def colorregions(region):
"""Set the colors to ensure a uniform scheme for each region """
d={}
d["ALL"]="k"
d["NHDA"]=cm.gray(.5)
d["NADA"]=cm.Purples(.5)
d["OWDA"]=cm.Blues(.5)
d["MXDA"]=cm.PiYG(.1)
d["ANZDA"]=cm.PiYG(.8)
d["MADA"]=cm.Oranges(.5)
d["GDA"]="k"
return d[region]
def get_dataset_color(dataset,depth=None):
"""Set the colors to ensure a uniform scheme for each dataset """
dataset=string.lower(dataset)
d={}
d["dai"]=cm.Blues(.5)
d["tree"]=cm.summer(.3)
d["cru"]=cm.Blues(.9)
#models
d["picontrol"]=cm.Purples(.8)
d["h85"]="k"
d["tree_noise"]=cm.PiYG(.2)
#Soil moisture
d["merra2"]={}
d["merra2"]["30cm"]=cm.copper(.3)
d["merra2"]["2m"]=cm.copper(.3)
d["gleam"]={}
d["gleam"]["30cm"]=cm.Reds(.3)
d["gleam"]["2m"]=cm.Reds(.7)
if depth is None:
return d[dataset]
else:
return d[dataset][depth]
backupCount=3) # file handler
fh.setLevel(logging.DEBUG)
ch = logging.StreamHandler() # console handler
ch.setLevel(logging.DEBUG)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
ch.setFormatter(formatter)
fh.setFormatter(formatter)
logger.addHandler(fh)
logger.addHandler(ch)
logger.info('Initializing %s', __name__)
colors = [
cm.RdBu(0.85),
cm.RdBu(0.7),
cm.PiYG(0.7),
cm.Spectral(0.38),
cm.Spectral(0.25)
]
if __name__ == '__main__':
# parameter setting
gps_dir = s.GPS_DIR
gps_counter_file = s.GPS_COUNTER_FILE
TWEET_COUNTER_FILE = s.TWEET_COUNTER_FILE
timestart = s.TIMESTART
timestart_text = s.TIMESTART_TEXT
timeend = s.TIMEEND
timeend_text = s.TIMEEND_TEXT
aoi = s.AOI
unit_temporal = s.UNIT_TEMPORAL
rys_expected_sig_from_sib.append(rys_total_signal*(sib_sig_d['nonchr']/sib_total_signal))
sib_posn_dif_from_expected.append((sib_posn_d['nonchr']-sib_expected_pos_d['nonchr'])/sib_expected_pos_d['nonchr'])
sib_sig_dif_from_expected.append((sib_sig_d['nonchr']-sib_expected_sig_d['nonchr'])/sib_expected_sig_d['nonchr'])
rys_posn_dif_from_sib.append((rys_posn_d['nonchr']-rys_expected_posn_from_sib[25])/rys_expected_posn_from_sib[25])
rys_sig_dif_from_sib.append((rys_sig_d['nonchr']-rys_expected_sig_from_sib[25])/rys_expected_sig_from_sib[25])
#!!sib nonchromosomal signal is blown out relative to other scaffolds. store value and annotate, color separately
NC_sig_dif = sib_sig_dif_from_expected[25]
sib_sig_dif_from_expected[25] = 0
#build proportional difference color arrays
sib_norm = colors.Normalize(vmin=-abs(max(sib_posn_dif_from_expected+sib_sig_dif_from_expected, key=abs)), vmax=abs(max(sib_posn_dif_from_expected+sib_sig_dif_from_expected, key=abs)))
rys_norm = colors.Normalize(vmin=-abs(max(rys_posn_dif_from_sib+rys_sig_dif_from_sib, key=abs)), vmax=abs(max(rys_posn_dif_from_sib+rys_sig_dif_from_sib, key=abs)))
sib_pos_colors = cm.PiYG(sib_norm(sib_posn_dif_from_expected))
sib_sig_colors = cm.PiYG(sib_norm(sib_sig_dif_from_expected))
sib_sig_colors[25] = [0, .1, 1, 1]
rys_pos_colors = cm.PRGn(rys_norm(rys_posn_dif_from_sib))
rys_sig_colors = cm.PRGn(rys_norm(rys_sig_dif_from_sib))
#build figure
fig = plt.figure(figsize=(6,6.5))
gs1 = GridSpec (2,2, wspace=.1, hspace=.05, left=.05, right=.95, top=.95, bottom=.15)
pos_sib = plt.subplot(gs1[0, 0])
pos_rys = plt.subplot(gs1[0, 1])
sig_sib = plt.subplot(gs1[1, 0])
sig_rys = plt.subplot(gs1[1, 1])
