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PGRRpost.py
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PGRRpost.py
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############################################################################
# Project: The Lenard effect of preciptation at the RUAO,
# Title: Ensemble processing of the PG, Time and Rain Rate data,
# Author: James Gilmore,
# Email: james.gilmore@pgr.reading.ac.uk.
# Version: 1.0.8
# Date: 18/01/16
# Status: Operational (Basic)
############################################################################
#Initialising the python script
from __future__ import absolute_import, division, print_function
from scipy import stats, interpolate
from lowess import lowess
from array import array
import sys,csv
import numpy as np
import matplotlib.pyplot as plt
execfile("externals.py")
np.set_printoptions(threshold='nan')
y = "y"
n = "n"
#User input for the further processing of the PGRR data
print("####################################################################")
print("The Lenard effect of preciptation at the RUAO. Using the processed ")
print("data collected from the PGRainRate.py script the average for each ")
print("rain rate can be found.")
print("####################################################################\n")
selectcase = input("Please select the averaging method: Type '1' for Mean, Type '2' for Median: ")
loop = str(input('Do you want to loop over many bins? y/n: '))
if loop == "n":
bincount = input("How many bins for the averaging would you like (recommended = 100): ")
loop = 1
elif loop == "y":
bincount = 30
loop = 10
#Import the processed data for the significantly charged rain. See PGRainRate.py
year, month, time, rainrate, pg = np.genfromtxt('processeddata/PGdata.csv', dtype=float, delimiter=',', unpack=True)
#Remove zero values from processed data. Used "year" as criteria as theres a chance of real
#zero values cropping up in other columns.
Month = month.copy()[year.copy()!=0]
Time = time.copy()[year.copy()!=0]
Rainrate = rainrate.copy()[year.copy()!=0]
PG = pg.copy()[year.copy()!=0]
Year = year.copy()[year.copy()!=0]
slope = np.zeros(int(loop))
intercept = np.zeros(int(loop))
r_value = np.zeros(int(loop))
p_value = np.zeros(int(loop))
std_err = np.zeros(int(loop))
lowessval = np.zeros([int(loop),bincount+30*loop])
PGRR = np.asarray(zip(Rainrate, PG))
PGRRsort = PGRR[np.lexsort((PGRR[:, 1], PGRR[:, 0]))]
print("mean = ", np.mean(PGRRsort[:, 1]), " median = ", np.median(PGRRsort[:, 1]))
PGsort = PGRRsort[:,1]
RRsort = PGRRsort[:,0]
for k in xrange(loop):
#Initalise the matrices and vectors
RainRateBin = np.zeros((bincount+30*k)-1)
RainRateBinLimit = np.zeros(bincount+30*(k))
TimeTipBin = np.zeros(bincount+30*(k))
PGTipBin = np.zeros(bincount+30*(k))
TotalBin = np.zeros(bincount+30*(k))
PGTipBinMedian = np.zeros([bincount+30*(k),len(Year)])
#PGTipBinMedianConst = np.zeros([bincount+30*k, len(PGRR)/(bincount+30*k)]) probs dont need it now (for case ==3)
PGTipPosition = np.zeros(bincount+30*(k))
PGTipBinMedianFinal = np.zeros(bincount+30*(k))
eps = sys.float_info.epsilon
#Define the Rain Rate for each bin with the centred values determined as well.
for i in range(bincount+30*(k)):
RainRateBinLimit[i] = i*5/(bincount+30*(k))
for i in range((bincount+30*(k))-1):
RainRateBin[i] = 0.5*(RainRateBinLimit[i+1]-RainRateBinLimit[i])
if selectcase == 1:
############################################################################
#Define the mean (ensemble) PG and Tip Times for the statistically significant data.
#Equal Bin Spacing, Variable Bin Counts
for j in range(len(Year)):
#print("PG[j]", PG[j])
for i in range(1,bincount+30*(k)):
if (Rainrate[j] < i*5/(bincount+30*(k)) and Rainrate[j] > (i-1)*5/(bincount+30*(k))):
PGTipBin[i] += PG[j]
TimeTipBin[i] += Time[j]
TotalBin[i] += 1
PGTipBinned = PGTipBin.copy()/(TotalBin.copy()+eps)
TimeTipBinned = TimeTipBin.copy()/(TotalBin.copy()+eps)
#Removes NaN values
PGTipBinned = [0 if np.isnan(x) else x for x in PGTipBinned]
TimeTipBinned = [0 if np.isnan(x) else x for x in TimeTipBinned]
#Select values for plotting
yvalue = np.asarray(PGTipBinned)
amethod = "Mean"
############################################################################
elif selectcase == 2:
############################################################################
#Define the median PG and Tip Times for the statistically significant data.
