plot-utilities
Plot Utilities
We provide a jupyter notebook to plot the results of matrix processing. notebook
# Initialize Notebook
from IPython.core.display import HTML,Image
#%run ../library/v1.0.5/init.ipy
HTML('''<script> code_show=true; function code_toggle() { if (code_show){ $('div.input').hide(); } else { $('div.input').show(); } code_show = !code_show } $( document ).ready(code_toggle); </script> <form action="javascript:code_toggle()"><input type="submit" value="Toggle Code"></form>''')cd ~/projects/exSEEK_training/import gc, argparse, sys, os, errno
from functools import reduce
%pylab inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm_notebook as tqdm
import scipy
import sklearn
from scipy.stats import pearsonr
import warnings
warnings.filterwarnings('ignore')
from bokeh.io import output_notebook, show
output_notebook()
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.metrics import roc_curve,roc_auc_score,auc,precision_recall_curve,average_precision_score
from sklearn.preprocessing import RobustScaler,MinMaxScaler,StandardScaler
from sklearn.neighbors import NearestNeighbors
from bokeh.palettes import Category20c
from ipywidgets import interact,interactive, FloatSlider,IntSlider, RadioButtons,Dropdown,Tab,Textload plotting functions
embed pdf; std_plot; display dataframe
def embed_pdf_figure(width='640', height='480', title='Image'):
data = BytesIO()
plt.savefig(data, format='pdf', metadata={'Title': title})
data = data.getvalue()
data = 'data:application/pdf;base64,'+ str(b64encode(data), encoding='utf-8')
display(HTML('<object width="{}" height="{}" data="{}" download="{}.pdf"></object>'.format(
width, height, data, title)))
plt.close()
from matplotlib.backends.backend_pdf import PdfPages, PdfFile
from IPython.display import HTML, display, FileLink
from base64 import b64encode, b64decode
from io import StringIO, BytesIO
from contextlib import contextmanager
@contextmanager
def embed_pdf_pages(width=960, height=480, title='Image'):
data = BytesIO()
try:
pdf = PdfPages(data, metadata={'Title': title})
yield pdf
finally:
pdf.close()
data = data.getvalue()
data = 'data:application/pdf;base64,'+ str(b64encode(data), encoding='utf-8')
display(HTML('<object width="{}" height="{}" data="{}"></object>'.format(width, height, data)))
plt.close()
@contextmanager
def embed_pdf_data(width=640, height=480, title='Image'):
try:
data = BytesIO()
yield data
finally:
data = data.getvalue()
data = 'data:application/pdf;base64,'+ str(b64encode(data), encoding='utf-8')
display(HTML('<object width="{}" height="{}" data="{}"></object>'.format(width, height, data)))
plt.close()
def gradient_func(val):
return '<span style="background: linear-gradient(90deg, #d65f5f {0}%, transparent 0%)">{0:.3f}</span>'.format(val)
def display_dataframe(df, filename=None, encoding='utf-8', format='csv', type='button',gradientfunc=False, **kwargs):
#display(df)
#if isinstance(df, pd.DataFrame):
# display(df.style.set_caption(filename))
#else:
if gradientfunc == False:
display(df.style.set_caption(filename))
else:
display(df.style.format(gradient_func).set_caption(filename))
if filename is None:
filename = "dataframe"
if format == 'csv':
data = df.to_csv(**kwargs)
mime_type = 'text/csv'
filename = filename + '.csv'
elif format == 'tsv':
data = df.to_csv(**kwargs)
mime_type = 'text/plain'
filename = filename + '.txt'
else:
raise ValueError('unknown file format: {}'.format(format))
data = 'data:{mime_type};base64,'.format(mime_type=mime_type) + str(b64encode(bytes(data, encoding=encoding)), encoding=encoding)
if type == 'hyperlink':
display(HTML('<a href=" " download={filename} target="_blank">{filename}</a >'.format(
mime_type=mime_type, filename=filename, data=data)))
elif type == 'button':
button_id = 'button_{}'.format(np.random.randint(1000000000))
display(HTML(r'<input type="button" id="{0}" value="Download">'.format(button_id)))
display(HTML('''<script>
document.getElementById("{button_id}").addEventListener("click", function(event){{
var filename = "{filename}";
var data = "{data}";
const element = document.createElement('a');
element.setAttribute('href', data);
element.setAttribute('download', filename);
element.style.display = 'none';
document.body.appendChild(element);
element.click();
document.body.removeChild(element);
}});
</script>'''.format(button_id=button_id, filename=filename, data=data)))
def log_transfrom(data,small=0.01):
return np.log2(data+small)fontsize = 6.5
fontscale = 1
fontweight = 'normal'
fonttitle = {'family':'Arial',
'weight' : fontweight,
'size' : fontsize*fontscale}
fontlabel = {'family':'Arial',
'weight' : fontweight,
'size' : fontsize*fontscale}
fontticklabel = {'family':'Arial',
'weight' : fontweight,
'size' : fontsize*fontscale}
fontlegend = {'family':'Arial',
'weight' : fontweight,
#'linewidth':0.5,
'size' : fontsize*fontscale}
fontcbarlabel = {'family':'Arial',
'weight' : fontweight,
#'Rotation' : 270,
#'labelpad' : 25,
'size' : fontsize*fontscale}
fontcbarticklabel = {'family':'Arial',#Helvetica
'weight' : fontweight,
'size' : (fontsize-1)*fontscale}
def std_plot(ax,xlabel=None,ylabel=None,title=None,
legendtitle=None,bbox_to_anchor=None,
labelspacing=0.2,borderpad=0.2,handletextpad=0.2,legendsort=False,markerscale=None,
xlim=None,ylim=None,
xbins=None,ybins=None,
cbar=None,cbarlabel=None,
moveyaxis=False,sns=False,left=True,rotation=None,xticklabel=None,legendscale=True,h=None,l=None,row=1,legend_adj=True,**kwards):
# height = 2 font = 6.5
def autoscale(fig):
if isinstance(fig,matplotlib.figure.Figure):
width,height = fig.get_size_inches()
elif isinstance(fig,matplotlib.axes.Axes):
width,height = fig.figure.get_size_inches()
fontscale = height/(2*row)
if width/fontscale > 8:
warnings.warn("Please reset fig's width. When scaling the height to 2 in, the scaled width '%.2f' is large than 8"%(width/fontscale),UserWarning)
return fontscale
class fontprop:
def init(self,fonttitle=None,fontlabel=None,fontticklabel=None,fontlegend=None,fontcbarlabel=None,fontcbarticklabel=None):
self.fonttitle = fonttitle
self.fontlabel = fontlabel
self.fontticklabel = fontticklabel
self.fontlegend = fontlegend
self.fontcbarlabel = fontcbarlabel
self.fontcbarticklabel = fontcbarticklabel
def update(self,fontscale):
self.fonttitle['size'] = self.fonttitle['size']*fontscale
self.fontlabel['size'] = self.fontlabel['size']*fontscale
self.fontticklabel['size'] = self.fontticklabel['size']*fontscale
self.fontlegend['size'] = self.fontlegend['size']*fontscale
self.fontcbarlabel['size'] = self.fontcbarlabel['size']*fontscale
self.fontcbarticklabel['size'] = self.fontcbarticklabel['size']*fontscale
def reset(self,fontscale):
self.fonttitle['size'] = self.fonttitle['size']/fontscale
self.fontlabel['size'] = self.fontlabel['size']/fontscale
self.fontticklabel['size'] = self.fontticklabel['size']/fontscale
self.fontlegend['size'] = self.fontlegend['size']/fontscale
self.fontcbarlabel['size'] = self.fontcbarlabel['size']/fontscale
self.fontcbarticklabel['size'] = self.fontcbarticklabel['size']/fontscale
fontscale = autoscale(ax)
font = fontprop()
font.init(fonttitle,fontlabel,fontticklabel,fontlegend,fontcbarlabel,fontcbarticklabel)
font.update(fontscale)
pyplot.draw()
#plt.figure(linewidth=30.5)
if xlim is not None:
ax.set(xlim=xlim)
if ylim is not None:
ax.set(ylim=ylim)
#pyplot.draw()
if xbins is not None:
locator = MaxNLocator(nbins=xbins)
locator.set_axis(ax.xaxis)
ax.set_xticks(locator())
if ybins is not None:
locator = MaxNLocator(nbins=ybins)
locator.set_axis(ax.yaxis)
ax.set_yticks(locator())
pyplot.draw()
ax.set_xticks(ax.get_xticks())
ax.set_yticks(ax.get_yticks())
ax.set_xlabel(xlabel,fontdict = font.fontlabel,labelpad=(fontsize-1)*fontscale)
ax.set_ylabel(ylabel,fontdict = font.fontlabel,labelpad=(fontsize-1)*fontscale)
if (rotation is not None) & (xticklabel is not None) :
ax.set_xticklabels(xticklabel,fontticklabel,rotation=rotation)
elif (xticklabel is not None) &(rotation is None):
