def atest_very_anisotropic(self):
""" Made to find error in very anisotropic case close to upper layer
"""
set_up_parameters = {
"sigma_G": [1.0, 1.0, 1.0], # Below electrode
"sigma_T": [0.1, 0.1, 1.0], # Tissue
"sigma_S": [0.0, 0.0, 0.0], # Saline
"slice_thickness": 200,
"steps": 2,
}
Moi = MoI(set_up_parameters=set_up_parameters)
imem = 1.2
a = set_up_parameters["slice_thickness"] / 2.0
high_position = [0, 0, 90]
low_position = [0, 0, -a + 10]
x_array = np.linspace(-200, 200, 41)
y_array = np.linspace(-100, 100, 21)
values_high = []
values_low = []
for y in y_array:
for x in x_array:
values_high.append([x, y, Moi.ad_hoc_anisotropic(charge_pos=high_position, elec_pos=[x, y, -100])])
values_low.append([x, y, Moi.ad_hoc_anisotropic(charge_pos=low_position, elec_pos=[x, y, -100])])
values_high = np.array(values_high)
values_low = np.array(values_low)
pl.subplot(211)
pl.scatter(values_high[:, 0], values_high[:, 1], c=values_high[:, 2])
pl.axis("equal")
pl.colorbar()
pl.subplot(212)
pl.scatter(values_low[:, 0], values_low[:, 1], c=values_low[:, 2])
pl.colorbar()
pl.axis("equal")
pl.show()
def test1():
import numpy as np
import pylab
from scipy import sparse
from regreg.algorithms import FISTA
from regreg.atoms import l1norm
from regreg.container import container
from regreg.smooth import quadratic
Y = np.random.standard_normal(500); Y[100:150] += 7; Y[250:300] += 14
sparsity = l1norm(500, lagrange=1.0)
#Create D
D = (np.identity(500) + np.diag([-1]*499,k=1))[:-1]
D = sparse.csr_matrix(D)
fused = l1norm.linear(D, lagrange=19.5)
loss = quadratic.shift(-Y, lagrange=0.5)
p = container(loss, sparsity, fused)
soln1 = blockwise([sparsity, fused], Y)
solver = FISTA(p)
solver.fit(max_its=800,tol=1e-10)
soln2 = solver.composite.coefs
#plot solution
pylab.figure(num=1)
pylab.clf()
pylab.scatter(np.arange(Y.shape[0]), Y, c='r')
pylab.plot(soln1, c='y', linewidth=6)
pylab.plot(soln2, c='b', linewidth=2)
def window_fn_matrix(Q,N,num_remov=None,save_tag=None,lms=None):
Q = n.matrix(Q); N = n.matrix(N)
Ninv = uf.pseudo_inverse(N,num_remov=None) # XXX want to remove dynamically
#print Ninv
info = n.dot(Q.H,n.dot(Ninv,Q))
M = uf.pseudo_inverse(info,num_remov=num_remov)
W = n.dot(M,info)
if save_tag!=None:
foo = W[0,:]
foo = n.real(n.array(foo))
foo.shape = (foo.shape[1]),
print foo.shape
p.scatter(lms[:,0],foo,c=lms[:,1],cmap=mpl.cm.PiYG,s=50)
p.xlabel('l (color is m)')
p.ylabel('W_0,lm')
p.title('First Row of Window Function Matrix')
p.colorbar()
p.savefig('{0}/{1}_W.pdf'.format(fig_loc,save_tag))
p.clf()
print 'W ',W.shape
p.imshow(n.real(W))
p.title('Window Function Matrix')
p.colorbar()
p.savefig('{0}/{1}_W_im.pdf'.format(fig_loc,save_tag))
p.clf()
return W
def generate_pr_scatter_plots(
query_prf, subject_prf, query_color="b", subject_color="r", x_label="Precision", y_label="Recall"
):
""" Generate scatter plot of precision versus recall for query and subject results
query_prf: precision, recall, and f-measure values as returned
from compute_prfs for query data
subject_prf: precision, recall, and f-measure values as returned
from compute_prfs for subject data
query_color: the color of the query points (defualt: blue)
subject_color: the color of the subject points (defualt: red)
x_label: x axis label for the plot (default: "Precision")
y_label: y axis label for the plot (default: "Recall")
"""
# Extract the query precisions and recalls and
# generate a scatter plot
query_precisions = [e[4] for e in query_prf]
query_recalls = [e[5] for e in query_prf]
scatter(query_precisions, query_recalls, c=query_color)
# Extract the subject precisions and recalls and
# generate a scatter plot
subject_precisions = [e[4] for e in subject_prf]
subject_recalls = [e[5] for e in subject_prf]
scatter(subject_precisions, subject_recalls, c=subject_color)
xlim(0, 1)
ylim(0, 1)
xlabel(x_label)
ylabel(y_label)
def geweke_plot(data, name, format='png', suffix='-diagnostic', path='./', fontmap = None,
verbose=1):
# Generate Geweke (1992) diagnostic plots
if fontmap is None: fontmap = {1:10, 2:8, 3:6, 4:5, 5:4}
# Generate new scatter plot
figure()
x, y = transpose(data)
scatter(x.tolist(), y.tolist())
# Plot options
xlabel('First iteration', fontsize='x-small')
ylabel('Z-score for %s' % name, fontsize='x-small')
# Plot lines at +/- 2 sd from zero
pyplot((nmin(x), nmax(x)), (2, 2), '--')
pyplot((nmin(x), nmax(x)), (-2, -2), '--')
# Set plot bound
ylim(min(-2.5, nmin(y)), max(2.5, nmax(y)))
xlim(0, nmax(x))
# Save to file
if not os.path.exists(path):
os.mkdir(path)
if not path.endswith('/'):
path += '/'
savefig("%s%s%s.%s" % (path, name, suffix, format))
def drunkTest(numTrials = 1000):
#stepsTaken = [10, 100, 1000, 10000]
stepsTaken = 1000
for dClass in (UsualDrunk, ColdDrunk, EDrunk, PhotoDrunk, DDrunk):
#initialize field
field = Field()
origin = Location(0, 0)
# initialize drunk
drunk = dClass('Drunk')
field.addDrunk(drunk, origin)
x_pos, y_pos = [], [] # initialize to empty
x, y = 0.0, 0.0
for trial in range(numTrials): # trials
x, y = walkVector(field, drunk, stepsTaken)
x_pos.append(x)
y_pos.append(y)
#pylab.plot(x_pos, y_pos, 'ro', s=5,
# label = dClass.__name__)
pylab.scatter(x_pos, y_pos,s=5, color='red')
pylab.title(str(dClass))
pylab.xlabel('x')
pylab.grid()
pylab.xlim(-100, 100)
pylab.ylim(-100,100)
pylab.ylabel('y')
pylab.show()
def plotslice(pos,filename='',boxsize=100.):
ng = pos.shape[0]
M.clf()
M.scatter(pos[ng/4,:,:,1].flatten(),pos[ng/4,:,:,2].flatten(),s=1.,lw=0.)
M.axis('tight')
if filename != '':
M.savefig(filename)
def plot_margin(X1_train, X2_train, clf):
def f(x, w, b, c=0):
# given x, return y such that [x,y] in on the line
# w.x + b = c
return (-w[0] * x - b + c) / w[1]
pl.plot(X1_train[:,0], X1_train[:,1], "ro")
pl.plot(X2_train[:,0], X2_train[:,1], "bo")
pl.scatter(clf.sv[:,0], clf.sv[:,1], s=100, c="g")
# w.x + b = 0
a0 = -4; a1 = f(a0, clf.w, clf.b)
b0 = 4; b1 = f(b0, clf.w, clf.b)
pl.plot([a0,b0], [a1,b1], "k")
# w.x + b = 1
a0 = -4; a1 = f(a0, clf.w, clf.b, 1)
b0 = 4; b1 = f(b0, clf.w, clf.b, 1)
pl.plot([a0,b0], [a1,b1], "k--")
# w.x + b = -1
a0 = -4; a1 = f(a0, clf.w, clf.b, -1)
b0 = 4; b1 = f(b0, clf.w, clf.b, -1)
pl.plot([a0,b0], [a1,b1], "k--")
pl.axis("tight")
pl.show()
def pseudoSystem():
#The corrolations discovered when answering this question shows the emergent effects of component evolution on CSE.
#We can further study these correlations by creating an example component system consisting of many components where a a different component has a new version released every day.
#By looking at users who Upgrade the system at different frequencies over 100 days, we present two graphs, Upgrade frequency to uttd and change.
