def test_radar_view():
lats=linspace(-13.0, -11.5, 40)
lons=linspace(130., 132., 40)
ber_loc=[-12.4, 130.85] #location of Berrimah radar
gp_loc=[-12.2492, 131.0444]#location of CPOL at Gunn Point
h=2.5*1000.0
t0=systime()
i_a, j_a, k_a=propigation.unit_vector_grid(lats, lons, h, gp_loc)
print "unit_vector_compute took ", systime()-t0, " seconds"
fw=0.1#degrees
m_bump=5.0
#b1=simul_winds.speed_bump(lats, lons, [-12.0, 131.0], fw)*m_bump
#b2=-1.0*simul_winds.speed_bump(lats, lons, [-12.0, 131.1], fw)*m_bump
u,v=simul_winds.unif_wind(lats, lons, 5.0, 75.0)
up,vp=array(simul_winds.vortex(lats, lons, [-12.5, 131.1], fw))*m_bump
#up=u+(b1-b2)
#vp=v-(b1-b2)
w=v*0.0
v_r=i_a*(up+u)+j_a*(vp+v)+k_a*w
f=figure()
mapobj=pres.generate_darwin_plot()
pres.contour_vr(mapobj,lats, lons, v_r)
savefig(os.getenv('HOME')+'/bom_mds/output/test_radar_view_gp.png')
close(f)
f=figure()
mapobj=pres.generate_darwin_plot()
pres.quiver_contour_winds(mapobj, lats, lons, (up+u),(vp+v))
savefig(os.getenv('HOME')+'/bom_mds/output/test_pert2.png')
close(f)
def test():
figure(figsize=(6,12))
t = linspace(0, 1, 1001)
y = sin(2*pi*t*6) + sin(2*pi*t*10) + sin(2*pi*t*13)
subplot(311)
plot(t, y, 'b-')
xlabel("TIME (sec)")
ylabel("SIGNAL MAGNITUDE")
# compute FFT and plot the magnitude spectrum
F = fft(y)
N = len(t) # number of samples
dt = 0.001 # inter-sample time difference
w = fftfreq(N, dt) # gives us a list of frequencies for the FFT
ipos = where(w>0)
freqs = w[ipos] # only look at positive frequencies
mags = abs(F[ipos]) # magnitude spectrum
subplot(312)
plot(freqs, mags, 'b-')
ylabel("POWER")
subplot(313)
plot(freqs, mags, 'b-')
xlim([0, 50]) # replot but zoom in on freqs 0-50 Hz
ylabel("POWER")
xlabel("FREQUENCY (Hz)")
savefig("signal_3freqs.jpg", dpi=150)
def show_dct_fig():
figure(figsize=(12,7))
for u in range(8):
subplot(2, 4, u+1)
ylim((-1, 1))
title(str(u))
plot(arange(0,8,0.1), dct0[u, :])
plot(dct[u, :],'ro')
def test_gracon():
#setup
noise_level=0.0#m/s
nx=40
ny=40
fw=0.1
m_bump=10.00
t0=systime()
lats=linspace(-13.5, -12.0, 40)
lons=linspace(130.5, 131.5, 40)
ber_loc=[-12.4, 130.85] #location of Berrimah radar
gp_loc=[-12.2492, 131.0444]#location of CPOL at Gunn Point
h=2.5*1000.0
print 'calculating berimah UV', systime()-t0
i_ber, j_ber, k_ber=propigation.unit_vector_grid(lats, lons, h, ber_loc)
print 'calculating gp UV', systime()-t0
i_gp, j_gp, k_gp=propigation.unit_vector_grid(lats, lons, h, gp_loc)
#make winds
u,v=simul_winds.unif_wind(lats, lons, 10.0, 75.0)
up,vp=array(simul_winds.vortex(lats, lons, [-12.5, 131.1], fw))*m_bump
#make V_r measurements
vr_ber=i_ber*(up+u)+j_ber*(vp+v) + (random.random([nx,ny])-0.5)*(noise_level*2.0)
vr_gp=i_gp*(up+u)+j_gp*(vp+v)+ (random.random([nx,ny])-0.5)*(noise_level*2.0)
#try to reconstruct the wind field
igu, igv= simul_winds.unif_wind(lats, lons, 0.0, 90.0)
gv_u=zeros(u.shape)
gv_v=zeros(v.shape)
f=0.0
print igu.mean()
angs=array(propigation.make_lobe_grid(ber_loc, gp_loc, lats,lons))
wts=zeros(angs.shape, dtype=float)+1.0
#for i in range(angs.shape[0]):
# for j in range(angs.shape[1]):
# if (angs[i,j] < 150.0) and (angs[i,j] > 30.0): wts[i,j]=1.0
print 'Into fortran'
gv_u,gv_v,f,u_array,v_array = gracon_vel2d.gracon_vel2d(gv_u,gv_v,f,igu,igv,i_ber,j_ber,i_gp,j_gp,vr_ber,vr_gp,wts, nx=nx,ny=ny)
print u_array.mean()
print f
bnds=[0.,20.]
