def ffcontour(grids, X0=None): # if X0==None: assume square if X0==None: X0=(len(grids))**.5 # # in this case, X0 is explicitly the width of a row, including borders. Xs=[] Ys=[] Z=scipy.array(grids) Z.shape=(X0, math.ceil(len(grids)/X0)) Xs=range(X0) Ys=range(X0) #for i in xrange(len(grids)): # Xs+=[i%X0] # Ys+=[int(i/X0)] # # Z+=[[i%X0, int(i/X0), grids[i]]] X,Y=numpy.meshgrid(Xs, Ys) #Z.shape=(X0, math.ceil(len(grids)/X0)) #print "lens: %d * %d = %d, %d" % (len(Xs), len(Ys), len(Xs)*len(Ys), len(grids)) Z.shape=(len(Xs), len(Ys)) plt.contourf(X,Y,Z, alpha=.35) #plt.colorbar(ticks=[-5,-4,-3,-2,-1]) plt.colorbar() plt.spectral()
def show_fdct(filename, channel=0, scales=4, angles=16):
image = pyplot.imread(filename)[:, :, channel]
transformation = fdct2(image.shape, scales, angles, True, norm=True)
cl = transformation.fwd(image)
#fig, axs = pyplot.subplots(angles, scales + 1)
pyplot.spectral()
fig = pyplot.figure(1)
# display original image
#img_orig = axs[0, 0].imshow(image)
img_orig = pyplot.imshow(image)
pyplot.colorbar(orientation="horizontal")
#pyplot.colorbar(img_orig, ax=axs[0, 0], orientation="horizontal")
#imgs = []
angles = [1, ] + [angles] * (scales - 1)
for scale in range(scales):
fig = pyplot.figure(scale + 100)
grid = ImageGrid(fig, 111, (1, angles[scale]))
for angle in range(angles[scale]):
print("displaying",scale,angle)
cl_img = cl(scale, angle)
print(cl_img.shape)
grid[angle].imshow(cl_img)
#img = axs[0, scale + 1].imshow(cl_img)
#pyplot.colorbar(img, ax=axs[0, scale + 1], orientation="horizontal")
#imgs.append(img)
pyplot.show()
def plotar_silhueta(silhueta_dados):
fig = plt.figure()
ax = fig.add_subplot(111)
flat_silhueta_dados = []
for cluster in silhueta_dados:
cluster = list(cluster)
cluster.sort(key=lambda x: x) #sort ordem decrescente
for dado in cluster:
flat_silhueta_dados.append(dado)
Y = np.arange(0, len(flat_silhueta_dados))
plt.spectral()
ax.fill_betweenx(Y, flat_silhueta_dados)
plt.show()
def GetExec(self, f, coverPanel, rec, geMat, colorList):
self.rec = rec
self.coverPanel = coverPanel
self.geMat = geMat
self.bPSize = self.coverPanel.GetSize()
self.plotter = mpl.PlotNotebook(self.coverPanel,
size = (self.bPSize[1], self.bPSize[1] * 1.08),
pos = ((self.bPSize[0]-self.bPSize[1])/2,
-self.bPSize[1] * .08))
self.plotter.Show(True)
self.axes1 = self.plotter.add('figure 1').gca()
colMat = GetExec(self.rec, geMat, 'tri')
self.Z3 = np.transpose(np.array(colMat))
plt.spectral()
self.DoDraw()
return self.geMat
def plot_clustering(n_clusters, cluster_labels, X):
plt.figure()
sample_silhouette_values = silhouette_samples(X, cluster_labels)
y_lower = 10
for i in range(n_clusters):
# Aggregate the silhouette scores for samples belonging to
# cluster i, and sort them
ith_cluster_silhouette_values = \
sample_silhouette_values[cluster_labels == i]
ith_cluster_silhouette_values.sort()
size_cluster_i = ith_cluster_silhouette_values.shape[0]
y_upper = y_lower + size_cluster_i
color = plt.spectral(float(i) / n_clusters)
plt.fill_betweenx(np.arange(y_lower, y_upper),
0, ith_cluster_silhouette_values,
facecolor=color, edgecolor=color, alpha=0.7)
# Label the silhouette plots with their cluster numbers at the middle
plt.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))
# Compute the new y_lower for next plot
y_lower = y_upper + 10 # 10 for the 0 samples
plt.set_title("The silhouette plot for the various clusters.")
plt.set_xlabel("The silhouette coefficient values")
plt.set_ylabel("Cluster label")
# The vertical line for average silhouette score of all the values
plt.axvline(x=silhouette_avg, color="red", linestyle="--")
plt.set_yticks([]) # Clear the yaxis labels / ticks
plt.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])
plt.show()
recvbuf=[counts, MPI.INTEGER],
root=MPI.ROOT)
worker.Gatherv(sendbuf=None,
recvbuf=[indices, (counts, None), MPI.INTEGER],
root=MPI.ROOT)
rowtype = MPI.INTEGER.Create_contiguous(w).Commit()
worker.Gatherv(sendbuf=None,
recvbuf=[result, (counts, None), rowtype],
root=MPI.ROOT)
rowtype.Free()
# disconnect worker
worker.Disconnect()
# reconstruct result
C = numpy.empty([h, w], dtype='i')
C[indices, :] = result
try:
from matplotlib import pyplot as plt
plt.imshow(C, aspect='equal')
plt.spectral()
if not plt.isinteractive():
import signal
def action(*args): raise SystemExit
signal.signal(signal.SIGALRM, action)
signal.alarm(2)
plt.show()
except:
pass
import matplotlib.pyplot as plt
import numpy as np
plt.spectral() #setting the default color map to "spectral"
plt.rcParams['contour.negative_linestyle'] = 'solid'
def getfile(ts=None, dtype=None, filename=None, fmt="%s.%06d_p000000.h5"):
'''
Return the hdf5 file pointer. "filename" is either given explicitly, or formatted from "ts"(timestep) and "dtype"('pfd'/'tfd') according to "fmt".
