Main Timeline Figure INVOLCAN¶
LON LAT DEP YYYY MM DD HH MI SS MAG
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import numpy as np
sns.set_theme(style="whitegrid")
import warningsdef make_category_columns(df):
df['Depth'] = 'Shallow (<17km)'
df.loc[df['Depth(km)'] >= 17, 'Depth'] = 'Deep (>17km)'
df['D'] = 'S'
df.loc[df['Depth(km)'] >= 17, 'D'] = 'D'
df['Mag'] = 0
df.loc[(df['Magnitude'] >= 1) & (df['Magnitude'] <= 2), 'Mag'] = 1
df.loc[(df['Magnitude'] >= 2) & (df['Magnitude'] <= 3), 'Mag'] = 2
df.loc[(df['Magnitude'] >= 3) & (df['Magnitude'] <= 4), 'Mag'] = 3
df.loc[(df['Magnitude'] >= 4) & (df['Magnitude'] <= 5), 'Mag'] = 4
return dfVisualising Long term earthquake data¶
Data taken directly from the IGN Catalog
from os import path
event_data_file = path.join('..','involcan','event.dat')
fields = ["Longitude", "Latitude", "Depth(km)", "Year", "Month", "Day", "Hour", "Minute", "Second", "Magnitude"];
events = []
with open(event_data_file) as f:
for line in map(lambda x: x.split(), f.readlines()):
events.append({ k:v for k,v in zip(fields, line)})df_raw = pd.DataFrame(events)
df_raw.head()Loading...
df = df_raw[["Longitude", "Latitude", "Depth(km)", "Magnitude"]].copy()
df['DateTime'] = pd.to_datetime(df_raw[['Year','Month','Day','Hour','Minute']])
df['Magnitude'] = df['Magnitude'].astype('float')
df['Depth(km)'] = df['Depth(km)'].astype('float')
df = make_category_columns(df)
df.head()Loading...
df_early = df[df['DateTime'] < '2021-09-11']
df_pre = df[(df['DateTime'] >= '2021-09-11')&(df['DateTime'] < '2021-09-19 14:13:00')]
df_phase1 = df[(df['DateTime'] >= '2021-09-19 14:13:00')&(df['DateTime'] < '2021-10-01')]
df_phase2 = df[(df['DateTime'] >= '2021-10-01')&(df['DateTime'] < '2021-12-01')]
df_phase3 = df[(df['DateTime'] >= '2021-12-01')&(df['DateTime'] <= '2021-12-31')]
df_erupt = df[(df['DateTime'] < '2022-01-01') & (df['DateTime'] > '2021-09-11')]
df_erupt_1 = df_erupt[df_erupt['Magnitude'] < 1.0]
df_erupt_2 = df_erupt[(df_erupt['Magnitude'] >= 1.0)&(df_erupt['Magnitude'] < 2.0)]
df_erupt_3 = df_erupt[(df_erupt['Magnitude'] >= 2.0)&(df_erupt['Magnitude'] < 3.0)]
df_erupt_4 = df_erupt[(df_erupt['Magnitude'] >= 3.0)&(df_erupt['Magnitude'] < 4.0)]
df_erupt_5 = df_erupt[df_erupt['Magnitude'] > 4.0]tab10_colors = (
(0.12156862745098039, 0.4666666666666667, 0.7058823529411765 ), # 1f77b4
(1.0, 0.4980392156862745, 0.054901960784313725), # ff7f0e
(0.17254901960784313, 0.6274509803921569, 0.17254901960784313 ), # 2ca02c
(0.8392156862745098, 0.15294117647058825, 0.1568627450980392 ), # d62728
(0.5803921568627451, 0.403921568627451, 0.7411764705882353 ), # 9467bd
(0.5490196078431373, 0.33725490196078434, 0.29411764705882354 ), # 8c564b
(0.8901960784313725, 0.4666666666666667, 0.7607843137254902 ), # e377c2
(0.4980392156862745, 0.4980392156862745, 0.4980392156862745 ), # 7f7f7f
(0.7372549019607844, 0.7411764705882353, 0.13333333333333333 ), # bcbd22
(0.09019607843137255, 0.7450980392156863, 0.8117647058823529), # 17becf
)
tab20_colors = (
(0.12156862745098039, 0.4666666666666667, 0.7058823529411765 ), # 1f77b4
(0.6823529411764706, 0.7803921568627451, 0.9098039215686274 ), # aec7e8
(1.0, 0.4980392156862745, 0.054901960784313725), # ff7f0e
