# ------------------------------------------------------------------------------
infec = np.convolve(infec, np.ones(2), 'same')
dead = np.convolve(dead, np.ones(2), 'same')
# ------------------------------------------------------------------------------
infec = np.diff(infec)
dead = np.diff(dead)
dates = dates[0:-1]
# ------------------------------------------------------------------------------
guarda_path = '../pics/cases/'
dpi = 120
size = [9.6, 5.76]
# ------------------------------------------------------------------------------
plt_ = fancy_figure(figsize=size,
x=dates,
curve=dead,
colop='k',
symbol='.-',
margin=(0.03, 0.03),
holdon='on').plotter_date()
# ------------------------------------------------------------------------------
plt_ = fancy_figure(
figsize=size,
x=dates,
curve=infec,
colop='r',
symbol='.-',
margin=(0.03, 0.03),
title='Daily COVID-19 cases in ' + country_,
ylabel='Number of people',
xlabel='Time (days)',
legends=['Dead', 'Infected'],
dates = np.array(dates, dtype=np.datetime64)
# ------------------------------------------------------------------------------
if place_name.size > 1:
place_name_ = ''
for i_ in range(place_name.size):
place_name_ = place_name_ + ' ' + place_name[i_]
place_name = place_name_
else:
place_name = place_name.item()
# ------------------------------------------------------------------------------
#
# ------------------------------------------------------------------------------
plt_ = fancy_figure(figsize=size,
x=dates,
curve=dead,
colop='k',
symbol='.-',
margin=(0.03, 0.03),
holdon='on').plotter_date()
# ------------------------------------------------------------------------------
plt_ = fancy_figure(figsize=size,
x=dates,
curve=active,
colop='r',
symbol='.-',
margin=(0.03, 0.03),
title='Active COVID-19 cases in ' + place_name,
ylabel='Number of people',
xlabel='Time (days)',
legends=['Dead', 'Active'],
fig_name=place_name + '-active',
import numpy as np
# ------------------------------------------------------------------------------
# load when saved as binary
uo_obs = np.fromfile('../bin/uo_obs.bin', dtype=float)
uo_rec = np.fromfile('../bin/uo_rec.bin', dtype=float)
uo_sig = np.fromfile('../bin/uo_sig.bin', dtype=float)
t = np.fromfile('../bin/t.bin', dtype=float)
# ------------------------------------------------------------------------------
#
# plotting routine begins
#
# ------------------------------------------------------------------------------
fancy_figure(
figsize=size,
curve=uo_obs,
x=t,
symbol='-',
# colop=((0.5,0,0)),
margin=(0.05, 0.1),
holdon='on').plotter()
fancy_figure(
figsize=size,
curve=uo_sig,
x=t,
symbol='-',
# colop=((0.5,0,0)),
margin=(0.05, 0.1),
holdon='on').plotter()
fancy_figure(
figsize=size,
vz_rx_real=vz_rx_real.reshape((nrx,nt)) vz_rx_imag=vz_rx_imag.reshape((nrx,nt)) vz_rx_real=vz_rx_real.transpose() vz_rx_imag=vz_rx_imag.transpose() # ------------------------------------------------------------------------------ # # plotting routine begins # # ------------------------------------------------------------------------------ fancy_figure( figsize=size, curve=sz_time[:,0], x=t, title=r'Source on $v_z$', xlabel='Time (s)', ylabel='Amplitude', symbol='-', colop=((0.3,0.3,0.3)), margin=(0.05,0.1), guarda_path=guarda_path, guarda=dpi, fig_name='elastic-lamb-source' ).plotter() # ------------------------------------------------------------------------------ extent_ = np.array([rx[0],rx[-1],t[-1],t[0]],dtype=float) # ------------------------------------------------------------------------------ plt_=fancy_figure(data=np.zeros((2,2)),holdon='close',colorbaron='off').matrix() fig,ax=plt_.subplots(1,2,figsize=size) # ------------------------------------------------------------------------------ fancy_figure( ax_=ax[0], aspect='auto',
[x_test.max(),
x_train.max(),
y_train.max(),
y_test.max(),
y_reco.max()])
mini = min(
[x_test.min(),
x_train.min(),
y_train.min(),
y_test.min(),
y_reco.min()])
n_samples, npix_x, npix_y = x_train.shape
n_tests, _, _ = x_test.shape
# ------------------------------------------------------------------------------
plt_ = fancy_figure(data=np.zeros((2, 2)), holdon='close',
colorbaron='off').matrix()
# ------------------------------------------------------------------------------
fig, ax = plt_.subplots(4, 6) #,figsize=size)
# ------------------------------------------------------------------------------
for i_ in range(n_samples - 1):
ic = int(i_ % 6)
ir = int((i_ - ic) / 6)
fancy_figure(
ax_=ax[ir, ic],
data=y_train[i_, :, :],
vmin=mini,
vmax=maxi,
x_ticklabels='off',
y_ticklabels='off',
# colo='Greys',
quarantined,_,_=fancy_figure.bring(path_,'quarantined',0,0) dead,_,_ =fancy_figure.bring(path_,'dead',0,0) it_infec,_,_ =fancy_figure.bring(path_,'it_infec',0,0) it_infec = it_infec[0,0] # ------------------------------------------------------------------------------ # matlab2py bullshit time_ = time_- 366 time_ = time_.squeeze(axis=1) it_infec = it_infec-1 # ------------------------------------------------------------------------------ # # ------------------------------------------------------------------------------ fancy_figure( figsize=size, x=time_, curve=infectious, colop='b', symbol='-', holdon='on').plotter_date() # ------------------------------------------------------------------------------ fancy_figure( x=time_, curve=dead, colop='k', symbol='-', holdon='on').plotter_date() # ------------------------------------------------------------------------------ fancy_figure( x=time_, curve=quarantined, colop='m',
