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585 KiB
585 KiB
Analysis of simulated IP and temperature grouped by latitude¶
Import libraries¶
In [1]:
import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.transforms as tf
import numpy as np
import pandas as pd
import scipy.stats as st
from matplotlib import cm, colormaps, colors, transforms
from functools import cache
import cartopy.crs as ccrs
Helper functions, variables and classes¶
In [2]:
month_name = ["J", "F", "M", "A", "M", "J", "J", "A", "S", "O", "N", "D"]
In [3]:
area_factor = (
np.cos(np.arange(180) * np.pi / 180) - np.cos(np.arange(1, 181) * np.pi / 180)
) / 2
In [4]:
def std_error(avg_val, avg_sqr, counter):
"""
Estimate the standard error from the average value
and the average value of the square.
:param avg_val: the average value
:param avg_sqr: the average square value
:param counter: the size of the sample
:return: the standard error
"""
return np.sqrt((avg_sqr - avg_val**2) / (counter - 1))
In [5]:
class LatitudeBand():
"""
Represents a latitude band object with specific
latitude range and model information
Attributes:
slice (slice): slice for the latitude range
indices (tuple): indices within the latitude range
model (str): model associated with the latitude band
color (str): color associated with the latitude band.
"""
def __init__(self, expression, model=None):
"""
Initializes a LatitudeBand object with the given latitude
expression and model.
Args:
expression (str): latitude range expr. like '0-8S', '8N-8S'
model (str, optional): 'WRF' or 'INMCM', defaults to None
"""
# split the expression into two parts
# and replace "0" with "0S" if necessary
s1, s2 = [s if s != "0" else "0S" for s in expression.split("-")]
assert all(s[-1] in "NS" and s[:-1].isdigit() for s in [s1, s2])
# calculate values for the latitude bands
# taking into account direction
N1 = int(s1[:-1]) * (-1 if s1[-1] == "S" else 1)
N2 = int(s2[:-1]) * (-1 if s2[-1] == "S" else 1)
# format latitude values for display using thin spaces
p1 = f"0°" if N1 == 0 else f"{abs(N1):d}° {'S' if N1 < 0 else 'N'}"
p2 = f"0°" if N2 == 0 else f"{abs(N2):d}° {'S' if N2 < 0 else 'N'}"
# set color for each latitude band
Z = (N1 + N2) / 2
cmap = colormaps.get_cmap('tab20')
if 0 < Z < 9:
self.color = cmap(7 / 40)
elif -9 < Z < 0:
self.color = cmap(5 / 40)
elif -18 < Z < -9:
self.color = cmap(39 / 40)
elif -90 < Z < -18:
self.color = cmap(37 / 40)
elif 9 < Z < 18:
self.color = cmap(35 / 40)
elif 18 < Z < 90:
self.color = cmap(33 / 40)
# ensure the model is valid and set degrees
# and central index based on the model
assert model in ["WRF", "INMCM"]
degrees = 1 if model == "WRF" else 1.5
central_idx = 90 if model == "WRF" else 60
# check that latitude values are divisible
# by the appropriate degrees
assert all(N % degrees == 0 for N in [N1, N2])
# convert latitude values to indices and sort them
N1, N2 = sorted([int(N // degrees + central_idx) for N in [N1, N2]])
# create a slice for the latitude range
# and generate indices within that range
self.slice = slice(N1, N2, 1)
self.indicies = tuple(np.arange(*self.slice.indices(1000000)).tolist())
# store latitude bands model and prettify title
self.model = model