f=figure()
mapobj=pres.generate_darwin_plot()
pres.quiver_contour_winds(mapobj, lats, lons, (up+u),(vp+v), bounds=bnds)
savefig(os.getenv('HOME')+'/bom_mds/output/orig_winds_clean.png')
close(f)
f=figure()
mapobj=pres.generate_darwin_plot()
pres.quiver_contour_winds(mapobj, lats, lons, (wts*u_array +0.001),(wts*v_array +0.001), bounds=bnds)
savefig(os.getenv('HOME')+'/bom_mds/output/recon_winds_clean.png')
close(f)
f=figure()
mapobj=pres.generate_darwin_plot()
pres.quiver_contour_winds(mapobj, lats, lons, (wts*u_array - (up+u)),(wts*v_array -(vp+v)))
savefig(os.getenv('HOME')+'/bom_mds/output/errors_clean.png')
close(f)
def generate_plot(array, vmin, vmax, figNumber=1):
plt.figure(figNumber)
plt.subplot(2,3,i)
print i
plt.imshow(array, vmin = vmin, vmax= vmax, interpolation = None)
plt.xlabel('Sample')
plt.ylabel('Line')
plt.title(row[0])
cb = plt.colorbar(orientation='hor', spacing='prop',ticks = [vmin,vmax],format = '%.2f')
cb.set_label('Reflectance / cos({0:.2f})'.format(incAnglerad*180.0/math.pi))
plt.grid(True)
def plot_f_score(self, disag_filename):
plt.figure()
from nilmtk.metrics import f1_score
disag = DataSet(disag_filename)
disag_elec = disag.buildings[building].elec
f1 = f1_score(disag_elec, test_elec)
f1.index = disag_elec.get_labels(f1.index)
f1.plot(kind='barh')
plt.ylabel('appliance');
plt.xlabel('f-score');
plt.title(type(self.model).__name__);
def plotSolutions(x,states=None): #Default func values is trivial
plt.figure(figsize=(11,8.5))
#get the exact values
f = open('exact_results.txt', 'r')
x_e = []
u_e = []
p_e = []
rho_e = []
e_e = []
for line in f:
if len(line.split())==1:
t = line.split()
else:
data = line.split()
x_e.append(float(data[0]))
u_e.append(float(data[1]))
p_e.append(float(data[2]))
rho_e.append(float(data[4]))
e_e.append(float(data[3]))
if states==None:
raise ValueError("Need to pass in states")
else:
u = []
p = []
rho = []
e = []
for i in states:
u.append(i.u)
p.append(i.p)
rho.append(i.rho)
e.append(i.e)
#get edge values
x_cent = [0.5*(x[i]+x[i+1]) for i in range(len(x)-1)]
if u != None:
plot2D(x_cent,u,"$u$",x_ex=x_e,y_ex=u_e)
if rho != None:
plot2D(x_cent,rho,r"$\rho$",x_ex=x_e,y_ex=rho_e)
if p != None:
plot2D(x_cent,p,r"$p$",x_ex=x_e,y_ex=p_e)
if e != None:
plot2D(x_cent,e,r"$e$",x_ex=x_e,y_ex=e_e)
plt.show(block=False) #show all plots generated to this point
raw_input("Press anything to continue...")
plot2D.fig_num=0
def plot(self, output):
plt.figure(figsize=output.fsize, dpi=output.dpi)
for ii in range(0, len(self.v)):
imsize = [self.t[0], self.t[-1], self.x[ii][-1], self.x[ii][0]]
lim = amax(absolute(self.v[ii])) / output.scale_sat
plt.imshow(self.v[ii], extent=imsize, vmin=-lim, vmax=lim, cmap=cm.gray, origin='upper', aspect='auto')
plt.title("%s-Velocity for Trace #%i" % (self.comp.upper(), ii))
plt.xlabel('Time (s)')
plt.ylabel('Offset (km)')
#plt.colorbar()
plt.savefig("Trace_%i_v%s.pdf" % (ii, self.comp))
plt.clf()
def compress_kmeans(im, k=4):
height, width, depth = im.shape
data = im.reshape((height * width, depth))
labels, centers = kmeans(data, k, 1e-2)
rep = closest(data, centers)
data_compressed = centers[rep]
im_compressed = data_compressed.reshape((height, width, depth))
plt.figure()
plt.imshow(im_compressed)
plt.show()
def plotCentroidFitDiagnostic(img, hdr, ccdMod, ccdOut, res, prfObj):
"""Some diagnostic plots showing the performance of fitPrfCentroid()
Inputs:
-------------
img
(np 2d array) Image of star to be fit. Image is in the
format img[row, col]. img should not contain Nans
hdr
(Fits header object) header associated with the TPF file the
image was drawn from
ccdMod, ccdOut
(int) CCD module and output of image. Needed to
create the correct PRF model
prfObj
An object of the class prf.KeplerPrf()
Returns:
-------------
**None**
Output:
----------
A three panel subplot is created
"""
mp.figure(1)
mp.clf()
mp.subplot(131)
plotTpf.plotCadence(img, hdr)
mp.colorbar()
mp.title("Input Image")
mp.subplot(132)
c,r = res.x[0], res.x[1]
bbox = getBoundingBoxForImage(img, hdr)
model = prfObj.getPrfForBbox(ccdMod, ccdOut, c, r, bbox)
model *= res.x[2]
plotTpf.plotCadence(model, hdr)
mp.colorbar()
mp.title("Best fit model")
mp.subplot(133)
diff = img-model
plotTpf.plotCadence(diff, hdr)
mp.colorbar()
mp.title("Residuals")
print "Performance %.3f" %(np.max(np.abs(diff))/np.max(img))
def plot_confusion_matrix(cm, labels, title='Confusion matrix', cmap=plt.cm.Blues, save=False):
plt.figure()
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(labels))
plt.xticks(tick_marks, labels, rotation=45)
plt.yticks(tick_marks, labels)
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()
if save:
plt.savefig(save)
def get_trajectories_for_DF(DF):
GetXYS=ct.get_xys(DF)
xys_s=ct.get_xys_s(GetXYS['xys'],GetXYS['Nmin'])
plt.figure(figsize=(5, 5),frameon=False)
for m in list(range(9)):
plt.plot()
plt.subplot(3,3,m+1)
xys_s_x_n=xys_s[m]['X']-min(xys_s[m]['X'])
xys_s_y_n=xys_s[m]['Y']-min(xys_s[m]['Y'])
plt.plot(xys_s_x_n,xys_s_y_n)
plt.axis('off')
axes = plt.gca()
axes.set_ylim([0,125])
axes.set_xlim([0,125])
def plotRetinaSpikes(retina=None, label=""):
assert retina is not None, "Network is not initialised! Visualising failed."
