def plotuv_freq(antennaPosition,L,dec,h,Nfreqs,lamb0,df):
c=3e8
#print "lamb0="+str(lamb0)
tabfreq=c/(lamb0)+np.arange(Nfreqs)*df
B = baseline_angles(antennaPosition,lamb0)
# print B.shape
#number of antennas
na = len(antennaPosition)
#number pair or baseline
nbl = na*(na-1)/2
maxuv=0.
for i in range (nbl):
uv = track_uv_freq(h, tabfreq,B[i, 0], 0., B[i, 1], L, dec, Nfreqs)/1e3;
# if i == 0 : print uv
if uv.max() > maxuv : maxuv=uv.max()
plt.plot(uv[:,0], uv[:,1], 'b.',ms=1,alpha=0.5)
plt.plot(-uv[:,0], -uv[:,1], 'r.',ms=1,alpha=0.5)
plt.xlabel('u (klambda)')
plt.ylabel('v (klambda)')
plt.title('uv coverage')
mb = maxuv*1.1 #5*np.sqrt((uv**2).sum(1)).max()
uv.shape
plt.axes().set_aspect('equal')
plt.xlim(-mb,mb)
plt.ylim(-mb,mb)
def scatter(x, y, equal=False, xlabel=None, ylabel=None, xinvert=False, yinvert=False):
"""
Plot a scatter with simple formatting options
"""
plt.scatter(x, y, 200, color=[0.3, 0.3, 0.3], edgecolors="white", linewidth=1, zorder=2)
sns.despine()
if xlabel:
plt.xlabel(xlabel)
if ylabel:
plt.ylabel(ylabel)
if equal:
plt.axes().set_aspect("equal")
plt.plot([0, max([x.max(), y.max()])], [0, max([x.max(), y.max()])], color=[0.6, 0.6, 0.6], zorder=1)
bmin = min([x.min(), y.min()])
bmax = max([x.max(), y.max()])
rng = abs(bmax - bmin)
plt.xlim([bmin - rng * 0.05, bmax + rng * 0.05])
plt.ylim([bmin - rng * 0.05, bmax + rng * 0.05])
else:
xrng = abs(x.max() - x.min())
yrng = abs(y.max() - y.min())
plt.xlim([x.min() - xrng * 0.05, x.max() + xrng * 0.05])
plt.ylim([y.min() - yrng * 0.05, y.max() + yrng * 0.05])
if xinvert:
plt.gca().invert_xaxis()
if yinvert:
plt.gca().invert_yaxis()
def display_channel_efficiency(self):
size = 0
start_time = self.pcap_file[0].time
end_time = self.pcap_file[len(self.pcap_file) - 1].time
duration = (end_time - start_time)/1000
for i in range(len(self.pcap_file) - 1):
size += len(self.pcap_file[i])
ans = (((size * 8) / duration) / BW_STANDARD_WIFI) * 100
ans = float("%.2f" % ans)
labels = ['utilized', 'unutilized']
sizes = [ans, 100.0 - ans]
colors = ['g', 'r']
# Make a pie graph
plt.clf()
plt.figure(num=1, figsize=(8, 6))
plt.axes(aspect=1)
plt.suptitle('Channel efficiency', fontsize=14, fontweight='bold')
plt.title("Bits/s: " + str(float("%.2f" % ((size*8)/duration))),fontsize = 12)
plt.rcParams.update({'font.size': 17})
plt.pie(sizes, labels=labels, autopct='%.2f%%', startangle=60, colors=colors, pctdistance=0.7, labeldistance=1.2)
plt.show()
def make_shape(pts, max_output_len=100):
"""
Args:
pts: a tuple of points (x,y) to be interpolated
max_output_len: the max number of points in the interpolated curve
Returns:
the pair of interpolated points (xnew,ynew)
Raises:
ValueError: pts defined a self-intersecting curve
"""
assert len(pts[0]) == len(pts[1])
pts = tuple(map(lambda x: np.append(x, x[0]), pts))
fit_pts = interp(pts)
if curve_intersects(fit_pts):
raise ValueError("Curve is self-intersecting")
if PLOT_SHAPE:
plt.figure()
plt.plot(pts[0],pts[1], 'x')
plt.plot(fit_pts[0],fit_pts[1])
plt.axes().set_aspect('equal', 'datalim')
plt.show()
sparse_pts = tuple(map(lambda ls: ls[::len(fit_pts[0]) // max_output_len + 1], fit_pts))
return sparse_pts
def display_PER(self):
number_of_pkts = len(self.pcap_file)
retransmission_pkts = 0
for pkt in self.pcap_file:
if (pkt[Dot11].FCfield & 0x8) != 0:
retransmission_pkts += 1
ans = (retransmission_pkts / number_of_pkts)*100
ans = float("%.2f" % ans)
labels = ['Standard packets', 'Retransmitted packets']
sizes = [100.0 - ans,ans]
colors = ['g', 'firebrick']
# Make a pie graph
plt.clf()
plt.figure(num=1, figsize=(8, 6))
plt.axes(aspect=1)
plt.suptitle('Retransmitted packet', fontsize=14, fontweight='bold')
plt.rcParams.update({'font.size': 13})
plt.pie(sizes, labels=labels, autopct='%.2f%%', startangle=60, colors=colors, pctdistance=0.7, labeldistance=1.2)
plt.show()
def png(self, start_timestamp, end_timestamp):
self.load(start_timestamp, end_timestamp)
plt.figure(figsize=(10, 7.52))
plt.rc("axes", labelsize=12, titlesize=14)
plt.rc("font", size=10)
plt.rc("legend", fontsize=7)
plt.rc("xtick", labelsize=8)
plt.rc("ytick", labelsize=8)
plt.axes([0.08, 0.08, 1 - 0.27, 1 - 0.15])
for plot in self.plots:
plt.plot(self.timestamps, self.plots[plot], self.series_fmt(plot), label=self.series_label(plot))
plt.axis("tight")
plt.gca().xaxis.set_major_formatter(
matplotlib.ticker.FuncFormatter(lambda x, pos=None: time.strftime("%H:%M\n%b %d", time.localtime(x)))
)
plt.gca().yaxis.set_major_formatter(
matplotlib.ticker.FuncFormatter(lambda x, pos=None: locale.format("%.*f", (0, x), True))
)
plt.grid(True)
plt.legend(loc=(1.003, 0))
plt.xlabel("Time/Date")
plt.title(
self.description()
+ "\n%s to %s"
% (
time.strftime("%H:%M %d-%b-%Y", time.localtime(start_timestamp)),
time.strftime("%H:%M %d-%b-%Y", time.localtime(end_timestamp)),
)
)
output_buffer = StringIO.StringIO()
plt.savefig(output_buffer, format="png")
return output_buffer.getvalue()
def drawVectors(transformed_features, components_, columns, plt, scaled):
if not scaled:
return plt.axes() # No cheating ;-)
num_columns = len(columns)
# This funtion will project your *original* feature (columns)
# onto your principal component feature-space, so that you can
# visualize how "important" each one was in the
# multi-dimensional scaling
# Scale the principal components by the max value in
# the transformed set belonging to that component
xvector = components_[0] * max(transformed_features[:,0])
yvector = components_[1] * max(transformed_features[:,1])
## visualize projections
# Sort each column by it's length. These are your *original*
# columns, not the principal components.
important_features = { columns[i] : math.sqrt(xvector[i]**2 + yvector[i]**2) for i in range(num_columns) }
important_features = sorted(zip(important_features.values(), important_features.keys()), reverse=True)
print "Features by importance:\n", important_features
ax = plt.axes()
for i in range(num_columns):
# Use an arrow to project each original feature as a
# labeled vector on your principal component axes
plt.arrow(0, 0, xvector[i], yvector[i], color='b', width=0.0005, head_width=0.02, alpha=0.75)
plt.text(xvector[i]*1.2, yvector[i]*1.2, list(columns)[i], color='b', alpha=0.75)
return ax
def gen_irregular_axes(axes_prop, spacing=0.05, offset=0.05, axis='vert', share=False):
if not axis in ['horiz', 'vert']:
raise Exception("'axis' must be either 'horiz' or 'vert'")
axes_prop = np.array(axes_prop).ravel()
axes_prop = axes_prop[::-1]
axes_prop /= sum(axes_prop)
axes_prop *= (1-offset*2)
axes_starts = np.r_[0, np.cumsum(axes_prop)]
if axis=='vert':
stuff = [ (s, e-s-spacing) for s, e in izip(axes_starts[:-1], axes_starts[1:])]
rectangles = [ [spacing, offset+ystart, 1-spacing*2, height] for ystart, height in stuff]
else:
stuff = [ (s, e-s-spacing) for s, e in izip(axes_starts[:-1], axes_starts[1:])]
rectangles = [ [ystart, spacing, height, 1-spacing] for ystart, height in stuff]
axes = [None]*len(rectangles)
for k,r in enumerate(rectangles):
if share and k > 0 and axis == 'vert':
axes[k] = plt.axes(r, sharex=axes[0])
elif share and k > 0 and axis=='horiz':
axes[k] = plt.axes(r, sharey=axes[0])
else:
axes[k] = plt.axes(r)
if k > 0 and share and axis=='vert':
#axes[k].set_xticks([])
#axes[k].set_xticklabels([])
for label in axes[k].get_xticklabels():
label.set_visible(False)
return axes
def _scatter(actual, prediction, args):
plt.figure()
plt.plot(actual, prediction, 'b'+args['plot_scatter_marker'])
xmin=min(actual)
xmax=max(actual)
ymin=min(prediction)
ymax=max(prediction)
diagxmin=min(math.fabs(x) for x in actual)
diagymin=min(math.fabs(y) for y in prediction)
diagpmin=min(diagxmin,diagymin)
pmin=min(xmin,ymin)
pmax=max(xmax,ymax)
plt.plot([diagpmin,pmax],[diagpmin,pmax],'k-')
if args['plot_identifier'] != 'NoName':
plt.title(args['plot_identifier'])
plt.xlabel('Observed')
plt.ylabel('Modeled')
if args['plot_performance_log'] == True:
plt.yscale('log')
plt.xscale('log')
if args['plot_scatter_free'] != True:
plt.axes().set_aspect('equal')
if args['plot_dump'] == True:
pfname=os.path.join(args['plot_dir'],args['plot_identifier']+'_eiger_scatter.pdf')
plt.savefig(pfname,format="pdf")
else:
plt.show()
def plot_curves(curve1, curve2, size_x, picture_name):
""" function eats .dat files """
size_y=size_x
shape = [int(size_x),int(size_y)]
data_1 = np.loadtxt(curve1)
x_1 = data_1[:,0].reshape(shape,order='C')
y_1 = data_1[:,1].reshape(shape,order='C')
f_1 = data_1[:,2].reshape(shape,order='C')
# plt.plot(x_end,rho_end, label='curve 1')
a = plt.contour(x_1, y_1, f_1,30, label='curve 1')
print np.max(f_1)
data_2 = np.loadtxt(curve2)
x_2 = data_2[:,0].reshape(shape,order='C')
y_2 = data_2[:,1].reshape(shape,order='C')
rho_2 = data_2[:,2].reshape(shape,order='C')
b = plt.contourf(x_2,y_2,rho_2,30, label='curve 2')
# b = plt.contour(xcoords, ycoords, step)
print np.max(rho_2)
plt.clabel(a, inline=1, fontsize=10)
plt.clabel(b, inline=1, fontsize=10)
plt.legend()
cbar = plt.colorbar(b)
# ymax = max(np.max(rho_end1),np.max(rho_end))
# ymin = min(np.min(rho_end),np.min(rho_end1))
# xmax = max(np.max(x_end1),np.max(x_end))
# xmin = min(np.min(x_end),np.min(x_end1))
#print xmax, xmin, ymax, ymin
#plt.axis([0,1/6.,0,1])
plt.axes().set_aspect('equal')
plt.savefig(str(picture_name)+'.png', dpi=300)
def POST(self):
data = web.data()
query_data=json.loads(data)
start_time=query_data["start_time"]
end_time=query_data["end_time"]
parameter=query_data["parameter"]
query="SELECT "+parameter+",timestamp FROM jplug_data WHERE timestamp BETWEEN "+str(start_time)+" AND "+str(end_time)
retrieved_data=list(db.query(query))
LEN=len(retrieved_data)
x=[0]*LEN
y=[0]*LEN
X=[None]*LEN
for i in range(0,LEN):
x[i]=retrieved_data[i]["timestamp"]
y[i]=retrieved_data[i][parameter]
X[i]=datetime.datetime.fromtimestamp(x[i],pytz.timezone(TIMEZONE))
#print retrieved_data["timestamp"]
with lock:
figure = plt.gcf() # get current figure
plt.axes().relim()
plt.title(parameter+" vs Time")
plt.xlabel("Time")
plt.ylabel(units[parameter])
plt.axes().autoscale_view(True,True,True)
figure.autofmt_xdate()
plt.plot(X,y)
filename=randomword(12)+".jpg"
plt.savefig("/home/muc/Desktop/Deployment/jplug_view_data/static/images/"+filename, bbox_inches=0,dpi=100)
plt.close()
web.header('Content-Type', 'application/json')
return json.dumps({"filename":filename})
def diff_viewer(expected_fname, result_fname, diff_fname):
plt.figure(figsize=(16, 16))
plt.suptitle(os.path.basename(expected_fname))
ax = plt.subplot(221)
ax.imshow(mimg.imread(expected_fname))
ax = plt.subplot(222, sharex=ax, sharey=ax)
ax.imshow(mimg.imread(result_fname))
ax = plt.subplot(223, sharex=ax, sharey=ax)
ax.imshow(mimg.imread(diff_fname))
def accept(event):
# removes the expected result, and move the most recent result in
print('ACCEPTED NEW FILE: %s' % (os.path.basename(expected_fname), ))
os.remove(expected_fname)
shutil.copy2(result_fname, expected_fname)
os.remove(diff_fname)
plt.close()
def reject(event):
print('REJECTED: %s' % (os.path.basename(expected_fname), ))
plt.close()
ax_accept = plt.axes([0.7, 0.05, 0.1, 0.075])
ax_reject = plt.axes([0.81, 0.05, 0.1, 0.075])
bnext = mwidget.Button(ax_accept, 'Accept change')
bnext.on_clicked(accept)
bprev = mwidget.Button(ax_reject, 'Reject')
bprev.on_clicked(reject)
plt.show()
def test_plot_raw_psd():
"""Test plotting of raw psds."""
import matplotlib.pyplot as plt
raw = _get_raw()
# normal mode
raw.plot_psd(tmax=2.0)
# specific mode
picks = pick_types(raw.info, meg='mag', eeg=False)[:4]
raw.plot_psd(picks=picks, area_mode='range')
ax = plt.axes()
# if ax is supplied:
assert_raises(ValueError, raw.plot_psd, ax=ax)
raw.plot_psd(picks=picks, ax=ax)
plt.close('all')
ax = plt.axes()
assert_raises(ValueError, raw.plot_psd, ax=ax)
ax = [ax, plt.axes()]
raw.plot_psd(ax=ax)
plt.close('all')
# topo psd
raw.plot_psd_topo()
plt.close('all')
# with a flat channel
raw[5, :] = 0
assert_raises(ValueError, raw.plot_psd)
def test_extents():
# tests that one can set the extents of a map in a variety of coordinate
# systems, for a variety of projection
uk = [-12.5, 4, 49, 60]
uk_crs = ccrs.Geodetic()
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent(uk, crs=uk_crs)
# enable to see what is going on (and to make sure it is a plot of the uk)
# ax.coastlines()
assert_array_almost_equal(ax.viewLim.get_points(),
np.array([[-12.5, 49.], [4., 60.]]))
