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172 KiB
172 KiB
Processing digitized data from external sources to construct Figure 1¶
Import libraries¶
In [1]:
import matplotlib.pyplot as plt
import numpy as np
Helper functions and variables¶
In [2]:
# Define an array of abbreviated month names to use as labels on the x-axis of plots
month_name = ['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N', 'D']
Digitized measurement results from external sources¶
In [3]:
# air—Earth current values measured at Kew in Jun 1930 — May 1931
# units are A·m^(−2)
# source: Scrase (1933), Table 2
kew_data = (
np.array([40, 66, 86, 76, 90, 114, 113, 138, 94, 93, 65, 55])
* 10**4 * 10**(-18)
)
In [4]:
# air—Earth current values measured at Mauna Loa in 1977–1983
# units are A·m^(−2)
# source: Adlerman & Williams (1996), Fig. 6 (data by Cobb)
mauna_loa_data = (
np.array([30.8, 31.7, 31.3, 28.0, 33.7, 36.4,
35.7, 34.1, 33.2, 33.9, 34.0, 32.2])
* 10**(-13)
)
In [5]:
# air—Earth current values measured at Athens in 1965–1980
# units are A·m^(−2)
# source: Retalis (1991), Fig. 6
athens_data = (
np.array([23.3, 19.7, 18.4, 21.6, 19.2, 24.1,
24.1, 26.0, 23.2, 20.5, 21.0, 19.0])
* 10**(-13)
)
In [6]:
# potential gradient values measured during Carnegie and Maud expeditions
# (1915–1929)
# units are V·m^(−1)
# source: Adlerman & Williams (1996), Fig. 7b
carnegie_maud_data = (
np.array([121, 131, 128, 121, 131, 161,
180, 111, 127, 121, 117, 126])
)
In [7]:
# ionospheric potential values measured at different sites over 1955–2004
# units are V
# source: Markson (2007), Fig. 7 (various data)
ip_data = (
np.array([212, 236, 239, 238, 245, 234,
240, 263, 260, 255, 229, 262])
* 10**3
)
In [8]:
# potential gradient values measured at Vostok station in (1998–2001)
# units are V·m^(−1)
# source: Burns et al. (2012), Table 2
vostok_old_data = (
np.array([195, 201, 205, 192, 188, 195,
209, 198, 209, 195, 193, 192])
)
Figure 1.1¶
Seasonal variation based on earlier measurement results
In [9]:
fig = plt.figure(figsize=(10, 14), constrained_layout=False)
# create a list of axes objects to hold subplots.
ax = [None for _ in range(6)]
# configure each subplot in the figure
# subplots are arranged in a 4×4 grid
for n in range(6):
ax[n] = fig.add_subplot(4, 4, (2*n + 1, 2*n + 2))
# lower, upper limits, y-axis ticks interval, y-scaling coefficient for each subplot
low = [100, 160, 0e-13, 15e-13, 25e-13, 200e3]
high = [200, 240, 20e-13, 30e-13, 40e-13, 280e3]
step = [20, 20, 5e-13, 5e-13, 5e-13, 20e3]
coeff = [1, 1, 1e-12, 1e-12, 1e-12, 1e3]
caption = ['Carnegie and Maud, 1915–1929',
'Vostok station, 1998–2001 (adjusted)',
'Kew, 1930–1931',
'Athens, 1965–1980',
'Mauna Loa, 1977–1983',
'Ion. potent. measurements, 1955–2004']
ins_caption = ['(after $\it{Adlerman~&~Williams}$, 1996)',
'(after $\it{Burns~et~al.}$, 2012)',
'(after $\it{Scrase}$, 1933)',
'(after $\it{Retalis}$, 1991)',
'(after $\it{Adlerman~&~Williams}$, 1996)',
'(after $\it{Markson}$, 2007)']
data = np.array([carnegie_maud_data,
vostok_old_data,
kew_data,
athens_data,
mauna_loa_data,
ip_data])
# assign colors for each dataset
col = ['orangered', 'orangered', 'teal', 'teal', 'teal', 'royalblue']
for n in range(6):
for axis in ['top', 'bottom', 'left', 'right']:
ax[n].spines[axis].set_linewidth(0.5)
ax[n].tick_params(length=6, width=0.5, axis='y')
ax[n].tick_params(length=0, width=0.5, axis='x')
ax[n].grid(color='0.', linewidth=0.5, axis='y')
ax[n].set_xlim((-0.5, 11.5))
ax[n].set_xticks(np.arange(12))
ax[n].set_xticklabels(month_name, fontsize='large', va='top')
ax[n].set_ylim((low[n], high[n]))
ax[n].set_yticks(np.arange(low[n], high[n] + step[n] / 2, step[n]))
if n in [2, 3, 4]:
ax[n].set_yticklabels([f'{x:.1f}' for x in
np.arange(low[n], high[n] + step[n] / 2,
step[n]) / coeff[n]
],
fontsize='large')
ax[n].set_ylabel('Monthly mean fair-weather\n'
'air—Earth current, pA/m²',
fontsize='large')
else:
ax[n].set_yticklabels((np.arange(low[n], high[n] + step[n] / 2,
step[n]) / coeff[n]).astype(int),
fontsize='large')
if n in [0, 1]:
ax[n].set_ylabel('Monthly mean fair-weather\n'
'potential gradient, V/m',
fontsize='large')
else:
ax[n].set_ylabel('Monthly mean\nionospheric potential, kV',
fontsize='large')
ax[n].set_title(caption[n], fontsize='large')
ax[n].text(0.5,
1 - 0.01
* (ax[n].get_position().x1 - ax[n].get_position().x0)
/ (ax[n].get_position().y1 - ax[n].get_position().y0)
* fig.get_size_inches()[0] / fig.get_size_inches()[1],
ins_caption[n],
fontsize='large', ha='center', va='top',
transform=ax[n].transAxes)
ax[n].annotate('', xy=(12, np.min(data[n])), xycoords='data',
xytext=(12, np.max(data[n])), textcoords='data',
annotation_clip=False,
arrowprops=dict(
arrowstyle='<|-|>,head_length=0.8,head_width=0.3',
patchA=None, patchB=None, shrinkA=0., shrinkB=0.,
connectionstyle='arc3,rad=0.', fc='black',
linewidth=0.5
))
ampl = (np.max(data[n]) - np.min(data[n])) / np.mean(data[n])
ax[n].text(12.2, (np.min(data[n]) + np.max(data[n])) / 2,
f'{ampl * 100:.0f}%',
fontsize='large', ha='left', va='center', rotation=270)
fig.align_ylabels([ax[0], ax[2], ax[4]])
fig.align_ylabels([ax[1], ax[3], ax[5]])
for n in range(6):
ax[n].bar(np.arange(12), data[n], 0.8, color=col[n])
for n in range(6):
ax[n].text(-0.3, 1.05, chr(ord('a') + n), fontsize='x-large',
fontweight='semibold', ha='left', va='bottom',
transform=ax[n].transAxes)
fig.subplots_adjust(hspace=0.3, wspace=1.6)
fig.savefig('figures_two_parts/earlier_measurements.eps', bbox_inches='tight')
