def Number_by_Source(tweets):
tweets['statusSource_new'] = ''
for i in range(len(tweets['statusSource'])):
m = re.search('(?<=>)(.*)', tweets['statusSource'][i])
try:
tweets['statusSource_new'][i] = m.group(0)
except AttributeError:
tweets['statusSource_new'][i] = tweets['statusSource'][i]
# print(tweets['statusSource_new'].head())
tweets['statusSource_new'] = tweets['statusSource_new'].str.replace('</a>', ' ', case=False)
tweets['statusSource_new'] = tweets['statusSource_new'].str.replace('</a>', ' ', case=False)
#print(tweets[['statusSource_new','retweetCount']])
tweets_by_type= tweets.groupby(['statusSource_new'])['retweetCount'].sum()
#print(tweets_by_type)
tweets_by_type.transpose().plot(kind='bar',figsize=(10, 5))
#plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.title('Number of retweetcount by Source')
plt._show()
def drawLine(f1, f2, feature1TestData, feature2TestData, test_Y, w1, w2, b):
color = ['grey', 'blue']
# test_Y += 1
plt.scatter(feature1TestData,
feature2TestData,
c=test_Y,
cmap=matplotlib.colors.ListedColormap(color))
plt.xlabel(f1, fontsize=20)
plt.ylabel(f2, fontsize=20)
cb = plt.colorbar()
loc = np.arange(0, max(test_Y), max(test_Y) / float(len(color)))
cb.set_ticks(loc)
lbls = le.inverse_transform(np.unique(test_Y))
cb.set_ticklabels(lbls)
pointX, pointY = [], []
x2 = min(feature1TestData) - 5
x1 = ((-w2 * x2) - b) / w1
pointX.append(x2)
pointY.append(x1[0])
x1 = max(feature1TestData) + 5
x2 = ((-w1 * x1) - b) / w2
pointX.append(x1)
pointY.append(x2[0])
plt.plot(pointX, pointY, color='green', linewidth=2)
plt._show()
def plot_simplearray(x_array=None,
y_array=None,
x_label=None,
y_label=None,
label=None,
show=True):
"""
Plot a simple two dimensional array
:param x_array: elements of x axis
:type x_array: tuple
:param y_array: elements of y axis
:type y_array: tuple
:param x_label: x label
:type x_label: str
:param y_label: y label
:type y_label: str
:param label: label for the entire plot
:type label: str
:param show: shot the plot
:type show: bool
:return: plot a simple 2d array
"""
pyplot.plot(x_array, y_array, label=label)
pyplot.ylabel(y_label)
pyplot.xlabel(x_label)
pyplot.legend(loc='upper center', shadow=True)
if show is True:
pyplot._show()
def step1():
image = [[255, 7, 3], [212, 240, 4], [218, 216, 230]]
imageConv = sg.convolve(image, [[1., -1]], mode="valid")
plt.imshow(imageConv)
plt._show()
print(imageConv)
def drawLine(f1, f2, feature1TestData, feature2TestData, classTestData, w1, w2,
b):
color = ['grey', 'blue']
classTestData += 1
plt.scatter(feature1TestData,
feature2TestData,
c=classTestData,
cmap=matplotlib.colors.ListedColormap(color))
plt.xlabel(f1, fontsize=20)
plt.ylabel(f2, fontsize=20)
cb = plt.colorbar()
loc = np.arange(0, max(classTestData),
max(classTestData) / float(len(color)))
cb.set_ticks(loc)
lbls = le.inverse_transform(np.unique(classTestData))
cb.set_ticklabels(lbls)
# line
x2 = 2
x1 = ((-w2 * x2) - b) / w1 # X1, 2
point1 = [x1, 2]
x1 = 3
x2 = ((-w1 * x1) - b) / w2 # 3, X2
point2 = [3, x2]
plt.plot(point1, point2, color='red', linewidth=3)
plt._show()
def draw_hist():
mu = 100
sigma = 20
x = mu + sigma * np.random.randn(20000) # 样本数量
plt.hist(x, bins=100, color='green',
normed=True) # bins：显示有几个直方，normed是否对数据进行标准化
plt._show()
def plot_angle_trajectories(self,trajs):
plt.title("2-D DMP demonstration")
plt.xlabel("Time(t)")
plt.ylabel("Angle Position(deg)")