l = 100
uttdxy = []
chxy = []
for uf in range(1,20):
uttd = range(uf)*(l*2/uf)
uttd = uttd[1:l+1]
uttdxy.append((uf,numpy.mean(uttd)))
sh = [0]*(uf-1) + [uf]
sh = sh*l
sh = sh[:l]
chxy.append((uf,sum(sh)))
pylab.figure(20)
x,y = zip(*sorted(uttdxy))
pylab.plot(x,y)
pylab.scatter(x,y)
saveFigure("q1bpseudouttd")
pylab.figure(21)
x,y = zip(*sorted(chxy))
pylab.plot(x,numpy.array(y))
pylab.scatter(x,numpy.array(y))
pylab.ylim([0,l+10])
saveFigure("q1bpseudochange")
def plot_iris_knn():
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features. We could
# avoid this ugly slicing by using a two-dim dataset
y = iris.target
knn = neighbors.KNeighborsClassifier(n_neighbors=3)
knn.fit(X, y)
x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1
y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1
xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),
np.linspace(y_min, y_max, 100))
Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
pl.figure()
pl.pcolormesh(xx, yy, Z, cmap=cmap_light)
# Plot also the training points
pl.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
pl.xlabel('sepal length (cm)')
pl.ylabel('sepal width (cm)')
pl.axis('tight')
def plot_polynomial_regression():
rng = np.random.RandomState(0)
x = 2*rng.rand(100) - 1
f = lambda t: 1.2 * t**2 + .1 * t**3 - .4 * t **5 - .5 * t ** 9
y = f(x) + .4 * rng.normal(size=100)
x_test = np.linspace(-1, 1, 100)
pl.figure()
pl.scatter(x, y, s=4)
X = np.array([x**i for i in range(5)]).T
X_test = np.array([x_test**i for i in range(5)]).T
regr = linear_model.LinearRegression()
regr.fit(X, y)
pl.plot(x_test, regr.predict(X_test), label='4th order')
X = np.array([x**i for i in range(10)]).T
X_test = np.array([x_test**i for i in range(10)]).T
regr = linear_model.LinearRegression()
regr.fit(X, y)
pl.plot(x_test, regr.predict(X_test), label='9th order')
pl.legend(loc='best')
pl.axis('tight')
pl.title('Fitting a 4th and a 9th order polynomial')
pl.figure()
pl.scatter(x, y, s=4)
pl.plot(x_test, f(x_test), label="truth")
pl.axis('tight')
pl.title('Ground truth (9th order polynomial)')
def plot_scatter(results, xvar='lat', yvar='max_CAPE'):
plt.xlabel(xvar)
plt.ylabel(yvar)
for res in results:
if res['max_CAPE'] != 0:
plt.scatter(res[xvar], res[yvar])
def plot_values(X, Y, xlabel, ylabel, suffix, ptype='plot'):
output_filename = constants.ATTRACTIVENESS_FOLDER_NAME + constants.DATASET + '_' + suffix
X1 = [X[i] for i in range(len(X)) if X[i]>0 and Y[i]>0]
Y1 = [Y[i] for i in range(len(X)) if X[i]>0 and Y[i]>0]
X = X1
Y = Y1
pylab.close("all")
pylab.figure(figsize=(8, 7))
#pylab.rcParams.update({'font.size': 20})
pylab.scatter(X, Y)
#pylab.axis(vis.get_bounds(X, Y, False, False))
#pylab.xscale('log')
pylab.yscale('log')
pylab.xlabel(xlabel)
pylab.ylabel(ylabel)
#pylab.xlim(0.1,1)
#pylab.ylim(ymin=0.01)
#pylab.tight_layout()
pylab.savefig(output_filename + '.pdf')
def pair_plot(data, savefile=None, display=True, **kwargs):
chan = data.channels
l = len(chan)
figure = pylab.figure()
pylab.subplot(l, l, 1)
for i in range(l):
for j in range(i + 1):
pylab.subplot(l, l, i * l + j + 1)
if i == j:
pylab.hist(data[:, i], bins=200, histtype='stepfilled')
else:
pylab.scatter(data[:, i], data[:, j], **kwargs)
if j == 0:
pylab.ylabel(chan[i])
if i == l - 1:
pylab.xlabel(chan[j])
if display:
pylab.show()
if savefile:
pylab.savefig(savefile)
return figure
def test():
from pandas import DataFrame
X = np.linspace(0.01, 1.0, 10)
Y = np.log(X)
Y -= Y.min()
Y /= Y.max()
Y *= 0.95
#Y = X
df = DataFrame({'X': X, 'Y': Y})
P = Pareto(df, 'X', 'Y')
data = []
for val in np.linspace(0,1,15):
data.append(dict(val=val, x=P.lookup_x(val), y=P.lookup_y(val)))
pl.axvline(val, alpha=.5)
pl.axhline(val, alpha=.5)
dd = DataFrame(data)
pl.scatter(dd.y, dd.val, lw=0, c='r')
pl.scatter(dd.val, dd.x, lw=0, c='g')
print dd
#P.scatter(c='r', lw=0)
P.show_frontier(c='r', lw=4)
pl.show()
def generateNetwork(aINDEX, dDIFF, AM, name):
R = 100
X = []
Y = []
Z = []
for i in range(len(aINDEX)):
#r = 100*R + sum(AM[i])*R
r = 1000*R + 10000*abs(dDIFF[aINDEX[i]])*R
t = 2*math.pi*random.random()
X.append(r*math.cos(t))
Y.append(r*math.sin(t))
Z.append(sum(AM[i]))
fig = P.figure()
P.axis('off')
P.scatter(X, Y, Z, edgecolor = '', c = 'lightblue', alpha = 0.5)
for i in range(len(aINDEX)):
for j in range(i):
if sum(AM[i]) > 200 and sum(AM[j]) > 200:
P.plot([X[i], X[j]], [Y[i], Y[j]],'k',lw = 0.01)
for i in range(len(aINDEX)):
if sum(AM[i])>200:
P.text(X[i], Y[i], aINDEX[i], fontsize = 8)
#P.show()
fig.savefig('figures/' + name + '.png')
return
def plot(n):
h = heighway(n)
x, y = [], []
for z in h:
x.append(z.real), y.append(z.imag)
scatter(x, y)
show()
def plot_values(values, suffix):
'''
#bins = [math.pow(BASE, i) for i in range(int(math.log(max(degrees), BASE)))]
bins = [i for i in range(max(degrees)+1) if i in degrees]
print bins
N, tempbins, temppatches = pylab.hist(degrees, bins)
H = [[bins[i], float(N[i])/sum(N)] for i in range(len(N)) if N[i]>0]
print N
pylab.close()
X = [h[0] for h in H if h[0]!=0]
Y = [h[1] for h in H if h[0]!=0]
'''
X = range(len(values))
Y = values
output_filename = constants.CHARTS_FOLDER_NAME + 'time_ordered' + '_' + suffix
pylab.figure(figsize=(9, 4))
pylab.rcParams.update({'font.size': 20})
#pylab.xscale('log')
#pylab.yscale('log')
pylab.scatter(X, Y)
#pylab.xlabel('# of edges')
#pylab.ylabel('Probability')
#pylab.title(output_filename)
pylab.savefig(output_filename + '.pdf')
pylab.close()
def plotTOPICS(name, sdDIFF, dKT):
vCOMM =[]
vDIFF = []
for i in range(len(sdDIFF)):
#print sdDIFF[i][0], -sdDIFF[i][1], dKT[sdDIFF[i][0]]
vDIFF.append(-sdDIFF[i][1])
vCOMM.append(dKT[sdDIFF[i][0]])
nDIFF = np.array(vDIFF)
nCOMM = np.array(vCOMM)
mDIFF = np.mean(nDIFF)
sDIFF = np.std(nDIFF)
mCOMM =np.mean(nCOMM)
sCOMM =np.std(nCOMM)
X = []
Y = []
for j in range(len(sdDIFF)):
x = (max(nCOMM) - nCOMM[j])/(max(nCOMM) - min(nCOMM))
y = (nDIFF[j] - min(nDIFF))/(max(nDIFF) - min(nDIFF))
X.append(x)
Y.append(y)
fig = P.figure()
P.scatter(X, Y)
fig.savefig('figures/DISTR_' + name + '.png')
return X, Y
def main():
args = sys.argv[1:]
dataset_path = None
if args and '-save' in args:
try: dataset_path = args[args.index('-save') + 1]
except: dataset_path = 'dataset.p'
# Generate the dataset
print "...Generating Dataset..."
X1, Y1 = make_circles(n_samples=800, noise=0.07, factor=0.4)
frac0 = len(np.where(Y1 == 0)[0]) / float(len(Y1))
frac1 = len(np.where(Y1 == 1)[0]) / float(len(Y1))
print "Percentage of '0' labels:", frac0
print "Percentage of '1' labels:", frac1
# (Optionally) save the dataset to DATASET_PATH
if dataset_path:
print "...Saving dataset to {0}...".format(dataset_path)
pickle.dump((X1, Y1, frac0, frac1), open(dataset_path, 'wb'))
# Plot the dataset
print "...Showing dataset in new window..."
pl.figure(figsize=(10, 8))
pl.subplots_adjust(bottom=.05, top=.9, left=.05, right=.95)
pl.subplot(111)
pl.title("Our Dataset: N=200, '0': {0} '1': {1} ".format(frac0, frac1), fontsize="large")
pl.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)
pl.show()
print "...Done."
def plot_vel_vs_h3(maps, out_suffix=''):
xaxis = (np.arange(len(maps.velocity[0])) - ppxf_m31.bhpos_pix[0]) * 0.05
yaxis = (np.arange(len(maps.velocity)) - ppxf_m31.bhpos_pix[1]) * 0.05
yy, xx = np.meshgrid(xaxis, yaxis)
radius = np.hypot(xx, yy)
good = np.where((np.abs(yy) < 0.5) & (np.abs(xx) < 1.0))
plt.scatter(maps.velocity[good], maps.h3[good], c=maps.sigma[good], s=5,
marker='o', vmin=0, vmax=450)
plt.xlim(-700, 0)
plt.ylim(-0.5, 0.5)
plt.colorbar(label='Sigma (km/s)')
plt.axhline(linestyle='--', color='grey')
plt.xlabel('Velocity (km/s)')
plt.ylabel('h3')
plt.savefig(plot_dir + 'vel_vs_h3' + out_suffix + '.png')
plt.clf()
plt.scatter(maps.sigma[good], maps.h3[good], c=maps.velocity[good], s=5,
marker='o', vmin=-700, vmax=0)
plt.xlim(0, 450)
plt.ylim(-0.5, 0.5)
plt.colorbar(label='Velocity (km/s)')
plt.axhline(linestyle='--', color='grey')
plt.xlabel('Sigma (km/s)')
plt.ylabel('h3')
plt.savefig(plot_dir + 'sig_vs_h3' + out_suffix + '.png')
return
def test_regress_vary_na(baselines, coeffs, nants=32, restrictChi=False):
"""
This function runs many tests of the linear regression, varying the number
of antennae in the array. Again, the global signal is hard-coded to be 1.
"""
for jj in n.arange(100):
nas = n.arange(2, nants)
gs_diff = n.zeros(len(nas))
for ii, na in enumerate(nas):
gs_recov, redchi, err = test_regress(baselines, coeffs, gs=1, n_sig=0.1, na=na, readFromFile=True)
if restrictChi:
if n.absolute(redchi - 1) < 0.1:
gs_diff[ii] = gs_recov - 1.0
else:
gs_diff[ii] = None
else:
gs_diff[ii] = gs_recov - 1.0
p.scatter(nas, gs_diff)
p.xlabel("Number of antenna")
p.ylabel("Difference between true and recovered global signal")
# p.show()
if restrictChi:
p.savefig("./figures/gs_diff_vs_na_good_chi.pdf")
else:
p.savefig("./figures/gs_diff_vs_na.pdf")
p.clf()
def plot_contour(X, X1, X2, clf, title):
pl.figure()
pl.title(title)
# Plot instances of class 1.
pl.plot(X1[:, 0], X1[:, 1], "ro")
# Plot instances of class 2.
pl.plot(X2[:, 0], X2[:, 1], "bo")
# Select "support vectors".
if hasattr(clf, "support_vectors_"):
sv = clf.support_vectors_
else:
sv = X[clf.coef_.ravel() != 0]
# Plot support vectors.
pl.scatter(sv[:, 0], sv[:, 1], s=100, c="g")
# Plot decision surface.
A, B = np.meshgrid(np.linspace(-6, 6, 50), np.linspace(-6, 6, 50))
C = np.array([[x1, x2] for x1, x2 in zip(np.ravel(A), np.ravel(B))])
Z = clf.decision_function(C).reshape(A.shape)
pl.contour(A, B, Z, [0.0], colors="k", linewidths=1, origin="lower")
pl.axis("tight")
def plot(self):
f = pylab.figure(figsize=(8,4))
co = [] #colors container
for zScore, r in itertools.izip(self.zScores, self.log2Ratio):
if zScore < self.pCut:
if r > 0:
co.append(Colors().greenColor)
elif r < 0:
co.append(Colors().redColor)
else:
raise Exception
else:
co.append(Colors().blueColor)
#print "Probability this is from a normal distribution: %.3e" %stats.normaltest(self.log2Ratio)[1]
ax = f.add_subplot(121)
pylab.axvline(self.meanLog2Ratio, color=Colors().redColor)
pylab.axvspan(self.meanLog2Ratio-(2*self.stdLog2Ratio),
self.meanLog2Ratio+(2*self.stdLog2Ratio), color=Colors().blueColor, alpha=0.2)
his = pylab.hist(self.log2Ratio, bins=50, color=Colors().blueColor)
pylab.xlabel("log2 Ratio %s/%s" %(self.sampleNames[1], self.sampleNames[0]))
pylab.ylabel("Frequency")
ax = f.add_subplot(122, aspect='equal')
pylab.scatter(self.genes1, self.genes2, c=co, alpha=0.5)
pylab.ylabel("%s RPKM" %self.sampleNames[1])
pylab.xlabel("%s RPKM" %self.sampleNames[0])
pylab.yscale('log')
pylab.xscale('log')
pylab.tight_layout()
def genderBoxplots(self, women, men, labels, path):
data = [women.edition_count.values, men.edition_count.values]
plt.figure()
plt.boxplot(data)
# mark the mean
means = [np.mean(x) for x in data]
print(means)
plt.scatter(range(1, len(data) + 1), means, color="red", marker=">", s=20)
plt.ylabel("num editions")
plt.xticks(range(1, len(data) + 1), labels)
plt.savefig(
path + "/numeditions_gender_box_withOutlier" + self.pre + "-" + self.post + ".png", bbox_inches="tight"
)
plt.figure()
plt.boxplot(data, sym="")
# mark the mean
means = [np.mean(x) for x in data]
print(means)
plt.scatter(range(1, len(data) + 1), means, color="red", marker=">", s=20)
plt.ylabel("num editions")
plt.xticks(range(1, len(data) + 1), labels)
plt.savefig(path + "/numeditions_gender_box" + self.pre + "-" + self.post + ".png", bbox_inches="tight")
def plot_all(x, y): pca = PCA(n_components=2) new_x = pca.fit(x).transform(x) for i in range(0, len(new_x)): l_color = color_list[y[i]] pl.scatter(new_x[i, 0], new_x[i, 1], color=l_color) pl.show()
def linearReg():
from sklearn import datasets
diabetes = datasets.load_diabetes()
diabetes_X_train = diabetes.data[:-20]
diabetes_X_test = diabetes.data[-20:]
diabetes_y_train = diabetes.target[:-20]
diabetes_y_test = diabetes.target[-20:]
from sklearn import linear_model
regr = linear_model.LinearRegression()
regr.fit(diabetes_X_train, diabetes_y_train)
print(regr.coef_)
import numpy as np
np.mean((regr.predict(diabetes_X_test) - diabetes_y_test) ** 2)
regr.score(diabetes_X_test, diabetes_y_test)
X = np.c_[.5, 1].T
y = [.5, 1]
test = np.c_[0, 2].T
regr = linear_model.LinearRegression()
import pylab as pl
pl.figure()
np.random.seed(0)
for _ in range(6):
this_X = .1 * np.random.normal(size=(2, 1)) + X
regr.fit(this_X, y)
pl.plot(test, regr.predict(test))
pl.scatter(this_X, y, s=3)
def scatter_from_csv(self, filename, sand = 'sand', silt = 'silt', clay = 'clay', diameter = '', hue = '', tags = '', **kwargs):
"""Loads data from filename (expects csv format). Needs one header row with at least the columns {sand, silt, clay}. Can also plot two more variables for each point; specify the header value for columns to be plotted as diameter, hue. Can also add a text tag offset from each point; specify the header value for those tags.