ax.set_xticklabels(xticklabel,fontticklabel)
elif (xticklabel is None) &(rotation is None):
ax.set_xticklabels(ax.get_xticklabels(),fontticklabel)
elif (rotation is not None) & (xticklabel is None):
ax.set_xticklabels(ax.get_xticklabels(),fontticklabel,rotation=rotation)
ax.set_yticklabels(ax.get_yticklabels(),font.fontticklabel)
if moveyaxis is True:
#fontticklabel
ax.spines['left'].set_position(('data',0))
ax.spines['left'].set_visible(left)
ax.spines['right'].set_visible(not left)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_linewidth(0.5*fontscale)
ax.spines['bottom'].set_linewidth(0.5*fontscale)
ax.spines['left'].set_linewidth(0.5*fontscale)
ax.spines['bottom'].set_color('k')
ax.spines['left'].set_color('k')
ax.spines['right'].set_color('k')
ax.tick_params(direction='out', pad=2*fontscale,width=0.5*fontscale)
#ax.spines['bottom']._edgecolor="#000000"
#ax.spines['left']._edgecolor="#000000"
if title is not None:
ax.set_title(title,fontdict = font.fonttitle)
if legendscale is True:
if (h is None)&(l is None):
legend = ax.legend(prop=font.fontlegend,
bbox_to_anchor=bbox_to_anchor,
labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,markerscale=markerscale,**kwards)
else:
legend = ax.legend(h,l,prop=font.fontlegend,
bbox_to_anchor=bbox_to_anchor,
labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,markerscale=markerscale,**kwards)
if legendtitle is not None:
#if legendloc is None:
# legendloc="best"
legend = ax.legend(title=legendtitle,prop=font.fontlegend,
bbox_to_anchor=bbox_to_anchor,
labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,markerscale=markerscale,**kwards)
ax.legend_.get_frame()._linewidth=0.5*fontscale
legend.get_title().set_fontweight('normal')
legend.get_title().set_fontsize(fontscale*fontsize)
if legendsort is True:
# h: handle l:label
h,l = ax.get_legend_handles_labels()
l,h = zip(*sorted(zip(l,h), key=lambda t: int(t[0])))
legend = ax.legend(h,l,title=legendtitle,prop=font.fontlegend,
bbox_to_anchor=bbox_to_anchor,
labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,markerscale=markerscale,**kwards)
ax.legend_.get_frame()._linewidth=0.5*fontscale
legend.get_title().set_fontweight('normal')
legend.get_title().set_fontsize(fontscale*fontsize)
if sns is True:
h,l = ax.get_legend_handles_labels()
#l,h = zip(*sorted(zip(l,h), key=lambda t: int(t[0])))
legend = ax.legend(h[1:],l[1:],title=legendtitle,prop=font.fontlegend,
bbox_to_anchor=bbox_to_anchor,
labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,markerscale=markerscale,**kwards)
ax.legend_.get_frame()._linewidth=0.5*fontscale
legend.get_title().set_fontweight('normal')
legend.get_title().set_fontsize(fontscale*fontsize)
elif legend_adj is True:
legend = ax.legend(handles=h,labels=l,title=legendtitle,prop=font.fontlegend,
bbox_to_anchor=bbox_to_anchor,
labelspacing=labelspacing,borderpad=borderpad,handletextpad=handletextpad,
edgecolor="#000000",fancybox=False,markerscale=markerscale,**kwards)
ax.legend_.get_frame()._linewidth=0.5*fontscale
legend.get_title().set_fontweight('normal')
legend.get_title().set_fontsize(fontscale*fontsize)
if cbar is not None:
#locator, formatter = cbar._get_ticker_locator_formatter()
#ticks, ticklabels, offset_string = cbar._ticker(locator, formatter)
#cbar.ax.spines['top'].set_visible(False)
#cbar.ax.spines['right'].set_visible(False)
#cbar.ax.spines['bottom'].set_visible(False)
#cbar.ax.spines['left'].set_visible(False)
cbar.ax.tick_params(direction='out', pad=3*fontscale,width=0*fontscale,length=0*fontscale)
cbar.set_label(cbarlabel,fontdict = font.fontcbarlabel,Rotation=270,labelpad=fontscale*(fontsize+1))
cbar.ax.set_yticks(cbar.ax.get_yticks())
cbar.ax.set_yticklabels(cbar.ax.get_yticklabels(),font.fontcbarticklabel)
font.reset(fontscale)
return ax#setup figure template
figure_template_path = 'bin'
if figure_template_path not in sys.path:
sys.path.append(figure_template_path)
from importlib import reload
import figure_template
#force reload of the module
reload(figure_template)
from figure_template import display_dataframe, embed_pdf_figure, embed_pdf_pages,std_plotPython
# use a tab
for i in range(3):
print(i)
# use 2 spaces
for i in range(3):
print(i)
# use 4 spaces
for i in range(3):
print(i)print("The \n makes a new line")
print("The \t is a tab")
print('I\'m going to the movies')firstVariable = 'Hello World!'
print(firstVariable)
print(firstVariable.lower())
print(firstVariable.upper())
print(firstVariable.title())print (1+1)
print (130-2.0)
print (126/3)
print (2*3)
print (2**3)
print (10%3)# Defining a list
z = [3, 7, 4, 2]print (z[0])
print (z[-1])
print (z[0:2])x = [3, 7, 2, 11, 8, 10, 4]
y = ['Steve', 'Rachel', 'Michael', 'Adam', 'Monica', 'Jessica', 'Lester']
x.append(3)
y.append('James')
print(x)
print(y)numpy and pandas
numpy
http://cs231n.github.io/python-numpy-tutorial/
import numpy as np
a = np.array([1, 2, 3]) # Create a rank 1 array
print(type(a)) # Prints "<class 'numpy.ndarray'>"
print(a.shape) # Prints "(3,)"
print(a[0], a[1], a[2]) # Prints "1 2 3"
a[0] = 5 # Change an element of the array
print(a) # Prints "[5, 2, 3]"
b = np.array([[1,2,3],[4,5,6]]) # Create a rank 2 array
print(b.shape) # Prints "(2, 3)"
print(b[0, 0], b[0, 1], b[1, 0]) # Prints "1 2 4"a = np.zeros((2,2)) # Create an array of all zeros
print(a) # Prints "[[ 0. 0.]
# [ 0. 0.]]"
b = np.ones((1,2)) # Create an array of all ones
print(b) # Prints "[[ 1. 1.]]"
c = np.full((2,2), 7) # Create a constant array
print(c) # Prints "[[ 7. 7.]
# [ 7. 7.]]"
d = np.eye(2) # Create a 2x2 identity matrix
print(d) # Prints "[[ 1. 0.]
# [ 0. 1.]]"
e = np.random.random((2,2)) # Create an array filled with random values
print(e) # Might print "[[ 0.91940167 0.08143941]
# [ 0.68744134 0.87236687]]"np.eye(15)imshow(np.eye(15),cmap=cm.gray_r)np.random.random(100).reshape(10,10).shapeimshow(np.random.random(100).reshape(10,10))Pandas
https://github.com/adeshpande3/Pandas-Tutorial/blob/master/Pandas Tutorial.ipynb
df = pd.read_csv('data/RegularSeasonCompactResults.csv')df.shapedf.head(10)df.tail(3)df.shapedf.describe()df['Wscore']df['Wscore'].max(), df['Wscore'].mean(), df['Wscore'].argmax()df['Season'].value_counts()np.unique(df['Season'],return_counts=True)df['Wscore'].argmax()df.iloc[df['Wscore'].argmax(),:3]df.loc[np.where(df['Wscore']==186)[0], 'Lscore']df.sort_values('Lscore').head()df[(df['Wscore'] > 150) & (df['Lscore'] < 100)]df.groupby('Wteam')['Wscore'].mean().head()df.groupby('Wteam')['Wloc'].value_counts().head(9)df.valuesax = df['Wscore'].plot.hist(bins=20)
ax.set_xlabel('Points for Winning Team')qgrid filtering
!pip install qgridimport numpy as np
import pandas as pd
import qgrid
randn = np.random.randn
df_types = pd.DataFrame({
'A' : 1.,
'B' : pd.Series(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
'2013-01-05', '2013-01-06', '2013-01-07', '2013-01-08', '2013-01-09'],index=list(range(9)),dtype='datetime64[ns]'),
'C' : pd.Series(randn(9),index=list(range(9)),dtype='float32'),
'D' : np.array([3] * 9,dtype='int32'),
'E' : pd.Categorical(["washington", "adams", "washington", "madison", "lincoln","jefferson", "hamilton", "roosevelt", "kennedy"]),
'F' : ["foo", "bar", "buzz", "bippity","boppity", "foo", "foo", "bar", "zoo"] })
df_types['G'] = df_types['F'] == 'foo'
qgrid_widget = qgrid.show_grid(df_types, show_toolbar=True)
qgrid_widgetqgrid_widget.get_changed_df()Matplotlib
xnp.sin(x)x = np.linspace(0, 2 * np.pi, 50)
plt.plot(x, np.sin(x))#use ax and figure
fig,ax=plt.subplots(figsize=(8,3))
x = np.linspace(0, 2 * np.pi, 50)
ax.plot(x, np.sin(x))#use ax and figure
fig,ax=plt.subplots(2,3,figsize=(18,6))
for i in range(2):
for j in range(3):
x = np.linspace(0, 2 * np.pi * (i*3+j+1), 50)
ax[i,j].plot(x, np.sin(x))fig,ax=plt.subplots(figsize=(4,4))
x = np.random.rand(100)
y = np.random.rand(100)
size = np.random.rand(100) * 50
colour = np.random.rand(100)
scatter = ax.scatter(x, y, size, colour)
fig.colorbar(scatter)fig,ax=plt.subplots(figsize=(6,3))