ax = plt.axes(projection=ccrs.NorthPolarStereo())
ax.set_extent(uk, crs=uk_crs)
# enable to see what is going on (and to make sure it is a plot of the uk)
# ax.coastlines()
assert_array_almost_equal(ax.viewLim.get_points(),
np.array([[-1034046.22566261, -4765889.76601514],
[333263.47741164, -3345219.0594531]])
)
# given that we know the PolarStereo coordinates of the UK, try using
# those in a PlateCarree plot
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_extent([-1034046, 333263, -4765889, -3345219],
crs=ccrs.NorthPolarStereo())
# enable to see what is going on (and to make sure it is a plot of the uk)
# ax.coastlines()
assert_array_almost_equal(ax.viewLim.get_points(),
np.array([[-17.17698577, 48.21879707],
[5.68924381, 60.54218893]])
)
def plot_q_qhat(q, t):
# Plot Potential Vorticity
plt.clf()
plt.subplot(2,1,1)
plt.pcolormesh(xx/1e3,yy/1e3,q)
plt.colorbar()
plt.axes([-Lx/2e3, Lx/2e3, -Ly/2e3, Ly/2e3])
name = "PV at t = %5.2f" % (t/(3600.0*24.0))
plt.title(name)
# compute power spectrum and shift ffts
qe = np.vstack((q0,-np.flipud(q)))
qhat = np.absolute(fftn(qe))
kx = fftshift((parms.ikx/parms.ikx[0,1]).real)
ky = fftshift((parms.iky/parms.iky[1,0]).real)
qhat = fftshift(qhat)
Sx, Sy = int(parms.Nx/2), parms.Ny
Sk = 1.5
# Plot power spectrum
plt.subplot(2,1,2)
#plt.pcolor(kx[Sy:Sy+20,Sx:Sx+20],ky[Sy:Sy+20,Sx:Sx+20],qhat[Sy:Sy+20,Sx:Sx+20])
plt.pcolor(kx[Sy:int(Sk*Sy),Sx:int(Sk*Sx)],ky[Sy:int(Sk*Sy),Sx:int(Sk*Sx)],
qhat[Sy:int(Sk*Sy),Sx:int(Sk*Sx)])
plt.axis([0, 10, 0, 10])
plt.colorbar()
name = "PS at t = %5.2f" % (t/(3600.0*24.0))
plt.title(name)
plt.draw()
def fit_circle_nonlin_lstsq(x, y, x0=0, y0=0, r0=1):
def residual(params, x, y):
xc, yc, r = params
return (x-xc)**2 + (y-yc)**2 - r**2
def Jacobian(params, x, y):
xc, yc, r = params
J = np.zeros((x.shape[0], params.shape[0]))
J[:,0] = -2*(x-xc)
J[:,1] = -2*(y-yc)
J[:,2] = -2*r
return J
# nonlinear least squares fit
params0 = np.array([x0, y0, r0])
params = scipy.optimize.leastsq(residual, params0, args=(x,y), Dfun=Jacobian)[0]
# plotting
xc, yc, r = params
t = np.linspace(0,2*np.pi,num=10000)
xx = xc + r*np.cos(t)
yy = yc + r*np.sin(t)
plt.close('all')
plt.xlabel('x'), plt.xlim([np.min(xx),np.max(xx)])
plt.ylabel('y'), plt.ylim([np.min(yy),np.max(yy)])
plt.plot(x, y, 'ok', ms=10) # input data points
plt.plot(xx, yy, '-b', lw=2) # nonlinear fit
plt.plot(xc, yc, 'or', ms=10) # nonlinear fit center
plt.axes().set_aspect('equal')
def advance(self, t, plotresult=False):
y0 = self.concs * self.molWeight
y0 = append(y0, self.thickness)
yt = odeint(self.rightSideofODE, y0, t)
if (plotresult):
import matplotlib.pyplot as plt
plt.figure()
plt.axes([0.1, 0.1, 0.6, 0.85])
plt.semilogy(t, yt)
plt.ylabel('mass concentrations (kg/m3)')
plt.xlabel('time(s)')
#plt.legend(self.speciesnames)
for i in range(len(self.speciesnames)):
plt.annotate(
self.speciesnames[i], (t[-1], yt[-1, i]),
xytext=(20, -5),
textcoords='offset points',
arrowprops=dict(arrowstyle="-"))
plt.show()
self.thickness = yt[-1][-1]
ytt = yt[-1][:-1]
# for iii in range(len(ytt)):
# if ytt[iii]<0:
# ytt[iii]=0.
molDens = ytt / self.molWeight
self.concs = molDens
self.molFrac = molDens / sum(molDens)
self.massFrac = ytt / sum(ytt)
def svm_sgd_plot(X, Y):
w = np.zeros(len(X[0]))
eta = 1
epochs = 10000
errors = []
for epoch in range(1, epochs):
error = 0
for i,x in enumerate(X):
if(Y[i] * np.dot(X[i], w)) < 1:
w = w + eta * ( (X[i] * Y[i]) + (-2 * (1/epoch) * w) )
error = 1
else:
w = w + eta * (-2 * (1/epoch) * w)
errors.append(error)
plt.plot(errors, '|')
plt.ylim(0.5,1.5)
plt.axes().set_yticklabels([])
plt.xlabel('Epoch')
plt.ylabel('Misclassified')
plt.show()
return w
def test_simple():
fig = plt.figure()
# un-comment to debug
# recursive_pickle(fig)
pickle.dump(fig, BytesIO(), pickle.HIGHEST_PROTOCOL)
ax = plt.subplot(121)
pickle.dump(ax, BytesIO(), pickle.HIGHEST_PROTOCOL)
ax = plt.axes(projection='polar')
plt.plot(list(xrange(10)), label='foobar')
plt.legend()
# recursive_pickle(fig)
pickle.dump(ax, BytesIO(), pickle.HIGHEST_PROTOCOL)
# ax = plt.subplot(121, projection='hammer')
# recursive_pickle(ax, 'figure')
# pickle.dump(ax, BytesIO(), pickle.HIGHEST_PROTOCOL)
plt.figure()
plt.bar(left=list(xrange(10)), height=list(xrange(10)))
pickle.dump(plt.gca(), BytesIO(), pickle.HIGHEST_PROTOCOL)
fig = plt.figure()
ax = plt.axes()
plt.plot(list(xrange(10)))
ax.set_yscale('log')
pickle.dump(fig, BytesIO(), pickle.HIGHEST_PROTOCOL)
def main():
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus'] = False
plt.title("显示正弦波")
plt.xlabel("x 轴")
plt.ylabel("y 轴")
X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
Y = np.sin(2*X)
plt.axes([0.025, 0.025, 0.95, 0.95])
plt.plot (X, Y+1, color='blue', alpha=1.00)
plt.fill_between(X, 1, Y+1, color='blue', alpha=.25)
plt.plot (X, Y-1, color='blue', alpha=1.00)
plt.fill_between(X, -1, Y-1, (Y-1) > -1, color='blue', alpha=.25)
plt.fill_between(X, -1, Y-1, (Y-1) < -1, color='red', alpha=.25)
plt.xlim(-np.pi, np.pi)
plt.xticks([])
plt.ylim(-3.5, 3.5)
plt.yticks([])
#plt.savefig('../figures/plot_ex.png',dpi=48)
plt.show()
def plot_bicycle():
plt.clf()
plt.axes()
ax = plt.gca()
#ax.add_patch(plt.Rectangle((10,0), 10, 20, fill=False, ec='k')) #car
ax.add_patch(plt.Rectangle((21,1), .75, 2, fill=False, ec='k')) #wheel
ax.add_patch(plt.Rectangle((21.33,10), .75, 2, fill=False, ec='k', angle=20)) #wheel
ax.add_patch(plt.Rectangle((21.,4.), .75, 2, fill=True, ec='k', angle=5, ls='dashdot', alpha=0.3)) #wheel
plt.arrow(0, 2, 20.5, 0, fc='k', ec='k', head_width=0.5, head_length=0.5)
plt.arrow(0, 2, 20.4, 3, fc='k', ec='k', head_width=0.5, head_length=0.5)
plt.arrow(21.375, 2., 0, 8.5, fc='k', ec='k', head_width=0.5, head_length=0.5)
plt.arrow(23, 2, 0, 2.5, fc='k', ec='k', head_width=0.5, head_length=0.5)
#ax.add_patch(plt.Rectangle((10,0), 10, 20, fill=False, ec='k'))
plt.text(11, 1.0, "R", fontsize=18)
plt.text(8, 2.2, r"$\beta$", fontsize=18)
plt.text(20.4, 13.5, r"$\alpha$", fontsize=18)
plt.text(21.6, 7, "w (wheelbase)", fontsize=18)
plt.text(0, 1, "C", fontsize=18)
plt.text(24, 3, "d", fontsize=18)
plt.plot([21.375, 21.25], [11, 14], color='k', lw=1)
plt.plot([21.375, 20.2], [11, 14], color='k', lw=1)
plt.axis('scaled')
plt.xlim(0,25)
plt.axis('off')
plt.show()
def plot(X, z, fname='plot.pdf'):
"""Plot data according to labels in z.
Use PCA to project in 2D in case data is higher dimensional.
"""
z_unique = np.unique(z)
if len(X[0]) > 2:
pca = PCA(n_components=2)
X_new = pca.fit_transform(X)
else:
X_new = X
fig = plt.figure()
ax = fig.add_subplot(111)
#colors = iter(['#1b9e77', '#d95f02', '#7570b3'])
colors = iter(['#e41a1c', '#377eb8', '#4daf4a'])
for k in z_unique:
idx = np.where(z==k)
x = X_new[idx][:,0]
y = X_new[idx][:,1]
ax.plot(x, y, 'bo', markersize=4, alpha=.5, color=next(colors))
ax.set_xticks([])
ax.set_yticks([])
plt.axes().set_aspect('equal', 'datalim')
fig.tight_layout()
fig.savefig(fname)
def plot_skew(y_cv, preds, N = 50, Nmax = 20, start=0, detailed=True):
'''
plot the roc curves with different skews
to see what the distribution of the data
is ...
'''
powers = np.linspace(start, N, Nmax)[1:]
aucs = []
if detailed:
plot_distribution(y_cv, preds**N)
for xx, i in enumerate(powers):
fpr, tpr, thresholds = metrics.roc_curve(y_cv, preds**i)
roc_auc = metrics.auc(fpr, tpr)
if detailed:
plot_roc(fpr, tpr, roc_auc, newPlot=(xx==0), label='%.1f'%i, color=(i/N,0.5,1-i/N))
aucs.append( roc_auc )
if detailed:
plt.legend()
plt.figure(figsize=(4, 3))
plt.axes([0.17, 0.18, 0.94-0.17, 0.96-0.18])
plt.plot(powers, aucs, 's')
intPowers = np.linspace(start, N, 100)[1:]
# plt.plot(intPowers, np.poly1d(np.polyfit(powers, aucs, 2))( intPowers ), color='black' )
plt.xlabel('power')
plt.ylabel('AUC')
return powers, aucs
def add_clusters(self, cl_class, num_clusters, cl_color):
mypatches=[]
for i,(x,y) in enumerate(self.coords):
if cl_class[i] == Cluster.NO_CLUSTER:
continue
if self.lattice == Lattice.Hex:
patch = RegularPolygon((x,-y), numVertices=6, radius=3,
facecolor=cl_color[cl_class[i] - 1],
edgecolor='none')
else:
patch = Rectangle((x, -y), 5.2, 5.2,
facecolor=cl_color[cl_class[i] - 1],
edgecolor='none')
mypatches.append(patch)
p = PatchCollection(mypatches, match_original=True)
self.ax.add_collection(p)
self.ax.autoscale_view()
plt.axes().set_aspect('equal', 'datalim')
# Double the size of the canvas
current_figure = plt.gcf()
w, h = current_figure.get_size_inches()
current_figure.set_size_inches(w*2, h*2)
# Shrink current axis by 20% to make space for the legend
box = self.ax.get_position()
self.ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
handles = []
for i in range(num_clusters):
handles.append(plt.Line2D((0,1),(0,0), color=cl_color[i]))
plt.legend(handles, [ "cluster " + str(x) for x in range(1, num_clusters + 1)],
loc='center left', bbox_to_anchor=(1, 0.5))
def display(workspace, **params):
def update(val):
vmax = smax.val
vmin = smin.val
im.set_clim(vmax=vmax, vmin=vmin)
fig.canvas.draw_idle()
fig = pylab.figure()
'''displays a gather using imshow'''
vmax = np.amax(workspace)
vmin = np.amin(workspace)
im = pylab.imshow(workspace.T, aspect='auto', cmap='Greys', vmax =vmax, vmin=vmin)
pylab.colorbar()
axcolor = 'lightgoldenrodyellow'
axmax = pylab.axes([0.08, 0.06, 0.65, 0.01], axisbg=axcolor) #rect = [left, bottom, width, height] in normalized (0, 1) units
smax = Slider(axmax, 'vmax', vmin, vmax, valinit=vmax)
smax.on_changed(update)
axmin = pylab.axes([0.08, 0.03, 0.65, 0.01], axisbg=axcolor) #rect = [left, bottom, width, height] in normalized (0, 1) units
smin = Slider(axmin, 'vmin', vmin, vmax, valinit=vmin)
smin.on_changed(update)
smin.on_changed(update)
pylab.show()
def make_ax3():
paper_single(TW=8, AR=0.9)
f = plt.figure()
from matplotlib.ticker import NullFormatter, MaxNLocator
nullfmt = NullFormatter() # no labels
# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.6
bottom_h = bottom+height+0.02
left_h = left+width+0.02
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
ax = plt.axes(rect_scatter)
plt.minorticks_on()
axx = plt.axes(rect_histx)
plt.minorticks_on()
axy = plt.axes(rect_histy)
plt.minorticks_on()
# no labels
axx.xaxis.set_major_formatter(nullfmt)
axy.yaxis.set_major_formatter(nullfmt)
axy.xaxis.set_major_locator(MaxNLocator(3))
axx.yaxis.set_major_locator(MaxNLocator(3))
return f,ax,axx,axy
def generateToy():
print 'loading values'
if not os.path.isfile('values2.p'):
z_data = np.loadtxt('values2.dat')
pkl.dump( z_data, open( 'values2.p', "wb" ),pkl.HIGHEST_PROTOCOL )
else:
z_data = pkl.load(open('values2.p',"rb"))
print 'loaded'
#x = np.random.normal(size=1000)
z_data_subset = z_data[0:20000]
plot_range = [50,400]
print 'max',max(z_data_subset),'min',min(z_data_subset)
plt.yscale('log', nonposy='clip')
plt.axes().set_ylim(0.0000001,0.17)
hist(z_data_subset,range=plot_range,bins=100,normed=1,histtype='stepfilled',
color=['lightgrey'], label=['100 bins'])
#hist(z_data_subset,range=plot_range,bins='knuth',normed=1,histtype='step',linewidth=1.5,
# color=['navy'], label=['knuth'])
hist(z_data_subset,range=plot_range,bins='blocks',normed=1,histtype='step',linewidth=2.0,
color=['crimson'], label=['b blocks'])
plt.legend()
#plt.yscale('log', nonposy='clip')
#plt.axes().set_ylim(0.0000001,0.17)
plt.xlabel(r'$m_{\ell\ell}$ (GeV)')
plt.ylabel('A.U.')