(1.0, 0.7333333333333333, 0.47058823529411764 ), # ffbb78
(0.17254901960784313, 0.6274509803921569, 0.17254901960784313 ), # 2ca02c
(0.596078431372549, 0.8745098039215686, 0.5411764705882353 ), # 98df8a
(0.8392156862745098, 0.15294117647058825, 0.1568627450980392 ), # d62728
(1.0, 0.596078431372549, 0.5882352941176471 ), # ff9896
(0.5803921568627451, 0.403921568627451, 0.7411764705882353 ), # 9467bd
(0.7725490196078432, 0.6901960784313725, 0.8352941176470589 ), # c5b0d5
(0.5490196078431373, 0.33725490196078434, 0.29411764705882354 ), # 8c564b
(0.7686274509803922, 0.611764705882353, 0.5803921568627451 ), # c49c94
(0.8901960784313725, 0.4666666666666667, 0.7607843137254902 ), # e377c2
(0.9686274509803922, 0.7137254901960784, 0.8235294117647058 ), # f7b6d2
(0.4980392156862745, 0.4980392156862745, 0.4980392156862745 ), # 7f7f7f
(0.7803921568627451, 0.7803921568627451, 0.7803921568627451 ), # c7c7c7
(0.7372549019607844, 0.7411764705882353, 0.13333333333333333 ), # bcbd22
(0.8588235294117647, 0.8588235294117647, 0.5529411764705883 ), # dbdb8d
(0.09019607843137255, 0.7450980392156863, 0.8117647058823529 ), # 17becf
(0.6196078431372549, 0.8549019607843137, 0.8980392156862745), # 9edae5
)
tab20c_colors = (
(0.19215686274509805, 0.5098039215686274, 0.7411764705882353 ), # 3182bd
(0.4196078431372549, 0.6823529411764706, 0.8392156862745098 ), # 6baed6
(0.6196078431372549, 0.792156862745098, 0.8823529411764706 ), # 9ecae1
(0.7764705882352941, 0.8588235294117647, 0.9372549019607843 ), # c6dbef
(0.9019607843137255, 0.3333333333333333, 0.050980392156862744), # e6550d
(0.9921568627450981, 0.5529411764705883, 0.23529411764705882 ), # fd8d3c
(0.9921568627450981, 0.6823529411764706, 0.4196078431372549 ), # fdae6b
(0.9921568627450981, 0.8156862745098039, 0.6352941176470588 ), # fdd0a2
(0.19215686274509805, 0.6392156862745098, 0.32941176470588235 ), # 31a354
(0.4549019607843137, 0.7686274509803922, 0.4627450980392157 ), # 74c476
(0.6313725490196078, 0.8509803921568627, 0.6078431372549019 ), # a1d99b
(0.7803921568627451, 0.9137254901960784, 0.7529411764705882 ), # c7e9c0
(0.4588235294117647, 0.4196078431372549, 0.6941176470588235 ), # 756bb1
(0.6196078431372549, 0.6039215686274509, 0.7843137254901961 ), # 9e9ac8
(0.7372549019607844, 0.7411764705882353, 0.8627450980392157 ), # bcbddc
(0.8549019607843137, 0.8549019607843137, 0.9215686274509803 ), # dadaeb
(0.38823529411764707, 0.38823529411764707, 0.38823529411764707 ), # 636363
(0.5882352941176471, 0.5882352941176471, 0.5882352941176471 ), # 969696
(0.7411764705882353, 0.7411764705882353, 0.7411764705882353 ), # bdbdbd
(0.8509803921568627, 0.8509803921568627, 0.8509803921568627 ), # d9d9d9
)from matplotlib.patches import Rectangle
import datetime as dt
from matplotlib.dates import date2num, num2date
matplotlib.rcParams['font.sans-serif'] = "Helvetica"
matplotlib.rcParams['font.family'] = "sans-serif"
matplotlib.rcParams['xtick.labelsize'] = 14
matplotlib.rcParams['ytick.labelsize'] = 14
matplotlib.rcParams['ytick.labelleft'] = True
matplotlib.rcParams['ytick.labelright'] = True
%matplotlib inline
fig = matplotlib.pyplot.figure(figsize=(24,12))
fig.tight_layout()