self.title = f"{p1}–{p2}" # like 9° S–18° S
Loading precalculated arrays¶
In [6]:
wrf_N_days = 4992
inm_N_days = 3650
In [7]:
wrf_dt_indicies = np.array(
[dt.date(1980, 1, 1) + dt.timedelta(i * 3) for i in range(wrf_N_days)]
)
inm_dt_indicies = np.array(
[dt.date(2022, 1, 1) + dt.timedelta(i % 365) for i in range(inm_N_days)]
)
In [8]:
wrf_LATxMON_t2 = np.load("./data/WRF/WRF_T2_LATxMON.npy")
wrf_LATxMON_ip = {
key: np.load(f"./data/WRF/WRF_IP_{parameters}_LATxMON.npy")
for key, parameters in zip([500, 800, 1000, 1200],
["500_T2_25", "800", "1000", "1200"])
}
inm_LATxMON_ip = {
key: np.load(f"./data/INMCM/INMCM_IP_{parameters}_LATxMON.npy")
for key, parameters in zip([800, 1000, 1200],
["800", "1000", "1200"])
}
wrf_hourly_total_ip = {
key: np.load(f"./data/WRF/WRF_HOURLY_TOTAL_IP_{parameters}.npy")
for key, parameters in zip([500, 800, 1000, 1200],
["500_T2_25", "800", "1000", "1200"])
}
inm_hourly_total_ip = {
key: np.load(f"./data/INMCM/INMCM_HOURLY_TOTAL_IP_{parameters}.npy")
for key, parameters in zip([800, 1000, 1200],
["800", "1000", "1200"])
}
Figure 4¶
In [9]:
band_names = ["0-9N", "0-9S", "9S-18S", "18S-90S", "9N-18N", "18N-90N"]
wrf_bands = [LatitudeBand(t, model="WRF") for t in band_names]
inmcm_bands = [LatitudeBand(t, model="INMCM") for t in band_names]
bar_data = np.zeros((6, len(band_names), 12))
# (axes, strips, months)
for j, cape in enumerate([800, 1000, 1200]):
ax_idx = j * 2
for i, band in enumerate(wrf_bands):
bar_data[ax_idx, i] = wrf_LATxMON_ip[cape][band.slice].sum(axis=0)
ax_idx = j * 2 + 1
for i, band in enumerate(inmcm_bands):
bar_data[ax_idx, i] = inm_LATxMON_ip[cape][band.slice].sum(axis=0)
In [10]:
fig = plt.figure(figsize=(10, 14), constrained_layout=False)
ax = [None for _ in range(6)]
for n in range(6):
ax[n] = fig.add_subplot(4, 4, (2*n + 1, 2*n + 2))
low = [0e3] * 6
high = [300e3] * 6
step = [50e3] * 6
coeff = [1e3] * 6
caption = ['WRF, 1980–2020, $\\varepsilon_0 = 0.8$ kJ/kg',
'INMCM, 10 years, $\\varepsilon_0 = 0.8$ kJ/kg',
'WRF, 1980–2020, $\\varepsilon_0 = 1$ kJ/kg',
'INMCM, 10 years, $\\varepsilon_0 = 1$ kJ/kg',
'WRF, 1980–2020, $\\varepsilon_0 = 1.2$ kJ/kg',
'INMCM, 10 years, $\\varepsilon_0 = 1.2$ kJ/kg']
lab = ['18° S–90° S',
'9° S–18° S',
'0°–9° S',
'0°–9° N',
'9° N–18° N',
'18° N–90° N']
# thin spaces (' ') are used between ‘°’ signs and letters
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]))
ax[n].set_yticklabels((np.arange(low[n], high[n] + step[n] / 2,
step[n]) / coeff[n]).astype(int),
fontsize='large')
ax[n].set_ylabel('Monthly mean\nionospheric potential, kV',
fontsize='large')
ax[n].set_title(caption[n], fontsize='large')
fig.align_ylabels([ax[0], ax[2], ax[4]])
fig.align_ylabels([ax[1], ax[3], ax[5]])
for n in range(6):
bottom_data = np.zeros(12)
for k in range(len(band_names)):
data = bar_data[n, k]
ax[n].bar(
np.arange(12),
data,
0.8,
color=wrf_bands[k].color,
bottom=bottom_data,
label=wrf_bands[k].title,
)
bottom_data += data
for n in range(6):
ax[n].text(-0.28, 1.05, chr(ord('a') + 3 * (n % 2) + n // 2),
fontsize='x-large',
fontweight='semibold', ha='left', va='bottom',
transform=ax[n].transAxes)
fig.subplots_adjust(hspace=0.3, wspace=1.)