time = np.arange(0, self.tau + self.dt, self.dt)
for i in range(0,len(trajs)):
# plt.plot(time,des_trajs[i])
plt.plot(time,trajs[i])
plt._show()
def plotdna2(DNA, dyads, nucl, plt_range=500):
'''
Plot DNA and show the location of the dyads
Parameters
----------
DNA : instance of HelixPose
the DNA to be plotted
dyads : list
list of bp indices of the dyads
plt_range : int, optional
xyz range (nm), default is 50 nm
'''
#plt.close()
rb_width = 2.0
rb_width = 2.0
color = 'kb'
rb_vec = DNA.rb_vec
coord = DNA.coord
NRL = 198
#NRL = dyads[1]-dyads[0]
fig = plt.figure()
ax = p3.Axes3D(fig)
ax.scatter3D(coord[dyads, 0], coord[dyads, 1], coord[dyads, 2])
ax.plot3D(coord[:, 0], coord[:, 1], coord[:, 2], color[0] + '-')
color2 = 'kbgr'
a = 100
for d in dyads:
fixed_frame = np.transpose(DNA.frames[d + nucl.fixed_i[7] - NRL % 2])
fixed_origin = coord[d + nucl.fixed_i[7] - NRL % 2]
Q = nuc.of2coords(fixed_origin, fixed_frame)
P = nuc.of2coords(nucl.coords[nucl.d_index+nucl.fixed_i[7]], \
np.transpose(nucl.frames[nucl.d_index+nucl.fixed_i[7]]))
tf = nuc.get_transformation(P, Q)
n_coord = nuc.apply_transf_coords(nucl.n_coords, tf)
for i in range(1, 4):
ax.plot3D([n_coord[0,0],n_coord[0,0]+a*(n_coord[i,0]-n_coord[0,0])],\
[n_coord[0,1],n_coord[0,1]+a*(n_coord[i,1]-n_coord[0,1])],\
[n_coord[0,2],n_coord[0,2]+a*(n_coord[i,2]-n_coord[0,2])], color2[i]+'-')
ax.scatter3D(coord[nucl.fixed_i + d, 0], coord[nucl.fixed_i + d, 1],
coord[nucl.fixed_i + d, 2])
ax.set_xlabel('X (nm)')
ax.set_ylabel('Y (nm)')
ax.set_zlabel('Z (nm)')
if plt_range is None:
plt_range = DNA.n_bp * 0.34
ax.set_xlim3d(-plt_range / 2, plt_range / 2)
ax.set_ylim3d(-plt_range / 2, plt_range / 2)
ax.set_zlim3d(0, plt_range)
plt._show()
def plotScatterFigure(X, Y, filename, xlabel, ylabel):
plt.scatter(X, Y, c="red", alpha=0.5, edgecolors="black")
plt.title(xlabel + "_VS_" + ylabel)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.savefig(filename)
plt._show()
pass
def draw_confusion_matrix(y_test, y_predict, c1, c2):
lbls = []
if c1 == -1:
c1Test = y_test[0:20]
lbls.append("Iris-setosa")
elif c1 == 0:
c1Test = y_test[20:40]
lbls.append("Iris-versicolor")
elif c1 == 1:
c1Test = y_test[40:60]
lbls.append("Iris-virginica")
if c2 == -1:
c2Test = y_test[0:20]
lbls.append("Iris-setosa")
elif c2 == 0:
c2Test = y_test[20:40]
lbls.append("Iris-versicolor")
elif c2 == 1:
c2Test = y_test[40:60]
lbls.append("Iris-virginica")
desired = np.append(c1Test, c2Test).reshape(40, 1)
# print("classTest type", type(classTest))
# print("classTest shape", classTest.shape)
desired.sort(axis=0)
# print("desired after sort")
# print(desired)
# print("\n\ny predict")
# print(y_predict)
# print("type y_predict in fn confusion", type(y_predict[0, 0]))
confusion = confusion_matrix(desired,
y_predict) # , labels=["AAAAAAA", "BBBBBB"])
print("confusion", confusion)
# print("confusion shape", confusion.shape)
# print("confusion type", type(confusion))
#
# print("lbls")
# print(lbls)
df_cm = pd.DataFrame(list(confusion),
index=[i for i in lbls],
columns=[i for i in lbls])
# print("\ndf_cm", df_cm)
# print("df_cm shape", df_cm.shape)
# print("df_cm type", type(df_cm))
plt.figure(figsize=(10, 7))
sn.heatmap(df_cm, annot=True)
plt._show()
def draw_confusion_matrix(y_test, y_predict):
lbls = ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
confusion = confusion_matrix(y_test, y_predict)
print("Confusion Matrix:")
print(confusion)
