def plot_values(X, Y, suffix):
output_filename = constants.CHARTS_FOLDER_NAME + constants.DATASET + '_' + suffix
if constants.DATASET=='etoro':
yfactor = 1.0 #100000
Y = [y*yfactor for y in Y]
pylab.figure(figsize=(7.5, 7))
pylab.rcParams.update({'font.size': 20})
pylab.scatter(X, Y)
pylab.axis(get_bounds(X, Y, False))
pylab.xlabel('Day')
if constants.DATASET=='etoro':
pylab.ylabel('Change (*10^' + str(int(math.log10(yfactor))) + ')')
else:
pylab.ylabel('Change')
pylab.tight_layout()
pylab.savefig(output_filename + '.pdf')
def param_set_averages_plot(results):
averages_ocr = [
a[1] for a in sorted(
param_set_averages(results, metric='ocr').items(),
key=lambda x: int(x[0].split('-')[1]))
]
averages_q = [
a[1] for a in sorted(
param_set_averages(results, metric='q').items(),
key=lambda x: int(x[0].split('-')[1]))
]
averages_mse = [
a[1] for a in sorted(
param_set_averages(results, metric='mse').items(),
key=lambda x: int(x[0].split('-')[1]))
]
fig = plt.figure(figsize=(6, 4))
# plt.tight_layout()
plt.plot(averages_ocr, label='OCR', linewidth=2.0)
plt.plot(averages_q, label='Q', linewidth=2.0)
plt.plot(averages_mse, label='MSE', linewidth=2.0)
plt.ylim([0, 1])
plt.xlabel(u'Paslėptų neuronų skaičius')
plt.ylabel(u'Vidurinė Q įverčio pokyčio reikšmė')
plt.grid(True)
plt.tight_layout()
plt.legend(loc='lower right')
plt.show()
def plot_sphere_x( s, fname ):
""" put plot of ionization fractions from sphere `s` into fname """
plt.figure()
s.Edges.units = 'kpc'
s.r_c.units = 'kpc'
xx = s.r_c
L = s.Edges[-1]
plt.plot( xx, np.log10( s.xHe1 ),
color='green', ls='-', label = r'$x_{\rm HeI}$' )
plt.plot( xx, np.log10( s.xHe2 ),
color='green', ls='--', label = r'$x_{\rm HeII}$' )
plt.plot( xx, np.log10( s.xHe3 ),
color='green', ls=':', label = r'$x_{\rm HeIII}$' )
plt.plot( xx, np.log10( s.xH1 ),
color='red', ls='-', label = r'$x_{\rm HI}$' )
plt.plot( xx, np.log10( s.xH2 ),
color='red', ls='--', label = r'$x_{\rm HII}$' )
plt.xlim( -L/20, L+L/20 )
plt.xlabel( 'r_c [kpc]' )
plt.ylim( -4.5, 0.2 )
plt.ylabel( 'log 10 ( x )' )
plt.grid()
plt.legend(loc='best', ncol=2)
plt.tight_layout()
plt.savefig( 'doc/img/x_' + fname )
def _create_histogram(M_c, data, columns, mc_col_indices, filename):
dir = S.path.web_resources_data_dir
full_filename = os.path.join(dir, filename)
num_rows = data.shape[0]
num_cols = data.shape[1]
p.figure()
# col_i goes from 0 to number of predicted columns
# mc_col_idx is the original column's index in M_c
for col_i in range(num_cols):
mc_col_idx = mc_col_indices[col_i]
data_i = data[:, col_i]
ax = p.subplot(1, num_cols, col_i, title=columns[col_i])
if M_c['column_metadata'][mc_col_idx]['modeltype'] == 'normal_inverse_gamma':
p.hist(data_i, orientation='horizontal')
else:
str_data = [du.convert_code_to_value(M_c, mc_col_idx, code) for code in data_i]
unique_labels = list(set(str_data))
np_str_data = np.array(str_data)
counts = []
for label in unique_labels:
counts.append(sum(np_str_data == label))
num_vals = len(M_c['column_metadata'][mc_col_idx]['code_to_value'])
rects = p.barh(range(num_vals), counts)
heights = np.array([rect.get_height() for rect in rects])
ax.set_yticks(np.arange(num_vals) + heights/2)
ax.set_yticklabels(unique_labels)
p.tight_layout()
p.savefig(full_filename)
def plot_Barycenter(dataset_name, feat, unfeat, repo):
if dataset_name==MNIST:
_, _, test=get_data(dataset_name, repo, labels=True)
xtest1,_,_, labels,_=test
else:
_, _, test=get_data(dataset_name, repo, labels=False)
xtest1,_,_ =test
labels=np.zeros((len(xtest1),))
# get labels
def bary_wdl2(index): return _bary_wdl2(index, xtest1, feat, unfeat)
n=xtest1.shape[-1]
num_class = (int)(max(labels)+1)
barys=[bary_wdl2(np.where(labels==i)) for i in range(num_class)]
pl.figure(1, (num_class, 1))
for i in range(num_class):
pl.subplot(1,10,1+i)
pl.imshow(barys[i][0,0,:,:],cmap='Blues',interpolation='nearest')
pl.xticks(())
pl.yticks(())
if i==0:
pl.ylabel('DWE Bary.')
if num_class >1:
pl.title('{}'.format(i))
pl.tight_layout(pad=0,h_pad=-2,w_pad=-2)
pl.savefig("imgs/{}_dwe_bary.pdf".format(dataset_name))
def plotCoeff(X, y, obj, featureNames, whichReg):
""" Plot Regression's Coeff
"""
clf = classifiers[whichReg]
clf,_,_ = fitAlgo(clf, X,y, opt= True, param_dict = param_dist_dict[whichReg])
if whichReg == "LogisticRegression":
coeff = np.absolute(clf.coef_[0])
else:
coeff = np.absolute(clf.coef_)
print coeff
indices = np.argsort(coeff)[::-1]
print indices
print featureNames
featureList = []
# num_features = len(featureNames)
print("Feature ranking:")
for f in range(num_features):
featureList.append(featureNames[indices[f]])
print("%d. feature %s (%.2f)" % (f, featureNames[indices[f]], coeff[indices[f]]))
fig = pl.figure(figsize=(8,6),dpi=150)
pl.title("Feature importances",fontsize=30)
# pl.bar(range(num_features), coeff[indices],
# yerr = std_importance[indices], color=paired[0], align="center",
# edgecolor=paired[0],ecolor=paired[1])
pl.bar(range(num_features), coeff[indices], color=paired[0], align="center",
edgecolor=paired[0],ecolor=paired[1])
pl.xticks(range(num_features), featureList, size=15,rotation=90)
pl.ylabel("Importance",size=30)
pl.yticks(size=20)
pl.xlim([-1, num_features])
# fix_axes()
pl.tight_layout()
save_path = 'plots/'+obj+'/'+whichReg+'_feature_importances.pdf'
fig.savefig(save_path)
def plot_number_alteration_by_tissue(self, fontsize=10, width=0.9):
"""Plot number of alterations
.. plot::
:width: 100%
:include-source:
from gdsctools import *
data = gdsctools_data("test_omnibem_genomic_alterations.csv.gz")
bem = OmniBEMBuilder(data)
bem.filter_by_gene_list(gdsctools_data("test_omnibem_genes.txt"))
bem.plot_number_alteration_by_tissue()
"""
count = self.unified.groupby(['TISSUE_TYPE'])['GENE'].count()
try:
count.sort_values(inplace=True, ascending=False)
except:
count.sort(inplace=True, ascending=False)
count.plot(kind="bar", width=width)
pylab.grid()
pylab.xlabel("Tissue Type", fontsize=fontsize)
pylab.ylabel("Total number of alterations in cell lines",
fontsize=fontsize)
try:pylab.tight_layout()
except:pass
def plotGaussian(X, y, obj, featureNames):
"""Plot Gausian fit on top of X.
"""
save_path = '../MSPrediction-Python/plots/'+obj+'/'+'BayesGaussian2'
clf = classifiers["BayesGaussian2"]
clf,_,_ = fitAlgo(clf, X,y, opt= True, param_dict = param_dist_dict["BayesGaussian2"])
unique_y = np.unique(y)
theta = clf.theta_
sigma = clf.sigma_
class_prior = clf.class_prior_
norm_func = lambda x, sigma, theta: 1 if np.isnan(x) else -0.5 * np.log(2 * np.pi*sigma) - 0.5 * ((x - theta)**2/sigma)
norm_func = np.vectorize(norm_func)
n_samples = X.shape[0]
for j in range(X.shape[1]):
fcol = X[:,j]
jfeature = featureNames[j]
jpath = save_path +'_'+jfeature+'.pdf'
fig = pl.figure(figsize=(8,6),dpi=150)
for i, y_i in enumerate(unique_y):
fcoli = fcol[y == y_i]
itfreq = itemfreq(fcoli)
uniqueVars = itfreq[:,0]
freq = itfreq[:,1]
freq = freq/sum(freq)
the = theta[i, j]
sig = sigma[i,j]
pred = np.exp(norm_func(uniqueVars, sig, the))
pl.plot(uniqueVars, pred, label= str(y_i)+'_model')
pl.plot(uniqueVars, freq, label= str(y_i) +'_true')
pl.xlabel(jfeature)
pl.ylabel("density")
pl.legend(loc='best')
pl.tight_layout()
# pl.show()
fig.savefig(jpath)
def plotMixNB(X, y, obj, featureNames, whichMix):
"""Plot MixNB fit on top of X.
"""
save_path = '../MSPrediction-Python/plots/'+obj+'/'+whichMix
clf = classifiers[whichMix]
clf,_,_ = fitAlgo(clf, X,y, opt= True, param_dict = param_dist_dict[whichMix])
unique_y = np.unique(y)
# norm_func = lambda x, sigma, theta: 1 if np.isnan(x) else -0.5 * np.log(2 * np.pi*sigma) - 0.5 * ((x - theta)**2/sigma)
# norm_func = np.vectorize(norm_func)
n_samples = X.shape[0]
for j in range(X.shape[1]):
fcol = X[:,j]
optmodel = clf.optmodels[:,j]
distname = clf.distnames[j]
jfeature = featureNames[j]
jpath = save_path +'_'+jfeature+'.pdf'
fig = pl.figure(figsize=(8,6),dpi=150)
for i, y_i in enumerate(unique_y):
fcoli = fcol[y == y_i]
itfreq = itemfreq(fcoli)
uniqueVars = itfreq[:,0]
freq = itfreq[:,1]
freq = freq/sum(freq)
pred = np.exp(optmodel[i](uniqueVars))
# print pred
# print pred
pl.plot(uniqueVars, pred, label= str(y_i)+'_model')
pl.plot(uniqueVars, freq, label= str(y_i) +'_true')
pl.xlabel(jfeature)
pl.ylabel("density")
pl.title(distname)
pl.legend(loc='best')
pl.tight_layout()
# pl.show()
fig.savefig(jpath)
def plot_importances(imp, clfName, obj):
imp=np.vstack(imp)
print imp
mean_importance = np.mean(imp,axis=0)
std_importance = np.std(imp,axis=0)
indices = np.argsort(mean_importance)[::-1]
print indices
print featureNames
featureList = []
# num_features = len(featureNames)
print("Feature ranking:")
for f in range(num_features):
featureList.append(featureNames[indices[f]])
print("%d. feature %s (%.2f)" % (f, featureNames[indices[f]], mean_importance[indices[f]]))
fig = pl.figure(figsize=(8,6),dpi=150)
pl.title("Feature importances",fontsize=30)
pl.bar(range(num_features), mean_importance[indices],
yerr = std_importance[indices], color=paired[0], align="center",
edgecolor=paired[0],ecolor=paired[1])
pl.xticks(range(num_features), featureList, size=15,rotation=90)
pl.ylabel("Importance",size=30)
pl.yticks(size=20)
pl.xlim([-1, num_features])
# fix_axes()
pl.tight_layout()
save_path = 'plots/'+obj+'/'+clfName+'_feature_importances.pdf'
fig.savefig(save_path)
def param_sweeping(clf, obj, X, y, param_dist, metric, param, clfName):
'''Plot a parameter sweeping (ps) curve with the param_dist as a axis, and the scoring based on metric as y axis.
Keyword arguments:
clf - - classifier
X - - feature matrix
y - - target array
param - - a parameter of the classifier
param_dist - - the parameter distribution of param
clfName - - the name of the classifier
metric - - the metric we use to evaluate the performance of the classifiers
obj - - the name of the dataset we are using'''
scores = []
for i in param_dist:
y_true = []
y_pred = []
# new classifer each iteration
newclf = eval("clf.set_params("+ param + "= i)")
y_pred, y_true, gs_score_list, amp = testAlgo(newclf, X, y, clfName)
mean_fpr, mean_tpr, mean_auc = plot_unit_prep(y_pred, y_true, metric)
scores.append(mean_auc)
print("Area under the ROC curve : %f" % mean_auc)
fig = pl.figure(figsize=(8,6),dpi=150)
paramdist_len = len(param_dist)
pl.plot(range(paramdist_len), scores, label = 'Parameter Sweeping Curve')
pl.xticks(range(paramdist_len), param_dist, size = 15, rotation = 45)
pl.xlabel(param.upper(),fontsize=30)
pl.ylabel(metric.upper(),fontsize=30)
pl.title('Parameter Sweeping Curve',fontsize=25)
pl.legend(loc='lower right')
pl.tight_layout()
fig.savefig('plots/'+obj+'/'+ clfName +'_' + param +'_'+'ps.pdf')
pl.show()
def plot_file_color(base, thin=True, start=0, size=14, save=False):
conf, track, pegs = load(base)
fig = pl.figure(figsize=(size,size*conf['top']/conf['wall']))
track = track[start:]
x = track[:,0]; y = track[:,1]
t = np.linspace(0,1,x.shape[0])
points = np.array([x,y]).transpose().reshape(-1,1,2)
segs = np.concatenate([points[:-1],points[1:]],axis=1)
lc = LineCollection(segs, linewidths=0.25, cmap=pl.cm.coolwarm)
lc.set_array(t)
pl.gca().add_collection(lc)
#pl.scatter(x, y, c=np.arange(len(x)),linestyle='-',cmap=pl.cm.coolwarm)
#pl.plot(track[-1000000:,0], track[-1000000:,1], '-', linewidth=0.0125, alpha=0.8)
for peg in pegs:
pl.gca().add_artist(pl.Circle(peg, conf['radius'], color='k', alpha=0.3))
pl.xlim(0, conf['wall'])
pl.ylim(0, conf['top'])
pl.xticks([])
pl.yticks([])
pl.tight_layout()
pl.show()
if save:
pl.savefig(base+".png", dpi=200)
def tracks_movie(base, skip=1, frames=500, size=10):
"""
A movie of each particle as a point
"""
conf, track, pegs = load(base)
fig = pl.figure(figsize=(size,size*conf['top']/conf['wall']))
plot = None
for t in xrange(1,max(frames, track.shape[1]/skip)):
tmp = track[:,t*skip,:]
if not ((tmp[:,0] > 0) & (tmp[:,1] > 0) & (tmp[:,0] < conf['wall']) & (tmp[:,1] < conf['top'])).any():
continue
if plot is None:
plot = pl.plot(tmp[:,0], tmp[:,1], 'k,', alpha=1.0, ms=0.1)[0]
pl.xticks([])
pl.yticks([])
pl.xlim(0,conf['wall'])
pl.ylim(0,conf['top'])
pl.tight_layout()
else:
plot.set_xdata(tmp[:,0])
plot.set_ydata(tmp[:,1])
pl.draw()
pl.savefig(base+'-movie-%05d.png' % (t-1))
def pca(inF,MIN):
df = pd.read_table(inF, header=0)
dc = list(df.columns)
dc[0]='GeneID'
df.columns = dc
print(df.shape)
sel = ~((df.ix[:,2] < MIN) & (df.ix[:,3]< MIN) & (df.ix[:,4]< MIN) & (df.ix[:,5]< MIN) & (df.ix[:,6]< MIN) & (df.ix[:,7]< MIN) & (df.ix[:,8]< MIN) & (df.ix[:,9]< MIN))
df = df.ix[sel,:]
print(df.shape)
X = df.ix[:,2:df.shape[1]].values.T
y = df.columns[2:df.shape[1]].values
X_std = StandardScaler().fit_transform(X)
#pca = PCA(n_components=2)
pca = PCA()
Y_sklearn = pca.fit_transform(X_std)
fig = plt.figure()
plt.style.use('ggplot')
#plt.style.use('seaborn-whitegrid')
ax = fig.add_subplot(111)
for lab, col in zip(y,('red','red', 'green','green', 'blue','blue','m','m')):
ax.scatter(Y_sklearn[y==lab, 0],Y_sklearn[y==lab, 1],label=lab,c=col, s=80)
ax.set_xlabel('Principal Component 1 : %.2f'%(pca.explained_variance_ratio_[0]*100) + '%')
ax.set_ylabel('Principal Component 2 : %.2f'%(pca.explained_variance_ratio_[1]*100) + '%')
ax.legend(loc='lower right', prop={'size':8})
plt.tight_layout()
plt.savefig(inF + '-RNASeq-MIN' + str(MIN) + '.pdf')
def pca(inF,MIN):
df = pd.read_table(inF, header=0)
dc = list(df.columns)
dc[0]='GeneID'
df.columns = dc
print(df.shape)
sel = True
for i in range(4, df.shape[1]-1):
sel = (df.ix[:,i] < MIN) & (df.ix[:,i+1]< MIN)
df = df.ix[~sel,:]
print(df.shape)
X = df.ix[:,4:df.shape[1]].values.T
y = df.columns[4:df.shape[1]].values
X_std = StandardScaler().fit_transform(X)
#pca = PCA(n_components=2)
pca = PCA()
Y_sklearn = pca.fit_transform(X_std)
fig = plt.figure()
plt.style.use('ggplot')
#plt.style.use('seaborn-whitegrid')
ax = fig.add_subplot(111)
for lab, col in zip(y,['r','g','b','c'] + sns.color_palette("cubehelix", df.shape[1]-4-4)):
ax.scatter(Y_sklearn[y==lab, 0],Y_sklearn[y==lab, 1],label=lab,c=col, s=80)
ax.set_xlabel('Principal Component 1 : %.2f'%(pca.explained_variance_ratio_[0]*100) + '%')
ax.set_ylabel('Principal Component 2 : %.2f'%(pca.explained_variance_ratio_[1]*100) + '%')
ax.legend(loc='upper right', prop={'size':8})
plt.tight_layout()
plt.savefig(inF + '-MIN' + str(MIN) + '.pdf')
def ShowRawToF():
raw_codes = GetTimecodes_AllFilesInDir(timepix_path_1, xmin, xmax, ymin, ymax, 0, checkerboard_phase = None)
offset = 0
tmin = 0
tmax = 11810
fig = pl.figure(figsize=(14,10))
n_codes, bins, patches = pl.hist([11810-i for i in raw_codes], bins = 11810, range = [0,11810])
pl.yscale('log', nonposy='clip')
for i,code in enumerate(raw_codes):
raw_codes[i] = (11810. - code) - offset
raw_codes[i] *= 20
fig = pl.figure(figsize=(14,10))
n_codes, bins, patches = pl.hist(raw_codes, bins = 1000, range = [0,40000])
# n_codes, bins, patches = pl.hist(raw_codes, bins = tmax-tmin, range = [tmin,tmax])
# pl.clf()
# n_codes = n_codes[:-1]
# pl.plot(bins, n_codes, 'k-.o', label="Timecodes")
pl.tight_layout()
pl.show()
def printHeatMap(marginals, words, outFile):
N = len(words)
words_uni = [i.decode('UTF-8') for i in words]
heatmap = np.zeros((N+1, N+1))
for chart in marginals:
heatmap[chart[0], chart[1]] = math.log(marginals[chart])
fig, ax = plt.subplots()
mask = np.tri(heatmap.shape[0], k=0)
heatmap = np.ma.array(heatmap, mask=mask)
cmap = plt.cm.get_cmap('RdBu')
cmap.set_bad('w')
im = ax.pcolor(heatmap, cmap=cmap, alpha=0.8)
font = mpl.font_manager.FontProperties(fname='/usr0/home/avneesh/spectral-scfg/data/wqy-microhei.ttf')
ax.grid(True)
ax.set_ylim([0,N])
ax.invert_yaxis()
ax.set_yticks(np.arange(heatmap.shape[1]-1)+0.5, minor=False)
ax.set_yticklabels(words_uni, minor=False, fontproperties=font)
ax.set_xticks(np.arange(heatmap.shape[0])+0.5, minor=True)
ax.set_xticklabels(np.arange(heatmap.shape[0]), minor=True)
ax.set_xticks([])
cbar = fig.colorbar(im, use_gridspec=True)
cbar.set_label('ln(sum)')
ax.set_xlabel('Span End')
ax.xaxis.set_label_position('top')
ax.xaxis.tick_top()
plt.ylabel('Span starting at word: ')
plt.tight_layout()
#ax.set_title('CKY Heat Map: Node Marginals')
fig.savefig(outFile)
def multiplot(x,y,row=True):
Nsp = len(y.keys())
if len(x.keys())==1:
xk = x.keys()[0]
for i,yk in enumerate(y.iterkeys()):
if row:
pl.subplot(Nsp,1,i+1)
else:
pl.subplot(1,Nsp,i+1)
pl.plot(x[xk],y[yk],label=yk)
pl.xlabel(xk)
pl.ylabel(yk)
else:
for i,(xk,yk) in enumerate(zip(x.iterkeys(),y.iterkeys())):
if row:
pl.subplot(Nsp,1,i+1)
else:
pl.subplot(1,Nsp,i+1)
pl.plot(x[xk],y[yk],label=yk)
pl.xlabel(xk)
pl.ylabel(yk)
pl.tight_layout()
def plotScalingFactor():
r=2*1e-8
l = 5e4
dpi = 300
j = 0
for nu0 in [0.005, 0.1]:
for s in [0.025, 0.1]:
t = np.arange(0, 2 * (utl.logit(0.995) - utl.logit(nu0)) / s + 1., 1)
fig, ax = plt.subplots(2, 1, figsize=(5.5, 2.5), dpi=dpi, sharex=True);
nu(t, s=s, nu0=nu0).plot(color='k', legend=False, ax=ax[0])
pplt.annotate(r'$s$={}, $\nu_0=${} ({} Sweep)'.format(s, nu0, ('Soft', 'Hard')[nu0 == 0.005]), fontsize=7,
ax=ax[0])
pplt.setSize(ax=ax[0], fontsize=6)
ax[0].set_ylabel(r'$\nu_t$')
#
H0 = H(t[0], s=s, nu0=nu0)
Ht = H(t, s=s, nu0=nu0)
df = pd.DataFrame([np.log(Ht / H0), -2 * r * t * l], columns=t, index=['log(Growth)', r'log(Decay)']).T
df['log(Growth) + log(Decay)'] = df.sum(1)
df.plot(ax=ax[1], grid=True, linewidth=2);
ax[1].set_xlabel('Generations');
ax[1].set_ylabel('Log(Scaling Factor)')
ax[1].axvline(df.iloc[1:, 2].abs().idxmin(), color='k', linestyle='--', linewidth=0.5)
# if j != 3:
# ax[1].legend_.remove()
# else:
ax[1].legend(['log(Growth)', r'log(Decay)', 'log(Growth) + log(Decay)'], bbox_to_anchor=(1.45, .75),
prop={'size': 6})
pplt.setSize(ax[1], fontsize=6)
plt.tight_layout(pad=0.1, rect=[0, 0, 0.7, 1])
plt.gcf().subplots_adjust(bottom=0.15)
pplt.savefig('decayFactors{}'.format(j), dpi=dpi)
j += 1
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 synthesize(t1, t2, sig=1, length=800):
td = trajdata.TrajData()
td.trajs.append( synthTraj('UBL1', length, t1, 1) )
td.trajs.append( synthTraj('UBL2', length, t2, sig) )
td.filename = 'test.qtd'
dio.trajDataSaveH5(td)
curves = xcorr.corrFile('test.qtd', ['UB'], [1.0, 1.0, 1.0], 100)
x1 = np.array(td.trajs[0].pointData)[:,0]
x2 = np.array(td.trajs[1].pointData)[:,0]
pl.figure(figsize=(5,6))
pl.subplot(3,1,1)
pl.plot(x1, 'r', linewidth=2)
pl.ylim(np.min(x1) - np.max(x1)*0.1, np.max(x1) * 1.1)
pl.ylabel("position of\nparticipant A (mm)")
pl.xlabel("time (msec)")
pl.subplot(3,1,2)
pl.plot(x2, 'b', linewidth=2)
if sig == 1:
pl.ylim(np.min(x2) - np.max(x2)*0.1, np.max(x2) * 1.1)
else:
pl.ylim(np.min(x2)*1.1, np.max(x2) - np.min(x2)*0.1)
pl.ylabel("position of\nparticipant B (mm)")
pl.xlabel("time (msec)")
pl.subplot(3,1,3)
curves.plot(10)
if sig == 1:
pl.ylim(-0.6, 1.2)
else:
pl.ylim(-1.2, 0.6)
pl.ylabel("cross-correlation\nof speed")
pl.xlabel("delay (msec)")
pl.xlim(-200, 200)
pl.tight_layout()
def walker_steps(samples, nwalkers, limit):
ur = N.load('/Users/becky/Projects/Green-Valley-Project/bayesian/find_t_tau_old/colour_plot/ur.npy')
nuv = N.load('/Users/becky/Projects/Green-Valley-Project/bayesian/find_t_tau_old/colour_plot/nuvu.npy')
s = samples.reshape(nwalkers, -1, 4)
s = s[:,:limit,:]
fig = P.figure(figsize=(9,9))
ax1 = P.subplot(221, autoscale_on = False, aspect='auto', xlim=[0,13.8], ylim=[0,3])
ax2 = P.subplot(222, autoscale_on = False, aspect='auto', xlim=[0,13.8], ylim=[0,3])
ax3 = P.subplot(223, autoscale_on = False, aspect='auto', xlim=[0,13.8], ylim=[0,3])
ax4 = P.subplot(224, autoscale_on = False, aspect='auto', xlim=[0,13.8], ylim=[0,3])
ax1.imshow(ur, origin='lower', aspect='auto', extent=[0, 13.8, 0, 3])
ax2.imshow(ur, origin='lower', aspect='auto', extent=[0, 13.8, 0, 3])
ax3.imshow(nuv, origin='lower', aspect='auto', extent=[0, 13.8, 0, 3])
ax4.imshow(nuv, origin='lower', aspect='auto', extent=[0, 13.8, 0, 3])
for n in range(len(s)):
ax1.plot(s[n,:,0], s[n,:,1], 'k', alpha=0.5)
ax2.plot(s[n,:,2], s[n,:,3], 'k', alpha=0.5)
ax3.plot(s[n,:,0], s[n,:,1], 'k', alpha=0.5)
ax4.plot(s[n,:,2], s[n,:,3], 'k', alpha=0.5)
ax1.set_xlabel(r'$t_{bar}$')
ax1.set_ylabel(r'$\tau_{bar}$')
ax2.set_xlabel(r'$t_{no bar}$')
ax2.set_ylabel(r'$\tau_{no bar}$')
ax3.set_xlabel(r'$t_{bar}$')
ax3.set_ylabel(r'$\tau_{bar}$')
ax4.set_xlabel(r'$t_{no bar}$')
ax4.set_ylabel(r'$\tau_{no bar}$')
P.tight_layout()
save_fig = '/Users/becky/Projects/Green-Valley-Project/bayesian/find_t_tau/bars/walkers_2d_steps_bars_'+str(nwalkers)+'_'+str(time.strftime('%H_%M_%d_%m_%y'))+'.pdf'
fig.savefig(save_fig)
return fig
def make_plotII(self):
# retrieve data
D=self.D
kmap={}
kmap['Q2 = 2'] = {'c':'r','ls':'-'}
kmap['Q2 = 5'] = {'c':'g','ls':'--'}
kmap['Q2 = 10'] = {'c':'b','ls':'-.'}
kmap['Q2 = 100'] = {'c':'k','ls':':'}
ax=py.subplot(111)
DF=D['AV18']
for Q2 in [2,5,10,100]:
k='Q2 = %d'%Q2
Q2=float(k.split('=')[1])
DF=D['AV18'][D['AV18'].Q2==Q2]
cls=kmap[k]['c']+kmap[k]['ls']
ax.plot(DF.X,DF.THEORY,cls,lw=2.0,label=r'$Q^2=%0.0f~{\rm GeV}^2$'%Q2)
ax.set_xlabel('$x$',size=25)
ax.set_ylabel(r'$F_2^d\, /\, F_2^N$',size=25)
ax.set_ylim(0.97,1.08)
ax.axhline(1,color='k',ls='-',alpha=0.2)
ax.legend(frameon=0,loc=2,fontsize=22)
py.tick_params(axis='both',labelsize=22)
py.tight_layout()
py.savefig('gallery/F2d_F2_II.pdf')
def runSimulation1(numSteps):
"""
Runs the simulation for `numSteps` time steps.