# Creating axis
# add_axes([xmin,ymin,dx,dy])
ax_min = fig.add_axes([0.01, 0.01, 0.01, 0.01])
ax_min.axis('off')
ax_max = fig.add_axes([0.99, 0.99, 0.01, 0.01])
ax_max.axis('off')
ax_timeline = fig.add_axes([0.04, 0.1, 0.92, 0.85])
ax_timeline.spines["top"].set_visible(False)
ax_timeline.spines["right"].set_visible(False)
ax_timeline.spines["left"].set_visible(False)
ax_timeline.grid(axis='x')
ax_timeline.axvline(x=dt.datetime(2021, 9, 19, 14, 13), ymin=0.005, ymax=0.972, color='r', linewidth=3)
def make_scatter(df, c, alpha=0.8):
M = 3*np.exp2(1.3*df['Magnitude'])
return ax_timeline.scatter(df['DateTime'], df['Depth(km)'], s=M, c=c, alpha=alpha, edgecolor='black', linewidth=0.5, zorder=2);
# make_scatter(df_erupt, c=tab20c_colors[-1])
points_1 = make_scatter(df_erupt_1, c=[tab20c_colors[-1]], alpha=0.8)
points_2 = make_scatter(df_erupt_2, c=[tab20c_colors[-1]], alpha=0.8)
points_3 = make_scatter(df_erupt_3, c=[tab20c_colors[-1]], alpha=0.8)
points_4 = make_scatter(df_erupt_4, c=[tab20c_colors[-1]], alpha=0.8)
points_5 = make_scatter(df_erupt_5, c=[tab20c_colors[-1]], alpha=0.8)
ax_timeline.tick_params(axis='x', labelrotation=0, bottom=True)
ax_timeline.set_ylabel('')
ax_timeline.yaxis.set_ticks_position('both')
ax_timeline.yaxis.set_ticks_position('both')
xticks = ax_timeline.get_xticks()
new_xticks = [date2num(pd.to_datetime('2021-09-11')),
date2num(pd.to_datetime('2021-09-19 14:13:00'))]
new_xticks = np.append(new_xticks, xticks[2:-1])
ax_timeline.set_xticks(new_xticks)
ax_timeline.invert_yaxis()
ax_timeline.spines['bottom'].set_position(('data', 45))
ax_timeline.margins(tight=True, x=0)
ax_timeline.legend(
[points_1, points_2, points_3, points_4, points_5],
['0 < M <= 1','1 < M <= 2','2 < M <= 3','3 < M <= 4','M > 4'],
loc='best', bbox_to_anchor=(1.02, 0.88, 0.15, 0.1), fancybox=True, borderpad=1.0, labelspacing=1, mode="expand", title="Event Magnitude (M)", fontsize=14, title_fontsize=14)
ax_timeline.add_patch(Rectangle((date2num(pd.to_datetime('2021-09-11')), -1), date2num(pd.to_datetime('2021-09-19 14:13:00'))-date2num(pd.to_datetime('2021-09-11')), 46, color=tab20_colors[0], zorder=1, alpha=0.05))
ax_timeline.add_patch(Rectangle((date2num(pd.to_datetime('2021-09-19 14:13:00')), -1), date2num(pd.to_datetime('2021-10-01'))-date2num(pd.to_datetime('2021-09-19 14:13:00')), 46, color=tab20_colors[2], zorder=1, alpha=0.05))
ax_timeline.add_patch(Rectangle((date2num(pd.to_datetime('2021-10-01')), -1), date2num(pd.to_datetime('2021-12-01'))-date2num(pd.to_datetime('2021-10-01')), 46, color=tab20_colors[4], zorder=1, alpha=0.05))
ax_timeline.add_patch(Rectangle((date2num(pd.to_datetime('2021-12-01')), -1), date2num(pd.to_datetime('2021-12-31'))-date2num(pd.to_datetime('2021-12-01'))+1, 46, color=tab20_colors[6], zorder=1, alpha=0.05));
# plt.savefig('timeline_grey_with_panels_involcan.eps', format='eps')
plt.savefig('timeline_grey_with_panels_involcan.png', format='png')
df_intensity = (df.groupby([pd.Grouper(key='DateTime', freq='D'), 'D'])['Mag']
.sum()
.reset_index())
df_intensity = df_intensity[df_intensity['DateTime'] >= '2021-09-11']
D, S = list(df_intensity.groupby('D'))
df_shallow = S[1]
df_deep = D[1]
df_shallow.head()Loading...
