#coding:utf-8
import numpy as np
import matplotlib.pyplot as plt
x=np.linspace(0,10,1000)
y=np.sin(x)
z=np.cos(x**2)
#控制图形的长和宽单位为英寸，
# 调用figure创建一个绘图对象，并且使它成为当前的绘图对象。
plt.figure(figsize=(8,4))
#$可以让字体变得跟好看
#给所绘制的曲线一个名字，此名字在图示(legend)中显示。
# 只要在字符串前后添加"$"符号，matplotlib就会使用其内嵌的latex引擎绘制的数学公式。
#color : 指定曲线的颜色
#linewidth : 指定曲线的宽度
plt.plot(x,y,label="$sin(x)$",color="red",linewidth=2)
#b-- 曲线的颜色和线型
plt.plot(x,z,"b--",label="$cos(x^2)$")
#设置X轴的文字
plt.xlabel("Time(s)")
#设置Y轴的文字
plt.ylabel("Volt")
#设置图表的标题
plt.title("PyPlot First Example")
#设置Y轴的范围
plt.ylim(-1.2,1.2)
#显示图示
plt.legend()
#显示出我们创建的所有绘图对象。
plt.show()

#coding:utf-8
import numpy as np
import matplotlib.pyplot as plt
x=np.arange(0,5,0.1)
## plot返回一个列表，通过line,获取其第一个元素
line,=plt.plot(x,x*x)
# 调用Line2D对象的set_*方法设置属性值 是否抗锯齿
line.set_antialiased(False)
# 同时绘制sin和cos两条曲线，lines是一个有两个Line2D对象的列表
lines = plt.plot(x, np.sin(x), x, np.cos(x))
## 调用setp函数同时配置多个Line2D对象的多个属性值
plt.setp(lines, color="r", linewidth=2.0)
plt.show()

import matplotlib.pyplot as plt

'''

subplot(numRows, numCols, plotNum)

numRows行 * numCols列个子区域

如果numRows，numCols和plotNum这三个数都小于10的话，可以把它们缩写为一个整数，例如subplot(323)和subplot(3,2,3)是相同的

'''

for idx, color in enumerate("rgbyck"):

    plt.subplot(330+idx+1, axisbg=color)

plt.show()

plt.subplot(221) # 第一行的左图

plt.subplot(222) # 第一行的右图

#第二行全占

plt.subplot(212) # 第二整行

plt.show()

#coding:utf-8

import matplotlib.pyplot as pl

from matplotlib.ticker import MultipleLocator, FuncFormatter

import numpy as np

x = np.arange(0, 4*np.pi, 0.01)

y = np.sin(x)

pl.figure(figsize=(8,4))

pl.plot(x, y)

ax = pl.gca()

def pi_formatter(x, pos):

    """

    比较罗嗦地将数值转换为以pi/4为单位的刻度文本

    """

    m = np.round(x / (np.pi/4))

    n = 4

    if m%2==0: m, n = m/2, n/2

    if m%2==0: m, n = m/2, n/2

    if m == 0:

        return ""

    if m == 1 and n == 1:

        return "$\pi$"

    if n == 1:

        return r"$%d \pi$" % m

    if m == 1:

        return r"$\frac{\pi}{%d}$" % n

    return r"$\frac{%d \pi}{%d}$" % (m,n)

# 设置两个坐标轴的范围

pl.ylim(-1.5,1.5)

pl.xlim(0, np.max(x))

# 设置图的底边距

pl.subplots_adjust(bottom = 0.15)

pl.grid() #开启网格

# 主刻度为pi/4

ax.xaxis.set_major_locator( MultipleLocator(np.pi/4) )

# 主刻度文本用pi_formatter函数计算

ax.xaxis.set_major_formatter( FuncFormatter( pi_formatter ) )

# 副刻度为pi/20

ax.xaxis.set_minor_locator( MultipleLocator(np.pi/20) )