Returns a tuple of two lists: (rabbit_populations, fox_populations)
where rabbit_populations is a record of the rabbit population at the
END of each time step, and fox_populations is a record of the fox population
at the END of each time step.
Both lists should be `numSteps` items long.
"""
rabbit_sim = []
fox_sim = []
for step in range(numSteps):
rabbitGrowth()
rabbit_sim.append(CURRENTRABBITPOP)
foxGrowth()
fox_sim.append(CURRENTFOXPOP)
#print "CURRENTRABBITPOP", CURRENTRABBITPOP
#print "CURRENTFOXPOP", CURRENTFOXPOP
pylab.plot(range(numSteps), rabbit_sim, '-g', label='Rabbit population')
pylab.plot(range(numSteps), fox_sim, '-o', label='Fox population')
pylab.title('Fox and rabbit population in the wood')
xlabel = "Plot for simulation of {} steps".format(numSteps)
pylab.xlabel(xlabel)
pylab.ylabel('Current fox and rabbit population')
pylab.legend(loc='upper right')
pylab.tight_layout()
pylab.show()
pylab.clf()
def make_plotI(self):
# retrieve data
D=self.D
kmap={}
kmap['AV18'] = {'c':'r','ls':'-'}
kmap['CDBONN'] = {'c':'g','ls':'--'}
kmap['WJC1'] = {'c':'k','ls':'-.'}
kmap['WJC2'] = {'c':'b','ls':':'}
ax=py.subplot(111)
for k in ['AV18','CDBONN','WJC1','WJC2']:
DF=D[k]
DF=DF[DF.Q2==10]
if k=='CDBONN':
label='CDBonn'
else:
label=k
cls=kmap[k]['c']+kmap[k]['ls']
ax.plot(DF.X,DF.THEORY,cls,lw=2.0,label=tex(label))
ax.set_xlabel('$x$',size=25)
ax.set_ylabel(r'$F_2^d\, /\, F_2^N$',size=25)
ax.set_ylim(0.97,1.08)
ax.axhline(1,color='k',ls='-',alpha=0.2)
ax.legend(frameon=0,loc=2,fontsize=22)
py.tick_params(axis='both',labelsize=22)
py.tight_layout()
py.savefig('gallery/F2d_F2_I.pdf')
py.close()
def plot_temp():
data = read_temps()
dates, values = map(np.array, zip(*[(d['date'], d['temperature'])
for d in data]))
tmp = (date2num(dates) % 1.0)*24.0
ii = np.where((tmp > 0) & (tmp < 8))[0]
continuum = get_continuum(dates, dates[ii], values[ii])
setup(figsize=(12,6))
setupplot(subplt=(1,2,1), autoticks=True, xlabel='Date',)
pylab.plot(dates, values)
pylab.plot(dates[ii], values[ii], '.r')
pylab.plot(dates, continuum, '.k')
plot_weather(np.min(date2num(dates)))
# pylab.plot(dates, values-continuum+38, '.r')
dateticks('%Y.%m.%d')
setupplot(subplt=(2,2,2), autoticks=False, xlabel='Hour of Day')
pylab.plot(tmp, values, '.')
setupplot(subplt=(2,2,2), ylabel='', secondax=True)
setupplot(subplt=(2,2,4), autoticks=False, xlabel='Hour of Day')
sc = pylab.scatter(tmp, values-continuum+TNORM,
c=date2num(dates)-np.min(date2num(dates)), s=15,
marker='.', edgecolor='none',
label='Days since Start')
setupplot(subplt=(2,2,4), ylabel='', secondax=True)
hcolorbar(sc, axes=[0.75, 0.42, 0.1, 0.01])
pylab.tight_layout()
pylab.show()
def createMP4(text, filename, thumbnail, faimsSync):
fig = pylab.plt.figure()
fig.suptitle(text)
ax = fig.add_subplot(111)
ax.set_aspect('equal')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
im = ax.imshow(numpy.random.rand(300,300),cmap='gray',interpolation='nearest')
im.set_clim([0,1])
fig.set_size_inches([3,3])
pylab.tight_layout()
if faimsSync:
path = 'files/app/'
else:
path = 'files/server/'
def update_img(n):
tmp = numpy.random.rand(300,300)
im.set_data(tmp)
return im
#legend(loc=0)
photoUuid = uuid4()
if thumbnail:
fig.savefig("%s%s_%s.thumbnail.jpg" % (path,photoUuid,filename))
ani = animation.FuncAnimation(fig,update_img,20,interval=3)
writer = animation.writers['ffmpeg'](fps=3)
ani.save('%s%s_%s.mp4' % (path,photoUuid,filename),writer=writer,dpi=dpi)
pylab.plt.close()
return "%s%s_%s.mp4" % (path,photoUuid,filename)
def run(self):
plts = {}
graphs = {}
pos = 0
plt.ion()
plt.style.use('ggplot')
for name in sorted(self.names.values()):
p = plt.subplot(math.ceil(len(self.names) / 2), 2, pos+1)
p.set_ylim([0, 100])
p.set_title(self.machine_classes[name] + " " + name)
p.get_xaxis().set_visible(False)
X = range(0, NUM_ENTRIES, 1)
Y = NUM_ENTRIES * [0]
graphs[name] = p.plot(X, Y)[0]
plts[name] = p
pos += 1
plt.tight_layout()
while True:
for name, p in plts.items():
graphs[name].set_ydata(self.loads[name])
plt.draw()
plt.pause(0.05)
def set_font(font):
pl.xlabel('String length', fontproperties=font)
pl.tight_layout()
for label in pl.axes().get_xticklabels():
label.set_fontproperties(font)
for label in pl.axes().get_yticklabels():
label.set_fontproperties(font)
def corner_plot(s, labels):
x, y = s[:,0], s[:,1]
fig = P.figure(figsize=(10,10))
ax2 = P.subplot(223)
ax2.set_xlabel(labels[0])
ax2.set_ylabel(labels[1])
im = triangle.histo2d(x, y, ax=ax2, extent=[[0, 13.807108309208775],[0, 3.0]])
[l.set_rotation(45) for l in ax2.get_xticklabels()]
[j.set_rotation(45) for j in ax2.get_yticklabels()]
ax1 = P.subplot(221, xlim=[0, 13.807108309208775])
ax1.tick_params(axis='x', labelbottom='off')
ax1.tick_params(axis='y', labelleft='off')
ax1.hist(x, bins=50, histtype='step', color='k', range=(0, 13.807108309208775))
ax3 = P.subplot(224)
ax3.tick_params(axis='x', labelbottom='off')
ax3.tick_params(axis='y', labelleft='off')
ax3.hist(y, bins=50, orientation='horizontal', histtype='step',color='k', range=(0,3))
P.subplots_adjust(wspace=0.05)
P.subplots_adjust(hspace=0.05)
cbar_ax = fig.add_axes([0.55, 0.565, 0.02, 0.405])
cb = fig.colorbar(im, cax = cbar_ax)
cb.solids.set_edgecolor('face')
cb.set_label(r'predicted SFR $[M_{\odot} yr^{-1}]$', labelpad = 20, fontsize=16)
P.tight_layout()
return fig
def plot_3_targs():
#name three objects
targNames = ['ob140613', 'ob150211', 'ob150029']
#object alignment directories
an_dirs = [
'/g/lu/microlens/cross_epoch/OB140613/a_2018_09_24/prop/',
'/g/lu/microlens/cross_epoch/OB150211/a_2018_09_19/prop/',
'/g/lu/microlens/cross_epoch/OB150029/a_2018_09_24/prop/'
]
align_dirs = ['align/align_t', 'align/align_t', 'align/align_t']
points_dirs = ['points_a/', 'points_d/', 'points_d/']
poly_dirs = ['polyfit_a/fit', 'polyfit_d/fit', 'polyfit_d/fit']
xlim = [1.2, 2.0, 1.5]
ylim = [1.0, 7.0, 1.5]
#Output file
#filename = '/Users/jlu/doc/proposals/keck/uc/19A/plot_3_targs.png'
filename = '/Users/jlu/plot_3_targs.png'
#figsize = (15, 4.5)
figsize = (10, 4.5)
ps = 9.92
plt.close(1)
plt.figure(1, figsize=figsize)
Ntarg = len(targNames) - 1
for i in range(Ntarg):
rootDir = an_dirs[i]
starName = targNames[i]
align = align_dirs[i]
poly = poly_dirs[i]
point = points_dirs[i]
s = starset.StarSet(rootDir + align)
s.loadPolyfit(rootDir + poly, accel=0, arcsec=0)
names = s.getArray('name')
mag = s.getArray('mag')
x = s.getArray('x')
y = s.getArray('y')
ii = names.index(starName)
star = s.stars[ii]
pointsTab = Table.read(rootDir + point + starName + '.points',
format='ascii')
time = pointsTab[pointsTab.colnames[0]]
x = pointsTab[pointsTab.colnames[1]]
y = pointsTab[pointsTab.colnames[2]]
xerr = pointsTab[pointsTab.colnames[3]]
yerr = pointsTab[pointsTab.colnames[4]]
if i == 0:
print('Doing MJD')
idx_2015 = np.where(time <= 57387)
idx_2016 = np.where((time > 57387) & (time <= 57753))
idx_2017 = np.where((time > 57753) & (time <= 58118))
idx_2018 = np.where((time > 58119) & (time <= 58484))
else:
idx_2015 = np.where(time < 2016)
idx_2016 = np.where((time >= 2016) & (time < 2017))
idx_2017 = np.where((time >= 2017) & (time < 2018))
idx_2018 = np.where((time >= 2018) & (time < 2019))
fitx = star.fitXv
fity = star.fitYv
dt = time - fitx.t0
fitLineX = fitx.p + (fitx.v * dt)
fitSigX = np.sqrt(fitx.perr**2 + (dt * fitx.verr)**2)
fitLineY = fity.p + (fity.v * dt)
fitSigY = np.sqrt(fity.perr**2 + (dt * fity.verr)**2)
from matplotlib.ticker import FormatStrFormatter
fmtX = FormatStrFormatter('%5i')
fmtY = FormatStrFormatter('%6.2f')
fontsize1 = 16
# Convert everything into relative coordinates:
x -= fitx.p
y -= fity.p
fitLineX -= fitx.p
fitLineY -= fity.p
# Change plate scale.
x = x * ps * -1.0
y = y * ps
xerr *= ps
yerr *= ps
fitLineX = fitLineX * ps * -1.0
fitLineY = fitLineY * ps
fitSigX *= ps
fitSigY *= ps
paxes = plt.subplot(1, Ntarg, i + 1)
plt.errorbar(x[idx_2015],
y[idx_2015],
xerr=xerr[idx_2015],
yerr=yerr[idx_2015],
fmt='r.',
label='2015')
plt.errorbar(x[idx_2016],
y[idx_2016],
xerr=xerr[idx_2016],
yerr=yerr[idx_2016],
fmt='g.',
label='2016')
plt.errorbar(x[idx_2017],
y[idx_2017],
xerr=xerr[idx_2017],
yerr=yerr[idx_2017],
fmt='b.',
label='2017')
plt.errorbar(x[idx_2018],
y[idx_2018],
xerr=xerr[idx_2018],
yerr=yerr[idx_2018],
fmt='c.',
label='2018')
# if i==1:
# plt.yticks(np.arange(np.min(y-yerr-0.1*ps), np.max(y+yerr+0.1*ps), 0.3*ps))
# plt.xticks(np.arange(np.min(x-xerr-0.1*ps), np.max(x+xerr+0.1*ps), 0.25*ps))
# else:
# plt.yticks(np.arange(np.min(y-yerr-0.1*ps), np.max(y+yerr+0.1*ps), 0.15*ps))
# plt.xticks(np.arange(np.min(x-xerr-0.1*ps), np.max(x+xerr+0.1*ps), 0.15*ps))
paxes.tick_params(axis='both', which='major', labelsize=fontsize1)
paxes.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
paxes.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))
plt.xlabel('X offset (mas)', fontsize=fontsize1)
plt.ylabel('Y offset (mas)', fontsize=fontsize1)
plt.plot(fitLineX, fitLineY, 'k-', label='_nolegend_')
plt.plot(fitLineX + fitSigX,
fitLineY + fitSigY,
'k--',
label='_nolegend_')
plt.plot(fitLineX - fitSigX,
fitLineY - fitSigY,
'k--',
label='_nolegend_')
