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regions.py
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regions.py
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import os
import matplotlib.pyplot as plt
import numpy as np
import datetime
from matplotlib.figure import Figure
from dateutil.relativedelta import relativedelta
from matplotlib.pyplot import figure, show
from matplotlib.patches import Rectangle
from matplotlib.mlab import find, prctile
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.mlab import movavg
from scipy.io import netcdf
from matplotlib.dates import YearLocator, MonthLocator, DateFormatter
from settings import WWDTNETCDF_DIR, WWDTNETCDF_DIR2, WWDTNETCDF_DIR3, ELEVATION_DATA
from regionDicts import *
WWDTNETCDF_DIR = WWDTNETCDF_DIR2
#from datetime import datetime
from matplotlib.font_manager import FontProperties
from matplotlib.ticker import ScalarFormatter
from matplotlib.ticker import MultipleLocator
class Climatology:
def __init__(self, region, variable, monthSpan, month, year):
'''Collect and Assign Parameters '''
self.variable = variable
self.monthSpan = monthSpan
self.region = region
self.month = month
self.year = year
# print self.region, self.variable, self.monthSpan
def Index(self, array, userInput):
'''Function to find the index value of an array value closest to user input value '''
# Convert array to list and find the value with the smallest difference between the array and user input.
userInput = float(userInput)
newList = [abs(userInput - i) for i in array]
originalList = [i for i in array]
newList.sort()
smallestDifference = newList[0]
# Return to index of the closest value to the original
index = userInput + smallestDifference
if index in originalList:
pass
else:
index = userInput - smallestDifference
closestIndex = originalList.index(index)
return closestIndex
def getData(self):
'''Open and plot the data '''
# Set station directory if needed
if int(self.region) in stationDict:
WWDTNETCDF_DIR = WWDTNETCDF_DIR3
else:
WWDTNETCDF_DIR = WWDTNETCDF_DIR2
# Open NetCDF Files
if self.variable == 'pdsi' or self.variable == 'scpdsi' or self.variable == 'pzi':
dataJan = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 1))
dataFeb = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 2))
dataMar = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 3))
dataApr = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 4))
dataMay = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 5))
dataJun = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 6))
dataJul = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 7))
dataAug = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 8))
dataSep = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 9))
dataOct = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 10))
dataNov = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 11))
dataDec = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 12))
else:
dataJan = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 1))
dataFeb = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 2))
dataMar = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 3))
dataApr = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 4))
dataMay = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 5))
dataJun = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 6))
dataJul = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 7))
dataAug = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 8))
dataSep = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 9))
dataOct = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 10))
dataNov = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 11))
dataDec = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 12))
#print dataJan, dataFeb, dataMar, dataApr, dataMay, dataJun, dataJul, dataAug, dataSep, dataOct, dataNov, dataDec
# Open the netcdf files
dataJan = netcdf.netcdf_file(dataJan, 'r')
dataFeb = netcdf.netcdf_file(dataFeb, 'r')
dataMar = netcdf.netcdf_file(dataMar, 'r')
dataApr = netcdf.netcdf_file(dataApr, 'r')
dataMay = netcdf.netcdf_file(dataMay, 'r')
dataJun = netcdf.netcdf_file(dataJun, 'r')
dataJul = netcdf.netcdf_file(dataJul, 'r')
dataAug = netcdf.netcdf_file(dataAug, 'r')
dataSep = netcdf.netcdf_file(dataSep, 'r')
dataOct = netcdf.netcdf_file(dataOct, 'r')
dataNov = netcdf.netcdf_file(dataNov, 'r')