Note! text values (header entries, tag values ) need to be quoted to be recognized as text. """
fh = file(filename, 'rU')
soilrec = csv2rec(fh)
count = 0
if (sand in soilrec.dtype.names):
count = count + 1
if (silt in soilrec.dtype.names):
count = count + 1
if (clay in soilrec.dtype.names):
count = count + 1
if (count < 3):
print "ERROR: need columns for sand, silt and clay identified in ', filename"
locargs = {'s': None, 'c': None}
for (col, key) in ((diameter, 's'), (hue, 'c')):
col = col.lower()
if (col != '') and (col in soilrec.dtype.names):
locargs[key] = soilrec.field(col)
else:
print 'ERROR: did not find ', col, 'in ', filename
for k in kwargs:
locargs[k] = kwargs[k]
values = zip(*[soilrec.field(sand), soilrec.field(clay), soilrec.field(silt)])
print values
(xs, ys) = self._toCart(values)
p.scatter(xs, ys, label='_', **locargs)
if (tags != ''):
tags = tags.lower()
for (x, y, tag) in zip(*[xs, ys, soilrec.field(tags)]):
print x,
print y,
print tag
p.text(x + 1, y + 1, tag, fontsize=12)
fh.close()
def draw_cluster(self):
for station in self.cluster.stations:
for detector in station.detectors:
x, y = detector.get_xy_coordinates()
plt.scatter(x, y, c='r', s=5, edgecolor='none')
x, y, alpha = station.get_xyalpha_coordinates()
plt.scatter(x, y, c='orange', s=10, edgecolor='none')
def test_regress_vary_bsln(baselines, coeffs, nants=32, restrictChi=False):
"""
This is the exact same function as test_regress_vary_na except that it
plots the number of baselines on the x axis instead of the number of
antennae.
"""
for jj in n.arange(100):
nas = n.arange(2, nants)
gs_diff = n.zeros(len(nas))
for ii, na in enumerate(nas):
gs_recov, redchi, err = test_regress(baselines, coeffs, gs=1, n_sig=0.1, na=na, readFromFile=True)
if restrictChi:
if n.absolute(redchi - 1) < 0.1:
gs_diff[ii] = gs_recov - 1.0
else:
gs_diff[ii] = None
else:
gs_diff[ii] = gs_recov - 1.0
p.scatter(nas * (nas - 1) / 2, gs_diff)
p.xlabel("Number of baselines")
p.ylabel("Difference between true and recovered global signal")
# p.show()
if restrictChi:
p.savefig("./figures/gs_diff_vs_bsln_good_chi.pdf")
else:
p.savefig("./figures/gs_diff_vs_bsln.pdf")
p.clf()
fignum = 1
# we create an instance of Neighbours Classifier and fit the data.
for name, clf in classifiers.iteritems():
clf.fit(X, Y)
# Plot the decision boundary. For that, we will asign a color to each
# point in the mesh [x_min, m_max]x[y_min, y_max].
x_min, x_max = X[:,0].min() - .5, X[:,0].max() + .5
y_min, y_max = X[:,1].min() - .5, X[:,1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
pl.figure(fignum, figsize=(4, 3))
pl.set_cmap(pl.cm.Paired)
pl.pcolormesh(xx, yy, Z)
# Plot also the training points
pl.scatter(X[:,0], X[:,1], c=Y)
pl.xlabel('Sepal length')
pl.ylabel('Sepal width')
pl.xlim(xx.min(), xx.max())
pl.ylim(yy.min(), yy.max())
pl.xticks(())
pl.yticks(())
fignum += 1
pl.show()
def plot(self,
to_plot=None,
do_save=None,
fig_path=None,
fig_args=None,
plot_args=None,
scatter_args=None,
axis_args=None,
as_dates=True,
interval=None,
dateformat=None,
font_size=18,
font_family=None,
use_grid=True,
use_commaticks=True,
do_show=True,
verbose=None):
'''
Plot the results -- can supply arguments for both the figure and the plots.
Args:
to_plot (dict): Nested dict of results to plot; see default_sim_plots for structure
do_save (bool or str): Whether or not to save the figure. If a string, save to that filename.
fig_path (str): Path to save the figure
fig_args (dict): Dictionary of kwargs to be passed to pl.figure()
plot_args (dict): Dictionary of kwargs to be passed to pl.plot()
scatter_args (dict): Dictionary of kwargs to be passed to pl.scatter()
axis_args (dict): Dictionary of kwargs to be passed to pl.subplots_adjust()
as_dates (bool): Whether to plot the x-axis as dates or time points
interval (int): Interval between tick marks
dateformat (str): Date string format, e.g. '%B %d'
font_size (int): Size of the font
font_family (str): Font face
use_grid (bool): Whether or not to plot gridlines
use_commaticks (bool): Plot y-axis with commas rather than scientific notation
do_show (bool): Whether or not to show the figure
verbose (bool): Display a bit of extra information
Returns:
fig: Figure handle
'''
if verbose is None:
verbose = self['verbose']
sc.printv('Plotting...', 1, verbose)
if to_plot is None:
to_plot = default_sim_plots
to_plot = sc.odict(to_plot) # In case it's supplied as a dict
# Handle input arguments -- merge user input with defaults
fig_args = sc.mergedicts({'figsize': (16, 14)}, fig_args)
plot_args = sc.mergedicts({'lw': 3, 'alpha': 0.7}, plot_args)
scatter_args = sc.mergedicts({'s': 70, 'marker': 's'}, scatter_args)
axis_args = sc.mergedicts(
{
'left': 0.1,
'bottom': 0.05,
'right': 0.9,
'top': 0.97,
'wspace': 0.2,
'hspace': 0.25
}, axis_args)
fig = pl.figure(**fig_args)
pl.subplots_adjust(**axis_args)
pl.rcParams['font.size'] = font_size
if font_family:
pl.rcParams['font.family'] = font_family
res = self.results # Shorten since heavily used
# Plot everything
for p, title, keylabels in to_plot.enumitems():
ax = pl.subplot(len(to_plot), 1, p + 1)
for key in keylabels:
label = res[key].name
this_color = res[key].color
y = res[key].values
pl.plot(res['t'], y, label=label, **plot_args, c=this_color)
if self.data is not None and key in self.data:
pl.scatter(self.data['day'],
self.data[key],
c=[this_color],
**scatter_args)
if self.data is not None and len(self.data):
pl.scatter(pl.nan,
pl.nan,
c=[(0, 0, 0)],
label='Data',
**scatter_args)
pl.grid(use_grid)
cvu.fixaxis(self)
if use_commaticks:
sc.commaticks()
pl.title(title)
# Optionally reset tick marks (useful for e.g. plotting weeks/months)
if interval:
xmin, xmax = ax.get_xlim()
ax.set_xticks(pl.arange(xmin, xmax + 1, interval))
# Set xticks as dates
if as_dates:
xticks = ax.get_xticks()
xticklabels = self.inds2dates(xticks, dateformat=dateformat)
ax.set_xticklabels(xticklabels)
# Plot interventions
for intervention in self['interventions']:
intervention.plot(self, ax)
# Ensure the figure actually renders or saves
if do_save:
if fig_path is None: # No figpath provided - see whether do_save is a figpath
if isinstance(do_save, str):
fig_path = do_save # It's a string, assume it's a filename
else:
fig_path = 'covasim.png' # Just give it a default name
fig_path = sc.makefilepath(
fig_path) # Ensure it's valid, including creating the folder
pl.savefig(fig_path)
if do_show:
pl.show()
else:
pl.close(fig)
return fig
point_on_plane = np.eye(n_obj)[0] # Point on Das-Dennis
reference_directions = get_ref_dirs_from_n(n_obj, 21) # Das-Dennis points
for point in ref_point:
# ref_proj = point - np.dot(point - point_on_plane, n_vector) * n_vector
# TODO: Compute which is faster, a copy.deepcopy, or recomputing all the points from get_ref_dirs_from_n
ref_dir = copy.deepcopy(
reference_directions) # Copy of computed reference directions
for i in range(n_obj): # Shrink Das-Dennis points by a factor of alpha
ref_dir[:, i] = point[i] + alpha * (ref_dir[:, i] - point[i])
for d in ref_dir: # Project shrunked Das-Dennis points back onto original Das-Dennis hyperplane
ref_dirs.append(d -
np.dot(d - point_on_plane, n_vector) * n_vector)
# TODO: Extreme points are only extreme of the scale is normalized between 0-1, how to make them truly extreme?
ref_dirs.extend(np.eye(n_obj)) # Add extreme points
return np.array(ref_dirs)
if __name__ == '__main__':
test = get_ref_dirs_from_n(2, 100)
# for i in [3]:
# for j in range(20):
# test = get_ref_dirs_from_section(i, j)
# print(j, len(test), get_number_of_reference_directions(i, j))
# print()
import pylab as pl
fig = pl.subplot()
pl.scatter(test[:, 0], test[:, 1])
a=-w[0]/w[1]
xx=np.linspace(-5,5)
yy=a*xx-(clf.intercept_[0])/w[1]
b=clf.support_vectors_[0]
yy_down=a*xx+(b[1]-a*b[0])
b=clf.support_vectors_[-1]
yy_up=a*xx+(b[1]-a*b[0])
print "w:",w
print "a:",a
print "support_vectors_",clf.support_vectors_
print "clf.coef_",clf.coef_
pl.plot(xx,yy,'k-')
pl.plot(xx,yy_down,'k--')
pl.plot(xx,yy_up,'k--')
pl.scatter(clf.support_vectors_[:,0],clf.support_vectors_[:,-1],s=80,facecolors='none')
pl.scatter(X[:,0],X[:,-1],c=Y,cmap=pl.cm.Paired) #scatter显示出离散的点
pl.axis('tight')
pl.show()
def discrepancy_plot(data,
name='discrepancy',
report_p=True,
format='png',
suffix='-gof',
path='./',
fontmap=None):
'''
Generate goodness-of-fit deviate scatter plot.
:Arguments:
data: list
List (or list of lists for vector-valued variables) of discrepancy values, output
from the `pymc.diagnostics.discrepancy` function .
name: string
The name of the plot.
report_p: bool
Flag for annotating the p-value to the plot.
format (optional): string
Graphic output format (defaults to png).
suffix (optional): string
Filename suffix (defaults to "-gof").
path (optional): string
Specifies location for saving plots (defaults to local directory).
fontmap (optional): dict
Font map for plot.