# Plot lines between observed point and the best fit value along the model line.
for ee in range(len(time)):
if ee in idx_2015[0].tolist():
color_line = 'red'
if ee in idx_2016[0].tolist():
color_line = 'green'
if ee in idx_2017[0].tolist():
color_line = 'blue'
if ee in idx_2018[0].tolist():
color_line = 'cyan'
plt.plot([fitLineX[ee], x[ee]], [fitLineY[ee], y[ee]],
color=color_line,
linestyle='dashed',
alpha=0.8)
plt.axis([xlim[i], -xlim[i], -ylim[i], ylim[i]])
plt.title(starName.upper())
if i == 0:
plt.legend(loc=1, fontsize=12)
plt.subplots_adjust(wspace=0.6,
hspace=0.6,
left=0.08,
bottom=0.05,
right=0.95,
top=0.90)
plt.tight_layout()
plt.show()
plt.savefig(filename)
return
def plot_ratio(wdir, data, istep, Q2_bins, dpi=200):
ratio_1 = 2.5
ratio_2 = -3.0
n_rows, n_columns = 4, 4
fig = py.figure(figsize=(n_columns * 4.5, n_rows * 3.0))
count = 0
ax = {}
label_1 = r'$\mathrm{E866}~pp$'
label_2 = r'$\mathrm{E866}~pd$'
for _ in data:
for i in range(len(Q2_bins)):
Q2_min, Q2_max = Q2_bins[i]
thyk = data[_]['thy']
ratio = data[_]['value'] / thyk
data[_].update({'ratio': ratio})
df = pd.DataFrame(data[_])
df = df.query('Q2>%f and Q2<%f' % (Q2_min, Q2_max))
count += 1
ax[count] = py.subplot(n_rows, n_columns, count)
df = df.query('ratio<%f' % (ratio_1))
df = df.query('ratio>%f' % (ratio_2))
thy = df['thy']
xF = df.xF
value = df.value
alpha = df.alpha
if _ == 10001:
ax[count].errorbar(xF,
value / thy,
alpha / thy,
color='firebrick',
marker='.',
linestyle='none',
label=label_1)
if _ == 10002:
ax[count].errorbar(xF,
value / thy,
alpha / thy,
color='darkgreen',
marker='^',
linestyle='none',
label=label_2)
count_max = count
ax[1].legend(fontsize=20, loc=9)
ax[9].legend(fontsize=20, loc='upper left')
## proton
ax[1].set_ylim(0.5, 1.6)
minorLocator = MultipleLocator(0.1)
majorLocator = MultipleLocator(0.5)
ax[1].yaxis.set_minor_locator(minorLocator)
ax[1].yaxis.set_major_locator(majorLocator)
ax[1].text(0.1, 0.6, r'$Q^2~\in~[37,42]\,\mathrm{GeV^2}$', fontsize=22)
ax[2].set_ylim(0.5, 1.3)
minorLocator = MultipleLocator(0.05)
majorLocator = MultipleLocator(0.25)
ax[2].yaxis.set_minor_locator(minorLocator)
ax[2].yaxis.set_major_locator(majorLocator)
ax[2].text(0.07, 0.57, r'$Q^2~\in~[47,49]\,\mathrm{GeV^2}$', fontsize=22)
ax[3].set_ylim(0.5, 2.1)
minorLocator = MultipleLocator(0.1)
majorLocator = MultipleLocator(0.5)
ax[3].yaxis.set_minor_locator(minorLocator)
ax[3].yaxis.set_major_locator(majorLocator)
ax[3].text(0.1, 1.8, r'$Q^2~\in~[54,58]\,\mathrm{GeV^2}$', fontsize=22)
ax[4].set_ylim(0.7, 2.1)
minorLocator = MultipleLocator(0.1)
majorLocator = MultipleLocator(0.5)
ax[4].yaxis.set_minor_locator(minorLocator)
ax[4].yaxis.set_major_locator(majorLocator)
ax[4].text(0.1, 1.8, r'$Q^2~\in~[63,66]\,\mathrm{GeV^2}$', fontsize=22)
ax[5].set_ylim(0.3, 2.5)
minorLocator = MultipleLocator(0.1)
majorLocator = MultipleLocator(0.5)
ax[5].yaxis.set_minor_locator(minorLocator)
ax[5].yaxis.set_major_locator(majorLocator)
ax[5].text(0.1, 2.1, r'$Q^2~\in~[70,73]\,\mathrm{GeV^2}$', fontsize=22)
ax[6].set_ylim(0.5, 2.5)
minorLocator = MultipleLocator(0.1)
majorLocator = MultipleLocator(0.5)
ax[6].yaxis.set_minor_locator(minorLocator)
ax[6].yaxis.set_major_locator(majorLocator)
ax[6].text(0.15, 2.1, r'$Q^2~\in~[118,131]\,\mathrm{GeV^2}$', fontsize=22)
ax[7].set_ylim(0.3, 2.5)
minorLocator = MultipleLocator(0.1)
majorLocator = MultipleLocator(0.5)
ax[7].yaxis.set_minor_locator(minorLocator)
ax[7].yaxis.set_major_locator(majorLocator)
ax[7].text(0.1, 2.1, r'$Q^2~\in~[148,156]\,\mathrm{GeV^2}$', fontsize=22)
ax[8].set_ylim(0.4, 2.7)
minorLocator = MultipleLocator(0.1)
majorLocator = MultipleLocator(0.5)
ax[8].yaxis.set_minor_locator(minorLocator)
ax[8].yaxis.set_major_locator(majorLocator)
ax[8].text(0.32, 2, r' $Q^2~\in~[173,222]\,\mathrm{GeV^2}$', fontsize=22)
## deuteron
ax[9].set_ylim(0.5, 1.7)
minorLocator = MultipleLocator(0.1)
majorLocator = MultipleLocator(0.5)
ax[9].yaxis.set_minor_locator(minorLocator)
ax[9].yaxis.set_major_locator(majorLocator)
ax[9].text(0.1, 0.6, r'$Q^2~\in~[37,42]\,\mathrm{GeV^2}$', fontsize=22)
ax[10].set_ylim(0.5, 1.6)
minorLocator = MultipleLocator(0.1)
majorLocator = MultipleLocator(0.5)
ax[10].yaxis.set_minor_locator(minorLocator)
ax[10].yaxis.set_major_locator(majorLocator)
ax[10].text(0.2, 1.4, r'$Q^2~\in~[47,49]\,\mathrm{GeV^2}$', fontsize=22)
ax[11].set_ylim(0.4, 1.72)
minorLocator = MultipleLocator(0.1)
majorLocator = MultipleLocator(0.5)
ax[11].yaxis.set_minor_locator(minorLocator)
ax[11].yaxis.set_major_locator(majorLocator)
ax[11].text(0.2, 1.5, r'$Q^2~\in~[52,57]\,\mathrm{GeV^2}$', fontsize=22)
ax[12].set_ylim(0.6, 1.42)
minorLocator = MultipleLocator(0.05)
majorLocator = MultipleLocator(0.25)
ax[12].yaxis.set_minor_locator(minorLocator)
ax[12].yaxis.set_major_locator(majorLocator)
ax[12].text(0.1, 1.27, r'$Q^2~\in~[62,65]\,\mathrm{GeV^2}$', fontsize=22)
ax[13].set_ylim(0.45, 1.35)
minorLocator = MultipleLocator(0.05)
majorLocator = MultipleLocator(0.25)
ax[13].yaxis.set_minor_locator(minorLocator)
ax[13].yaxis.set_major_locator(majorLocator)
ax[13].text(0.2, 1.2, r'$Q^2~\in~[70,72]\,\mathrm{GeV^2}$', fontsize=22)
ax[14].set_ylim(0.45, 2.5)
minorLocator = MultipleLocator(0.1)
majorLocator = MultipleLocator(0.5)
ax[14].yaxis.set_minor_locator(minorLocator)
ax[14].yaxis.set_major_locator(majorLocator)
ax[14].text(0.2, 2.2, r'$Q^2~\in~[124,129]\,\mathrm{GeV^2}$', fontsize=22)
ax[15].set_ylim(0.45, 1.75)
minorLocator = MultipleLocator(0.1)
majorLocator = MultipleLocator(0.5)
ax[15].yaxis.set_minor_locator(minorLocator)
ax[15].yaxis.set_major_locator(majorLocator)
ax[15].text(0.05,
1.55,
r'$Q^2~\in~[145,154]\,\mathrm{GeV^2}$',
fontsize=22)
ax[16].set_ylim(0.0, 2.0)
minorLocator = MultipleLocator(0.1)
majorLocator = MultipleLocator(0.5)
ax[16].yaxis.set_minor_locator(minorLocator)
ax[16].yaxis.set_major_locator(majorLocator)
ax[16].text(0.05, 0.2, r'$Q^2~\in~[73,280]\,\mathrm{GeV^2}$', fontsize=22)
for count in range(1, count_max + 1):
if count % 4 == 1:
ax[count].set_ylabel(r'\boldmath$\mathrm{data/theory}$', size=24)
ax[count].yaxis.set_label_coords(-0.17, 0.5)
for count in range(count_max, count_max - n_columns, -1):
ax[count].set_xlabel(r'\boldmath$x_{\rm F}$', size=24)
for i in range(1, 13):
ax[i].set_xticklabels([])
for _ in ax:
ax[_].axhline(1, color='black', ls=':')
#AX[_].set_ylim(0.8,1.2)
ax[_].set_xlim(0.0, 1.0)
ax[_].xaxis.set_label_coords(0.90, -0.12)
ax[_].tick_params(axis='x', which='major', labelsize=18)
ax[_].tick_params(axis='y', which='major', labelsize=18)
ax[_].xaxis.set_tick_params(which='major', length=6)
ax[_].xaxis.set_tick_params(which='minor', length=3)
ax[_].yaxis.set_tick_params(which='major', length=6)
ax[_].yaxis.set_tick_params(which='minor', length=3)
minorLocator = MultipleLocator(0.05)
majorLocator = MultipleLocator(0.2)
ax[_].xaxis.set_minor_locator(minorLocator)
ax[_].xaxis.set_major_locator(majorLocator)
py.tight_layout()
py.subplots_adjust(left=0.08,
bottom=0.08,
right=0.99,
top=0.97,
wspace=0.2,
hspace=0.1)
py.savefig('%s/gallery/dy-%d.png' % (wdir, istep), dpi=dpi)
py.close()
norm.pdf(beta_axis[2], loc=mean[2], scale=std[2]),
color='red',
lw=2)
plt.xlabel('beta[2]')
plt.subplot(4, 4, 13)
plt.scatter(samples['beta'][0][:, 0], samples['beta'][3][:, 0])
plt.xlabel('beta[0]')
plt.ylabel('beta[3]')
plt.subplot(4, 4, 14)
plt.scatter(samples['beta'][1][:, 0], samples['beta'][3][:, 0])
plt.xlabel('beta[1]')
plt.ylabel('beta[3]')
plt.subplot(4, 4, 15)
plt.scatter(samples['beta'][2][:, 0], samples['beta'][3][:, 0])
plt.xlabel('beta[2]')
plt.ylabel('beta[3]')
plt.subplot(4, 4, 16)
plt.hist(samples['beta'][3][:, 0], normed=True)
plt.plot(beta_axis[3],
norm.pdf(beta_axis[3], loc=mean[3], scale=std[3]),
color='red',
lw=2)
plt.xlabel('beta[3]')
plt.tight_layout()
plt.show()
def plotSOWFAvsFLORIS(prob, just_SOWFA=True, plot_prefix=""):
# prob['floris_params:me'] = params[0:3]
# prob['floris_params:MU'] = params[3:6]
# prob['floris_params:cos_spread'] = params[6]
# Define turbine locations and orientation
prob['turbineX'] = np.array([1118.1, 1881.9])
prob['turbineY'] = np.array([1279.5, 1720.5])
ICOWESdata = loadmat('YawPosResults.mat')
yawrange = ICOWESdata['yaw'][0]
FLORISpower = list()
for yaw1 in yawrange:
# print 'yaw in = ', yaw1*np.pi/180.
# assign values to yaw
prob['yaw0'] = np.array([yaw1, 0.0])
# run the problem at given conditions
prob.run()
# print np.sqrt((turbineY[0]-turbineY[1])**2+(turbineX[0]-turbineX[1])**2)/rotorDiameter[0] # print downwind distance
FLORISpower.append(list(prob['wtPower0']))
time.sleep(0.001)
# time.sleep(10)
# quit()
FLORISpower = np.array(FLORISpower)
SOWFApower = np.array([
ICOWESdata['yawPowerT1'][0], ICOWESdata['yawPowerT2'][0]
]).transpose() / 1000.
error_turbine2 = np.sum(np.abs(FLORISpower[:, 1] - SOWFApower[:, 1]))
fig, axes = plt.subplots(ncols=2, nrows=1, sharey=False)
axes[0].plot(yawrange.transpose(),
SOWFApower[:, 0],
'o',
markeredgecolor='r',
markerfacecolor='None',
markersize=4,
label='Turbine 1, SOWFA Results')
axes[0].plot(yawrange.transpose(),
SOWFApower[:, 1],
'o',
markeredgecolor='b',
markerfacecolor='None',
markersize=4,
label='Turbine 2, SOWFA Results')
axes[0].plot(yawrange.transpose(),
SOWFApower[:, 0] + SOWFApower[:, 1],
'k+',
label='Total, SOWFA results')
axes[0].plot(yawrange.transpose(), FLORISpower[:, 0], 'k-')
axes[0].plot(yawrange.transpose(), FLORISpower[:, 1], 'k-')
axes[0].plot(yawrange.transpose(),
FLORISpower[:, 0] + FLORISpower[:, 1],
'k-',
label='FLORIS model')
# axes[0, 0].plot(yawrange, FLORISpower[:, 1], '#7CFC00')
axes[0].set_xlabel('yaw angle (deg.)')
axes[0].set_ylabel('power (kW)')
# axes[0, 0].set_ylim(500, 6000)
# axes[0, 0].legend(loc=2)
posrange = ICOWESdata['pos'][0]
prob['yaw0'] = np.array([0.0, 0.0])
FLORISpower = list()
for pos2 in posrange:
# Define turbine locations and orientation
effUdXY = 0.523599
Xinit = np.array([1118.1, 1881.9])
Yinit = np.array([1279.5, 1720.5])
XY = np.array([Xinit, Yinit]) + np.dot(
np.array([[np.cos(effUdXY), -np.sin(effUdXY)],
[np.sin(effUdXY), np.cos(effUdXY)]]),
np.array([[0., 0], [0, pos2]]))
prob['turbineX'] = XY[0, :]
prob['turbineY'] = XY[1, :]
# run the problem at given conditions
prob.run()
# print np.sqrt((turbineY[0]-turbineY[1])**2+(turbineX[0]-turbineX[1])**2)/rotorDiameter[0] # print downwind distance
FLORISpower.append(list(prob['wtPower0']))
FLORISpower = np.array(FLORISpower)
SOWFApower = np.array([
ICOWESdata['posPowerT1'][0], ICOWESdata['posPowerT2'][0]
]).transpose() / 1000.
error_turbine2 += np.sum(np.abs(FLORISpower[:, 1] - SOWFApower[:, 1]))
# print error_turbine2
axes[1].plot(posrange / rotorDiameter[0], FLORISpower[:, 0], 'k-')
axes[1].plot(posrange / rotorDiameter[0],
SOWFApower[:, 0],
'o',
markeredgecolor='r',
markerfacecolor='None',
markersize=4,
label='Turbine 1, SOWFA Results')
axes[1].plot(posrange / rotorDiameter[0], FLORISpower[:, 1], 'k-')
axes[1].plot(posrange / rotorDiameter[0],
SOWFApower[:, 1],
'o',
markeredgecolor='b',
markerfacecolor='None',
markersize=4,
label='Turbine 2, SOWFA Results')
axes[1].plot(posrange / rotorDiameter[0],
FLORISpower[:, 0] + FLORISpower[:, 1],
'k-',
label='FLORIS model')
axes[1].plot(posrange / rotorDiameter[0],
SOWFApower[:, 0] + SOWFApower[:, 1],
'k+',
label='Total, SOWFA Results')
axes[1].set_xlabel('y/D')
axes[1].set_ylabel('power (kW)')
axes[1].set_xlim(min(posrange / rotorDiameter[0]),
max(posrange / rotorDiameter[0]))
# lgd = axes[1].legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
lgd = axes[0].legend(loc='lower left',
bbox_to_anchor=(0.0, 1.05),
fancybox=False,
shadow=False,
ncol=2)
plt.tight_layout()
if not plot_prefix == "":
plt.savefig(plot_prefix + "SOWFA.pdf",
bbox_extra_artists=(lgd, ),
bbox_inches='tight')
# ############
if not just_SOWFA:
fig, axes = plt.subplots(ncols=2, nrows=1, sharey=False)
posrange = np.linspace(-3. * rotorDiameter[0],
30. * rotorDiameter[0],
num=1000)
yaw = np.array([0.0, 0.0])
wind_direction = 270.
prob['yaw0'] = yaw
prob['wind_direction'] = wind_direction
FLORISpower = list()
FLORISvelocity = list()
for pos2 in posrange:
# assign values to yaw
prob['turbineX'] = np.array([0., pos2])
prob['turbineY'] = np.array([0.0, 0.0])
# run the problem at given conditions
prob.run()
# print np.sqrt((turbineY[0]-turbineY[1])**2+(turbineX[0]-turbineX[1])**2)/rotorDiameter[0] # print downwind distance
FLORISpower.append(list(prob['wtPower0']))
FLORISvelocity.append(list(prob['wtVelocity0']))
FLORISpower = np.array(FLORISpower)
FLORISvelocity = np.array(FLORISvelocity)
# np.savetxt("downstream_velocity_original.txt", np.c_[posrange/rotorDiameter[0], FLORISvelocity[:, 1]], delimiter=',')
axes[1].plot(posrange / rotorDiameter[0],
FLORISpower[:, 1],
'#7CFC00',
label='FLORIS model')
axes[1].plot(np.array([7, 7]),
np.array([0, 1800]),
'--k',
label='tuning point')
axes[1].set_xlabel('x/D')
axes[1].set_ylabel('Power (kW)')
axes[1].legend(loc=4)
axes[0].plot(posrange / rotorDiameter[0],
FLORISvelocity[:, 1],
'#7CFC00',
label='FLORIS model')
axes[0].plot(np.array([7, 7]),
np.array([2, 9]),
'--k',
label='tuning point')
axes[0].set_xlabel('x/D')
axes[0].set_ylabel('Valocity (m/s)')
axes[0].legend(loc=4)
# plt.show()
plt.tight_layout()
if not plot_prefix == "":
plt.savefig(plot_prefix + "DownwindProfile.pdf")
FLORIScenters = list()
FLORISdiameters = list()
FLORISoverlap = list()
# prob['wakeCentersYT'][2] = 0.
for pos2 in posrange:
# assign values to yaw
prob['turbineX'] = np.array([0., pos2])
# prob['turbineY'] = np.array([0.0, prob['wakeCentersYT'][2]])
prob['turbineY'] = np.array([0.0, 0.0])
# run the problem at given conditions
prob.run()
# print np.sqrt((turbineY[0]-turbineY[1])**2+(turbineX[0]-turbineX[1])**2)/rotorDiameter[0] # print downwind distance
# print prob['wakeCentersYT']
FLORIScenters.append(list(prob['wakeCentersYT']))
FLORISdiameters.append(list(prob['wakeDiametersT']))
FLORISoverlap.append(list(prob['wakeOverlapTRel']))
# print prob['wtVelocity0']
# print prob['wakeOverlapTRel'][6:]
FLORIScenters = np.array(FLORIScenters)
FLORISdiameters = np.array(FLORISdiameters)
FLORISoverlap = np.array(FLORISoverlap)
fig, axes = plt.subplots(ncols=2, nrows=2, sharey=False, sharex=False)
# plot wake center
axes[0, 0].plot(posrange / rotorDiameter[0],
FLORIScenters[:, 2] / rotorDiameter[0],
'k',
label='Wake Center')
# axes[0, 0].set_xlabel('x/D')
axes[0, 0].set_ylabel('y/D')
axes[0, 0].legend(loc=1)
# plot wake diameters
axes[0,
1].plot(posrange / rotorDiameter[0],
FLORISdiameters[:, 3 * nTurbines + 0] / rotorDiameter[0],
'b',
label='Near Wake')
axes[0,
1].plot(posrange / rotorDiameter[0],
FLORISdiameters[:, 3 * nTurbines + 2] / rotorDiameter[0],
'r',
label='Far Wake')
axes[0,
1].plot(posrange / rotorDiameter[0],
FLORISdiameters[:, 3 * nTurbines + 4] / rotorDiameter[0],
'y',
label='Mixing Zone')
# axes[0, 1].set_xlabel('x/D')
axes[0, 1].set_ylabel('Wake Diameter / Rotor Diameter')
axes[0, 1].legend(loc=2)
axes[0, 1].set_ylim([-1., 5.])
# plot wake relative overlap
axes[1, 0].plot(posrange / rotorDiameter[0],
FLORISoverlap[:, 3 * nTurbines + 0],
'b',
label='Near Wake')
axes[1, 0].plot(posrange / rotorDiameter[0],
FLORISoverlap[:, 3 * nTurbines + 2],
'r',
label='Far Wake')
axes[1, 0].plot(posrange / rotorDiameter[0],
FLORISoverlap[:, 3 * nTurbines + 4],
'y',
label='Mixing Zone')
axes[1, 0].set_xlabel('x/D')
axes[1, 0].set_ylabel('Relative Overlap')
axes[1, 0].legend(loc=0)
axes[1, 0].set_ylim([-0.1, 1.1])
posrange = np.linspace(-3. * rotorDiameter[0],
7. * rotorDiameter[0],
num=300)
yaw = np.array([0.0, 0.0])
wind_direction = 270.
prob['yaw0'] = yaw
prob['wind_direction'] = wind_direction
FLORISpower = list()
for pos2 in posrange:
# assign values to yaw
prob['turbineX'] = np.array([0., 5 * rotorDiameter[0]])
prob['turbineY'] = np.array([0.0, pos2])
# run the problem at given conditions
prob.run()
# print np.sqrt((turbineY[0]-turbineY[1])**2+(turbineX[0]-turbineX[1])**2)/rotorDiameter[0] # print downwind distance
FLORISpower.append(list(prob['wtPower0']))
FLORISpower = np.array(FLORISpower)
axes[1, 1].plot(posrange / rotorDiameter[0], FLORISpower[:, 1],
'#7CFC00') #, label="FLORIS model")
axes[1, 1].set_xlabel('x/D')
axes[1, 1].set_ylabel('Power (kW)')
axes[1, 1].legend(loc=4)
if not plot_prefix == "":
plt.savefig(plot_prefix + "WakeProfile.pdf")
#####################
# FLORISaxialInd = list()
# windspeed = np.linspace(0, 30, 100)
# # prob['wakeCentersYT'][2] = 0.
# for pos2 in posrange:
#
# # assign values to yaw
# prob['turbineX'] = np.array([0., pos2])
# # prob['turbineY'] = np.array([0.0, prob['wakeCentersYT'][2]])
# prob['turbineY'] = np.array([0.0, 0.0])
#
# # run the problem at given conditions
# prob.run()
#
# # print np.sqrt((turbineY[0]-turbineY[1])**2+(turbineX[0]-turbineX[1])**2)/rotorDiameter[0] # print downwind distance
# # print prob['wakeCentersYT']
# FLORIScenters.append(list(prob['wakeCentersYT']))
# FLORISdiameters.append(list(prob['wakeDiametersT']))
# FLORISoverlap.append(list(prob['wakeOverlapTRel']))
# # print prob['wtVelocity0']
#
# # print prob['wakeOverlapTRel'][6:]
#
# FLORIScenters = np.array(FLORIScenters)
# FLORISdiameters = np.array(FLORISdiameters)
# FLORISoverlap = np.array(FLORISoverlap)
# fig, axes = plt.subplots(ncols=2, nrows=2, sharey=False, sharex=True)
# # plot wake center
# axes[0, 0].plot(posrange/rotorDiameter[0], FLORIScenters[:, 2], 'k', label='Wake Center')
# # axes[0, 0].set_xlabel('x/D')
# axes[0, 0].set_ylabel('Position')
# axes[0, 0].legend(loc=1)
plt.tight_layout()
plt.show()
x0 = 10 * xc
#plot the two calculations as an overlay, for both x and v
figure(j)
subplot(2, 1, 1)
plot(t, xRel, t, xClass)
legend(('Relativistic', 'Classical'))
xlabel('time(s)')
subplot(2, 1, 2)
plot(t, vRel, t, vClass)
xlabel('time(s)')
title('v')
suptitle('x0 = ' + str(x0) + 'm')
tight_layout()
savefig('Lab03-Q1a-Fig' + str(j + 1) + '.png')
#------------------------------------------------------------------------
# Relativistic period
x0_range = linspace(1, 10 * xc, 1000)
def g(x):
return c*sqrt(((0.5*k*(x0**2-x**2)*(2.0*m*c**2+0.5*k*(x0**2-x**2))) \
/(m*c**2+0.5*k*(x0**2-x**2))**2))
def integrand(x):
return 4.0 * g(x)**-1
def plot_map(self):
import pylab
# compile a list of reflexes allowed by current spectro config
allowed, limited = self.generate_hkls()
# calculate limits of movement from limits of phi and psi
lim1, lim2, lim3, lim4 = [], [], [], []
psimin, psimax = self.tasinfo['psilim']
phimin, phimax = self.tasinfo['philim']
coupling = self.tasinfo['axiscoupling']
if self.mode == 'CKI':
ki = self.const
kf = self.cell.cal_kf(self.ny, ki)
else:
kf = self.const
ki = self.cell.cal_ki1(self.ny, kf)
for phival in linspace(phimin, phimax, 100):
xy1 = self.cell.angle2Qcart([ki, kf, phival, psimin], coupling)
xy2 = self.cell.angle2Qcart([ki, kf, phival, psimax], coupling)
lim1.append(xy1[:2])
lim2.append(xy2[:2])
for psival in linspace(psimin, psimax, 100):
xy1 = self.cell.angle2Qcart([ki, kf, phimin, psival], coupling)
xy2 = self.cell.angle2Qcart([ki, kf, phimax, psival], coupling)
lim3.append(xy1[:2])
lim4.append(xy2[:2])
# directions of axes
dir1 = self.cell._orient1
dir2 = cross(self.cell.cal_zone(), self.cell._orient1)
# normalize second direction to smallest length
comp = [abs(c) for c in dir2 if c != 0]
complen = len(comp)
if complen == 0:
# should be impossible: all entries == 0, no change
f = 1
elif complen == 1:
f = comp[0]
elif complen == 2:
f = gcd(comp[0], comp[1])
else:
f = min(gcd(comp[0], comp[1]), gcd(comp[1], comp[2]),
gcd(comp[0], comp[2]))
dir2 /= f
# set up pylab figure
pylab.ion()
pylab.figure('Reciprocal space visualization',
figsize=(7, 7),
dpi=120,
facecolor='1.0')
pylab.clf()
pylab.rc('text', usetex=True)
pylab.rc('text.latex',
preamble=
r'\usepackage{amsmath}\usepackage{helvet}\usepackage{sfmath}')
ax = pylab.subplot(111, aspect='equal')
ax.set_axisbelow(True) # draw grid lines below plotted points
# register event handler to pylab
pylab.connect('key_press_event', pylab_key_handler)
def click_handler(event):
if not event.inaxes: return
canvas = pylab.gcf().canvas
if canvas.toolbar.mode: return
hkl = self.display_hkl(event.xdata, event.ydata, self.clicktext)
if hkl is not None:
self.plot_ellipsoid(hkl, event.xdata, event.ydata)
canvas.draw()
pylab.connect('button_release_event', click_handler)
# monkey-patch formatting coordinates in the status bar
pylab.gca().format_coord = self.format_coord
pylab.title(
'Available reciprocal space for %s %.3f \\AA$^{-1}$, E = %.3f %s' %
(self.mode, self.const, self.E,
self.tasinfo['energytransferunit']))
pylab.xlabel('$Q_1$ (\\AA$^{-1}$) $\\rightarrow$ ( %d %d %d )' %
tuple(dir1))
pylab.ylabel('$Q_2$ (\\AA$^{-1}$) $\\rightarrow$ ( %d %d %d )' %
tuple(dir2))
pylab.grid(color='0.5', zorder=-2)
if hasattr(pylab, 'tight_layout'):
pylab.tight_layout()
pylab.subplots_adjust(bottom=0.1)
self.clicktext = pylab.text(0.5,
0.01,
'(click to show angles)',
size='small',
horizontalalignment='center',
transform=pylab.gcf().transFigure)
# plot origin
self.plot_hkl((0, 0, 0), color='black')
# plot allowed Bragg indices
self.plot_hkls(allowed,
labels=['(%d%d%d)' % tuple(v) for v in allowed])
# plot Bragg indices allowed by scattering plane, but limited by devices
self.plot_hkls(limited, color='red')
# plot user-supplied points
for hkl in self.hkls:
self.plot_hkl(hkl,
color='#009900',
edgecolor='black',
linewidth=0.5,
label='(%.3g %.3g %.3g)' % tuple(hkl))
# plot user-supplied propagation vectors
for tau in self.taus:
tau = array(tau)
allowed1, limited = self.generate_hkls(tau, origin=True)
allowed2, limited = self.generate_hkls(-tau, origin=True)
self.plot_hkls(allowed1 + allowed2,
color='#550055',
s=3,
marker='s')
# plot user-supplied scan points
if self.scan is not None:
allowed, limited = self.check_hkls(self.scan)
self.plot_hkls(allowed, color='#009900', s=2)
self.plot_hkls(limited, color='red', s=2)
# plot current spectrometer position
current_pos = self.tasinfo['actpos']
if abs(current_pos[3] - self.E) < 0.01:
self.plot_hkls([current_pos[:3]], color='#990033', marker='x')
# plot last calpos() result
calpos = self.tasinfo['calpos']
if calpos is not None and abs(calpos[3] - self.E) < 0.01:
self.plot_hkls([calpos[:3]], color='#006600', marker='*')
# plot limits of phi/psi
for v in lim1, lim2, lim3, lim4:
v = array(v)
pylab.plot(v[:, 0], v[:, 1], color='0.7', zorder=-1)
P.fill_between([1e8, 4e9],
1e-3,
1e3,
linewidths=0.,
color='k',
alpha=0.1,
zorder=-100)
P.xlabel(r"$M_\star$ $[M_\odot]$", fontsize=18)
P.ylabel(r"$\psi_{\rm SFR}$ $[M_\odot/{\rm yr}]$", fontsize=18)
P.gca().tick_params(axis='both',
which='major',
labelsize=20,
size=8.,
width=1.5,
pad=8.)