df_cm = pd.DataFrame(list(confusion), index=[i for i in lbls], columns=[i for i in lbls])
plt.figure(figsize=(10, 7))
sn.heatmap(df_cm, annot=True)
plt._show()
def wordcloud_by_province_Demonetization(tweets):
stopwords = set(STOPWORDS)
stopwords.add("https")
stopwords.add("00A0")
stopwords.add("00BD")
stopwords.add("00B8")
stopwords.add("ed")
stopwords.add("demonetization")
stopwords.add("Demonetization co")
stopwords.add("lakh")
wordcloud = WordCloud(background_color="white",stopwords=stopwords,random_state = 2016).generate(" ".join([i for i in tweets['text_new'].str.upper()]))
plt.imshow(wordcloud)
plt.axis("off")
plt.title("Demonetization")
plt._show()
def Number_by_Source_bis(tweets):
tweets['statusSource_new2'] = ''
for i in range(len(tweets['statusSource_new'])):
if tweets['statusSource_new'][i] not in ['Twitter for Android ','Twitter Web Client ','Twitter for iPhone ']:
tweets['statusSource_new2'][i] = 'Others'
else:
tweets['statusSource_new2'][i] = tweets['statusSource_new'][i]
#print(tweets['statusSource_new2'])
tweets_by_type2 = tweets.groupby(['statusSource_new2'])['retweetCount'].sum()
tweets_by_type2.transpose().plot(kind='pie',figsize=(6.5, 4))
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.title('Number of retweetcount by Source bis')
plt._show()
def wordcloud_by_province_narendramodi(tweets):
a = pd.DataFrame(tweets['text'].str.contains("narendramodi").astype(int))
b = list(a[a['text']==1].index.values)
stopwords = set(STOPWORDS)
stopwords.add("narendramodi")
stopwords.add("https")
stopwords.add("00A0")
stopwords.add("00BD")
stopwords.add("00B8")
stopwords.add("ed")
stopwords.add("demonetization")
stopwords.add("Demonetization co")
stopwords.add("lakh")
wordcloud = WordCloud(background_color="white",stopwords=stopwords,random_state = 2016).generate(" ".join([i for i in tweets.ix[b,:]['text_new'].str.upper()]))
plt.imshow(wordcloud)
plt.axis("off")
plt.title("Tweets with word 'narendramodi'")
plt._show()
def PlotGraph(self, hoursAccuracyTuplesList, xlabel, ylabel, classifier, postType):
import matplotlib.pyplot as plt
import numpy as np
accuracies = None
hourList = []
for idx, hoursAccuracyTuples in enumerate(hoursAccuracyTuplesList):
hour, AccuracyListFrom2Fold = hoursAccuracyTuples
hourList.append(hour)
if idx == 0:
accuracies = np.array([AccuracyListFrom2Fold])
else:
accuracies = np.concatenate((accuracies, [AccuracyListFrom2Fold]), axis=0)
averageAccuracies = np.average(accuracies, axis=0)
indexOfMaxAvg = np.argmax(averageAccuracies)
kValueForMaxAvg = indexOfMaxAvg + 2
avgMaxAccuracies = np.array(
[[accuracies[rowIndex][indexOfMaxAvg] for rowIndex in range(0, len(accuracies))]])
maxHourIndex = np.argmax(avgMaxAccuracies)
avgMaxAccuracies = avgMaxAccuracies * 100
plt.plot(hourList, avgMaxAccuracies[0], c="red")
plt.axis([0, 28, 30, 100])
#plt.annotate('Best k-fold value ' + str(kValueForMaxAvg),
# xytext=(hourList[maxHourIndex]-3, avgMaxAccuracies[0][maxHourIndex] - 10),
# )
plt.title(classifier + "K = " + str(kValueForMaxAvg) +": "+ xlabel + "_vs_" + ylabel)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.savefig("Images_" + postType +"/"+classifier + "Classifier.png")
plt._show()
pass
def plot_simplearray(x_array, y_array, x_label, y_label, label, show=True):
# print label
# fig, ax = pyplot.subplots()
pyplot.plot(x_array, y_array, label=label)
pyplot.ylabel(y_label)
pyplot.xlabel(x_label)