# 设置刻度文本的大小

for tick in ax.xaxis.get_major_ticks():

    tick.label1.set_fontsize(16)

pl.show()

import matplotlib.pyplot as plt

from sklearn.datasets import load_boston

X1 = load_boston()['data'][:, [8]]

X2 = load_boston()['data'][:, [10]]

plt.scatter(X1,X2, marker = 'o')

plt.show()

m=pd.read_csv(sportinte)

x,y,z = m['ydra'],m['zyd'],m['rs12612420']

ax=plt.subplot(111,projection='3d') #创建一个三维的绘图工程

ax.scatter(x[:],y[:],z[:],c='r')

#将数据点分成三部分画，在颜色上有区分度

plt.scatter(y,z, marker = 'o')

ax.set_zlabel('rs12612420') #坐标轴

ax.set_ylabel(u'周运动')

ax.set_xlabel(u'运动热爱')

plt.show()

#coding:utf-

import  numpy as np

#pip install seaborn

import seaborn as sns

sns.set(style="whitegrid", color_codes=True)

np.random.seed(sum(map(ord, "categorical")))

titanic = sns.load_dataset("titanic")

tips = sns.load_dataset("tips")

iris = sns.load_dataset("iris")

#在带状图中，散点图通常会重叠。这使得很难看到数据的完全分布

sns.stripplot(x="day", y="total_bill", data=tips)

sns.plt.show()

#加入随机抖动”来调整位置

sns.stripplot(x="day", y="total_bill", data=tips, jitter=True);

#避免重叠点

sns.swarmplot(x="day", y="total_bill", data=tips)

#加入说明label

sns.swarmplot(x="day", y="total_bill", hue="sex", data=tips);

sportinte="/Users/similarface/Documents/sport耐力与爆发/sportinter.csv"

m=pd.read_csv(sportinte)

sns.swarmplot(x="ydra", y="zyd", hue="rs12612420", data=m);

sns.plt.show()

sportinte="/Users/similarface/Documents/sport耐力与爆发/sportinter.csv"

m=pd.read_csv(sportinte)

sns.boxplot(x="ydra", y="zyd", hue="rs12612420", data=m)

sns.plt.show()

sportinte="/Users/similarface/Documents/sport耐力与爆发/sportinter.csv"

m=pd.read_csv(sportinte)

sns.violinplot(x="ydra", y="zyd", hue="rs12612420", data=m)

sns.plt.show()

sportinte="/Users/similarface/Documents/sport耐力与爆发/sportinter.csv"

m=pd.read_csv(sportinte)

sns.violinplot(x="ydra", y="zyd", hue="rs12612420", data=m,bw=.1, scale="count", scale_hue=False)

sns.plt.show()

sns.violinplot(x="day", y="total_bill", hue="sex", data=tips, split=True);

sns.plt.show()

#加入每个观测值

sns.violinplot(x="day", y="total_bill", hue="sex", data=tips,

               split=True, inner="stick", palette="Set3");

#加入点柱状图和小提琴图
sns.violinplot(x="day", y="total_bill", data=tips, inner=None)

sns.swarmplot(x="day", y="total_bill", data=tips, color="w", alpha=.5);

sns.barplot(x="sex", y="survived", hue="class", data=titanic);

sns.plt.show()

#count 计数图

sns.countplot(x="deck", data=titanic, palette="Greens_d");

sns.countplot(y="deck", hue="class", data=titanic, palette="Greens_d");

#泰坦尼克号获取与船舱等级

sns.pointplot(x="sex", y="survived", hue="class", data=titanic)

sns.factorplot(x="time", y="total_bill", hue="smoker", col="day", data=tips, kind="box", size=4, aspect=.5);

sns.plt.show()

g = sns.PairGrid(tips,

                 x_vars=["smoker", "time", "sex"],

                 y_vars=["total_bill", "tip"],

                 aspect=.75, size=3.5)

g.map(sns.violinplot, palette="pastel");

sns.plt.show()

import matplotlib.pyplot as plt

import seaborn as sns

_ = sns.pairplot(df[:50], vars=[1,2,3,4,5,6,7,8,9,10,11], hue="class", size=1)

plt.show()