x = np.random.randn(1000)
ax.hist(x, 50)Seaborn
use boxplot as an example https://seaborn.pydata.org/generated/seaborn.boxplot.html
import numpy as np
import pandas as pd
np.random.seed(44)
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
# Let us also get tableau colors we defined earlier:
tableau_20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
# Scaling above RGB values to [0, 1] range, which is Matplotlib acceptable format:
for i in range(len(tableau_20)):
r, g, b = tableau_20[i]
tableau_20[i] = (r / 255., g / 255., b / 255.)# Loading built-in Tips dataset:
tips = sns.load_dataset("tips")
tips# Plotting basic Box Plot:
sns.boxplot(x="day", y="total_bill", data=tips)sns.boxplot(x="day", y="total_bill", hue="smoker", data=tips, palette="coolwarm")sns.boxplot(x="day", y="total_bill", data=tips)
sns.swarmplot(x="day", y="total_bill", data=tips, color=tableau_20[7])sns.violinplot(x="day", y="total_bill", data=tips)
sns.swarmplot(x="day", y="total_bill", data=tips, color=tableau_20[7])interactive plotting
It is useful to use ipywidgets to tune the parameters to get a perfect plot https://ipywidgets.readthedocs.io/en/stable/examples/Using Interact.html#Basic-interact
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgetsdef f(x):
return xinteract(f, x=10)interact(f, x=True)interact(f, x='Hi there!')fixed arguments
def h(p, q):
return (p, q)interact(h, p=5, q=fixed(20))Slider
interact(f, x=widgets.IntSlider(min=-10,max=30,step=1,value=10))interact(f, x=(-10,10,1))widgets.FloatSlider(
value=7.5,
min=0,
max=10.0,
step=0.1,
description='Test:',
disabled=False,
continuous_update=False,
orientation='vertical',
readout=True,
readout_format='.1f',
)widgets.IntProgress(
value=9,
min=0,
max=10,
step=1,
description='Loading:',
bar_style='success', # 'success', 'info', 'warning', 'danger' or ''
orientation='horizontal'
)widgets.BoundedFloatText(
value=7.5,
min=0,
max=10.0,
step=0.1,
description='Text:',
disabled=False
)#Dropdown
widgets.Dropdown(
options=['1', '2', '3'],
value='2',
description='Number:',
disabled=False,
)widgets.RadioButtons(
options=['holiday', 'work', 'study'],
# value='pineapple',
description='Pizza topping:',
disabled=False
)widgets.Select(
options=['Linux', 'Windows', 'OSX'],
value='OSX',
# rows=10,
description='OS:',
disabled=False
)widgets.SelectMultiple(
options=['Ubuntu', 'CentOS','RedHat','Raspberry','Windows10', 'Mac OS Majove'],
description='OS:',
)import datetime
dates = [datetime.date(2015,i,1) for i in range(1,13)]
options = [(i.strftime('%b'), i) for i in dates]
widgets.SelectionRangeSlider(
options=options,
index=(0,11),
description='Months (2015)',
disabled=False
)play = widgets.Play(
# interval=10,
value=50,
min=0,
max=100,
step=1,
description="Press play",
disabled=False
)
slider = widgets.IntSlider()
widgets.jslink((play, 'value'), (slider, 'value'))
widgets.HBox([play, slider])widgets.DatePicker(
description='Pick a Date',
disabled=False
)widgets.ColorPicker(
concise=False,
description='Pick a color',
value='blue',
disabled=False
)based on the above things, you can create some fancy plotting...
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.axes3d import *Xs = np.repeat(np.arange(0,100),100).reshape(-1,100).T.ravel()
Ys = np.repeat(np.arange(0,100),100).ravel()
Zs = np.random.random(10000).ravel()
def plot_3d_grid_surface(width,height,azim,elev,contextind,styind,featureind,savefig):
fig = plt.figure(figsize=(width,height))
p = 0.05
f = -0.01
def get_data(p):
X = np.arange(-5, 5, 0.25)
Y = np.arange(-5, 5, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
return X,Y,Z
x, y, z = get_data(p)
x_min, x_max = np.min(x), np.max(x)
y_min, y_max = np.min(y), np.max(y)
z_min, z_max = np.min(z), np.max(z)
fig= plt.figure(figsize=(15, 10))
ax = plt.axes(projection='3d')
ax.tick_params(labelsize=8)
#ax.view_init(azim=azim, elev=elev)
#ax.plot_surface(x, y, z, rstride=10, cstride=10, alpha=1)
ax.contourf(x, y, z, zdir='z', offset=-2, cmap=cm.coolwarm)
surf = ax.plot_surface(x,y,z, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
fig.colorbar(surf, shrink=0.5, aspect=5)
ax.set_xlabel('X')
ax.set_xlim(x_min, x_max)
ax.set_ylabel('Y')
ax.set_ylim(y_min, y_max)
ax.set_zlabel('Z')
ax.set_zlim(-2, 1.01)
ax.view_init(azim=azim,elev=elev)
interact(plot_3d_grid_surface,
contextind=FloatSlider(min=0,max=3,step=1,value=2),
styind=FloatSlider(min=0,max=2,step=1),
width =FloatSlider(min=4,max=40,step=1,value=25),
featureind=IntSlider(min=0,max=30,step=1,value=0),
height= FloatSlider(min=4,max=30,step=1,value=16),
azim= FloatSlider(min=0,max=180,step=2,value=45,continuous_update=False),
savefig= RadioButtons(options=['show','save']),
elev= FloatSlider(min=0,max=180,step=1,value=32,continuous_update=False,))display dataframe, std_plot, pdf_figure...
import numpy as np
import pandas as pd
import qgrid
randn = np.random.randn
df_types = pd.DataFrame({
'A' : 1.,
'B' : pd.Series(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
'2013-01-05', '2013-01-06', '2013-01-07', '2013-01-08', '2013-01-09'],index=list(range(9)),dtype='datetime64[ns]'),
'C' : pd.Series(randn(9),index=list(range(9)),dtype='float32'),
'D' : np.array([3] * 9,dtype='int32'),
'E' : pd.Categorical(["washington", "adams", "washington", "madison", "lincoln","jefferson", "hamilton", "roosevelt", "kennedy"]),
'F' : ["foo", "bar", "buzz", "bippity","boppity", "foo", "foo", "bar", "zoo"] })
df_types['G'] = df_types['F'] == 'foo'
qgrid_widget = qgrid.show_grid(df_types, show_toolbar=True)
qgrid_widgetqgrid_widget.get_changed_df()display_dataframe(qgrid_widget.get_changed_df(),filename='qgrid_widget_data')tips = sns.load_dataset('tips')
sns.boxplot(x="day", y="total_bill", hue="smoker", data=tips, palette="coolwarm")ax,xlabel,ylabel,title=None,
legendtitle=None,bbox_to_anchor=None,
labelspacing=1.2,borderpad=1,handletextpad=0.5,legendsort=False,markerscale=None,
xlim=None,ylim=None,
xbins=None,ybins=None,
cbar=None,cbarlabel=None,
moveyaxis=False,sns=False,left=True,rotation=None,xticklabel=Nonefig,ax=plt.subplots(figsize=(6,4))
sns.boxplot(x="day", y="total_bill", hue="smoker", data=tips, palette="coolwarm",
ax=ax)
ax = std_plot(ax=ax,xlabel='day',ylabel='total_bill',title='boxplot',
ylim=[0,80])
fig.tight_layout()
#embed_pdf_figure()Basic plot
Now we try to use the following commands to get summary plot of exSEEK modules:
mapping
count matrix
differetial expression
def interactive_config_settings(dataset,sequencing_type,classifier,value_change,example_cancer,reads_preprocess,stage_info,saveformat):
if sequencing_type == 'short':
exp_mx_name = 'mirna_and_domains'
elif sequencing_type =='long':
exp_mx_name = 'featurecounts'
elif sequencing_type =='domain_only':
exp_mx_name = 'domains_long'
elif sequencing_type =='transcript':
exp_mx_name = 'transcript'
elif sequencing_type =='transcript_small':
exp_mx_name = 'transcript_small'
elif sequencing_type =='transcript_long_bg':
exp_mx_name = 'transcript_long_bg'
return dataset,sequencing_type,classifier,value_change,exp_mx_name,example_cancer,reads_preprocess,stage_info,saveformat
widget =interactive(interactive_config_settings,
dataset= ['lulab_hcc','scirep','exorbase','exosome_small','pico_3v3'],
sequencing_type=['short','long','domain_only','transcript','transcript_small','transcript_long_bg'],
classifier = ['random_forest','logistic_regression','linear_svm','decision_tree','logistic_regression_l1'],
value_change = ['any','up','down'],
example_cancer=['Normal-CRC','Normal-PAAD','Normal-PRAD','Normal-HCC'],
reads_preprocess=[True,False],
stage_info = ['No Stage','With Stage'],
saveformat=['.pdf','.eps']) # if start from preprocessing
display(widget)dataset,sequencing_type,classifier_use,value_change,exp_mx_name,example_cancer,reads_preprocess,stage_info,saveformat = widget.result
dataset,sequencing_type,classifier_use,value_change,exp_mx_name,example_cancer,reads_preprocess,stage_info,saveformatfile_counts = 'output/'+dataset+'/summary/read_counts.txt'