plt.title(r'Z$\to\mu\mu$ Data')
plt.savefig('z_data_hist_comp.png')
plt.show()
def display(ncube, ngrid, path):
# Time delay before displaying new plot.
delay = 0.001
# Determines the number of files over which to loop.
# NOTE: These files must be the only files in the directory for this approach to work.
files = os.listdir(path)
numFiles = len(files)
# Prepares the plot to be displayed.
pl.ion()
pl.figure(1)
# Loops over all files and extracts the component-based data.
for i in xrange(1, numFiles + 1):
x, y, z, vx, vy, vz = np.genfromtxt(path + "TimeStamp" + str(i) + ".txt", dtype = float, unpack = True)
# Plots x positions against y positions to get an xy-plane slice.
pl.scatter(x, y, s = 3)
pl.axes().set_xlim((0., float(ngrid * ncube)))
pl.axes().set_ylim((0., float(ngrid * ncube)))
pl.xlabel("X Position")
pl.ylabel("Y Position")
pl.title("N-Body Simulation: 2D Slice")
# Draws the plot and then delays the next iteration.
pl.draw()
time.sleep(delay)
pl.clf()
# Closes the plot at the very end.
pl.close(1)
def generate(self): # path = os.path.join(self.output_root, self.make_filename) plt.figure() plt.axes(**self.plot_settings['axis']) plt.imshow(self.frames(self.data), **self.plot_settings['image']) plt.savefig(self.path, bbox_inches="tight", pad_inches=0, format='png') plt.close()
def __save(self, n, plot, sfile):
p.figure(figsize=sfile)
p.xlabel(plot.xlabel)
p.ylabel(plot.ylabel)
p.xscale(plot.xscale)
p.yscale(plot.yscale)
p.grid()
for curvetype, args, kwargs in plot.curves:
if curvetype == "plot":
p.plot(*args, **kwargs)
elif curvetype == "imshow":
p.imshow(*args, **kwargs)
elif curvetype == "hist":
p.hist(*args, **kwargs)
elif curvetype == "bar":
p.bar(*args, **kwargs)
p.axes().set_aspect(plot.aspect)
if plot.legend:
p.legend(shadow=0, loc=plot.loc)
if not os.path.isdir(plot.dir):
os.mkdir(plot.dir)
if plot.pgf:
p.savefig(plot.dir + plot.name + ".pgf")
print(plot.name + ".pgf")
if plot.pdf:
p.savefig(plot.dir + plot.name + ".pdf", bbox_inches="tight")
print(plot.name + ".pdf")
p.close()
a0 = board.get_pin('a:0:i')
# Tkinter canvas
top = Tkinter.Tk()
top.title("Tkinter + matplotlib")
# Create flag to work with indefinite while loop
flag = Tkinter.BooleanVar(top)
flag.set(True)
pyplot.ion()
pData = [0.0] * 25
fig = pyplot.figure()
pyplot.title('Potentiometer')
ax1 = pyplot.axes()
l1, = pyplot.plot(pData)
pyplot.ylim([0, 1])
# Create Start button and associate with onStartButtonPress method
startButton = Tkinter.Button(top, text="Start", command=onStartButtonPress)
startButton.grid(column=1, row=2)
# Create Stop button and associate with onStopButtonPress method
pauseButton = Tkinter.Button(top, text="Pause", command=onPauseButtonPress)
pauseButton.grid(column=2, row=2)
# Create Exit button and destroy the window
exitButton = Tkinter.Button(top, text="Exit", command=onExitButtonPress)
exitButton.grid(column=3, row=2)
def recreate_image(codebook, labels, w, h):
"""Recreate the (compressed) image from the code book & labels"""
d = codebook.shape[1]
image = np.zeros((w, h, d))
label_idx = 0
for i in range(w):
for j in range(h):
image[i][j] = codebook[labels[label_idx]]
label_idx += 1
return image
# Display all results, alongside original image
plt.figure(1)
plt.clf()
ax = plt.axes([0, 0, 1, 1])
plt.axis('off')
plt.title('Original image (96,615 colors)')
plt.imshow(china)
plt.figure(2)
plt.clf()
ax = plt.axes([0, 0, 1, 1])
plt.axis('off')
plt.title('Quantized image (64 colors, K-Means)')
plt.imshow(recreate_image(kmeans.cluster_centers_, labels, w, h))
plt.figure(3)
plt.clf()
ax = plt.axes([0, 0, 1, 1])
plt.axis('off')
def main():
for p_level in plot_levels:
# Set pressure height contour min/max
if p_level == 925:
clev_min = 660.
clev_max = 810.
elif p_level == 850:
clev_min = 1435.
clev_max = 1530.
elif p_level == 700:
clev_min = 3090.
clev_max = 3155.
elif p_level == 500:
clev_min = 5800.
clev_max = 5890.
else:
print 'Contour min/max not set for this pressure level'
# Set potential temperature min/max
if p_level == 925:
clevpt_min = 300.
clevpt_max = 312.
elif p_level == 850:
clevpt_min = 302.
clevpt_max = 310.
elif p_level == 700:
clevpt_min = 312.
clevpt_max = 320.
elif p_level == 500:
clevpt_min = 325.
clevpt_max = 332.
else:
print 'Potential temperature min/max not set for this pressure level'
# Set specific humidity min/max
if p_level == 925:
clevsh_min = 0.012
clevsh_max = 0.020
elif p_level == 850:
clevsh_min = 0.007
clevsh_max = 0.017
elif p_level == 700:
clevsh_min = 0.002
clevsh_max = 0.010
elif p_level == 500:
clevsh_min = 0.001
clevsh_max = 0.005
else:
print 'Specific humidity min/max not set for this pressure level'
#clevs_col = np.arange(clev_min, clev_max)
clevs_lin = np.arange(clev_min, clev_max, 5)
p_level_constraint = iris.Constraint(pressure=p_level)
for plot_diag in plot_diags:
for experiment_id in experiment_ids:
expmin1 = experiment_id[:-1]
pp_file = '%s_%s_on_p_levs_mean_by_hour.pp' % (experiment_id,
plot_diag)
pfile = '%s%s/%s/%s' % (pp_file_path, expmin1, experiment_id,
pp_file)
pcube = iris.load_cube(pfile, p_level_constraint)
# For each hour in cube
height_pp_file = '%s_408_on_p_levs_mean_by_hour.pp' % (
experiment_id)
height_pfile = '%s%s/%s/%s' % (pp_file_path, expmin1,
experiment_id, height_pp_file)
height_cube = iris.load_cube(height_pfile, p_level_constraint)
print pcube
print height_cube
#time_coords = cube_f.coord('time')
add_hour_of_day(pcube, pcube.coord('time'))
add_hour_of_day(height_cube, height_cube.coord('time'))
#pcube.remove_coord('time')
#cube_diff.remove_coord('time')
#height_cube.remove_coord('time')
#height_cube_diff.remove_coord('time')
#p_cube_difference = iris.analysis.maths.subtract(pcube, cube_diff, dim='hour')
#height_cube_difference = iris.analysis.maths.subtract(height_cube, height_cube_diff, dim='hour')
#pdb.set_trace()
#del height_cube, pcube, height_cube_diff, cube_diff
for t, time_cube in enumerate(
pcube.slices(['grid_latitude', 'grid_longitude'])):
#pdb.set_trace()
print time_cube
height_cube_slice = height_cube.extract(
iris.Constraint(hour=time_cube.coord('hour').points))
# Get time of averagesfor plot title
h = u.num2date(
np.array(time_cube.coord('hour').points,
dtype=float)[0]).strftime('%H%M')
#Convert to India time
from_zone = tz.gettz('UTC')
to_zone = tz.gettz('Asia/Kolkata')
h_utc = u.num2date(
np.array(time_cube.coord('hour').points,
dtype=float)[0]).replace(tzinfo=from_zone)
h_local = h_utc.astimezone(to_zone).strftime('%H%M')
fig = plt.figure(**figprops)
cmap = plt.cm.RdBu_r
ax = plt.axes(projection=ccrs.PlateCarree(),
extent=(lon_low, lon_high,
lat_low + degs_crop_bottom,
lat_high - degs_crop_top))
m =\
Basemap(llcrnrlon=lon_low,llcrnrlat=lat_low,urcrnrlon=lon_high,urcrnrlat=lat_high, rsphere = 6371229)
#pdb.set_trace()
lat = time_cube.coord('grid_latitude').points
lon = time_cube.coord('grid_longitude').points
cs = time_cube.coord_system('CoordSystem')
lons, lats = np.meshgrid(lon, lat)
lons, lats = iris.analysis.cartography.unrotate_pole\
(lons,lats, cs.grid_north_pole_longitude, cs.grid_north_pole_latitude)
x, y = m(lons, lats)
if plot_diag == 'temp':
min_contour = clevpt_min
max_contour = clevpt_max
cb_label = 'K'
main_title = '8km Explicit model (dklyu) minus 8km parametrised model geopotential height (grey contours), potential temperature (colours),\
and wind (vectors) %s UTC %s IST' % (
h, h_local)
tick_interval = 2
clev_number = max_contour - min_contour + 1
elif plot_diag == 'sp_hum':
min_contour = clevsh_min
max_contour = clevsh_max
cb_label = 'kg/kg'
main_title = '8km Explicit model (dklyu) minus 8km parametrised model geopotential height (grey contours), specific humidity (colours),\
and wind (vectors) %s UTC %s IST' % (
h, h_local)
tick_interval = 0.002
clev_number = (max_contour - min_contour +
0.001) * (10**3)
clevs = np.linspace(min_contour, max_contour, clev_number)
#clevs = np.linspace(-3, 3, 32)
cont = plt.contourf(x,
y,
time_cube.data,
clevs,
cmap=cmap,
extend='both')
#cont = iplt.contourf(time_cube, clevs, cmap=cmap, extend='both')
cs_lin = iplt.contour(height_cube_slice,
clevs_lin,
colors='#262626',
linewidths=1.)
plt.clabel(cs_lin, fontsize=14, fmt='%d', color='black')
#del time_cube
#plt.clabel(cont, fmt='%d')
#ax.stock_img()
ax.coastlines(resolution='110m', color='#262626')
gl = ax.gridlines(draw_labels=True,
linewidth=0.5,
color='#262626',
alpha=0.5,
linestyle='--')
gl.xlabels_top = False
gl.ylabels_right = False
#gl.xlines = False
dx, dy = 10, 10
gl.xlocator = mticker.FixedLocator(
range(int(lon_low_tick),
int(lon_high_tick) + dx, dx))
gl.ylocator = mticker.FixedLocator(
range(int(lat_low_tick),
int(lat_high_tick) + dy, dy))
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.xlabel_style = {'size': 12, 'color': '#262626'}
#gl.xlabel_style = {'color': '#262626', 'weight': 'bold'}
gl.ylabel_style = {'size': 12, 'color': '#262626'}
cbar = fig.colorbar(cont,
orientation='horizontal',
pad=0.05,
extend='both')
cbar.set_label('%s' % cb_label,
fontsize=10,
color='#262626')
#cbar.set_label(time_cube.units, fontsize=10, color='#262626')
cbar.set_ticks(
np.arange(min_contour, max_contour + tick_interval,
tick_interval))
ticks = (np.arange(min_contour,
max_contour + tick_interval,
tick_interval))
cbar.set_ticklabels(['${%.1f}$' % i for i in ticks])
cbar.ax.tick_params(labelsize=10, color='#262626')
#main_title='Mean Rainfall for EMBRACE Period -%s UTC (%s IST)' % (h, h_local)
#main_title=time_cube.standard_name.title().replace('_',' ')
#model_info = re.sub(r'[(\']', ' ', model_info)
#model_info = re.sub(r'[\',)]', ' ', model_info)
#print model_info
file_save_name = '%s_%s_%s_hPa_and_geop_height_%s' % (
experiment_id, plot_diag, p_level, h)
save_dir = '%s%s/%s' % (save_path, experiment_id,
plot_diag)
if not os.path.exists('%s' % save_dir):
os.makedirs('%s' % (save_dir))
#plt.show()
fig.savefig('%s/%s_notitle.png' %
(save_dir, file_save_name),
format='png',
bbox_inches='tight')
plt.title('%s UTC %s IST' % (h, h_local))
fig.savefig('%s/%s_short_title.png' %
(save_dir, file_save_name),
format='png',
bbox_inches='tight')
model_info = re.sub(
'(.{68} )', '\\1\n',
str(model_name_convert_title.main(experiment_id)), 0,
re.DOTALL)
plt.title('\n'.join(
wrap('%s\n%s' % (main_title, model_info),
1000,
replace_whitespace=False)),
fontsize=16)
fig.savefig('%s/%s.png' % (save_dir, file_save_name),
format='png',
bbox_inches='tight')
fig.clf()
plt.close()
#del time_cube
gc.collect()
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import TextBox
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.2)
t = np.arange(-2.0, 2.0, 0.001)
s = t**2
initial_text = "t ** 2"
l, = plt.plot(t, s, lw=2)
def submit(text):
ydata = eval(text)
l.set_ydata(ydata)
ax.set_ylim(np.min(ydata), np.max(ydata))
plt.draw()
axbox = plt.axes([0.1, 0.05, 0.8, 0.075])
text_box = TextBox(axbox, 'Evaluate', initial=initial_text)
text_box.on_submit(submit)
plt.show()
calgo = l_alg[ja]
print(' *** '+calgo+' => '+cf_nemo[ja])
id_nemo = Dataset(cf_nemo[ja])
F_nemo[:,ja] = id_nemo.variables[cv_nemo][:,1,1] ; # it's 3x3 spatial domain, taking middle point !
id_nemo.close()
if l_more:
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# How does all this look on on a figure?
cfig = l_var_rf[jv]+'_'+calgo+'.'+fig_ext
print(' *** will plot '+cfig)
fig = plt.figure(num=1, figsize=size_fig, facecolor='w', edgecolor='k')
ax1 = plt.axes([0.08, 0.25, 0.9, 0.7])
#
plt.plot(vtime, F_nemo[:,ja], label='NEMO['+calgo+']', zorder=1)
plt.plot(vtime, F_rf_m[:], color='k', label='MEAN REF!', zorder=10)
# +- rtol enveloppe:
plt.fill_between(vtime, F_rf_m[:]-F_rf_t[:], F_rf_m[:]+F_rf_t[:], alpha=0.2)
#
ax1.grid(color='k', linestyle='-', linewidth=0.3)
plt.legend(loc='best', ncol=1, shadow=True, fancybox=True)
plt.savefig(cfig, dpi=int(rDPI), transparent=False)
plt.close(1)
print('')
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Does the field look okay with respect to reference +- tolerance ?