from matplotlib.patches import Rectangle
import datetime as dt
from matplotlib.dates import date2num, num2date
%matplotlib inline
fig, ax = matplotlib.pyplot.subplots(1,1,figsize=(24,5))
ax.axvline(x=dt.datetime(2021, 9, 19, 14, 13), ymin=0.005, ymax=0.972, color='r', linewidth=3)
shallow = ax.bar(df_shallow['DateTime'], df_shallow['Mag'])
deep = ax.bar(df_deep['DateTime'], -df_deep['Mag'])
ax.tick_params(axis='x', labelrotation=0, bottom=True)
ax.set_ylabel('')
ax.yaxis.set_ticks_position('both')
ax.yaxis.set_ticks_position('both')
with warnings.catch_warnings():
warnings.simplefilter("ignore")
ax.set_yticklabels(np.abs(ax.get_yticks()))
xticks = ax.get_xticks()
new_xticks = [date2num(pd.to_datetime('2021-09-11')),
date2num(pd.to_datetime('2021-09-19 14:13:00'))]
new_xticks = np.append(new_xticks, xticks[2:-1])
ax_timeline.set_xticks(new_xticks)
plt.title('Daily Earthquake Event Frequency', fontsize=18)
plt.ylabel('# events', fontsize=18)
ax.margins(tight=True, x=0)
ax.legend(
[shallow, deep],
['# shallow events','# deep events',],
loc='best', bbox_to_anchor=(1.05, 0.88, 0.15, 0.1), fancybox=True, borderpad=1.0, labelspacing=1, mode="expand", title="Event Magnitude (M)", fontsize=14, title_fontsize=14)
ax.add_patch(Rectangle((date2num(pd.to_datetime('2021-09-11')), -200), date2num(pd.to_datetime('2021-09-19 14:13:00'))-date2num(pd.to_datetime('2021-09-11')), 1050, color=tab20_colors[0], zorder=1, alpha=0.05))
ax.add_patch(Rectangle((date2num(pd.to_datetime('2021-09-19 14:13:00')), -200), date2num(pd.to_datetime('2021-10-01'))-date2num(pd.to_datetime('2021-09-19 14:13:00')), 1050, color=tab20_colors[2], zorder=1, alpha=0.05))
ax.add_patch(Rectangle((date2num(pd.to_datetime('2021-10-01')), -200), date2num(pd.to_datetime('2021-12-01'))-date2num(pd.to_datetime('2021-10-01')), 1050, color=tab20_colors[4], zorder=1, alpha=0.05))
ax.add_patch(Rectangle((date2num(pd.to_datetime('2021-12-01')), -200), date2num(pd.to_datetime('2021-12-31'))-date2num(pd.to_datetime('2021-12-01'))+1, 1050, color=tab20_colors[6], zorder=1, alpha=0.05));
fig.tight_layout()
# plt.savefig('histotimeline_grey_with_panels_involcan.eps', format='eps')
plt.savefig('histotimeline_grey_with_panels_involcan_bars.png', format='png')
from matplotlib.patches import Rectangle
import datetime as dt
from matplotlib.dates import date2num, num2date
%matplotlib inline
fig, ax = matplotlib.pyplot.subplots(1,1,figsize=(24,5))
ax.axvline(x=dt.datetime(2021, 9, 19, 14, 13), ymin=0.005, ymax=0.972, color='r', linewidth=3)
shallow, = ax.plot(df_shallow['DateTime'], df_shallow['Mag'])
deep, = ax.plot(df_deep['DateTime'], df_deep['Mag'])
ax.tick_params(axis='x', labelrotation=0, bottom=True)
ax.set_ylabel('')
ax.yaxis.set_ticks_position('both')
ax.yaxis.set_ticks_position('both')
with warnings.catch_warnings():
warnings.simplefilter("ignore")
ax.set_yticklabels(np.abs(ax.get_yticks()))
xticks = ax.get_xticks()
new_xticks = [date2num(pd.to_datetime('2021-09-11')),
date2num(pd.to_datetime('2021-09-19 14:13:00'))]
new_xticks = np.append(new_xticks, xticks[2:-1])
ax_timeline.set_xticks(new_xticks)
plt.title('Daily Earthquake Event Frequency', fontsize=18)
plt.ylabel('# events', fontsize=18)
ax.margins(tight=True, x=0)
ax.legend(
[shallow, deep],
['# shallow events','# deep events',],
loc='best', bbox_to_anchor=(1.05, 0.88, 0.15, 0.1), fancybox=True, borderpad=1.0, labelspacing=1, mode="expand", title="Event Magnitude (M)", fontsize=14, title_fontsize=14)