leg_width_rel = (ax[5].get_position().x1 - ax[4].get_position().x0) \
/ (ax[4].get_position().x1 - ax[4].get_position().x0)
leg = ax[4].legend(bbox_to_anchor=(0., -1./3, leg_width_rel, 1./6), ncols=6,
loc='lower center', borderaxespad=0.,
mode='expand', fontsize='large',
framealpha=1, edgecolor='0.',
handlelength=1., handletextpad=0.5)
leg.get_frame().set_linewidth(0.5)
fig.savefig('figures_two_parts/ip_decomposed.eps', bbox_inches='tight')
Figure 2¶
In [11]:
band_names = ["18N-90N", "9N-18N", "0-9N", "0-9S", "9S-18S", "18S-90S"]
wrf_bands = [LatitudeBand(t, model="WRF") for t in band_names]
inm_bands = [LatitudeBand(t, model="INMCM") for t in band_names]
wrf_t2_bands = [LatitudeBand(t, model="WRF") for t in ["18N-30N"]+band_names[1:-1]+["18S-30S"]]
In [12]:
wrf_slice = LatitudeBand("30S-30N", model="WRF").slice
inm_slice = LatitudeBand("30S-30N", model="INMCM").slice
WRF_IP_LxM = wrf_LATxMON_ip[1000][wrf_slice] / 360
INM_IP_LxM = inm_LATxMON_ip[1000][inm_slice] / 180
T2_LxM = wrf_LATxMON_t2[wrf_slice]
In [13]:
strip_map = np.full((180, 360, 4), 1.)
for band in wrf_t2_bands:
for i in band.indicies:
for j in range(360):
strip_map[i, j] = band.color
In [14]:
wrf_ip_sum_over_bands = np.zeros((6, 12))
inm_ip_sum_over_bands = np.zeros((6, 12))
wrf_t2_avg_over_bands = np.zeros((6, 12))
for j in range(6):
wrf_ip_sum_over_bands[j] = wrf_LATxMON_ip[1000][wrf_bands[j].slice].sum(axis=0)
inm_ip_sum_over_bands[j] = inm_LATxMON_ip[1000][inm_bands[j].slice].sum(axis=0)
wrf_t2_avg_over_bands[j] = np.sum(
wrf_LATxMON_t2[wrf_t2_bands[j].slice]
* area_factor[wrf_t2_bands[j].slice, np.newaxis],
axis=0,
) / np.sum(area_factor[wrf_t2_bands[j].slice, np.newaxis])
In [15]:
fig_width = 10
fig_height = 12
width = 3.
wsp = 1.
tot_width = 3 * width + 4 * wsp
widthM = 0.6 * tot_width
height = np.array([2., 2., 3., 3., 2., 1.])
hsp = 0.8
hsep1 = 2.
hsep2 = 3.5
assert len(height) == 6
heightD = 4.
heightM = widthM/tot_width * 60/360 * fig_width/fig_height
tot_height = (sum(height) + heightD + 8 * hsp + hsep1 + hsep2) / (1 - heightM)
heightM *= tot_height
sizeT = np.array([6., 3., 2., 2., 3., 4.])
hspT = hsp
assert len(sizeT) == 6
heightT = sizeT * (sum(height) + 5 * hsp - 5 * hspT) / sum(sizeT)
minT = np.array([16., 24., 24., 24., 22., 18.])