'''
if verbose > 0:
print_('Plotting', name + suffix)
if fontmap is None:
fontmap = {1: 10, 2: 8, 3: 6, 4: 5, 5: 4}
# Generate new scatter plot
figure()
try:
x, y = transpose(data)
except ValueError:
x, y = data
scatter(x, y)
# Plot x=y line
lo = nmin(ravel(data))
hi = nmax(ravel(data))
datarange = hi - lo
lo -= 0.1 * datarange
hi += 0.1 * datarange
pyplot((lo, hi), (lo, hi))
# Plot options
xlabel('Observed deviates', fontsize='x-small')
ylabel('Simulated deviates', fontsize='x-small')
if report_p:
# Put p-value in legend
count = sum(s > o for o, s in zip(x, y))
text(lo + 0.1 * datarange,
hi - 0.1 * datarange,
'p=%.3f' % (count / len(x)),
horizontalalignment='center',
fontsize=10)
# Save to file
if not os.path.exists(path):
os.mkdir(path)
if not path.endswith('/'):
path += '/'
savefig("%s%s%s.%s" % (path, name, suffix, format))
def runMetroSingleChain(self, individual0, NSteps=1000, chain_dict={}):
df = self.PM.NPixListData
self.rv = chi2(df)
_, Chi2 = self.GiveFitness(individual0)
self.MinChi2 = Chi2
logProb = self.rv.logpdf(Chi2)
x = np.linspace(0, 2 * self.rv.moment(1), 1000)
lP = self.rv.logpdf(x)
iMax = np.argmax(lP)
self.Chi2PMax = x[iMax]
# #####################
# # V0
#self.Var=self.MinChi2/self.Chi2PMax
#Chi20_n=self.MinChi2/self.Var
#VarMin=(3e-3)**2
#ThVar=np.max([self.Var,VarMin])
#ShrinkFactor=np.min([1.,self.Var/ThVar])
# # print
# # print ShrinkFactor
# # print
# # stop
# #####################
VarMin = (3e-4)**2
#self.Var=np.max([self.EstimatedStdFromResid**2,VarMin])
Var = self.MinChi2 / self.Chi2PMax
S = self.PM.ArrayToSubArray(individual0, Type="S")
B = np.sum(np.abs(S)) / float(S.size)
B0 = 7e-4
Sig0 = 3e-3
Sig = B * Sig0 / B0
# print
# print "%f %f %f -> %f"%(B,B0,Sig0,Sig)
# print
self.Var = np.max([4. * self.EstimatedStdFromResid**2, Sig**2])
Chi20_n = self.MinChi2 / self.Var
ShrinkFactor = 1.
# #####################
DicoChains = {}
Parms = individual0
# ##################################
DoPlot = True
if DoPlot:
import pylab
pylab.figure(1)
x = np.linspace(0, 2 * self.rv.MeanChi2, 1000)
P = self.rv.pdf(x)
pylab.clf()
pylab.plot(x, P)
Chi2Red = Chi2_0 #/self.Var
pylab.scatter(Chi2Red, np.mean(P), c="black")
pylab.draw()
pylab.show(False)
# ##################################
# ##################################
DoPlot = False
# DoPlot=True
if DoPlot:
import pylab
x = np.linspace(0, 2 * self.rv.moment(1), 1000)
P = self.rv.pdf(x)
pylab.clf()
pylab.plot(x, P)
pylab.scatter(Chi20_n, np.mean(P), c="black")
pylab.draw()
pylab.show(False)
# ##################################
DicoChains["Parms"] = []
DicoChains["Chi2"] = []
DicoChains["logProb"] = []
logProb0 = self.rv.logpdf(Chi20_n)
Mut_pFlux, Mut_p0, Mut_pMove = 0.2, 0., 0.3
#T.disable()
FactorAccelerate = 1.
lAccept = []
NBurn = self.GD["MetroClean"]["MetroNBurnin"]
NSteps = NSteps + NBurn
NAccepted = 0
iStep = 0
NMax = NSteps #10000
#for iStep in range(NSteps):
while NAccepted < NSteps and iStep < NMax:
iStep += 1
#print "========================"
#print iStep
individual1, = self.MutMachine.mutGaussian(individual0.copy(),
Mut_pFlux, Mut_p0,
Mut_pMove) #,
#FactorAccelerate=FactorAccelerate)
# ds=Noise
# individual1,=self.MutMachine.mutNormal(individual0.copy(),ds*1e-1*FactorAccelerate)
# #T.timeit("mutate")
_, Chi2 = self.GiveFitness(individual1)
# if Chi2<self.MinChi2:
# self.Var=Chi2/self.Chi2PMax
# #print " >>>>>>>>>>>>>> %f"%np.min(Chi2)
Chi2_n = Chi2 / self.Var
Chi2_n = Chi20_n + ShrinkFactor * (Chi2_n - Chi20_n)
logProb = self.rv.logpdf(Chi2_n)
p1 = logProb
p0 = logProb0 #DicoChains["logProb"][-1]
if p1 - p0 > 5:
R = 1
elif p1 - p0 < -5:
R = 0
else:
R = np.min([1., np.exp(p1 - p0)])
r = np.random.rand(1)[0]
#print "%5.3f [%f -> %f]"%(R,p0,p1)
# print "MaxDiff ",np.max(np.abs(self.pop[iChain]-DicoChains[iChain]["Parms"][-1]))
lAccept.append((r < R))
if r < R: # accept
individual0 = individual1
logProb0 = logProb
NAccepted += 1
if NAccepted > NBurn:
DicoChains["logProb"].append(p1)
DicoChains["Parms"].append(individual1)
DicoChains["Chi2"].append(Chi2_n)
if DoPlot:
pylab.scatter(Chi2_n, np.exp(p1), lw=0)
pylab.draw()
pylab.show(False)
pylab.pause(0.1)
# print " accept"
# # Model=self.StackChain()
# # Asq=self.ArrayMethodsMachine.PM.ModelToSquareArray(Model,TypeInOut=("Parms","Parms"))
# # _,npol,NPix,_=Asq.shape
# # A=np.mean(Asq,axis=0).reshape((NPix,NPix))
# # Mask=(A==0)
# # pylab.clf()
# # pylab.imshow(A,interpolation="nearest")
# # pylab.draw()
# # pylab.show(False)
# # pylab.pause(0.1)
else:
# # #######################
if DoPlot:
pylab.scatter(Chi2_n, np.exp(p1), c="red", lw=0)
pylab.draw()
pylab.show(False)
pylab.pause(0.1)
# # #######################
pass
#T.timeit("Compare")
AccRate = np.count_nonzero(lAccept) / float(len(lAccept))
#print "[%i] Acceptance rate %f [%f with ShrinkFactor %f]"%(iStep,AccRate,FactorAccelerate,ShrinkFactor)
if (iStep % 50 == 0) & (iStep > 10):
if AccRate > 0.234:
FactorAccelerate *= 1.5
else:
FactorAccelerate /= 1.5
FactorAccelerate = np.min([3., FactorAccelerate])
FactorAccelerate = np.max([.01, FactorAccelerate])
lAccept = []
#T.timeit("Acceptance")
T.timeit("Chain")
chain_dict["logProb"] = np.array(DicoChains["logProb"])
chain_dict["Parms"] = np.array(DicoChains["Parms"])
chain_dict["Chi2"] = np.array(DicoChains["Chi2"])
rateVariationCell = []
rateVariationD2d = []
rateVariationBoth = []
print("\n Variation of Throughput With No. of D2D Users\n")
for Nd in Ndvariation:
sys.stdout.write("\r")
progress = int(100 * ((Nd - Ndmin) / (Ndmax - Ndmin - step)))
percent = "{:2}".format(progress)
sys.stdout.write(" " + percent + " % ")
[sys.stdout.write("##") for x in range(int(Nd / step))]
sys.stdout.flush()
throughputCell, throughPutD2d = core.core(Rc, Pc, bw, N0, tSNR, cellUsers,
Nc, Nd, Nrb, d2dDistance,
rbPerD2DPair, RWindowSize, 400, False)
rateVariationCell.append(throughputCell)
rateVariationD2d.append(throughPutD2d)
rateVariationBoth.append(throughputCell + throughPutD2d)
print("")
pl.figure(2)
pl.plot(Ndvariation, np.asarray(rateVariationCell) / 1e6, label="Cell Users")
pl.scatter(Ndvariation, np.asarray(rateVariationCell) / 1e6, marker=">")
pl.plot(Ndvariation, np.asarray(rateVariationD2d) / 1e6, label="D2D Pairs")
pl.scatter(Ndvariation, np.asarray(rateVariationD2d) / 1e6, marker=">")
pl.plot(Ndvariation, np.asarray(rateVariationBoth) / 1e6, label="Both")
pl.scatter(Ndvariation, np.asarray(rateVariationBoth) / 1e6, marker=">")
pl.xlabel("No. of D2D Pairs")
pl.ylabel("Throughput (Mbits/sec)")
pl.legend(loc="upper left")
pl.grid(True)
pl.show()
def test_budget(self):
"Test the budget heuristic."
w = np.random.uniform(-1, 1, size=self.D)
w_orig = w.copy()
def L0(threshold):
ww = w_orig.copy()
self.prox(threshold, ww)
return (np.abs(ww) > 0).sum()
# Check that the find_threshold gives a conservative estimate for L0 budget
M = len(w)
f = {}
est = {}
for budget in range(M + 1):
est[budget] = self.find_threshold(budget, w)
l0 = L0(est[budget])
f[budget] = l0
assert l0 <= budget
# Check end points
assert f[0] == 0
assert f[M] == M
# Check coverage against a numerical sweep.
numerical_x = np.linspace(0, M + 1, 10000)
numerical_y = np.array([L0(threshold) for threshold in numerical_x])
heuristic_x = np.array(sorted(est.values()))
heuristic_y = np.array([L0(threshold) for threshold in heuristic_x])
if 0:
pl.title('threshold vs L0 coverage')
keep = numerical_y > 0
pl.plot(numerical_x[keep],
numerical_y[keep],
c='b',
alpha=0.5,
lw=2,
label='numerical')
pl.plot(heuristic_x,
heuristic_y,
alpha=0.5,
c='r',
lw=2,
label='heuristic')
pl.scatter(heuristic_x, heuristic_y, lw=0)
pl.legend(loc='best')
pl.show()
# How many operating points (budgets) do we miss that the numerical
# method achieves?
#
# Note that we don't expect perfect coverage because the heuristic
# pretends that groups don't overlap.
#
# ^^ We appear to be getting great coverage. Should we revise this
# statement?
numerical_points = list(sorted(set(numerical_y)))
heuristic_points = list(sorted(set(heuristic_y)))
print('numerical:', numerical_points)
print('heuristic:', heuristic_points)
recall = len(set(numerical_points) & set(heuristic_points)) / len(
set(numerical_points))
print('recall: %.2f' % recall)
if 0:
# This plot is for debugging the conservativeness of the budget
# heuristic, which is now asserted above.
pl.title('Ability to conservatively meet the budget')
xs, ys = list(zip(*sorted(f.items())))
pl.plot(xs, xs, c='k', alpha=0.5, linestyle=':')
pl.plot(xs, ys, alpha=0.5, c='r', lw=2)
pl.scatter(xs, ys, lw=0)
pl.show()
print('[test budget]', colors.light.green % 'pass')
import numpy as np
import pylab as pl
N = 1000
n = 10
np.random.seed(3) #use always the same seed
x, y = np.random.randn(2, N) / 10 + 0.5
X, Y = np.mgrid[0:1:n * 1j, 0:1:n * 1j]
xfloor = X[:, 0][np.floor(n * x).astype(int)]
yfloor = Y[0][np.floor(n * y).astype(int)]
z = xfloor + n * yfloor
Z = X + n * Y
histo = np.histogram(z.ravel(), bins=np.r_[Z.T.ravel(), 2 * n**2])
pl.pcolor(X - 1. / (2 * n), Y - 1. / (2 * n), histo[0].reshape(
(n, n))) #shifted to
# have centered bins
pl.scatter(x, y)
pl.show()
def test_one_feature_mixture(component_model_type,
num_clusters=3,
show_plot=False,
seed=None):
"""
"""
random.seed(seed)
N = 300
separation = .9
get_next_seed = lambda: random.randrange(2147483647)
cluster_weights = [[1.0 / float(num_clusters)] * num_clusters]
cctype = component_model_type.cctype
T, M_c, structure = sdg.gen_data([cctype],
N, [0],
cluster_weights, [separation],
seed=get_next_seed(),
distargs=[distargs[cctype]],
return_structure=True)
T_list = list(T)
T = numpy.array(T)
# pdb.set_trace()
# create a crosscat state
M_c = du.gen_M_c_from_T(T_list, cctypes=[cctype])
state = State.p_State(M_c, T_list)
# Get support over all component models
discrete_support = qtu.get_mixture_support(
cctype,
component_model_type,
structure['component_params'][0],
nbins=250)
# calculate simple predictive probability for each point
Q = [(N, 0, x) for x in discrete_support]
# transitions
state.transition(n_steps=200)
# get the sample
X_L = state.get_X_L()
X_D = state.get_X_D()
# generate samples
# kstest has doesn't compute the same answer with row and column vectors
# so we flatten this column vector into a row vector.
predictive_samples = sdg.predictive_columns(
M_c, X_L, X_D, [0], seed=get_next_seed()).flatten(1)
probabilities = su.simple_predictive_probability(M_c, X_L, X_D,
[] * len(Q), Q)