import matplotlib.pyplot as plt
from matplotlib import animation
print "Visualising {0} Spikes...".format(label)
spikes = [x.getSpikes() for x in retina]
# print spikes
sortedSpikes = sortSpikes(spikes)
# print sortedSpikes
framesOfSpikes = generateFrames(sortedSpikes)
# print framesOfSpikes
x = range(0, dimensionRetinaX)
y = range(0, dimensionRetinaY)
from numpy import meshgrid
rows, pixels = meshgrid(x,y)
fig = plt.figure()
initialData = createInitialisingData()
imNet = plt.imshow(initialData, cmap='green', interpolation='none', origin='upper')
plt.xticks(range(0, dimensionRetinaX))
plt.yticks(range(0, dimensionRetinaY))
args = (framesOfSpikes, imNet)
anim = animation.FuncAnimation(fig, animate, fargs=args, frames=int(simulationTime)*10, interval=30)
plt.show()
def get_trajectories_singleAxis_for_DF(DF):
from random import randint
sns.set_palette(sns.color_palette("Paired"))
fig = plt.figure(figsize=(5, 5),frameon=False)
ax=fig.add_subplot(1, 1, 1)
ax.spines['left'].set_position('zero')
ax.spines['right'].set_color('none')
ax.spines['bottom'].set_position('zero')
ax.spines['top'].set_color('none')
#ax.spines['left'].set_smart_bounds(True)
#ax.spines['bottom'].set_smart_bounds(True)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.set_ylim([-300,300])
ax.set_xlim([-300,300])
ticklab = ax.xaxis.get_ticklabels()[0]
ax.xaxis.set_label_coords(300, -40,transform=ticklab.get_transform())
ax.set_xlabel('x($\mu$m)',fontsize=14)
ticklab = ax.yaxis.get_ticklabels()[0]
ax.yaxis.set_label_coords(90, 280,transform=ticklab.get_transform())
ax.set_ylabel('y($\mu$m)',rotation=0,fontsize=14)
GetXYS=ct.get_xys(DF)
xys_s=ct.get_xys_s(GetXYS['xys'],GetXYS['Nmin'])
for n in list(range(12)):
m=randint(0,len(xys_s))-1
xys_s_x_n=xys_s[m]['X']-(xys_s[m]['X'][xys_s[m]['X'].index[0]])
xys_s_y_n=xys_s[m]['Y']-(xys_s[m]['Y'][xys_s[m]['X'].index[0]])
xys_s_x_n=[x*(100/(60.)) for x in xys_s_x_n]
xys_s_y_n=[x*(100/(60.)) for x in xys_s_y_n]
ax.plot(xys_s_x_n,xys_s_y_n)
return fig
def plot_images(header):
''' function to plot images from header.
It plots images, return nothing
Parameters
----------
header : databroker header object
header pulled out from central file system
'''
# prepare header
if type(list(headers)[1]) == str:
header_list = list()
header_list.append(headers)
else:
header_list = headers
for header in header_list:
uid = header.start.uid
img_field = _identify_image_field(header)
imgs = np.array(get_images(header, img_field))
print('Plotting your data now...')
for i in range(imgs.shape[0]):
img = imgs[i]
plot_title = '_'.join(uid, str(i))
# just display user uid and index of this image
try:
fig = plt.figure(plot_title)
plt.imshow(img)
plt.show()
except:
pass # allow matplotlib to crash without stopping other function
def showScatterPlot(data, labels, idx1, idx2):
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
# X-axis data, Y-axis data, Size for each sample, Color for each sample
ax.scatter(data[:,idx1], data[:,idx2], 100.0*(1 + np.array(labels)), 100.0*(1 + np.array(labels)))
plt.show()
def plotColorCodedNetworkSpikes(network):
assert network is not None, "Network is not initialised! Visualising failed."
import matplotlib as plt
from NetworkBuilder import sameDisparityInd
cellsOutSortedByDisp = []
spikes = []
for disp in range(0, maxDisparity+1):
cellsOutSortedByDisp.append([network[x][2] for x in sameDisparityInd[disp]])
spikes.append([x.getSpikes() for x in cellsOutSortedByDisp[disp]])
sortedSpikes = sortSpikesByColor(spikes)
print sortedSpikes
framesOfSpikes = generateColoredFrames(sortedSpikes)
print framesOfSpikes
fig = plt.figure()
initialData = createInitialisingDataColoredPlot()
imNet = plt.imshow(initialData[0], c=initialData[1], cmap=plt.cm.coolwarm, interpolation='none', origin='upper')
plt.xticks(range(0, dimensionRetinaX))
plt.yticks(range(0, dimensionRetinaY))
plt.title("Disparity Map {0}".format(disparity))
args = (framesOfSpikes, imNet)
anim = animation.FuncAnimation(fig, animate, fargs=args, frames=int(simulationTime)*10, interval=30)
plt.show()
def showScatterPlot(data, idx1, idx2):
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
# X-axis data, Y-axis data, Size for each sample, Color for each sample
ax.scatter(data[:,idx1], data[:,idx2])
plt.show()
def simple_reconstruction_3d_pytest(tim, lvl_str, use_guess):
lvl=int(lvl_str)
ber, gp=netcdf_utis.load_cube('/bm/gdata/scollis/cube_data/20060122_'+tim+'_ver_hr_big.nc')
print gp['levs'][lvl]
Re=6371.0*1000.0
rad_at_radar=Re*sin(pi/2.0 -abs(gp['zero_loc'][0]*pi/180.0))#ax_radius(float(lat_cpol), units='degrees')
lons=gp['zero_loc'][1]+360.0*gp['xar']/(rad_at_radar*2.0*pi)
lats=gp['zero_loc'][0] + 360.0*gp['yar']/(Re*2.0*pi)
ber_loc=[-12.457, 130.925]
gp_loc= [-12.2492, 131.0444]
if use_guess=='none':
igu=ones(ber['CZ'].shape, dtype=float)*0.0
igv=ones(ber['CZ'].shape, dtype=float)*0.0
else:
ber_ig, gp_ig=netcdf_utis.load_cube(use_guess)
print gp_ig.keys()
igu=gp_ig['u_array']
igv=gp_ig['v_array']
mywts=ones(ber['CZ'].shape, dtype=float)
angs=array(propigation.make_lobe_grid(ber_loc, gp_loc, lats,lons))
wts_ang=zeros(gp['CZ'][:,:,0].shape, dtype=float)
for i in range(angs.shape[0]):
for j in range(angs.shape[1]):
if (angs[i,j] < 150.0) and (angs[i,j] > 30.0): wts_ang[i,j]=1.0
for lvl_num in range(len(gp['levs'])):
#create a weighting grid
mask_reflect=10.0#dBZ
mask=(gp['CZ'][:,:,lvl_num]/mask_reflect).round().clip(min=0., max=1.0)
mask_vel_ber=(ber['VR'][:,:,lvl_num]+100.).clip(min=0., max=1.)