fig = plt.figure(figsize=(fig_width, fig_height), constrained_layout=False)
ax = [None for _ in range(12)]
axT = [None for _ in range(6)]
for n in range(6):
ax[2*n] = fig.add_axes(
(
(width + 2 * wsp) / tot_width,
(sum(height[n+1:]) + heightM + (6 - n) * hsp + hsep1) / tot_height,
width / tot_width,
height[n] / tot_height
)
)
ax[2*n + 1] = fig.add_axes(
(
(2 * width + 3 * wsp) / tot_width,
(sum(height[n+1:]) + heightM + (6 - n) * hsp + hsep1) / tot_height,
width / tot_width,
height[n] / tot_height
)
)
axT[n] = fig.add_axes(
(
wsp / tot_width,
(sum(heightT[n+1:]) + (5 - n) * hspT
+ heightM + hsp + hsep1) / tot_height,
width / tot_width,
heightT[n] / tot_height
)
)
axD = [None for _ in range(3)]
for n in range(3):
axD[n] = fig.add_axes(
(
(n * width + (n + 1) * wsp) / tot_width,
(sum(height) + heightM + 7 * hsp + hsep1 + hsep2) / tot_height,
width / tot_width,
heightD / tot_height
)
)
axM = fig.add_axes(
(
(1 - widthM / tot_width) / 2,
hsp / tot_height,
widthM / tot_width,
heightM / tot_height
),
projection=ccrs.PlateCarree(central_longitude=0)
)
axM.coastlines('110m', linewidth=0.5)
caption = ['WRF, 1980–2020, $\\varepsilon_0 = 1$ kJ/kg',
'INMCM, 10 years, $\\varepsilon_0 = 1$ kJ/kg']
captionT = 'WRF, 1980–2020'
captionD = ['WRF, 1980–2020',
'WRF, 1980–2020, $\\varepsilon_0 = 1$ kJ/kg',
'INMCM, 10 years, $\\varepsilon_0 = 1$ kJ/kg']
for n in range(12):
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')
low = 0
high = 50e3 * height[n // 2]
step = 50e3
coeff = 1e3
ax[n].set_ylim((low, high))
ax[n].set_yticks(np.arange(low, high + step / 2, step))
ax[n].set_yticklabels((np.arange(low, high + step / 1,
step) / coeff).astype(int),
fontsize='large')
if n < 2:
ax[n].set_title(caption[n], fontsize='large')
for n in range(2):
lbl = ax[n].set_ylabel('Monthly mean contributions '
'to the ionospheric potential, kV',
fontsize='large', labelpad=0.)
lbl.set_position((0., 1 - (sum(height) + 5 * hsp) / 2 / height[0]))
for n in range(6):
for axis in ['top', 'bottom', 'left', 'right']:
axT[n].spines[axis].set_linewidth(0.5)
axT[n].tick_params(length=6, width=0.5, axis='y')
axT[n].tick_params(length=0, width=0.5, axis='x')
axT[n].grid(color='0.', linewidth=0.5, axis='y')
axT[n].set_xlim((-0.5, 11.5))
axT[n].set_xticks(np.arange(12))
axT[n].set_xticklabels(month_name, fontsize='large', va='top')
low = minT[n]
step = 2
high = minT[n] + step * sizeT[n]
coeff = 1
axT[n].set_ylim((low, high))
axT[n].set_yticks(np.arange(low, high + step / 2, step))
axT[n].set_yticklabels((np.arange(low, high + step / 2,
step) / coeff).astype(int),
fontsize='large')
if n == 0:
axT[n].set_title(captionT, fontsize='large')
lbl = axT[0].set_ylabel('Monthly mean surface air temperature, °C',
fontsize='large')
lbl.set_position((0., 1 - (sum(heightT) + 5 * hspT) / 2 / heightT[0]))
for n in range(3):
for axis in ['top', 'bottom', 'left', 'right']:
axD[n].spines[axis].set_linewidth(0.5)
axD[n].tick_params(length=6, width=0.5)