def __init__(self, images, shape):
fig = plt.figure(figsize=(10, 6))
# subplot positions
h_nav = [Size.Fixed(0.5), Size.Fixed(2.5)]
v_nav = [Size.Fixed(0.5), Size.Scaled(1.0), Size.Fixed(0.5)]
h_im = [Size.Fixed(0.5), Size.Scaled(1.0), Size.Fixed(0.5)]
v_im = [Size.Fixed(0.5), Size.Scaled(1.0), Size.Fixed(0.5)]
nav = Divider(fig, (0.0, 0.0, 0.2, 1.), h_nav, v_nav, aspect=False)
image = Divider(fig, (0.2, 0.0, 0.8, 1.), h_im, v_im, aspect=True)
image.set_anchor('C')
# Toolbar menu box
ax1 = LocatableAxes(fig, nav.get_position())
ax1.set_axes_locator(nav.new_locator(nx=1, ny=1))
ax1.get_xaxis().set_visible(False)
ax1.get_yaxis().set_visible(False)
fig.add_axes(ax1, label='toolbar')
ax1.text(0.05, 0.45, "Filter", weight='heavy',
transform=ax1.transAxes)
# Image space
ax2 = LocatableAxes(fig, image.get_position())
ax2.set_axes_locator(image.new_locator(nx=1, ny=1))
fig.add_axes(ax2, label='image_space')
self.callback = ImageIndex(images, shape, fig)
# Navigation
## Go to
ax_text_index = plt.axes([0.59, 0.05, 0.1, 0.075])
ip = InsetPosition(ax1, [0.2, 0.84, 0.3, 0.05])
ax_text_index.set_axes_locator(ip)
entry_index = TextBox(ax_text_index, 'Go to', initial="0")
entry_index.on_submit(self.callback.submit_index)
## Previous
ax_prev = plt.axes([0.7, 0.05, 0.075, 0.075])
ip = InsetPosition(ax1, [0.55, 0.84, 0.15, 0.05])
ax_prev.set_axes_locator(ip)
bprev = Button(ax_prev, '<<')
bprev.on_clicked(self.callback.prev)
## Next
ax_next = plt.axes([0.81, 0.05, 0.075, 0.075])
ip = InsetPosition(ax1, [0.75, 0.84, 0.15, 0.05])
ax_next.set_axes_locator(ip)
bnext = Button(ax_next, '>>')
bnext.on_clicked(self.callback.next)
# Bounding Boxes
ax_chec = plt.axes([0.1, 0.05, 0.35, 0.075])
ip = InsetPosition(ax1, [0.05, 0.5, 0.9, 0.3])
ax_chec.set_axes_locator(ip)
ax_chec.text(0.05, 0.85, "Bounding Boxes", transform=ax_chec.transAxes)
check = CheckButtons(ax_chec,
('characters', 'lines'),
(False, False))
check.on_clicked(self.callback.update_bboxes)
# Filtering
## Image
ax_text_image = plt.axes([0.1, 0.1, 0.1, 0.075])
ip = InsetPosition(ax1, [0.26, 0.38, 0.64, 0.05])
ax_text_image.set_axes_locator(ip)
entry_image = TextBox(ax_text_image, 'images',
initial="image_id,image_id")
entry_image.on_submit(self.callback.submit_images)
## Characters
ax_text_char = plt.axes([0.1, 0.2, 0.1, 0.075])
ip = InsetPosition(ax1, [0.21, 0.3, 0.69, 0.05])
ax_text_char.set_axes_locator(ip)
entry_char = TextBox(ax_text_char, 'chars',
initial="U+3055,U+3056")
entry_char.on_submit(self.callback.submit_chars)
## Reset
ax_reset = plt.axes([0., 0., 1., 1.])
ip = InsetPosition(ax1, [0.05, 0.2, 0.2, 0.05])
ax_reset.set_axes_locator(ip)
breset = Button(ax_reset, 'Reset')
breset.on_clicked(self.callback.reset)
plt.show()
np.matmul(np.linalg.pinv(sigma),
np.atleast_2d(np.array([x - mu[0, 0], y - mu[0, 1]])).T)
))[0]) + 10000 * (1 / (2 * np.pi * np.linalg.det(sigma))) * np.exp(
-.5 * (np.matmul(
np.array([x - mu[1, 0], y - mu[1, 1]]),
np.matmul(np.linalg.pinv(sigma),
np.array([x - mu[1, 0], y - mu[1, 1]])))))
x = np.linspace(0, 100, 100)
y = np.linspace(0, 100, 100)
z = f(x[:, None], y[None, :])
z = np.rot90(np.fliplr(z))
# fig = plt.figure()
# ax = plt.axes(projection='3d')
# ax.contour3D(x, y, z, 100)
# ax.set_xlabel('x')
# ax.set_ylabel('y')
# ax.set_zlabel('z')
# ax.set_title('3D Contour')
# plt.show()
fig = plt.figure()
ax = plt.axes()
ax.contour(x, y, z)
ax.set_xlabel('x')
ax.set_ylabel('y')
plt.show()
def populate_figure(self):
# updates GUI button placement, colors, etc
self.display.clear()
button_color = [0.792156862745098, 0.8823529411764706, 1.0]
if self.saving == 1:
self.saving_text = self.fig.text(0.875,
0.879,
'Saving',
fontsize=10,
fontweight='bold',
color=[0.933, 0.463, 0])
else:
try:
self.saving_text.remove()
except (ValueError, AttributeError):
pass
self.notes_text = self.fig.text(0.852,
0.575,
'Notes',
fontsize=10,
color="black")
axmodnotes = plt.axes([0.852, 0.45, 0.125, 0.12])
initial_modnotes = str(self.obj.clone.modification_notes)
if initial_modnotes == "nan":
initial_modnotes = ""
self.notestextbox = TextBox(axmodnotes, '', initial=initial_modnotes)
self.notestextbox.on_submit(self.set_notes)
axmodifier = plt.axes([0.875, 0.37, 0.1, 0.035])
self.modifiertextbox = TextBox(axmodifier,
'Initials',
initial=str(
self.params['default_modifier']))
self.modifiertextbox.on_submit(self.change_default_modifier)
axflip = plt.axes([0.50, 0.01, 0.1, 0.075])
self.flipbutton = Button(axflip,
'Flip',
color=button_color,
hovercolor=button_color)
self.flipbutton.on_clicked(self.obj.flip_button_press)
axedges = plt.axes([0.60, 0.01, 0.1, 0.075])
self.edgebutton = Button(axedges,
'Toggle Edges',
color=button_color,
hovercolor=button_color)
self.edgebutton.on_clicked(self.obj.edge_button_press)
axtogdorsal = plt.axes([0.70, 0.01, 0.1, 0.075])
self.togdorsalbutton = Button(axtogdorsal,
'Toggle Dorsal Fit',
color=button_color,
hovercolor=button_color)
self.togdorsalbutton.on_clicked(self.obj.toggle_dorsal_button_press)
axdel = plt.axes([0.025, 0.01, 0.1, 0.075])
self.delcheckbutton = Button(axdel,
'Delete Checkpoint',
color=button_color,
hovercolor=button_color)
self.delcheckbutton.on_clicked(self.obj.delete_selected_checkpoint)
axsave = plt.axes([0.875, 0.8, 0.1, 0.075])
self.savebutton = Button(axsave,
'Save',
color=button_color,
hovercolor=button_color)
self.savebutton.on_clicked(self.save)
axblur = plt.axes([0.25, 0.01, 0.1, 0.035])
self.blurtextbox = TextBox(axblur,
'Gaussian Blur StDev',
initial=str(self.obj.edge_blur))
self.blurtextbox.on_submit(self.set_edge_blur)
axreset = plt.axes([0.40, 0.01, 0.1, 0.075])
self.resetbutton = Button(axreset,
'Reset',
color=button_color,
hovercolor=button_color)
self.resetbutton.on_clicked(self.reset_button_press)
if self.curr_idx + 1 < len(self.clone_list):
axnext = plt.axes([0.875, 0.01, 0.1, 0.075])
self.nextbutton = Button(axnext,
'Next',
color=button_color,
hovercolor=button_color)
self.nextbutton.on_clicked(self.next_button_press)
if self.curr_idx > 0:
axprev = plt.axes([0.875, 0.085, 0.1, 0.075])
self.prevbutton = Button(axprev,
'Previous',
color=button_color,
hovercolor=button_color)
self.prevbutton.on_clicked(self.prev_button_press)
self.obj.draw()
'ResultsDirectory': Results_Path
}
ResultImage, TransformParameterMap = ElastixRegistration(Dictionary)
ResultImage_Array = sitk.GetArrayFromImage(ResultImage)
## Plot registration results
Figure, Axes = plt.subplots(1, 1, figsize=(8.25, 6.75), dpi=100)
Mid_Position = int(round(uCT_Scan_Array.shape[2] / 2))
uCT_Show = Axes.imshow(uCT_Scan_Array[:, :, Mid_Position],
cmap='bone',
alpha=1)
HRpQCT_Show = Axes.imshow(ResultImage_Array[:, :, Mid_Position],
cmap='jet',
alpha=0.5)
SliderAxis = plt.axes([0.25, 0.15, 0.65, 0.03])
Mid_Position_Slider = Slider(SliderAxis,
'Y Position',
0,
uCT_Scan_Array.shape[1],
valinit=Mid_Position)
def Update(Value):
Position = Mid_Position_Slider.Value
uCT_Show.set_data(uCT_Scan_Array[:, :, int(Position)])
HRpQCT_Show.set_data(ResultImage_Array[:, :, int(Position)])
Figure.canvas.draw_idle()
Mid_Position_Slider.on_changed(Update)
Axes.set_title(Sample + ' Registration Results')
def main():
parser = build_parser()
options = parser.parse_args()
if not os.path.isfile(options.network):
parser.error("Network %s does not exist. (Did you forget to download it?)" % options.network)
if not os.path.isfile(options.model_save_dir + 'checkpoint'):
parser.error("Pretrained model %s does not exist.)" % options.network)
if not options.from_webcam or options.from_screenshot:
parser.error("You must choose either getting the image from webcam or from screen shot." % options.network)
style_images = [imread(style) for style in options.styles]
# Start with the first one with 1.0 weight and 0.0 for all other ones.
current_style_blend_weights = [1.0 if i == 0 else 0.0 for i,_ in enumerate(style_images)]
one_hot_vector_container = n_style_feedforward_net.one_hot_vector_container(np.array([current_style_blend_weights]))
plt.ion()
fig, ax = plt.subplots()
plt.subplots_adjust(left=0.25, bottom=0.25)
ax.set_title("Real Time Neural Style")
im = ax.imshow(np.zeros((options.height, options.width, 3)) + 128,vmin=0,vmax=255) # Blank starting image
axcolor = 'lightgoldenrodyellow'
slider_axes = [plt.axes([0.25, 0.21 - i * 0.02, 0.65, 0.02], axisbg=axcolor) for i, style in enumerate(
options.styles)]
sliders = [Slider(slider_axes[i], style, 0.0, 1.0, valinit=current_style_blend_weights[i]) for i, style in enumerate(options.styles)]
fig.show()
im.axes.figure.canvas.draw()
plt.pause(0.001)
tstart = None
def update(val):
for i, slider in enumerate(sliders):
current_style_blend_weights[i] = slider.val
one_hot_vector_container.vec = np.array([current_style_blend_weights])
for slider in sliders:
slider.on_changed(update)
for iteration, image in n_style_feedforward_net.style_synthesis_net(path_to_network=options.network,
height=options.height, width=options.width,
styles=style_images, iterations=None,
batch_size=1,
one_hot_vector_for_restore_and_generate=one_hot_vector_container,
style_only=options.texture_synthesis_only,
use_johnson=options.use_johnson,
use_skip_noise_4=options.use_skip_noise_4,
save_dir=options.model_save_dir,
do_restore_and_generate=True,
from_screenshot=options.from_screenshot,
from_webcam=options.from_webcam):
# We must do this clip step before we display the image. Otherwise the color will be off.
image = np.clip(image, 0, 255).astype(np.uint8)
if tstart is None:
tstart = time.time()
# Change the data in place instead of create a new window.
ax.set_title(str(iteration))
im.set_data(image)
im.axes.figure.canvas.draw()
plt.pause(0.001)
print ('FPS:', iteration / (time.time() - tstart + 0.001))
plt.ioff()
plt.show()
def test_check_bunch_of_radio_buttons():
rax = plt.axes([0.05, 0.1, 0.15, 0.7])
widgets.RadioButtons(rax, ('B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8',
'B9', 'B10', 'B11', 'B12', 'B13', 'B14', 'B15'))
def draw(self):
# updates all visualizations depending on current state of the GUI,
# and redraws the GUI image and buttons
self.display.clear()
buttoncolor = [0.792156862745098, 0.8823529411764706, 1.0]
axaddcheck = plt.axes([0.025, 0.085, 0.1, 0.075])
self.addcheckbutton = Button(axaddcheck,
'Add Checkpoint',
color=buttoncolor,
hovercolor=buttoncolor)
self.addcheckbutton.on_clicked(self.add_checkpoint_button_press)
if self.add_checkpoint:
self.addcheckbutton.color = "green"
self.addcheckbutton.hovercolor = "green"
self.display.imshow(self.image, cmap="gray")
axaccept = plt.axes([0.875, 0.7, 0.1, 0.075])
self.acceptbutton = Button(axaccept,
'Accept Changes',
color=buttoncolor,
hovercolor=buttoncolor)
self.acceptbutton.on_clicked(self.accept)
if self.clone.accepted:
self.acceptbutton.color = "blue"
self.acceptbutton.hovercolor = "blue"
axmodified = plt.axes([0.875, 0.6, 0.1, 0.075])
self.modifiedbutton = Button(axmodified,
'Mark as Modified',
color=buttoncolor,
hovercolor=buttoncolor)
self.modifiedbutton.on_clicked(self.modified_clicked)
if self.clone.modified:
self.modifiedbutton.color = "blue"
self.modifiedbutton.hovercolor = "blue"
axmask = plt.axes([0.025, 0.185, 0.05, 0.075])
self.maskbutton = Button(axmask,
'Mask',
color=buttoncolor,
hovercolor=buttoncolor)
self.maskbutton.on_clicked(self.mask)
axunmask = plt.axes([0.075, 0.185, 0.05, 0.075])
self.unmaskbutton = Button(axunmask,
'Unmask',
color=buttoncolor,
hovercolor=buttoncolor)
self.unmaskbutton.on_clicked(self.unmask)
if self.mask_clicked:
self.maskbutton.color = "green"
self.maskbutton.hovercolor = "green"
if self.unmask_clicked:
self.unmaskbutton.color = "green"
self.unmaskbutton.hovercolor = "green"
if self.show_dorsal_edge:
try:
self.display.scatter(self.de[:, 1], self.de[:, 0], c="blue")
except TypeError:
pass
if self.edges:
checkpoint_color = "yellow"
else:
checkpoint_color = "black"
try:
self.display.scatter(self.checkpoints[:, 1],
self.checkpoints[:, 0],
c=checkpoint_color)
except TypeError:
pass
self.display.scatter(self.clone.tail_tip[1],
self.clone.tail_tip[0],
c='red')
self.display.text(self.clone.tail_tip[1],
self.clone.tail_tip[0],
'tail_tip',
color='red')
self.display.scatter(self.clone.tail[1], self.clone.tail[0], c='red')
self.display.text(self.clone.tail[1],
self.clone.tail[0],
'tail',
color='red')
self.display.axis('off')
self.display.set_title(self.clone.filepath, color="black")
self.display.figure.canvas.draw()
def jview(var, tindex, jindex, gridid, filename=None, \
cmin=None, cmax=None, clev=None, clbformat='%.2f', \
fill=False, contour=False, d=4, irange=None, \
hrange=None, fts=None, title=None, map=False, \
pal=None, clb=True, outfile=None):
"""
jview(var, tindex, jindex, gridid, {optional switch})
optional switch:
- filename if defined, load the variable from file
- cmin set color minimum limit
- cmax set color maximum limit
- clev set the number of color step
- fill use contourf instead of pcolor
- contour overlay contour (request fill=True)
- d contour density (default d=4)
- irange i range
- hrange h range
- fts set font size (default: 12)
- title add title to the plot
- map if True, draw a map showing islice location
- pal set color map (default: cm.jet)
- clb add colorbar (defaul: True)
- outfile if defined, write figure to file
plot a constante-j slice of variable var. If filename is provided,
var must be a string and the variable will be load from the file.
grid can be a grid object or a gridid. In the later case, the grid
object correponding to the provided gridid will be loaded.