P.gca().tick_params(axis='both',
which='minor',
labelsize=20,
size=5.,
width=1.5,
pad=8.)
P.xscale('log')
P.yscale('log')
P.tight_layout()
#P.savefig('../draft/sfr_mstar_bounds.pdf')
P.show()
def finish(self):
# Finish presentation
#
if not self.isReady:
return
if self.isUserAbort:
# Clear screen
#
self.View.clear()
self.View.present()
ssp.Log.write("ABORT", "User aborted program")
else:
ssp.Log.write("ok", "Program finished")
# Log timing information
#
if self.Conf.isTrackTime:
self.avRendDur_s = self.avRendDur_s / self.nRendTotal
self.avPresDur_s = self.avPresDur_s / self.nRendTotal
self.avFrDur_s = self.avFrDur_s / self.nFrTotal
ssp.Log.write(
"INFO", "{0:.3f} ms/frame ({1:.3f} Hz), rendering: "
"{2:.3f} ms/frame ({3} frames in total)".format(
self.avFrDur_s * 1000.0, 1 / self.avFrDur_s,
self.avRendDur_s * 1000.0, self.nFrTotal))
ssp.Log.write(
"INFO", "presenting: {0:.3f} ms/frame".format(
self.avPresDur_s * 1000.0))
if glo.QDSpy_frRateStatsBufferLen > 0:
if not self.dataDtFrOver:
data = self.dataDtFr[:self.dataDtFrLen]
else:
data = self.dataDtFr
else:
data = np.array(self.dataDtFr)
av = data.mean() * 1000.0
std = data.std() * 1000.0
ssp.Log.write(
"INFO", "{0:.3f} +/- {1:.3f} ms/frame (over the last {2}"
" frames) = {3:.3} Hz".format(av, std, len(data), 1000.0 / av))
if self.nDroppedFr > 0:
pcDrFr = 100.0 * self.nDroppedFr / self.nFrTotal
ssp.Log.write(
"WARNING", "{0} frames dropped (={1:.3f} %)".format(
self.nDroppedFr, pcDrFr))
ssp.Log.write(
"DATA", {
"avgFreq_Hz": 1 / self.avFrDur_s,
"nFrames": self.nFrTotal,
"nDroppedFrames": self.nDroppedFr
}.__str__())
if QDSpy_verbose:
# Generate a plot ...
#
pylab.title("Timing")
pylab.subplot(2, 1, 1)
pylab.plot(list(range(len(data))), data * 1000, "-")
xArr = [0, len(data) - 1]
dtFr_ms = self.dtFr_meas_s * 1000
yMin = dtFr_ms + self.Conf.maxDtTr_ms
yMax = dtFr_ms - self.Conf.maxDtTr_ms
pylab.plot(xArr, [yMin, yMin], "k--")
pylab.plot(xArr, [yMax, yMax], "k--")
pylab.ylabel("frame duration [ms]")
pylab.xlabel("frame #")
pylab.xlim([10, len(data)])
pylab.ylim([dtFr_ms - 10, dtFr_ms + 10])
pylab.subplot(2, 1, 2)
n, bins, pat = pylab.hist(data * 1000,
100,
histtype="bar",
rwidth=0.9)
pylab.setp(pat, 'facecolor', 'b', 'alpha', 0.75)
#pylab.xlim([dtFr_ms-5,dtFr_ms+5])
pylab.ylabel("# frames")
pylab.xlabel("frame duration [ms]")
pylab.tight_layout()
pylab.show()
def calculate_T_and_R_in_time(total_time=params.total_time,Etot=params.E,true_h=params.h,output_file=params.path_outputs,
dilution_factor = params.dillution_factor,v=params.v):
space_x=np.linspace(0,Lx,num=Nx+1)
delta_x=space_x[1]-space_x[0]
space_y=np.linspace(0,Ly,num=Ny+1)
delta_y=space_y[1]-space_y[0]
space_z=np.linspace(0,h,num=Nz+1)
delta_z=space_z[1]-space_z[0]
delta_t=F/((1/delta_x**2)+(1/delta_y**2)+(1/delta_z**2))
Nt = int(round(total_time / float(delta_t)))
print('given the total time,F and L, I have {0} time meshpoints'.format(Nt))
#checking the normalization
a=4.
Ei=Etot/true_h**3
print('the initial volumetric energy is',Ei)
already_run=False
if already_run==False:
u,spacex,spacey,spacez,time=solver_3D.solver_3D_norm(Nx,Ny,Nz,Lx=Lx,Ly=Ly,Initial_xyz=Initial_trivariate,F=F,v=v,a=a,total_time=total_time,reflecting=True,Ei=Ei,output_file=output_file+'/images2D')#53.125)
if os.path.exists(output_file+'/images2D')!=True:
logger.info('the output file for the 2D images did not exist yet. I am creating it now')
os.makedirs(output_file+'/images2D')
plotter_3D.plot_u_latest_time(u,space_z,time,time_Units='tD',output_file=output_file,show_plots=False)
#Temperature
sig_cgs=5.6704e-5
c_cgs=3e10
initial_energy_density=1.#5000.0 #erg/cm^3
a_bb = 4 * sig_cgs/ c_cgs
print('I am calculating the temperature at the surface of the slab')
temp=np.zeros((Nt + 1, Nx + 1, Ny+1))
temp[:,:,:] = np.power(1. / a_bb * u[:,:,:,Nz], 0.25)
#plotter_3D.plot_cuts_2D_temp(temp,spacez,time,time_Units='tD',output_file=output_file,dilution_factor=dilution_factor,show_plots=True)
#plotter_3D.plot_temp_latest_time(temp,spacez,time,time_Units=None,output_file=output_file,show_plots=False)
pylab.matshow(temp[-1,:,:].T)
pylab.xlabel('x axis')
pylab.ylabel('y axis')
pylab.title('final temperature at the surface of the slab',y=1.08)#z=h/2
pylab.colorbar()
pylab.savefig(output_file + '/temperature_surface.png', bbox_inches='tight')
#pylab.tight_layout()
#pylab.show()
#pdb.set_trace()
else:
time = np.linspace(0, total_time, num=Nt + 1)
time_observed=[]
print('dilution_factor',dilution_factor)
time_diluted = time[::dilution_factor]
index_dilute = range(Nt+1)[::dilution_factor]
#print(np.shape(time_diluted))
#print(np.shape(index_dilute))
Sum_BB_fromfile=np.zeros((len(time[::dilution_factor]),100))
already_run=False
if already_run==False:
for t, s in enumerate(time_diluted):
Sum_BB_ind = np.zeros((100))
#print(t)
#print(s)
print('*********************')
print('Computing the observables at the surface of the slab for t/tD={0} (total time is {1})'.format(s,total_time))
# edge = u[t, :, :, Nz]
#if s >= tmin and s <= tmax:
time_observed.append(s)
#pylab.figure()
#Sum_BB[t,:,0]=np.linspace(1e-10,1e-6,num=100)
for i,l in enumerate(spacex):
for j,k in enumerate(spacey):
#print(temp[int(index_dilute[t]),i,j,Nz]
#if temp[int(index_dilute[t]),i,j,Nz]!=0.:
#pdb.set_trace()
#print('I am calculating a cell {0} spectrum'.format()
bb_flux_spectrum=black_body_flux_density.black_body_flux_density(temp[int(index_dilute[t]),i,j],np.linspace(1e-10,1e-6,num=100),type='P',verbose=False,distance_pc=1,Radius=None)[2]#les luminosites seront celle de bb 1 solar radius a 1 pc!
#pylab.plot(bb_flux_spectrum[:,0]*1e9,bb_flux_spectrum[:,1])
#else:
# bb_flux_spectrum=np.zeros(100,2)
# bb_flux_spectrum[:, 0]=np.linspace(1e-10,1e-6,num=100)
Sum_BB_ind[:] = Sum_BB_ind[:] + bb_flux_spectrum[:, 1] #erg/sec/cm^2/Ang
#Sum_BB[t,:]=Sum_BB[t,:]+bb_flux_spectrum[:,1]
#pylab.xlabel('wavelegth [$nm$]')
#pylab.ylabel(r'$B_\lambda$ [$erg/s/cm^2/\AA$]')
#pylab.title('Edge cells spectra at t={0}'.format(s))
#pylab.savefig(output_file+'/cells_bb_spectra/cell_bb_' + str(t).zfill(5) + '.png', bbox_inches='tight')
np.savetxt(output_file+'/Sum_BB_time_{0}.txt'.format(s), Sum_BB_ind[:])
time_observed_x=[]
for t, s in enumerate(time_diluted):
#if s >= tmin and s <= tmax:
#print('I am looking at the edge at time-meshpoint {0}, i.e time {1}'.format(int(index_dilute[t]), s))
time_observed_x.append(s)
np.savetxt(output_file+'/time_observed.txt',time_observed_x)
distance_modulus=37.77
distance_pc=distances_conversions.DM_to_pc(distance_modulus)
time_observed_fromfile=np.genfromtxt(output_file+'/time_observed.txt')
#Sum_BB_fromfile=np.genfromtxt(output_file+'/Sum_BB.txt',skip_header=1)
for t, s in enumerate(time_diluted): #10s per step 1days~6 steps (60 s per day), 50 days en 50 min
Sum_BB_fromfile[t,:]=np.genfromtxt(output_file+'/Sum_BB_time_{0}.txt'.format(s))
#print(np.shape(Sum_BB_fromfile))
#print(Sum_BB_fromfile[0,:])
pylab.figure()
for t,s in enumerate(time_observed_fromfile):
#print(t)
pylab.plot(np.linspace(1e-10,1e-6,num=100)*1e9,Sum_BB_fromfile[t,:])
pylab.xlabel('wavelegth [$nm$]')
pylab.ylabel(r'$B_\lambda$ [$erg/s/cm^2/\AA$]')
#pylab.title('Sum of all edge cells spectra from day {0} to {1} days'.format(tmin,tmax/(3600*24)))
if os.path.exists(output_file + '/cells_bb_spectra') != True:
logger.info('the output file for the 2D images did not exist yet. I am creating it now')
os.makedirs(output_file + '/cells_bb_spectra')
pylab.savefig(output_file+'/cells_bb_spectra/sum_cells_bb.png', bbox_inches='tight')
already_fit=False
if already_fit==False:
BB_evo=np.zeros((np.shape(time_observed_fromfile)[0],4))
for t, s in enumerate(time_observed_fromfile):
#print('I am looking at time {0}'.format(s))
Sum_BB_fromfile_w_wavelengths = np.zeros((100, 2))
Sum_BB_fromfile_w_wavelengths[:, 0] = np.linspace(1e-10, 1e-6, num=100)
Sum_BB_fromfile_w_wavelengths[:, 1] = Sum_BB_fromfile[t, :]
Xi_array, best_temp, index_min, best_coeff1, best_radius, best_luminosity=fit_black_body_flux_spec.fit_black_body_flux_spec(Sum_BB_fromfile_w_wavelengths,TempVec=np.logspace(3,6,1000),WaveUnits='m',distance=distance_pc,output_file=output_file)
BB_evo[t,0]=s
BB_evo[t,1]=best_temp
BB_evo[t,2]=best_radius
BB_evo[t,3]=best_luminosity
np.savetxt(output_file+'/Sum_BB_properties.txt',BB_evo,header='time,best-fit temperature,best-fit radius,best-fit luminosity')
BB_evo_fromfile=np.genfromtxt(output_file+'/Sum_BB_properties.txt',skip_header=1)
'''
pylab.figure()
pylab.plot(BB_evo_fromfile[:,0],BB_evo_fromfile[:,1],'r-')
pylab.xlabel(r'$\frac{t}{h^2/D(z=h)}$',fontsize=25)
ax=pylab.gca()
ax.set_xscale("log")
pylab.grid()
pylab.ylabel('temperature [K]',fontsize=25)
pylab.xlim(BB_evo_fromfile[1,0],BB_evo_fromfile[-1,0])
#pylab.title('Spectrum BB temperature')
pylab.savefig(output_file+'/edge_spectrum_BB_T.png', bbox_inches='tight')
BB_evo_fromfile=np.genfromtxt(output_file+'/Sum_BB_properties.txt',skip_header=1)
pylab.figure()
pylab.plot(BB_evo_fromfile[:,0],BB_evo_fromfile[:,2],'bo')
pylab.xlabel(r'time [t/t_D]')
pylab.ylabel('radius [arb]')
pylab.title('Spectrum BB radius')
pylab.grid()
pylab.savefig(output_file+'/edge_spectrum_BB_R.png', bbox_inches='tight')
pylab.show()
'''
#########################################################################
u_total_Dcst = np.zeros(np.shape(time))
u_grid = np.zeros(np.shape(time))
flux = np.zeros((np.shape(time)[0], Nx + 1, Ny + 1))
energy_released_in_time = np.zeros(np.shape(time))
#print(np.shape(flux))
# pdb.set_trace()
flux_in_time = np.zeros((np.shape(time)[0]))
print('I am caluclating u at the edge over time and u in the grid over time')
for t, s in enumerate(time):
u_total_Dcst[t] = np.sum(u[t, :, :, -1])
u_grid[t] = np.sum(u[t, :, :, :])
kernel_Dcst = np.zeros((np.shape(u_total_Dcst)[0], 2))
kernel_Dcst[:, 0] = time
kernel_Dcst[:, 1] = u_total_Dcst
coeff_boundary = v / a
surface_elementaire = (space_x[1] - space_x[0]) * (space_y[1] - space_y[0])
Luminosities = c_cgs / a * u_total_Dcst * surface_elementaire * true_h ** 2 # u est en erg/cm^3, surface_elementaire *true_h**2 est en cm2, c^2/a est le vrai coeff buondary
np.savetxt(output_file+'/Luminosities_Dcst.txt', Luminosities)
#else:
#time = np.linspace(0, total_time, num=Nt + 1)
#########################################################################
temperatures=np.genfromtxt(output_file+'/Sum_BB_properties.txt',skip_header=1)[:,1]
Luminosities=np.genfromtxt(output_file+'/Luminosities_Dcst.txt')
dilution_factor_1=dilution_factor
dilution_factor_2=1
Luminosities_diluted=Luminosities[::dilution_factor_1]
pylab.figure()
pylab.plot(time,Luminosities)
pylab.xlabel(r'$\frac{t}{t_D}$', fontsize=25)
pylab.ylabel(r'$erg/s$', fontsize=25)
pylab.title('Total brightness at the surface')
pylab.tight_layout()
pylab.savefig(output_file + '/brightness_surface.png', bbox_inches='tight')
#pylab.show()
#print(Luminosities_diluted)
#pdb.set_trace()
radii=np.power((1./(4*math.pi*sig_cgs)*np.multiply(Luminosities_diluted,1/np.power(temperatures,4))),0.5)
time_diluted=time[::dilution_factor_1]
time_diluted_more=time_diluted[::dilution_factor_2]
radii_diluted_more=radii[::dilution_factor_2]
pylab.figure()
pylab.plot(time_diluted_more,radii_diluted_more,'b-')
pylab.grid()
pylab.xlabel(r'$\frac{t}{t_D}$',fontsize=25)
pylab.ylabel(r'Radius $r_{BB}$ [cm]', fontsize=25)
pylab.tight_layout()
pylab.savefig(output_file+'/Radius_surface.png',bbox_inches='tight')
#pylab.show()
#only if you ran solver_diffusion_and_calc_spectrum_w_true_values.py before
BB_evo_fromfile=np.genfromtxt(output_file+'/Sum_BB_properties.txt',skip_header=1)
#pylab.figure()
#pylab.title('Spectrum BB temperature')
#pylab.savefig(output_file/edge_spectrum_BB_T.png', bbox_inches='tight')
#pylab.figure()
f = plt.figure(figsize=(8,8))
ax1 = plt.subplot(212)
plt.plot(BB_evo_fromfile[:,0],BB_evo_fromfile[:,1],'r-')
plt.xlabel(r'$\rm{\frac{t}{t_d}}$',fontsize=25)
ax=pylab.gca()
ax.set_xscale("log")
pylab.grid(True, which="both")
plt.ylabel('temperature [K]',fontsize=23)
plt.xlim(BB_evo_fromfile[1,0],BB_evo_fromfile[-1,0])
plt.yticks(fontsize=16)
plt.xticks(fontsize=16)
# share x only
ax2 = plt.subplot(211, sharex=ax1)
plt.plot(time_diluted_more,radii_diluted_more/1e15,'b-')
pylab.grid(True, which="both")
#plt.xlabel(r'$\frac{t}{h^2/D(z=h)}$',fontsize=25)
plt.ylabel(r'Radius $r_{BB}$ [$\rm{10^{15}}\,$cm]', fontsize=18)
#plt.savefig(output_file/edge_spectrum_BB_R_2.png',bbox_inches='tight')
plt.yticks(fontsize=16)
plt.xticks(fontsize=16)
plt.xlim(0.01,1)
#plt.ylim(0,16e15)
plt.setp(ax2.get_xticklabels(), visible=False)
pylab.tight_layout()
#plt.savefig('./paper_figures/kernel_comparision_same_xaxis.pdf', facecolor='w', edgecolor='w',
# orientation='portrait',
# papertype=None, format='pdf', transparent=False, bbox_inches=None, pad_inches=0.5)
plt.savefig(output_file+'/R_T_surface.png',bbox_inches='tight')
pylab.show()
xmin, xmax = pylab.xlim()
ymin, ymax = pylab.ylim()
if name == 'F11':
pylab.text(0.4 * xmax,
0.2 * ymax,
r'$F_{{{0}}}$'.format(str(number)),
fontsize=12)
elif name == 'F12':
pylab.text(0.4 * xmax,
0.75 * ymax,
'-' + r'$F_{{{0}}}$'.format(str(number)) +
r'$\rm /\textit{$F$}_{11}$',
fontsize=12)
else:
pylab.text(0.4 * xmax,
0.75 * ymax,
r'$F_{{{0}}}$'.format(str(number)) +
r'$\rm /\textit{$F$}_{11}$',
fontsize=12)
pylab.xticks([0, 45, 90, 135, 180])
pylab.gca().xaxis.set_minor_locator(MultipleLocator(15))
pylab.tight_layout()
pylab.legend(loc='center left',
bbox_to_anchor=(1, 0.5),
title='Vdc=' + str(vdc) + ' Vac=' + str(vac))
pylab.savefig(sample_name, bbox_inches='tight', dpi=400)
pylab.show()
def plot_snapshot(filepath, snapshot_idx, do_slice, do_flip):
fig = plt.figure()
#ax1 = fig.add_subplot(121)
#ax2 = plt.axes([0.58, 0.58, 0.29, 0.29])
ax2 = fig.add_subplot(111)
for core_idx in range(0, num_cores):
print("Plotting chunk {0}".format(core_idx))
if snapshot_idx < 66:
fname = "{2}snapdir_{0:03d}/snapdir_{0:03d}/snapshot_{0:03d}.{1}.hdf5".format(
snapshot_idx, core_idx, snapdir)
else:
fname = "{2}snapdir_{0:03d}/snapshot_{0:03d}.{1}.hdf5".format(
snapshot_idx, core_idx, snapdir)
fname = "/lustre/projects/p004_swin/bsmith/1.6Gpc/means/halo_1721673/dm_gadget/data/snapdir_020/snapshot_020.0.hdf5"
print("Reading from file {0}".format(fname))
with h5py.File(fname, 'r') as f:
for type_idx in range(TypeMax):
print("plotting ParticleType {0}".format(type_idx))
tmp = "PartType{0}".format(type_idx)
try:
part = f[tmp]['Coordinates'][:]
except KeyError:
pass
else:
if (do_slice == 1):
#w = np.where((part[:,2] > 800.0) & (part[:,2] < 804.0))[0]
w = np.where((part[:, 0] > 817.0)
& (part[:, 1] < 779.0))[0]
print(
"There is {0} particles within this slice.".format(
len(w)))
else:
if (len(part) > dilute):
print(
"There are {0} particles and we are diluting it down to {1}"
.format(len(part), dilute))
w = sample(list(np.arange(0, len(part))), dilute)
# w = np.sort(w)
else:
w = np.arange(0, len(part))
# print("The x coords range from [{0:.4f}, {1:.4f}]".format(min(part[:,0]), max(part[:,0])))
# print("The y coords range from [{0:.4f}, {1:.4f}]".format(min(part[:,1]), max(part[:,1])))
# print("The z coords range from [{0:.4f}, {1:.4f}]".format(min(part[:,2]), max(part[:,2])))
# ax1.scatter(part[w,0], part[w,1], marker = 'o', alpha = 0.5, color = PlotScripts.colors[type_idx-1], s = 1)
if (do_flip == 1):
#ax2.scatter(part[w,1], part[w,0], marker = 'o', alpha = 0.5, color = PlotScripts.colors[type_idx-1], s = 1)
ax2.scatter((part[w, 1] - 775.0), (part[w, 0] - 775.0),
marker='o',
alpha=0.5,
color=PlotScripts.colors[type_idx - 1],
s=1)
else:
#ax2.scatter(part[w,0] - 775.0, part[w,1] - 775.0, marker = 'o', alpha = 0.5, color = PlotScripts.colors[type_idx-1], s = 1)
ax2.scatter(part[w, 2] - 775.0,
part[w, 0] - 775.0,
marker='o',
alpha=0.5,
color=PlotScripts.colors[type_idx - 1],
s=1)
# ax2.scatter((part[w,0] - 775.0), (part[w,1] - 775.0), marker = 'o', alpha = 0.5, color = PlotScripts.colors[type_idx-1], s = 1)
#ax2.add_patch(patches.Rectangle((817, 779), 1.0, 1.0, fill=False))
#ax2.add_patch(patches.Rectangle((45.0, 45.0), 5.0, 5.0, fill=False))
#ax2.add_patch(patches.Rectangle((10.0, 45.0), 5.0, 5.0, fill=False))
ax2.add_patch(patches.Rectangle((45.0, 45.0), 5.0, 5.0, fill=False))
ax2.add_patch(patches.Rectangle((45.0, 0.0), 5.0, 10.0, fill=False))
for type_idx in range(1, TypeMax): # PartType0 has no particles ever.