pos_sib.set_ylabel('Positions', fontsize=12, weight='bold')
fig2 = plt.figure("y")
plt.clf()
plt.subplots_adjust(right=0.75)
fig3 = plt.figure("z")
plt.clf()
plt.subplots_adjust(right=0.75)
# Loop thru particles
count = 0
for particle_id in particle_ids:
count = count + 1
particle_data = partextract(extract_exe, work_dir, uda_name, plot_dir,
particle_id[0], error_file)
plt_color = cm.PiYG(float(count) / float(len(particle_ids)))
particle_pos = "Sim: (%0.3f, %0.3f)" % (float(
particle_id[1]), float(particle_id[2]))
plot_x(particle_data, particle_pos, plt_color, '-')
plot_y(particle_data, particle_pos, plt_color, '-')
plot_z(particle_data, particle_pos, plt_color, '-')
pos_all = exact_solution()
count = 0
for pos in pos_all:
count = count + 1
plt_color = cm.winter(float(count) / float(len(pos_all)))
particle_pos = "Exact: (%0.3f,%0.3f)" % (pos[0][1], pos[0][2])
plot_x(pos, particle_pos, plt_color, '--')
plot_y(pos, particle_pos, plt_color, '--')
plot_z(pos, particle_pos, plt_color, '--')
# Get the locations of the detector (change if needed)
positions = getPositions(x, y, z, radii, theta, phi)
print positions
data_file = os.path.join(plot_dir, uda_name + "_acc.data")
fid = open(data_file, 'w+')
for idx, pos in enumerate(positions):
xpos = pos[0]
ypos = pos[1]
zpos = pos[2]
rad = np.sqrt((x - xpos) * (x - xpos) + (y - ypos) * (y - ypos) +
(z - zpos) * (z - zpos))
data = timeextract(extract_exe, work_dir, plot_dir, uda_name, xpos,
ypos, zpos, material_id)
plt_color = cm.PiYG(float(idx) / float(len(positions)))
line_style = '-'
plot_acc('x-acc', data, 'x', rad, plt_color, line_style)
plot_acc('y-acc', data, 'y', rad, plt_color, line_style)
plot_acc('z-acc', data, 'z', rad, plt_color, line_style)
for timedata in data:
fid.write(
'%s %s %s %s %s %s %s %s %s %s %s\n' %
(xpos, ypos, zpos, rad, timedata[0], timedata[1], timedata[2],
timedata[3], timedata[4], timedata[5], timedata[6]))
fid.close()
plt.figure('x-acc')
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, prop={'size': 10})
plt.figure('y-acc')
#!/usr/bin/env python
# coding: utf-8
# In[2]:
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
fig, (ax1, ax2, ax3, ax4, ax5, ax6, ax7) = plt.subplots(nrows=7, sharex=True)
x = np.linspace(0, 2 * np.pi, 100)
for i in range(30):
y = i * np.sin(x)
ax1.plot(x, y, color=cm.rainbow(i / 30.0))
ax2.plot(x, y, color=cm.Reds(i / 30.0))
ax3.plot(x, y, color=cm.binary(i / 30.0))
ax4.plot(x, y, color=cm.PiYG(i / 30.0))
ax5.plot(x, y, color=cm.twilight(i / 30.0))
ax6.plot(x, y, color=cm.Pastel1(i / 30.0))
ax7.plot(x, y, color=cm.flag(i / 30.0))
ax1.set_xlim(0, 2 * np.pi)
fig.show()
# In[ ]:
print(N_ITERATIONS, REG_COEFF)
fin = open(f'../../results/dataset_size/SICK/reg_coeff_{REG_COEFF}_it_{N_ITERATIONS}.txt', 'r')
accs[i, :] = np.array([float(line.strip().split(' ')[0]) for line in fin]) * 100
plt.errorbar(x_axis, np.mean(accs, axis=1), yerr=np.std(accs, axis=1), color='k', linestyle='--', label='SICK uncleaned')
for c, DATASET in enumerate(['RNN-priming-short-500', 'RNN-priming-short-1000', 'RNN-priming-short-5000', 'RNN-priming-short-10000']):
for s, N_ITERATIONS in enumerate([1, 2, 5, 10]):
accs = np.zeros((len(x_axis), N_SAMPLES))