"""
# get grid
if type(gridid).__name__ == 'ROMS_Grid':
grd = gridid
else:
grd = pyroms.grid.get_ROMS_grid(gridid)
# get variable
if filename == None:
var = var
else:
data = pyroms.io.Dataset(filename)
var = data.variables[var]
Np, Mp, Lp = grd.vgrid.z_r[0,:].shape
if tindex is not -1:
assert len(var.shape) == 4, 'var must be 4D (time plus space).'
K, N, M, L = var.shape
else:
assert len(var.shape) == 3, 'var must be 3D (no time dependency).'
N, M, L = var.shape
# determine where on the C-grid these variable lies
if N == Np and M == Mp and L == Lp:
Cpos='rho'
lon = grd.hgrid.lon_vert
lat = grd.hgrid.lat_vert
mask = grd.hgrid.mask_rho
if N == Np and M == Mp and L == Lp-1:
Cpos='u'
lon = 0.5 * (grd.hgrid.lon_vert[:,:-1] + grd.hgrid.lon_vert[:,1:])
lat = 0.5 * (grd.hgrid.lat_vert[:,:-1] + grd.hgrid.lat_vert[:,1:])
mask = grd.hgrid.mask_u
if N == Np and M == Mp-1 and L == Lp:
Cpos='v'
lon = 0.5 * (grd.hgrid.lon_vert[:-1,:] + grd.hgrid.lon_vert[1:,:])
lat = 0.5 * (grd.hgrid.lat_vert[:-1,:] + grd.hgrid.lat_vert[1:,:])
mask = grd.hgrid.mask_v
if N == Np+1 and M == Mp and L == Lp:
Cpos='w'
lon = grd.hgrid.lon_vert
lat = grd.hgrid.lat_vert
mask = grd.hgrid.mask_rho
# get constante-j slice
if tindex == -1:
var = var[:,:,:]
else:
var = var[tindex,:,:,:]
if fill == True:
jslice, zj, lonj, latj, = pyroms.tools.jslice(var, jindex, grd, \
Cpos)
else:
jslice, zj, lonj, latj, = pyroms.tools.jslice(var, jindex, grd, \
Cpos, vert=True)
# plot
if cmin is None:
cmin = jslice.min()
else:
cmin = float(cmin)
if cmax is None:
cmax = jslice.max()
else:
cmax = float(cmax)
if clev is None:
clev = 100.
else:
clev = float(clev)
dc = (cmax - cmin)/clev ; vc = np.arange(cmin,cmax+dc,dc)
if pal is None:
pal = cm.jet
else:
pal = pal
if fts is None:
fts = 12
else:
fts = fts
#pal.set_over('w', 1.0)
#pal.set_under('w', 1.0)
#pal.set_bad('w', 1.0)
pal_norm = colors.BoundaryNorm(vc,ncolors=256, clip = False)
# clear figure
#plt.clf()
if map is True:
# set axes for the main plot in order to keep space for the map
if fts < 12:
ax=None
else:
ax = plt.axes([0.15, 0.08, 0.8, 0.65])
else:
if fts < 12:
ax=None
else:
ax=plt.axes([0.15, 0.1, 0.8, 0.8])
if fill is True:
cf = plt.contourf(lonj, zj, jslice, vc, cmap = pal, norm = pal_norm, axes=ax)
else:
cf = plt.pcolor(lonj, zj, jslice, cmap = pal, norm = pal_norm, axes=ax)
if clb is True:
clb = plt.colorbar(cf, fraction=0.075,format=clbformat)
for t in clb.ax.get_yticklabels():
t.set_fontsize(fts)
if contour is True:
if fill is not True:
raise Warning, 'Please run again with fill=True for overlay contour.'
else:
plt.contour(lonj, zj, jslice, vc[::d], colors='k', linewidths=0.5, linestyles='solid', axes=ax)
if irange is not None:
plt.xlim(irange)
if hrange is not None:
plt.ylim(hrange)
if title is not None:
if map is True:
# move the title on the right
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
xt = xmin - (xmax-xmin)/9.
yt = ymax + (ymax-ymin)/7.
plt.text(xt, yt, title, fontsize=fts+4)
else:
plt.title(title, fontsize=fts+4)
plt.xlabel('Latitude', fontsize=fts)
plt.ylabel('Depth', fontsize=fts)
if map is True:
# draw a map with constant-i slice location
ax_map = plt.axes([0.4, 0.76, 0.2, 0.23])
varm = np.ma.masked_where(mask[:,:] == 0, var[var.shape[0]-1,:,:])
xmin, xmax = ax.get_xlim()
dd = (lon[jindex,:] - xmin) * (lon[jindex,:] - xmin)
start = np.where(dd == dd.min())
dd = (lon[jindex,:] - xmax) * (lon[jindex,:] - xmax)
end = np.where(dd == dd.min())
lon_min = lon.min()
lon_max = lon.max()
lon_0 = (lon_min + lon_max) / 2.
lat_min = lat.min()
lat_max = lat.max()
lat_0 = (lat_min + lat_max) / 2.
map = Basemap(projection='merc', llcrnrlon=lon_min, llcrnrlat=lat_min, \
urcrnrlon=lon_max, urcrnrlat=lat_max, lat_0=lat_0, lon_0=lon_0, \
resolution='i', area_thresh=10.)
x, y = map(lon,lat)
# fill land and draw coastlines
map.drawcoastlines()
map.fillcontinents(color='grey')
#map.drawmapboundary()
Basemap.pcolor(map, x, y, varm, axes=ax_map)
Basemap.plot(map, x[jindex,start[0]:end[0]], y[jindex,start[0]:end[0]], \
'k-', linewidth=3, axes=ax_map)
if outfile is not None:
if outfile.find('.png') != -1 or outfile.find('.svg') != -1 or outfile.find('.eps') != -1:
print 'Write figure to file', outfile
plt.savefig(outfile, dpi=200, facecolor='w', edgecolor='w', orientation='portrait')
else:
print 'Unrecognized file extension. Please use .png, .svg or .eps file extension.'
return
def plot_diff(slice1, title, lim1, lim2, step):
#plt.figure()
plt.figure(figsize=(12, 6)) # this works
#plt.figure(figsize=(12, 4.8))# this works
data = slice1.data
lon = slice1.coord('longitude').points
lat = slice1.coord('latitude').points
new_lon = []
for k in range(len(lon)):
if lon[k] > 180:
#temp=lon[k]
temp = lon[k] - 360
new_lon = np.append(new_lon, temp)
else:
new_lon = np.append(new_lon, lon[k])
#lon=lon-180 #basemap correction
#new_lon=temp
#..............basemap requires lat and lon to be in increasing order
data_1 = data[:, 0:96]
data_2 = data[:, 96:]
data_21 = np.hstack((data_2, data_1))
new_lon_1 = new_lon[0:96]
new_lon_2 = new_lon[96:]
new_lon_21 = np.hstack((new_lon_2, new_lon_1))
data_final = data_21
new_lon_final = new_lon_21
ticks = np.arange(lim1, lim2, step)
ticks6 = [1e0, 1e3, 1e5, 1e8, 1e10]
ticks6_label = ['1e0', '1e3', '1e5', '1e8', '1e10']
ticks6 = [1e-2, 1e0, 1e1, 1e2, 1e3, 1e4, 1e5, 1e6, 1e7, 1e8, 1e9]
ticks6_label = [
'-1e2', '1e0', '1e1', '1e2', '1e3', '1e4', '1e5', '1e6', '1e7', '1e8',
'1e9'
]
ticks6 = [1e-33, 1e0, 1e1, 1e2, 1e3, 1e4, 1e5, 1e6, 1e7, 1e8, 1e9]
ticks6_label = [
'-1e2', '1e0', '1e1', '1e2', '1e3', '1e4', '1e5', '1e6', '1e7', '1e8',
'1e9'
]
ticks6 = [1e-35, 1e-30, 1e-25, 1e-20, 1e-15]
ticks6_label = ['1e-35', '1e-30', '1e-25', '1e-20', '1e-15']
ticks6 = [1e-35, 1e-32, 1e-29, 1e-26, 1e-23, 1e-20, 1e-17, 1e-14]
ticks6_label = [
'1e-35', '1e-32', '1e-29', '1e-26', '1e-23', '1e-20', '1e-17', '1e-14'
]
ticks6 = [1e-20, 1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1e0]
ticks6 = [1e-20, 1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1e0]
ticks6 = [-300, -250, -200, -150, -100, -50, 0,
50] # this is a percentage change plot
ticks6 = [-100, -90, -80, -70, -60, -50, -40, -30, -20, -10,
0] # this is a percentage change plot
ticks6_label = [str(o) + '%' for o in ticks6]
#ticks6_label = ['1e-35','1e-32','1e-29','1e-26','1e-23','1e-20','1e-17','1e-14']
#ticks6_label = ['1e-35','1e-30','1e-25','1e-20','1e-15']
#ticks6_label = ['-1e2','1e0','1e1','1e2','1e3','1e4','1e5','1e6','1e7','1e8','1e9']
# ticks6 = [-1e-22,1e-30,1e-16]
ax = plt.axes(projection=ccrs.PlateCarree())
#data_final = np.where(data_final>1.0,data_final,1e-1)
#x=plt.contourf(new_lon_final,lat,data_final,transform=ccrs.PlateCarree(),cmap='RdYlBu_r',levels=ticks)
#x=plt.contourf(new_lon_final,lat,data_final,norm=colors.LogNorm(vmin=1e-8,vmax=1e0),transform=ccrs.PlateCarree(),cmap='RdYlBu_r',levels=ticks6)
#x=plt.contourf(new_lon_final,lat,data_final,vmin=-100,vmax=0,transform=ccrs.PlateCarree(),cmap='RdYlBu_r',levels=ticks6)
x = plt.contourf(new_lon_final,
lat,
data_final,
vmin=-100,
vmax=0,
transform=ccrs.PlateCarree(),
cmap=ruths_colors('davos'),
levels=ticks6)
#x=plt.contourf(new_lon_final,lat,data_final,norm=colors.LogNorm(vmin=pow(10,-35),vmax=1e-15),transform=ccrs.PlateCarree(),cmap='RdYlBu_r',levels=ticks6)
norm = mpl.colors.Normalize(vmin=lim1, vmax=lim2)
#plt.title(title,fontdict={'fontsize':16})
plt.title(title, fontsize=18)
ax.coastlines()
gl = ax.gridlines(crs=ccrs.PlateCarree(),
draw_labels=True,
linewidth=2,
color='black',
alpha=0.5,
linestyle='--')
gl.xlabels_top = False
gl.ylabels_right = False
#gl.xlines = False
gl.xlocator = mticker.FixedLocator([-180, -90, 0, 90, 180])
gl.ylocator = mticker.FixedLocator([-90, -45, 0, 45, 90])
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.xlabel_style = {'size': 15, 'color': 'gray'}
gl.xlabel_style = {'color': 'black', 'weight': 'bold'}
gl.ylabel_style = {'size': 15, 'color': 'gray'}
gl.ylabel_style = {'color': 'black', 'weight': 'bold'}
"""
cbar=plt.colorbar(x,ax=ax,ticks=ticks)
cbar.ax.tick_params(labelsize=16)
ax.set_aspect('auto')
plt.savefig(title+'.png')
"""
cbar = plt.colorbar(x, ax=ax)
cbar.set_ticks(ticks6)
cbar.set_ticklabels(ticks6_label)
cbar.ax.tick_params(labelsize=16)
ax.set_aspect('auto')
nTotal = sum(nResponses)
df = nComponents * (nGroups - 1)
#(1) Generate Gaussian 1D fields, compute test stat, store field maximum:
X2 = []
generator = rft1d.random.GeneratorMulti1D(nTotal, nNodes, nComponents, FWHM,
W0)
for i in range(nIterations):
y = generator.generate_sample()
chi2 = here_manova1(y, GROUP)
X2.append(chi2.max())
X2 = np.asarray(X2)
#(2) Compute survival function (SF) for the field maximumimum:
heights = np.linspace(10, 18, 21)
sf = np.array([(X2 > h).mean() for h in heights])
sfE = rft1d.chi2.sf(heights, df, nNodes, FWHM) #theoretical
sf0D = rft1d.chi2.sf0d(heights, df) #theoretical (0D)
#(3) Plot results:
pyplot.close('all')
ax = pyplot.axes()
ax.plot(heights, sf, 'o', label='Simulated')
ax.plot(heights, sfE, '-', label='Theoretical')
ax.plot(heights, sf0D, 'r-', label='Theoretical (0D)')
ax.set_xlabel('$u$', size=20)
ax.set_ylabel('$P (\chi^2_\mathrm{max} > u)$', size=20)
ax.legend()
ax.set_title("MANOVA validation (1D)", size=20)
pyplot.show()
import matplotlib.pyplot as plt
from random_walk import RandomWalk
while True:
rw = RandomWalk(50000)
rw.fill_walk()
plt.figure(dpi=128, figsize=(10, 6))
point_num = list(range(rw.num_points))
plt.scatter(rw.x_label, rw.y_label, c=point_num, cmap=plt.cm.Blues,
edgecolors='none', s=15)
plt.scatter(0, 0, c='green', edgecolors='none', s=100)
plt.scatter(rw.x_label[-1], rw.y_label[-1], c='red', edgecolors='none', s=100)
plt.axes().get_xaxis().set_visible(False)
plt.axes().get_yaxis().set_visible(False)
plt.title("Random Walk", fontsize=24)
# plt.xlabel("Value", fontsize=14)
# plt.ylabel("Square Value", fontsize=14)
# plt.savefig("random_walk.png", bbox_inches="tight")
plt.show()
keep_running = input("Make another walk? (y/n): ")
if keep_running == 'n':
break
def plot_rating(ratings: list,
daily_games: list,
username,
export_video=False,
show_graph=True,
game_mode="Blitz",
big=False,
upload=False):
if big:
fig = plt.figure(figsize=(16, 10))
ax = plt.axes(autoscale_on=True, position=[0.06, 0.06, 0.88, 0.94])
ax2 = plt.twinx(ax)
ax2.set_position([0.06, 0.06, 0.88, 0.94])
ax3 = plt.twinx(ax)
ax.tick_params(labelsize=15)
ax2.tick_params(labelsize=15)
ax3.tick_params(labelsize=15)
size = 'big'
else:
fig = plt.figure()
ax = plt.axes()
ax2 = plt.twinx(ax)
ax3 = plt.twinx(ax)
size = 'small'
line_color = "#3F5D7D"
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.set_ylabel(f"{username}'s Lichess {game_mode} Rating",
color=line_color,
fontsize=15)
ax.tick_params(labelcolor=line_color, left=False, bottom=False)
ax.set_xlabel("Number of games", fontsize=15)
ax3.spines["top"].set_visible(False)
ax3.spines["bottom"].set_visible(False)
ax3.spines["right"].set_visible(False)
ax3.spines["left"].set_visible(False)
ax3.tick_params(labelright=False, left=False, bottom=False)
bar_color = 'grey'
ax2.spines["top"].set_visible(False)
ax2.spines["bottom"].set_visible(False)
ax2.spines["right"].set_visible(False)
ax2.spines["left"].set_visible(False)
ax2.set_ylabel('Games per day', color=bar_color, fontsize=15)
ax2.tick_params(labelcolor=bar_color, right=False, bottom=False)
number_of_games = len(ratings)
for y in range(800, 3000, 50):
ax.plot(range(0, number_of_games),
[y] * len(range(0, number_of_games)),
"--",
lw=0.5,
color="black",
alpha=0.3)
ax.set(ylim=(round_to(min(ratings), 50),
round_to(max(ratings), 50, False)))
ax3.set(ylim=(round_to(min(ratings), 50),
round_to(max(ratings), 50, False)))
ax2.set(ylim=(0, round_to(max(daily_games), 5, False)))
def animate(i):
if i >= len(ratings) - 2:
return
if i > 0:
ax.set(xlim=(0, i + 1))
ax3.set(xlim=(0, i + 1))
ax2.bar(i, daily_games[i], color="black", alpha=0.1, width=1.0)
ax.plot([i - 1, i], ratings[i - 1:i + 1], color=line_color)
if i > 100:
ax3.plot([i - 1, i], [
sum(ratings[i - 100 - 1:i - 1]) / 100,
sum(ratings[i - 100:i]) / 100
],
color='orange')
anim = animation.FuncAnimation(fig,
animate,
interval=17,
frames=number_of_games - 1)
writer = animation.writers['ffmpeg']
writer(fps=60, metadata=dict(artist='kewko'))
if export_video:
print("Exporting video...")
