def PlotConfusionMatrix(cm, target_names, Title="Neuropeptides"):
'''
Works!
'''
import matplotlib as mpl
np.set_printoptions(suppress=True)
mpl.rc("figure", figsize=(12, 9))
hm = sns.heatmap(
cm,
cbar=True,
annot=True,
square=True,
fmt='d',
yticklabels=target_names,
xticklabels=target_names,
# cmap='Blues'
)
plt.fig(dpi=200)
plt.title('Confusion matrix: ' + Title)
plt.ylabel('Actual class')
plt.xlabel('Predicted class')
plt.tight_layout()
## plt.savefig('./images/confmat.png', dpi=300)
plt.show()
def visualize_source_distribution(sim, superpose=True, options=None):
if not mp.am_master():
return
options = options if options else def_src_options
for ns, s in enumerate(sim.sources):
sc, ss = s.center, s.size
J2 = sum(
[abs2(sim.get_source_slice(c, center=sc, size=ss)) for c in Exyz])
# M2=sum([abs2(sim.get_source_slice(c,center=sc,size=ss)) for c in Hxyz])
if superpose == False:
if ns == 0:
plt.ion()
plt.figure()
plt.title('Source regions')
plt.fig().subplot(len(sim.sources), 1, ns + 1)
plt.fig().title('Currents in source region {}'.format(ns))
# plot_data_curves(sim,superpose,[J2,M2],labels=['||J||','||M||'],
# styles=['bo-','rs-'],center=sc,size=ssu
plot_data_curves(sim,
center=sc,
size=ss,
superpose=superpose,
data=[J2],
labels=['J'],
options=options)
def plot_latent(vae_enc, vae_dec):
# output axa images
a = 30
# size of digit
dig_sz = 28
# scale
s = 2.0
# figure size
fig_sz = 15
fig = npy.zeros((dig_sz * a, dig_sz * a))
# linear scaling
x = npy.linspace(-s, s, a)
y = npy.linspace(-s, s, a)[::-1]
# for all rows and columns
for c, col in enumerate(y):
for r, row in enumerate(x):
smpl = npy.array([[row, col]])
xd = vae_dec.predict(smpl)
dig = xd[0].reshape(dig_sz, dig_sz)
figure[
c * dig_sz : (c + 1) * dig_sz,
r * dig_sz : (r + 1) * dig_sz,
] = dig
# plot figure
plot.fig(fig_sz=(fig_sz, fig_sz))
# start range
st = dig_sz // 2
# end range
en = a * dig_sz + 1 + st
pixel_range = npy.arange(st, en, dig_sz)
# sample range, x and y
x_range = npy.round(x, 1)
y_range = npy.round(y, 1)
# show plot
plot.imshow(fig, cmap="Greys_r")
plot.show()
def detect_edge_display(path):
# Load image as greyscale
image_gray = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
# Calculate median intensity
median_intensity = np.median(image_gray)
# Set thresholds to be one standard deviation above and below median intensity
lower_threshold = int(max(0, (1.0 - 0.33) * median_intensity))
upper_threshold = int(min(255, (1.0 + 0.33) * median_intensity))
# Apply canny edge detector
image_canny = cv2.Canny(image_gray, lower_threshold, upper_threshold)
# Show image
plt.imshow(image_canny, cmap='gray'), plt.axis("off")
plt.show()
plt.fig()
def plot_data_samples(MNIST, fig=None):
if fig is None: fig = plt.fig()
img_indices = np.random.random_integers(0, 5000, 10)
ax = fig.subplots(10, 10)
for i in range(10):
for j in range(10):
ax[i, j].imshow(MNIST["train" + str(i)][img_indices[j]].reshape(
(28, 28)))
ax[i, j].axis('off')
return fig, ax
def visualize_source_distribution(sim, superpose=True, options=None):
if not mp.am_master():
return
options=options if options else def_src_options
for ns,s in enumerate(sim.sources):
sc,ss=s.center,s.size
J2=sum([abs2(sim.get_source_slice(c,center=sc,size=ss)) for c in Exyz])
# M2=sum([abs2(sim.get_source_slice(c,center=sc,size=ss)) for c in Hxyz])
if superpose==False:
if ns==0:
plt.ion()
plt.figure()
plt.title('Source regions')
plt.fig().subplot(len(sim.sources),1,ns+1)
plt.fig().title('Currents in source region {}'.format(ns))
# plot_data_curves(sim,superpose,[J2,M2],labels=['||J||','||M||'],
# styles=['bo-','rs-'],center=sc,size=ssu
plot_data_curves(sim,center=sc,size=ss, superpose=superpose,
data=[J2], labels=['J'], options=options)
def plot(self):
fig=plt.fig(figsize=(10,6))
fig.subtitle("Pendulo Invertido",fontsize=14,fontweight='bold')