#coding:utf-

__author__ = 'similarface'

'''

耳垢项目

'''

import pandas as pd

import seaborn as sns

from scipy.stats import spearmanr

meat_to_phenotypes = {

  'Dry': ,

  'Wet': ,

  'Un': ,

}

meat_to_genotype = {

  'TT': ,

  'CT': ,

  'CC': ,

}

filepath="/Users/similarface/Documents/phenotypes/耳垢/ergou20170121.txt"

data=pd.read_csv(filepath,sep="\t")

data['phenotypesid'] = data['phenotypes'].map(meat_to_phenotypes)

data['genotypeid'] = data['genotype'].map(meat_to_genotype)

#######################################################################################

#均线图

#剔除不清楚

#####

# data=data[data.phenotypesid!=]

# p=sns.pointplot(x="genotype", y='phenotypesid', data=data,markers="o")

# p.axes.set_title(u"均线图[湿耳0干耳1]")

#####

#######################################################################################

#######################################################################################

#####

#联结图

# p=sns.jointplot(x="genotypeid", y="phenotypesid", data=data,stat_func=spearmanr)

#####

#######################################################################################

#######################################################################################

#####

# data=data[data.phenotypesid!=]

# sns.countplot(x="genotype", hue="phenotypes", data=data)

#p.axes.set_title(u"柱状图[湿耳0干耳1]")

#####

#######################################################################################

#sns.plt.show()

#######################################################################################

#####

import matplotlib.pyplot as plt

#

data=data[data.phenotypesid!=]

plt.subplot()

g=data.groupby(['phenotypes'])

label_list=g.count().index

plt.pie(g.count()['genotype'],labels=label_list,autopct="%1.1f%%")

plt.title(u"问卷统计饼图(不含Unkown)")

datag=data[data.genotype=='TT']

g=datag.groupby(['phenotypes'])

label_list=g.count().index

plt.subplot()

plt.pie(g.count()['genotype'],labels=label_list,autopct="%1.1f%%")

plt.title(u"耳垢TT")

datag=data[data.genotype=='CT']

g=datag.groupby(['phenotypes'])

label_list=g.count().index

plt.subplot()

plt.pie(g.count()['genotype'],labels=label_list,autopct="%1.1f%%")

plt.title(u"耳垢CT")

datag=data[data.genotype!='TT']

g=datag.groupby(['phenotypes'])

label_list=g.count().index

plt.subplot()

plt.pie(g.count()['genotype'],labels=label_list,autopct="%1.1f%%")

plt.title(u"耳垢!=TT")

plt.show()

#####

#######################################################################################

#coding:utf-8

__author__ = 'similarface'

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

import numpy as np

def fun(x,y):

    #return np.power(x,2)+np.power(y,2)

    return 2*(x*0.8+y*0.1)*(x*0.2+y*0.9)*(x*0.3+y*0.7)*(x*0.3+y*0.7)*(x*0.4+y*0.7)*(x*0.4+y*0.7)

def fun2(xx,yy):

    return xx

fig1=plt.figure()

ax=Axes3D(fig1)

X=np.arange(0,1,0.01)

Y=np.arange(0,1,0.01)

XX=np.arange(0,1,0.01)

YY=np.arange(1,0,-0.01)

ZZ=np.arange(0,1,0.01)

ZZ,ZZ=np.meshgrid(ZZ,ZZ)

#ZZ=fun2(XX,YY)

X,Y=np.meshgrid(X,Y)

Z=fun(X,Y)

plt.title("This is main title")

ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.cm.coolwarm)

ax.plot_surface(XX, YY, ZZ, rstride=1, cstride=1, cmap=plt.cm.coolwarm)

ax.set_xlabel(u'θ1', color='r')

ax.set_ylabel(u'θ2', color='g')

ax.set_zlabel('z label', color='b')

plt.show()