export_file_path = f'export/ChessGraph_{username}_{game_mode}_{size}.mp4'
try:
os.mkdir('export')
print("Export Directory Created ")
except FileExistsError:
pass
anim.save(export_file_path)
print(f'Saved {export_file_path}.')
if upload:
from secrets import streamable_password, streamable_username
files = {'file': open(export_file_path, 'rb')}
print("Uploading to streamable...")
response = requests.post('https://api.streamable.com/upload',
auth=HTTPBasicAuth(
streamable_username,
streamable_password),
files=files)
graph_url = f"https://streamable.com/{response.json()['shortcode']}"
print(graph_url)
return graph_url
if show_graph:
plt.draw()
plt.show()
def plot_trajectory(self):
ax = plt.axes(projection='3d')
ax.plot3D(self.x_, self.y_, self.z_)
plt.show()
def main():
# image_path = os.path.join('data', 'uEye_Image_002767.png')
# zoom = ((714, 1920), (920, 1830))
image_path = os.path.join('data', 'uEye_Image_000827.png')
zoom = ([100, 1312], [400, 1900])
calib = 0.00124 / 400 # mm / px
# Arbitrary first guess for gamma
initial_surface_tension = 0.04 # N/m
surface_tension_range = (0.02, 0.1) # N/m
fluid_density = 1000
image1 = load_image(image_path, region=zoom)
edges, RZ_edges = detect_edges(image1, method='contour')
# Guess parameters
center_Z, center_R, radius = fit_circle_tip(edges.shape,
RZ_edges,
method='ransac',
debug=False)
theta = guess_angle(edges, center_Z, center_R)
# Note that below the method method='SLSQP' can be used also.
# Step 1: consider only the surface tension
# as it is not guessed so far
ini_variables = np.array((initial_surface_tension))
res = minimize(deviation_edge_model_simple,
ini_variables,
args=(theta, center_R, center_Z, radius, RZ_edges,
fluid_density, calib),
method='L-BFGS-B',
bounds=(surface_tension_range, ),
options={
'maxiter': 10,
'ftol': 1e-2,
'disp': True
})
guessed_surface_tension = res.x[0]
print(f'Step 1-RMS: {res.fun}')
# Step 2: consider all the parameters
ini_variables2 = np.array((
guessed_surface_tension,
theta,
center_R,
center_Z,
radius,
))
param_bounds = (
(guessed_surface_tension - 2e-3, guessed_surface_tension + 2e-3),
(theta * 0.7, theta * 1.3),
(center_R - 5, center_R + 5),
(center_Z - 5, center_Z + 5),
(radius - 10, radius + 10),
)
res = minimize(deviation_edge_model_full,
ini_variables2,
args=(RZ_edges, fluid_density, calib),
method='L-BFGS-B',
bounds=param_bounds,
options={
'maxiter': 100,
'ftol': 1e-6,
'disp': True
})
optimal_variables = res.x
print(f'Step 2-ini params BFGS: {ini_variables2}')
print(f'Step 2-opt params BFGS: {optimal_variables}')
print(f'Step 2-RMS: {res.fun}')
# Plot
RZ_model = young_laplace(*optimal_variables,
fluid_density,
calib,
RZ_edges=RZ_edges,
num_points=1e4)
oRMS = orthogonal_RMS(RZ_model, RZ_edges)
rRMS = radial_RMS(RZ_model, RZ_edges)
print(f'OrthoRMS: {oRMS}, RadialRMS {rRMS}')
plt.figure()
ax = plt.axes()
plt.imshow(image1, cmap='gray')
circle = plt.Circle((center_R, center_Z),
radius=radius,
color='c',
fill=False)
ax.add_patch(circle)
plt.plot(*RZ_edges, '*g', markersize=1)
plt.plot(*RZ_model, 'r-', markersize=2)
plt.plot(center_R, center_Z, 'bo')
plt.title(f'Gamma = {optimal_variables[0]:.4} N/m')
plt.show()
def main(docopts):
docopts["--batch_size"] = int(docopts["--batch_size"])
docopts["--gpu"] = int(docopts["--gpu"])
docopts["--lambda_l2_reg"] = float(docopts["--lambda_l2_reg"])
docopts["--learning_rate"] = float(docopts["--learning_rate"])
docopts["--max_epochs"] = int(docopts["--max_epochs"])
# Logging
logging.basicConfig(level=logging.INFO)
#
# Following http://nbviewer.jupyter.org/github/dmlc/mxnet/blob/master/example/notebooks/simple_bind.ipynb
#
X, Y = data.get_mnist()
iter = mx.io.NDArrayIter(data=X, label=Y, batch_size=docopts["--batch_size"], shuffle=True)
if docopts["train"] or docopts["continue"]:
m = vae.VAE(ARCHITECTURE)
sym = m.training_model()
dbatch = iter.next()
exe = sym.simple_bind(ctx=mx.gpu(docopts["--gpu"]), data = dbatch.data[0].shape)
args = exe.arg_dict
grads = exe.grad_dict
outputs = dict(zip(sym.list_outputs(), exe.outputs))
if docopts["continue"]:
loaded_args = mx.nd.load(os.path.join(docopts["--log"], "parameters"))
for name in args:
if name != "data":
args[name][:] = loaded_args[name]
# Initialize parameters
xavier = mx.init.Xavier()
for name, nd_array in args.items():
if name != "data":
xavier(name, nd_array)
optimizer = mx.optimizer.create(name="adam",
learning_rate=docopts["--learning_rate"],
wd=docopts["--lambda_l2_reg"])
updater = mx.optimizer.get_updater(optimizer)
# Train
keys = sym.list_arguments()
optimizer = mx.optimizer.Adam()
if docopts["--visualize"]:
# Random image
last_image_time = time.time()
plt.ion()
figure = plt.figure()
imshow = plt.imshow(np.random.uniform(size=(28,28)), cmap="gray")
for epoch in range(docopts["--max_epochs"]):
iter.reset()
epoch_start_time = time.time()
batch = 0
for dbatch in iter:
args["data"][:] = dbatch.data[0]
exe.forward(is_train=True)
exe.backward()
if docopts["--visualize"]:
# Throttle refresh ratio
if time.time() - last_image_time > 0.1:
last_image_time = time.time()
imshow.set_data(exe.outputs[2][
random.randint(0, docopts["--batch_size"])].reshape(
(28,28)).asnumpy())
figure.canvas.draw()
figure.canvas.flush_events()
for index, key in enumerate(keys):
updater(index=index, grad=grads[key], weight=args[key])
kl_divergence = exe.outputs[3].asnumpy()
cross_entropy = exe.outputs[4].asnumpy()
logging.info("Batch %d: %f mean kl_divergence", batch, kl_divergence.mean())
logging.info("Batch %d: %f mean cross_entropy", batch, cross_entropy.mean())
batch += 1
logging.info("Finish training epoch %d in %f seconds",
epoch,
time.time() - epoch_start_time)
# Save model parameters (including data, to simplify loading / binding)
mx.nd.save(os.path.join(docopts["--log"], "parameters"),
{x[0]: x[1] for x in args.items() if x[0] != "data"})
elif docopts["test"]:
from matplotlib.widgets import Button
m = vae.VAE(ARCHITECTURE)
sym = m.testing_model()
exe = sym.simple_bind(ctx=mx.gpu(docopts["--gpu"]),
data=(docopts["--batch_size"], ARCHITECTURE[-1]))
args = exe.arg_dict
grads = exe.grad_dict
outputs = dict(zip(sym.list_outputs(), exe.outputs))
loaded_args = mx.nd.load(os.path.join(docopts["--log"], "parameters"))
for name in args:
if name != "data":
args[name][:] = loaded_args[name]
args["data"][:] = np.random.randn(docopts["--batch_size"], ARCHITECTURE[-1])
exe.forward(is_train=True)
# testing_model has only 1 output
batch = exe.outputs[0].asnumpy().reshape(-1, 28, 28)
np.save(os.path.join(docopts["--log"], "output"), batch)
imshow = plt.imshow(batch[0], cmap="gray")
callback = Index(imshow, batch)
axnext = plt.axes([0.8, 0.7, 0.1, 0.075])
axprev = plt.axes([0.8, 0.6, 0.1, 0.075])
next_button = Button(axnext, 'Next')
next_button.on_clicked(callback.next)
prev_button = Button(axprev, 'Previous')
prev_button.on_clicked(callback.prev)
plt.show()
plt.waitforbuttonpress()
def scat_hist(xdata,
ydata,
color,
x_axis,
y_axis,
system,
analysis,
num_b=100,
average=False,
t0=0,
**kwargs):
""" Creates 1D scatter plot w/ a 1D histogram
Usage: scat_hist(xdata, ydata, color, x_axis, y_axis, system, analysis, num_b)
Arguments:
xdata, ydata: self-explanatory
color: color to be used to plot data
x_axis, y_axis: strings to be printed on the axi labels
system: descriptor for the system analyzed
analysis: descriptor for the analysis performed and plotted
num_b: number of bins to be used when binning the data; Default is 100
average: [False|True]; Default is False; if set to True, the function will calc the average, standard dev, and standard dev of mean of the y-data # THERE IS A BUG; if average = True, need to read in xunits for this function to work...
t0: index to begin averaging from; Default is 0
kwargs:
xunits, yunits: string with correct math text describing the units for the x/y data
x_lim, y_lim: list w/ two elements, setting the limits of the x/y ranges of plot
plt_title: string to be added as the plot title
"""
# INITIATING THE PLOT SIZES
left, width = 0.1, 0.65
bottom, height = 0.1, 0.8
bottom_h = left_h = left + width + 0.01
rect_scatter = [left, bottom, width, height]
rect_histy = [left_h, bottom, 0.2, height]
# INITIATING THE PLOT...
plt.figure(1, figsize=(10, 8))
axScatter = plt.axes(rect_scatter)
axScatter.plot(xdata, ydata, '%s.' % (color))
# READING IN KWARG DICTIONARY INTO SPECIFIC VARIABLES
for name, value in kwargs.items():
if name == 'xunits':
x_units = value
x_axis = '%s (%s)' % (x_axis, value)
elif name == 'yunits':
y_units = value
y_axis = '%s (%s)' % (y_axis, value)
elif name == 'x_lim':
plt.xlim(value)
elif name == 'y_lim':
plt.ylim(value)
elif name == 'plt_title':
plt.title(r'%s' % (value), size='14')
plt.grid(b=True,
which='major',
axis='both',
color='#808080',
linestyle='--')
plt.ylabel(r'%s' % (y_axis), size=20)
plt.xlabel(r'%s' % (x_axis), size=20)
axScatter.tick_params(axis='both', which='major', labelsize=14)
if average != False:
avg = np.sum(ydata[t0:]) / len(ydata[t0:])
SD = stdev(ydata[t0:])
SDOM = SD / sqrt(len(ydata[t0:]))
plt.axhline(avg, xmin=0.0, xmax=1.0, c='r')
axHisty = plt.axes(rect_histy)
axHisty.yaxis.set_major_formatter(nullfmt)
axHisty.xaxis.set_major_formatter(nullfmt)
plt.grid(b=True,
which='major',
axis='both',
color='#808080',
linestyle='--')
axHisty.hist(ydata,
bins=num_b,
orientation='horizontal',
facecolor='grey',
edgecolor='black')
axHisty.set_ylim(axScatter.get_ylim())
# CALCULATING THE AVERAGE/SD/SDOM OF THE Y-DATA
if average != False:
plt.axhline(avg, xmin=0.0, xmax=1.0, c='r')
plt.figtext(0.775,
0.810,
'%s\n%6.4f $\\pm$ %6.4f %s \nSD = %4.3f %s' %
(analysis, avg, SDOM, y_units, SD, y_units),
bbox=dict(boxstyle='square', ec='r', fc='w'),
fontsize=12)
plt.savefig('%s.%s.scat_hist.png' % (system, analysis), dpi=300)
plt.close()
#plt.plot(m2_k11_evo_opt*0.975,r2_k11_evo_opt*0.981,c='r',linewidth=1) # m2=0.195
'''
plt.plot(m2_k11_evo_opt, r2_k11_evo_opt, c='r', linewidth=1,
alpha=0.8) #,linestyle='dashed',alpha=0.5)
plt.plot(m2_k11_evo_std, r2_k11_evo_std, c='k', linewidth=1,
alpha=0.8) #,linestyle='dashed')
plt.xlabel(r'$M_{2}\ (\rm{M}_{\odot})$', fontsize=16)
plt.ylabel(r'$R_{2}\ (\rm{R}_{\odot})$', fontsize=16)
plt.tick_params(axis='both', which='major', labelsize=13, width=1.0)
plt.tick_params(top='on', right='on')
# Period gap
#plt.axhspan(0.2254,0.2926,xmin=0.59,xmax=0.70,color='k',alpha=0.05)
#plt.axvline(1.363,linestyle='dashed',color='k',linewidth=1,alpha=0.5)
#plt.axvspan(1.348,1.378,color='k',alpha=0.1)
#plt.scatter(1.373,0.1175,marker='o',color='w',s=25)
#plt.errorbar(1.373,0.035,xerr=0.048,ls='none',color='k',capsize=5,linewidth=1,alpha=0.5)
#plt.errorbar(1.373,0.061,xerr=0.048,ls='none',color='k',capsize=5,linewidth=1,alpha=0.5)
#plt.errorbar(1.373,0.106,xerr=0.048,ls='none',color='k',capsize=5,linewidth=1,alpha=0.5)
#plt.axvline(1.373,linestyle='dashed',color='gray')
plt.subplots_adjust(bottom=0.10, top=0.98, left=0.10, right=0.99)
#for axis in ['top','bottom','left','right']:
# plt.subplot(1,1,1).spines[axis].set_linewidth(1.5)
plt.axes().set_aspect('auto')
plt.savefig("r2vsm2_curr.pdf")
plt.show()
def run_all_plots():
# # CEI timeseries
if True:
# from https://www.ncdc.noaa.gov/extremes/cei/graph/us/03-05/4 (Spring, Step4 indicator)
cei = read_cei("CEI_step4_figure_data.csv")
smoothed = binomialfilter(cei.data, -99.9, 9, pad=False)
smoothed = np.ma.masked_where(smoothed == -99.9, smoothed)
fig = plt.figure(figsize=(8, 5))
plt.clf()
ax1 = plt.axes([0.11, 0.08, 0.86, 0.90])
ax1.bar(cei.times,
cei.data,
color="g",
label=cei.name,
align="center",
width=1,
edgecolor="darkgreen")
ax1.plot(cei.times, smoothed, "r", lw=LW)
ax1.plot([cei.times[0], cei.times[-1]],
[np.mean(cei.data), np.mean(cei.data)],
"k",
lw=1)
ax1.set_xlim([1910, int(settings.YEAR) + 2])
ax1.set_ylabel("%", fontsize=settings.FONTSIZE)
minorLocator = MultipleLocator(1)
ax1.xaxis.set_minor_locator(minorLocator)
utils.thicken_panel_border(ax1)
ax1.yaxis.set_tick_params(right=False)
for tick in ax1.xaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
for tick in ax1.yaxis.get_major_ticks():
tick.label.set_fontsize(settings.FONTSIZE)
# ax1.text(0.02, 0.9, "(e)", transform=ax1.transAxes, fontsize=settings.FONTSIZE)
plt.savefig(settings.IMAGELOC + "PEX_CEI_ts{}".format(settings.OUTFMT))
plt.close()
#*************************
# GHCNDEX indices
rank_bounds = [-4.5, -3.5, -2.5, -1.5, 1.5, 2.5, 3.5, 4.5]
if True:
for index in ETCCDI_INDICES:
cube_list = iris.load(
DATALOC +
"GHCND_{}_1951-{}_RegularGrid_global_2.5x2.5deg_LSmask.nc".