label = 'PartType{0}'.format(type_idx)
ax2.scatter(np.nan,
np.nan,
color=PlotScripts.colors[type_idx - 1],
label=label)
#ax1.set_xlim([0, 1600])
#ax1.set_ylim([0, 1600])
#ax2.set_xlim([774, 827])
#ax2.set_ylim([774, 827])
ax2.set_xlim([0, 50])
ax2.set_ylim([0, 50])
#ax1.set_xlabel("")
#ax1.set_ylabel("y [Mpc/h]")
ax2.set_xlabel("z [Mpc/h]")
ax2.set_ylabel("x [Mpc/h]")
leg = ax2.legend(loc='upper left', numpoints=1, labelspacing=0.1)
leg.draw_frame(False) # Don't want a box frame
for t in leg.get_texts(): # Reduce the size of the text
t.set_fontsize(PlotScripts.global_legendsize)
t.set_alpha(1)
plt.tight_layout()
if do_flip == 1:
flip_tag = "flip"
else:
flip_tag = "noflip"
if do_slice == 1:
outputFile = './Slice_Core{2}_{0}{3}{1}'.format(
snapshot_idx, output_format, core_idx, flip_tag)
else:
outputFile = './Grid_AllPart_Core{2}_{0}{3}{1}'.format(
snapshot_idx, output_format, core_idx, flip_tag)
outputFile = './Grid_AllPart_Snapshot20_chunk0_zx.png'
plt.savefig(outputFile, bbox_inches='tight') # Save the figure
print('Saved file to {0}'.format(outputFile))
plt.close()
def plot_dirty_PCA(
kinship_mat,
figure_fn='pca.png',
k_figure_fn='kinship_heatmap.png',
title=None,
pcs34=True,
figure_dir='C:/Users/MariaIzabel/Desktop/MASTER/PHD/Bjarnicode',
strains=None):
from scipy import linalg
evals, evecs = linalg.eig(kinship_mat) #PCA via eigen decomp
evals[evals < 0] = 0
sort_indices = sp.argsort(evals, )
ordered_evals = evals[sort_indices]
print ordered_evals[-10:] / sp.sum(ordered_evals)
pc1, pc2 = evecs[:, sort_indices[-1]], evecs[:, sort_indices[-2]]
pc3, pc4 = evecs[:, sort_indices[-3]], evecs[:, sort_indices[-4]]
if pcs34 == True:
pc1 = pc3
pc2 = pc4
pylab.clf()
tot = sum(evals)
var_exp = [(i / tot) * 100 for i in sorted(evals, reverse=True)]
np.savetxt('test.out', var_exp, delimiter=',')
cum_var_exp = np.cumsum(var_exp)
print cum_var_exp
# Ploting the variance explained
with plt.style.context('seaborn-whitegrid'):
plt.figure(figsize=(6, 6))
plt.bar(range(198),
var_exp,
alpha=1,
align='center',
label='individual explained variance')
plt.step(range(198),
cum_var_exp,
where='mid',
label='cumulative explained variance')
plt.ylabel('Explained variance ratio')
plt.xlabel('Principal components')
plt.legend(loc='best')
#plt.tight_layout()
plt.savefig('variance_explained_PC1234')
plt.show()
#Trying PCA 3D
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d import proj3d
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111, projection='3d')
plt.rcParams['legend.fontsize'] = 10
ax.plot(pc2,
pc3,
'o',
markersize=8,
color='blue',
alpha=0.5,
label='data points')
plt.title('Principal Component Analysis (2 and 3)')
ax.legend(loc='upper right')
plt.xlabel('PC2 (' + str(round(var_exp[2])) + '%)')
plt.ylabel('PC3 (' + str(round(var_exp[3])) + '%)')
plt.savefig('PCA23_3D')
if strains is not None:
ct_marker_map = {'DK': '*', 'UK': '^', 'F': 'o', 'nan': 's'}
gs_color_map = {
'gsA': 'm',
'gsB': 'g',
'gsC': 'r',
'gsE': 'b',
'gsD': 'y',
'nan': 'c'
}
pop_map = parse_pop_map()
for i, strain in enumerate(strains):
d = pop_map.get(strain, 'nan')
if d == 'nan':
gs = 'nan'
country = 'nan'
print 'na', strain, pc1[i], pc2[i]
else:
gs = d['genospecies']
country = d['country']
pylab.scatter(pc1[i],
pc2[i],
marker=ct_marker_map[country],
c=gs_color_map[gs],
alpha=0.3,
s=100,
edgecolor='none')
for gs in gs_color_map:
pylab.scatter([], [],
color=gs_color_map[gs],
marker='s',
label=gs,
s=100,
edgecolor='none')
for country in ct_marker_map:
if country != 'nan':
pylab.scatter([], [],
color='k',
marker=ct_marker_map[country],
label=country,
s=100,
facecolors='none')
pylab.legend(scatterpoints=1)
else:
pylab.plot(pc1, pc2, 'k.')
if title is not None:
pylab.title(title)
pylab.xlabel('PC3')
pylab.xlabel('PC4')
pylab.tight_layout()
pylab.savefig(figure_dir + '/' + figure_fn, format='pdf')
pylab.clf()
pylab.imshow(kinship_mat, cmap='hot', interpolation='nearest')
pylab.savefig(figure_dir + '/' + k_figure_fn)
import pylab as pl
def dft(s):
N = len(s)
out = pl.zeros(N)*1j;
for k in range(0,N):
for t in range(0, N):
out[k] += s[t]*(pl.cos(2*pl.pi*k*t/N)
- 1j*pl.sin(2*pl.pi*k*t/N))
return out/N
saw = pl.zeros(100)
t = pl.arange(0,100)
T = 100
N = T // 2
for k in range(1,N):
saw += (1/k)*pl.sin(2*pl.pi*k*t/T)
saw *= 2/(pl.pi)
spec = dft(saw)
mags = abs(spec)
scal = max(mags)
pl.figure(figsize=(8,3))
pl.stem(t,mags/scal, 'k-', linewidth=1)
pl.ylim(0,1.1)
pl.tight_layout()
pl.show()
def plot_stf(target):
data, tabnames, tag = get_data(target)
#--collect data from different groups
for tabname in tabnames:
data[tabname] = {}
nrep = data['nrep'][tabname]
idxs = data['idx'][tabname]
for q2 in Q2:
data[tabname][q2] = {_: [] for _ in idxs}
for i in range(nrep + 1):
stf = lhapdf.mkPDF(tabname, i)
for q2 in Q2:
for idx in idxs:
F = np.array([x * stf.xfxQ2(idx, x, q2) for x in X])
data[tabname][q2][idx].append(F)
#--plot data
#--all channels
nrows, ncols = 3, 3
fig = py.figure(figsize=(ncols * 5, nrows * 3))
cnt = 0
for q2 in Q2:
cnt += 1
ax = py.subplot(nrows, ncols, cnt)
ax.tick_params(axis='both',
which='both',
top=True,
right=True,
direction='in',
labelsize=20)
for tabname in tabnames:
if 908 not in data[tabname][q2]: continue
for i in range(data['nrep'][tabname] + 1):
if i == 0: label, alpha = data['label'][tabname], 1.0
else: label, alpha = None, 0.1
F2 = data[tabname][q2][908][i]
ax.plot(X,
F2,
label=label,
color=data['color'][tabname],
alpha=alpha)
#ax.set_ylim(0,2)
ax.semilogx()
ax.set_xticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 1])
ax.axhline(0, 0, 1, ls='--', color='black', alpha=0.5)
if cnt == 1:
ax.legend(loc='lower left',
bbox_to_anchor=(0, 0.1),
frameon=False,
fontsize=15)
if cnt == 1:
ax.text(0.1,
0.7,
r'$xF_2^{(%s)}$' % tag,
size=35,
transform=ax.transAxes)
if cnt == 7: ax.set_xlabel(r'$x$', size=20)
ax.set_ylabel(r'$Q^2=%0.1f~{\rm GeV^2}$' % q2, size=20)
cnt += 1
ax = py.subplot(nrows, ncols, cnt)
ax.tick_params(axis='both',
which='both',
top=True,
right=True,
direction='in',
labelsize=20)
for tabname in tabnames:
if 909 not in data[tabname][q2]: continue
for i in range(data['nrep'][tabname] + 1):
if i == 0: label, alpha = data['label'][tabname], 1.0
else: label, alpha = None, 0.1
FL = data[tabname][q2][909][i]
ax.plot(X, FL, color=data['color'][tabname], alpha=alpha)
#ax.set_ylim(0,0.3)
ax.semilogx()
ax.set_xticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 1])
ax.axhline(0, 0, 1, ls='--', color='black', alpha=0.5)
if cnt == 2:
ax.text(0.1,
0.7,
r'$xF_L^{(%s)}$' % tag,
size=35,
transform=ax.transAxes)
if cnt == 8: ax.set_xlabel(r'$x$', size=20)
cnt += 1
ax = py.subplot(nrows, ncols, cnt)
ax.tick_params(axis='both',
which='both',
top=True,
right=True,
direction='in',
labelsize=20)
for tabname in tabnames:
if 910 not in data[tabname][q2]: continue
for i in range(data['nrep'][tabname] + 1):
if i == 0: label, alpha = data['label'][tabname], 1.0
else: label, alpha = None, 0.1
F3 = data[tabname][q2][910][i]
ax.plot(X, F3, color=data['color'][tabname], alpha=alpha)
ax.semilogx()
ax.set_xticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 1])
ax.axhline(0, 0, 1, ls='--', color='black', alpha=0.5)
#ax.set_ylim(0,0.9)
if cnt == 3:
ax.text(0.1,
0.7,
r'$xF_3^{(%s)}$' % tag,
size=35,
transform=ax.transAxes)
if cnt == 9: ax.set_xlabel(r'$x$', size=20)
py.tight_layout()
filename = 'gallery/%s/FX.png' % target
py.savefig(filename)
print('Saving figure to %s' % filename)
py.clf()
#--gamma channel
nrows, ncols = 3, 2
fig = py.figure(figsize=(ncols * 5, nrows * 3))
cnt = 0
for q2 in Q2:
cnt += 1
ax = py.subplot(nrows, ncols, cnt)
ax.tick_params(axis='both',
which='both',
top=True,
right=True,
direction='in',
labelsize=20)
for tabname in tabnames:
if 900 not in data[tabname][q2]: continue
for i in range(data['nrep'][tabname] + 1):
if i == 0: label, alpha = data['label'][tabname], 1.0
else: label, alpha = None, 0.1
F2 = data[tabname][q2][900][i]
ax.plot(X,
F2,
label=label,
color=data['color'][tabname],
alpha=alpha)
#ax.set_ylim(0,2)
ax.semilogx()
ax.set_xticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 1])
ax.axhline(0, 0, 1, ls='--', color='black', alpha=0.5)
if cnt == 1:
ax.legend(loc='lower left',
bbox_to_anchor=(0, 0.1),
frameon=False,
fontsize=15)
if cnt == 1:
ax.text(0.1,
0.7,
r'$xF_2^{(%s)\gamma}$' % tag,
size=35,
transform=ax.transAxes)
if cnt == 7: ax.set_xlabel(r'$x$', size=20)
ax.set_ylabel(r'$Q^2=%0.1f~{\rm GeV^2}$' % q2, size=20)
cnt += 1
ax = py.subplot(nrows, ncols, cnt)
ax.tick_params(axis='both',
which='both',
top=True,
right=True,
direction='in',
labelsize=20)
for tabname in tabnames:
if 901 not in data[tabname][q2]: continue
for i in range(data['nrep'][tabname] + 1):
if i == 0: label, alpha = data['label'][tabname], 1.0
else: label, alpha = None, 0.1
FL = data[tabname][q2][901][i]
ax.plot(X, FL, color=data['color'][tabname], alpha=alpha)
#ax.set_ylim(0,0.3)
ax.semilogx()
ax.set_xticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 1])
ax.axhline(0, 0, 1, ls='--', color='black', alpha=0.5)
if cnt == 2:
ax.text(0.1,
0.7,
r'$xF_L^{(%s)\gamma}$' % tag,
size=35,
transform=ax.transAxes)
if cnt == 8: ax.set_xlabel(r'$x$', size=20)
py.tight_layout()
filename = 'gallery/%s/FXg.png' % target
py.savefig(filename)
print('Saving figure to %s' % filename)
py.clf()
#--Z channel
nrows, ncols = 3, 3
fig = py.figure(figsize=(ncols * 5, nrows * 3))
cnt = 0
for q2 in Q2:
cnt += 1
ax = py.subplot(nrows, ncols, cnt)
ax.tick_params(axis='both',
which='both',
top=True,
right=True,
direction='in',
labelsize=20)
for tabname in tabnames:
if 905 not in data[tabname][q2]: continue
for i in range(data['nrep'][tabname] + 1):
if i == 0: label, alpha = data['label'][tabname], 1.0
else: label, alpha = None, 0.1
F2 = data[tabname][q2][905][i]
ax.plot(X,
F2,
label=label,
color=data['color'][tabname],
alpha=alpha)
#ax.set_ylim(0,2)
ax.semilogx()
ax.set_xticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 1])
ax.axhline(0, 0, 1, ls='--', color='black', alpha=0.5)
if cnt == 1:
ax.legend(loc='lower left',
bbox_to_anchor=(0, 0.1),
frameon=False,
fontsize=15)
if cnt == 1:
ax.text(0.1,
0.7,
r'$xF_2^{(%s)Z}$' % tag,
size=35,
transform=ax.transAxes)
if cnt == 7: ax.set_xlabel(r'$x$', size=20)
ax.set_ylabel(r'$Q^2=%0.1f~{\rm GeV^2}$' % q2, size=20)
cnt += 1
ax = py.subplot(nrows, ncols, cnt)
ax.tick_params(axis='both',
which='both',
top=True,
right=True,
direction='in',
labelsize=20)
for tabname in tabnames:
if 906 not in data[tabname][q2]: continue
for i in range(data['nrep'][tabname] + 1):
if i == 0: label, alpha = data['label'][tabname], 1.0
else: label, alpha = None, 0.1
FL = data[tabname][q2][906][i]
ax.plot(X, FL, color=data['color'][tabname], alpha=alpha)
#ax.set_ylim(0,0.3)
ax.semilogx()
ax.set_xticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 1])
ax.axhline(0, 0, 1, ls='--', color='black', alpha=0.5)
if cnt == 2:
ax.text(0.1,
0.7,
r'$xF_L^{(%s)Z}$' % tag,
size=35,
transform=ax.transAxes)
if cnt == 8: ax.set_xlabel(r'$x$', size=20)
cnt += 1
ax = py.subplot(nrows, ncols, cnt)
ax.tick_params(axis='both',
which='both',
top=True,
right=True,
direction='in',
labelsize=20)
for tabname in tabnames:
if 907 not in data[tabname][q2]: continue
for i in range(data['nrep'][tabname] + 1):
if i == 0: label, alpha = data['label'][tabname], 1.0
else: label, alpha = None, 0.1
F3 = data[tabname][q2][907][i]
ax.plot(X, F3, color=data['color'][tabname], alpha=alpha)
ax.semilogx()
ax.set_xticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 1])
ax.axhline(0, 0, 1, ls='--', color='black', alpha=0.5)
#ax.set_ylim(0,0.9)
if cnt == 3:
ax.text(0.1,
0.7,
r'$xF_3^{(%s)Z}$' % tag,
size=35,
transform=ax.transAxes)
if cnt == 9: ax.set_xlabel(r'$x$', size=20)
py.tight_layout()
filename = 'gallery/%s/FXZ.png' % target
py.savefig(filename)
print('Saving figure to %s' % filename)
py.clf()
#--gamma/Z channel
nrows, ncols = 3, 3
fig = py.figure(figsize=(ncols * 5, nrows * 3))
cnt = 0
for q2 in Q2:
cnt += 1
ax = py.subplot(nrows, ncols, cnt)
ax.tick_params(axis='both',
which='both',
top=True,
right=True,
direction='in',
labelsize=20)
for tabname in tabnames:
if 902 not in data[tabname][q2]: continue
for i in range(data['nrep'][tabname] + 1):
if i == 0: label, alpha = data['label'][tabname], 1.0
else: label, alpha = None, 0.1
F2 = data[tabname][q2][902][i]
ax.plot(X,
F2,
label=label,
color=data['color'][tabname],
alpha=alpha)
#ax.set_ylim(0,2)
ax.semilogx()
ax.set_xticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 1])
ax.axhline(0, 0, 1, ls='--', color='black', alpha=0.5)
if cnt == 1:
ax.legend(loc='lower left',
bbox_to_anchor=(0, 0.1),
frameon=False,
fontsize=15)
if cnt == 1:
ax.text(0.1,
0.7,
r'$xF_2^{(%s)\gamma Z}$' % tag,
size=35,
transform=ax.transAxes)
if cnt == 7: ax.set_xlabel(r'$x$', size=20)
ax.set_ylabel(r'$Q^2=%0.1f~{\rm GeV^2}$' % q2, size=20)
cnt += 1
ax = py.subplot(nrows, ncols, cnt)
ax.tick_params(axis='both',
which='both',
top=True,
right=True,
direction='in',
labelsize=20)
for tabname in tabnames:
if 903 not in data[tabname][q2]: continue
for i in range(data['nrep'][tabname] + 1):
if i == 0: label, alpha = data['label'][tabname], 1.0
else: label, alpha = None, 0.1
FL = data[tabname][q2][903][i]
ax.plot(X, FL, color=data['color'][tabname], alpha=alpha)
#ax.set_ylim(0,0.3)
ax.semilogx()
ax.set_xticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 1])
ax.axhline(0, 0, 1, ls='--', color='black', alpha=0.5)
if cnt == 2:
ax.text(0.1,
0.7,
r'$xF_L^{(%s)\gamma Z}$' % tag,
size=35,
transform=ax.transAxes)
if cnt == 8: ax.set_xlabel(r'$x$', size=20)
cnt += 1
ax = py.subplot(nrows, ncols, cnt)
ax.tick_params(axis='both',
which='both',
top=True,
right=True,
direction='in',
labelsize=20)
for tabname in tabnames:
if 904 not in data[tabname][q2]: continue
for i in range(data['nrep'][tabname] + 1):
if i == 0: label, alpha = data['label'][tabname], 1.0
else: label, alpha = None, 0.1
F3 = data[tabname][q2][904][i]
ax.plot(X, F3, color=data['color'][tabname], alpha=alpha)
#ax.set_ylim(0,0.25)
ax.semilogx()
ax.set_xticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 1])
ax.axhline(0, 0, 1, ls='--', color='black', alpha=0.5)
if cnt == 3:
ax.text(0.1,
0.7,
r'$xF_3^{(%s)\gamma Z}$' % tag,
size=35,
transform=ax.transAxes)
if cnt == 9: ax.set_xlabel(r'$x$', size=20)
py.tight_layout()
filename = 'gallery/%s/FXgZ.png' % target
py.savefig(filename)
print('Saving figure to %s' % filename)
py.clf()
#--no charged current for deuteron/helium
if target == 'deuteron': return
if target == 'helium': return
#--W plus
nrows, ncols = 3, 3
fig = py.figure(figsize=(ncols * 5, nrows * 3))
cnt = 0
for q2 in Q2:
cnt += 1
ax = py.subplot(nrows, ncols, cnt)
ax.tick_params(axis='both',
which='both',
top=True,
right=True,
direction='in',
labelsize=20)
for tabname in tabnames:
if 940 not in data[tabname][q2]: continue
for i in range(data['nrep'][tabname] + 1):
if i == 0: label, alpha = data['label'][tabname], 1.0
else: label, alpha = None, 0.1
F2 = data[tabname][q2][940][i]
ax.plot(X,
F2,
label=label,
color=data['color'][tabname],
alpha=alpha)
#ax.set_ylim(0,12)
ax.semilogx()
ax.set_xticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 1])
ax.axhline(0, 0, 1, ls='--', color='black', alpha=0.5)
if cnt == 1:
ax.legend(loc='lower left',
bbox_to_anchor=(0, 0.1),
frameon=False,
fontsize=15)
if cnt == 1:
ax.text(0.1,
0.7,
r'$xF_2^{(%s)W^+}$' % tag,
size=35,
transform=ax.transAxes)
if cnt == 7: ax.set_xlabel(r'$x$', size=20)
ax.set_ylabel(r'$Q^2=%0.1f~{\rm GeV^2}$' % q2, size=20)
cnt += 1
ax = py.subplot(nrows, ncols, cnt)
ax.tick_params(axis='both',
which='both',
top=True,
right=True,
direction='in',
labelsize=20)
for tabname in tabnames:
if 941 not in data[tabname][q2]: continue
for i in range(data['nrep'][tabname] + 1):
if i == 0: label, alpha = data['label'][tabname], 1.0
else: label, alpha = None, 0.1
FL = data[tabname][q2][941][i]
ax.plot(X, FL, color=data['color'][tabname], alpha=alpha)
#ax.set_ylim(0,2.5)
ax.semilogx()
ax.set_xticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 1])
ax.axhline(0, 0, 1, ls='--', color='black', alpha=0.5)
if cnt == 2:
ax.text(0.1,
0.7,
r'$xF_L^{(%s)W^+}$' % tag,
size=35,
transform=ax.transAxes)
if cnt == 8: ax.set_xlabel(r'$x$', size=20)
cnt += 1
ax = py.subplot(nrows, ncols, cnt)
ax.tick_params(axis='both',
which='both',
top=True,
right=True,
direction='in',
labelsize=20)
for tabname in tabnames:
if 942 not in data[tabname][q2]: continue
for i in range(data['nrep'][tabname] + 1):
if i == 0: label, alpha = data['label'][tabname], 1.0
else: label, alpha = None, 0.1
F3 = data[tabname][q2][942][i]
ax.plot(X, F3, color=data['color'][tabname], alpha=alpha)
#ax.set_ylim(0,20)
ax.semilogx()
ax.set_xticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 1])
ax.axhline(0, 0, 1, ls='--', color='black', alpha=0.5)
if cnt == 3:
ax.text(0.1,
0.7,
r'$xF_3^{(%s)W^+}$' % tag,
size=35,
transform=ax.transAxes)
if cnt == 9: ax.set_xlabel(r'$x$', size=20)
py.tight_layout()
filename = 'gallery/%s/WXp.png' % target
py.savefig(filename)
print('Saving figure to %s' % filename)
py.clf()
#--W minus
nrows, ncols = 3, 3
fig = py.figure(figsize=(ncols * 5, nrows * 3))
cnt = 0
for q2 in Q2:
cnt += 1
ax = py.subplot(nrows, ncols, cnt)
ax.tick_params(axis='both',
which='both',
top=True,
right=True,
direction='in',
labelsize=20)
for tabname in tabnames:
if 930 not in data[tabname][q2]: continue
for i in range(data['nrep'][tabname] + 1):
if i == 0: label, alpha = data['label'][tabname], 1.0
else: label, alpha = None, 0.1
F2 = data[tabname][q2][930][i]
ax.plot(X,
F2,
label=label,
color=data['color'][tabname],
alpha=alpha)
#ax.set_ylim(0,12)
ax.semilogx()
ax.set_xticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 1])
ax.axhline(0, 0, 1, ls='--', color='black', alpha=0.5)
if cnt == 1:
ax.legend(loc='lower left',
bbox_to_anchor=(0, 0.1),
frameon=False,
fontsize=15)
if cnt == 1:
ax.text(0.1,
0.7,
r'$xF_2^{(%s)W^-}$' % tag,
size=35,
transform=ax.transAxes)
if cnt == 7: ax.set_xlabel(r'$x$', size=20)
ax.set_ylabel(r'$Q^2=%0.1f~{\rm GeV^2}$' % q2, size=20)
cnt += 1
ax = py.subplot(nrows, ncols, cnt)
ax.tick_params(axis='both',
which='both',
top=True,
right=True,
direction='in',
labelsize=20)
for tabname in tabnames:
if 931 not in data[tabname][q2]: continue
for i in range(data['nrep'][tabname] + 1):
if i == 0: label, alpha = data['label'][tabname], 1.0
else: label, alpha = None, 0.1
FL = data[tabname][q2][931][i]
ax.plot(X, FL, color=data['color'][tabname], alpha=alpha)
#ax.set_ylim(0,2.5)
ax.semilogx()
ax.set_xticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 1])
ax.axhline(0, 0, 1, ls='--', color='black', alpha=0.5)
if cnt == 2:
ax.text(0.1,
0.7,
r'$xF_L^{(%s)W^-}$' % tag,
size=35,
transform=ax.transAxes)
if cnt == 8: ax.set_xlabel(r'$x$', size=20)
cnt += 1
ax = py.subplot(nrows, ncols, cnt)
ax.tick_params(axis='both',
which='both',
top=True,
right=True,
direction='in',
labelsize=20)
for tabname in tabnames:
if 932 not in data[tabname][q2]: continue
for i in range(data['nrep'][tabname] + 1):
if i == 0: label, alpha = data['label'][tabname], 1.0
else: label, alpha = None, 0.1
F3 = data[tabname][q2][932][i]
ax.plot(X, F3, color=data['color'][tabname], alpha=alpha)
ax.semilogx()
ax.set_xticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 1])
ax.axhline(0, 0, 1, ls='--', color='black', alpha=0.5)
#ax.set_ylim(0,20)
if cnt == 3:
ax.text(0.1,
0.7,
r'$xF_3^{(%s)W^-}$' % tag,
size=35,
transform=ax.transAxes)
if cnt == 9: ax.set_xlabel(r'$x$', size=20)
py.tight_layout()
filename = 'gallery/%s/WXm.png' % target
py.savefig(filename)
print('Saving figure to %s' % filename)
py.clf()
def __init__(self):
fig = pp.figure(figsize=(10, 6))
self.States = np.load('States.npy')
self.Covariances = np.load('Covariances.npy')
self.times = np.load('Times.npy')
self.IncomingTMass = np.load('Incoming.npy', )
self.tau_s0s = np.load('tau_s.npy', )
self.Q = np.load('Q.npy')
self.T = np.load('Turbidity.npy')
ax1 = pp.subplot2grid((4, 3), (0, 0), colspan=2, rowspan=2)
ax1.plot(self.times, self.Q, 'k--', label='Measured Flow')
ax1.set_ylabel('Applied Shear (Pa)')
self.line1 = Line2D([], [], color='red')
self.line1a = Line2D([], [], color='red', alpha=0.5)
self.line1b = Line2D([], [], color='red', alpha=0.5)
ax1.add_line(self.line1)
ax1.add_line(self.line1a)
ax1.add_line(self.line1b)
ax2 = pp.subplot2grid((4, 3), (2, 0), colspan=2, rowspan=2)
ax2.plot(self.times, self.T, 'k--', label='Measured Turbidity')
ax2.set_ylabel('Turbidity (turbidity units)')
ax2.set_xlabel('Time (time units)')
self.line2 = Line2D([], [], color='red')
self.line2a = Line2D([], [], color='red', alpha=0.5)
self.line2b = Line2D([], [], color='red', alpha=0.5)
ax2.add_line(self.line2)
ax2.add_line(self.line2a)
ax2.add_line(self.line2b)
self.ax3 = pp.subplot2grid((4, 3), (0, 2))
self.ax3.set_xlabel('Upstream Turbidity (turbidity units)')
self.ax3.set_ylim(0, 10)
self.line3 = Line2D([], [], color='red')
self.line3a = Line2D([], [], color='black', linestyle='--')
self.ax3.add_line(self.line3)
self.ax3.add_line(self.line3a)
self.ax4 = pp.subplot2grid((4, 3), (1, 2))
self.ax4.set_xlabel('tau_s (N/m2)')
self.ax4.set_ylim(0, 10.)