'''
import h5py, os
if filename == None:
if ts != None and dtype != None:
filename = fmt % (dtype, ts)
else:
print "ERROR: either ts & dtype together, or filename alone should be given"
return
try:
f = h5py.File(filename, 'r')
except IOError:
print "ERROR: Cannot open %s" % filename
return
return f
def getfld_file(fld,
f,
dims='xz',
n=0,
i1b=0,
i1e=None,
local_height = height - default_local_height * (size - 1)
local_array_len = local_height * width
local_result = np.zeros(shape=[local_array_len],
dtype='i')
dx = a / height
dy = b / width
for row in range(local_height):
shift = default_local_height * rank
y = -b / 3 + (row + shift) * dy
for col in range(width):
x = -a + col * dx
local_result[col + row * width] = mandelbrot(x, y, max_iter)
print('{0} process for {1} seconds'.format(rank, time.time() - t0))
local_result.shape = (local_height, width)
pyplot.imshow(local_result, aspect='equal')
pyplot.spectral()
pyplot.show()
result = None
if rank == 0:
result = np.empty([height * width], dtype='i')
comm.Gather(sendbuf=local_result,
recvbuf=result,
root=0)
if rank == 0:
result.shape = (height, width)
print('all time for execution {}'.format(time.time() - t0))
pyplot.imshow(result, aspect='equal')
pyplot.spectral()
pyplot.show()
# without load balancing # for i in range(rank*strip_size,rank*strip_size+strip_size): # y = y1 + i*dx # for j in range(ny): # x = x1 + j*dy # c[i,j] = mandlebrot(x,y,127) # with load balancing by cyclic distribution i = rank while i < nx: y = y1 + i*dx for j in range(ny): x = x1 + j*dy c[i,j] = mandlebrot(x,y,127) i += size end_local = MPI.Wtime() print "Total time taken for local calculation by process "+str(rank)+" : "+str(end_local-start) comm.Allreduce(MPI.IN_PLACE,c,op=MPI.MAX) if rank == 0: # print c end_comm = time.time() print "Total time taken for all communications : "+str(end_comm-start) pyplot.imshow(c, aspect='equal') pyplot.spectral() pyplot.show()
warnings = ['No','Low','Guarded','Elevated','High','Severe']
#-----------------------------------------------------------------------------#
Z_i = numpy.loadtxt('tmp/Zarray.dat')
Y_i = 37.0 + .1*numpy.arange(Z_i.shape[0])
X_i = -127.5 + .1*numpy.arange(Z_i.shape[1])
print(Z_i.shape, len(Y_i), len(X_i))
#-----------------------------------------------------------------------------#
# Create the KML file
cs = plt.contourf(X_i, Y_i, Z_i, LevelsNumber,
cm=plt.spectral()).collections
file = open('doc.kml','w')
file.write('<?xml version="1.0" encoding="UTF-8"?>\n')
file.write('<kml xmlns="http://www.opengis.net/kml/2.2">\n')
file.write('<Document>\n')
for i in range(0,len(cs)):
bgra_array = 255*cs[i].get_facecolor()[0]
file.write('<Style id="l%d">\n' % i)
file.write('<LineStyle><color>00000000</color></LineStyle>\n')
file.write('<PolyStyle><color>7d%02x%02x%02x</color></PolyStyle>\n' %\
( bgra_array[2] , bgra_array[1] , bgra_array[0] ) )
file.write('</Style>\n')
file.write('<ScreenOverlay id="scale">\n')
import matplotlib.pyplot as plt
import numpy as np
plt.spectral()#setting the default color map to "spectral"
plt.rcParams['contour.negative_linestyle'] = 'solid'
def getfile(ts=None,dtype=None,filename=None,fmt="%s.%06d_p000000.h5"):
'''
Return the hdf5 file pointer. "filename" is either given explicitly, or formatted from "ts"(timestep) and "dtype"('pfd'/'tfd') according to "fmt".
'''
import h5py,os
if filename==None:
if ts!=None and dtype!=None:
filename=fmt%(dtype,ts)
else:
print "ERROR: either ts & dtype together, or filename alone should be given"
return
try:
f=h5py.File(filename,'r')
except IOError:
print "ERROR: Cannot open %s"%filename
return
return f
def getfld_file(fld,f,dims='xz',n=0,i1b=0,i1e=None,i2b=0,i2e=None,stride1=None,stride2=None,stride=1):
'''
Retrieve field named by "fld" from a hdf5 file pointer. For the first dimension(e.g., 'x' in 'xz'), indices ;i1b'/'i1e' specify a range from which data is retrieved. Data array will also be strided every 'stride1' step. If 'stride1' is not set, the value of 'stride' will be used, which is 1 (no striding) by default. Similarly, for the second dimension, 'i2b', 'i2e', 'stride2' can be used.
'''
if stride1==None:
stride1=stride
if stride2==None:
stride2=stride