file_length_path = 'output/'+dataset+'/stats/mapped_read_length_by_sample/'
file_length_path_insert = 'output/'+dataset+'/stats/mapped_insert_size_by_sample/'
save_path = 'output/'+dataset+'/plots/'
if not os.path.exists(save_path ):
os.makedirs(save_path )#Get table
def get_counts_ratio_table(file_counts,sequencing_type='short'):
df = pd.read_table(file_counts, index_col=0)
if reads_preprocess == True:
if sequencing_type == 'short':
rna_types = [s.split('.')[0] for s in df.index.tolist() if s.endswith('.mapped')]
rna_types = 'rRNA,miRNA,piRNA,Y_RNA,srpRNA,tRNA,snRNA,snoRNA,lncRNA,mRNA,tucpRNA,intron,promoter,enhancer,repeats,circRNA,other'.split(',')
mapped_ratio = {}
mapped_count = {}
for sample_id in df.columns.tolist():
mapped_ratio[sample_id] = {}
mapped_count[sample_id] = {}
clean_counts = float(df.loc['clean.unmapped', sample_id])
for rna_type in rna_types:
mapped_ratio[sample_id][rna_type] = df.loc[rna_type + '.mapped', sample_id]/(
clean_counts - df.loc['other.unmapped', sample_id])
mapped_count[sample_id][rna_type] = df.loc[rna_type + '.mapped', sample_id]
for region in ('promoter', 'enhancer', 'intron','repeats','circRNA'):
mapped_ratio[sample_id]['other'] -= mapped_ratio[sample_id][region]
mapped_count[sample_id]['other'] -= mapped_count[sample_id][region]
mapped_count = pd.DataFrame.from_records(mapped_count)
mapped_ratio = pd.DataFrame.from_records(mapped_ratio)
mapped_count = mapped_count.loc[rna_types, :]
mapped_ratio = mapped_ratio.loc[rna_types, :]
return df,mapped_count.T, mapped_ratio.T
elif sequencing_type =='long':
read_counts = pd.read_table(file_counts, index_col=0)
read_counts = read_counts.T
rna_types = read_counts.columns.tolist()
rna_types.remove('clean')
rna_types_included = list(rna_types)
for rna_type in ('genome', 'rRNA'):
rna_types_included.remove(rna_type)
# percentage by clean reads
#display(read_counts.style.set_caption('Read counts'))
percent_by_clean = read_counts.loc[:, ['clean', 'rRNA', 'genome', 'circRNA']].copy()
percent_by_clean = 100.0*percent_by_clean.div(percent_by_clean.loc[:, 'clean'], axis=0)
percent_by_clean.drop(columns='clean', inplace=True)
percent_by_clean['unmapped'] = 100 - percent_by_clean.sum(axis=1)
#display(percent_by_clean.style.format(gradient_func).set_caption('Percentage by clean reads'))
percent_by_mapped = read_counts.copy()
percent_by_mapped = 100.0*percent_by_mapped.div(percent_by_mapped.loc[:, ['genome', 'circRNA']].sum(axis=1), axis=0)
percent_by_mapped.drop(columns=['rRNA', 'genome', 'clean'], inplace=True)
percent_by_mapped['other'] = 100.0 - percent_by_mapped.sum(axis=1)
#display(percent_by_mapped.style.format(gradient_func).set_caption('Percentage by mapped reads'))
return read_counts,percent_by_clean,percent_by_mapped
else:
if sequencing_type == 'short':
pass
elif sequencing_type =='long':
read_counts = pd.read_table(file_counts, index_col=0)
read_counts = read_counts.T
rna_types = read_counts.columns.tolist()
rna_types.remove('clean')
rna_types_included = list(rna_types)
for rna_type in ('genome', 'rRNA'):
rna_types_included.remove(rna_type)
# percentage by clean reads
#display(read_counts.style.set_caption('Read counts'))
percent_by_clean = read_counts.loc[:, ['clean', 'rRNA', 'genome', 'circRNA']].copy()
percent_by_clean = 100.0*percent_by_clean.div(percent_by_clean.loc[:, 'clean'], axis=0)
percent_by_clean.drop(columns='clean', inplace=True)
percent_by_clean['unmapped'] = 100 - percent_by_clean.sum(axis=1)
#display(percent_by_clean.style.format(gradient_func).set_caption('Percentage by clean reads'))
percent_by_mapped = read_counts.copy()
percent_by_mapped = 100.0*percent_by_mapped.div(percent_by_mapped.loc[:, ['genome', 'circRNA']].sum(axis=1), axis=0)
percent_by_mapped.drop(columns=['rRNA', 'genome', 'clean'], inplace=True)
percent_by_mapped['other'] = 100.0 - percent_by_mapped.sum(axis=1)
#display(percent_by_mapped.style.format(gradient_func).set_caption('Percentage by mapped reads'))
return read_counts,percent_by_clean,percent_by_mappeddef get_length_table(file_length_path,sequencing_type='short'):
length_table = {}
for i in os.listdir(file_length_path):
if sequencing_type =='short':
length_table[i] = pd.read_table(file_length_path+i,index_col=0).iloc[16:52]
elif sequencing_type =='long':
length_table[i] = pd.read_table(file_length_path+i,index_col=0).iloc[16:152]
sample_names = np.array(os.listdir(file_length_path))
length_table_sum = length_table[sample_names[0]]
for i in sample_names[1:]:
length_table_sum += length_table[i]
return length_table_sum/length_table_sum.sum(axis=0)if sequencing_type == 'short':
read_counts,table_count, table_ratio = get_counts_ratio_table(file_counts)
length_table = get_length_table(file_length_path,sequencing_type=sequencing_type)
rnanames=np.array(table_ratio.columns).astype('str')
elif sequencing_type == 'long':
read_counts,percent_by_clean,percent_by_mapped = \
get_counts_ratio_table(file_counts,sequencing_type='long')
rnanames_1=np.array(percent_by_clean.columns).astype('str')
rnanames_2=np.array(percent_by_mapped.columns).astype('str')
length_table_1 = get_length_table(file_length_path,sequencing_type=sequencing_type)
length_table_2 = get_length_table(file_length_path_insert,sequencing_type=sequencing_type)if sequencing_type == 'short':
table_percent = table_ratio*100
display_dataframe(table_count,filename='Mapped counts',gradientfunc=False)
display_dataframe(table_percent,filename='Percentage by clean reads',gradientfunc=True)
elif sequencing_type == 'long':
display_dataframe(read_counts,filename='Read counts',gradientfunc=False)
display_dataframe(percent_by_clean,filename='Percentage by clean reads',gradientfunc=True)
display_dataframe(percent_by_mapped,filename='Percentage by mapped reads',gradientfunc=True)pie plot of RNA ratio
from bokeh.io import output_file, show
from bokeh.palettes import Category20
from bokeh.plotting import figure
from bokeh.transform import cumsumdef plot_pie(data, rnanames):
'''
data: table_ratio
rnanames: rna type names
adjustment: merge RNA with small percent together
'''
x = np.array(rnanames)
y = np.array(data.loc[:,x].mean())+10e-8
z_ = np.array([x[i] + str(' {:.2f}'.format(y[i]*100)+'%') for i in range(y.shape[0])])
z = np.array([float('{:.10f}'.format(y[i]*100)) for i in range(y.shape[0])])
labels = rnanames
dataframe = pd.DataFrame(np.concatenate((x.reshape(-1,1),z.reshape(-1,1),z_.reshape(-1,1)),axis=1))
dataframe.columns=['rna','percent','label']
dataframe["percent"] = pd.to_numeric(dataframe["percent"])
dataframe['angle'] = dataframe['percent']/dataframe['percent'].sum() * 2*pi
dataframe['color'] = Category20[len(x)]
p = figure(plot_height=500,width=750, title="Pie Chart", toolbar_location=None,
tools="hover", tooltips="@label", x_range=(-0.5, 1.0))
p.wedge(x=0.14, y=1, radius=0.45,
start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
line_color="black", fill_color='color', legend="label", source=dataframe)
p.axis.axis_label=None
p.axis.visible=False
p.grid.grid_line_color = None
show(p)
def plot_for_pie(sequencing_type=sequencing_type,by_sample=False):
if by_sample:
if sequencing_type == 'short':
for i in range(table_ratio.shape[0]):
plot_pie(pd.DataFrame(table_ratio.iloc[i]).T, rnanames)
elif sequencing_type == 'long':
for i in range(percent_by_mapped.shape[0]):
plot_pie(pd.DataFrame(percent_by_mapped.iloc[i]/100.).T, rnanames_2)
else:
if sequencing_type == 'short':
plot_pie(table_ratio, rnanames)
elif sequencing_type == 'long':
plot_pie(percent_by_mapped/100. , rnanames_2)
plot_for_pie(sequencing_type=sequencing_type,by_sample=False)boxplot of rna ratio
def plot_ratio_boxplot(data, rnanames, points_show = True, width=20, height=10, savefig=False):
'''
data: table_ratio
rnanames: rna type names
points_show: plot scatter points too
'''
fig, ax = plt.subplots(figsize=(width, height))
sns.boxplot(data = data,ax=ax,boxprops=dict(alpha=.001),color='gray',width=0.65,saturation=0.01)
if points_show:
sns.stripplot(data = data,ax=ax,size=2, edgecolor='black')
ax = std_plot(ax,'type','percentage','boxplot',ylim=[0,np.ceil(np.max(np.max(data))*10)/10],
xticklabel=rnanames,rotation=90,legendscale=False,legend_adj=False)
fig.tight_layout()
if savefig:
fig.savefig(save_path+'rna_ratio_box_plot.png', bbox_inches='tight')
#embed_pdf_figure()
if sequencing_type == 'long':
plot_ratio_boxplot(percent_by_mapped/100, rnanames_2, points_show = True,
width=7, height=5, savefig=1)
else:
plot_ratio_boxplot(table_ratio, rnanames, points_show = True, width=4, height=4, savefig=1)line plot of rna length
def plot_length_line(data, rnanames, width=7, height=20, savefig=False):
'''
data: length_table
rnanames: rna type names
'''
data=data.fillna(0)
length = np.array(data.T)
fig,ax=plt.subplots(length.shape[0],1,figsize=(width, height))
for i in range(length.shape[0]):
ax[i].plot(length[i],label=data.columns[i], color=Category20c[20][i],linewidth=1)
ax[i].legend(loc='upper right')
std_plot(ax[i],'','',ylim=[0,max(length[i])],ybins=5,row=length.shape[0])
legend = ax[i].legend(prop=fontlegend,
bbox_to_anchor=None,
borderpad=1,
edgecolor="#000000",fancybox=False)
ax[i].legend_.get_frame()._linewidth=0.5
legend.get_title().set_fontweight('normal')
legend.get_title().set_fontsize(6.5)
if savefig:
fig.savefig(save_path+'rna_length_line_plot'+saveformat, bbox_inches='tight')
fig.tight_layout()
#embed_pdf_figure()if sequencing_type == 'long':
plot_length_line(length_table_1, rnanames_1, width=7, height=length_table_1.shape[1], savefig=1)
plot_length_line(length_table_2, rnanames_2, width=7, height=length_table_2.shape[1], savefig=1)
else:
plot_length_line(length_table, rnanames, width=7, height=length_table.shape[1], savefig=1)3D barplot of rna length
from mpl_toolkits.mplot3d import Axes3D
def plot_3d(data, width=7, height=5, azim = 45, elev = 32,savefig=False):
'''
data: length_table
'''
#data = length_table
data=data.fillna(0)
fig = plt.figure(figsize=(width,height))
ax1 = fig.gca(projection="3d")
num = data.shape[1]
count_ = data.shape[0]
xpos = np.repeat(np.arange(1,count_+1),num).reshape(count_,-1).T.ravel()
ypos = np.repeat(range(num),count_).ravel()
num_elements = len(xpos)
zpos = np.zeros(count_*num)
dx = np.ones(count_*num)/5
dy = np.ones(count_*num)/5
dz = np.array(data.T).ravel()
for i in range(num):
ax1.bar3d(xpos[count_*i:count_*(i+1)], ypos[count_*i:count_*(i+1)], zpos[count_*i:count_*(i+1)],
dx[count_*i:count_*(i+1)], dy[count_*i:count_*(i+1)],dz[count_*i:count_*(i+1)], color=(np.array(Category20c[20]))[:num][i],alpha=0.9)
plt.xticks(range(count_), [str(i+16) for i in range(count_)], size=6.5,color='red',weight='normal',family='Arial',rotation=-azim)
ax1.set_yticks(range(num))
ax1.set_yticklabels(data.columns, color='blue',weight='normal',family='Arial', size=6.5)
fig.canvas.draw()
ax1.set_zticks(ax1.get_zticks())
ax1.set_zticklabels(ax1.get_zticklabels(),weight='normal',family='Arial', size=6.5)
for color,tick in zip((Category20c[20])[:num],ax1.yaxis.get_major_ticks()):
tick.label1.set_color(color)
ax1.view_init(azim=azim,elev=elev)
ax1.xaxis.pane.fill = False
ax1.yaxis.pane.fill = False
ax1.zaxis.pane.fill = False
ax1.xaxis.pane.set_edgecolor('w')
ax1.yaxis.pane.set_edgecolor('w')
ax1.zaxis.pane.set_edgecolor('w')
fig.tight_layout()
if savefig:
fig.savefig(save_path+'rna_length_3D_barplot'+saveformat, bbox_inches='tight')if sequencing_type == 'long':
plot_3d(length_table_1,width=7, height=5, savefig=1, azim = 45, elev = 32)
embed_pdf_figure()
plot_3d(length_table_2,width=7, height=5, savefig=1, azim = 45, elev = 32)
#embed_pdf_figure()
else:
plot_3d(length_table,width=7, height=5, savefig=True, azim = 45, elev = 32)
#embed_pdf_figure()stack bar plot of rna counts and ratio
from matplotlib.colors import LinearSegmentedColormap
def stack_bar_ratio(table, ax,statistics = 'ratio',savefig=1):
table.plot(kind='bar', stacked=True,ax=ax,width=0.5,
legend=True,colormap=matplotlib.colors.ListedColormap ( Category20c[20]))
#ax.legend(bbox_to_anchor=(1,1),fontsize='large')#, loc="lower right",
#ax.set_title('Stacked Bar plot',fontsize=40)
if savefig:
fig.savefig(save_path+statistics+'_stack_barplot'+saveformat, bbox_inches='tight')
return ax@contextmanager
def embed_pdf_data(width=640, height=480, title='Image'):
try:
data = BytesIO()
yield data
finally:
data = data.getvalue()
data = 'data:application/pdf;base64,'+ str(b64encode(data), encoding='utf-8')
display(HTML('<object width="{}" height="{}" data="{}"></object>'.format(width, height, data)))
plt.close()fig,ax=plt.subplots(figsize=(14,4))
if sequencing_type == 'long':
ax = stack_bar_ratio(percent_by_mapped/100.,ax=ax)
else:
ax = stack_bar_ratio(table_ratio,ax=ax)
std_plot(ax,'sample','percentage',legendtitle='type',legendsort=False,ylim=[0,1],bbox_to_anchor=(1,1),borderpad=0.2,ncol=2)
fig.tight_layout()
with embed_pdf_data(title='Clustermap') as data:
fig.savefig(data, format='pdf')
#embed_pdf_figure()fig,ax=plt.subplots(figsize=(14,4))
if sequencing_type == 'long':
ax = stack_bar_ratio(percent_by_mapped/100,ax=ax)
else:
ax = stack_bar_ratio(table_ratio,ax=ax)
std_plot(ax,'sample','counts(1e7)',legendtitle='type',legendsort=False,bbox_to_anchor=(1,1),borderpad=0.2,ncol=2)
fig.tight_layout()
#embed_pdf_figure()fig,ax=plt.subplots(figsize=(14,4))
if sequencing_type == 'long':
ax = stack_bar_ratio(read_counts,ax=ax)
else:
ax = stack_bar_ratio(table_count,ax=ax)
std_plot(ax,'sample','counts(1e7)',legendtitle='type',legendsort=False,bbox_to_anchor=(1,1),borderpad=0.2,ncol=2)
fig.tight_layout()
#embed_pdf_figure()bar plot of RNA by sample
def plot_bar_by_rna(fig,ax,table,rnaname,savefig=1, statistics = 'ratio',height = 4, width=20):
'''
table: ratio or count table, rows are rna type
statistics: ratio or count
'''
table = table.T
count = np.array(table[table.index ==rnaname]).ravel()
#fig,ax=plt.subplots(1,figsize=(width,height))
counttable = pd.DataFrame(np.concatenate((np.arange(1,table.shape[1]+1).reshape(-1,1),
count[np.argsort(-count)].reshape(-1,1)),axis=1),columns=['sample',statistics])
sns.barplot(ax=ax,x='sample',y=statistics,data = counttable,color=Category20c[20][np.random.randint(0,20)],alpha=1)
ax.set_xticks(np.arange(0,table.shape[1],5))
ax.set_xticklabels(np.arange(0,table.shape[1],5))
ax.set_title(statistics+' of '+rnaname,fontsize=15)
if savefig:
fig.savefig(save_path+'sample_'+rnaname+'_'+statistics+'_bar_plot'+saveformat, bbox_inches='tight')def plot_bar_by_rna_total(table,datatype='ratio'):
fignum= table.columns.shape[0]
fig,ax=plt.subplots(fignum ,1,figsize=(7 , 24 ))
for i in range(fignum):
plot_bar_by_rna(fig,ax[i],table,table.columns[i],statistics = datatype)
std_plot(ax[i],'sample','ratio',datatype+' of '+table.columns[i])
fig.tight_layout()if sequencing_type == 'long': plot_bar_by_rna_total(percent_by_mapped/100.,datatype='ratio') else: plot_bar_by_rna_total(table_ratio,datatype='ratio')
embed_pdf_figure()
if sequencing_type == 'long': plot_bar_by_rna_total(percent_by_mapped/100.,datatype='count') else: plot_bar_by_rna_total(table_ratio,datatype='count')
embed_pdf_figure()
FastQC
summary = pd.read_table('output/'+dataset+'/summary/fastqc.txt', sep='\t')
qc_status = summary.iloc[:, 9:]
qc_status.fillna('NA')
qc_status = qc_status.astype('str')
sample_ids = summary.sample_id
sections = qc_status.columns.values
def style_func(val):
status, row, col = val.split('|')
row, col = int(row), int(col)
color = {'pass': 'green', 'fail': 'red', 'warn': 'orange'}.get(status, 'gray')
return '<a href="../output/'+dataset+'/fastqc/{sample_id}_fastqc.html#M{section}" style="color: {color}">{status}</a>'.format(
sample_id=sample_ids[row], color=color, status=status, section=col + 1)
pd.DataFrame(qc_status.values \
+ '|' + np.arange(qc_status.shape[0]).astype('str')[:, np.newaxis] \
+ '|' + np.arange(qc_status.shape[1]).astype('str')[np.newaxis, :],
index=qc_status.index, columns=qc_status.columns) \
.style.format(style_func)Sample QC