dataDec = netcdf.netcdf_file(dataDec, 'r')
# Get closest Lat/Lon
if int(self.region) in stationDict:
closestRegion = self.Index(dataJan.variables['station_ID'], self.region)
else:
closestRegion = self.Index(dataJan.variables['polygon'], self.region)
# Set up time features
today = datetime.datetime.today()
currentMonth = today.month
currentDay = today.day
# Set len of index for current year
currentYear = today.year
currentYear = range(1895, currentYear+1, 1)
currentYear = len(currentYear)
# Select all data that exists
if currentMonth > 1 and currentDay > 2:
dataJan = np.array(dataJan.variables['data'][:currentYear,closestRegion])
else:
dataJan = np.array(dataJan.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 2 and currentDay > 2:
dataFeb = np.array(dataFeb.variables['data'][:currentYear,closestRegion])
else:
dataFeb = np.array(dataFeb.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 3 and currentDay > 2:
dataMar = np.array(dataMar.variables['data'][:currentYear,closestRegion])
else:
dataMar = np.array(dataMar.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 4 and currentDay > 2:
dataApr = np.array(dataApr.variables['data'][:currentYear,closestRegion])
else:
dataApr = np.array(dataApr.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 5 and currentDay > 2:
dataMay = np.array(dataMay.variables['data'][:currentYear,closestRegion])
else:
dataMay = np.array(dataMay.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 6 and currentDay > 2:
dataJun = np.array(dataJun.variables['data'][:currentYear,closestRegion])
else:
dataJun = np.array(dataJun.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 7 and currentDay > 2:
dataJul = np.array(dataJul.variables['data'][:currentYear,closestRegion])
else:
dataJul = np.array(dataJul.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 8 and currentDay > 2:
dataAug = np.array(dataAug.variables['data'][:currentYear,closestRegion])
else:
dataAug = np.array(dataAug.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 9 and currentDay > 2:
dataSep = np.array(dataSep.variables['data'][:currentYear,closestRegion])
else:
dataSep = np.array(dataSep.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 10 and currentDay > 2:
dataOct = np.array(dataOct.variables['data'][:currentYear,closestRegion])
else:
dataOct = np.array(dataOct.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 11 and currentDay > 2:
dataNov = np.array(dataNov.variables['data'][:currentYear,closestRegion])
else:
dataNov = np.array(dataNov.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 12 and currentDay > 2:
dataDec = np.array(dataDec.variables['data'][:currentYear,closestRegion])
else:
dataDec = np.array(dataDec.variables['data'][:(currentYear-1),closestRegion])
# Convert C to F
if self.variable == 'mdn':
dataJan = ((dataJan* 9.0/5) + 32)
dataFeb = ((dataFeb* 9.0/5) + 32)
dataMar = ((dataMar* 9.0/5) + 32)
dataApr = ((dataApr* 9.0/5) + 32)
dataMay = ((dataMay* 9.0/5) + 32)
dataJun = ((dataJun* 9.0/5) + 32)
dataJul = ((dataJul* 9.0/5) + 32)
dataAug = ((dataAug* 9.0/5) + 32)
dataSep = ((dataSep* 9.0/5) + 32)
dataOct = ((dataOct* 9.0/5) + 32)
dataNov = ((dataNov* 9.0/5) + 32)
dataDec = ((dataDec* 9.0/5) + 32)
# Convert Precip to inches
if self.variable == 'pon':
dataJan = dataJan/24.5
dataFeb = dataFeb/24.5
dataMar = dataMar/24.5
dataApr = dataApr/24.5
dataMay = dataMay/24.5
dataJun = dataJun/24.5
dataJul = dataJul/24.5
dataAug = dataAug/24.5
dataSep = dataSep/24.5
dataOct = dataOct/24.5
dataNov = dataNov/24.5
dataDec = dataDec/24.5
# Pull in the monthly percentages
DATA = np.array([dataJan,
dataFeb,
dataMar,
dataApr,
dataMay,
dataJun,
dataJul,
dataAug,
dataSep,
dataOct,
dataNov,
dataDec], dtype=object)
# Pull out means for each month
meanJan = sum(DATA[0])/len(DATA[0])
meanFeb = sum(DATA[1])/len(DATA[1])
meanMar = sum(DATA[2])/len(DATA[2])
meanApr = sum(DATA[3])/len(DATA[3])
meanMay = sum(DATA[4])/len(DATA[4])
meanJun = sum(DATA[5])/len(DATA[5])
meanJul = sum(DATA[6])/len(DATA[6])
meanAug = sum(DATA[7])/len(DATA[7])
meanSep = sum(DATA[8])/len(DATA[8])
meanOct = sum(DATA[9])/len(DATA[9])
meanNov = sum(DATA[10])/len(DATA[10])
meanDec = sum(DATA[11])/len(DATA[11])
#print meanJan
#print len(DATA[0]), len(DATA[11])
# Colors for fill between, median and months legend colors
perc_colors = ( [.85, .99, .99], [.7, .99, .99], [.5, .99, .99], [0, 0, 0], [1, 0, 0], [1,1,1] )
plevels = [5., 10., 25., 50., 75., 90., 95.]