mywts[:,:,lvl_num]=mask*mask_vel_ber*wts_ang
f=0.0
gv_u=zeros(ber['CZ'].shape, dtype=float)
gv_v=zeros(ber['CZ'].shape, dtype=float)
wts=mask*mask_vel_ber*wts_ang
gu,gv,f= grad_conj_solver_plus_plus.meas_cost(gv_u, gv_v, f, igu, igv, ber['i_comp'], ber['j_comp'], gp['i_comp'], gp['j_comp'], ber['VR'], gp['VR'], mywts)
print "Mean U gradient", gu.mean(), "gv mean", gv.mean(), "F ", f
for i in range(len(gp['levs'])):
print "U,V ", (igu[:,:,i]).sum()/mywts[:,:,i].sum(), (igv[:,:,i]).sum()/mywts[:,:,i].sum()
#gv_u,gv_v,cost = vel_2d_cost(gv_u,gv_v,cost,u_array,v_array,i_cmpt_r1,j_cmpt_r1,i_cmpt_r2,j_cmpt_r2,vr1,vr2,weights,nx=shape(gv_u,0),ny=shape(gv_u,1))
print gracon_vel2d.vel_2d_cost(gv_u[:,:,i]*0.0,gv_v[:,:,i]*0.0,0.0,igu[:,:,i],igv[:,:,i],ber['i_comp'][:,:,i], ber['j_comp'][:,:,i], gp['i_comp'][:,:,i], gp['j_comp'][:,:,i], ber['VR'][:,:,i], gp['VR'][:,:,i],mywts[:,:,i])[2]
gv_u,gv_v,f,u_array,v_array = grad_conj_solver_plus_plus.gracon_vel2d_3d( gv_u, gv_v, f, igu, igv, ber['i_comp'], ber['j_comp'], gp['i_comp'], gp['j_comp'], ber['VR'], gp['VR'], mywts)#, nx=nx, ny=ny, nz=nz)
gp.update({'u_array': u_array, 'v_array':v_array})
netcdf_utis.save_data_cube(ber, gp, '/bm/gdata/scollis/cube_data/20060122_'+tim+'_winds_ver1.nc', gp['zero_loc'])
plotit=True
if plotit:
for lvl in range(len(gp['levs'])):
print lvl
f=figure()
mapobj=pres.generate_darwin_plot(box=[130.8, 131.2, -12.4, -12.0])
diff=gp['VR']-(u_array*gp['i_comp']+ v_array*gp['j_comp'])
gp.update({'diff':diff})
pres.reconstruction_plot(mapobj, lats, lons, gp, lvl, 'diff',u_array[:,:,lvl],v_array[:,:,lvl], angs, mywts[:,:,lvl])
#pres.quiver_contour_winds(mapobj, lats, lons, (wts*u_array).clip(min=-50, max=50),(wts*v_array).clip(min=-50, max=50))
t1='Gunn Point CAPPI (dBZ) and reconstructed winds (m/s) at %(lev)05dm \n 22/01/06 ' %{'lev':gp['levs'][lvl]}
title(t1+tim)
ff=os.getenv('HOME')+'/bom_mds/output/recons_22012006/real_%(lev)05d_' %{'lev':gp['levs'][lvl]}
savefig(ff+tim+'_2d_3d.png')
close(f)
def plot_roc_curve(fpr, tpr, roc_auc, title='ROC curve'):
plt.figure()
lw = 2
colors = cycle(['darkorange', 'blue', 'red', 'green', 'black', 'purple',
'yellow'])
for i, color in zip(range(len(fpr), colors)):
plt.plot(fpr[i], tpr[i], color=color, lw=lw,
label='ROC area {0:.4f}'.format(roc_auc[i]))
# plt.plot(fpr, tpr, color='darkorange', lw=lw,
# label='ROC area under curve = {}'.format(roc_auc))
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.title(title)
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.legend(loc='lower right')
plt.show()
def draw_bar(savename):
import matplotlib.pyplot as plt
size = 3
x = np.arange(size)
a = [34.89, 33.87, 72.37]
b = [551.72, 552.87, 698.34]
xl = ['training time \n on MNIST', 'training time on \n Fashion-MNIST', 'training time \n on CIFAR-10']
total_width, n = 0.54, 2
width = total_width / n
x = x - (total_width - width)
plt.figure()
plt.bar(x, a, width=width)
plt.bar(x + width, b, width=width)
plt.ylabel('time(s)')
plt.xticks(x, xl,)
plt.legend()
plt.savefig('../eps/' + savename)
plt.show()
def sh2(data):
dpi = 80.0
xpixels, ypixels = data.shape[::-1]
margin = 0.05
figsize = (1 + margin) * ypixels / dpi, (1 + margin) * xpixels / dpi
fig = plt.figure(figsize=figsize, dpi=dpi)
ax = fig.add_axes([margin, margin, 1 - 2 * margin, 1 - 2 * margin])
ax.imshow(data, interpolation='none')
plt.show()
def plot_df(
df1, plot_title, x_axis, y_axis, plot, save
): # function for plotting high-dimension and low-dimension eigenvalues
y1 = df1[df1.columns[1]]
x1 = df1[df1.columns[0]]
plt.figure(figsize=(8, 8))
plt.plot(x1, y1, color='red')
plt.grid(color='black', linestyle='-',
linewidth=0.1) # parameters for plot grid
plt.title(plot_title).set_position([0.5, 1.05])
plt.xlabel(x_axis)
plt.ylabel(y_axis)
plt.legend(loc='best') # creating legend and placing in at the top right
if save == 'yes':
plt.savefig(plot_title)
if plot == 'yes':
plt.show()
plt.close()
def plot_probability_distributions(pmfs, lcolors, tam=[15, 7]):
fig = plt.figure(figsize=tam)
ax = fig.add_subplot(111)
for k, m in enumerate(pmfs, start=0):
m.plot(ax, color=lcolors[k])
handles0, labels0 = ax.get_legend_handles_labels()
ax.legend(handles0, labels0)
def test_uniform_winds():
f=figure()
lats=linspace(-13.0, -11.5, 20)
lons=linspace(130., 132., 20)
u,v=simul_winds.unif_wind(lats, lons, 10.0, 45.0)
mapobj=pres.generate_darwin_plot()
pres.quiver_winds(mapobj, lats, lons, u,v)
savefig(os.getenv('HOME')+'/bom_mds/output/test_uniform.png')
close(f)
def plot(tf_confidence, num_groundtruthbox):
"""
从上到下截取tf_confidence, 计算并画图
:param tf_confidence: np.array, 存放所有检测框对应的tp或fp和置信度
:param num_groundtruthbox: int, 标注框的总数
"""
fp_list = []
recall_list = []
precision_list = []
auc = 0
mAP = 0
for num in range(len(tf_confidence)):
arr = tf_confidence[:(num + 1), 0] # 截取, 注意要加1
tp = np.sum(arr)
fp = np.sum(arr == 0)
recall = tp / num_groundtruthbox
precision = tp / (tp + fp)
auc = auc + recall
mAP = mAP + precision
fp_list.append(fp)
recall_list.append(recall)
precision_list.append(precision)
auc = auc / len(fp_list)
mAP = mAP * max(recall_list) / len(recall_list)
plt.figure()
plt.title('ROC')
plt.xlabel('False Positives')
plt.ylabel('True Positive rate')
plt.ylim(0, 1)
plt.plot(fp_list, recall_list, label='AUC: ' + str(auc))
plt.legend()
plt.figure()
plt.title('Precision-Recall')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.axis([0, 1, 0, 1])
plt.plot(recall_list, precision_list, label='mAP: ' + str(mAP))
plt.legend()
plt.show()
def log_transform(data, columns, plot=True, figsize=(12, 6)):
'''
Nomralizes the dataset to be as close to the gaussian distribution.