# get histogram. Different behavior for discrete and continuous types. For some reason
# the normed property isn't normalizing the multinomial histogram to 1.
# T = T[:,0]
if is_discrete[component_model_type.model_type]:
bins = range(len(discrete_support))
T_hist = numpy.array(qtu.bincount(T, bins=bins))
S_hist = numpy.array(qtu.bincount(predictive_samples, bins=bins))
T_hist = T_hist / float(numpy.sum(T_hist))
S_hist = S_hist / float(numpy.sum(S_hist))
edges = numpy.array(discrete_support, dtype=float)
else:
T_hist, edges = numpy.histogram(T,
bins=min(50, len(discrete_support)),
normed=True)
S_hist, _ = numpy.histogram(predictive_samples,
bins=edges,
normed=True)
edges = edges[0:-1]
# Goodness-of-fit-tests
if not is_discrete[component_model_type.model_type]:
# do a KS tests if the distribution in continuous
# cdf = lambda x: component_model_type.cdf(x, model_parameters)
# stat, p = stats.kstest(predictive_samples, cdf) # 1-sample test
stat, p = stats.ks_2samp(predictive_samples, T[:, 0]) # 2-sample test
test_str = "KS"
else:
# Cressie-Read power divergence statistic and goodness of fit test.
# This function gives a lot of flexibility in the method <lambda_> used.
freq_obs = S_hist * N
freq_exp = numpy.exp(probabilities) * N
stat, p = stats.power_divergence(freq_obs, freq_exp, lambda_='pearson')
test_str = "Chi-square"
if show_plot:
pylab.clf()
lpdf = qtu.get_mixture_pdf(discrete_support, component_model_type,
structure['component_params'][0],
[1.0 / num_clusters] * num_clusters)
pylab.axes([0.1, 0.1, .8, .7])
# bin widths
width = (numpy.max(edges) - numpy.min(edges)) / len(edges)
pylab.bar(edges,
T_hist,
color='blue',
alpha=.5,
width=width,
label='Original data',
zorder=1)
pylab.bar(edges,
S_hist,
color='red',
alpha=.5,
width=width,
label='Predictive samples',
zorder=2)
# plot actual pdf of support given data params
pylab.scatter(discrete_support,
numpy.exp(lpdf),
c="blue",
edgecolor="none",
s=100,
label="true pdf",
alpha=1,
zorder=3)
# plot predictive probability of support points
pylab.scatter(discrete_support,
numpy.exp(probabilities),
c="red",
edgecolor="none",
s=100,
label="predictive probability",
alpha=1,
zorder=4)
pylab.legend()
ylimits = pylab.gca().get_ylim()
pylab.ylim([0, ylimits[1]])
title_string = "%i samples drawn from %i %s components: \ninference after 200 crosscat transitions\n%s test: p = %f" \
% (N, num_clusters, component_model_type.cctype, test_str, round(p,4))
pylab.title(title_string, fontsize=12)
filename = component_model_type.model_type + "_mixtrue.png"
pylab.savefig(filename)
pylab.close()
return p
# Since we have class labels for the training data, we can
# initialize the GMM parameters in a supervised manner.
classifier.means_ = np.array(
[X_train[y_train == i].mean(axis=0) for i in xrange(n_classes)])
# Train the other parameters using the EM algorithm.
classifier.fit(X_train)
h = pl.subplot(2, n_classifiers / 2, index + 1)
make_ellipses(classifier, h)
for n, color in enumerate('rgb'):
data = iris.data[iris.target == n]
pl.scatter(data[:, 0],
data[:, 1],
0.8,
color=color,
label=iris.target_names[n])
# Plot the test data with crosses
for n, color in enumerate('rgb'):
data = X_test[y_test == n]
pl.plot(data[:, 0], data[:, 1], 'x', color=color)
y_train_pred = classifier.predict(X_train)
train_accuracy = np.mean(y_train_pred.ravel() == y_train.ravel()) * 100
pl.text(0.05,
0.9,
'Train accuracy: %.1f' % train_accuracy,
transform=h.transAxes)
y_test_pred = classifier.predict(X_test)
import random, pylab
meanArrival = 60
arrivals = []
for i in range(2000):
interArrivalTime = random.expovariate(1.0 / meanArrival)
arrivals.append(interArrivalTime)
ave = sum(arrivals) / len(arrivals)
print 'Distance from intended mean:', meanArrival - ave
xAxis = pylab.arange(0, len(arrivals), 1)
pylab.scatter(xAxis, arrivals)
pylab.axhline(meanArrival, linewidth=4)
pylab.title('Exponential Inter-arrival Times')
pylab.ylabel('Inter-arrival Time (secs)')
pylab.xlabel('Job Number')
pylab.figure()
pylab.hist(arrivals)
pylab.title('Exponential Inter-arrival Times')
pylab.xlabel('Inter-arrival Time (secs)')
pylab.ylabel('Number of Jobs')
pylab.show()
def plot_samples(type, data, data_ql, samples, samples_ql, var_name1,
var_name2, scaler, ncomp, z, path):
print('Plot samples')
# type: normalised data or original data
# data: data from LES output
# data_ql: ql data from LES output
# samples: sample data drawn from GMM PDF
# samples_ql: ql data calculated from sample data
# var_name1: thl or s
# var_name : qt
# scaler: scaling factors for normalisation
# ncomp: # of components for GMM PDF
# path: output path for saving figure
# print('shapes: ', data.shape, data_ql.shape, samples.shape, samples_ql.shape)
# print('min/max: ', np.amin(data_ql), np.amax(data_ql), np.amin(samples_ql), np.amax(samples_ql))
# ql_min = np.min([np.amin(data_ql), np.amin(samples_ql)])
ql_min = 0.0
ql_max = np.max([np.amax(data_ql), np.amax(samples_ql)])
scale_thl = scaler.scale_[0]
scale_qt = scaler.scale_[1]
xmin = np.min([np.amin(data[:, 0]), np.amin(samples[:, 0])])
xmax = np.max([np.amax(data[:, 0]), np.amax(samples[:, 0])])
ymin = np.min([np.amin(data[:, 1]), np.amin(samples[:, 1])])
ymax = np.max([np.amax(data[:, 1]), np.amax(samples[:, 1])])
cm = plt.cm.get_cmap('RdYlBu')
cm = plt.cm.get_cmap('viridis')
plt.figure(figsize=(8, 9))
plt.subplot(2, 2, 1)
plt.scatter(data[:, 0], data[:, 1], s=5, alpha=0.2)
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
if type == 'norm':
plt.title('data norm (n=' + str(np.shape(data)[0]) + ')')
else:
plt.title('data (n=' + str(np.shape(data)[0]) + ')')
labeling(var_name1, var_name2, scale_thl, scale_qt)
plt.subplot(2, 2, 2)
plt.scatter(samples[:, 0], samples[:, 1], s=5, alpha=0.2)
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
plt.title('samples (n=' + str(np.size(samples_ql)) + ')')
labeling(var_name1, var_name2, 0, 0)
plt.subplot(2, 2, 3)
try:
ax = plt.scatter(data[:, 0],
data[:, 1],
c=data_ql[:],
s=6,
alpha=0.5,
edgecolors='none',
vmin=ql_min,
vmax=ql_max) #, cmap = cm)
if np.amax(data_ql[:]) > 0.0:
plt.colorbar(ax, shrink=0.6)
except:
print('except data - color arr: ', data.shape, data_ql.shape)
# traceback.print_exc()
color_arr = np.zeros(shape=np.size(data_ql))
for i in range(np.size(data_ql)):
color_arr[i] = data_ql[i] * 1e3
print('data_ql except', np.amin(data_ql), np.amax(data_ql),
np.amin(color_arr), np.amax(color_arr), np.shape(color_arr))
print(color_arr.shape)
print(color_arr)
try:
ax = plt.scatter(data[:, 0],
data[:, 1],
c=color_arr[:],
s=6,
alpha=0.5,
edgecolors='none',
vmin=ql_min,
vmax=ql_max) # , cmap = cm)
if np.amax(data_ql[:]) > 0.0:
plt.colorbar(ax, shrink=0.6)
except:
# traceback.print_exc()
print('except except (data ql)')
ax = plt.scatter(data[:, 0],
data[:, 1],
s=6,
alpha=0.5,
edgecolors='none',
vmin=ql_min,
vmax=ql_max) # , cmap = cm)
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
if type == 'norm':
plt.title('data norm (n=' + str(np.shape(data)[0]) + ')')
else:
plt.title('data (n=' + str(np.shape(data)[0]) + ')')
labeling(var_name1, var_name2, scale_thl, scale_qt)
plt.subplot(2, 2, 4)
color_arr = np.zeros(shape=np.size(samples_ql))
for i in range(np.size(samples_ql)):
color_arr[i] = samples_ql[i] * 1e3
print('samples_ql ', np.amin(samples_ql), np.amax(samples_ql),
np.amin(color_arr), np.amax(color_arr), np.shape(color_arr))
try:
print('try')
# ax = plt.scatter(samples[:, 0], samples[:, 1], c=color_arr[:], s=6, alpha=0.5, edgecolors='none')
ax = plt.scatter(samples[:, 0],
samples[:, 1],
c=samples_ql[:],
s=6,
alpha=0.5,
edgecolors='none',
vmin=ql_min,
vmax=ql_max) #, cmap = cm)
if np.amax(data_ql[:]) > 0.0:
plt.colorbar(ax, shrink=0.6)
except:
try:
print('except-try')
ax = plt.scatter(samples[:, 0],
samples[:, 1],
c=color_arr[:],
s=6,
alpha=0.5,
edgecolors='none',
vmin=ql_min,
vmax=ql_max) #, cmap = cm)
# ax = plt.scatter(samples[:, 0], samples[:, 1], c=samples_ql[:], s=6, alpha=0.5, edgecolors='none')
if np.amax(data_ql[:]) > 0.0:
plt.colorbar(ax, shrink=0.6)
except:
print('except-except')
ax = plt.scatter(samples[:, 0],
samples[:, 1],
s=6,
alpha=0.5,
edgecolors='none') #, cmap = cm)
# pass
# pass
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
plt.title('samples (n=' + str(np.size(samples_ql)) + ')')
labeling(var_name1, var_name2, 0, 0)
