Scripts templete
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on %(date)s
@author: %(ZYJ)s (Python 3.7)
@email: %(lianlizyj@mail.dlut.edu.cn)s
Description:
"""
import numpy as np
import matplotlib.pyplot as plt
#s=str 3 # SyntaxError
#prnt(s) # NameError
"""
for i in np.arange(-1,8):
print(i,np.log(i))
RuntimeWarning: divid by zero encontered in log
"""
"""
same as error function in matlab
for i in np.arange(-1,8):
assert (i>0),"I don't know how to take the log of {}!".format(i) # AssertionEeeor
print(i,np.log(i))
"""
# first_script.py
# Jesse M. Kinder -- 2014 Nov 11
"""
Calculate how long an object is in the air when thrown from a specied height
with a range of initial speeds assuming constant acceleration due to gravity:
o.5*g*t**2-v0*t-y0=0
"""
# %% Initialize variables.
ini_speed=0.0 # v0 = initial vertical speed of ball [m/s]
impact_time= 0.0 # t = time of impact [s] (computered in loop)
# %% Initialize parameters.
g = 9.8066 # Gravitational acceleration [m/s^2]
ini_height = 2.0 # y0 = height ball is thrown from [m]
speed_increament = 5.0 # Speed increment for each iteration [m/s]
cutoff_time = 10.0 # Stop computing after impact time exceeds cutoff
# %% Calculate and display impact time. Incerement initial speed each step.
# Repeat until impact time exceeds cutoff
while impact_time < cutoff_time:
# Use quadratic equation to solve kinematic equation for impact time:
impact_time = (np.sqrt(ini_speed**2+2*g*ini_height)\
+ini_speed)/g
print("Speed = {} m/s; time= {:.1f} s".format(ini_speed,impact_time))
ini_speed+=speed_increament
print("Calculation complete.")
# %% comments for variables define and unit
# Variables:
# ------------------------------------------
# length = length of the microtubule [um]
# velocity = velocity of motor [um/s]
# rate_constant = rate constant [1/days]
或有行为
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 26 20:35:33 2020
@author: Yanjie Zhang (Python 3.7)
@email: lianlizyj@mail.dlut.edu.cn
Description:
Purpose:
This script illustrates branching.
"""
import numpy as np
import matplotlib.pyplot as plt
for trial in range(5):
userInput=input('Pick a number: ')
number=float(userInput)
if number<0:
print('Square root is not real')
else:
print('Square root of {} is {:.4f}'.format(number,np.sqrt(number)))
userAgain=input('Another [y/n]? ')
if userAgain!='y':
break
# %% Check whether the loop terminated normally
if trial==4:
print('Sorry, only 5 per customer.')
elif userAgain=='n':
print("Bye!")
else:
print('Sorry, i didn\'t understand that.')
# %% nested loops
rows=3
columns=4
a=np.zeros((rows,columns))
for i in range(rows):
for j in range(columns):
a[i,j]=i**2+j**3
print("New a is : ", a)
数据输入、结果输出
pandas=numpy+pyplot+电子表+数据库（功能统一的组合接口）
https://pandas.pydata.org
https://docs.scipy.org
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 26 21:36:28 2020
@author: Yanjie Zhang (Python 3.7)
@email: lianlizyj@mail.dlut.edu.cn
Description:
Purpose:
import data to python from *,csv(separator is ',' )
"""
import numpy as np
import matplotlib.pyplot as plt
path='HIVseries.csv'
data_set=np.loadtxt(path,delimiter=',')
# %% other data type process (open method)
my_file=open(path)
temp_data=[]
for line in my_file:
print(line)
x,y=line.strip().split(',')
temp_data=[float(x),float(y)]
my_file.close()
data_set=np.array(temp_data)
# %% with open method
with open(path) as my_file:
for line in my_file:
print(line)
x,y=line.strip().split(',')
temp_data=[float(x),float(y)]
data_set=np.array(temp_data)
# %% get data_set from web
# import urllib
# web_file=urllib.request.urlopen('http://www.physics.upenn.edu/biophys/'+\
# 'PMLS/Datasets/01HIVseries/HIVseries.csv')
# print(" read data from web")
# print(web_file)
# data_set=np.loadtxt(web_file)
# url='http://www.baidu.com'
# content=urllib.request.urlopen(url).read()
# print(content)
# %% save data in files
x=np.linspace(0,1,1001)
y=3*np.sin(x)**3-np.sin(x)
np.savetxt('x_value.dat',x) # readable
np.savetxt('y_value.dat',y)
np.save('x_values',x) # unreadable
np.save('y_values',y)
np.savez('xy_values',x_vals=x,y_vals=y) # unreadable
print('save data in file sucessfully!')