Parameter:
-----------------------------------------
data: DataFrame, Series.
Data to Log transform.
columns: List, Series
Columns to be transformed to normality using log transformation
plot: bool, default True
Plots a before and after log transformation plot
Returns:
Log-transformed dataframe
'''
if data is None:
raise ValueError(
"data: Expecting a DataFrame/ numpy2d array, got 'None'")
if columns is None:
raise ValueError(
"columns: Expecting at least a column in the list of columns but got 'None'"
)
df = data.copy()
for col in columns:
df[col] = np.log1p(df[col])
if plot:
for col in columns:
_ = plt.figure(figsize=figsize)
plt.subplot(1, 2, 1)
sns.distplot(data[col],
color="m",
label="Skewness : %.2f" % (df[col].skew()))
plt.title('Distribution of ' + col + " before Log transformation")
plt.legend(loc='best')
plt.subplot(1, 2, 2)
sns.distplot(df[col],
color="m",
label="Skewness : %.2f" % (df[col].skew()))
plt.title('Distribution of ' + col + " after Log transformation")
plt.legend(loc='best')
plt.tight_layout(2)
plt.show()
return df
def plottrace(self, point):
# 使用matplotlib之pyplot绘制船舶轨迹
# point = 38
def initial(ax):
ax.axis("equal") #设置图像显示的时候XY轴比例
ax.set_xlabel('Horizontal Position')
ax.set_ylabel('Vertical Position')
ax.set_title('Vessel trajectory')
plt.grid(True) #添加网格
return ax
es_time = np.zeros([point])
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax = initial(ax)
# test
ax2 = fig.add_subplot(1, 1, 1)
ax2 = initial(ax2)
plt.ion() #interactive mode on 动态绘制
# IniObsX=0000
# IniObsY=4000
# IniObsAngle=135
# IniObsSpeed=10*math.sqrt(2) #米/秒
# print('开始仿真')
obsX = []
obsX2 = []
# obsY = [4000,]
obsY = []
obsY2 = []
for t in range(point):
# t0 = time.time()
#障碍物船只轨迹
# obsX.append(IniObsX+IniObsSpeed*math.sin(IniObsAngle/180*math.pi)*t)
obsX.append(sim_res.SHIP1POS[t][0])
obsX2.append(sim_res.SHIP2POS[t][0])
# obsY.append(IniObsY+IniObsSpeed*math.cos(IniObsAngle/180*math.pi)*t)
obsY.append(sim_res.SHIP1POS[t][1])
obsY2.append(sim_res.SHIP2POS[t][1])
plt.cla()
ax = initial(ax)
ax.plot(obsX, obsY, '-g', marker='*') #散点图
# test
ax2 = initial(ax2)
ax2.plot(obsX2, obsY2, '-r', marker='o')
risk_value_text = 'Risk value: ' + str(
sim_res.RISKVALUE[t])
plt.text(0, 7, risk_value_text)
plt.pause(0.5)
# es_time[t] = 1000*(time.time() - t0)
plt.pause(0)
# return es_time
pass
def save_image(image, filename):
fig = plt.figure()
fig.set_size_inches(image.shape)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.imshow(image, cmap='gray')
if DEBUG_enable_write:
plt.savefig(filename)
def plotNNFilter(units):
filters = 3
fig = plt.figure(1, figsize=(20, 20))
n_columns = 6
n_rows = math.ceil(filters / n_columns) + 1
for i in range(filters):
plt.subplot(n_rows, n_columns, i + 1)
plt.title('Filter ' + str(i))
plt.imshow(units[0, :, :, i], interpolation="nearest", cmap="gray")
plt.savefig('/Users/raghav/Documents/Uni/oc-nn/models/representation_sigmoid_dog.png')
def plot_roc(y_true: List[str], y_score: List[float]) -> None:
"""Plot a ROC curve for these results"""
fpr, tpr, threshold = roc_curve(y_true, y_score, pos_label="target")
fnr = 1 - tpr
EER = fpr[np.nanargmin(np.absolute((fnr - fpr)))]
print("EER: {}%".format(EER * 100))
plt.figure()
lw = 2
plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve')
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.show()
def drawTKY():
"""
Function plots 10 most popular Venue Categories in Tokyo.