# plt.subplot(2, 3, 6)
# color_arr = np.zeros(shape=np.shape(data_ql))
# for i in range(np.shape(data_ql)[0]):
# color_arr[i] = data_ql[i] * 1e4
# print('data_ql. ', np.amin(data_ql), np.amax(data_ql), np.amin(color_arr), np.amax(color_arr))
# plt.scatter(data[:, 0], data[:, 1], c=color_arr[:], s=5, alpha=0.2)
# # plt.gray()
# if type == 'norm':
# plt.title('data norm')
# else:
# plt.title('data')
# labeling(var_name1, var_name2, scale_thl, scale_qt)
plt.suptitle('Data & Samples: ncomp=' + str(ncomp) + ', z=' + str(z) + 'm',
fontsize=18)
if type == 'norm':
savename = 'sample_figure_' + 'ncomp' + str(ncomp) + '_norm_' + str(
z) + 'm.png'
else:
savename = 'sample_figure_' + 'ncomp' + str(ncomp) + '_' + str(
z) + 'm.png'
plt.savefig(os.path.join(path, 'CloudClosure_figures', savename))
plt.close()
return
elif args.DataSetName == "batvsuper":
classify_twitter_data(file_name="redmi.txt")
elif args.DataSetName == "junglebook":
classify_twitter_data(file_name="iphone.txt")
elif args.DataSetName == "zootopia":
classify_twitter_data(file_name="googlepixel.txt")
elif args.DataSetName == "deadpool":
classify_twitter_data(file_name="oneplus.txt")
else:
print("ERROR while specifying Movie Tweets File, please check the name again")
pca = PCA(n_components=2).fit(data_X)
pca_2d = pca.transform(data_X)
svmClassifier_2d = svm.LinearSVC(random_state=111).fit( pca_2d, data_Y)
for i in range(0, pca_2d.shape[0]):
if data_Y[i] == float('negative'):
c1 = pl.scatter(pca_2d[i,0],pca_2d[i,1],c='r', s=50,marker='+')
elif data_Y[i] == float('positive'):
c2 = pl.scatter(pca_2d[i,0],pca_2d[i,1],c='g', s=50,marker='o')
elif data_Y[i] == float('neutral'):
c3 = pl.scatter(pca_2d[i,0],pca_2d[i,1],c='b', s=50,marker='*')
pl.legend([c1, c2], ['Positive', 'Negative','Neutral'])
x_min, x_max = pca_2d[:, 0].min() - 1, pca_2d[:,0].max() + 1
y_min, y_max = pca_2d[:, 1].min() - 1, pca_2d[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, .01), np.arange(y_min, y_max, .01))
Z = svmClassifier_2d.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
pl.contour(xx, yy, Z)
pl.title('Support Vector Machine Decision Surface')
pl.axis('off')
pl.show()
def draw(self, W, data=[], C=[], file_name=1):
if file_name != 1:
pl.figure(figsize=(14, 6))
colValue = ['y', 'g', 'b', 'c', 'k', 'm', 'peru', 'darkorchid']
# 聚类图像
pl.subplot(1,2,1)
# 普通的样本
if len(data) != 0:
pl.plot(data[:,0], data[:,1], 'ko', markersize="5")
# 分类的样本
if len(C) != 0:
for i in range(len(C)):
coo_X = [] # x坐标列表
coo_Y = [] # y坐标列表
for j in range(len(C[i])):
coo_X.append(C[i][j][0])
coo_Y.append(C[i][j][1])
pl.scatter(coo_X, coo_Y, marker='o', color=colValue[i % len(colValue)], label=i)
pl.legend(loc='upper right')
# 神经元
pl.plot(W[:,0], W[:,1], "ro", marker='o', markersize="7")
for index in range(self.output[0]*self.output[1]):
for i in range(self.output[0]*self.output[1]):
dist = self.Manhattan_dist(i, index)
if dist == 1:
pl.plot(W[[i,index],0], W[[i,index],1], "r")
pl.xlabel('X')
pl.ylabel('Y')
pl.title('SOM')
pl.xlim(-5, 25)
pl.ylim(-5, 25)
# 邻域半径图像
pl.subplot(2,2,2)
pl.title('Radius of Neighborhood')
pl.ylabel('Manhattan Distance')
x = np.linspace(0, self.iteration, self.iteration+1)
y = np.power(1.115, -x) * 4
pl.plot(x,y)
font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 18}
pl.text(20,3.5,r'$D = 4 \times 1.115^{-t}$', font1)
pl.plot(self.count, np.power(1.115, -self.count) * 4, 'go', markersize="12")
# 绘制学习率图像
pl.subplot(2,2,4)
pl.title('Learning Rate')
pl.xlabel('Iteration')
pl.ylabel('Rate')
x = np.linspace(0, self.iteration, self.iteration+1)
y = np.power(np.e, 0) / (x + 40)
pl.plot(x,y)
pl.text(20,0.024,'Batch Size=100, D=0', font1)
pl.text(20,0.022, r'$R = e^{-D} / (t + 40)$', font1)
pl.plot(self.count, np.power(np.e, 0) / (self.count + 40), 'go', markersize="12")
# 保存图片
pl.savefig(file_name, dpi=300)
pl.close()
else:
pl.figure(figsize=(22, 10))
colValue = ['y', 'g', 'b', 'c', 'k', 'm', 'peru', 'darkorchid']
# 聚类图像
pl.subplot(1,2,1)
# 普通的样本
if len(data) != 0:
pl.plot(data[:,0], data[:,1], 'ko', markersize="5")
# 分类的样本
if len(C) != 0:
for i in range(len(C)):
coo_X = [] # x坐标列表
coo_Y = [] # y坐标列表
for j in range(len(C[i])):
coo_X.append(C[i][j][0])
coo_Y.append(C[i][j][1])
pl.scatter(coo_X, coo_Y, marker='o', color=colValue[i % len(colValue)], label=i)
pl.legend(loc='upper right')
# 神经元
pl.plot(W[:,0], W[:,1], "ro", marker='o', markersize="7")
for index in range(self.output[0]*self.output[1]):
for i in range(self.output[0]*self.output[1]):
dist = self.Manhattan_dist(i, index)
if dist == 1:
pl.plot(W[[i,index],0], W[[i,index],1], "r")
pl.xlabel('X')
pl.ylabel('Y')
pl.title('SOM')
pl.xlim(-5, 25)
pl.ylim(-5, 25)
###########################################################################
num_tmp = 1000
for i in range(len(C)):
for j in range(len(C[i])):
num_tmp = min([len(C[i][j]), num_tmp])
pl.subplot(3,6,4)
pl.title("Cluster 0 (n=50)")
pl.ylim(-1,25)
pl.plot([1,2],self.W[0,:].A[0], 'ko-')
pl.subplot(3,6,5)
pl.title("Cluster 1 (n=0)")
pl.ylim(-1,25)
pl.subplot(3,6,6)
pl.title("Cluster 2 (n=50)")
pl.ylim(-1,25)
pl.plot([1,2],self.W[2,:].A[0], 'ko-')
pl.subplot(3,6,10)
pl.title("Cluster 3 (n=" + str(50-num_tmp) + ")")
pl.ylim(-1,25)
pl.plot([1,2],self.W[3,:].A[0], 'ko-')
pl.subplot(3,6,11)
pl.title("Cluster 4 (n=" + str(num_tmp) + ")")
pl.ylim(-1,25)
pl.plot([1,2],self.W[4,:].A[0], 'ko-')
pl.subplot(3,6,12)
pl.title("Cluster 5 (n=0)")
pl.ylim(-1,25)
pl.subplot(3,6,16)
pl.title("Cluster 6 (n=50)")
pl.ylim(-1,25)
pl.plot([1,2],self.W[6,:].A[0], 'ko-')
pl.subplot(3,6,17)
pl.title("Cluster 7 (n=50)")
pl.ylim(-1,25)
pl.plot([1,2],self.W[7,:].A[0], 'ko-')
pl.subplot(3,6,18)
pl.title("Cluster 8 (n=50)")
pl.ylim(-1,25)
pl.plot([1,2],self.W[8,:].A[0], 'ko-')
# 保存图片
pl.savefig('./fig5/fig36.png', dpi=300)
pl.show()
def plot_PDF(data, data_norm, var_name1, var_name2, nvar, clf, scaler, ncomp,
error, z, path):
print('Plot PDF: ncomp=' + str(ncomp) + ', z=' + str(z))
xmin = np.amin(data_norm[:, 0])
xmax = np.amax(data_norm[:, 0])
ymin = np.amin(data_norm[:, 1])
ymax = np.amax(data_norm[:, 1])
n_sample = np.int(1e2)
x_ = np.linspace(xmin, xmax, n_sample)
y_ = np.linspace(ymin, ymax, n_sample)
XX_ = np.ndarray(shape=(n_sample**nvar, nvar))
# (1) print PDF computed by GMM
delta_i = n_sample
for i in range(n_sample):
for j in range(n_sample):
shift = i * delta_i + j
XX_[shift, 0] = x_[i]
XX_[shift, 1] = y_[j]
ZZ_ = clf.score_samples(XX_)
ZZ = np.ndarray(shape=(n_sample, n_sample))
for j in range(n_sample**nvar):
jshift = np.mod(j, delta_i)
ishift = (j - jshift) / delta_i
ZZ[ishift, jshift] = ZZ_[j]
# XXii = np.zeros(shape=XX_.shape)
# print('XX before: ', ZZ.shape, XX_.shape)
# print(XX_[0:3, 0])
# print(XX_[0:3, 1])
# print('XX after inverse')
# XXi = scaler.inverse_transform(XX_)
# print(XXi[0:3,0])
# print(XXi[0:3, 1])
# XXii[:,0] = XX_[:,0]/scaler.scale_[0]
# XXii[:, 1] = XX_[:, 1]/ scaler.scale_[1]
# print(scaler.scale_)
# print('XX after rescale')
# print(XXii[0:3, 0])
# print(XXii[0:3, 1])
# (2) compute normal distribution from parameters given from GMM
# X_ = np.ndarray()
# Z_pred =
plt.figure(figsize=(16, 8))
plt.subplot(1, 4, 1)
plt.scatter(data[:, 0], data[:, 1], s=5, alpha=0.2)
plt.title('data')
labeling(var_name1, var_name2, 1, 1)
plt.xlim(np.amin(data[:, 0]), np.amax(data[:, 0]))
plt.ylim(np.amin(data[:, 1]), np.amax(data[:, 1]))
plt.subplot(1, 4, 2)
ax = plt.contour(x_, y_, np.exp(ZZ).T)
plt.scatter(data_norm[:, 0], data_norm[:, 1], s=5, alpha=0.2)
plt.colorbar(ax)
plt.title('data')
labeling(var_name1, var_name2, scaler.scale_[0], scaler.scale_[1])
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
plt.subplot(1, 4, 3)
ax = plt.contourf(x_, y_, np.exp(ZZ).T)
plt.colorbar(ax)
# plt.scatter(data_norm[:, 0], data_norm[:, 1], s=5, alpha=0.2)
plt.title('PDF(thl, qt)')
labeling(var_name1, var_name2, scaler.scale_[0], scaler.scale_[1])
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
plt.subplot(1, 4, 4)
try:
ax = plt.contourf(x_, y_, np.exp(ZZ).T, norm=LogNorm())
plt.colorbar(ax)
except:
print('except in: plot_PDF, subplot(1,4,3)')
# plt.scatter(data_norm[:, 0], data_norm[:, 1], s=5, alpha=0.2)
plt.title('PDF(thl, qt)')
labeling(var_name1, var_name2, scaler.scale_[0], scaler.scale_[1])
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
plt.suptitle('GMM, ncomp=' + str(ncomp) + ', error: ' + str(error) +
' (z=' + str(z) + ')')
print('')
print(
ncomp, z,
os.path.join(
path, 'CloudClosure_figures',
'PDF_figures_' + str(z) + 'm' + '_ncomp' + str(ncomp) + '.png'))
try:
plt.savefig(
os.path.join(
path, 'CloudClosure_figures', 'PDF_figures_' + str(z) + 'm' +
'_ncomp' + str(ncomp) + '.png'))
except:
print('!!!!! figure with ncomp=' + str(ncomp) + ', z=' + str(z) +
' not saved !!!!!')