use PCA and tSNE to visualize outliters
original_mx_file = 'output/'+dataset+'/count_matrix/'+exp_mx_name+'.txt'
original_mx = pd.read_table(original_mx_file,index_col=0)def PCA_plot_basic(ax,data,sampleclass,method = 'PCA'):
X = log_transfrom(data).T
X = StandardScaler().fit_transform(X)
if method == 'PCA':
transform = PCA()
elif method == 'tSNE':
transform = TSNE()
elif method == 'UMAP':
transform = umap.UMAP(n_neighbors=5,min_dist=0.3,metric='correlation')
X_pca = transform.fit_transform(X)
plot_table = pd.DataFrame(X_pca[:,:2])
plot_table.columns = ['dimension_1','dimension_2']
g = sns.scatterplot(ax=ax,data=plot_table,x="dimension_1", y="dimension_2",
s=50)
return g
#plt.figure(linewidth=30.5)
#ax.spines['right'].set_visible(False)
#ax.spines['top'].set_visible(False)
def sample_qc_visualize_outlier(original_mx,table_ratio,method='PCA'):
fig, ax = plt.subplots(1,2,figsize=(7, 4))
if method=='PCA':
method_PCA = True
elif method=='tSNE':
method_PCA = False
g = PCA_plot_basic(ax[0],original_mx, method_PCA)
std_plot(g,'Dimension 1','Dimension 2','Original matrix',legendscale=False,legend_adj=False)
g = PCA_plot_basic(ax[1],table_ratio, method_PCA)
std_plot(g,'Dimension 1','Dimension 2','Table ratio',legendscale=False,legend_adj=False)
#embed_pdf_figure()if sequencing_type=='short':
sample_qc_visualize_outlier(original_mx,table_ratio.T,method='tSNE')
elif sequencing_type=='long':
sample_qc_visualize_outlier(original_mx,percent_by_clean.T,method='tSNE')Differential Expression
compare_list_use = np.array(['Normal-CRC','Normal-CRC_S1','Normal-HCC','Normal-stage_A'])
#,'Normal-PAAD','Normal-PRAD'
if dataset =='scirep':
compare_group_list = ['Normal-CRC','Normal-PAAD','Normal-PRAD','Normal-CRC_S1','Normal-CRC_S2','Normal-CRC_S3','Normal-CRC_S4']
elif dataset =='exorbase':
compare_group_list = ['Normal-HCC','Normal-CRC','Normal-PAAD']
elif dataset =='pico_3v3':
compare_group_list = ['Normal-CRC']
elif dataset =='lulab_hcc':
compare_group_list = ['Normal-HCC','Normal-stage_A']def volcano_plot():
for compare_group in compare_group_list:
if np.isin( compare_group,compare_list_use):
detable = pd.read_table('output/'+dataset+'/differential_expression/'+exp_mx_name+'/'+compare_group+'/deseq2.txt'
,index_col=0)
de_plot_mx = pd.DataFrame(data={'feature':[name.split('|')[0] for name in detable.index.values],
'log2FoldChange':detable['log2FoldChange'].tolist(),
'padj':detable['padj'].tolist()})
de_plot_mx.set_index('feature',inplace=True)
de_plot_mx['threshold'] = (abs(de_plot_mx['log2FoldChange'])>1) & (de_plot_mx['padj']<0.05)
de_plot_mx['-log10(q values)'] = [-math.log10(qvalue) for qvalue in de_plot_mx['padj'].tolist()]
#de_plot_mx.iloc[np.where(de_plot_mx['threshold']==True)]
de_plot_mx['color'] = de_plot_mx['threshold']
for i in np.where(de_plot_mx['threshold']==True):
de_plot_mx['color'][i]='#DA706F'
for i in np.where(de_plot_mx['threshold']==False):
de_plot_mx['color'][i]='#5876B9'
fig,ax=plt.subplots(figsize=(4,4))
ax.scatter(de_plot_mx['log2FoldChange'], de_plot_mx['-log10(q values)'], c=de_plot_mx['color'],s=10,alpha=0.8,edgecolors='none')
ax.vlines(-1,-1,10,linewidth=0.5)
ax.vlines(1,-1,10,linewidth=0.5)
ax.hlines(-math.log10(0.05),-4,4,linewidth=0.5)
std_plot(ax,'log2FoldChange','-log10(q values)','volcano plot of '+compare_group,ylim=[-2,18])
ax.tick_params(direction='out', pad=2)
fig.tight_layout()
fig.savefig(save_path+'volcano_plot_of_'+compare_group+saveformat)
#embed_pdf_figure()
volcano_plot()class_info = 'data/'+dataset+'/sample_classes.txt'
original_mx_file = 'output/'+dataset+'/count_matrix/'+exp_mx_name+'.txt'
filter_mx_file = 'output/'+dataset+'/matrix_processing/filter.'+exp_mx_name+'.txt'
original_mx = pd.read_table(original_mx_file,index_col=0)
filter_mx = pd.read_table(filter_mx_file,index_col=0)
if dataset=='lulab_hcc':
sample_class_stage = pd.read_table(class_info,sep='\t',index_col=0)
sample_classes = sample_class_stage.copy()
sample_classes[sample_class_stage.label !='Normal'] ='HCC'
elif dataset=='exorbase':
sample_classes = pd.read_table(class_info,sep='\t',index_col=0)DE_methods = ['deseq2','edger_exact','edger_glmlrt','edger_glmqlf','wilcox']def heamap_plot(mat,sample_class=sample_classes):
if sequencing_type=='short':
norm_mx = mat/mat.sum(axis=0)*10e6
norm_mx = (norm_mx+0.01).apply(np.log2,0)
if sequencing_type=='long':
norm_mx = mat/mat.sum(axis=0)*10e6
length = np.array([mat.index[i].split('|')[-1] for i in range(mat.index.shape[0])]).astype('int')-\
np.array([mat.index[i].split('|')[-2] for i in range(mat.index.shape[0])]).astype('int')
norm_mx = (norm_mx.T/length).T*1000
norm_mx = (norm_mx+0.01).apply(np.log2,0)
for compare_group in tqdm(compare_group_list):
if np.isin( compare_group,compare_list_use):
if dataset=='scirep':
if compare_group=='Normal-CRC_S1':
class_compare = np.array([ 'Colorectal Cancer Stage 1', 'Healthy Control'])
if compare_group=='Normal-CRC_S1':
class_compare = np.array([ 'Colorectal Cancer Stage 1', 'Healthy Control'])
if compare_group=='Normal-CRC_S1':
class_compare = np.array([ 'Colorectal Cancer Stage 1', 'Healthy Control'])
if compare_group=='Normal-CRC_S1':
class_compare = np.array([ 'Colorectal Cancer Stage 1', 'Healthy Control'])
if compare_group == 'Normal-CRC':
class_select = ['Healthy Control','Colorectal Cancer']
if compare_group == 'Normal-PAAD':
class_select = ['Healthy Control','Pancreatic Cancer']
if compare_group == 'Normal-PRAD':
class_select = ['Healthy Control','Prostate Cancer']
elif dataset=='exorbase':
if compare_group == 'Normal-CRC':
class_select = ['Healthy','CRC']
if compare_group == 'Normal-HCC':
class_select = ['Healthy','HCC']
if compare_group == 'Normal-PAAD':
class_select = ['Healthy','PAAD']
elif dataset=='lulab_hcc':
if compare_group == 'Normal-HCC':
class_select = ['Normal','HCC']
sample_class = sample_classes
if compare_group == 'Normal-stage_A':
class_select = ['Normal','stage_A']
sample_class = sample_class_stage
elif dataset=='pico_3v3':
if compare_group == 'Normal-CRC':
class_select = ['Control','CRC']
print (compare_group)
norm_mx_delete = norm_mx
norm_mx_delete = norm_mx_delete.loc[:,np.array(sample_class.iloc[np.where(np.isin(sample_class['label'],class_select)==1)[0]].index)]
norm_z_mx = norm_mx_delete.apply(scipy.stats.zscore,1)
detable = pd.read_table('output/'+dataset+'/differential_expression/'+exp_mx_name+'/'+compare_group+'/deseq2.txt'
,index_col=0)
de_plot_mx = pd.DataFrame(data={'feature':[name.split('|')[0] for name in detable.index.values],
'log2FoldChange':detable['log2FoldChange'].tolist(),
'padj':detable['padj'].tolist()})
de_plot_mx.set_index('feature',inplace=True)
de_plot_mx['threshold'] = (abs(de_plot_mx['log2FoldChange'])>1) & (de_plot_mx['padj']<0.05)
de_plot_mx['-log10(q values)'] = [-math.log10(qvalue) for qvalue in de_plot_mx['padj'].tolist()]
detable['-log10(q values)']=-np.log10(detable['padj'])
detable['metrics']=detable['-log10(q values)']*np.abs(detable['log2FoldChange'])
de_mx = pd.DataFrame(norm_z_mx).loc[detable.sort_values('metrics',ascending=False).iloc[np.where(de_plot_mx.sort_values('-log10(q values)',ascending=False)['threshold']==True)].index]
type_select =pd.DataFrame(sample_class.loc[de_mx.columns])
column_colors = np.zeros(type_select.shape[0]).astype('str')
column_colors[np.where(type_select.label==class_select[0])] = np.array(["#DA706F"])
column_colors[np.where(type_select.label==class_select[1])] = np.array(["#5876B9"])
tmpind = de_mx.index
tmpind = np.array([tmpind[i].split('|')[0]+'|'+tmpind[i].split('|')[1]+'|'+tmpind[i].split('|')[2] for i in range(tmpind.shape[0])])
de_mx.index = tmpind
de_mx = de_mx.fillna(0)
g = sns.clustermap(de_mx.iloc[:20], row_cluster=False,cmap="vlag",
col_colors=column_colors,linewidths=.005,vmax=3,vmin=-3)#,z_score=0)
g.savefig(save_path+compare_group+'_DE_heatmap'+saveformat)
#with embed_pdf_data() as data:
#g.savefig(data, format='pdf', metadata={'Title': compare_group})
heamap_plot(original_mx)if you would like to try...