# Get percentages for months e.g. perc = Jan percentages
perc = prctile(DATA[0], plevels[:])
perc2 = prctile(DATA[1], plevels[:])
perc3 = prctile(DATA[2], plevels[:])
perc4 = prctile(DATA[3], plevels[:])
perc5 = prctile(DATA[4], plevels[:])
perc6 = prctile(DATA[5], plevels[:])
perc7 = prctile(DATA[6], plevels[:])
perc8 = prctile(DATA[7], plevels[:])
perc9 = prctile(DATA[8], plevels[:])
perc10 = prctile(DATA[9], plevels[:])
perc11 = prctile(DATA[10], plevels[:])
perc12 = prctile(DATA[11], plevels[:])
# Set number of months to go back
xMonth = self.monthSpan
# Set the span to the x-month span
span = xMonth
## Date format for red line
oneDay = 1
oneMonth = self.month
oneYear = self.year
endDate = datetime.datetime(oneYear,oneMonth,oneDay)
startDate = endDate + relativedelta(months =- self.monthSpan)
delta = relativedelta(months=+1)
#print 'startDate: ', startDate
#print 'endDate: ', endDate
# varibale to become new date
newDate = startDate
# create list of dates to use for x-axis
dateList = []
while newDate <= endDate:
dateList.append(newDate)
newDate += delta
#print dateList
# Get months from start date to end date
monthSpanListData = []
# Create list for months, years
monthDates = []
yearDates = []
while startDate <= endDate:
#print 'Month: ', startDate.month, startDate.year
if self.variable == 'pdsi' or self.variable == 'scpdsi' or self.variable == 'pzi':
dataFile = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, startDate.month))
#print dataFile
dataFile = netcdf.netcdf_file(dataFile, 'r')
#dataFile = netcdf.netcdf_file('/media/drought/WWDTNETCDF/PRISM/%s/%s_%s_PRISM.nc'%(self.variable, self.variable, startDate.month), 'r')
#print ('/media/drought/WWDTNETCDF/PRISM/%s/%s_%s_PRISM.nc'%(self.variable, self.variable, startDate.month), 'r')
else:
dataFile = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, startDate.month))
dataFile = netcdf.netcdf_file(dataFile, 'r')
#closestRegion = self.Index(dataFile.variables['station_ID'], self.region)
# Get closest Lat/Lon
#if int(self.region) in stationDict:
# closestRegion = self.Index(dataJan.variables['station_ID'], self.region)
#else:
# closestRegion = self.Index(dataJan.variables['polygon'], self.region)
data = np.array(dataFile.variables['data'][startDate.year-1895:startDate.year-1894 ,closestRegion])
for value in data:
# Convert C to F
if self.variable == 'mdn':
value = ((value* 9.0/5) + 32)
# Convert Precip to inches
if self.variable == 'pon':
value = value/24.5
monthSpanListData.append(value)
# Create value to append for date to return through django
monthString = '%2.0f' % startDate.month
yearString = '%4.0f' % startDate.year
monthDates.append(monthString)
yearDates.append(yearString)
startDate += delta
# Add y-axis data
# Use y = monthSpanListData[:-1] when all data present
lastMonthY = monthSpanListData[1:]
# Set back a month if data has not been made for last month
#if today.day < 2:
# xMonth = xMonth + 1
# Set the starting month
startDate = (datetime.date(oneYear, oneMonth, 25) - datetime.timedelta(xMonth*365/12))
startMonth = startDate.month
# print startDate
# Create label for x axis
xLabelCombiner = ['J','F','M','A','M','J','J','A','S','O','N','D']
xLabelList = []
# List to hold the month span
spanList = []
# Needed after update to tool that includes the selected month to go back from
startMonth+=1