ax.add_patch(Rectangle((date2num(pd.to_datetime('2021-09-11')), -200), date2num(pd.to_datetime('2021-09-19 14:13:00'))-date2num(pd.to_datetime('2021-09-11')), 1050, color=tab20_colors[0], zorder=1, alpha=0.05))
ax.add_patch(Rectangle((date2num(pd.to_datetime('2021-09-19 14:13:00')), -200), date2num(pd.to_datetime('2021-10-01'))-date2num(pd.to_datetime('2021-09-19 14:13:00')), 1050, color=tab20_colors[2], zorder=1, alpha=0.05))
ax.add_patch(Rectangle((date2num(pd.to_datetime('2021-10-01')), -200), date2num(pd.to_datetime('2021-12-01'))-date2num(pd.to_datetime('2021-10-01')), 1050, color=tab20_colors[4], zorder=1, alpha=0.05))
ax.add_patch(Rectangle((date2num(pd.to_datetime('2021-12-01')), -200), date2num(pd.to_datetime('2021-12-31'))-date2num(pd.to_datetime('2021-12-01'))+1, 1050, color=tab20_colors[6], zorder=1, alpha=0.05));
fig.tight_layout()
# plt.savefig('histotimeline_grey_with_panels_involcan.eps', format='eps')
plt.savefig('histotimeline_grey_with_panels_involcan_lines.png', format='png')
from matplotlib.patches import Rectangle
import datetime as dt
from matplotlib.dates import date2num, num2date
%matplotlib inline
fig, ax = matplotlib.pyplot.subplots(1,1,figsize=(24,5))
ax.axvline(x=dt.datetime(2021, 9, 19, 14, 13), ymin=0.005, ymax=0.972, color='r', linewidth=3)
shallow, = ax.plot(df_shallow['DateTime'], df_shallow['Mag'])
deep, = ax.plot(df_deep['DateTime'], 3*df_deep['Mag'])
ax.tick_params(axis='x', labelrotation=0, bottom=True)
ax.set_ylabel('')
ax.yaxis.set_ticks_position('both')
ax.yaxis.set_ticks_position('both')
with warnings.catch_warnings():
warnings.simplefilter("ignore")
ax.set_yticklabels(np.abs(ax.get_yticks()))
xticks = ax.get_xticks()
new_xticks = [date2num(pd.to_datetime('2021-09-11')),
date2num(pd.to_datetime('2021-09-19 14:13:00'))]
new_xticks = np.append(new_xticks, xticks[2:-1])
ax_timeline.set_xticks(new_xticks)
plt.title('Daily Earthquake Event Frequency (deep event count x3)', fontsize=18)
plt.ylabel('# events', fontsize=18)
ax.margins(tight=True, x=0)
ax.legend(
[shallow, deep],
['# shallow events','# (x3) deep events',],
loc='best', bbox_to_anchor=(1.05, 0.88, 0.15, 0.1), fancybox=True, borderpad=1.0, labelspacing=1, mode="expand", title="Event Magnitude (M)", fontsize=14, title_fontsize=14)
ax.add_patch(Rectangle((date2num(pd.to_datetime('2021-09-11')), -200), date2num(pd.to_datetime('2021-09-19 14:13:00'))-date2num(pd.to_datetime('2021-09-11')), 1050, color=tab20_colors[0], zorder=1, alpha=0.05))
ax.add_patch(Rectangle((date2num(pd.to_datetime('2021-09-19 14:13:00')), -200), date2num(pd.to_datetime('2021-10-01'))-date2num(pd.to_datetime('2021-09-19 14:13:00')), 1050, color=tab20_colors[2], zorder=1, alpha=0.05))
ax.add_patch(Rectangle((date2num(pd.to_datetime('2021-10-01')), -200), date2num(pd.to_datetime('2021-12-01'))-date2num(pd.to_datetime('2021-10-01')), 1050, color=tab20_colors[4], zorder=1, alpha=0.05))
ax.add_patch(Rectangle((date2num(pd.to_datetime('2021-12-01')), -200), date2num(pd.to_datetime('2021-12-31'))-date2num(pd.to_datetime('2021-12-01'))+1, 1050, color=tab20_colors[6], zorder=1, alpha=0.05));
fig.tight_layout()
# plt.savefig('histotimeline_grey_with_panels_involcan.eps', format='eps')
plt.savefig('histotimeline_grey_with_panels_involcan_lines.png', format='png')
from matplotlib.patches import Rectangle