Find suitable methods and metrics for better visualization
cluster_methods = ['single','average','weighted','centroid','median','ward']
cluster_metrics=['euclidean','correlation','cosine','seuclidean',
'braycurtis', 'canberra','chebyshev', 'cityblock','dice',
'hamming','jaccard','kulsinski','matching',
'minkowski','rogerstanimoto','russellrao','sokalmichener','sokalsneath',
'sqeuclidean']def heamap_plot(mat,sample_class=sample_classes):
if sequencing_type=='short':
norm_mx = mat/mat.sum(axis=0)*10e6
norm_mx = (norm_mx+0.01).apply(np.log2,0)
if sequencing_type=='long':
norm_mx = mat/mat.sum(axis=0)*10e6
length = np.array([mat.index[i].split('|')[-1] for i in range(mat.index.shape[0])]).astype('int')-\
np.array([mat.index[i].split('|')[-2] for i in range(mat.index.shape[0])]).astype('int')
norm_mx = (norm_mx.T/length).T*1000
norm_mx = (norm_mx+0.01).apply(np.log2,0)
for compare_group in tqdm(compare_group_list):
if np.isin( compare_group,compare_list_use):
if dataset=='scirep':
if compare_group=='Normal-CRC_S1':
class_compare = np.array([ 'Colorectal Cancer Stage 1', 'Healthy Control'])
if compare_group=='Normal-CRC_S1':
class_compare = np.array([ 'Colorectal Cancer Stage 1', 'Healthy Control'])
if compare_group=='Normal-CRC_S1':
class_compare = np.array([ 'Colorectal Cancer Stage 1', 'Healthy Control'])
if compare_group=='Normal-CRC_S1':
class_compare = np.array([ 'Colorectal Cancer Stage 1', 'Healthy Control'])
if compare_group == 'Normal-CRC':
class_select = ['Healthy Control','Colorectal Cancer']
if compare_group == 'Normal-PAAD':
class_select = ['Healthy Control','Pancreatic Cancer']
if compare_group == 'Normal-PRAD':
class_select = ['Healthy Control','Prostate Cancer']
elif dataset=='exorbase':
if compare_group == 'Normal-CRC':
class_select = ['Healthy','CRC']
if compare_group == 'Normal-HCC':
class_select = ['Healthy','HCC']
if compare_group == 'Normal-PAAD':
class_select = ['Healthy','PAAD']
elif dataset=='lulab_hcc':
if compare_group == 'Normal-HCC':
class_select = ['Normal','HCC']
sample_class = sample_classes
if compare_group == 'Normal-stage_A':
class_select = ['Normal','stage_A']
sample_class = sample_class_stage
elif dataset=='pico_3v3':
if compare_group == 'Normal-CRC':
class_select = ['Control','CRC']
print (compare_group)
norm_mx_delete = norm_mx
norm_mx_delete = norm_mx_delete.loc[:,np.array(sample_class.iloc[np.where(np.isin(sample_class['label'],class_select)==1)[0]].index)]
norm_z_mx = norm_mx_delete.apply(scipy.stats.zscore,1)
detable = pd.read_table('output/'+dataset+'/differential_expression/'+exp_mx_name+'/'+compare_group+'/deseq2.txt'
,index_col=0)
de_plot_mx = pd.DataFrame(data={'feature':[name.split('|')[0] for name in detable.index.values],
'log2FoldChange':detable['log2FoldChange'].tolist(),
'padj':detable['padj'].tolist()})
de_plot_mx.set_index('feature',inplace=True)
de_plot_mx['threshold'] = (abs(de_plot_mx['log2FoldChange'])>1) & (de_plot_mx['padj']<0.05)
de_plot_mx['-log10(q values)'] = [-math.log10(qvalue) for qvalue in de_plot_mx['padj'].tolist()]
detable['-log10(q values)']=-np.log10(detable['padj'])
detable['metrics']=detable['-log10(q values)']*np.abs(detable['log2FoldChange'])
de_mx = pd.DataFrame(norm_z_mx).loc[detable.sort_values('metrics',ascending=False).iloc[np.where(de_plot_mx.sort_values('-log10(q values)',ascending=False)['threshold']==True)].index]
type_select =pd.DataFrame(sample_class.loc[de_mx.columns])
column_colors = np.zeros(type_select.shape[0]).astype('str')
column_colors[np.where(type_select.label==class_select[0])] = np.array(["#DA706F"])
column_colors[np.where(type_select.label==class_select[1])] = np.array(["#5876B9"])
tmpind = de_mx.index
tmpind = np.array([tmpind[i].split('|')[0]+'|'+tmpind[i].split('|')[1]+'|'+tmpind[i].split('|')[2] for i in range(tmpind.shape[0])])
de_mx.index = tmpind
de_mx = de_mx.fillna(0)
g = sns.clustermap(de_mx.iloc[:20], row_cluster=False,cmap="vlag",
col_colors=column_colors,linewidths=.005,vmax=3,vmin=-3)#,z_score=0)
g.savefig(save_path+compare_group+'_DE_heatmap'+saveformat)
#with embed_pdf_data() as data:
# g.savefig(data, format='pdf', metadata={'Title': compare_group})
heamap_plot(original_mx)cpm_table_origin_ = pd.read_table('output/'+dataset+'/count_matrix/'+exp_mx_name+'.txt',index_col=0)
cpm_table_origin = cpm_table_origin_/cpm_table_origin_.sum(axis=0)*10e6
length_tmp = np.array([cpm_table_origin.index[i].split('|')[-1] for i in range(cpm_table_origin.index.shape[0])]).astype('int')-\
np.array([cpm_table_origin.index[i].split('|')[-2] for i in range(cpm_table_origin.index.shape[0])]).astype('int')
rpkm_table_origin = (cpm_table_origin.T/length_tmp*1000).Tfrom matplotlib.colors import ListedColormap, LinearSegmentedColormap
viridisBig = cm.get_cmap('BuGn', 512)
newcmp = ListedColormap(viridisBig(np.linspace(0.4, 1, 256)))
def DE_scatter(area_=(6.0,8.0),nameshort=True,savefig=True,DE_method='deseq2',up_regulated=1):
for compare_group in compare_group_list:
if np.isin( compare_group,compare_list_use):
detable = pd.read_table('output/'+dataset+'/differential_expression/'+exp_mx_name+'/'+compare_group+'/'+DE_method+'.txt'
,index_col=0)
de_plot_mx = pd.DataFrame(data={'feature':detable.index,
'log2FoldChange':detable['log2FoldChange'].tolist(),
'padj':detable['padj'].tolist()})
de_plot_mx.set_index('feature',inplace=True)
if up_regulated:
de_plot_mx['threshold'] = (de_plot_mx['log2FoldChange']>1) & (de_plot_mx['padj']<0.05)
else:
de_plot_mx['threshold'] = (abs(de_plot_mx['log2FoldChange'])>1) & (de_plot_mx['padj']<0.05)
de_plot_mx['-log10(q values)'] = [-math.log10(qvalue) for qvalue in de_plot_mx['padj'].tolist()]
de_plot_mx['color'] = de_plot_mx['threshold']
for i in np.where(de_plot_mx['threshold']==True):
de_plot_mx['color'][i]='#DA706F'
for i in np.where(de_plot_mx['threshold']==False):
de_plot_mx['color'][i]='#5876B9'
if dataset=='scirep':
rpkmtable = cpm_table_origin
matrix_type='cpm'
elif dataset=='lulab_hcc':
rpkmtable = cpm_table_origin
matrix_type='cpm'
elif dataset=='exorbase':
rpkmtable = rpkm_table_origin
matrix_type='rpkm'
elif dataset=='pico_3v3':
rpkmtable = rpkm_table_origin
matrix_type='rpkm'
de_plot_mx['metrics']=de_plot_mx['-log10(q values)']*np.abs(de_plot_mx['log2FoldChange'])
de_plot_mx = de_plot_mx.sort_values(['metrics'],ascending=0).iloc[:10]
de_plot_mx = de_plot_mx.sort_values(['log2FoldChange'],ascending=0)
rpkmtable = rpkmtable.loc[de_plot_mx.index]
de_plot_mx['log2RPKM'] = np.mean(log_transfrom(rpkmtable),axis=1)
de_plot_mx.index = np.array([name.split('|')[2]+'|'+name.split('|')[1]
#+'|'+name.split('|')[3]+'|'+name.split('|')[4]
for name in de_plot_mx.index.values])
fig, (ax) = plt.subplots(1, figsize=(6,3))
im = ax.scatter(de_plot_mx['log2FoldChange'],de_plot_mx.index,s=((de_plot_mx['log2RPKM']/area_[0]-0.5)*area_[1])**2,c=de_plot_mx['-log10(q values)'],cmap=newcmp)