# plt.show()
plt.close()
return
xaxisthetis1.append(i)
bathymetrythetis1.append(-solver_obj.fields.bathymetry_2d.at([i, 0.55]))
# plot thetis results against sisyphe and experiment
from matplotlib import colors as mcolors
colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)
plt.rc('font', family='Helvetica')
data = pd.read_excel('data/experimental_data.xlsx',
sheet_name='recreatepaperrun',
header=None)
diff_sisyphe = pd.read_excel('data/sisyphe_results.xlsx')
thetisdf = pd.read_csv('model_outputs/trench_bed_output.csv')
plt.scatter(data[0], data[1], label='Experimental Data')
plt.plot(thetisdf['x'],
thetisdf['bathymetry'],
'--',
linewidth=3,
c=colors['darkgreen'],
label=r'Thetis ($\Delta x = 0.2m$)')
plt.plot(xaxisthetis1,
bathymetrythetis1,
colors['mediumblue'],
label=r'Thetis ($\Delta x = 0.5m$)')
plt.plot(diff_sisyphe['x'][diff_sisyphe['y'] == 0.55],
-diff_sisyphe['Sisyphe'][diff_sisyphe['y'] == 0.55],
color=colors['orange'],
label='Sisyphe')
def plot_drawn_pos(ANTENNAS_i,ANTENNAS_sel,pos_tab,output_folder,task,DISPLAY):
#ANTENNAS_i are the antenna positions corrected by the position of the intersection between the shower axis and the ground
#ANTENNAS_sel are the selected antenna positions
#CORE is the random position
fig, ax = pl.subplots()
pl.scatter(ANTENNAS_i[:,0]+pos_tab[-1,0],ANTENNAS_i[:,1]+pos_tab[-1,1],c='b',edgecolors='none') #arrays are move back around (0,0,GdAlt)
pl.scatter(ANTENNAS_sel[:,0]+pos_tab[-1,0],ANTENNAS_sel[:,1]+pos_tab[-1,1],c='g',edgecolors='none') #arrays are move back around (0,0,GdAlt)
pl.scatter(pos_tab[:,0],pos_tab[:,1],c='k',edgecolors='none')
pl.scatter(pos_tab[-1,0],pos_tab[-1,1],c='r',edgecolors='none')
figname = output_folder+'/inp/fig/plots_'+task+'.png'
pl.savefig(figname,dpi=350)
if DISPLAY:
pl.show()
pl.close()
fig, ax = pl.subplots()
pl.scatter(ANTENNAS_i[:,0],ANTENNAS_i[:,1],c='b',edgecolors='none') #arrays are move back around (0,0,GdAlt)
pl.scatter(ANTENNAS_sel[:,0],ANTENNAS_sel[:,1],c='g',edgecolors='none') #arrays are move back around (0,0,GdAlt)
pl.scatter(0.,0.,c='r',edgecolors='none')
figname = output_folder+'/inp/fig/plots_'+task+'_real.png'
pl.savefig(figname,dpi=350)
if DISPLAY:
pl.show()
pl.close()
return
def plot():
print "Load tracking.txt"
d = numpy.genfromtxt("tracking.txt", names=True)
n = len(d)
# one per particle
print "Find unique inital particles"
ids_source = unique_ids(d['ID0'], d['E0'], d['X0'], d['Y0'], d['Z0'], d['P0x'], d['P0y'], d['P0z'], d['ID1'], d['E1'], d['X1'], d['Y1'], d['Z1'], d['P1x'], d['P1y'], d['P1z'])
nids_source = ids_source.max() + 1
print "Unique Source: ", nids_source
# one per initial
print "Find unique final particles"
ids_particle = unique_ids(d['ID0'], d['E0'], d['X0'], d['Y0'], d['Z0'], d['P0x'], d['P0y'], d['P0z'])
nids_particle = ids_particle.max() + 1
print "Unique Particle: ", nids_source
# collect data
print "Detect at different distances"
select_1000kpc = numpy.ones(nids_source, dtype=int) * -1
select_500kpc = numpy.ones(nids_source, dtype=int) * -1
select_200kpc = numpy.ones(nids_source, dtype=int) * -1
select_100kpc = numpy.ones(nids_source, dtype=int) * -1
select_50kpc = numpy.ones(nids_source, dtype=int) * -1
select_closest = numpy.ones(nids_source, dtype=int) * -1
dist = ((d['X'] - 64)**2 + (d['Y'] - 64)**2 + (d['Z'] - 64)**2)**0.5
for i in xrange(nids_source):
if i % 1000 == 0:
sys.stdout.write(" %5.2f%%\r" % (100. * float(i) / nids_source))
sys.stdout.flush()
# select i'th initial particle
s = numpy.nonzero(ids_source == i)[0]
s_d = d[s]
s_dist = dist[s]
# distances sorted by trajectory
D_sort = numpy.argsort(s_d['D'])
dist_sort = s_dist[D_sort]
idx = numpy.nonzero(dist_sort < 1.000)[0]
if len(idx) > 0:
select_1000kpc[i] = s[D_sort[idx[0]]]
idx = numpy.nonzero(dist_sort < 0.500)[0]
if len(idx) > 0:
select_500kpc[i] = s[D_sort[idx[0]]]
idx = numpy.nonzero(dist_sort < 0.200)[0]
if len(idx) > 0:
select_200kpc[i] = s[D_sort[idx[0]]]
idx = numpy.nonzero(dist_sort < 0.100)[0]
if len(idx) > 0:
select_100kpc[i] = s[D_sort[idx[0]]]
idx = numpy.nonzero(dist_sort < 0.05)[0]
if len(idx) > 0:
select_50kpc[i] = s[D_sort[idx[0]]]
select_closest[i] = s[numpy.argmin(s_dist)]
# plot first 10 tracks
first_ten = ids_source < 10
pylab.figure()
pylab.scatter(d['X'][first_ten], d['Y'][first_ten], c=ids_source[first_ten], s=50*(d['Z'][first_ten]-63))
first_ten_1000kpc = select_1000kpc[:10]
pylab.scatter(d['X'][first_ten_1000kpc], d['Y'][first_ten_1000kpc], marker='+', s=1000)
first_ten_500kpc = select_500kpc[:10]
pylab.scatter(d['X'][first_ten_500kpc], d['Y'][first_ten_500kpc], marker='+', s=500)
first_ten_200kpc = select_200kpc[:10]
pylab.scatter(d['X'][first_ten_200kpc], d['Y'][first_ten_200kpc], marker='+', s=200)
first_ten_100kpc = select_100kpc[:10]
pylab.scatter(d['X'][first_ten_100kpc], d['Y'][first_ten_100kpc], marker='+', s=100)
first_ten_50kpc = select_50kpc[:10]
pylab.scatter(d['X'][first_ten_50kpc], d['Y'][first_ten_50kpc], marker='+', s=50)
first_ten_closest = select_closest[:10]
pylab.scatter(d['X'][first_ten_closest], d['Y'][first_ten_closest], marker='x', s=50)
pylab.xlim(63, 65)
pylab.ylim(63, 65)
pylab.savefig("scatter.png")
pylab.show()
pylab.close()
# plot skplots
x, y, z = -d['Px'], -d['Py'], -d['Pz']
phi = numpy.arctan2(y, x)
theta = numpy.arctan2(z, (x * x + y * y) ** .5)
plot_uhecrs(phi, theta, dist, cmap='jet_r')
pylab.savefig("all.png")
pylab.show()
pylab.close()
plot_uhecrs(phi, theta, 1./dist)
pylab.savefig("all_w.png")
pylab.show()
pylab.close()
plot_uhecrs(phi, theta, 1./(dist**2))
pylab.savefig("all_w2.png")
pylab.show()
pylab.close()
plot_uhecrs(phi[select_1000kpc], theta[select_1000kpc], None)
pylab.savefig("1000kpc.png")
pylab.show()
pylab.close()
plot_uhecrs(phi[select_500kpc], theta[select_500kpc], None)
pylab.savefig("500kpc.png")
pylab.show()
pylab.close()
plot_uhecrs(phi[select_200kpc], theta[select_200kpc], None)
pylab.savefig("200kpc.png")
pylab.show()
pylab.close()
plot_uhecrs(phi[select_100kpc], theta[select_100kpc], None)
pylab.savefig("100kpc.png")
pylab.show()
pylab.close()
plot_uhecrs(phi[select_50kpc], theta[select_50kpc], None)
pylab.savefig("50kpc.png")
pylab.show()
pylab.close()
plot_uhecrs(phi[select_closest], theta[select_closest], None)
pylab.savefig("closest.png")
pylab.show()
pylab.close()
plot_uhecrs(phi[select_closest], theta[select_closest], 1./dist[select_closest])
pylab.savefig("closest_w.png")
pylab.show()
pylab.close()
plot_uhecrs(phi[select_closest], theta[select_closest], 1./(dist[select_closest]**2))
pylab.savefig("closest_w2.png")
pylab.show()
pylab.close()
if __name__ == '__main__':
listener()
vel_rect = np.array(vel_rect)
vel_rect_x = np.array(vel_rect_x)
vel_rect_y = np.array(vel_rect_y)
vel1 = np.array(vel1)
vel1_x = np.array(vel1_x)
vel1_y = np.array(vel1_y)
arr_rect = np.array(arr_rect)
arr1 = np.array(arr1)
# print ("vel1:",(vel1))
# print ("vel_rect:",vel_rect)
pl.plot(vel_rect_x[:, 0], vel_rect_x[:, 1], color='b')
pl.plot(vel1_x[:, 0], vel1_x[:, 1], color='r')
pl.title('vel_x')
pl.figure()
pl.plot(vel_rect_y[:, 0], vel_rect_y[:, 1], color='b')
pl.plot(vel1_y[:, 0], vel1_y[:, 1], color='r')
pl.title('vel_y')
pl.figure()
pl.plot(vel_rect[:, 0], vel_rect[:, 1], color='b')
pl.plot(vel1[:, 0], vel1[:, 1], color='r')
pl.title('vel_mod')
# pl.show()
pl.figure()
pl.scatter(arr1[:, 0], arr1[:, 1], color='r')
pl.scatter(arr_rect[:, 0], arr_rect[:, 1], color='b')
pl.title('pos')
# pl.show()
# support vectors
margin = 1 / np.sqrt(np.sum(clf.coef_**2))
yy_down = yy + a * margin
yy_up = yy - a * margin
# plot the line, the points, and the nearest vectors to the plane
pl.figure(fignum, figsize=(4, 3))
pl.clf()
pl.set_cmap(pl.cm.Paired)
pl.plot(xx, yy, 'k-')
pl.plot(xx, yy_down, 'k--')
pl.plot(xx, yy_up, 'k--')
pl.scatter(clf.support_vectors_[:, 0],
clf.support_vectors_[:, 1],
s=80,
facecolors='none',
zorder=10)
pl.scatter(X[:, 0], X[:, 1], c=Y, zorder=10)
pl.axis('tight')
x_min = -4.8
x_max = 4.2
y_min = -6
y_max = 6
XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
Z = clf.predict(np.c_[XX.ravel(), YY.ravel()])
# Put the result into a color plot
Z = Z.reshape(XX.shape)
Ordinary Least Squares with SGD
===============================
Simple Ordinary Least Squares example with stochastic
gradient descent, we draw the linear least
squares solution for a random set of points in the plane.
"""
print __doc__
import pylab as pl
from sklearn.linear_model import SGDRegressor
from sklearn.datasets.samples_generator import make_regression
# this is our test set, it's just a straight line with some
# gaussian noise
X, Y = make_regression(n_samples=100,
n_features=1,
n_informative=1,
random_state=0,
noise=35)
# run the classifier
clf = SGDRegressor(alpha=0.1, n_iter=20)
clf.fit(X, Y)
# and plot the result
pl.scatter(X, Y, color='black')
pl.plot(X, clf.predict(X), color='blue', linewidth=3)
pl.show()
def line_search(rays, K_ne, m_tci, i0, gradient, g, dobs, CdCt, figname=None):
M = m_tci.get_shaped_array()
#g = forward_equation_dask(rays,K_ne,m_tci,i0)
dd = (g - dobs)**2 / (CdCt + 1e-15)
S0 = np.sum(dd) / 2.
ep_a = []
S_a = []
S = S0
#initial epsilon_n
dd = (g - dobs) / (CdCt + 1e-15)
ep = 1e-3
g_ = forward_equation(
rays, K_ne,
TriCubic(m_tci.xvec, m_tci.yvec, m_tci.zvec, M - ep * gradient), i0)
Gm = (g - g_) / ep
#numerator
dd *= Gm
numerator = 2. * np.sum(dd)
#denominator
Gm *= Gm
Gm /= (CdCt + 1e-15)
denominator = np.sum(Gm)
epsilon_n0 = np.abs(numerator / denominator)
epsilon_n = epsilon_n0
iter = 0
while S >= S0 or iter < 3:
epsilon_n /= 2.