import datetime as dt
from matplotlib.dates import date2num, num2date
matplotlib.rcParams['font.sans-serif'] = "Helvetica"
matplotlib.rcParams['font.family'] = "sans-serif"
matplotlib.rcParams['xtick.labelsize'] = 14
matplotlib.rcParams['ytick.labelsize'] = 14
matplotlib.rcParams['ytick.labelleft'] = True
matplotlib.rcParams['ytick.labelright'] = True
%matplotlib inline
fig = matplotlib.pyplot.figure(figsize=(24,12))
fig.tight_layout()
# Creating axis
# add_axes([xmin,ymin,dx,dy])
ax_min = fig.add_axes([0.01, 0.01, 0.01, 0.01])
ax_min.axis('off')
ax_max = fig.add_axes([0.99, 0.99, 0.01, 0.01])
ax_max.axis('off')
ax_timeline = fig.add_axes([0.04, 0.1, 0.92, 0.85])
ax_timeline.spines["top"].set_visible(False)
ax_timeline.spines["right"].set_visible(False)
ax_timeline.spines["left"].set_visible(False)
ax_timeline.grid(axis='x')
ax_timeline.axvline(x=dt.datetime(2021, 9, 19, 14, 13), ymin=0.005, ymax=0.972, color='r', linewidth=3)
def make_scatter(df, c, alpha=0.8):
M = 3*np.exp2(1.3*df['Magnitude'])
return ax_timeline.scatter(df['DateTime'], df['Depth(km)'], s=M, c=c, alpha=alpha, edgecolor='black', linewidth=0.5, zorder=2);
# make_scatter(df_erupt, c=tab20c_colors[-1])
points_1 = make_scatter(df_erupt_1, c=[tab20_colors[12]], alpha=0.3)
points_2 = make_scatter(df_erupt_2, c=[tab20_colors[16]], alpha=0.4)
points_3 = make_scatter(df_erupt_3, c=[tab20_colors[4]], alpha=0.5)
points_4 = make_scatter(df_erupt_4, c=[tab20_colors[2]], alpha=0.6)
points_5 = make_scatter(df_erupt_5, c=[tab20_colors[6]], alpha=0.8)
ax_timeline.tick_params(axis='x', labelrotation=0, bottom=True)
ax_timeline.set_ylabel('')
ax_timeline.yaxis.set_ticks_position('both')
ax_timeline.yaxis.set_ticks_position('both')
xticks = ax_timeline.get_xticks()
new_xticks = [date2num(pd.to_datetime('2021-09-11')),
date2num(pd.to_datetime('2021-09-19 14:13:00'))]
new_xticks = np.append(new_xticks, xticks[2:-1])
ax_timeline.set_xticks(new_xticks)
ax_timeline.invert_yaxis()
ax_timeline.spines['bottom'].set_position(('data', 45))
ax_timeline.margins(tight=True, x=0)
ax_timeline.legend(
[points_1, points_2, points_3, points_4, points_5],
['0 < M <= 1','1 < M <= 2','2 < M <= 3','3 < M <= 4','M > 4'],
loc='best', bbox_to_anchor=(1.02, 0.88, 0.15, 0.1), fancybox=True, borderpad=1.0, labelspacing=1, mode="expand", title="Event Magnitude (M)", fontsize=14, title_fontsize=14)
plt.savefig('timeline_colors_involcan.eps', format='eps')The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
from matplotlib.patches import Rectangle
import datetime as dt
from matplotlib.dates import date2num, num2date
matplotlib.rcParams['font.sans-serif'] = "Helvetica"
matplotlib.rcParams['font.family'] = "sans-serif"
matplotlib.rcParams['xtick.labelsize'] = 14
matplotlib.rcParams['ytick.labelsize'] = 14
matplotlib.rcParams['ytick.labelleft'] = True
matplotlib.rcParams['ytick.labelright'] = True
%matplotlib inline
fig = matplotlib.pyplot.figure(figsize=(24,12))
fig.tight_layout()
# Creating axis
# add_axes([xmin,ymin,dx,dy])
ax_min = fig.add_axes([0.01, 0.01, 0.01, 0.01])
ax_min.axis('off')
ax_max = fig.add_axes([0.99, 0.99, 0.01, 0.01])
ax_max.axis('off')
ax_timeline = fig.add_axes([0.04, 0.1, 0.92, 0.85])
ax_timeline.spines["top"].set_visible(False)
ax_timeline.spines["right"].set_visible(False)
ax_timeline.spines["left"].set_visible(False)
ax_timeline.grid(axis='x')