# %% load the data
y2=np.loadtxt('y_value.dat')
print('read data by loadtxt success!',y2)
x2=np.load('x_values.npy')
print("read data by load success!",x2)
w=np.load('xy_values.npz')
print('read data of *.npz by load success!',w)
print("read data of *.npz by key, key() is ",w.keys())
print('w[\'x_vals\'] is',w['x_vals'])
# %% print into file by open function
with open('power.txt','w') as my_file:
print(" N \t\t2**N\t\t3**N\n") # print labels for column
print("---\t\t----\t\t----\n") # print separator.
my_file.write(" N \t\t2**N\t\t3**N\n") # write labels to file
my_file.write("---\t\t----\t\t----\n") # write separator to file
# %% Loop over integerfrom 0 to 10 and print/write results.
for N in range(11):
print("{:d}\t\t{:d}\t\t{:d}\n".format(N,pow(2,N),pow(3, N)))
my_file.write("{:d}\t\t{:d}\t\t{:d}\n".format(N,pow(2,N),pow(3, N)))
re_read=np.loadtxt('power.txt',skiprows=2)
print(re_read)
print("How to read power.txt, this is a question!")
# read by specifing the dtype
re_read=np.loadtxt('power.txt',\
dtype={'names':('col1','col2','col3')\
,'formats':('float','float','float')},\
skiprows=2)
print(list(re_read))
print("Sorry, i can't read complex format txt file now.")
print("Now, i can't read txt by formats type. Yeah!")
# re_read=np.fromregex()
print("I think i don't need to use fromregex to read complex data type, now.")
数据可视化
python | preferfences
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 26 23:52:26 2020
@author: Yanjie Zhang (Python 3.7)
@email: lianlizyj@mail.dlut.edu.cn
Description:
Purpose:
Learning to visualize the data
"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import (MultipleLocator,FormatStrFormatter,\
AutoMinorLocator)
num_points=50
x_min,x_max=0,4
x_values=np.linspace(x_min, x_max,num_points)
y_values=x_values**2
# check for the length of x and y
assert len(x_values)==len(y_values),\
"Length-mismtch: {:d} versus {:d}".format(len(x_values),len(y_values))
plt.plot(x_values, y_values,'r--d')
# plt.scatter(x_values, y_values)
# plt.semilogy(x_values, y_values,'r-o')
# plt.loglog(x_values, y_values,'r-o')
plt.xlim(1,3) # xlim([1,3]) in matlab
plt.axis('auto') # axis equal/square/tight/auto in matlab
ax=plt.gca()
ax.set_title("My first plot in python",size=24, weight='bold',family='Times New Roman',fontsize=30,fontweight=1.5)
ax.set_xlabel("x [$\mu$m]",family='Times New Roman', weight='bold',fontsize=15,fontweight=1.5)
ax.set_ylabel('y',family='Times New Roman', weight='bold',fontsize=15,fontweight=1.5)
lines=ax.get_lines()
plt.setp(lines[0],linestyle='--',linewidth=3,color='c') # set_property
# %% use "label" keyword to set labels when plotting
plt.plot(x_values,y_values,label="population 1")
plt.plot(x_values,x_values**3,label= "population 2")
# plt.plot(x_values,y_values)
# plt.plot(x_values,x_values**3)
plt.legend()
# use line objects to set labels after plotting.
ax=plt.gca()
lines=ax.get_lines()
lines[0].set_label("Infected population")
lines[1].set_label("Cured population")
ax.legend()