# Grab months in order
while len(spanList) < span:
if startMonth < 13:
#print startMonth, xLabelCombiner[startMonth-1]
spanList.append(startMonth)
xLabelList.append(xLabelCombiner[startMonth-1])
else:
startMonth=0
startMonth+=1
# Combine the spanlist with the percentage lines
combiner = [perc,perc2,perc3,perc4,perc5,perc6,perc7,perc8,perc9,perc10,perc11,perc12]
meanCombiner = [meanJan, meanFeb, meanMar, meanApr, meanMay, meanJun, meanJul, meanAug, meanSep, meanOct, meanNov, meanDec]
# Create empty lists to fill lines
meanLine = []
percLine = []
perc2Line = []
perc3Line = []
perc4Line = []
perc5Line = []
perc6Line = []
perc7Line = []
# Collect for all 7 lines
for x in spanList:
index = x-1
adder = combiner[index]
meanAdder = meanCombiner[index]
meanLine.append(meanAdder)
percLine.append(adder[0])
perc2Line.append(adder[1])
perc3Line.append(adder[2])
perc4Line.append(adder[3])
perc5Line.append(adder[4])
perc6Line.append(adder[5])
perc7Line.append(adder[6])
# Set range for months x-axis
x = range(0,span, 1)
#plevels = [5., 10., 25., 50., 75., 90., 95.]
#Date,Value,Median,5th,95th,10th,90th,25th,75th
#stringValue = len(monthDates)
#zeroValue = 0
#while zeroValue < stringValue:
# print monthDates[zeroValue],yearDates[zeroValue],lastMonthY[zeroValue],meanLine[zeroValue],percLine[zeroValue],perc7Line[zeroValue],perc2Line[zeroValue],perc6Line[zeroValue],perc3Line[zeroValue],perc4Line[zeroValue]
# zeroValue+=1
# Create Figure
fig = Figure(figsize=(11,8), facecolor='w')
ax = fig.add_axes([0.08, 0.15, .70, 0.78])
# Eliminate extra month from dateList
x = dateList[1:]
# Plot percentage lines
#ax.plot_date(x, percLine, color='white')
#ax.plot_date(x, perc2Line, color='white')
#ax.plot_date(x, perc3Line, color='white')
#ax.plot_date(x, perc4Line, color='white')
#ax.plot_date(x, perc5Line, color='white')
#ax.plot_date(x, perc6Line, color='white')
#ax.plot_date(x, perc7Line, color='white')
# Plot mean line
#meanLine = ax.plot(meanLine, 'k', linewidth=1.5)
# Plot the x's and the lastXmonth line
#
# Check here to make sure red line is correct
#
#print len(lastMonthY)
lastXLine = ax.plot_date(x, lastMonthY, 'r--', linewidth=1)
lastX = ax.plot_date(x, lastMonthY, 'rx', linewidth=2, markersize=12, markeredgewidth=3, markerfacecolor="red" )
# Fill in between percentage lines
ax.fill_between(x, percLine, perc2Line, facecolor=perc_colors[0], edgecolor='w', interpolate=True)
ax.fill_between(x, perc2Line, perc3Line, facecolor=perc_colors[1], edgecolor='w', interpolate=True)
ax.fill_between(x, perc3Line, perc4Line, facecolor=perc_colors[2], edgecolor='w', interpolate=True)
ax.fill_between(x, perc4Line, perc5Line, facecolor=perc_colors[2], edgecolor='black', interpolate=True)
ax.fill_between(x, perc5Line, perc6Line, facecolor=perc_colors[1], edgecolor='w', interpolate=True)
ax.fill_between(x, perc6Line, perc7Line, facecolor=perc_colors[0], edgecolor='w', interpolate=True)
# set up locator for formating
years = YearLocator() # every year
months = MonthLocator() # every month
threeMonths = MonthLocator(bymonth=None, bymonthday=1, interval=3, tz=None)
yearsFmt = DateFormatter('%B %Y')
monthsFmt = DateFormatter('%B')
# Set x-axis labels
if span > 48:
ax.xaxis.set_major_locator(years)
elif span > 36:
ax.xaxis.set_major_locator(threeMonths)
elif span > 1:
ax.xaxis.set_major_locator(months)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
ax.autoscale_view()
ax.grid(True)
ax.tick_params(axis='both', which='major', labelsize=8)
fig.autofmt_xdate()
# Set title
#ax.set_title('%s Last %s-Months'%(self.variable,span))
# Used to set month name in plots based on month index
monthList = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
# Set name from region dictionary
#regionName = stateDict[self.region]
regionName = allRegionDict[int(self.region)]
# add climate division to name if needed
if int(self.region) in divDict:
regionName = regionName + " Climate Division"
# Sets the correct year for use in plot header
oneYear = oneYear = dateList[-1].year
# Setup plots based on Variable
if self.variable == 'pdsi':
ax.set_title(u'Palmer Drought Severity Index, %s-Months Ending in %s,%4.0f \n %s' % (span, monthList[(oneMonth-1)], oneYear, regionName))
ax.set_ylabel("PDSI")
if self.variable == 'scpdsi':
ax.set_title(u' Self Calibrated Palmer Drought Severity Index, %s-Months Ending in %s,%4.0f \n %s' % (span, monthList[(oneMonth-1)], oneYear, regionName))
ax.set_ylabel("SCPDSI")
if self.variable == 'pzi':
ax.set_title(u' Palmer Z-Index, %s-Months Ending in %s,%4.0f \n %s' % (span, monthList[(oneMonth-1)], oneYear, regionName))
ax.set_ylabel("PZI")
if self.variable == 'mdn':
ax.set_title(u'Mean Temperature, %s-Months Ending in %s,%4.0f \n %s' % (span, monthList[(oneMonth-1)], oneYear, regionName))
ax.set_ylabel(u"Temperature \u00b0F")
if self.variable == 'spi':
ax.set_title(u'Standardized Precipitation Index, %s-Months Ending in %s,%4.0f \n %s' % (span, monthList[(oneMonth-1)], oneYear, regionName))
ax.set_ylabel(u"SPI")
if self.variable == 'spei':
ax.set_title(u'Standardized Precipitation-Evapotranspiration Index, %s-Months Ending in %s,%4.0f \n %s' % (span, monthList[(oneMonth-1)], oneYear, regionName), fontsize=12)
ax.set_ylabel(u"SPEI")
if self.variable == 'pon':
ax.set_title(u'Precipitation, %s-Months Ending in %s,%4.0f \n %s' % (span, monthList[(oneMonth-1)], oneYear, regionName))
ax.set_ylabel("Inches")
# Set axes
#ax.set_ylabel('%s'%self.variable)
#ax.set_xlabel('From %s-%s - %s-%s'%(startDate.month, startDate.year, self.month, oneYear))
ax.autoscale_view(tight=False)
# Set text
#ax.text(1.1, 01.10, "Western Regional\nClimate Center\nUniversity of Idaho",{'size':"x-small", 'family':"monospace"}, color='black')#, ha='right', va='top', alpha=0.9)
# Set legend
labels = ( r"$5-95^{th}$", r"$10-90^{th}$", r"$25-75^{th}$", r"$Median$", r"$Monthly$",r"$Values$")
# Set legend outside of plot axes
ax.legend([Rectangle((0,0),1,1, facecolor=c, edgecolor='w') for c in perc_colors ], labels, bbox_to_anchor=(1.01, 1.), loc=2, borderaxespad=0., shadow=True)
currentYear = today.year
ax.set_xlabel("Data Source: WRCC/UI\n Created: %s-%s-%s" % (currentMonth,currentDay,currentYear), fontsize=9)
ax.xaxis.set_label_coords(1.106, .59, transform=None)
return fig
def getText(self):
'''Open and plot the data '''
# Set station directory if needed
if int(self.region) in stationDict:
WWDTNETCDF_DIR = WWDTNETCDF_DIR3
else:
WWDTNETCDF_DIR = WWDTNETCDF_DIR2
# Open NetCDF Files
if self.variable == 'pdsi' or self.variable == 'scpdsi' or self.variable == 'pzi':
dataJan = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 1))
