df1['01 h'] =df1['15min'].rolling(window=4,center=False).sum()
import numpy as np # sample x and y data - example x = [7.76,10.11,11.89,14.81,15.49] y = [1.851,1.971,1.953,1.842,1.805] # the polyfit functions does the nth degree polynomial best fit on the data, # returning the polynomial coefficients n = 4 # 4th degree polynomial, you can change for whatever degree you want coefs = np.polyfit(x,y,n) # The poly1d function applies the polynomial function to our calculated coefficients polyf = np.poly1d(coefs) #if we want to apply our polynomial function to a range of x values xf = np.linspace(0,20) yf = polyf(xf)
#-*-coding:utf-8-*-;
import matplotlib.pyplot as plt
from matplotlib import gridspec
import numpy as np
def plotHH(t,sP,sQ):
fig = plt.figure()
gs = gridspec.GridSpec(2, 1, height_ratios=[1, 2])
# HYDROGRAM CHART
ax = plt.subplot(gs[1])
ax.plot(t,sQ)
ax.set_ylabel(u'Q(m³/s)', color='b')
ax.set_xlabel('Time (min.)')
ax.tick_params(axis='y', colors='b')
ax.xaxis.grid(b=True, which='major', color='.7', linestyle='-')
ax.yaxis.grid(b=True, which='major', color='.7', linestyle='-')
ax.set_xlim(min(t), max(t))
ax.set_ylim(0, max(sQ)*1.2)
# PRECIPITATION/HYETOGRAPH CHART
ax2 = plt.subplot(gs[0])
ax2.bar(t, sP, 1, color='#b0c4de')
ax2.xaxis.grid(b=True, which='major', color='.7', linestyle='-')
ax2.yaxis.grid(b=True, which='major', color='0.7', linestyle='-')
ax2.set_ylabel('P(mm)')
ax2.set_xlim(min(t), max(t))
plt.setp(ax2.get_xticklabels(), visible=False)
plt.tight_layout()
ax2.invert_yaxis()
plt.gcf().subplots_adjust(bottom=0.15)
plt.show()
#plt.savefig(filename,format='pdf')
plt.close(fig)
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.linear_model import LinearRegression
# Importing the dataset
dataset = pd.read_csv('data.csv')
# separate last column of dataset as dependent variable - y
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values
# build the regressor and print summary results
regressor = LinearRegression()
regressor.fit(X,y)
print('Coefficients:\t','\t'.join([str(c) for c in regressor.coef_]))
print('R2 =\t',regressor.score(X,y, sample_weight=None))
#plot the results if you like
y_pred = regressor.predict(X)
plt.scatter(y_pred,y)
plt.plot([min(y_pred),max(y_pred)],[min(y_pred),max(y_pred)])
plt.legend()
plt.show()
from math import sqrt
def centroid(lstP):
sumCx = 0
sumCy = 0
sumAc= 0
for i in range(len(lstP)-1):
cX = (lstP[i][0]+lstP[i+1][0])*(lstP[i][0]*lstP[i+1][1]-lstP[i+1][0]*lstP[i][1])
cY = (lstP[i][1]+lstP[i+1][1])*(lstP[i][0]*lstP[i+1][1]-lstP[i+1][0]*lstP[i][1])
pA = (lstP[i][0]*lstP[i+1][1])-(lstP[i+1][0]*lstP[i][1])
sumCx+=cX
sumCy+=cY
sumAc+=pA
print cX,cY,pA
ar = sumAc/2.0
print ar
centr = ((1.0/(6.0*ar))*sumCx,(1.0/(6.0*ar))*sumCy)
return centr