#Equal Bin Spacing, Variable Bin Counts
for j in range(len(Year)):
for i in range(1,bincount+30*(k)):
if (Rainrate[j] < i*5/(bincount+30*(k)) and Rainrate[j] > (i-1)*5/(bincount+30*(k))):
PGTipBinMedian[i,PGTipPosition[i]] = PG[j]
PGTipPosition[i]+=1
for i in range(bincount+30*(k)):
PGTipBinMedianFinal[i] = np.median(PGTipBinMedian[i,:].copy()[PGTipBinMedian[i,:].copy()!=0])
PGTipBinMedianFinal[np.isnan(PGTipBinMedianFinal)] = 0
#Select values for plotting
yvalue = np.asarray(PGTipBinMedianFinal)
amethod = "Median"
print("PGTipBinMedian", PGTipBinMedianFinal)
############################################################################
elif selectcase == 3:
############################################################################
#Define the mean (ensemble) PG and Tip Times for the statistically significant data.
#Variable Bin Spacing, Equal Bin Counts
PGTipBinned = np.zeros(bincount+30*(k))
PGTipBinned[0] = np.mean(PGRRsort[0:(len(PGRR)/(bincount+30*k)), 1].copy())
for i in range(1,(bincount+30*(k))-1):
PGTipBinned[i] = np.mean(PGRRsort[(len(PGRR)/(bincount+30*k)*i):(len(PGRR)/(bincount+30*k)*(i+1)), 1].copy())
PGTipBinned[np.isnan(PGTipBinned)] = 0
#Select values for plotting
yvalue = np.asarray(PGTipBinned)
amethod = "Mean"
#print("PGTipBinned", PGTipBinned)
#Define the Rain Rate for each bin with the centred values determined as well.
RainRateBinLimit[0] = 0.5*PGRRsort[(len(PGRR)/(bincount+30*k)), 0]
for i in range(1,bincount+30*(k)):
RainRateBinLimit[i] = 0.5*(PGRRsort[(len(PGRR)/(bincount+30*k)*i), 0]-PGRRsort[(len(PGRR)/(bincount+30*k)*(i-1)), 0])+PGRRsort[(len(PGRR)/(bincount+30*k)*(i-1)), 0]
print(RainRateBinLimit)
############################################################################
elif selectcase == 4:
############################################################################
#Define the median PG and Tip Times for the statistically significant data.
#Variable Bin Spacing, Equal Bin Counts
PGTipBinMedianFinal[0] = np.median(PGRRsort[0:(len(PGRR)/(bincount+30*k)), 1].copy())
for i in range(1,(bincount+30*(k))-1):
PGTipBinMedianFinal[i] = np.median(PGRRsort[(len(PGRR)/(bincount+30*k)*i):(len(PGRR)/(bincount+30*k)*(i+1)), 1].copy())
PGTipBinMedianFinal[np.isnan(PGTipBinMedianFinal)] = 0
#Select values for plotting
yvalue = np.asarray(PGTipBinMedianFinal)
amethod = "Median"
print("PGTipBinMedian", np.sort(PGTipBinMedianFinal))
#Define the Rain Rate for each bin with the centred values determined as well.
RainRateBinLimit[0] = 0.5*PGRRsort[(len(PGRR)/(bincount+30*k)), 0]
for i in range(1,bincount+30*(k)):
RainRateBinLimit[i] = 0.5*(PGRRsort[(len(PGRR)/(bincount+30*k)*i), 0]-PGRRsort[(len(PGRR)/(bincount+30*k)*(i-1)), 0])+PGRRsort[(len(PGRR)/(bincount+30*k)*(i-1)), 0]
print(RainRateBinLimit)
############################################################################
else:
sys.exit("Please select either the Mean (1) or Median (2) case.")
print("Bin Counts", PGTipPosition)
#Calculation of the linear regression model along with statistical parameters.
slope[k], intercept[k], r_value[k], p_value[k], std_err[k] = stats.linregress(RainRateBinLimit, yvalue)
#print("RainRateBinLimit", RainRateBinLimit)
#print("yvalue", yvalue)
for m in xrange(len(lowess(RainRateBinLimit+eps, yvalue+eps, 1/2))):
lowessval[k,m] = lowess(RainRateBinLimit+eps, yvalue+eps, 1/2)[m]
if loop == 10:
PGRainEnsembleMulti(np.max(RainRateBinLimit)+0.2, np.max(yvalue)+0.2, "PGEnsembleMulti" + str(amethod) + str(bincount), "png", RainRateBinLimit, yvalue, lowessval)
print(slope, intercept, r_value, p_value, std_err)
print("P-Value: ", p_value)
print("R^2 Value: ", r_value**2)
print("Standard Error: ", std_err)
#print(lowessval[0,:])
PGEnsembleData = zip(RainRateBinLimit, PGTipBinMedianFinal)
with open("processeddata/PGEnsembleData.csv", "wb") as output:
writer = csv.writer(output, lineterminator='\n')
writer.writerows(PGEnsembleData)
PGEnsembleLowess = zip(RainRateBinLimit, lowessval[0,:])
with open("processeddata/PGEnsembleLowess.csv", "wb") as output:
writer = csv.writer(output, lineterminator='\n')
writer.writerows(PGEnsembleLowess)
#Plot the ensemble PG against Rain Rate. See external.py for the source function.
PGRainSlim(np.max(RainRateBinLimit)+0.2, np.max(yvalue)+0.2, "PGEnsemble" + str(amethod) + str(bincount), "png", RainRateBinLimit, yvalue, slope, intercept)