ax = a3d.Axes3D(fig,rect=[0,0,0.6,1])
ax.set_autoscale_on(False)
ax.set_xlim3d((0,30))
ax.set_ylim3d((-1,1))
ax.set_zlim3d((-1,1))
ax.set_xlabel(r'$t$')
ax.set_ylabel(r'$x$')
ax.set_zlabel(r'$y$')
ax.plot3D(self.tau, self.x(), self.y())
fig.subplots_adjust(left=0.66,bottom=0.05,top=0.95)
bx = fig.add_subplot(211)
bx.set_autoscale_on(True)
bx.set_ylabel(r'$\theta$')
bx.set_title('t')
bx.plot(self.tau,self.theta())
cx = fig.add_subplot(212)
cx.set_autoscale_on(True)
cx.set_ylabel(r'$\omega$')
cx.plot(self.tau,self.omega())
fig, dx = plt.subplots(4,1, figsize = (10,8), sharex=True)
dx[0].plot(self.tau, self.x(), label="x", color="blue")
dx[1].plot(self.tau, self.xprima(), label="x prima", color="green")
dx[2].plot(self.tau, self.theta(), label="theta", color="blue")
dx[3].plot(self.tau, self.thetap(), label=" theta prima", color="green")
dx[0].set_ylabel("x (m)")
dx[0].set_xlabel("tiempo (s)")
dx[1].set_ylabel("x prima (m/s)")
dx[1].set_xlabel("tiempo (s)")
dx[2].set_ylabel("theta (radianes)")
dx[2].set_xlabel("tiempo (s)")
dx[3].set_ylabel("theta prima (rad/s)")
dx[3].set_xlabel("tiempo (s)")
plt.show()
def create_grid_category_bar_chart(self, out_file):
# bar chart will show the block counts of the following:
# 1. Total
# 2. EoC != 0.5
# 3. EoC == 0.5
# 4. VoC == 0
# 5. VoC == 1
# 6. EoC == 0.5 & VoC == 1
bar_names = [
'Total', 'EoC != 0.5', 'EoC == 0.5', 'VoC == 0', 'VoC == 1',
'EoC == 0.5 &\nVoC == 1'
]
y = self.get_grid_category_counts()
colors = ['green', 'blue', 'purple', 'grey', 'teal', 'red']
fig(figsize=(8, 4))
plt.bar(bar_names, y, color=colors)
plt.title('Overall block categories')
plt.xlabel('Category')
plt.ylabel('Number of Blocks')
plt.tight_layout()
plt.savefig(out_file)
plt.clf()
print(f"wrote: {out_file}")
def PlotConfusionMatrix (cm,target_names,Title="Neuropeptides"):
'''
Works!
'''
import matplotlib as mpl
np.set_printoptions(suppress=True)
mpl.rc("figure", figsize=(12, 9))
hm = sns.heatmap(cm,
cbar=True,
annot=True,
square=True,
fmt='d',
yticklabels=target_names,
xticklabels=target_names,
# cmap='Blues'
)
plt.fig(dpi=200)
plt.title('Confusion matrix: '+Title)
plt.ylabel('Actual class')
plt.xlabel('Predicted class')
plt.tight_layout()
## plt.savefig('./images/confmat.png', dpi=300)
plt.show()
def plot_acc(x, train, val):
fig = plt.fig()
fig.suptitle('training and validation accuracy')
axis = fig.add_subplot(111)
axis.set_ylim(0, 50)
axis.set_xlim(0, 1)
plt.plot(x, train, c='r', label='Training Accuracy')
plt.plot(x, val, c='g', label='Validation Accuracy')
axis.set_xlabel('Epochs')
axis.set_ylabel('Accuracy')
axis.legend()
plt.savefig('/home/users/traviscwelch/cifarplot.png')
def plot_featured(*args, **kwargs):
"""
Wrapper for matplotlib.pyplot.plot() / errorbar().
Takes options:
* 'error': if true, use :func:`matplotlib.pyplot.errorbar` instead of :func:`matplotlib.pyplot.plot`. *\*args* and *\*\*kwargs* passed through here.
* 'fig': figure to use.
* 'figlabel': figure label.
* 'legend': legend location.
* 'toplabel': top label of plot.
* 'xlabel': x-label of plot.
* 'ylabel': y-label of plot.
"""
# Strip off options specific to plot_featured
toplabel = kwargs.pop('toplabel', None)
xlabel = kwargs.pop('xlabel', None)
ylabel = kwargs.pop('ylabel', None)
legend = kwargs.pop('legend', None)
error = kwargs.pop('error', None)
# save = kwargs.pop('save', False)
figlabel = kwargs.pop('figlabel', None)
fig = kwargs.pop('fig', None)
if figlabel is not None:
fig = _figure(figlabel)
elif fig is None:
try:
fig = _plt.gcf()
except:
fig = _plt.fig()
# Pass everything else to plot
if error is None:
_plt.plot(*args, **kwargs)
else:
_plt.errorbar(*args, **kwargs)
# Format plot as desired
_addlabel(toplabel, xlabel, ylabel, fig=fig)
if legend is not None:
_plt.legend(legend)
return fig
def plot_featured(*args,**kwargs):
"""Wrapper for matplotlib.pyplot.plot()/errorbar().