# Now add the legend with some customizations.
pyplot.legend(loc='upper center', shadow=True)
#
# # The frame is matplotlib.patches.Rectangle instance surrounding the legend.
# frame = legend.get_frame()
# frame.set_facecolor('0.90')
#
# # Set the fontsize
# for label in legend.get_texts():
# label.set_fontsize('large')
#
# for label in legend.get_lines():
# label.set_linewidth(1.5) # the legend line width
if show is True:
pyplot._show()
def draw_confusion_matrix(y_test, y_predict, c1, c2):
lbls = []
if c1 == -1:
lbls.append("Iris-setosa")
elif c1 == 0:
lbls.append("Iris-versicolor")
elif c1 == 1:
lbls.append("Iris-virginica")
if c2 == -1:
lbls.append("Iris-setosa")
elif c2 == 0:
lbls.append("Iris-versicolor")
elif c2 == 1:
lbls.append("Iris-virginica")
print("y predict", y_predict)
print(" len y predict", len(y_predict))
print("y_test", y_test)
print("y_test", len(y_test))
confusion = confusion_matrix(y_test, y_predict)
print("confusion")
print(confusion)
#
# print("lbls")
# print(lbls)
df_cm = pd.DataFrame(list(confusion),
index=[i for i in lbls],
columns=[i for i in lbls])
# print("\ndf_cm", df_cm)
# print("df_cm shape", df_cm.shape)
# print("df_cm type", type(df_cm))
plt.figure(figsize=(10, 7))
sn.heatmap(df_cm, annot=True)
plt._show()
plt.pie(y)
plt.title("pie")
plt.subplot(234)
plt.bar(x, y)
plt.title("bar")
# 2D data
import numpy as np
delta = 0.025
x = y = np.arange(-3.0, 3.0, delta)
X, Y = np.meshgrid(x, y)
Z = Y**2 + X**2
plt.subplot(235)
plt.contour(X, Y, Z)
plt.colorbar()
plt.title("contour")
# read image
import matplotlib.image as mpimg
img = mpimg.imread('marvin.jpg')
plt.subplot(236)
plt.imshow(img)
plt.title("imshow")
plt.savefig("matplot_sample.jpg")
plt._show("matplot_sample.jpg")
# Reference : http://www.cnblogs.com/vamei/archive/2013/01/30/2879700.html
import matplotlib
matplotlib.use('TkAgg')
matplotlib.get_backend()
matplotlib.get_configdir
import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [5, 6, 7])
plt._show()
#import gensim
#gensim.download()
#import nltk
#nltk.download('averaged_perceptron_tagger')
#from nltk.tokenize import sent_tokenize, word_tokenize
#exapleText='My name is Ujwal Singh. I can Cook food. I can Drive Car. I am a smart guy'
#print(sent_tokenize(exapleText))
#print(word_tokenize(exapleText))
def drawIrisData():
#################x1,X2#################
x1 = X1_train.append(X1_test)
x2 = X2_train.append(X2_test)
label = np.append(labeled_Y_train + 1, labeled_Y_test + 1)
colors = ['grey', 'blue', 'purple']
plt.scatter(x1, x2, c=label, cmap=matplotlib.colors.ListedColormap(colors))
plt.xlabel('X1', fontsize=20)
plt.ylabel('X2', fontsize=20)
cb = plt.colorbar()
loc = np.arange(0, max(label), max(label) / float(len(colors)))
cb.set_ticks(loc)
cb.set_ticklabels(['C1-setosa', 'C2-versicolor', 'C3-virginica'])
plt._show()
#################x1,X3#################
x1 = X1_train.append(X1_test)
x3 = X3_train.append(X3_test)
label = np.append(labeled_Y_train + 1, labeled_Y_test + 1)
colors = ['grey', 'blue', 'purple']
plt.scatter(x1, x3, c=label, cmap=matplotlib.colors.ListedColormap(colors))
plt.xlabel('X1', fontsize=20)
plt.ylabel('X3', fontsize=20)
cb = plt.colorbar()
loc = np.arange(0, max(label), max(label) / float(len(colors)))
cb.set_ticks(loc)
cb.set_ticklabels(['C1-setosa', 'C2-versicolor', 'C3-virginica'])
plt._show()
