get_offset(), # Let the ScalarFormatter do the formatting work
transform=wf_ax.transAxes,
ha='left',
va='top',
)
return fig, spec_line, wf_line, time_box, wf_progress
def add_subtitle(self, subtitle, location='upper left'):
anchored_text = AnchoredText(subtitle, loc=location, borderpad=0.0)
anchored_text.zorder = 15
self.ax.add_artist(anchored_text)