ax_timeline.axvline(x=dt.datetime(2021, 9, 19, 14, 13), ymin=0.005, ymax=0.972, color='r', linewidth=3)
def make_scatter(df, c, alpha=0.8):
M = 3*np.exp2(1.3*df['Magnitude'])
return ax_timeline.scatter(df['DateTime'], df['Depth(km)'], s=M, c=c, alpha=alpha, edgecolor='black', linewidth=0.5, zorder=2);
# make_scatter(df_erupt, c=tab20c_colors[-1])
points_1 = make_scatter(df_erupt_1, c=[tab20_colors[12]], alpha=0.3)
points_2 = make_scatter(df_erupt_2, c=[tab20_colors[16]], alpha=0.4)
points_3 = make_scatter(df_erupt_3, c=[tab20_colors[4]], alpha=0.5)
points_4 = make_scatter(df_erupt_4, c=[tab20_colors[2]], alpha=0.6)
points_5 = make_scatter(df_erupt_5, c=[tab20_colors[6]], alpha=0.8)
ax_timeline.tick_params(axis='x', labelrotation=0, bottom=True)
ax_timeline.set_ylabel('')
ax_timeline.yaxis.set_ticks_position('both')
ax_timeline.yaxis.set_ticks_position('both')
xticks = ax_timeline.get_xticks()
new_xticks = [date2num(pd.to_datetime('2021-09-11')),
date2num(pd.to_datetime('2021-09-19 14:13:00'))]
new_xticks = np.append(new_xticks, xticks[2:-1])
ax_timeline.set_xticks(new_xticks)
ax_timeline.invert_yaxis()
ax_timeline.spines['bottom'].set_position(('data', 45))
ax_timeline.margins(tight=True, x=0)
ax_timeline.legend(
[points_1, points_2, points_3, points_4, points_5],
['0 < M <= 1','1 < M <= 2','2 < M <= 3','3 < M <= 4','M > 4'],
loc='best', bbox_to_anchor=(1.02, 0.88, 0.15, 0.1), fancybox=True, borderpad=1.0, labelspacing=1, mode="expand", title="Event Magnitude (M)", fontsize=14, title_fontsize=14)
ax_timeline.add_patch(Rectangle((date2num(pd.to_datetime('2021-09-11')), -1), date2num(pd.to_datetime('2021-09-19 14:13:00'))-date2num(pd.to_datetime('2021-09-11')), 46, color=tab20_colors[0], zorder=1, alpha=0.1))
ax_timeline.add_patch(Rectangle((date2num(pd.to_datetime('2021-09-19 14:13:00')), -1), date2num(pd.to_datetime('2021-10-01'))-date2num(pd.to_datetime('2021-09-19 14:13:00')), 46, color=tab20_colors[2], zorder=1, alpha=0.1))
ax_timeline.add_patch(Rectangle((date2num(pd.to_datetime('2021-10-01')), -1), date2num(pd.to_datetime('2021-12-01'))-date2num(pd.to_datetime('2021-10-01')), 46, color=tab20_colors[4], zorder=1, alpha=0.1))
ax_timeline.add_patch(Rectangle((date2num(pd.to_datetime('2021-12-01')), -1), date2num(pd.to_datetime('2021-12-31'))-date2num(pd.to_datetime('2021-12-01'))+1, 46, color=tab20_colors[6], zorder=1, alpha=0.1));
plt.savefig('timeline_colors_no_panels_involcan.eps', format='eps')The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
Cumulative Distrubtion Plots¶
def cumulative_events_mag_depth(df, hue='Depth', kind='scatter', ax=None, dpi=300, palette=None, kde=True):
matplotlib.rcParams['ytick.labelright'] = False
g = sns.jointplot(x="Magnitude", y="Depth(km)", data=df,
kind=kind, hue=hue, height=10, space=0.1, marginal_ticks=False, ratio=8, alpha=0.6,
hue_order=['Shallow (<18km)', 'Interchange (18km>x>28km)', 'Deep (>28km)'],
ax=ax, palette=palette, ylim=(-2,50), xlim=(0.3,5.6), edgecolor=".2", marginal_kws=dict(bins=20, hist_kws={'edgecolor': 'black'}))
if kde:
g.plot_joint(sns.kdeplot, color="b", zorder=1, levels=15, ax=ax)
g.fig.axes[0].invert_yaxis();
g.fig.set_dpi(dpi)
def cumulative_events_spatial(df, hue='Depth', kind='scatter', ax=None, dpi=300, palette=None):
g = sns.jointplot(x="Longitude", y="Depth(km)", data=df,
kind=kind, hue=hue, color="m", height=10, palette=palette,
hue_order=['Shallow (<18km)', 'Interchange (18km>x>28km)', 'Deep (>28km)', ], ax=ax, ylim=(-2,50))