#################x1,X4#################
x1 = X1_train.append(X1_test)
x4 = X4_train.append(X4_test)
label = np.append(labeled_Y_train + 1, labeled_Y_test + 1)
colors = ['grey', 'blue', 'purple']
plt.scatter(x1, x4, c=label, cmap=matplotlib.colors.ListedColormap(colors))
plt.xlabel('X1', fontsize=20)
plt.ylabel('X4', fontsize=20)
cb = plt.colorbar()
loc = np.arange(0, max(label), max(label) / float(len(colors)))
cb.set_ticks(loc)
cb.set_ticklabels(['C1-setosa', 'C2-versicolor', 'C3-virginica'])
plt._show()
#################x2,X3#################
x2 = X2_train.append(X2_test)
x3 = X3_train.append(X3_test)
label = np.append(labeled_Y_train + 1, labeled_Y_test + 1)
colors = ['grey', 'blue', 'purple']
plt.scatter(x2, x3, c=label, cmap=matplotlib.colors.ListedColormap(colors))
plt.xlabel('X2', fontsize=20)
plt.ylabel('X3', fontsize=20)
cb = plt.colorbar()
loc = np.arange(0, max(label), max(label) / float(len(colors)))
cb.set_ticks(loc)
cb.set_ticklabels(['C1-setosa', 'C2-versicolor', 'C3-virginica'])
plt._show()
#################x2,X4#################
x2 = X2_train.append(X2_test)
x4 = X4_train.append(X4_test)
label = np.append(labeled_Y_train + 1, labeled_Y_test + 1)
colors = ['grey', 'blue', 'purple']
plt.scatter(x2, x4, c=label, cmap=matplotlib.colors.ListedColormap(colors))
plt.xlabel('X2', fontsize=20)
plt.ylabel('X4', fontsize=20)
cb = plt.colorbar()
loc = np.arange(0, max(label), max(label) / float(len(colors)))
cb.set_ticks(loc)
cb.set_ticklabels(['C1-setosa', 'C2-versicolor', 'C3-virginica'])
plt._show()
#################x3,X4#################
x3 = X3_train.append(X3_test)
x4 = X4_train.append(X4_test)
label = np.append(labeled_Y_train + 1, labeled_Y_test + 1)
colors = ['grey', 'blue', 'purple']
plt.scatter(x3, x4, c=label, cmap=matplotlib.colors.ListedColormap(colors))
plt.xlabel('X3', fontsize=20)
plt.ylabel('X4', fontsize=20)
cb = plt.colorbar()
loc = np.arange(0, max(label), max(label) / float(len(colors)))
cb.set_ticks(loc)
cb.set_ticklabels(['C1-setosa', 'C2-versicolor', 'C3-virginica'])
plt._show()
def __pyplot_show(*args, **kwargs):
return pyplot._show(*args, **kwargs)
def plotvlines(cdfdata, labels, filename,gfolder, xlabel='',ylabel='', islog=False,figwidth=6, figlen=4, resolution=0.0001, neglect=[], showlen=False,colors=True, legendposition = 'lower right', title =None,xlimit=None,topdata=None, mindatalen = 1,extension='.pdf',xrange=None):
return
fig = plt.figure(figsize=(figwidth,figlen))
plt.axvline(x=5, ymax=10)
plt.axvline(x=10, ymax=100)
plt.axvline(x=0, ymax=5)
plt.show()
plt.ylim([0,100])
empty = True
markervalues = ['*','o','.']#,'_', '^','x','s','v', '<', '>', '1', '2', '3', '4', '8', 's', 'p', 'D', 'd', '_' ]
colorvalues = ['b','g', 'r', 'k', 'y', 'm', 'c','#CCCCCC', '#808080']
dashes = [[]]#[3,3,3,3], [],[3, 3, 3, 3], [], [3, 3, 3, 3], [5, 1, 5, 1]]
leng = 0
maxx = 0
try:
if topdata != None:
mindatalen = min(sorted([len(x) for x in cdfdata], reverse=True)[:topdata])
except:
mindatalen = 1
i = 0
numdata = len(labels)
for v in range(len(labels)):
vindex = v
color = colorvalues[v%len(colorvalues)]
if topdata != None:
color = colorvalues[i%len(colorvalues)]
marker = markervalues[v%len(markervalues)]
dash = dashes[v%len(dashes)]
label = labels[v]
dat = cdfdata[v]
i+=1
#try:
for d in dat:
print dat.index(d)*numdata+vindex,d