"""
#Import dataset of New York from root directory
TKY = pd.read_csv('dataset_TSMC2014_TKY.csv')
TKY=TKY[["venueCategory","venueCategoryId"]]
grouped2=TKY.groupby(["venueCategory"]).count()
grouped2=grouped2.sort_values('venueCategoryId')
grouped2=grouped2[237:247]
#Plot bars of most popular venue categories
plt.figure(figsize=(16,6))
plt.style.use('fivethirtyeight')
plt.bar(grouped2.index,grouped2["venueCategoryId"])
plt.title("10 Most Popular Venue Categories \n Tokyo: 2012-2013",fontsize=14,color='black')
plt.ylabel("Check-ins per Venue Category",fontsize=14)
plt.show()
def plot_results_multiple(predicted_data, true_data, prediction_len):
fig = plt.figure(facecolor='white')
ax = fig.add_subplot(111)
ax.plot(true_data, label='True Data')
# Pad the list of predictions to shift it in the graph to it's correct start
for i, data in enumerate(predicted_data):
padding = [None for p in range(i * prediction_len)]
plt.plot(padding + data, label='Prediction')
plt.legend()
plt.show()
def show3d(coords):
global iterator, color
fig = plt.figure(int(iterator))
ax = fig.gca(projection='3d')
ax.plot(coords[0], coords[1], coords[2], color="b")
ax.plot([coords[0][-1]], [coords[1][-1]], [coords[2][-1]], 'ro')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
iterator += 1
def plot_stats(training_stats,
val_stats,
x_label='Training Steps',
stats='loss'):
stats, x_label = stats.title(), x_label.title()
legend_loc = 'upper right' if stats == 'loss' else 'lower right'
training_steps = len(training_stats)
test_steps = len(val_stats)
plt.figure()
plt.ylabel(stats)
plt.xlabel(x_label)
plt.plot(training_stats, label='Training ' + stats)
plt.plot(np.linspace(0, training_steps, test_steps),
val_stats,
label='Validation ' + stats)
plt.ylim([0, max(plt.ylim())])
plt.legend(loc=legend_loc)
plt.show()
def drawNYC():
"""
Function plots 10 most popular Venue Categories in New York.
"""
#Import dataset of New York from root directory
NYC = pd.read_csv('dataset_TSMC2014_NYC.csv')
NYC=NYC[["venueCategory","venueCategoryId"]]
grouped=NYC.groupby(["venueCategory"]).count()
grouped=grouped.sort_values('venueCategoryId')
grouped=grouped[241:251]
#Plot bars of most popular venue categories
plt.figure(figsize=(16,6))
plt.style.use('fivethirtyeight')
plt.bar(grouped.index,grouped["venueCategoryId"])
plt.title("10 Most Popular Venue Categories \n New York: 2012-2013",fontsize=14,color='black')
plt.ylabel("Check-ins per Venue Category",fontsize=14)
plt.show()
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 curve in plot.curves:
if curve[1] == None: p.plot(curve[0],curve[2], label=curve[3])
else: p.plot(curve[0], curve[1], curve[2], label=curve[3])
p.rc('legend', fontsize='small')
p.legend(shadow=0, loc='best')
p.axes().set_aspect(plot.aspect)
if not plot.dir: plot.dir = './plots/'
if not plot.name: plot.name = self.__global_name+'_%0*i'%(2,n)
if not os.path.isdir(plot.dir): os.mkdir(plot.dir)
if plot.pgf: p.savefig(plot.dir+plot.name+'.pgf')
else: p.savefig(plot.dir+plot.name+'.pdf', bbox_inches='tight')
p.close()
def __init__(self):
super(CDataMingQtUi, self).__init__()
self.setupUi(self)
#CARgraphview 是一个QGraphicView的实例
self.figure = plt.figure()
self.canvas = FigureCanvas(self.figure)
self.graph_sence = CARscene()
self.graph_sence.addWidget(self.canvas)
self.CARgraphview.setScene(self.graph_sence)
def plot_rewards(self):
plt.figure(1)
plt.clf()
plt.title('Training...')
plt.xlabel('Time')
plt.ylabel('Reward')
cumulative = []
for i in range(len(self.rewards[self.n_training])):
if i == 0:
cumulative.append(self.rewards[self.n_training][i])
else:
cumulative.append(self.rewards[self.n_training][i] +
cumulative[-1])
x = np.linspace(2.0,
len(self.rewards[self.n_training]),
num=len(self.rewards[self.n_training]))
plt.plot(x, cumulative)
plt.show()
plt.pause(0.001) # pause a bit so that plots are updated
def sns_pair_plot_fb(target, var_list, file, redshift, fb):
'''
pair plot for fixed broadening parameter
'''
sns.set_style("white")
sns.set_style("ticks")
sns.set_context("talk")
data = pd.read_pickle(file)
plt.figure()
data_f = {}
for var in var_list:
if var in ['NTOTH2', 'TEMP']:
data_f[var] = data[var][0]
if var == 'A_Z':
data_f[var] = data[var][0]/100000.0 + redshift
df = pd.DataFrame(data_f)
g = sns.PairGrid(df) #, diag_kws=dict(color="blue", shade=True))
g.map_upper(sns.kdeplot, cmap="bone_r",n_levels=10,shade=True,
shade_lowest=False)
g.map_lower(sns.kdeplot, cmap="bone_r",n_levels=10,shade=True,
shade_lowest=False)
g.map_diag(plt.hist) #, lw=2);
#g.axes[0,0].set_ylim(redshift-0.0002,redshift+0.00005)
g.axes[0,0].set_ylabel(r"$z$")
g.axes[1,0].set_ylabel(r"$N(H_2)$")
#g.axes[2,0].set_ylabel(r"$N(HI)$")
g.axes[2,0].set_ylabel(r"$T$")
#g.axes[3,0].set_xlim(redshift-0.0002,redshift+0.00005)
g.axes[2,0].set_xlabel(r"$z$")
g.axes[2,1].set_xlabel(r"$N(H_2)$")
#g.axes[3,2].set_xlabel(r"$N(HI)$")
g.axes[2,2].set_xlabel(r"$T$")
g.savefig(target+"_b_"+str(fb)+"_H2_pairplot.pdf")
def plotSolutions(x,
u=None,
rho=None,
p=None,
e=None): #Default func values is trivial
plt.figure(figsize=(11, 8.5))
#get edge values
x_edge = [0.5 * (x[i] + x[i + 1]) for i in range(len(x) - 1)]
#get the exact values
f = open('exact_results.txt', 'r')
x_e = []
u_e = []
p_e = []
rho_e = []
e_e = []
for line in f:
if len(line.split()) == 1:
t = line.split()
else:
data = line.split()
x_e.append(float(data[0]))
u_e.append(float(data[1]))
p_e.append(float(data[2]))
rho_e.append(float(data[4]))
e_e.append(float(data[3]))
if u != None:
plot2D(x, u, x_e, u_e, "$u$")
if rho != None:
plot2D(x_edge, rho, x_e, rho_e, r"$\rho$")
if p != None:
plot2D(x_edge, p, x_e, p_e, r"$p$")
if e != None:
plot2D(x_edge, e, x_e, e_e, r"$e$")
plt.show(block=False) #show all plots generated to this point
raw_input("Press anything to continue...")