g.plot_joint(sns.kdeplot, color="b", zorder=1, levels=15, ax=ax)
g.fig.axes[0].invert_yaxis();cumulative_events_mag_depth(df, hue=None)
plt.suptitle('Cumulative Events 01-01-2017 to 01-01-2022', y=1.01);
plt.savefig('cuml_events_all_involcan.eps', format='eps')/opt/homebrew/Caskroom/miniforge/base/envs/lapalma-earthquakes/lib/python3.10/site-packages/seaborn/axisgrid.py:2203: UserWarning: The marginal plotting function has changed to `histplot`, which does not accept the following argument(s): hist_kws.
warnings.warn(msg, UserWarning)
The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
cumulative_events_mag_depth(df_early, hue=None)
plt.suptitle(f'Events prior to 2021-09-11 ({len(df_early.index)} events)', y=1.01)
plt.savefig('cuml_events_early_involcan.eps', format='eps')/opt/homebrew/Caskroom/miniforge/base/envs/lapalma-earthquakes/lib/python3.10/site-packages/seaborn/axisgrid.py:2203: UserWarning: The marginal plotting function has changed to `histplot`, which does not accept the following argument(s): hist_kws.
warnings.warn(msg, UserWarning)
The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
cumulative_events_mag_depth(df_pre, hue=None)
plt.suptitle(f"Events Immediately before Eruption 11-19 September 2021 ({len(df_pre.index)} events)", y=1.01);
plt.savefig('cuml_events_preerupt_involcan.eps', format='eps')/opt/homebrew/Caskroom/miniforge/base/envs/lapalma-earthquakes/lib/python3.10/site-packages/seaborn/axisgrid.py:2203: UserWarning: The marginal plotting function has changed to `histplot`, which does not accept the following argument(s): hist_kws.
warnings.warn(msg, UserWarning)
The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
cumulative_events_mag_depth(df_phase1, hue=None)
plt.suptitle(f"Events in Phase 1 of Eruption 2021-09-11 to 2021-09-30 ({len(df_phase1.index)} events)", y=1.01);
plt.savefig('cuml_events_phase1_involcan.eps', format='eps')/opt/homebrew/Caskroom/miniforge/base/envs/lapalma-earthquakes/lib/python3.10/site-packages/seaborn/axisgrid.py:2203: UserWarning: The marginal plotting function has changed to `histplot`, which does not accept the following argument(s): hist_kws.
warnings.warn(msg, UserWarning)
The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
cumulative_events_mag_depth(df_phase2, hue=None)
plt.suptitle(f"Events in Phase 2 of Eruption 2021-10-01 to 2021-11-31 ({len(df_phase2.index)} events)", y=1.01);
plt.savefig('cuml_events_phase2_involcan.eps', format='eps')/opt/homebrew/Caskroom/miniforge/base/envs/lapalma-earthquakes/lib/python3.10/site-packages/seaborn/axisgrid.py:2203: UserWarning: The marginal plotting function has changed to `histplot`, which does not accept the following argument(s): hist_kws.
warnings.warn(msg, UserWarning)
The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
cumulative_events_mag_depth(df_phase3, hue=None)
plt.suptitle(f"Events in Phase 3 of Eruption 2021-12-01 to 2021-12-31 ({len(df_phase3.index)} events)", y=1.01);
plt.savefig('cuml_events_phase3_involcan.eps', format='eps')/opt/homebrew/Caskroom/miniforge/base/envs/lapalma-earthquakes/lib/python3.10/site-packages/seaborn/axisgrid.py:2203: UserWarning: The marginal plotting function has changed to `histplot`, which does not accept the following argument(s): hist_kws.
warnings.warn(msg, UserWarning)
The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