axD[n].tick_params(length=0, width=0.5, which='minor')
axD[n].grid(color='0.', linewidth=0.5, axis='y')
axD[n].set_xlim((0, 12))
axD[n].set_xticks(np.arange(0, 12.5, 1))
axD[n].set_xticks(np.arange(0.5, 12, 1), minor=True)
axD[n].set_xticklabels([])
axD[n].set_xticklabels(month_name, fontsize='large', va='top',
minor=True)
axD[n].set_ylim((-30, 30))
axD[n].set_yticks(np.arange(-30, 31, 10))
axD[n].set_yticklabels(['0' if v == 0 else f'{-v}S' if v < 0 else f'{v}N'
for v in range(-30, 31, 10)],
fontsize='large')
axD[n].set_title(captionD[n], fontsize='large')
cax = [None for _ in range(3)]
for n in range(3):
cax[n] = fig.add_axes(
[axD[n].get_position().x0,
axD[n].get_position().y0 - 0.035,
axD[n].get_position().x1 - axD[n].get_position().x0,
0.01])
min_val = [10, 0, 0]
max_val = [30, 50, 120]
step_val = [5, 10, 20]
cbar_cmap = ['inferno', 'plasma_r', 'plasma_r']
cbar_norm = [None] * 3
for n in range(3):
cbar_norm[n] = colors.Normalize(vmin=min_val[n], vmax=max_val[n])
cbar = fig.colorbar(cm.ScalarMappable(norm=cbar_norm[n],
cmap=cbar_cmap[n]),
cax=cax[n], orientation='horizontal')
cbar.outline.set_linewidth(0.5)
cbar.ax.tick_params(length=6, width=0.5)
cbar.set_ticks(np.arange(min_val[n], max_val[n] + 1, step_val[n]))
cbar.ax.set_xticklabels(['0' if v == 0 else f'−{-v}' if v < 0 else f'{v}'
for v in range(min_val[n], max_val[n] + 1,
step_val[n])],
fontsize='large')
if n == 0:
cbar.set_label('Mean surface air temperature, °C',
fontsize='large')
else:
cbar.set_label('Mean grid cell contributions\n'
'to the ionospheric potential, V',
fontsize='large')
for axis in ['geo']:
axM.spines[axis].set_linewidth(0.5)
axM.tick_params(top=True, right=True, which='both')
axM.tick_params(length=6, width=0.5)
axM.tick_params(length=3, width=0.5, which='minor')
axM.grid(False)
axM.set_xticks(np.arange(-180, 181, 30))
axM.set_xticks(np.arange(-180, 181, 10), minor=True)
axM.set_xticklabels(['180°', '', '120° W', '', '60° W', '', '0°',
'', '60° E', '', '120° E', '', '180°'],
fontsize='large', va='top')
# thin spaces (' ') are used between ‘°’ signs and letters
axM.set_yticks(np.arange(-90, 91, 30))
axM.set_yticks(np.arange(-90, 91, 10), minor=True)
axM.set_yticklabels(['', '', '30° S', '0°', '30° N', '', ''],
fontsize='large')
# thin spaces (' ') are used between ‘°’ signs and letters
axM.set_title('Partition of the Earth’s surface into regions',
fontsize='large', pad=10)
#####################
### Plotting data ###
#####################
for j in range(6):
ax[2*j].bar(range(12), wrf_ip_sum_over_bands[j],
0.8, color=wrf_bands[j].color)
ax[2*j+1].bar(range(12), inm_ip_sum_over_bands[j],
0.8, color=inm_bands[j].color)
axT[j].bar(range(12), wrf_t2_avg_over_bands[j],
0.8, color=wrf_t2_bands[j].color)
offset = lambda p: transforms.ScaledTranslation(0, p/72, fig.dpi_scale_trans)
for AX in [ax[2*j], ax[2*j+1]]:
AX.text(0.01 if j<2 else 0.99, 1,
wrf_bands[j].title,
fontsize='large',
ha='left' if j<2 else 'right',