def sns_velo_pair_plot(target, line, file, nvoigts):
sns.set_style("darkgrid")
#sns.set_style("ticks")
#sns.set_context("talk")
for i in np.arange(1, nvoigts + 1, 1):
data = pd.read_pickle(file)
plt.figure()
data_f = {}
for key in data:
if key.startswith(("v0", "N", "b")) and key.endswith(str(i)):
data_f[key] = data[key][0]
#if key in ["a", "a1", "a2"]:
# data_f[key] = data[key][0]
df = pd.DataFrame(data_f)
#g = sns.pairplot(df, diag_kind="kde")
g = sns.PairGrid(df) #, diag_kws=dict(color="blue", shade=True))
g.map_upper(sns.kdeplot, cmap="bone_r",n_levels=10,shade=True,
shade_lowest=False)
g.map_lower(sns.kdeplot, cmap="bone_r",n_levels=10,shade=True,
shade_lowest=False)
#g.map_diag(sns.kdeplot, lw=2);
g.map_diag(plt.hist, alpha=0.8)
plt.subplots_adjust(hspace=0.1, wspace=0.1)
g.axes[0,0].set_ylabel(r"$\mathrm{log} N\, ({\rm cm}^{-2})$")
g.axes[1,0].set_ylabel(r"$b\, {\rm (km/s)}$")
g.axes[2,0].set_ylabel(r"$v_0\, {\rm (km/s)}$")
g.axes[2,0].set_xlabel(r"$\mathrm{log} N\, ({\rm cm}^{-2})$")
g.axes[2,1].set_xlabel(r"$b\, {\rm (km/s)}$")
g.axes[2,2].set_xlabel(r"$v_0\, {\rm (km/s)}$")
g.savefig(target+"_"+str(nvoigts)+"_"+str(i)+"_"+str(line)+"_"+"v.pdf")
def print_for_test():
f = h5py.File("lex-chocolate-1min.hdf5")
ar = f.values()
npar = np.array(ar)
x1 = np.linspace(0, len(npar[0]), num=len(npar[0]))
wavFile = wave.open("lex-chocolate-1min.wav")
(nchannels, sampwidth, framerate, nframes, comptype, compname) = wavFile.getparams()
frames = wavFile.readframes(-1)
data = np.fromstring(frames, "Int16")
x2 = np.linspace(0, len(data), num=len(data))
f1 = plt.figure(1)
plt.plot(x1*160, npar[0]*max(data), x2, data);
f1.show()
xA = np.linspace(0, len(A), num=len(A))
f2 = plt.figure(2)
plt.plot(xA*160/16000, A*max(data), x2/16000, data);
f2.show()
def show_signature(img_path, fignum=1):
print('Showing signature: '+img_path)
img = plt.imread(join(imgdir, img_path))
sig_img = sliding_window_signature(img)
fig = plt.figure(fignum)
ax1 = fig.add_subplot(1,2,1)
ax2 = fig.add_subplot(1,2,2)
ax1.imshow(img)
ax2.imshow(sig_img)
fig.show()
fig.canvas.draw()
def print_save_all_mean_faces(x_class_mean,global_mean,show,save):
rows = 4
cols = 14
index = 1
font_size = 10
plt.figure(figsize=(20,10))
plt.subplot(rows,cols,index), plt.imshow(np.reshape(global_mean,(46,56)).T, cmap = 'gist_gray')
title = str("Global Mean")
plt.title(title, fontsize=font_size).set_position([0.5,0.95]), plt.xticks([]), plt.yticks([])
index=index+1
for i in range(0,x_class_mean.shape[1]):
title = str("Class Mean "+str(i+1))
plt.subplot(rows,cols,index), plt.imshow(np.reshape(x_class_mean[:,i],(46,56)).T, cmap = 'gist_gray')
plt.title(title, fontsize=font_size).set_position([0.5,0.95]), plt.xticks([]), plt.yticks([])
index = index+1
if show == 'yes':
plt.show()
if save == 'yes':
plt.savefig('Global and Class Mean')
plt.close()
def plot_top_M_fisherfaces(eigenvec,M,Mpca,mode):
if mode == 'None':
return None
cols = 10
rows = M/cols
index = 1
font_size = 10
plt.figure(figsize=(20,20))
for i in range(0,M):
plt.subplot(rows,cols,index),plt.imshow(np.reshape(eigenvec[:,i],(46,56)).T, cmap = 'gist_gray')
face_title = str("M="+str(i+1))
plt.title(face_title, fontsize=font_size).set_position([0.5,1.05]), plt.xticks([]), plt.yticks([]) # set_position([a,b]) adjust b for height
index = index+1
if mode == 'show':
plt.show()
plt.close()
if mode == 'save':
title = str("Top 50 Fisherfaces for Mpca="+str(Mpca))
plt.savefig(title)
plt.close()
def sunSat_plot(saturday_mean, sunday_mean, df):
plt.figure()
# declaring values to be plotted
xs = [1, 2]
ys = [saturday_mean, sunday_mean]
# setting range
xrng = np.arange(len(xs))
yrng = np.arange(0, max(ys)+50, 50)
# making bar chart and alligning items properly
plt.bar(xrng, ys, 0.45, align="center")
# labeling
plt.xticks(xrng, ["Saturday", "Sunday"])
plt.yticks(yrng)
plt.grid(True)
plt.show()
def visual_importnance(X, forest):
importances = forest.feature_importances_
std = np.std([tree.feature_importances_ for tree in forest.estimators_],
axis=0)
indices = np.argsort(importances)[::-1]
# Print the feature ranking
print("Feature ranking:")
for f in range(X.shape[1]):
print("%d. feature %d (%f)" % (f + 1, indices[f], importances[indices[f]]))
# Plot the feature importances of the forest
plt.figure()
plt.title("Feature importances")
plt.bar(range(X.shape[1]), importances[indices],
color="r", yerr=std[indices], align="center")
plt.xticks(range(X.shape[1]), indices)
plt.xlim([-1, X.shape[1]])
plt.show()
def show_signature(img_path, fignum=1):
print('Showing signature: ' + img_path)
img = plt.imread(join(imgdir, img_path))
sig_img = sliding_window_signature(img)
fig = plt.figure(fignum)
ax1 = fig.add_subplot(1, 2, 1)
ax2 = fig.add_subplot(1, 2, 2)