plt.axvline(x=d, ymax=float(d/max(dat)))
continue
plt.axvline(x=dat.index(d)*numdata+vindex,ymin=0,ymax=d, color=color)
#, label=label,color=color,dashes=dash)#, marker=marker)#, dashes=dash)#+'('+str(len(data))+')'
#except:
#pass
#plt.legend(loc=legendposition, fontsize=10)
if xlimit != None:
plt.xlim(xlimit)
if xrange != 0:
plt.xlim(xrange)
def setFigLinesBW(fig):
"""
Take each axes in the figure, and for each line in the axes, make the
line viewable in black and white.
"""
for ax in fig.get_axes():
setAxLinesBW(ax)
if not colors:
setFigLinesBW(fig)
plt.locator_params(nbins=5, axis='x')
if title != None:
plt.title(title)
plt.ylabel(ylabel)
plt.xlabel(xlabel)
title = filename.replace(' ','')
if islog:
plt.gca().set_xscale('log')
plt.tight_layout()
plt.savefig(gfolder+'/'+title+extension)
plt._show()
plt.close()
import matplotlib
import matplotlib.pyplot as pt
import numpy as np
from mpl_toolkits.mplot3d import axes3d
fig = pt.figure()
chart = fig.add_subplot(1, 1, 1, projection='3d')
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
y = [1, 2, 3, 5, 7, 9, 3, 2, 1, 0]
z = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
dx = np.ones(10)
dy = np.ones(10)
dz = [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
chart.set_xlabel("X axis")
chart.set_ylabel('Y axis')
chart.set_zlabel('Z axis')
chart.bar3d(x, y, z, dx, dy, dz, color="red")
pt._show()
def CreateFigures(LTM=10):
urk = np.loadtxt('SolutionDet.txt')
ussls = np.loadtxt('SolutionSto.txt')
uas = np.loadtxt('SolutionStoAs.txt')
t= ussls[:,0]
#Pots Vs Time
plt.close()
w=1.0
fig_width_pt =538.58*w # Get this from LaTeX using \showthe\columnwidth(190mm)
inches_per_pt = 1.0/72.27 # Convert pt to inch
golden_mean = (np.sqrt(5) - 1.0) / 2.0 # Aesthetic ratio
fig_width = fig_width_pt * inches_per_pt # width in inches
fig_height = fig_width*golden_mean # height in inches
fig_size = [fig_width, fig_height]
params = {'backend': 'ps',
'axes.labelsize': 10,
'text.fontsize': 10,
'legend.fontsize': 8,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'text.usetex': True,
'figure.figsize': fig_size}
rcParams.update(params)
##
fig1, ax1 = plt.subplots()
#plt.title('Jerez-Chen Model Experimental Parameters')
OC_line = mlines.Line2D([], [], color='#000000', linestyle='-', lw=2, marker='', markersize=1)
OB_line = mlines.Line2D([], [], color='#666666', linestyle='-', lw=2, marker='', markersize=1)
sOC_line = mlines.Line2D([], [], color='#666666', linestyle='-.', lw=4, marker='', markersize=1)
sOB_line = mlines.Line2D([], [], color='#000000', linestyle='-.', lw=4, marker='', markersize=1)
#
ax1.plot(urk[:, 0], urk[:, 1],
color='#000000',
linestyle='-',
lw=2,
marker='',
label='osteoclast'
)
ax1.plot(ussls[:, 0], ussls[:, 1],
color='#666666',
linestyle='-.',
lw=3,
marker='',
label='Sto osteoclast'
)
ax1.set_xlabel('time (days)')
ax1.set_ylabel('OCs (u1)', color='k')
ax2 = ax1.twinx()
#
ax2.plot(urk[:, 0], urk[:, 2],
color='#666666',
lw = 2,
linestyle='-',
marker='',
label='osteoblast'
)
ax2.plot(ussls[:, 0], ussls[:, 2],
color='#000000',
linestyle='-.',
lw=3,
marker='',
label='Sto osteoblast'
)
ax2.set_xlabel('time (days)')
ax2.set_ylabel(r'OBs (u2)')
ax2.grid(True)
plt.legend([OC_line, OB_line, sOC_line, sOB_line],
["OCs", "OBs", "stoOCs", "stoOBs"],
bbox_to_anchor=(0., 1.02, 1., .5),
loc=3,
ncol=4,
mode="expand",
borderaxespad=0.