va='top',
transform=AX.transAxes + offset(-2))
axT[j].text(0.01 if j<2 else 0.99, 1,
wrf_t2_bands[j].title,
fontsize='large',
ha='left' if j<2 else 'right',
va='top',
transform=axT[j].transAxes + offset(-2))
axD[0].imshow(T2_LxM,
cmap=cbar_cmap[0], norm=cbar_norm[0],
extent=[0, 12, 30, -30], aspect='auto', interpolation='none',
rasterized=True)
axD[1].imshow(WRF_IP_LxM,
cmap=cbar_cmap[1], norm=cbar_norm[1],
extent=[0, 12, 30, -30], aspect='auto', interpolation='none',
rasterized=True)
axD[2].imshow(INM_IP_LxM,
cmap=cbar_cmap[2], norm=cbar_norm[2],
extent=[0, 12, 30, -30], aspect='auto', interpolation='none',
rasterized=True)
axM.imshow(np.flip(strip_map[60:-60], axis=0), extent=[-180, 180, -30, 30])
for n in range(3):
axD[n].text(-0.28 * width/width, 1 + 0.1 * heightD/heightD,
chr(ord('a') + 2 * n), fontsize='x-large',
fontweight='semibold', ha='left', va='bottom',
transform=axD[n].transAxes)
axT[0].text(-0.28 * width/width, 1 + 0.1 * heightD/heightT[0],
'b', fontsize='x-large',
fontweight='semibold', ha='left', va='bottom',
transform=axT[0].transAxes)
for n in range(2):
ax[n].text(-0.28 * width/width, 1 + 0.1 * heightD/height[0],
chr(ord('d') + 2 * n), fontsize='x-large',
fontweight='semibold', ha='left', va='bottom',
transform=ax[n].transAxes)
axM.text(-0.28 * width/widthM, 1 + 0.1 * heightD/heightM,
'g', fontsize='x-large',
fontweight='semibold', ha='left', va='bottom',
transform=axM.transAxes)
fig.savefig('figures_two_parts/ip_separate.eps', bbox_inches='tight')
Figure 6¶
In [16]:
vostok_diurnal = np.load('./data/Vostok/vostok_diurnal_2006_2020.npy')
vostok_seasonal = np.load('./data/Vostok/vostok_2006_2020_results.npz')['mean'][0]
In [17]:
wrf_diurnal = wrf_hourly_total_ip[1000].mean(axis=0) / 240e3
wrf_new_diurnal = wrf_hourly_total_ip[500].mean(axis=0) / 240e3
vostok_diurnal /= vostok_diurnal.mean()
In [18]:
# ring
vostok_diurnal = [*vostok_diurnal, vostok_diurnal[0]]
wrf_new_diurnal = [*wrf_new_diurnal, wrf_new_diurnal[0]]
wrf_diurnal = [*wrf_diurnal, wrf_diurnal[0]]
In [19]:
band_names = ["0-9N", "0-9S", "9S-18S", "18S-90S", "9N-18N", "18N-90N"]
wrf_bands = [LatitudeBand(t, model="WRF") for t in band_names]
inmcm_bands = [LatitudeBand(t, model="INMCM") for t in band_names]
bar_data = np.zeros((2, len(band_names), 12))
# (axes, bands, months)
for i, band in enumerate(wrf_bands):
bar_data[0, i] = wrf_LATxMON_ip[1000][band.slice].sum(axis=0)
bar_data[1, i] = wrf_LATxMON_ip[500][band.slice].sum(axis=0)
In [20]:
fig = plt.figure(figsize=(10, 15), constrained_layout=False)
ax = [None for _ in range(6)]
for n in range(6):
ax[n] = fig.add_subplot(4, 4, (2*n + 1, 2*n + 2))
low = [0.8, 0] + [0.8, 0e3] * 2
high = [1.2, 180] + [1.2, 300e3] * 2
step = [0.1, 30] + [0.1, 50e3] * 2
coeff = [1] * 2 + [1, 1e3] * 2
caption = ['Vostok station, 2006–2020',
'Vostok station, 2006–2020',
'WRF, 1980–2020, $\\varepsilon_0 = 1$ kJ/kg',
'WRF, 1980–2020, $\\varepsilon_0 = 1$ kJ/kg',