format(index,
int(settings.YEAR) + 1))
names = np.array([cube.name() for cube in cube_list])
#*************
# plot annual map
selected_cube, = np.where(names == "Ann")
total_cube = cube_list[selected_cube[0]]
total_cube.coord('latitude').guess_bounds()
total_cube.coord('longitude').guess_bounds()
anoms = copy.deepcopy(total_cube)
anoms = ApplyClimatology(anoms)
# select the year to plot
years = GetYears(total_cube)
loc, = np.where(years == SELECTED_YEAR)
# sort the bounds and colourbars
if index in ["Rx1day"]:
bounds = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
elif index in ["Rx5day"]:
bounds = [0, 20, 40, 60, 80, 100, 120, 140, 160, 180]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
elif index in ["R10mm"]:
bounds = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
elif index in ["R20mm"]:
bounds = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
elif index in ["R95p"]:
bounds = [0, 25, 50, 75, 100, 125, 150, 175, 200, 225]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
elif index in ["PRCPTOT"]:
bounds = [0, 25, 50, 75, 100, 125, 150, 200, 300, 400]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"PEX_{}_{}_ghcndex".format(index, settings.YEAR),
total_cube[loc[0]],
cmap,
bounds,
"{} ({})".format(index, ETCCDI_UNITS[index]),
title="GHCNDEX {} - {}".format(index, ETCCDI_LABELS[index]))
# sort the bounds and colourbars
if index in ["Rx1day"]:
bounds = [-100, -10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10, 100]
bounds = [
-100, -40, -30, -20, -10, -5, 0, 5, 10, 20, 30, 40, 100
]
cmap = settings.COLOURMAP_DICT["hydrological"]
elif index in ["Rx5day"]:
bounds = [
-100, -40, -30, -20, -10, -5, 0, 5, 10, 20, 30, 40, 100
]
cmap = settings.COLOURMAP_DICT["hydrological"]
elif index in ["R10mm"]:
bounds = [-20, -8, -6, -4, -2, 0, 2, 4, 6, 8, 20]
cmap = settings.COLOURMAP_DICT["hydrological"]
elif index in ["R20mm"]:
bounds = [-20, -4, -3, -2, -1, 0, 1, 2, 3, 4, 20]
cmap = settings.COLOURMAP_DICT["hydrological"]
elif index in ["R95p"]:
bounds = [
-1000, -100, -80, -60, -40, -20, 0, 20, 40, 60, 80, 100,
1000
]
cmap = settings.COLOURMAP_DICT["hydrological"]
elif index in ["PRCPTOT"]:
bounds = [
-1000, -150, -80, -60, -40, -20, 0, 20, 40, 60, 80, 150,
1000
]
cmap = settings.COLOURMAP_DICT["hydrological"]
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"PEX_{}_{}_anoms_ghcndex".format(index, settings.YEAR),
anoms[loc[0]],
cmap,
bounds,
"{} ({})".format(index, ETCCDI_UNITS[index]),
title="GHCNDEX {} - {}".format(index, ETCCDI_LABELS[index]))
if index == "Rx1day":
utils.plot_smooth_map_iris(
settings.IMAGELOC + "p2.1_PEX_{}_{}_anoms_ghcndex".format(
index, settings.YEAR),
anoms[loc[0]],
cmap,
bounds,
"{} ({})".format(index, ETCCDI_UNITS[index]),
figtext="(k) Maximum 1 Day Precipitation Amount")
rank_cube = get_ranks(anoms)
plot_rank_map(
settings.IMAGELOC +
"PEX_{}_{}_rank_ghcndex".format(index, settings.YEAR),
rank_cube[loc[0]],
cmap,
rank_bounds,
"Rank",
title="{} - {}".format(index, ETCCDI_UNITS[index]))
#*************
# plot season maps (2x2)
if index in ["Rx1day", "Rx5day"]:
for sc, cube in enumerate([total_cube, anoms]):
season_list = []
for season in SEASONS:
# extract each month
month_data = []
months = SEASON_DICT[season]
for month in months:
selected_cube, = np.where(names == month)
cube = cube_list[selected_cube[0]]
if month == "Dec":
# need to extract from previous year - cheat by rolling data around
cube.data = np.roll(cube.data, 1, axis=0)
cube.data.mask[
0, :, :] = True # and mask out the previous years'
month_data += [cube.data]
# finished getting all months, make a dummy cube to populate
month_data = np.ma.array(month_data)
season_cube = copy.deepcopy(cube)
# take appropriate seasonal value
season_cube.data = np.ma.max(month_data, axis=0)
# mask if fewer that 2 months present
nmonths_locs = np.ma.count(month_data, axis=0)
season_cube.data = np.ma.masked_where(
nmonths_locs < 2, season_cube.data)
# make anomalies
if sc == 1:
season_cube = ApplyClimatology(season_cube)
# fix for plotting
season_cube.coord('latitude').guess_bounds()
season_cube.coord('longitude').guess_bounds()
# select the year to plot
years = GetYears(cube)
loc, = np.where(years == SELECTED_YEAR)
# add to list
season_list += [season_cube[loc[0]]]
# sort the bounds and colourbars
if sc == 0:
if index in ["Rx1day", "Rx5day"]:
bounds = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
# pass to plotting routine
utils.plot_smooth_map_iris_multipanel(
settings.IMAGELOC +
"PEX_{}_{}_seasons_ghcndex".format(
index, settings.YEAR),
season_list,
cmap,
bounds,
"{} ({})".format(index, ETCCDI_UNITS[index]),
shape=(2, 2),
title=SEASONS,
figtext=["(a)", "(b)", "(c)", "(d)"],
figtitle="{} - {}".format(index,
ETCCDI_LABELS[index]))
elif sc == 1:
cmap = settings.COLOURMAP_DICT["hydrological"]
if index in ["Rx1day"]:
bounds = [
-100, -10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10,
100
]
elif index in ["Rx5day"]:
bounds = [
-100, -20, -16, -12, -8, -4, 0, 4, 8, 12, 16,
20, 100
]
# pass to plotting routine
utils.plot_smooth_map_iris_multipanel(
settings.IMAGELOC +
"PEX_{}_{}_anoms_seasons_ghcndex".format(
index, settings.YEAR),
season_list,
cmap,
bounds,
"{} ({})".format(index, ETCCDI_UNITS[index]),
shape=(2, 2),
title=SEASONS,
figtext=["(a)", "(b)", "(c)", "(d)"],
figtitle="{} - {}".format(index,
ETCCDI_LABELS[index]))
#*************************
# DWD indices
if True:
for index in DWD_INDICES:
print(index)
if not os.path.exists(
DATALOC +
"First_Guess_Daily_{}_{}.nc".format(settings.YEAR, index)):
print("File {} missing".format(
"First_Guess_Daily_{}_{}.nc".format(settings.YEAR, index)))
continue
cube_list = iris.load(
DATALOC +
"First_Guess_Daily_{}_{}.nc".format(settings.YEAR, index))
if len(cube_list) == 1:
cube = cube_list[0]
else:
# these have two fields
for cube in cube_list:
if cube.units == "mm per 5 day" and index == "RX5":
break
elif cube.var_name == "consecutive_wet_days_index_per_time_period" and index == "CWD":
break
elif cube.var_name == "consecutive_dry_days_index_per_time_period" and index == "CDD":
break
else:
print(
"Check cube for {} for extra fields".format(index))
cube = cube[0] # take only single slice
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
if index in ["CDD"]:
bounds = [0, 20, 40, 60, 80, 100, 120, 140, 160, 180]
cmap = settings.COLOURMAP_DICT["precip_sequential_r"]
elif index in ["CWD"]:
bounds = [0, 20, 40, 60, 80, 100, 120, 140, 160, 180]
bounds = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
elif index in ["DD"]:
bounds = [0, 40, 80, 120, 160, 200, 240, 280, 320, 360]
cmap = settings.COLOURMAP_DICT["precip_sequential_r"]
elif index in ["PD"]:
bounds = [0, 40, 80, 120, 160, 200, 240, 280, 320, 360]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
elif index in ["RX1", "PD10"]:
bounds = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
elif index in ["RX5"]:
bounds = [0, 50, 100, 150, 200, 250, 300, 350, 400, 450]
bounds = [0, 20, 40, 60, 80, 100, 120, 140, 160, 180]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
elif index in ["R10", "R20", "R95P", "SDII", "PD20"]:
bounds = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"PEX_{}_{}_dwd".format(index, settings.YEAR),
cube,
cmap,
bounds,
"{} ({})".format(index, DWD_UNITS[index]),
title="{} - {}".format(index, DWD_LABELS[index]))
#*************************
# DWD differences indices
if True:
for index in DWD_INDICES:
print(index)
if not os.path.exists(
DATALOC +
"Diff_{}-Mean_{}.nc".format(index, settings.YEAR)):
print("File {} missing".format("Diff_{}-Mean_{}.nc".format(
index, settings.YEAR)))
continue
cube_list = iris.load(
DATALOC + "Diff_{}-Mean_{}.nc".format(index, settings.YEAR))
if len(cube_list) == 1:
cube = cube_list[0]
else:
# these have two fields
for cube in cube_list:
if cube.units == "mm per 5 day" and index == "RX5":
break
cube = cube[0] # take only single slice
cube.coord('latitude').guess_bounds()
cube.coord('longitude').guess_bounds()
if index in ["RX1"]:
bounds = [-100, -50, -25, -10, -5, 0, 5, 10, 25, 50, 100]
cmap = settings.COLOURMAP_DICT["hydrological"]
elif index in ["RX5"]:
bounds = [-50, -30, -20, -10, -5, 0, 5, 10, 20, 30, 50]
bounds = [-100, -50, -25, -10, -5, 0, 5, 10, 25, 50, 100]
cmap = settings.COLOURMAP_DICT["hydrological"]
elif index in ["PD10", "PD20", "R95P"]:
bounds = [-50, -30, -20, -10, -5, 0, 5, 10, 20, 30, 50]
cmap = settings.COLOURMAP_DICT["hydrological"]
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"PEX_{}_{}_diff_dwd".format(index, settings.YEAR),
cube,
cmap,
bounds,
"Anomalies from 1982-2016 {} ({})".format(
index, DWD_UNITS[index]),
title="{} - {}".format(index, DWD_LABELS[index]))
if index == "PD10":
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"p2.1_PEX_{}_{}_diff_dwd".format("R10mm", settings.YEAR),
cube,
cmap,
bounds,
"Anomalies from 1982-2016 {} ({})".format(
"R10mm", ETCCDI_UNITS["R10mm"]),
figtext="(l) {} anomalies".format("R10mm"))
else:
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"p2.1_PEX_{}_{}_diff_dwd".format(index, settings.YEAR),
cube,
cmap,
bounds,
"Anomalies from 1982-2016 {} ({})".format(
index, DWD_UNITS[index]),
figtext="(l) {} anomalies".format(index))
#*************************
# MERRA map
if True:
index = "R10mm"
cube = iris.load(DATALOC + "MERRA2_ann_2019_r10mm_gl_anom.nc")[0]
bounds = [-50, -30, -20, -10, -5, 0, 5, 10, 20, 30, 50]
cmap = settings.COLOURMAP_DICT["hydrological"]
utils.plot_smooth_map_iris(
settings.IMAGELOC + "PEX_R10mm_{}_merra2".format(settings.YEAR),
cube[0],
cmap,
bounds,
"{} ({})".format(index, ETCCDI_UNITS[index]),
title="MERRA-2 {} - {}".format(index, ETCCDI_LABELS[index]))
#*************************
# ERA5 map
if False:
index = "Rx1day"
cube = read_era5("prmax_{}.txt".format(settings.YEAR))
bounds = [0, 2, 5, 10, 20, 40, 80, 160, 300, 450]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
utils.plot_smooth_map_iris(
settings.IMAGELOC + "PEX_Rx1day_{}_era5".format(settings.YEAR),
cube,
cmap,
bounds,
"{} ({})".format(index, ETCCDI_UNITS[index]),
title="ERA5 {} - {}".format(index, ETCCDI_LABELS[index]))
#*************************
# ERA5 map
if True:
index = "Rx1day"
cube = read_era5("prmax1d_anomaly_for_{}_wrt_1981-2010.txt".format(
settings.YEAR))
bounds = [-400, -100, -75, -50, -25, 0, 25, 50, 75, 100, 400]
bounds = [-400, -100, -50, -20, -10, 0, 10, 20, 50, 100, 400]
bounds = [-100, -40, -30, -20, -10, -5, 0, 5, 10, 20, 30, 40, 100]
cmap = settings.COLOURMAP_DICT["hydrological"]
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"PEX_Rx1day_{}_anoms_era5".format(settings.YEAR),
cube,
cmap,
bounds,
"{} ({})".format(index, ETCCDI_UNITS[index]),
title="ERA5 {} - {}".format(index, ETCCDI_LABELS[index]))
# utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_PEX_Rx1day_{}_anoms_era5".format(settings.YEAR), cube, cmap, bounds, "{} ({})".format(index, ETCCDI_UNITS[index]), figtext="(i) Rx1day anomalies")
# cube = read_era5("prmax_for_{}_as_a_percentage_of_1981-2010_mean.txt".format(settings.YEAR))
# bounds = [0, 10, 25, 50, 75, 100, 150, 200, 250, 300, 1000]
# cmap = settings.COLOURMAP_DICT["hydrological"]
# utils.plot_smooth_map_iris(settings.IMAGELOC + "PEX_Rx1day_{}_anoms_percent_era5".format(settings.YEAR), cube, cmap, bounds, "Anomalies from 1981-2010 (%)", title="ERA5 - Rx1day %")
# utils.plot_smooth_map_iris(settings.IMAGELOC + "p2.1_PEX_Rx1day_{}_anoms_percent_era5".format(settings.YEAR), cube, cmap, bounds, "Anomalies from 1981-2010 (%)", figtext="(m) Rx1day anomalies")
#*************************
# DWD percentile
if True:
# dwd_cube = read_dwd_percentile(DATALOC + "GPCC_perzentile_{}.xyzras".format(settings.YEAR))
cube_list = iris.load(DATALOC + "Quantile_12month_{}01-{}12.nc".format(
settings.YEAR, settings.YEAR))
dwd_cube = cube_list[0][0]
bounds = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
cmap = settings.COLOURMAP_DICT["hydrological"]