self.line4 = Line2D([], [], color='red')
self.line4a = Line2D([], [], color='black', linestyle='--')
self.ax4.add_line(self.line4)
self.ax4.add_line(self.line4a)
self.ax5 = pp.subplot2grid((4, 3), (2, 2))
self.ax5.plot([5, 5], [0, 1000000000], 'k--')
self.ax5.set_xlabel('alpha (mass/m2)')
self.line5 = Line2D([], [], color='red')
self.ax5.add_line(self.line5)
self.ax6 = pp.subplot2grid((4, 3), (3, 2))
self.ax6.plot([0.2, 0.2], [0, 100000000], 'k--')
self.ax6.set_xlabel('beta (1/s)')
self.line6 = Line2D([], [], color='red')
self.ax6.add_line(self.line6)
pp.tight_layout()
animation.TimedAnimation.__init__(self, fig, interval=50, blit=False)
def ex_adjust():
import numpy as np
import pylab as pl
###########################################################################
# 1d Example ##############################################################
###########################################################################
# gage and radar coordinates
obs_coords = np.array([5, 10, 15, 20, 30, 45, 65, 70, 77, 90])
radar_coords = np.arange(0, 101)
# true rainfall
truth = np.abs(1.5 + np.sin(0.075 * radar_coords)) + np.random.uniform(
-0.1, 0.1, len(radar_coords))
# radar error
erroradd = 0.7 * np.sin(0.2 * radar_coords + 10.)
errormult = 0.75 + 0.015 * radar_coords
noise = np.random.uniform(-0.05, 0.05, len(radar_coords))
# radar observation
radar = errormult * truth + erroradd + noise
# gage observations are assumed to be perfect
obs = truth[obs_coords]
# add a missing value to observations (just for testing)
obs[1] = np.nan
# number of neighbours to be used
nnear_raws = 3
# adjust the radar observation by additive model
add_adjuster = adjust.AdjustAdd(obs_coords,
radar_coords,
nnear_raws=nnear_raws)
add_adjusted = add_adjuster(obs, radar)
# adjust the radar observation by multiplicative model
mult_adjuster = adjust.AdjustMultiply(obs_coords,
radar_coords,
nnear_raws=nnear_raws)
mult_adjusted = mult_adjuster(obs, radar)
# adjust the radar observation by MFB
mfb_adjuster = adjust.AdjustMFB(obs_coords,
radar_coords,
nnear_raws=nnear_raws)
mfb_adjusted = mfb_adjuster(obs, radar)
# adjust the radar observation by AdjustMixed
mixed_adjuster = adjust.AdjustMixed(obs_coords,
radar_coords,
nnear_raws=nnear_raws)
mixed_adjusted = mixed_adjuster(obs, radar)
# plotting
pl.plot(radar_coords,
radar,
'k-',
label="Unadjusted radar",
linewidth=2.,
linestyle="dashed")
pl.xlabel("Distance (km)")
pl.ylabel("Rainfall intensity (mm/h)")
pl.plot(radar_coords, truth, 'k-', label="True rainfall", linewidth=2.)
pl.plot(obs_coords,
obs,
'o',
label="Gage observation",
markersize=10.0,
markerfacecolor="grey")
pl.plot(radar_coords,
add_adjusted,
'-',
color="red",
label="Additive adjustment")
pl.plot(radar_coords,
mult_adjusted,
'-',
color="green",
label="Multiplicative adjustment")
pl.plot(radar_coords,
mfb_adjusted,
'-',
color="orange",
label="Mean Field Bias adjustment")
pl.plot(radar_coords,
mixed_adjusted,
'-',
color="blue",
label="Mixed (mult./add.) adjustment")
pl.legend(prop={'size': 12})
pl.show()
# Verification for this example
rawerror = verify.ErrorMetrics(truth, radar)
mfberror = verify.ErrorMetrics(truth, mfb_adjusted)
adderror = verify.ErrorMetrics(truth, add_adjusted)
multerror = verify.ErrorMetrics(truth, mult_adjusted)
mixerror = verify.ErrorMetrics(truth, mixed_adjusted)
# Verification reports
maxval = 4.
fig = pl.figure(figsize=(14, 8))
ax = fig.add_subplot(231, aspect=1.)
rawerror.report(ax=ax, unit="mm", maxval=maxval)
ax.text(0.2, 0.9 * maxval, "Unadjusted radar")
ax = fig.add_subplot(232, aspect=1.)
adderror.report(ax=ax, unit="mm", maxval=maxval)
ax.text(0.2, 0.9 * maxval, "Additive adjustment")
ax = fig.add_subplot(233, aspect=1.)
multerror.report(ax=ax, unit="mm", maxval=maxval)
ax.text(0.2, 0.9 * maxval, "Multiplicative adjustment")
ax = fig.add_subplot(234, aspect=1.)
mixerror.report(ax=ax, unit="mm", maxval=maxval)
ax.text(0.2, 0.9 * maxval, "Mixed (mult./add.) adjustment")
mixerror.report(ax=ax, unit="mm", maxval=maxval)
ax = fig.add_subplot(235, aspect=1.)
mfberror.report(ax=ax, unit="mm", maxval=maxval)
ax.text(0.2, 0.9 * maxval, "Mean Field Bias adjustment")
pl.show()
###########################################################################
# 2d Example ##############################################################
###########################################################################
# a) CREATE SYNTHETIC DATA ------------------------------------------------
# grid axes
xgrid = np.arange(0, 10)
ygrid = np.arange(20, 30)
# number of observations
num_obs = 10
# create grid
gridshape = len(xgrid), len(ygrid)
grid_coords = util.gridaspoints(ygrid, xgrid)
# Synthetic true rainfall
truth = np.abs(10. * np.sin(0.1 * grid_coords).sum(axis=1))
# Creating radar data by perturbing truth with multiplicative and additive error
# YOU CAN EXPERIMENT WITH THE ERROR STRUCTURE
radar = 0.6 * truth + 1. * np.random.uniform(
low=-1., high=1, size=len(truth))
radar[radar < 0.] = 0.
# indices for creating obs from raw (random placement of gauges)
obs_ix = np.random.uniform(low=0, high=len(grid_coords),
size=num_obs).astype('i4')
# creating obs_coordinates
obs_coords = grid_coords[obs_ix]
# creating gauge observations from truth
obs = truth[obs_ix]
# b) GAUGE ADJUSTMENT -----------------------------------------------------
# Mean Field Bias Adjustment
mfbadjuster = adjust.AdjustMFB(obs_coords, grid_coords)
mfbadjusted = mfbadjuster(obs, radar)
# Additive Error Model
addadjuster = adjust.AdjustAdd(obs_coords, grid_coords)
addadjusted = addadjuster(obs, radar)
# Multiplicative Error Model
multadjuster = adjust.AdjustMultiply(obs_coords, grid_coords)
multadjusted = multadjuster(obs, radar)
# c) PLOTTING
# Maximum value (used for normalisation of colorscales)
maxval = np.max(np.concatenate((truth, radar, obs, addadjusted)).ravel())
# Helper functions for repeated plotting tasks
def scatterplot(x, y, title):
"""Quick and dirty helper function to produce scatter plots
"""
pl.scatter(x, y)
pl.plot([0, 1.2 * maxval], [0, 1.2 * maxval], '-', color='grey')
pl.xlabel("True rainfall (mm)")
pl.ylabel("Estimated rainfall (mm)")
pl.xlim(0, maxval + 0.1 * maxval)
pl.ylim(0, maxval + 0.1 * maxval)
pl.title(title)
def gridplot(data, title):
"""Quick and dirty helper function to produce a grid plot
"""
xplot = np.append(xgrid, xgrid[-1] + 1.) - 0.5
yplot = np.append(ygrid, ygrid[-1] + 1.) - 0.5
grd = ax.pcolormesh(xplot,
yplot,
data.reshape(gridshape),
vmin=0,
vmax=maxval)
ax.scatter(obs_coords[:, 0],
obs_coords[:, 1],
c=obs.ravel(),
marker='s',
s=50,
vmin=0,
vmax=maxval)
pl.colorbar(grd, shrink=0.7)
pl.title(title)
# open figure
fig = pl.figure(figsize=(10, 10))
# True rainfall
ax = fig.add_subplot(331, aspect='equal')
gridplot(truth, 'True rainfall')
# Unadjusted radar rainfall
ax = fig.add_subplot(332, aspect='equal')
gridplot(radar, 'Radar rainfall')
# Scatter plot radar vs. observations
ax = fig.add_subplot(333, aspect='equal')
scatterplot(truth, radar, 'Radar vs. Truth (red: Gauges)')
pl.plot(obs, radar[obs_ix], linestyle="None", marker="o", color="red")
# Adjusted radar rainfall (MFB)
ax = fig.add_subplot(334, aspect='equal')
gridplot(mfbadjusted, 'Adjusted (MFB)')
# Adjusted radar rainfall (additive)
ax = fig.add_subplot(335, aspect='equal')
gridplot(addadjusted, 'Adjusted (Add.)')
# Adjusted radar rainfall (multiplicative)
ax = fig.add_subplot(336, aspect='equal')
gridplot(multadjusted, 'Adjusted (Mult.)')
# Adjusted (MFB) vs. radar (for control purposes)
ax = fig.add_subplot(337, aspect='equal')
#scatterplot(obs, mfbadjusted[obs_ix], 'Adjusted (MFB) vs. Gauges\n(no x-validation!)')
scatterplot(truth, mfbadjusted, 'Adjusted (MFB) vs. Truth')
# Adjusted (Add) vs. radar (for control purposes)
ax = fig.add_subplot(338, aspect='equal')
#scatterplot(obs, addadjusted[obs_ix], 'Adjusted (Add.) vs. Gauges\n(no x-validation!)')
scatterplot(truth, addadjusted, 'Adjusted (Add.) vs. Truth')
# Adjusted (Mult.) vs. radar (for control purposes)
ax = fig.add_subplot(339, aspect='equal')
#scatterplot(obs, multadjusted[obs_ix], 'Adjusted (Mult.) vs. Gauges\n(no x-validation!)')
scatterplot(truth, multadjusted, 'Adjusted (Mult.) vs. Truth')
pl.tight_layout()
pl.show()
ax0 = fig.add_subplot(221)
ax1 = fig.add_subplot(222)
ax2 = fig.add_subplot(223)
ax3 = fig.add_subplot(224)
# %% Import Lena
lena = cv2.imread("zelda.tif")
lena = cv2.cvtColor(lena, cv2.COLOR_RGB2GRAY) # RGBtoGray
lena = cv2.normalize(lena.astype("float"), None, 0, 1,
cv2.NORM_MINMAX) # mat2gray
plt.gray()
plt.subplot(2, 2, 1)
plt.subplot(2, 2, 2)
plt.subplot(2, 2, 3)
plt.subplot(2, 2, 4)
plt.tight_layout(pad=1.2)
# plt.imshow(lena)
flag_record = 1
width = 128
image = np.zeros((width, width))
image_fusion = np.zeros((width * 2, width * 2))
bead_volume_bool = np.empty((width, width, width), bool)
radius = 6
beads = 8
fov = np.arange(-width / 2, width / 2, 1)
[x, y, z] = np.meshgrid(fov, fov, fov)
# r = np.sqrt(x**2 + y**2)
def bench(self, data, plot=True, get=False):
""" Function to test drift rate search
note this bench mark contains compilation times and multiple kernel calls.
larger input would be faster.