)
plt.savefig("CellsTime.eps")
plt._show()
#
#
#Phase Potrait
fig1, ax3 = plt.subplots()
#plt.title('Stochastic Power Law Bone Remodeling')
ax3.set_xlim(-1, 13)
ax3.set_ylim(-50, urk[:,2].max()*1.15)
ax3.plot(urk[:, 1], urk[:, 2],
color='#000000',
linestyle='-',
lw=3,
label='Deterministic'
)
ax3.plot(StoPlbrmJC.Ubar[0], StoPlbrmJC.Ubar[1],
'o',
mfc='none',
ms=8,
#label=r'$\bar{u}$'
)
ax3.plot(StoPlbrmJC.Uzero[0], StoPlbrmJC.Uzero[1],
's',
mfc='#666666',
ms=8
)
ax3.plot(ussls[:, 1], ussls[:, 2],
color='#666666',
ms=1,
label='Stochastic Short Time'
)
ax3.plot(uas[:, 1], uas[:, 2],
color='#666666',
lw=1,
linestyle='-.',
label='Long Time Stochastic')
#ax3.plot(uas[0,0], uas[0,1], 'x', mfc='green', ms=12)
ax3.set_xlabel(r'$u_1$')
ax3.set_ylabel(r'$u_2$')
ax3.grid(True)
ax3.legend(loc=0)
plt.savefig("PhasePotrait.eps")
plt.show()
#
#
fig = plt.figure()
#ax = fig.gca(projection='3d')
ax = Axes3D(fig)
ax.set_xlabel(r'$u_1$')
ax.set_ylabel(r'$t$')
ax.set_zlabel(r'$u_2$')
ax.view_init(elev=20., azim=195)
#
ax.plot(urk[:, 1], t, urk[:, 2],
color='#000000',
lw=1,
linestyle='-',
label='Deterministic'
)
ax.plot(ussls[:, 1], t, ussls[:, 2],
color='#666666',
lw=1,
linestyle='-',
#label='Sto Short Time'
)
ax.plot(urk[:, 1], LTM*t[-1]+t, urk[:, 2],
color='#000000',
lw=1,
linestyle='-',
#label='Det Long Time'
)
ax.plot(uas[:, 1], LTM*t[-1]+t, uas[:, 2],
color='#666666',
lw=1,
linestyle='-',
label='Stochastic'
)
ax.legend()
plt.savefig("PhasePotrait3d.eps")
plt.show()
nsamp=[90,90,90,90,90,90,90,699]
ncancer=[0,2,1,5,2,3,6,9]
dose=[0,1,2,4,1,2,4,0]
if (False):
#all schwannomas in males, no control
nsamp=[90,90,90,90,90,90,90]
ncancer=[3,3,5,7,4,4,7]
dose=[0,1,2,4,1,2,4]
param_guess=numpy.asarray([0.03,0.03])
step_size=numpy.asarray([0.01,0.01])
mychain=run_chain(param_guess,20000,step_size,nsamp,ncancer,dose)
plt.clf()
plt.plot(mychain[:, 0], mychain[:, 1], ".")
ax = plt.gca()
ax.set_yscale("log")
ax.set_xscale("log")
plt._show()