#m_tci.m = m - epsilon_n*gradient.ravel('C')
g = forward_equation(
rays, K_ne,
TriCubic(m_tci.xvec, m_tci.yvec, m_tci.zvec,
M - epsilon_n * gradient), i0)
#print(np.mean(g),np.var(g))
dd = (g - dobs)**2 / (CdCt + 1e-15)
S = np.sum(dd) / 2.
ep_a.append(epsilon_n)
S_a.append(S)
#print(epsilon_n,S)
if not np.isnan(S):
if S < 1 << 64:
iter += 1
epsilon_n, S_p = vertex(*ep_a[-3:], *S_a[-3:])
g = forward_equation_dask(
rays, K_ne,
TriCubic(m_tci.xvec, m_tci.yvec, m_tci.zvec, M - epsilon_n * gradient),
i0)
dd = (g - dobs)**2 / (CdCt + 1e-15)
S = np.sum(dd) / 2.
print("S0: {} | Estimated epsilon_n: {}".format(S0, epsilon_n0))
print("Parabolic minimum | epsilon_n = {}, S = {}".format(epsilon_n, S_p))
print("Actual | S = {}".format(S))
print("Misfit Reduction: {:.2f}%".format(S / S0 * 100. - 100.))
if figname is not None:
plt.plot(ep_a, S_a)
plt.scatter(epsilon_n, S, c='green', label='Final misfit')
#plt.scatter(epsilon_n,S_p,c='red',label='Parabolic minimum')
plt.yscale('log')
plt.plot([min(epsilon_n, np.min(ep_a)),
max(epsilon_n, np.max(ep_a))], [S0, S0],
ls='--',
c='red')
plt.xscale('log')
plt.legend(frameon=False)
plt.savefig("{}.png".format(figname), format='png')
return epsilon_n, S, (S / S0 - 1.)
iY = momentum * iY - eta * (gains * dY)
Y = Y + iY
Y = Y - np.tile(np.mean(Y, 0), (n, 1))
# Compute current value of cost function
if (iter + 1) % 10 == 0:
C = np.sum(P * np.log(P / Q))
print("Iteration %d: error is %f" % (iter + 1, C))
# Stop lying about P-values
if iter == 100:
P = P / 4.
# Return solution
return Y
if __name__ == "__main__":
print(
"Run Y = tsne.tsne(X, no_dims, perplexity) to perform t-SNE on your dataset."
)
print("Running example on 2,500 MNIST digits...")
X = np.load("signatures.npy")
labels = np.load("labels_name.npy")
num_label = np.squeeze(labels)
print("labels:", labels)
print("numlabels:", num_label)
Y = tsne(X, 2, 50, 20.0)
pylab.scatter(Y[:, 0], Y[:, 1], num_label, labels)
pylab.show()
data_ols = np.column_stack((y1,x1,x2))
np.savetxt('OLS.csv',data_ols,delimiter = ",")
print(data_ols[1:10])
## Data Visualization ##
pylab.plot(samples1[:, 0], samples1[:, 1],'*', color = 'red')
pylab.plot(samples2[:, 0], samples2[:, 1],'o',color = 'blue')
pylab.plot(samples3[:, 0], samples3[:, 1],'+',color = 'green')
pylab.show()
pylab.plot(x1)
pylab.plot(x2, color='red')
pylab.plot(y1, color = 'orange')
pylab.show()
pylab.scatter(y1,x1, color='blue')
pylab.scatter(y1,x2, color = 'red')
pylab.scatter(x1,x2, color = 'orange')
pylab.show()
m1, b1 = np.polyfit(x1, y1, 1)
pylab.plot(x1, y1, '.', color = 'blue')
pylab.plot(x1, m1*x1 + b1, '-')
pylab.show()
m2, b2 = np.polyfit(x2, y1, 1)
pylab.plot(x2, y1, '.', color = 'red')
pylab.plot(x2, m1*x2 + b2, '-')
pylab.show()
m3, b3 = np.polyfit(x2, x1, 1)
pylab.plot(x2, x1, '.', color = 'orange')
pylab.plot(x2, m3*x2 + b3, '-')
pylab.show()
else:
momentum = final_momentum
gains = (gains + 0.2) * ((dY > 0) != (iY > 0)) + (gains * 0.8) * (
(dY > 0) == (iY > 0))
gains[gains < min_gain] = min_gain
iY = momentum * iY - eta * (gains * dY)
Y = Y + iY
Y = Y - Math.tile(Math.mean(Y, 0), (n, 1))
# Compute current value of cost function
if (iter + 1) % 10 == 0:
C = Math.sum(P * Math.log(P / Q))
print "Iteration ", (iter + 1), ": error is ", C
# Stop lying about P-values
if iter == 100:
P = P / 4
# Return solution
return Y
if __name__ == "__main__":
print "Run Y = tsne.tsne(X, no_dims, perplexity) to perform t-SNE on your dataset."
print "Running example on 2,500 MNIST digits..."
X = Math.loadtxt("mnist2500_X.txt")
labels = Math.loadtxt("mnist2500_labels.txt")
Y = tsne(X, 2, 50, 20.0)
Plot.scatter(Y[:, 0], Y[:, 1], 20, labels)
Plot.show()
# -0.27342691, -0.40625428, -0.57759724, -0.36327529, -0.68233917, -0.54106024,
# -0.594002, -0.54278992, -0.72141841, -0.84960626, -0.99065906, -0.95052521,
# -0.77855943, -1.1411366, -0.82951384, -0.98987168, -0.73749955, -1.03039024,
# -0.87363562, -0.96550525, -0.78238156, -1.02137395, -0.89266227, -0.9028713,
# -0.7883536, -0.72666741, -1.04589251, -0.73791169, -0.71817151, -0.64454691,
# -0.50123652, -0.73558315, -0.3898374, -0.40887712, -0.25264427, -0.02445753,
# -0.44606617, -0.16614301]
# y = np.array(y)
# # x = [[1], [2], [3], [4], [5]]
# x = [[1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12]]
# x = np.array(x)
# # y = [1, 2, 4, 3, 2]
# y = [1, 2, 4.3, 3, 2, 2, 1.5, 1.3, 1.5, 1.7, 1.8, 2]
# y = np.array(y)
print("Nadaray Watson's yest:")
yest_nadaray = nadaray_watson(x, y)
print(yest_nadaray)
print("Lowess yest:")
yest_lowess = lowess(x, y)
print(yest_lowess)
pl.clf()
pl.scatter(x, y, label='data', color="black")
pl.plot(x, yest_nadaray, label='y nadaray-watson', color="red")
pl.plot(x, yest_lowess, label='y lowess', color="blue")
pl.title('Nadaray Watson vs Lowess')
pl.legend()
pl.show()
def tharfwhm(image,lines=lines, doplot=False, silent=False, pltname='fwhm.png'):
imdat = pyfits.getdata(image).transpose()
if not silent: print( "Pixel xy guess FWHM peak ADU")
allfwhm = []
allpeaks = []
x = []
y = []
for i in range(lines.shape[0]):
lines[i] = np.round(lines[i])
imbox = imdat[lines[i,0]-win[0]/2:lines[i,0]+win[0]/2, lines[i,1]-1-win[1]/2:lines[i,1]-1+win[1]/2]
if np.max(imbox) < 500:
if not silent: print( "Line flux too small!")
continue
if np.max(imbox) > 40000:
if not silent: print( "Line too close to saturation!")
continue
bins, edges = np.histogram(imbox, bins=50)
mode = edges[np.argmax(bins)]
imflat = np.sum(imbox-mode,axis=0)
xfit = np.arange(-win[0]/2, win[0]/2, 1)
p0 = [np.max(imflat)-np.min(imflat), 0.0, 2.5, np.min(imflat)]
#pfin,covar = curve_fit(gauss, xfit, imflat, p0=p0)
pfin = leastsq(errfunc, p0, args=(xfit, imflat), maxfev=10000)[0]
yfit = gauss(xfit, pfin)
fwhm = 2*np.sqrt(2*np.log(2))*pfin[2]
if fwhm < 0:
if not silent: print( "Negative FWHM, bad fit!")
continue
allfwhm.append(fwhm)
x.append(lines[i,0])
y.append(lines[i,1])
allpeaks.append(np.max(imbox))
if False and doplot:
import pylab as pl
pl.ion()
pl.clf()
xplot = np.linspace(min(xfit), max(xfit), 200)
yfit = yfit = gauss(xplot, pfin)
pl.plot(xfit, imflat,'k-o', markersize=8)
pl.plot(xplot, yfit, 'b-')
pl.ylabel('ADU')
pl.xlabel('$\\Delta$pixel')
pl.draw()
time.sleep(0.2)
if not silent: print( "%s\t%6.4f\t%d" % (lines[i], fwhm, np.max(imbox)))
if not silent: print( "-------------------------------\n")
if doplot:
import pylab as pl
pl.ioff()
from scipy.interpolate import griddata
x = np.array(x)
y = np.array(y)
z = np.array(allfwhm)
xi = np.linspace(min(x), max(x), 1000)
yi = np.linspace(min(y), max(y), 1000)
zi = griddata((x,y),z, (xi[None,:],yi[:,None]), method='nearest')
pl.imshow(np.log10(imdat), vmin=np.log10(1000), vmax=np.log10(1500), aspect=7)
#pl.contourf(yi,xi,zi, 50)
pl.scatter(y,x,c=z,s=100, vmin=2, vmax=5)
pl.xlim(min(y), max(y))
pl.ylim(min(x), max(x))
pl.colorbar()
pl.title("median FWHM = %4.2f" % np.median(allfwhm))
pl.xlabel('row [px]')
pl.ylabel('col [px]')
pl.savefig(pltname)
#pl.show()
pl.clf()
return np.median(allfwhm), np.max(allpeaks)
fs.append(f)
if i % viz_every == 0:
np_samples.append(
np.vstack([sess.run(samples) for _ in xrange(n_batches_viz)]))
xx, yy = sess.run([samples, data])
import seaborn as sns
data_t = sample_mog(params['batch_size'])
data = sess.run(data_t)
fig = pylab.gcf()
fig.set_size_inches(16.0, 16.0)
pylab.clf()
pylab.scatter(data[:, 0],
data[:, 1],
s=20,
marker="o",
edgecolors="none",
color='blue')
pylab.xlim(-4, 4)
pylab.ylim(-4, 4)
pylab.savefig("gt_scatter.png")
color = "Greens"
fig = pylab.gcf()
fig.set_size_inches(16.0, 16.0)
pylab.clf()
bg_color = sns.color_palette(color, n_colors=256)[0]
ax = sns.kdeplot(data[:, 0],
data[:, 1],
shade=True,
cmap=color,
X2 = list(np.linspace(0.65, 0.85, 5))
X1.extend(X2)
X = X1
X = np.array(X)
Y = np.sin(2 * np.pi * X)
N = X.shape[0]
alpha = []
for i in range(N):
alpha_ = 0.01
alpha.append(alpha_)
alpha = np.array(alpha)
X_plot = X
Y_plot = Y + np.random.normal(0, alpha)
pylab.scatter(X_plot, Y_plot)
kernel = C(1.0, (0.01, 100)) \
* ManifoldKernel.construct(base_kernel=RBF(length_scale=10), architecture=((1, 6, 2),),
transfer_fct="tanh", max_nn_weight=1)
gp = GaussianProcessRegressor(kernel=kernel,
alpha=alpha**2,
n_restarts_optimizer=1)
'''
kernel = C(1.0) * RBF(length_scale=0.1)
gp = GaussianProcessRegressor(kernel=kernel, alpha=alpha ** 2, n_restarts_optimizer=10)
'''
gp.fit(X[:, None], Y)
XX = np.linspace(-1.5, 1.5, 100)