"""
start = cu.Event()
copy_htod = cu.Event()
compute = cu.Event()
stop = cu.Event()
start.record()
self.spectr_d = gpuarray.to_gpu(data)
copy_htod.record()
copy_htod.synchronize()
self.sweep_kernel(self.spectr_d,
self.output_d,
self.delay_table_d,
self.nfreqs,
self.ntimes,
self.ndelays,
block=self.block_size,
grid=self.grid_size)
compute.record()
compute.synchronize()
if get:
out = self.output_d.get()
else:
operand = self.output_d[self.ndelays // 2]
mean = gpuarray.sum(operand / np.float32(self.nfreqs)).get()
var = gpuarray.sum(
(operand - mean) * (operand - mean) / np.float32(self.nfreqs))
std = np.sqrt(var.get())
self.threshold_kernel(self.output_d,
self.mask_d,
np.float32(3 * std + mean),
self.nfreqs,
self.ndelays,
block=self.block_size,
grid=self.grid_size)
out = (self.output_d * self.mask_d).get()
stop.record()
stop.synchronize()
print "{} seconds to load data".format(
start.time_till(copy_htod) * 1.e-3)
print "{} seconds to compute {} channels, with {} delays".format(
copy_htod.time_till(compute) * 1.e-3, self.nfreqs, self.ndelays)
print copy_htod.time_till(compute) / (
self.nfreqs *
self.ndelays) * 1.e6, "nanoseconds per channel, per delay"
if plot:
import pylab as plt
f, axes = plt.subplots(2, 1)
axes[0].imshow(
data,
aspect='auto',
extent=[0, self.nfreqs, 0, self.ntimes * self.delta_t],
origin='lower')
axes[0].set_xlabel('Freq [Hz]')
axes[0].set_ylabel('Time [s]')
axes[1].imshow(out,
aspect='auto',
extent=[0, self.nfreqs, -0.1, 0.1],
origin='lower')
axes[1].set_xlabel('Freq [Hz]')
axes[1].set_ylabel('Drift [Hz/s]')
plt.tight_layout()
plt.show()
return out
def table_visulization(self):
outcome_dataframe = self.outcome_dataframe
factor_filtered_list = list(set(outcome_dataframe["FactorPortfolio"]))
market_filtered_list = list(
set(outcome_dataframe["MarketValuePortfolio"]))
factor_count, market_count = len(factor_filtered_list), len(
market_filtered_list)
indicator = [
'annual_return', 'annual_volatility', 'annual_sharpe',
'max_retrace', 'max_retrace_date', 'max_win_streak_number',
'max_lose_streak_number', 'profit_to_lose_percentage',
'profit_to_lose', 'turnover_rate'
]
data_storage = pd.DataFrame(np.zeros(
(factor_count * market_count, len(indicator))),
index=[(x, y) for x in factor_filtered_list
for y in market_filtered_list],
columns=indicator)
for factorIndex in range(factor_count):
for marketIndex in range(market_count):
profit_series = outcome_dataframe["ret"][
(outcome_dataframe["FactorPortfolio"] ==
factor_filtered_list[factorIndex])
& (outcome_dataframe["MarketValuePortfolio"] ==
market_filtered_list[marketIndex])]
symbol_series = outcome_dataframe["symbols"][
(outcome_dataframe["FactorPortfolio"] ==
factor_filtered_list[factorIndex])
& (outcome_dataframe["MarketValuePortfolio"] ==
market_filtered_list[marketIndex])]
return_temp = profit_series.apply(lambda x: (1 + x)).product()
data_storage['annual_return'][(factor_filtered_list[factorIndex], market_filtered_list[marketIndex])] = \
return_temp**(252/len(profit_series)) - 1
data_storage['annual_volatility'][(factor_filtered_list[factorIndex], market_filtered_list[marketIndex])] = \
profit_series.std()*np.sqrt(252)
data_storage['annual_sharpe'][(factor_filtered_list[factorIndex], market_filtered_list[marketIndex])] = \
profit_series.mean()/profit_series.std()*np.sqrt(252)
max_retrace = profit_series.min()
data_storage['max_retrace'][(
factor_filtered_list[factorIndex],
market_filtered_list[marketIndex])] = max_retrace
data_storage['max_retrace_date'][(factor_filtered_list[factorIndex], market_filtered_list[marketIndex])] = \
outcome_dataframe['trade_date'][(outcome_dataframe['ret'] == max_retrace)].to_string()
data_storage['max_win_streak_number'][(factor_filtered_list[factorIndex], market_filtered_list[marketIndex])] = \
max([len(list(v)) for k, v in itertools.groupby(profit_series > 0)])
data_storage['max_lose_streak_number'][(factor_filtered_list[factorIndex], market_filtered_list[marketIndex])] = \
max([len(list(v)) for k, v in itertools.groupby(profit_series < 0)])
profit_count, lose_count = len(
profit_series[profit_series > 0]), len(
profit_series[profit_series < 0])
profit_return, lose_return = profit_series[profit_series > 0].apply(lambda x: 1 + x).product() - 1, \
profit_series[profit_series < 0].apply(lambda x: 1 + x).product() - 1
data_storage['profit_to_lose_percentage'][(factor_filtered_list[factorIndex], market_filtered_list[marketIndex])] =\
(profit_return / profit_count) / (abs(lose_return) / lose_count)
data_storage['profit_to_lose'][(
factor_filtered_list[factorIndex],
market_filtered_list[marketIndex]
)] = profit_count / lose_count
data_storage['turnover_rate'][(
factor_filtered_list[factorIndex],
market_filtered_list[marketIndex])] = 0
for tableIndex in range(len(data_storage.columns)):
series_temp = data_storage[
data_storage.columns[tableIndex]].values.reshape(
factor_count, market_count)
plt.figure(3, figsize=(20, 40))
pic_temp = plt.subplot(len(data_storage.columns), 1,
tableIndex + 1)
pic_temp.axis('off')
pic_temp.axis('tight')
table_temp = pic_temp.table(cellText=series_temp,
loc='center',
cellLoc='center',
rowLabels=factor_filtered_list,
colLabels=market_filtered_list,
colLoc='center',
colWidths=[0.1] *
len(market_filtered_list))
plt.title(data_storage.columns[tableIndex], loc='left', size=25)
table_temp.set_fontsize(20)
table_temp.scale(2, 2)
plt.tight_layout()
figure_temp = plt.figure(3)
return figure_temp
] for T in Tls])
rewls[0] = R0
perf = (rewls - R0) / (Rmax - R0)
np.save('results/performance', perf)
pl.figure()
errorfill(range(len(perf)),
np.mean(perf, axis=1),
yerr=np.std(perf, axis=1) / np.sqrt(len(perf[0])))
pl.xticks([0, 200, 400], [0, 200, 400])
pl.yticks([0, .5, 1.0], [0, .5, 1.0])
pl.xlim([0, 400])
pl.ylim([0, 1])
pl.xlabel('Time [ms]')
pl.ylabel('Performance')
simpleaxis(pl.gca())
pl.tight_layout(0)
pl.savefig('performance.pdf', dpi=600)
# plot value:
h = .1
try:
V = np.load('results/value_obt.npy')
except IOError:
S = np.load('results/spikes.npz')['S']
y = np.array(map(lambda a: a.sum(axis=0), S))[:, 0]
V = []
for run, x in enumerate(y):
V += [
np.array([[
np.dot(map(get_R4pv, get_path(x, [p, v])),
gamma**np.arange(301))
np.save(Directory + 'InitialCOV.npy', COV)
COR = np.corrcoef(Outputs.T)
AxisLabels = ['Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'H1', 'H2', 'H3', 'H4', 'H5', 'H6']
cmap = pp.get_cmap('jet', 30)
f, ax = pp.subplots(figsize=(9, 6))
sns.heatmap(COV,
fmt="0.3f",
annot=True,
linewidths=.5,
ax=ax,
cmap=cmap,
xticklabels=AxisLabels,
yticklabels=AxisLabels)
#sns.heatmap(COR, fmt="0.3f",annot=True, linewidths=.5, ax=ax,cmap=cmap,xticklabels = AxisLabels,yticklabels=AxisLabels)
pp.tight_layout()
pp.show()
pp.savefig(Directory + '5_Pipes_Initial_Covariance_Matrix.png')
### Plot of the convergence of the mean and variance
#means = []
#variances = []
#NoSamples = np.arange(2,6.1,0.1)
##NoSamples = [10,30,100,300,1000,3000,10000,30000,100000,300000,1000000]
#for i in 10**NoSamples:
# means.append(np.mean(Outputs[:int(i),-1]))
# variances.append(np.std(Outputs[:int(i),-1])**2)
#
#pp.figure(figsize=(6, 4))
#pp.semilogx(10**NoSamples,means)
def daily_return_visulization(self, xtickNumber=5, visionType=""):
global pic_temp
outcome_dataframe = self.outcome_dataframe
dataNumber = len(self.outcome_dataframe)
factorFilteredList = list(set(outcome_dataframe["FactorPortfolio"])
) #将factor指标压缩成只包含不同指标的数组，如[1,2,3,4,5]
marketFilteredList = list(
set(outcome_dataframe["MarketValuePortfolio"]))
if visionType == "factor":
for factorIndex in range(len(factorFilteredList)):
for marketIndex in range(len(marketFilteredList)):
profit_series = outcome_dataframe["ret"][
(outcome_dataframe["FactorPortfolio"] ==
factorFilteredList[factorIndex])
& (outcome_dataframe["MarketValuePortfolio"] ==
marketFilteredList[marketIndex])]
date_series = outcome_dataframe["trade_date"][
(outcome_dataframe["FactorPortfolio"] ==
factorFilteredList[factorIndex])
& (outcome_dataframe["MarketValuePortfolio"] ==
marketFilteredList[marketIndex])]
profit_cumulate_series = np.exp(
np.log(1 + profit_series).expanding(
1).sum()) - 1 # 抽取累积收益Series
dataframe_temp = pd.DataFrame({
'date':
date_series.apply(lambda x: str(x)).tolist(),
'profit':
profit_cumulate_series.tolist()
}) # 生成一个新的dataframe来储存/此处只是为了update index
#Draw Pic
plt.figure(1, figsize=(20, 30))
pic_temp = plt.subplot(len(factorFilteredList), 1,
factorIndex + 1)
plt.plot(dataframe_temp['date'],
dataframe_temp['profit'],
label="Market=" +
str(marketFilteredList[marketIndex]))
pic_temp.legend()
xticks_increment = int(dataNumber / xtickNumber /
len(factorFilteredList) /
len(marketFilteredList)) # 设置画图步长
plt.xticks(
[xticks_increment * (x) for x in range(xtickNumber)],
dataframe_temp['date'].loc[[
xticks_increment * (x) for x in range(xtickNumber)
]]) # 设置x轴刻度
plt.xlabel('Trade Date')
plt.ylabel('Returns')
plt.title("Factor = " + str(factorFilteredList[factorIndex]))
plt.tight_layout()
figure_temp = plt.figure(1)
return figure_temp
if visionType == "market":
for marketIndex in range(len(marketFilteredList)):
for factorIndex in range(len(factorFilteredList)):
profit_series = outcome_dataframe["ret"][
(outcome_dataframe["FactorPortfolio"] ==
factorFilteredList[factorIndex])
& (outcome_dataframe["MarketValuePortfolio"] ==
marketFilteredList[marketIndex])]
date_series = outcome_dataframe["trade_date"][
(outcome_dataframe["FactorPortfolio"] ==
factorFilteredList[factorIndex])
& (outcome_dataframe["MarketValuePortfolio"] ==
marketFilteredList[marketIndex])]
profit_cumulate_series = np.exp(
np.log(1 + profit_series).expanding(
1).sum()) - 1 # 抽取累积收益Series
dataframe_temp = pd.DataFrame({
'date':
date_series.apply(lambda x: str(x)).tolist(),
'profit':
profit_cumulate_series.tolist()
}) # 生成一个新的dataframe来储存/此处只是为了update index
#Draw Pic
plt.figure(2, figsize=(20, 30))
pic_temp = plt.subplot(len(marketFilteredList), 1,
marketIndex + 1)
plt.plot(dataframe_temp['date'],
dataframe_temp['profit'],
label="Factor=" +
str(factorFilteredList[factorIndex]))
pic_temp.legend()
xticks_increment = int(dataNumber / xtickNumber /
len(factorFilteredList) /
len(marketFilteredList)) # 设置画图步长
plt.xticks(
[xticks_increment * (x) for x in range(xtickNumber)],
dataframe_temp['date'].loc[[
xticks_increment * (x) for x in range(xtickNumber)
]]) # 设置x轴刻度
plt.title("Market = " + str(marketFilteredList[marketIndex]))
plt.xlabel('Trade Date')
plt.ylabel('Returns')
plt.tight_layout()
figure_temp = plt.figure(2)
return figure_temp
def Doplots_diurnal_monthly(mypathforResults, PlottingDF, variable_to_fill,
Site_ID, units, list_out, index_str, is_this_all):
for index, item in enumerate(list_out):
print "Doing diurnal plot for month and index ", index_str
#Do Diurnal Plots for all 12 months
#create an X axis series for all 24 hours
t = np.arange(1, 25, 1)
NN_label = str(item + "_NN")
if is_this_all == True: NN_label = str(item + "_NN_all")
Plottemp = PlottingDF[[NN_label, item]]
fig = pl.figure(index,
figsize=(16, 12),
dpi=80,
facecolor='w',
edgecolor='k')
fig.suptitle('Monthly ANN ensemble diurnal average for variable ' +
item + ' at ' + Site_ID + ' index ' + index_str)
pl.subplots_adjust(top=100)
pl.subplot(321)
pl.title('Diurnal ' + item + ' month = 1')
try:
xdata1a = Plottemp[(PlottingDF.index.month == 1)][item].groupby(
[lambda x: x.hour]).mean()
plotxdata1a = True
except:
plotxdata1a = False
try:
xdata1b = Plottemp[(
PlottingDF.index.month == 1)][NN_label].groupby(
[lambda x: x.hour]).mean()
plotxdata1b = True
except:
plotxdata1b = False
if (len(xdata1a) == 0): plotxdata1a = False
if (len(xdata1b) == 0): plotxdata1b = False
if plotxdata1a == True:
pl.plot(t, xdata1a, 'b', label=item)
if plotxdata1b == True:
pl.plot(t, xdata1b, 'r', label=NN_label)
pl.ylabel('Flux')
pl.legend(loc='best')
pl.subplot(322)
pl.title('Diurnal ' + item + ' month = 3')
try:
xdata1a = Plottemp[(PlottingDF.index.month == 3)][item].groupby(
[lambda x: x.hour]).mean()
plotxdata1a = True
except:
plotxdata1a = False
try:
xdata1b = Plottemp[(
PlottingDF.index.month == 3)][NN_label].groupby(
[lambda x: x.hour]).mean()
plotxdata1b = True
except:
plotxdata1b = False
if (len(xdata1a) == 0): plotxdata1a = False
if (len(xdata1b) == 0): plotxdata1b = False
if plotxdata1a == True:
pl.plot(t, xdata1a, 'b', label=item)
if plotxdata1b == True:
pl.plot(t, xdata1b, 'r', label=NN_label)
pl.ylabel('Flux')
pl.legend(loc='best')
pl.subplot(323)
pl.title('Diurnal ' + item + ' month = 5')
try:
xdata1a = Plottemp[(PlottingDF.index.month == 5)][item].groupby(
[lambda x: x.hour]).mean()
plotxdata1a = True
except:
plotxdata1a = False
try:
xdata1b = Plottemp[(
PlottingDF.index.month == 5)][NN_label].groupby(
[lambda x: x.hour]).mean()
plotxdata1b = True
except:
plotxdata1b = False
if (len(xdata1a) == 0): plotxdata1a = False
if (len(xdata1b) == 0): plotxdata1b = False
if plotxdata1a == True:
pl.plot(t, xdata1a, 'b', label=item)
if plotxdata1b == True:
pl.plot(t, xdata1b, 'r', label=NN_label)
pl.ylabel('Flux')
pl.legend(loc='best')
pl.subplot(324)
pl.title('Diurnal ' + item + ' month = 7')
try:
xdata1a = Plottemp[(PlottingDF.index.month == 7)][item].groupby(
[lambda x: x.hour]).mean()
plotxdata1a = True
except:
plotxdata1a = False
try:
xdata1b = Plottemp[(
PlottingDF.index.month == 7)][NN_label].groupby(
[lambda x: x.hour]).mean()
plotxdata1b = True
except:
plotxdata1b = False
if (len(xdata1a) == 0): plotxdata1a = False
if (len(xdata1b) == 0): plotxdata1b = False
if plotxdata1a == True:
pl.plot(t, xdata1a, 'b', label=item)
if plotxdata1b == True:
pl.plot(t, xdata1b, 'r', label=NN_label)
pl.ylabel('Flux')
pl.legend(loc='best')
pl.subplot(325)
pl.title('Diurnal ' + item + ' month = 9')
try:
xdata1a = Plottemp[(PlottingDF.index.month == 9)][item].groupby(
[lambda x: x.hour]).mean()
plotxdata1a = True
except:
plotxdata1a = False
try:
xdata1b = Plottemp[(
PlottingDF.index.month == 9)][NN_label].groupby(
[lambda x: x.hour]).mean()
plotxdata1b = True
except:
plotxdata1b = False
if (len(xdata1a) == 0): plotxdata1a = False
if (len(xdata1b) == 0): plotxdata1b = False
if plotxdata1a == True:
pl.plot(t, xdata1a, 'b', label=item)
if plotxdata1b == True:
pl.plot(t, xdata1b, 'r', label=NN_label)
pl.ylabel('Flux')
pl.legend(loc='best')
pl.subplot(326)
pl.title('Diurnal ' + item + ' month = 11')
try:
xdata1a = Plottemp[(PlottingDF.index.month == 11)][item].groupby(
[lambda x: x.hour]).mean()
plotxdata1a = True
except:
plotxdata1a = False
try:
xdata1b = Plottemp[(
PlottingDF.index.month == 11)][NN_label].groupby(
[lambda x: x.hour]).mean()
plotxdata1b = True
except:
plotxdata1b = False
if (len(xdata1a) == 0): plotxdata1a = False
if (len(xdata1b) == 0): plotxdata1b = False
if plotxdata1a == True:
pl.plot(t, xdata1a, 'b', label=item)
if plotxdata1b == True:
pl.plot(t, xdata1b, 'r', label=NN_label)
pl.ylabel('Flux')
pl.legend(loc='best')
pl.tight_layout()
pl.savefig(mypathforResults +
'/Monthly ANN ensemble diurnal average for variable ' +
item + ' at ' + Site_ID + ' index ' + index_str)
#pl.show()
pl.close(index)
pl.close()
time.sleep(2)
def make_cog(infile,
lmin=650.,
lmax=700.,
sigfac=7.,
interact=False,
no_stamp=False):
"""Loads IFU frame "imfile" and extracts spectra using "fine".
Args:
infile (string): input extractions file
lmin (float): lower wavelength limit for image generation
lmax (float): upper wavelength limit for image generation
sigfac (float): sigma multiplier for Gaussian extent of aperture
interact (bool): set for interactive plotting
no_stamp (bool): set to prevent printing DRP version stamp on plot
Returns:
None
Raises:
None
"""
# The spaxel extraction file must already exist, so load extractions in
if os.path.isfile(infile):
print("USING extractions in %s!" % infile)
ex, meta = np.load(infile)
# No file found
else:
print("File not found: %s" % infile)
return
outname = infile.split('.')[0]
# Automatic extraction using Gaussian fit for Standard Stars
sixa, posa, adcpos, ellipse, status = \
identify_spectra_gauss_fit(ex,
prlltc=Angle(meta['PRLLTC'], unit='deg'),
lmin=lmin, lmax=lmax,
airmass=meta['airmass'],
sigfac=sigfac)
# Use all sky spaxels in image
kixa, skystat = identify_sky_spectra(ex, adcpos, ellipse=ellipse)
for ix in sixa:
ex[ix].is_obj = True
for ix in kixa:
ex[ix].is_sky = True
# Get sky spectrum
if skystat == 0:
skya = interp_spectra(ex, kixa, sky=True)
else:
skya = interp_spectra(ex, kixa)
# Define our standard wavelength grid
ll = None
# Resample sky onto standard wavelength grid
try:
sky_a = interp1d(skya[0]['nm'],
skya[0]['ph_10m_nm'],
bounds_error=False)
except:
sky_a = None
# Set up curve of growth
kt = SedSpec.Spectra(ex)
elrat = ellipse[1] / ellipse[0]
xc = ellipse[2]
yc = ellipse[3]
theta = ellipse[4] * np.pi / 180.
# Set up plot
pl.figure(1)
pl.clf()
if not no_stamp:
pl.title(outname)
xs = range(20)
rs = list(np.linspace(0, ellipse[0], 20))
rs.reverse()
print("max semi-major axis is %.2f asec" % ellipse[0])
c1 = []
c2 = []
c3 = []
c4 = []
c5 = []
px = []
resout = []
# Loop over semi-major axes
for ix in xs:
if rs[ix] <= 0.:
continue
a = rs[ix]
b = rs[ix] * elrat
kix = find_positions_ellipse(kt.KT.data, xc, yc, a, b, -theta)
sixa = kt.good_positions[kix]
print("%02d found %04d spaxels with a = %.3f" % (ix, len(sixa), a))
if len(sixa) > 0:
# get the summed spectrum over the selected spaxels
resa = interp_spectra(ex, sixa)
# get common wavelength scale
if ll is None:
ll = resa[0]['nm']
# Copy and resample object spectrum onto standard wavelength grid
res = {
"doc": resa[0]["doc"],
"ph_10m_nm": np.copy(resa[0]["ph_10m_nm"]),
"spectra": np.copy(resa[0]["spectra"]),
"coefficients": np.copy(resa[0]["coefficients"]),
"nm": np.copy(ll)
}
fl = interp1d(resa[0]['nm'],
resa[0]['ph_10m_nm'],
bounds_error=False)
# Calculate output corrected spectrum
# Account for sky and aperture
if sky_a is not None:
res['ph_10m_nm'] = (fl(ll) - sky_a(ll)) * len(sixa)
else:
res['ph_10m_nm'] = fl(ll) * len(sixa)
cog = res['ph_10m_nm']
# 400 - 500 nm
f1 = np.nanmean(cog[(ll > 400) * (ll < 500)])
c1.append(f1)
# 500 - 600 nm
f2 = np.nanmean(cog[(ll > 500) * (ll < 600)])
c2.append(f2)
# 600 - 700 nm
f3 = np.nanmean(cog[(ll > 600) * (ll < 700)])
c3.append(f3)
# 700 - 800 nm
f4 = np.nanmean(cog[(ll > 700) * (ll < 800)])
c4.append(f4)
# 800 - 900 nm
f5 = np.nanmean(cog[(ll > 800) * (ll < 900)])
c5.append(f5)
# Semi-major axis in arcsec
px.append(a)
# Output results
res['a'] = a
res['ix'] = ix
res['Nspax'] = len(sixa)
res['fl_400_500nm'] = f1
res['fl_500_600nm'] = f2
res['fl_600_700nm'] = f3
res['fl_700_800nm'] = f4
res['fl_800_900nm'] = f5
# Store this iteration
resout.append(res)
maxph = np.nanmax([c1, c2, c3, c4, c5])
# Normalize and get minimum half-max radius
hmr = 1.e9
# 400-500 nm
c1 /= maxph
if np.max(c1) == c1[0] and np.max(c1) > 0.5:
c1f = interp1d(c1, px)
hmx = c1f(0.5)
if hmx < hmr:
hmr = hmx
# 500-600 nm
c2 /= maxph
if np.max(c2) == c2[0] and np.max(c2) > 0.5:
c2f = interp1d(c2, px)
hmx = c2f(0.5)
if hmx < hmr:
hmr = hmx
# 600-700 nm
c3 /= maxph
if np.max(c3) == c3[0] and np.max(c3) > 0.5:
c3f = interp1d(c3, px)
hmx = c3f(0.5)
if hmx < hmr:
hmr = hmx
# 700-800 nm
c4 /= maxph
if np.max(c4) == c4[0] and np.max(c4) > 0.5:
c4f = interp1d(c4, px)
hmx = c4f(0.5)
if hmx < hmr:
hmr = hmx
# 800-900 nm
c5 /= maxph
if np.max(c5) == c5[0] and np.max(c5) > 0.5:
c5f = interp1d(c5, px)
hmx = c5f(0.5)
if hmx < hmr:
hmr = hmx
if not no_stamp:
pl.plot(px, c1, label='400-500 nm')
pl.plot(px, c2, label='500-600 nm')
pl.plot(px, c3, label='600-700 nm')
pl.plot(px, c4, label='700-800 nm')
pl.plot(px, c5, label='800-900 nm')
else:
pl.plot(px, c1, label='400-500 nm', linestyle=':')
pl.plot(px, c2, label='500-600 nm', linestyle='-.')