dataFeb = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 2))
dataMar = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 3))
dataApr = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 4))
dataMay = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 5))
dataJun = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 6))
dataJul = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 7))
dataAug = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 8))
dataSep = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 9))
dataOct = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 10))
dataNov = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 11))
dataDec = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, 12))
else:
dataJan = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 1))
dataFeb = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 2))
dataMar = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 3))
dataApr = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 4))
dataMay = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 5))
dataJun = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 6))
dataJul = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 7))
dataAug = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 8))
dataSep = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 9))
dataOct = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 10))
dataNov = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 11))
dataDec = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, 12))
#print dataJan, dataFeb, dataMar, dataApr, dataMay, dataJun, dataJul, dataAug, dataSep, dataOct, dataNov, dataDec
# Open the netcdf files
dataJan = netcdf.netcdf_file(dataJan, 'r')
dataFeb = netcdf.netcdf_file(dataFeb, 'r')
dataMar = netcdf.netcdf_file(dataMar, 'r')
dataApr = netcdf.netcdf_file(dataApr, 'r')
dataMay = netcdf.netcdf_file(dataMay, 'r')
dataJun = netcdf.netcdf_file(dataJun, 'r')
dataJul = netcdf.netcdf_file(dataJul, 'r')
dataAug = netcdf.netcdf_file(dataAug, 'r')
dataSep = netcdf.netcdf_file(dataSep, 'r')
dataOct = netcdf.netcdf_file(dataOct, 'r')
dataNov = netcdf.netcdf_file(dataNov, 'r')
dataDec = netcdf.netcdf_file(dataDec, 'r')
# Get closest Lat/Lon
if int(self.region) in stationDict:
closestRegion = self.Index(dataJan.variables['station_ID'], self.region)
else:
closestRegion = self.Index(dataJan.variables['polygon'], self.region)
# Set up time features
today = datetime.datetime.today()
currentMonth = today.month
currentDay = today.day
# Set len of index for current year
currentYear = today.year
currentYear = range(1895, currentYear+1, 1)
currentYear = len(currentYear)
# Select all data that exists
if currentMonth > 1 and currentDay > 2:
dataJan = np.array(dataJan.variables['data'][:currentYear,closestRegion])
else:
dataJan = np.array(dataJan.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 2 and currentDay > 2:
dataFeb = np.array(dataFeb.variables['data'][:currentYear,closestRegion])
else:
dataFeb = np.array(dataFeb.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 3 and currentDay > 2:
dataMar = np.array(dataMar.variables['data'][:currentYear,closestRegion])
else:
dataMar = np.array(dataMar.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 4 and currentDay > 2:
dataApr = np.array(dataApr.variables['data'][:currentYear,closestRegion])
else:
dataApr = np.array(dataApr.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 5 and currentDay > 2:
dataMay = np.array(dataMay.variables['data'][:currentYear,closestRegion])
else:
dataMay = np.array(dataMay.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 6 and currentDay > 2:
dataJun = np.array(dataJun.variables['data'][:currentYear,closestRegion])
else:
dataJun = np.array(dataJun.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 7 and currentDay > 2:
dataJul = np.array(dataJul.variables['data'][:currentYear,closestRegion])
else:
dataJul = np.array(dataJul.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 8 and currentDay > 2:
dataAug = np.array(dataAug.variables['data'][:currentYear,closestRegion])
else:
dataAug = np.array(dataAug.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 9 and currentDay > 2:
dataSep = np.array(dataSep.variables['data'][:currentYear,closestRegion])
else:
dataSep = np.array(dataSep.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 10 and currentDay > 2:
dataOct = np.array(dataOct.variables['data'][:currentYear,closestRegion])
else:
dataOct = np.array(dataOct.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 11 and currentDay > 2:
dataNov = np.array(dataNov.variables['data'][:currentYear,closestRegion])
else:
dataNov = np.array(dataNov.variables['data'][:(currentYear-1),closestRegion])
if currentMonth > 12 and currentDay > 2:
dataDec = np.array(dataDec.variables['data'][:currentYear,closestRegion])
else:
dataDec = np.array(dataDec.variables['data'][:(currentYear-1),closestRegion])
# Convert C to F
if self.variable == 'mdn':
dataJan = ((dataJan* 9.0/5) + 32)
dataFeb = ((dataFeb* 9.0/5) + 32)
dataMar = ((dataMar* 9.0/5) + 32)
dataApr = ((dataApr* 9.0/5) + 32)
dataMay = ((dataMay* 9.0/5) + 32)
dataJun = ((dataJun* 9.0/5) + 32)
dataJul = ((dataJul* 9.0/5) + 32)
dataAug = ((dataAug* 9.0/5) + 32)
dataSep = ((dataSep* 9.0/5) + 32)
dataOct = ((dataOct* 9.0/5) + 32)
dataNov = ((dataNov* 9.0/5) + 32)
dataDec = ((dataDec* 9.0/5) + 32)
# Convert Precip to inches
if self.variable == 'pon':
dataJan = dataJan/24.5
dataFeb = dataFeb/24.5
dataMar = dataMar/24.5
dataApr = dataApr/24.5
dataMay = dataMay/24.5
dataJun = dataJun/24.5
dataJul = dataJul/24.5
dataAug = dataAug/24.5
dataSep = dataSep/24.5
dataOct = dataOct/24.5
dataNov = dataNov/24.5
dataDec = dataDec/24.5
# Pull in the monthly percentages
DATA = np.array([dataJan,
dataFeb,
dataMar,
dataApr,
dataMay,
dataJun,
dataJul,
dataAug,
dataSep,
dataOct,
dataNov,
dataDec], dtype=object)
# Pull out means for each month
meanJan = sum(DATA[0])/len(DATA[0])
meanFeb = sum(DATA[1])/len(DATA[1])
meanMar = sum(DATA[2])/len(DATA[2])
meanApr = sum(DATA[3])/len(DATA[3])
meanMay = sum(DATA[4])/len(DATA[4])
meanJun = sum(DATA[5])/len(DATA[5])
meanJul = sum(DATA[6])/len(DATA[6])
meanAug = sum(DATA[7])/len(DATA[7])
meanSep = sum(DATA[8])/len(DATA[8])
meanOct = sum(DATA[9])/len(DATA[9])
meanNov = sum(DATA[10])/len(DATA[10])
meanDec = sum(DATA[11])/len(DATA[11])
#print meanJan
#print len(DATA[0]), len(DATA[11])
# Colors for fill between, median and months legend colors
perc_colors = ( [.85, .99, .99], [.7, .99, .99], [.5, .99, .99], [0, 0, 0], [1, 0, 0], [1,1,1] )
plevels = [5., 10., 25., 50., 75., 90., 95.]