Example: plot_featured(x,y,[arguments to matplotlib.pyplot.plot()/errorbar()],
[toplabel=],[xlabel=],[ylabel=],
[legend=],
[error=]
)
"""
# Strip off options specific to plot_featured
toplabel = kwargs.pop('toplabel',None)
xlabel = kwargs.pop('xlabel',None)
ylabel = kwargs.pop('ylabel',None)
legend = kwargs.pop('legend',None)
error = kwargs.pop('error',None)
save = kwargs.pop('save',False)
figlabel = kwargs.pop('figlabel',None)
fig = kwargs.pop('fig',None)
if not (figlabel == None):
fig=_figure(figlabel)
elif fig==None:
try:
fig=_plt.gcf()
except:
fig=_plt.fig()
# Pass everything else to plot
if ( error == None ):
_plt.plot(*args,**kwargs)
else:
_plt.errorbar(*args,**kwargs)
# Format plot as desired
_addlabel(toplabel,xlabel,ylabel)
if ( legend != None ):
_plt.legend(legend)
return fig
import matplotlib.pyplot as plt
def F(x, t):
x, xp = X
xpp = -wO**2 * x - w0 / Q * xp
Xp = np.array([xp, xpp])
return Xp
w0 = 10.
Q = 1.
X = np.array([1., 0.])
N = 1000
t0, t1 = 0., 10.
t = np.linspace(t0, t1, N)
Lx = np.zeros(N) #séquence pour x
Lxp = np.zeros(N) #séquence pour xp
Lx[0], Lxp[0] = X #CI
for i in range(N):
h = t[n] - t[n - 1]
X = X + F(x, t[n - 1]) * h
Lx[n] = X[0]
Lxp[n] = X[1]
fig = plt.fig() #Chronogramme
plt.plot(t, Lx)
plt.show()
fig = plt.figure() #Portrait de phase
plt.plot()
plt.show()
Created on Sun Aug 13 22:19:22 2017
@author: VX
"""
# 圖像
import matplotlib.pyplot as plt
import numpy as np
def f(x):
return x**2
x = np.linspace(-10, 10, 100)
x1 = np.linspace(-10, 10, 5)
'''plt.fig、plt.plot 用法
plt.fig(num=編號, figsize=(長,寬))
plt.plot(x, y, color='顏色' ,linewidth=線粗, linestyle='樣式')
'''
plt.figure(num=2, figsize=(5, 8))
plt.plot(x, f(x), color='red', linewidth=5, linestyle='--')
# 下面演示簡易打法
plt.figure(3, (5, 8))
plt.plot(x, f(x), 'g--', linewidth=10)
# 在線上加上點，不需要分開打
# 太麻煩
plt.figure()
plt.plot(x, f(x), 'r')
"""
import numpy as np
import matplotlib.pyplot as plt
from info import Patch
from binary_search import BFS
patch = Patch('/Users/zhiyiwu/Documents/pharmfit/12061415.csv')
patch.scan()
cluster = patch[1]
opening = cluster.open_period
log_opening = np.log10(opening)
plt.hist(log_opening)
sep_open = sorted(BFS(log_opening[:, np.newaxis]))
print(sep_open)
shutting = cluster.shut_period
plt.hist(np.log10(shutting))
log_shutting = np.log10(shutting)
plt.hist(log_shutting)
sep_shut = sorted(BFS(log_shutting[:, np.newaxis]))
print(sep_shut)
period = cluster.period
fig = plt.fig()
for i in range(10):
ax = fig.add_subplot(10,1,i+1)
plt
import matplotlib.pyplot as plt
import sys
import chrombits
from matplotlib_venn import venn3, venn3_circles
import fileinput
import copy
arr=chrombits.ChromosomeLocationBitArrays(sys.argv[1])
A_arr = copy.deepcopy(arr)
B_arr = copy.deepcopy(arr)
C_arr = copy.deepcopy(arr)
A_arr.set_bits_from_file(sys.argv[2])
B_arr.set_bits_from_file(sys.argv[3])
C_arr.set_bits_from_file(sys.argv[4])
A_arr.Start_End_Bed(sys.argv[2])
B_arr.Start_End_Bed(sys.argv[3])
C_arr.Start_End_Bed(sys.argv[4])
union_table = A_arr.union(B_arr).union(C_arr)
print union_table
plt.fig()
v = venn3(subsets = union_table, set_labels = ("CTCF, BEAF, Su(HW)"))
plt.savefig("union_venn.png")
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import imageio
import os
data = np.loadtxt("rungekutta.dat")
t = data[0]
x = data[1]
y = data[2]
plt.fig()
plt.plot(x, y)
plt.title('Trayectoria de la particula')
plt.xlabel('Coordenada X')
plt.ylabel('Coordenada Y')
plt.savefig('LaraDaniel_final_15.pdf')
plt.show()