pl.plot(px, c3, label='600-700 nm', linestyle='--')
pl.plot(px, c4, label='700-800 nm', linestyle='-')
pl.plot(px, c5, label='800-900 nm', linestyle=':')
if hmr < 1.e9:
pl.plot([hmr, hmr], [-0.05, 1.05],
ls='--',
c='black',
label='HalfLight')
# pl.plot([0.05, hmr], [0.5, 0.5], ls='--', c='gray')
pl.xlim(0.05, np.max(px) + 0.05)
pl.ylim(-0.05, 1.05)
pl.xlabel('Semi-major axis (arcsec)', {'fontsize': 14})
pl.ylabel('Relative Irradiance', {'fontsize': 14})
pl.legend()
if not no_stamp:
plot_drp_ver()
else:
ax = pl.gca()
ax.tick_params(axis='both', which='major', labelsize=14)
ltext = ax.get_legend().get_texts()
pl.setp(ltext[0], fontsize=16)
pl.setp(ltext[1], fontsize=16)
pl.setp(ltext[2], fontsize=16)
pl.setp(ltext[3], fontsize=16)
pl.setp(ltext[4], fontsize=16)
pl.tight_layout()
if interact:
pl.show()
else:
pl.savefig('cog_' + outname + '.pdf')
print("Wrote cog_" + outname + ".pdf")
np.save("cog_" + outname, resout)
print("Wrote cog_" + outname + ".npy")
def plot_E615_xF(wdir, istep, data):
nrows, ncols = 1, 1
fig = py.figure(figsize=(ncols * 5, nrows * 7))
ax = py.subplot(nrows, ncols, 1)
Xbins = []
#adjust bins
Xbins.append([0, 0.1])
Xbins.append([0.1, 0.2])
Xbins.append([0.2, 0.3])
Xbins.append([0.3, 0.4])
Xbins.append([0.4, 0.5])
Xbins.append([0.5, 0.6])
Xbins.append([0.7, 0.8])
Xbins.append([0.8, 0.9])
Xbins.append([0.9, 1])
Xbins = Xbins[::-1]
tab = {}
tab['pT'] = data['pT']
tab['xF'] = data['xF']
tab['value'] = data['value']
tab['alpha'] = data['alpha']
tab['thy'] = np.mean(data['prediction-rep'], axis=0)
tab['dthy'] = np.std(data['prediction-rep'], axis=0)
for k in range(len(data['prediction-rep'])):
tab['thy%d' % k] = data['prediction-rep'][k]
tab = pd.DataFrame(tab)
f = 1 #40
for i in range(len(Xbins)):
d = tab.query('xF>%f and xF<%f' % (Xbins[i][0], Xbins[i][1]))
if len(d['xF']) != 0:
ii = np.argsort(d['pT'].values)
X = d['pT'].values[ii]
Y = d['thy'].values[ii]
YP = Y + d['dthy'].values[ii]
YM = Y - d['dthy'].values[ii]
if len(d['pT'].values) > 1:
msg = r'$xF\in[%0.1f,%0.1f]$' % (Xbins[i][0], Xbins[i][1])
p, = ax.plot(X[:-1], (Y * f**i)[:-1], alpha=0.3, label=None)
ax.fill_between(X[:-1], (YM * f**i)[:-1], (YP * f**i)[:-1],
color=p.get_color(),
alpha=0.3,
label=None)
ax.errorbar(d['pT'][:-1], (d['value'] * f**i)[:-1],
(d['alpha'] * f**i)[:-1],
zorder=10,
marker='o',
ls='none',
markeredgecolor='k',
ecolor='k',
color=p.get_color(),
label=msg)
#ax.set_title('Q bin: '+str(Qbins[i][0])+'-'+str(Qbins[i][1]))
#ax.semilogy()
#ax.tick_params(axis='both',which='both',direction='in',pad=4,labelsize=18)
#ax.set_xlabel(r'$pT$',size=20)
#ax.xaxis.set_label_coords(0.95,-0.03)
#ax.text(0.06,0.06,r'$d^2\sigma/dQdpT$',transform=ax.transAxes,size=20)
#ax.text(0.78,0.91,r'$\bf E615 pT$',transform=ax.transAxes,size=17)
#py.tight_layout()
ax.legend(loc=4)
ax.semilogy()
ax.tick_params(axis='both',
which='both',
direction='in',
pad=4,
labelsize=18)
ax.set_xlim(None, 4.5)
#ax.set_ylim(1e-38,1e-20)
#ax.set_xticks([0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8])
#ax.set_xticklabels([r'$0$',r'',r'$0.2$',r'',r'$0.4$',r'',r'$0.6$',r'',r''])
#ax.set_xlabel(r'$pT$',size=20)
ax.xaxis.set_label_coords(0.95, -0.03)
#ax.set_ylim(2.5e-1,8e2)
ax.set_ylabel(r'$d^2\sigma/dxFdp_{\rm T}~\times %d^i$' % f, size=20)
ax.set_xlabel(r'$p_{\rm T}$', size=20)
ax.text(0.1, 0.23, r'$i=0$', transform=ax.transAxes, size=14)
ax.text(0.5, 0.92, r'$i=10$', transform=ax.transAxes, size=14)
ax.text(0.7,
0.9,
r'\boldmath{${\rm E615}$}',
transform=ax.transAxes,
size=17)
py.tight_layout()
py.savefig('%s/gallery/dy-pion-E615-xF-%d.png' % (wdir, istep))
def fitLines(coordPairs,lineBasicData, CutLinesValues, bPlot = False) :
fitData = []
j = 1
for i in range(0,len(coordPairs),1) :
print i
print coordPairs[i]
print lineBasicData[i]
for Values, basicData in itertools.izip(CutLinesValues[1:],lineBasicData[1:]):
#chnage slicer values to skip lines which cannot be minimised i.e edge effects or detected false lines
x = Values[0]
f = Values[1]
Err = Values[2]
#chi2 = lambda bg, a, mu, sigma, R: (((f-convolutionfuncG(a, mu, sigma, x, R)+bg)/Err)**2).sum()
#chi2 = lambda bg,a,mu,sigma: ((f -(a/sigma*pylab.sqrt(2*math.pi))*pylab.exp(-(x-mu)**2/sigma**2)+bg)**2/Err**2).sum() #just gauss
#chi2 = lambda bg, a, mu, gamma,R: ((f-convolutionfuncL(a, mu, gamma, x, R)+bg)**2/Err**2).sum() #circleLorentz
chi2 = lambda bg, a, mu, gamma, sigma, R:(((f-convolutionfuncvoi(a, x, mu, sigma, gamma, R)+bg)/Err)**2).sum() #circlevoigt
#chi2 = lambda bg, a, mu, gamma, sigma:(((f-voiSb((x-mu), sigma, gamma,a)+bg)/Err)**2).sum() #voigt
#m=minuit.Minuit(chi2, bg=-505864.835856, a = -408608, mu= 666.961236213, gamma=11.1507399367, sigma=0.750795227728, R=4.87)
#m=minuit.Minuit(chi2, bg=-510099.995856, a = -890000, mu= 668.961236213, gamma=11.1507399367, sigma=0.750795227728)#voigt beta extended
#m=minuit.Minuit(chi2, bg=-505864.835856, a = -408608, mu= 666.961236213, gamma=11.1507399367, sigma=0.750795227728)#voigt shallow
#m=minuit.Minuit(chi2, bg = -500000, a = -basicData[1], mu = basicData[0], gamma = basicData[2], sigma = basicData[2], R=4.87)#tester
#m=minuit.Minuit(chi2, bg = -603807.71721, a = -801908.440558, mu = 389.77133122, gamma = 10.133591096, sigma = 10.161872412, R=4.255771982)
m=minuit.Minuit(chi2, bg = -494532.214627, a = -275192.356194, mu = 389.755898794, gamma = 9.06574437224, sigma = 2, R=11.0398160531)
m.tol = 10000000000
m.printMode = 1
m.migrad()
m.hesse()
m.values
bg = m.values['bg']
a = m.values['a']
mu = m.values['mu']
gamma = m.values['gamma']
sigma = m.values['sigma']
R = m.values['R']
#fit =(a/sigma*pylab.sqrt(2*math.pi))*pylab.exp(-((x-mu)**2)/sigma**2)-bg
#fit =convolutionfuncL(a, mu, gamma, x, R)-bg
#fit = convolutionfuncG(a, mu, sigma, x, R)-bg
fit = convolutionfuncvoi(a, x, mu, sigma, gamma,R)-bg
#fit = voiSb((x-mu), sigma, gamma,a)-bg
#fitData.append([a,mu,bg,sigma,m.errors])
fitData.append([a,mu,bg,gamma,sigma,R,m.errors])
print'fitLines> Covariance Matrix:\n', m.covariance
if bPlot:
pylab.figure()
pylab.errorbar(x,f,Err,label='Data')
pylab.xlabel('Pixels')
pylab.ylabel('no. Photo electrons')
pylab.plot(x,fit,label='Fitted Voigt X Circle')
pylab.legend(loc='best')
pylab.figure(108)
pylab.subplot(5,2,j)
pylab.tight_layout()
ax = pylab.gca()
for tick in ax.xaxis.get_major_ticks():
tick.label1.set_fontsize(9)
for tick in ax.yaxis.get_major_ticks():
tick.label1.set_fontsize(9)
pylab.errorbar(x,f,Err)
pylab.plot(x,fit,label='line:%s'%(j))
pylab.legend(loc='best',prop={'size':8})
j+=1
fitData = pylab.reshape(fitData,(len(fitData),7))
#out_txt(fitData,'')
print 'fitLines> fitData:\n',fitData
return[fitData]
def test_acs_sa_wrapper():
print '\ntest_acs_sa_wrapper pradžia'
rez_dist_b, rez_dist_w = [], []
rez_dura_b, rez_dura_w = [], []
rez_ants_b, rez_ants_w = [], []
rez_iter_b, rez_iter_w = [], []
rez_alfa_b, rez_alfa_w = [], []
rez_beta_b, rez_beta_w = [], []
cities = [6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 25, 30]
for c in cities:
print '\t', c, 'miestai'
graph = AntGraph.generate_complete_graph((1000, 1000), c)
acsim = ACSSA(graph, 30, 60)
acsim.decrement = 0.99
acsim.simulate(True)
print '\t\tworst', acsim.worst_solution
print '\t\tbest', acsim.best_solution
rez_dist_b.append(acsim.best_solution['distance'])
rez_dist_w.append(acsim.worst_solution['distance'])
rez_dura_b.append(acsim.best_solution['duration'])
rez_dura_w.append(acsim.worst_solution['duration'])
rez_ants_b.append(acsim.best_solution['parameters']['ants'])
rez_ants_w.append(acsim.worst_solution['parameters']['ants'])
rez_iter_b.append(acsim.best_solution['parameters']['iterations'])
rez_iter_w.append(acsim.worst_solution['parameters']['iterations'])
rez_alfa_b.append(acsim.best_solution['parameters']['alfa'])
rez_alfa_w.append(acsim.worst_solution['parameters']['alfa'])
rez_beta_b.append(acsim.best_solution['parameters']['beta'])
rez_beta_w.append(acsim.worst_solution['parameters']['beta'])
fig = figure(200)
g1 = fig.add_subplot(211)
g1.set_title(
u'Geriausio (žalia) ir blogiausio (raudona) maršruto algoritmo atstumai.'
)
g1.set_xlabel(u'Miestu skaičius')
g1.set_ylabel(u'Atstumas')
g1.plot(cities, rez_dist_b, 'g-', cities, rez_dist_w, 'r--')
g1.grid(True)
g2 = fig.add_subplot(212)
g2.set_title(
u'Geriausio (žalia) ir blogiausio (raudona) maršruto algoritmo laikai.'
)
g2.set_xlabel(u'Miestu skaičius')
g2.set_ylabel(u'Laikas (s)')
g2.plot(cities, rez_dura_b, 'g-', cities, rez_dura_w, 'r--')
g2.grid(True)
tight_layout()
savefig('output/test_acs_sa_wrapper/test_acs_sa_wrapper_dist_dur.png')
fig = figure(201)
g1 = fig.add_subplot(211)
g1.set_title(
u'Geriausio (žalia) ir blogiausio (raudona) maršruto algoritmo skrudeliu skaičius.'
)
g1.set_xlabel(u'Miestu skaičius')
g1.set_ylabel(u'Skruzdeles')
g1.axis([cities[0], cities[len(cities) - 1], 0.0, OPTIMAL_ANTS * 2])
g1.plot(cities, rez_ants_b, 'g-', cities, rez_ants_w, 'r--')
g1.grid(True)
g2 = fig.add_subplot(212)
g2.set_title(
u'Geriausio (žalia) ir blogiausio (raudona) maršruto algoritmo iteraciju skaičius.'
)
g2.set_xlabel(u'Miestu skaičius')
g2.set_ylabel(u'Iteracijos')
g2.axis(
[cities[0], cities[len(cities) - 1], 0.0, OPTIMAL_ANT_ITERATIONS * 2])
g2.plot(cities, rez_iter_b, 'g-', cities, rez_iter_w, 'r--')
g2.grid(True)
tight_layout()
savefig('output/test_acs_sa_wrapper/test_acs_sa_wrapper_ant_iter.png')
fig = figure(202)
g1 = fig.add_subplot(211)
g1.set_title(
u'Geriausio (žalia) ir blogiausio (raudona) maršruto algoritmo alfa parametras.'
)
g1.set_xlabel(u'Miestu skaičius')
g1.set_ylabel(u'alfa')
g1.axis([cities[0], cities[len(cities) - 1], 0.0, 1.0])
g1.plot(cities, rez_alfa_b, 'g-', cities, rez_alfa_w, 'r--')
g1.grid(True)
g2 = fig.add_subplot(212)
g2.set_title(
u'Geriausio (žalia) ir blogiausio (raudona) maršruto algoritmo beta parametras.'
)
g2.set_xlabel(u'Miestu skaičius')
g2.set_ylabel(u'beta')
g2.axis([cities[0], cities[len(cities) - 1], 0.0, 75.0])
g2.plot(cities, rez_beta_b, 'g-', cities, rez_beta_w, 'r--')
g2.grid(True)
tight_layout()
savefig('output/test_acs_sa_wrapper/test_acs_sa_wrapper_alfa_beta.png')
print 'test_acs_sa_wrapper pabaiga\n'
def plot_E615_xF(wdir, istep, data):
Xbins = []
#adjust bins
Xbins.append([0, 0.1])
Xbins.append([0.1, 0.2])
Xbins.append([0.2, 0.3])
Xbins.append([0.3, 0.4])
Xbins.append([0.4, 0.5])
Xbins.append([0.5, 0.6])
Xbins.append([0.7, 0.8])
Xbins.append([0.8, 0.9])
Xbins.append([0.9, 1])
Xbins = Xbins[::-1]
tab = {}
tab['pT'] = data['pT']
tab['xF'] = data['xF']
tab['value'] = data['value']
tab['alpha'] = data['alpha']
tab['thy'] = np.mean(data['prediction-rep'], axis=0)
tab['dthy'] = np.std(data['prediction-rep'], axis=0)
#for k in range(len(data['prediction-rep'])):
# tab['thy%d'%k]=data['prediction-rep'][k]
tab = pd.DataFrame(tab)
nrows, ncols = 8, 1
fig = py.figure(figsize=(ncols * 5, nrows * 1))
AX = {cnt: py.subplot(nrows, ncols, cnt) for cnt in range(1, 9)}
cnt = 0
for i in range(len(Xbins)):
d = tab.query('xF>%f and xF<%f' % (Xbins[i][0], Xbins[i][1]))
if len(d['xF']) != 0:
ii = np.argsort(d['pT'].values)
X = d['pT'].values[ii]
Y = d['thy'].values[ii]
YP = Y + d['dthy'].values[ii]
YM = Y - d['dthy'].values[ii]
if len(d['pT'].values) > 1:
cnt += 1
ax = AX[cnt]
ax.fill_between(X[:-1], (YM / Y)[:-1], (YP / Y)[:-1],
color='y',
alpha=0.3,
label=None)
ax.errorbar(d['pT'][:-1], (d['value'] / Y)[:-1],
(d['alpha'] / Y)[:-1],
zorder=10,
fmt='r.')
msg = r'$x_F\in[%0.1f,%0.1f]$' % (Xbins[i][0], Xbins[i][1])
ax.text(0.75, 0.8, msg, transform=ax.transAxes)
#ax.set_title('Q bin: '+str(Qbins[i][0])+'-'+str(Qbins[i][1]))
#ax.semilogy()
#ax.tick_params(axis='both',which='both',direction='in',pad=4,labelsize=18)
#ax.set_xlabel(r'$pT$',size=20)
#ax.xaxis.set_label_coords(0.95,-0.03)
#ax.text(0.06,0.06,r'$d^2\sigma/dQdpT$',transform=ax.transAxes,size=20)
#ax.text(0.78,0.91,r'$\bf E615 pT$',transform=ax.transAxes,size=17)
#py.tight_layout()
for _ in AX:
AX[_].set_xlim(2.0, 4.5)
AX[_].axhline(1, color='k', alpha=0.3)
if _ != 8: AX[_].set_xticklabels([])
AX[_].tick_params(axis='both',
which='both',
direction='in',
labelsize=18)
#AX[_].set_ylim(0,10)
#AX[_].set_yticks([1,5])
AX[4].set_ylabel(r'$\rm Ratio~to~theory$', size=30)
AX[4].yaxis.set_label_coords(-.1, 0.03)
AX[8].set_xlabel(r'$p_{\rm T}$', size=20)
AX[8].xaxis.set_label_coords(0.95, -0.03)
AX[8].set_xticks([2.5, 3, 3.5, 4])
AX[1].set_ylim(0, 11)
AX[2].set_ylim(0, 3)
AX[3].set_ylim(0, 3)
AX[4].set_ylim(0, 3)
AX[5].set_ylim(0, 4)
AX[6].set_ylim(0, 4)
AX[7].set_ylim(0, 6)
AX[8].set_ylim(0, 11)
AX[1].set_yticks([1, 5, 10])
AX[2].set_yticks([1, 2])
AX[3].set_yticks([1, 2])
AX[4].set_yticks([1, 2])
AX[5].set_yticks([1, 3])
AX[6].set_yticks([1, 3])
AX[7].set_yticks([1, 5])
AX[8].set_yticks([1, 5, 10])
AX[8].text(0.7,
0.2,
r'\boldmath{${\rm E615}$}',
transform=AX[8].transAxes,
size=17)
#AX[3].xaxis.set_label_coords(0.95,-0.03)
#ax.set_ylim(1e-38,1e-20)
#ax.set_xticks([0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8])
#ax.set_xticklabels([r'$0$',r'',r'$0.2$',r'',r'$0.4$',r'',r'$0.6$',r'',r''])
#ax.set_xlabel(r'$pT$',size=20)
#ax.xaxis.set_label_coords(0.95,-0.03)
#ax.set_ylim(2.5e-1,8e2)
#ax.set_ylabel(r'$d^2\sigma/dQdp_{\rm T}~\times %d^i$'%f,size=20)
#ax.text(0.1,0.23,r'$i=0$',transform=ax.transAxes,size=14)
#ax.text(0.5,0.92,r'$i=10$',transform=ax.transAxes,size=14)
#ax.text(0.7,0.9,r'\boldmath{${\rm E615}$}',transform=ax.transAxes,size=17)
py.tight_layout()
py.subplots_adjust(wspace=0.1, hspace=0.0)
py.savefig('%s/gallery/dy-pion-E615-xF-%d.png' % (wdir, istep))
def test_acs_sa_bf():
print '\ntest_acs_sa_bf pradžia'
bf = {'name': 'Pilnas perrinkimas'}
acs = {'name': 'Skruzdeliu kolonijos'}
sa = {'name': 'Simuliuoto \"atkaitinimo\"'}
methods = [bf, acs, sa]
cities = [6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 25, 30, 40, 50, 60, 80, 100]
bf_dist = []
bf_dur = []
acs_dist = []
acs_dur = []
sa_dist = []
sa_dur = []
for c in cities:
print '\t', c, 'miestai'
g = AntGraph.generate_complete_graph([1000, 1000], c)
if c <= 10:
bf_rez = g.brute_force_tsp()
bf_dist.append(bf_rez['distance'])
bf_dur.append(bf_rez['duration'])
acs_sim = AntColonySystem(g, OPTIMAL_ANTS, OPTIMAL_ANT_ITERATIONS, 0.5,
20)
acs_sim.find_path()
acs_rez = acs_sim.best_route
acs_dist.append(acs_rez['distance'])
acs_dur.append(acs_rez['duration'])
sasim = TSPSA(g)
sasim.t = 1000
sasim.decrement = 0.9995
sasim.simulate()
sa_rez = sasim.result
sa_dist.append(sa_rez['distance'])
sa_dur.append(sa_rez['duration'])
bf['distance'] = bf_dist
bf['duration'] = bf_dur
acs['distance'] = acs_dist
acs['duration'] = acs_dur
sa['distance'] = sa_dist
sa['duration'] = sa_dur
fig = figure(500)
g1 = fig.add_subplot(211)
g1.set_title(u'Rasti trumpiausi marsrutai')
g1.set_xlabel(u'Miestu skaičius')
g1.set_ylabel(u'Atstumas')
for m in methods:
lbl = "Paieskos metodas: " + m['name']
g1.plot(cities[:len(m['distance'])], m['distance'], label=unicode(lbl))
fontP = FontProperties()
fontP.set_size('small')
box1 = g1.get_position()
g1.set_position([box1.x0, box1.y0, box1.width * 0.8, box1.height])
lgd = g1.legend(prop=fontP, loc='center left', bbox_to_anchor=(1, 0.5))
g1.grid(True)
g2 = fig.add_subplot(212)
g2.set_title(u'Algoritmu skaičiavo laikai')
g2.set_xlabel(u'Miestu skaičius')
g2.set_ylabel(u'Laikas (s)')
for m in methods:
lbl = "Paieskos metodas: " + m['name']
g2.plot(cities[:len(m['duration'])], m['duration'], label=unicode(lbl))
fontP = FontProperties()
fontP.set_size('small')
box2 = g2.get_position()
g2.set_position([box2.x0, box2.y0, box2.width * 0.8, box2.height])
lgd = g2.legend(prop=fontP, loc='center left', bbox_to_anchor=(1, 0.5))
g2.grid(True)
tight_layout()
savefig('output/test_acs_sa_bf/test_acs_sa_bf.png',
bbox_extra_artists=(lgd, ),
bbox_inches='tight')
fig = figure(501)
g3 = fig.add_subplot(111)
g3.set_title(u'Algoritmu skaičiavo laikai')
g3.set_xlabel(u'Miestu skaičius')
g3.set_ylabel(u'Laikas (s)')
for m in methods:
if m['name'] == bf['name']:
continue
lbl = "Paieskos metodas: " + m['name']
g3.plot(cities[:len(m['duration'])], m['duration'], label=unicode(lbl))
fontP = FontProperties()
fontP.set_size('small')
box3 = g3.get_position()
g3.set_position([box3.x0, box3.y0, box3.width * 0.8, box3.height])
lgd = g3.legend(prop=fontP, loc='center left', bbox_to_anchor=(1, 0.5))
g3.grid(True)
tight_layout()
savefig('output/test_acs_sa_bf/test_acs_sa_bf2.png',
bbox_extra_artists=(lgd, ),
bbox_inches='tight')
print 'test_acs_sa_bf pabaiga\n'
def plot_E615_Q(wdir, istep, data):
Qbins = []
#adjust bins
Qbins.append([4.05, 4.5])
Qbins.append([4.5, 4.95])
Qbins.append([4.95, 5.4])
Qbins.append([5.4, 5.85])
Qbins.append([5.85, 6.75])
Qbins.append([6.75, 7.65])
Qbins.append([7.65, 9])
Qbins.append([9, 10.35])
Qbins.append([10.35, 11.7])
Qbins.append([11.7, 13.05])
nrows, ncols = 6, 1
fig = py.figure(figsize=(ncols * 5, nrows * 1))
AX = {cnt: py.subplot(nrows, ncols, cnt) for cnt in range(1, 7)}
tab = {}
tab['pT'] = data['pT']
tab['Q'] = data['Q']
tab['value'] = data['value']
tab['alpha'] = data['alpha']
tab['thy'] = np.mean(data['prediction-rep'], axis=0)
tab['dthy'] = np.std(data['prediction-rep'], axis=0)
#for k in range(len(data['prediction-rep'])):
# tab['thy%d'%k]=data['prediction-rep'][k]
tab = pd.DataFrame(tab)
cnt = 0
for i in range(len(Qbins)):
d = tab.query('Q>%f and Q<%f' % (Qbins[i][0], Qbins[i][1]))
if len(d['Q']) != 0:
ii = np.argsort(d['pT'].values)
X = d['pT'].values[ii]
Y = d['thy'].values[ii]
YP = Y + d['dthy'].values[ii]
YM = Y - d['dthy'].values[ii]
if len(d['pT'].values) > 1:
cnt += 1
if cnt > sorted(AX.keys())[-1]: continue
ax = AX[cnt]
msg = r'$Q\in[%0.1f,%0.1f]$' % (Qbins[i][0], Qbins[i][1])
ax.text(0.75, 0.8, msg, transform=ax.transAxes)
#p,=ax.plot(X[:-1],(Y*f**i)[:-1],alpha=0.3,label=None)
ax.fill_between(X[:-1], (YM / Y)[:-1], (YP / Y)[:-1],
color='y',
alpha=0.3,
label=None)
#for k in range(len(data['prediction-rep'])):
##for k in range(70):
# Yk=d['thy%d'%k].values[ii]
# #print k, np.sum(((d['value']-Yk)/d['alpha'])**2)/X.size
# ax.plot(X[:-1],(Yk*f**i)[:-1],ls='-'
# ,zorder=0
# ,color=p.get_color(),alpha=0.3)
ax.errorbar(d['pT'][:-1], (d['value'] / Y)[:-1],
(d['alpha'] / Y)[:-1],
zorder=10,
fmt='r.')
#ax.set_title('Q bin: '+str(Qbins[i][0])+'-'+str(Qbins[i][1]))
#ax.semilogy()
#ax.tick_params(axis='both',which='both',direction='in',pad=4,labelsize=18)
#ax.set_xlabel(r'$pT$',size=20)
#ax.xaxis.set_label_coords(0.95,-0.03)
#ax.text(0.06,0.06,r'$d^2\sigma/dQdpT$',transform=ax.transAxes,size=20)
#ax.text(0.78,0.91,r'$\bf E615 pT$',transform=ax.transAxes,size=17)
#py.tight_layout()
#ax.legend(loc=4)
#ax.semilogy()
#ax.tick_params(axis='both',which='both',direction='in',pad=4,labelsize=18)
for _ in AX:
AX[_].set_xlim(2.0, 4.5)
AX[_].set_ylim(0, 3)
AX[_].axhline(1, color='k', alpha=0.3)
if _ != 6: AX[_].set_xticklabels([])
AX[_].tick_params(axis='both',
which='both',
direction='in',
labelsize=18)
AX[_].set_yticks([1, 2])
AX[3].set_ylabel(r'$\rm Ratio~to~theory$', size=30)
AX[3].yaxis.set_label_coords(-.1, -0.03)
AX[6].set_xlabel(r'$p_{\rm T}$', size=20)
AX[6].xaxis.set_label_coords(0.95, -0.03)
AX[6].set_xticks([2.5, 3, 3.5, 4])
#AX[3].xaxis.set_label_coords(0.95,-0.03)
#ax.set_ylim(1e-38,1e-20)
#ax.set_xticks([0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8])
#ax.set_xticklabels([r'$0$',r'',r'$0.2$',r'',r'$0.4$',r'',r'$0.6$',r'',r''])
#ax.set_xlabel(r'$pT$',size=20)
#ax.xaxis.set_label_coords(0.95,-0.03)
#ax.set_ylim(2.5e-1,8e2)
#ax.set_ylabel(r'$d^2\sigma/dQdp_{\rm T}~\times %d^i$'%f,size=20)
#ax.text(0.1,0.23,r'$i=0$',transform=ax.transAxes,size=14)
#ax.text(0.5,0.92,r'$i=10$',transform=ax.transAxes,size=14)
AX[6].text(0.7,
0.4,
r'\boldmath{${\rm E615}$}',
transform=AX[6].transAxes,
size=17)
py.tight_layout()
py.subplots_adjust(wspace=0.1, hspace=0.0)
py.savefig('%s/gallery/dy-pion-E615-Q-%d.png' % (wdir, istep))