'WRF, 1980–2020, $\\varepsilon_0 = 0.5$ kJ/kg, $T_0 = 25$ °C',
'WRF, 1980–2020, $\\varepsilon_0 = 0.5$ kJ/kg, $T_0 = 25$ °C']
lab = ['18° S–90° S',
'9° S–18° S',
'0°–9° S',
'0°–9° N',
'9° N–18° N',
'18° N–90° N']
# thin spaces (' ') are used between ‘°’ signs and letters
for n in range(0, 5, 2):
for axis in ['top', 'bottom', 'left', 'right']:
ax[n].spines[axis].set_linewidth(0.5)
ax[n].tick_params(length=6, width=0.5)
ax[n].grid(color='0.', linewidth=0.5)
ax[n].set_xlim((0, 24))
ax[n].set_xticks(np.arange(0, 25, 2))
ax[n].set_xticklabels(np.arange(0, 25, 2), fontsize='large')
ax[n].set_xlabel('UTC, hours', fontsize='large')
ax[n].set_ylim((low[n], high[n]))
ax[n].set_yticks(np.arange(low[n], high[n] + step[n] / 2, step[n]))
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')
if n == 0:
ax[n].set_ylabel('Fair-weather pot. gradient\n'
'as a fraction of the mean',
fontsize='large')
else:
ax[n].set_ylabel('Ionospheric potential\nas a fraction of the mean',
fontsize='large')
ax[n].set_title(caption[n], fontsize='large')
for n in range(1, 6, 2):
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]))
ax[n].set_yticklabels((np.arange(low[n], high[n] + step[n] / 2,
step[n]) / coeff[n]).astype(int),
fontsize='large')
if n == 1:
ax[n].set_ylabel('Monthly mean fair-weather\npotential 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')
fig.align_ylabels([ax[0], ax[2], ax[4]])
fig.align_ylabels([ax[1], ax[3], ax[5]])
ax[0].plot(np.arange(0, 25),
vostok_diurnal,
linewidth=1.5, color='purple', clip_on=False,
zorder=4)
ax[1].bar(np.arange(12), vostok_seasonal, 0.8, color='orangered')
ax[2].plot(np.arange(0, 25),
wrf_diurnal,
linewidth=1.5, color='purple', clip_on=False,
zorder=4)
ax[4].plot(np.arange(0, 25),
wrf_new_diurnal,
linewidth=1.5, color='purple', clip_on=False,
zorder=4)
for n in range(2):
bottom_data = np.zeros(12)
for k in range(len(band_names)):
data = bar_data[n, k]
ax[2*n+3].bar(
np.arange(12),
data,
0.8,
color=wrf_bands[k].color,
bottom=bottom_data,
label=wrf_bands[k].title,
)
bottom_data += data
for n in range(0, 5, 2):
ax[n].text(-0.27, 1.05, chr(ord('a') + n),
fontsize='x-large',
fontweight='semibold', ha='left', va='bottom',
transform=ax[n].transAxes)
for n in range(1, 6, 2):
ax[n].text(-0.28, 1.05, chr(ord('a') + n),
fontsize='x-large',
fontweight='semibold', ha='left', va='bottom',
transform=ax[n].transAxes)
fig.subplots_adjust(hspace=0.45, wspace=1.)
leg_width_rel = (ax[5].get_position().x1 - ax[4].get_position().x0) \
/ (ax[5].get_position().x1 - ax[5].get_position().x0)
leg = ax[5].legend(bbox_to_anchor=(1. - leg_width_rel, -1./2,
leg_width_rel, 1./6), ncols=6,
loc='lower center', borderaxespad=0.,
mode='expand', fontsize='large',
framealpha=1, edgecolor='0.',
handlelength=1., handletextpad=0.5)
leg.get_frame().set_linewidth(0.5)
fig.savefig('figures_two_parts/new_parameterisation.eps', bbox_inches='tight')
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