for i, REG_COEFF in enumerate(REG_COEFFS):
fin = open('../../results/dataset_size/' + DATASET + f'/reg_coeff_{REG_COEFF}_it_{N_ITERATIONS}.txt', 'r')
accs[i, :] = np.array([float(line.strip().split(' ')[-1]) for line in fin]) * 100
sent_count = DATASET.split('-')[-1]
plt.errorbar(x_axis, np.mean(accs, axis=1), yerr=np.std(accs, axis=1), c=cm.prism((3-c)*10), linestyle=styles[s], label=f'SICK cleaned by RNN-{sent_count} at {N_ITERATIONS} it')
print(cm.PiYG(0), cm.PiYG(100))
plt.xticks(x_axis, x_labels)
plt.ylim((50, 100))
plt.xlabel('Inverse regularization strength C')
plt.ylabel('accuracy (%)')
plt.legend(loc='lower left', bbox_to_anchor=(0, 0), shadow=True, fontsize='x-small')
plt.grid(True)
plt.show()
print(df_city_main_count)
df_city_main_count['gsmc'] = df_city_main_count['gsmc'] / (
df_city_main_count['gsmc'].sum())
df_city_main_count.columns = ['number', 'percentage', 'ad'] #
print(df_city_main_count)
df_city_main_count['label'] = df_city_main_count.index + ' ' + (
(df_city_main_count['percentage'] *
100).round()).astype('int').astype('str') + '%'
# print(type(d
label = df_city_main_count['label']
sizes = df_city_main_count['number']
# 设置绘图区域大小
fig, axes = plt.subplots(figsize=(10, 6), ncols=2)
ax1, ax2 = axes.ravel()
colors = cm.PiYG(np.arange(len(sizes)) / len(sizes))
# colormaps: Paired, autumn, rainbow, gray,spring,Darks#
# 由于城市数量太多，饼图中不显示labels和百分比
patches, texts = ax1.pie(sizes,
labels=None,
shadow=False,
startangle=0,
colors=colors)
ax1.axis('equal')
ax1.set_title('城市分布', loc='center')
# ax2 只显示图例（legend）
ax2.axis('off')
ax2.legend(patches, label, loc='center left', fontsize=9)
plt.savefig('job_distribute.jpg')
size=22,
va="baseline",
ha="left",
fontweight='medium',
color='#343837')
#plt.title(model.upper(), fontsize=22)
plt.savefig('%s/wsi_ssp2_2010s_%s_7plants.pdf' % (dir_fig, model))
plt.show()
### WSI relative change map
var_min = -90
var_max = 90
bnd = np.linspace(var_min, var_max,
10) ### 10 ticks on the colorbar, should be even
cmap_div = custom_div_cmap(numcolors=11,
mincol=cm.PiYG(255),
maxcol=cm.PiYG(0),
midcol='#f0f0f0') ### for contribution
norm = colors.BoundaryNorm(bnd, cmap_div.N)
ticks = np.linspace(var_min, var_max, 10)
imshow_Pakistan(wsi_ratio[::-1],
vmin=var_min,
vmax=var_max,
norm=norm,
cmap=cmap_div,
ticks=ticks,
fig_name='wsi_ssp2_changes_2050s_2010s_7plants')
### WSI trend map
sig_mask = np.ma.masked_greater_equal(pva_ssp2, 0.05).mask
print("# width_out = " + str(np.round(p84_out, 2) - np.round(p16_out, 2)))
### plot
figure = plt.figure(figsize=(10, 2))
gs = gridspec.GridSpec(nrows=8, ncols=17)
plt.subplots_adjust(bottom=0.22, left=0.05, right=0.98, top=0.88)
ax1 = plt.subplot(gs[0:8, 0:8])
ax2 = plt.subplot(gs[0:8, 9:17])
ax1.grid(axis="x")
ax2.grid(axis="both")
plt.rcParams["font.size"] = 11
plt.rcParams["legend.fontsize"] = 9
# ax1
ylim = [0.0001, np.r_[y_in, y_out].max() * 1.4]
ax1.step(x_in, y_in, color=cm.PiYG(1 / 1.), lw=1, alpha=1.0, where="mid")
ax1.bar(x_in,
y_in,
lw=0,
color=cm.PiYG(1 / 1.),
alpha=0.2,
width=x_in[1] - x_in[0],
align="center",
label="inside mask")
ax1.plot(p50_in_norm,
ylim[1] * 0.95,
"o",
markeredgewidth=0,
c=cm.PiYG(1 / 1.),
markersize=7,
zorder=1)