ax1.imshow(img)
ax2.imshow(sig_img)
fig.show()
fig.canvas.draw()
def fit_background(q,I):
## Working on background calculation
## mkak 2016.09.28
x = q
y = I
pfit = np.polyfit(x,y,4)
yfit = np.polyval(pfit,x)
#panel.plot(xrd_spectra[0], xrd_spectra[1]-yfit, label='no bkg')
#panel.plot(xrd_spectra[0], yfit, color='blue', label='bkg')
### calculation works, but plotting here wipes previous plots - only shows last
import matplotlib as plt
plt.figure()
plt.plot(x,y,label='raw data')
plt.plot(x,yfit,label='background')
plt.plot(x,y-yfit,label='background subtracted')
plt.legend()
plt.show()
def show3d_kalman_vs_ema(coordsK, coordsEMA):
global iterator, color
fig = plt.figure(int(iterator))
ax = fig.gca(projection='3d')
ax.plot(coordsK[0], coordsK[1], coordsK[2], color="b")
ax.plot(coordsEMA[0], coordsEMA[1], coordsEMA[2], color="r")
ax.plot([coordsK[0][-1]], [coordsK[1][-1]], [coordsK[2][-1]], 'ro')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
iterator += 1
def plot_sample(x, title="", width=28, fName=None):
fig = plt.figure()
sample = x.reshape(width, width)
# interploation can be 'nearest' to put grid at the center the pixels
plt.imshow(sample, interpolation='None', cmap='gray')
plt.title(title)
plt.xticks([])
plt.yticks([])
if fName != None:
savefig(fName, bbox_inches='tight')
plt.show()
def show_image(image, grayscale=True, ax=None, title=''):
if ax is None:
plt.figure()
plt.axis('off')
if len(image.shape) == 2 or grayscale == True:
if len(image.shape) == 3:
image = np.sum(np.abs(image), axis=2)
vmax = np.percentile(image, 99)
vmin = np.min(image)
plt.imshow(image, cmap=plt.cm.gray, vmin=vmin, vmax=vmax)
plt.title(title)
else:
image = image + 127.5
image = image.astype('uint8')
plt.imshow(image)
plt.title(title)
def plot_confusion_matrix(cm, classes,
normalize=False, title='Confusion matrix',
cmap=plt.cm.Blues, filename='viz\\confusion_matrix.png'):
plt.figure()
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j], horizontalalignment="center", color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.savefig(filename)
def plotSolutions(x,u=None,rho=None,p=None,e=None): #Default func values is trivial
plt.figure(figsize=(11,8.5))
#get edge values
x_edge = [0.5*(x[i]+x[i+1]) for i in range(len(x)-1)]
#get the exact values
f = open('exact_results.txt', 'r')
x_e = []
u_e = []
p_e = []
rho_e = []
e_e = []
for line in f:
if len(line.split())==1:
t = line.split()
else:
data = line.split()
x_e.append(float(data[0]))
u_e.append(float(data[1]))
p_e.append(float(data[2]))
rho_e.append(float(data[4]))
e_e.append(float(data[3]))
if u != None:
plot2D(x,u,x_e,u_e,"$u$")
if rho != None:
plot2D(x_edge,rho,x_e,rho_e,r"$\rho$")
if p != None:
plot2D(x_edge,p,x_e,p_e,r"$p$")
if e != None:
plot2D(x_edge,e,x_e,e_e,r"$e$")
plt.show(block=False) #show all plots generated to this point
raw_input("Press anything to continue...")
def plotCompared(original,forecasts,labels,title):
fig = plt.figure(figsize=[13,6])
ax = fig.add_subplot(111)
ax.plot(original,color='k',label="Original")
for c in range(0,len(forecasted)):
ax.plot(forecasted[c],label=labels[c])
handles0, labels0 = ax.get_legend_handles_labels()
ax.legend(handles0,labels0)
ax.set_title(title)
ax.set_ylabel('F(T)')
ax.set_xlabel('T')
ax.set_xlim([0,len(original)])
ax.set_ylim([min(original),max(original)])
def compareModelsPlot(original,models_fo,models_ho):
fig = plt.figure(figsize=[13,6])
fig.suptitle("Comparação de modelos ")
ax0 = fig.add_axes([0, 0, 1, 1]) #left, bottom, width, height
rows = []
for model in models_fo:
fts = model["model"]
ax0.plot(model["forecasted"], label=model["name"])
for model in models_ho:
fts = model["model"]
ax0.plot(model["forecasted"], label=model["name"])
handles0, labels0 = ax0.get_legend_handles_labels()
ax0.legend(handles0, labels0)
def test_pert_winds():
f=figure()
lats=linspace(-13.0, -11.5, 20)
lons=linspace(130., 132., 20)
u,v=simul_winds.unif_wind(lats, lons, 5.0, 45.0)
mapobj=pres.generate_darwin_plot()
fw=0.2#degrees
m_bump=15.0
b1=simul_winds.speed_bump(lats, lons, [-12.0, 131.0], fw)*m_bump
b2=-1.0*simul_winds.speed_bump(lats, lons, [-12.0, 131.2], fw)*m_bump
pres.quiver_contour_winds(mapobj, lats, lons, u+(b1+b2),v-(b1+b2))
savefig(os.getenv('HOME')+'/bom_mds/output/test_pert.png')
close(f)
def execute():
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
x = random.normal(5, .5, 1000)
y = random.normal(3, 1, 1000)
a = x*cos(pi/4) + y*sin(pi/4)
b = -x*sin(pi/4) + y*cos(pi/4)
plt.plot(a, b, '.')
plt.xlabel('x')
plt.ylabel('y')
plt.title('原数据集')
data = zeros((1000, 2))
data[:, 0] = a
data[:, 1] = b
x, y, evals, evecs = pca(data, 1)
print(y)
plt.figure()
plt.plot(y[:, 0], y[:, 1], '.')
plt.xlabel('x')
plt.ylabel('y')
plt.title('重新构造数据')
plt.show()