two_d = two_d.transpose()
# ------------------------------------------------------------------------------
print('shape of 1D data ', one_d.shape)
print('shape of 2D data ', two_d.shape)
# ------------------------------------------------------------------------------
#
# plotting routine begins
#
# ------------------------------------------------------------------------------
# 2D matrix
fancy_figure(aspect='auto',
figsize=[5, 5],
data=two_d,
x_ticklabels='off',
y_ticklabels='off',
title='2D data',
ylabel='\# of rows',
xlabel='\# of columns',
guarda_path=guarda_path,
guarda=dpi,
fig_name='fortran-2D').matrix()
# ------------------------------------------------------------------------------
fancy_figure(figsize=size,
curve=one_d,
x=t,
title='1D data',
xlabel='Time (time units)',
ylabel='Amplitude',
symbol='-',
colo_accu='%2i',
colop=((0.0471, 0.4824, 0.8627)),
# ------------------------------------------------------------------------------
size=[4*2,4]
guarda_path = '../pics/'
dpi=120
# ------------------------------------------------------------------------------
import numpy as np
f = np.loadtxt('frequency.dat')
t = np.loadtxt('time.dat')
g_= np.loadtxt('data_f.dat')
g = np.loadtxt('data_t.dat')
# ------------------------------------------------------------------------------
#
# plotting routine begins
#
# ------------------------------------------------------------------------------
plt_=fancy_figure(data=np.zeros((2,2)),holdon='close',colorbaron='off').matrix()
# ------------------------------------------------------------------------------
fig,ax=plt_.subplots(1,2,figsize=size)
# ------------------------------------------------------------------------------
fancy_figure(
ax_=ax[0],
figsize=size,
# curve=g_[:,0],
curve=np.fft.fftshift(g_[:,0]),
x=f,
title=r'$\mathbf{R}$',
xlabel='Frequency (Hz)',
ylabel='Amplitude',
symbol='-',
colo_accu='%2i',
colop=((0.0471,0.4824,0.8627)),
for iy in range(ny):
tmp_ = np.array([x_approx_[ix], y_approx_[iy]])
tmp_ = tmp_.reshape(1, -1)
tmp_ = exp_imp(tmp_, x_samples, y_samples, gau_pro_)
y_approx[iy, ix] = tmp_[0]
# ------------------------------------------------------------------------------
#
#
#
# actual plotting
#
#
#
#
# ------------------------------------------------------------------------------
plt_ = fancy_figure(data=np.zeros((2, 2)), holdon='close',
colorbaron='off').matrix()
# ------------------------------------------------------------------------------
fig, ax = plt_.subplots(1, 2) #,figsize=size)
# ------------------------------------------------------------------------------
extents_ = [-5, 5, 5, -5]
fancy_figure(ax_=ax[0],
x=2.06,
curve=2.14,
symbol='*',
colop='b',
markersize=10,
holdon='on').plotter()
fancy_figure(ax_=ax[0],
x=x_samples[0:n_init, 0],
y_inte = np.fromfile('y_inte.dat', dtype=float)
# ------------------------------------------------------------------------------
nt = t.size
# ------------------------------------------------------------------------------
# # here you transpose/reshape vector to matrix
# data=data.reshape((ncols,nrows))
# data=data.transpose()
# ------------------------------------------------------------------------------
#
# plotting routine begins
#
# ------------------------------------------------------------------------------
# differentiate
fancy_figure(figsize=size,
curve=y,
x=t,
symbol='-',
colop=((0.3, 0.5, 0.3)),
holdon='on').plotter()
fancy_figure(figsize=size,
curve=y_diff,
x=t,
title=r'Differentiate',
xlabel='Time',
ylabel='Amplitude',
symbol='-',
colop=((0.5, 0.3, 0.3)),
margin=(0.05, 0.1),
guarda_path=guarda_path,
guarda=dpi,
fig_name='differentiate').plotter()
mini = -1
maxi = 1
extents_xz = [x[0], x[-1], z[-1], z[0]]
# ------------------------------------------------------------------------------
nrows = 2
ncols = 3
width_max = x[nx - 1]
height_max = z[nz - 1]
wi, he = figaspect(nrows * width_max / (ncols * height_max))
# ------------------------------------------------------------------------------
# 🎨🎨
# ------------------------------------------------------------------------------
plt_ = fancy_figure(data=np.zeros((2, 2)), holdon='close',
colorbaron='off').matrix()
fig = plt_.figure()
fig.set_size_inches(wi, he)
ax0 = plt_.subplot2grid((1, 2), (0, 0), rowspan=1, colspan=1)
ax1 = plt_.subplot2grid((1, 2), (0, 1), rowspan=1, colspan=1)
# ------------------------------------------------------------------------------
# fig, ax = plt_.subplots()
# fig.suptitle(title_mega,fontsize=20)
# ------------------------------------------------------------------------------
fancy_figure(ax_=ax0,
x=x,
y=z,
extent=extents_xz,
data=0 * psi1_,
midi=midi,