utils.plot_smooth_map_iris(
settings.IMAGELOC + "PEX_{}_{}_dwd".format("R90", settings.YEAR),
dwd_cube,
cmap,
bounds,
"{} ({})".format("percentile", "%"),
title="Percentile of the annual precipitation total")
utils.plot_smooth_map_iris(
settings.IMAGELOC +
"p2.1_PEX_{}_{}_dwd".format("R90", settings.YEAR),
dwd_cube,
cmap,
bounds,
"{} ({})".format("percentile", "%"),
figtext="(l) Percentile of the Annual Precipitation Total")
#*************************
# DWD drought
if True:
# dwd_cube = read_dwd_percentile(DATALOC + "GPCC_perzentile_{}.xyzras".format(settings.YEAR))
cube_list = iris.load(DATALOC + "GPCC_DI_201912_12.nc")
dwd_cube = cube_list[0][0]
bounds = [-10, -5, -3, -2, -1, 0, 1, 2, 3, 5, 10]
cmap = settings.COLOURMAP_DICT["hydrological"]
utils.plot_smooth_map_iris(settings.IMAGELOC +
"PEX_{}_{}_dwd".format("DI", settings.YEAR),
dwd_cube,
cmap,
bounds,
"{} ({})".format("DI", "-"),
title="12 month Drought Index")
#*************************
# GHCND totals and ratios
if False:
import cartopy.feature as cfeature
NAMES = {"japan": "Japan", "neaus": "Australia", "hawaii": "Hawaii"}
EXTENTS = {
"japan": (132, 33, [126, 139, 30, 36]),
"neaus": (145, -17, [139, 151, -26, -8]),
"hawaii": (-158, 20, [-161, -154, 18.5, 22.5])
}
land_10m = cfeature.NaturalEarthFeature(
'physical',
'land',
'10m',
edgecolor='face',
facecolor=cfeature.COLORS['land'])
land_50m = cfeature.NaturalEarthFeature(
'physical',
'land',
'50m',
edgecolor='face',
facecolor=cfeature.COLORS['land'])
for index in ["Rx5day", "Rx1day"]:
for state in ["03-neaus", "07-japan", "08-hawaii"]:
print("{} - {}".format(state, index))
lats, lons, value_mm, prev_value_mm = read_ghcnd(
"{}/{}-{}-non0-sorted-{}.txt".format(
DATALOC, index, settings.YEAR, state))
ratio = value_mm / prev_value_mm
# set up figure
if "neaus" in state:
fig = plt.figure(figsize=(8, 5))
else:
fig = plt.figure(figsize=(8, 3.5))
if index == "Rx5day":
bounds = [0, 100, 200, 300, 400, 500, 600, 700, 800, 900]
elif index == "Rx1day":
bounds = [0, 50, 100, 150, 200, 250, 300, 350, 400, 450]
cmap = settings.COLOURMAP_DICT["precip_sequential"]
norm = mpl.cm.colors.BoundaryNorm(bounds, cmap.N)
# ratio_cmap = plt.cm.YlOrRd
# ratio_bounds = [1, 1.05, 1.1, 1.15, 1.2, 1.3, 1.5, 1.75, 2.0 ]
# ratio_cmap = settings.COLOURMAP_DICT["hydrological"]
# ratio_bounds = [0.01, 0.5, 0.75, 0.9, 0.95, 1.0, 1.05, 1.1, 1.25, 2.0, 10.0]
ratio_cmap = plt.cm.Blues
ratio_bounds = [
0.01, 0.5, 0.7, 0.9, 1.0, 1.1, 1.3, 1.5, 2.0, 10.0
]
ratio_norm = mpl.cm.colors.BoundaryNorm(
ratio_bounds, ratio_cmap.N)
plt.clf()
# set up axes
ax0 = plt.axes([0.01, 0.12, 0.45, 0.85],
projection=cartopy.crs.Stereographic(
central_longitude=EXTENTS[state[3:]][0],
central_latitude=EXTENTS[state[3:]][1]))
ax1 = plt.axes([0.51, 0.12, 0.45, 0.85],
projection=cartopy.crs.Stereographic(
central_longitude=EXTENTS[state[3:]][0],
central_latitude=EXTENTS[state[3:]][1]))
for ax in [ax0, ax1]:
ax.set_extent(EXTENTS[state[3:]][2],
cartopy.crs.PlateCarree())
states_provinces = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
ax.add_feature(states_provinces, edgecolor='gray')
ax.coastlines(resolution="10m", linewidth=0.5)
ax.add_feature(land_50m,
zorder=0,
facecolor="0.9",
edgecolor="k")
# add other features
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.BORDERS,
zorder=0,
facecolor="0.9",
edgecolor="k")
# plot
for ax, data, cm, bnds, nrm, label in zip(
[ax0, ax1], [value_mm, ratio], [cmap, ratio_cmap],
[bounds, ratio_bounds], [norm, ratio_norm],
["{} (mm)".format(index), "Ratio to previous record"]):
scatter = ax.scatter(lons, lats, c=data, cmap=cm, norm=nrm, s=50, \
transform=cartopy.crs.Geodetic(), edgecolor='0.5', linewidth=0.5)
# thicken border of colorbar and the dividers
# http://stackoverflow.com/questions/14477696/customizing-colorbar-border-color-on-matplotlib
if "Ratio" in label:
cb = fig.colorbar(scatter, ax=ax, orientation='horizontal', pad=0.05, fraction=0.05, \
aspect=30, ticks=bnds[1:-1], label=label, drawedges=True)
cb.set_ticklabels(
["{:g}".format(b) for b in bnds[1:-1]])
else:
cb = fig.colorbar(scatter, ax=ax, orientation='horizontal', pad=0.05, fraction=0.05, \
aspect=30, ticks=bnds[1:-1], label=label, drawedges=True)
cb.set_ticklabels(["{:g}".format(b) for b in bnds])
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
cb.ax.tick_params(axis='x',
labelsize=settings.FONTSIZE * 0.6,
direction='in')
if index == "Rx5day" and state[3:] == "hawaii":
ax0.text(0.05,
1.05,
"(a)",
transform=ax0.transAxes,
fontsize=settings.FONTSIZE * 0.8)
ax1.text(0.05,
1.05,
"(b)",
transform=ax1.transAxes,
fontsize=settings.FONTSIZE * 0.8)
elif index == "Rx1day" and state[3:] == "hawaii":
ax0.text(0.05,
1.05,
"(c)",
transform=ax0.transAxes,
fontsize=settings.FONTSIZE * 0.8)
ax1.text(0.05,
1.05,
"(d)",
transform=ax1.transAxes,
fontsize=settings.FONTSIZE * 0.8)
plt.savefig(
settings.IMAGELOC +
"PEX_{}_{}-{}".format(index, NAMES[state[3:]], state[:2]) +
settings.OUTFMT)
plt.close()
return # run_all_plots
EASING_1 = np.datetime64('2021-07-28')
LOCKDOWN_AGAIN = np.datetime64('2021-08-06')
CURFEW = np.datetime64('2021-08-16')
CONSTRUCTION_SHUTDOWN = np.datetime64('2021-09-21')
END_CONSTRUCTION_SHUTDOWN = CONSTRUCTION_SHUTDOWN + 14
PHASE_B = np.datetime64('2021-10-22')
PHASE_C = np.datetime64('2021-10-30')
PHASE_D = np.datetime64('2021-11-19')
MASKS_AGAIN = np.datetime64('2021-12-24')
DENSITY_LIMITS = np.datetime64('2022-01-07')
END_DENSITY_LIMITS = np.datetime64('2022-02-19')
END_MASKS = np.datetime64('2022-02-26')
fig1 = plt.figure(figsize=(10, 6))
ax1 = plt.axes()
ax1.fill_betweenx(
[-10, 10],
[PREV_LOCKDOWN, PREV_LOCKDOWN],
[PREV_EASING_1, PREV_EASING_1],
color=whiten("red", 0.35),
linewidth=0,
label="Lockdown",
)
ax1.fill_betweenx(
[-10, 10],
[PREV_EASING_1, PREV_EASING_1],
[PREV_EASING_2, PREV_EASING_2],
color=whiten("orange", 0.5),
def run(self):
""" runs the segment picker """
# show the images
self.fig, self.axes = plt.subplots(nrows=1,
ncols=2,
sharex=True,
sharey=True,
squeeze=True)
plt.subplots_adjust(bottom=0.2)
ax_img, ax_feat = self.axes
imshow_args = {
'interpolation': 'none',
'aspect': 1,
'cmap': plt.get_cmap('gray')
}
ax_img.imshow(self._frame, **imshow_args)
ax_img.set_title('First _frame of video')
ax_img.set_autoscale_on(False)
ax_feat.imshow(self.features, **imshow_args)
ax_feat.set_title('Automatic feature extraction')
ax_feat.set_autoscale_on(False)
# internal data
self.active = True
self.result = 'cancel'
self.bounds = self._frame.shape
self._ax_segments = [[] for _ in xrange(len(self.axes))]
# initialize data
if self.segments:
segments = self.segments
self.segments = []
for segment in segments:
self._add_segment(segment)
# drawtype is 'box' or 'line' or 'none'
useblit = backend.supports_blitting()
self.selectors = [
widgets.RectangleSelector(
ax,
self.select_callback,
drawtype='line',
lineprops=self.lineprops,
useblit=useblit,
button=[1], # don't use middle button
minspanx=5,
minspany=5,
spancoords='pixels') for ax in (ax_img, ax_feat)
]
# add buttons
ax_active = plt.axes([0.5, 0.05, 0.1, 0.075])
check = widgets.CheckButtons(ax_active, ['active'], [self.active])
check.on_clicked(self.clicked_check)
ax_ok = plt.axes([0.7, 0.05, 0.1, 0.075])
bn_ok = widgets.Button(ax_ok, 'Ok')
bn_ok.on_clicked(self.clicked_ok)
ax_cancel = plt.axes([0.81, 0.05, 0.1, 0.075])
bp_cancel = widgets.Button(ax_cancel, 'Cancel')
bp_cancel.on_clicked(self.clicked_cancel)
self.msg()
# initialize the interaction with the image
self.fig.canvas.mpl_connect('button_press_event', self.click_image)
# process result
plt.show()
return self.result
except:
raise
import networkx as nx
z=nx.create_degree_sequence(400,nx.utils.powerlaw_sequence,exponent=2.1)
nx.is_valid_degree_sequence(z)
print "Configuration model"
G=nx.configuration_model(z) # configuration model
degree_sequence=sorted(nx.degree(G),reverse=True) # degree sequence
#print "Degree sequence", degree_sequence
dmax=max(degree_sequence)
plt.loglog(degree_sequence,'b-',marker='o')
plt.title("Degree rank plot")
plt.ylabel("degree")
plt.xlabel("rank")
# draw graph in inset
plt.axes([0.45,0.45,0.45,0.45])
Gcc=nx.connected_component_subgraphs(G)[0]
pos=nx.spring_layout(Gcc)
plt.axis('off')
nx.draw_networkx_nodes(Gcc,pos,node_size=20)
nx.draw_networkx_edges(Gcc,pos,alpha=0.4)
plt.show()
import numpy as np import matplotlib.pyplot as plt def f(x,y): return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2) n = 10 x = np.linspace(-3,3,4*n) y = np.linspace(-3,3,3*n) X,Y = np.meshgrid(x,y) Z = f(X, Y) plt.axes([0.025, 0.025, 0.95, 0.95]) plt.imshow(Z, interpolation='bicubic', cmap='bone', origin='lower') plt.colorbar(shrink=.8) plt.xticks([]), plt.yticks([]) plt.show()
def plot_rank_map(outname,
cube,
cmap,
bounds,
cb_label,
scatter=[],
figtext="",
title=""):
'''
Standard scatter map
:param str outname: output filename root
:param array cube: cube to plot
:param obj cmap: colourmap to use
:param array bounds: bounds for discrete colormap
:param str cb_label: colorbar label
'''
norm = mpl.cm.colors.BoundaryNorm(bounds, cmap.N)
fig = plt.figure(figsize=(8, 5.5))
plt.clf()
ax = plt.axes([0.01, 0.12, 0.98, 0.88], projection=cartopy.crs.Robinson())
ax.gridlines() #draw_labels=True)
ax.add_feature(cartopy.feature.LAND,
zorder=0,
facecolor="0.9",
edgecolor="k")
ax.coastlines()
ext = ax.get_extent() # save the original extent
mesh = iris.plot.pcolormesh(cube, cmap=cmap, norm=norm)
if len(scatter) > 0:
lons, lats, data = scatter
plt.scatter(lons, lats, c=data, cmap=cmap, norm=norm, s=25, \
transform=cartopy.crs.Geodetic(), edgecolor='0.5', linewidth=0.5)
cb = plt.colorbar(mesh, orientation='horizontal', pad=0.05, fraction=0.05, \
aspect=30, ticks=bounds[1:-1], drawedges=True)
# thicken border of colorbar and the dividers
# http://stackoverflow.com/questions/14477696/customizing-colorbar-border-color-on-matplotlib
cb.set_ticklabels(["{:g}".format(b) for b in bounds[1:-1]])
cb.ax.tick_params(axis='x',
labelsize=settings.FONTSIZE,
direction='in',
size=0)
cb.set_label(label=cb_label, fontsize=settings.FONTSIZE)
# cb.outline.set_color('k')
cb.outline.set_linewidth(2)
cb.dividers.set_color('k')
cb.dividers.set_linewidth(2)
# label colorbar with sensibly placed and named labels
cb.ax.get_xaxis().set_ticks([])
cb.ax.get_xaxis().set_ticklabels([""])
for j, lab in enumerate([
'lowest', '2nd lowest', '3rd lowest', '', '3rd highest',
'2nd highest', 'highest'
]):
cb.ax.text((2 * j + 1) / 14.0,
-0.5,
lab,
ha='center',
va='center',
fontsize=settings.FONTSIZE * 0.8)
ax.set_extent(ext, ax.projection) # fix the extent change from colormesh
plt.title(title)
fig.text(0.03, 0.95, figtext, fontsize=settings.FONTSIZE)
plt.savefig(outname + settings.OUTFMT)
plt.close()
return # plot_rank_map_iris
import serial
from matplotlib import pyplot as plt
from matplotlib import animation
import numpy as np
import time
arduino = serial.Serial('COM3', 9600)
time.sleep(1)
fig = plt.figure()
ax = plt.axes(xlim=(0, 10), ylim=(24, 26))
max_points = 10
# fill initial artist with nans (so nothing draws)
line, = ax.plot(np.arange(max_points),
np.ones(max_points, dtype=np.float) * np.nan,
lw=2)
def init():
return line,
def animate(i):
rawString = arduino.readline()
y = float(rawString.decode()) # I assume this
y = (1.1 * y * 100) / 1024
print(y)
old_y = line.get_ydata() # grab current data
fig = plt.figure()
nframes = predict.shape[0]
predict = np.reshape(predict, [nframes, -1, 3])
scats = []
lns = []
for j in frames:
joint_xyz = np.squeeze(predict[j, :, :])
x_data = joint_xyz[:, 0]
y_data = joint_xyz[:, 1]
z_data = joint_xyz[:, 2]
ax = plt.axes(xlim=(xmin, xmax),
ylim=(ymin, ymax),
zlim=(zmin, zmax),
projection='3d')
ax.set_axis_off()
ax.view_init(azim=10, elev=60)
plt.gca().set_aspect('equal', adjustable='box')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.set_zticklabels([])
lns.append(
ax.plot3D(x_data[chain[:]],
y_data[chain[:]],