# =============================================================================
# Make a plot with major ticks that are multiples of 20 and minor ticks that
# are multiples of 5. Label mahor ticks with '%d' formatting but don't label
# minorticks.
# =============================================================================
# ax.xaxis.grid(True,which='minor')
# ax.yaxis.grid(True,which='minor')
ax.xaxis.set_major_locator(MultipleLocator(0.5))
ax.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))
ax.yaxis.set_major_locator(MultipleLocator(10))
ax.yaxis.set_major_formatter(FormatStrFormatter('%d'))
# =============================================================================
# For the minor ticks, use no labels; default NullFormatter
# =============================================================================
ax.xaxis.set_minor_locator(MultipleLocator(0.1))
# ax.yaxis.set_minor_locator(MultipleLocator(2))
ax.yaxis.set_minor_locator(AutoMinorLocator())
# change the orientation of ticks (in/out/inout)
plt.rcParams['xtick.direction']='in'
plt.rcParams['ytick.direction']='in'
ax.tick_params(which='both',width=2)
ax.tick_params(which='major', length=7,color='b')
ax.tick_params(which='minor', length=4,color='r')
# change the tick label property
plt.xticks(family='Times New Roman', weight='normal',fontsize=12)
plt.yticks(family='Times New Roman', weight='normal',fontsize=12)
# You can specify a rotation for tick labels in degrees or with keywords
plt.yticks(rotation='vertical')
# =============================================================================
# # Use Axes object to set labels after plotting (cmd+4)
# =============================================================================
ax.legend(("Healthy","Recovered"),loc=0) # upper/lower right/left/center
plt.show()
# %% plot with errorbar
plt.figure()
x_errors=np.random.random(len(x_values))
y_errors=np.random.random(len(x_values))
# plt.errorbar(x_values,y_values,yerr=y_errors,xerr=x_errors)
plt.errorbar(x_values,y_values,yerr=y_errors,fmt='o',ecolor='r',color='b',elinewidth=2,capsize=4)
plt.show()
# %% plot 3D graphy
from mpl_toolkits.mplot3d import Axes3D
fig=plt.figure() # create a new figure
ax=Axes3D(fig) # create 3D plotterattached to figure
t=np.linspace(0,5*np.pi,501) # define parameter for parameteric plot
ax.plot(np.cos(t),np.sin(t),t) # draw 3D plot
ax.view_init(elev=30,azim=30) # change the view
# %% multiple plot
x=np.linspace(0,1,5)
y1=np.exp(x)
y2=x**2
fig=plt.figure()
plt.plot(x,y1,'r--^',x,y2,'k-o')
# =============================================================================
# test: 3B
# =============================================================================
num_curves=3
x=np.linspace(0,1,501)
y=np.zeros((x.size,num_curves))
for n in range(num_curves):
y[:,n]=np.sin((n+1)*x*2*np.pi)
plt.figure()
plt.plot(x,y)
plt.legend(("2$\pi$","4$\pi$","6$\pi$"),loc='best')
# =============================================================================
# cla & hold
# =============================================================================
# fig=plt.figure()
t=np.linspace(0,5*np.pi,501)
plt.plot(t,np.sin(t)**2)
plt.plot(t,np.sin(t)**3)
ax=plt.gca()
plt.cla()
# ax.hold(True)
print("Hold attribute has been removed from matplotlib 3")
plt.plot(t,np.sin(t)**4)
print("You can close figure by plt.close() or plt.close('name') or plt.close('all')")
# %% subplot
from numpy.random import random
t=np.linspace(0,1,101)
plt.figure()
plt.subplot(2,2,1);plt.hist(random(20))
plt.subplot(2,2,2);plt.plot(t,t**2,t,t**3-t)
plt.subplot(2,2,3);plt.plot(random(20),random(20),'r*')
plt.subplot(2,2,4);plt.plot(t*np.cos(10*t),t*np.sin(10*t))
plt.tight_layout()
# fig=plt.gcf()
plt.savefig("greatest_figure_ever.pdf")
function
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 29 15:10:44 2020
@author: Yanjie Zhang
@email: lianlizyj@mail.dlut.edu.cn
Description:
Purpose:
"""
import numpy as np
import matplotlib as plt
from matplotlib.ticker import (MultipleLocator,FormatStrFormatter,\
AutoMinorLocator)
def taxicab(pointA,pointB):
"""
Taxicab metric for computing distance between points A and B
pointA=(x1,y1)
pointB=(x2,y2)
Return |x2-x1|+|y2-y1|, Distances are measured in city blocks.
"""
interval=abs(pointB[0]-pointA[0])+abs(pointB[1]-pointA[1])
return interval
# %%
fare_rate=0.4
start=(1,2)
stop = (4,5)
a=taxicab(start, stop) *fare_rate
print(a)
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