cbar =fig.colorbar(im, ax=ax)
cbar.outline.set_visible(False)
interval = np.max(de_plot_mx.log2RPKM) - np.min(de_plot_mx.log2RPKM)
ratiointer = interval/2
pws = set(np.round(np.arange(np.min(de_plot_mx.log2RPKM),np.max(de_plot_mx.log2RPKM),ratiointer),0).astype(int))
for pw in pws:
ax.scatter([], [], s=((pw/area_[0]-0.5)*area_[1])**2, c="k",label=str(pw))
ax = std_plot(ax,'Fold Change','Feature Name','DE bar plot of '+compare_group+' '+DE_method,'log('+matrix_type+')',
borderpad=0.1,labelspacing=0.2,handletextpad=1,cbar=cbar,cbarlabel='-log10(q values)',xlim=[np.min(de_plot_mx['log2FoldChange'])-0.1,np.max(de_plot_mx['log2FoldChange'])+0.1])
fig.tight_layout()
fig.savefig(save_path+'DE_bar_plot_of_'+compare_group+'.'+DE_method+saveformat)
#embed_pdf_figure()
de_plot_mx.to_csv(save_path+'DE_selected_features.'+compare_group+'.'+DE_method+'.txt',sep='\t')DE_scatter()abundance and diversity
def filter_mx(expression_mx,cutoff_ratio = 0.2,counts_threshold = 10 ): retain_index = np.where(np.sum(expression_mx > counts_threshold,axis=1) >=round(cutoff_ratio*expression_mx.shape[1]))[0] return expression_mx.iloc[retain_index,:]
filter_mx=pd.read_table('output/'+dataset+'/matrix_processing/filter.'+exp_mx_name+'.txt')
def div_abu_plot(expression_mx,savefig=True):
total_counts = expression_mx.shape[0]
type_counts_sample = pd.DataFrame()
for samplename in expression_mx.columns.values:
filter_zero_samplename = expression_mx.iloc[np.where(expression_mx[samplename]>0)[0],:]
names = filter_zero_samplename.index
names_type = np.array([names[i].split('|')[1] for i in range(names.shape[0])])
type_counts = np.unique(names_type, return_counts = True)
new = pd.DataFrame({'type' : type_counts[0],
samplename : type_counts[1],
})
new = new.set_index('type')
type_counts_sample = pd.concat([type_counts_sample, new], axis=1)#, join_axes=[df1.index]
typelist = np.unique([expression_mx.index[i].split('|')[1] for i in range(expression_mx.shape[0])])
type_mx = pd.DataFrame(index=typelist,columns=expression_mx.columns)
for sample in expression_mx.columns:
sample_feature = pd.DataFrame(data=expression_mx.loc[:,sample],index=expression_mx.index)
sample_feature['type']=[expression_mx.index[i].split('|')[1] for i in range(expression_mx.shape[0])]
for i in typelist:
type_mx.loc[i,sample] = sample_feature.iloc[np.where(sample_feature['type']==i)].iloc[:,0].sum()
table_ratio = (type_mx/type_mx.sum()).T
xticks = type_counts_sample.index.tolist()
Means = type_counts_sample.mean(axis=1).values.tolist()
Std=type_counts_sample.std(axis=1).values.tolist()
mean_sd = pd.DataFrame(data = {'type':xticks,'mean':Means,'std':Std})
mean_sd = mean_sd.sort_values(by='mean',ascending=False)
mean_sd = mean_sd.set_index('type')
Std = [[0]*len(mean_sd['std'].tolist()),mean_sd['std'].tolist()]
ab = table_ratio*100
xticks_ab = ab.columns.tolist()
Means_ab = ab.mean(axis=0).values.tolist()
Std_ab = ab.std(axis=0).values.tolist()
mean_sd_ab = pd.DataFrame(data = {'type':xticks_ab,'mean_ab':Means_ab,'std_ab':Std_ab})
mean_sd_ab = mean_sd_ab.set_index('type')
N = type_counts_sample.shape[0]
ind = np.arange(N)
merge = pd.concat([mean_sd,mean_sd_ab],axis=1,join_axes=[mean_sd.index])
Std_ab = [[0]*len(merge['std_ab'].tolist()),merge['std_ab'].tolist()]
merge = merge.sort_values(by='mean_ab',ascending=False)
xticks = np.array(merge.index.tolist())
tmpname,tmpcount = np.unique(np.array([i.split('|')[1] for i in expression_mx.index]),return_counts=1)
tmpdataframe = pd.DataFrame(np.zeros(tmpname.shape[0]))
tmpdataframe.index = tmpname
tmpdataframe.iloc[:,0] = tmpcount
fig,(ax,ax1) = plt.subplots(1,2,figsize = (5,2.6))
ax.barh(ind,tmpdataframe.loc[merge.index].iloc[:,0],0.7,xerr=None,color=np.array(['#cc3399','#3300ff','#006699','#339999','#66ffcc','#00ff00','#006600','#FFFF00','#FF6600','#FF0000','#FF9999']))
ax.invert_xaxis()
ax.set_yticks(ind)
ax.set_yticks([])
ax = std_plot(ax,'Number of RNA domains/miRNAs','','Number of RNA domains/miRNAs (cfRNA)',left=False,rotation=0,legendscale=False,legend_adj=False)#,ylim=[np.min(ind),np.max(ind)]
ax1.barh(ind,merge['mean_ab'],0.7,xerr=Std_ab,color=np.array(['#cc3399','#3300ff','#006699','#339999','#66ffcc','#00ff00','#006600','#FFFF00','#FF6600','#FF0000','#FF9999']))
ax1.set_yticks(ind)
xticks[np.where(xticks=='Y_RNA')] = 'Y RNA'
ax1.set_yticklabels(xticks)
ax1 = std_plot(ax1,'Percentage of mapped reads (%)', '','Abundance',rotation=0,legendscale=False,legend_adj=False)
ax1.tick_params(direction='out', pad=4,length=0)
fig.tight_layout(w_pad=0.2)
if savefig is True:
fig.savefig(save_path+'diversity_abundance_cfRNA.eps')
#embed_pdf_figure()
return tmpdataframe.loc[merge.index],_
diversity,ratio =div_abu_plot(expression_mx=filter_mx,
savefig=True)de_table=pd.read_table('output/'+dataset+'/differential_expression/'+exp_mx_name+'/Normal-HCC/deseq2.txt')
rnaname_tmp,count_tmp = np.unique(np.array([i.split('|')[1] for i in de_table[de_table.padj<=0.05].index]),return_counts=1)
div_df = pd.DataFrame([count_tmp]).T
div_df.index = rnaname_tmp
display_dataframe(div_df.loc[pd.DataFrame(diversity).index])def plot_pie_de(df):
'''
data: table_ratio
rnanames: rna type names
adjustment: merge RNA with small percent together
'''
x = np.array(df.index)
y = (np.array(df.values)+10e-8).ravel()
y = y/y.sum()
z_ = np.array([x[i] + str(' {:.2f}'.format(y[i]*100)+'%') for i in range(y.shape[0])])
z = np.array([float('{:.10f}'.format(y[i]*100)) for i in range(y.shape[0])])
dataframe = pd.DataFrame(np.concatenate((x.reshape(-1,1),z.reshape(-1,1),z_.reshape(-1,1)),axis=1))
dataframe.columns=['rna','percent','label']
dataframe["percent"] = pd.to_numeric(dataframe["percent"])
dataframe['angle'] = dataframe['percent']/dataframe['percent'].sum() * 2*pi
dataframe['color'] = Category20c[len(x)]
p = figure(height=500,width=750, title="Pie Chart", toolbar_location=None,
tools="hover", tooltips="@label", x_range=(-0.5, 1.0))
p.wedge(x=0.14, y=1, radius=0.45,
start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
line_color="black", fill_color='color', legend="label", source=dataframe)
p.axis.axis_label=None
p.axis.visible=False
p.grid.grid_line_color = None
show(p)plot_pie_de(div_df.loc[pd.DataFrame(diversity).index].fillna(0))domain_mx = filter_mx.iloc[np.where(np.array([i.split("|")[1] for i in filter_mx.index])!='genomic')]
domain_size = np.array([int(i.split('|')[-1]) -int(i.split('|')[-2]) for i in domain_mx.index])if sequencing_type=='short':
fig ,ax=plt.subplots(1,figsize=(2.5,2.5))
ax.hist(domain_size,bins=200,alpha=0.9)
ax = std_plot(ax,'Domain Size','','Domain size distribution',xlim=[20,150],legendscale=False,legend_adj=False)
fig.tight_layout()
fig.savefig(save_path+'domainsize.eps')
#embed_pdf_figure()Last updated