# Get percentages for months e.g. perc = Jan percentages
perc = prctile(DATA[0], plevels[:])
perc2 = prctile(DATA[1], plevels[:])
perc3 = prctile(DATA[2], plevels[:])
perc4 = prctile(DATA[3], plevels[:])
perc5 = prctile(DATA[4], plevels[:])
perc6 = prctile(DATA[5], plevels[:])
perc7 = prctile(DATA[6], plevels[:])
perc8 = prctile(DATA[7], plevels[:])
perc9 = prctile(DATA[8], plevels[:])
perc10 = prctile(DATA[9], plevels[:])
perc11 = prctile(DATA[10], plevels[:])
perc12 = prctile(DATA[11], plevels[:])
# Set number of months to go back
xMonth = self.monthSpan
# Set the span to the x-month span
span = xMonth
## Date format for red line
oneDay = 1
oneMonth = self.month
oneYear = self.year
endDate = datetime.datetime(oneYear,oneMonth,oneDay)
startDate = endDate + relativedelta(months =- self.monthSpan)
delta = relativedelta(months=+1)
#print 'startDate: ', startDate
#print 'endDate: ', endDate
# Get months from start date to end date
monthSpanListData = []
# Create list for months, years
monthDates = []
yearDates = []
while startDate <= endDate:
#print 'Month: ', startDate.month, startDate.year
if self.variable == 'pdsi' or self.variable == 'scpdsi' or self.variable == 'pzi':
dataFile = os.path.join(WWDTNETCDF_DIR, self.variable, '%s_%s_PRISM.nc' % (self.variable, startDate.month))
#print dataFile
dataFile = netcdf.netcdf_file(dataFile, 'r')
#dataFile = netcdf.netcdf_file('/media/drought/WWDTNETCDF/PRISM/%s/%s_%s_PRISM.nc'%(self.variable, self.variable, startDate.month), 'r')
#print ('/media/drought/WWDTNETCDF/PRISM/%s/%s_%s_PRISM.nc'%(self.variable, self.variable, startDate.month), 'r')
else:
dataFile = os.path.join(WWDTNETCDF_DIR, '%s%s' % (self.variable, 1), '%s%s_%s_PRISM.nc' % (self.variable, 1, startDate.month))
dataFile = netcdf.netcdf_file(dataFile, 'r')
#closestRegion = self.Index(dataFile.variables['polygon'], self.region)
data = np.array(dataFile.variables['data'][startDate.year-1895:startDate.year-1894 ,closestRegion])
for value in data:
# Convert C to F
if self.variable == 'mdn':
value = ((value* 9.0/5) + 32)
# Convert Precip to inches
if self.variable == 'pon':
value = value/24.5
monthSpanListData.append(value)
# Create value to append for date to return through django
monthString = '%2.0f' % startDate.month
yearString = '%4.0f' % startDate.year
monthDates.append(monthString)
yearDates.append(yearString)
startDate += delta
# Add y-axis data
# Use y = monthSpanListData[:-1] when all data present
lastMonthY = monthSpanListData[:]
# Set back a month if data has not been made for last month
#if today.day < 2:
# xMonth = xMonth + 1
# Set the starting month
startDate = (datetime.date(oneYear, oneMonth, 25) - datetime.timedelta(xMonth*365/12))
startMonth = startDate.month
# print startDate
# Create label for x axis
xLabelCombiner = ['J','F','M','A','M','J','J','A','S','O','N','D']
xLabelList = []
# List to hold the month span
spanList = []
# Grab months in order
while len(spanList) < span:
if startMonth < 13:
#print startMonth, xLabelCombiner[startMonth-1]
spanList.append(startMonth)
xLabelList.append(xLabelCombiner[startMonth-1])
else:
startMonth=0
startMonth+=1
# Combine the spanlist with the percentage lines
combiner = [perc,perc2,perc3,perc4,perc5,perc6,perc7,perc8,perc9,perc10,perc11,perc12]
meanCombiner = [meanJan, meanFeb, meanMar, meanApr, meanMay, meanJun, meanJul, meanAug, meanSep, meanOct, meanNov, meanDec]
# Create empty lists to fill lines
meanLine = []
percLine = []
perc2Line = []
perc3Line = []
perc4Line = []
perc5Line = []
perc6Line = []
perc7Line = []
# Collect for all 7 lines
for x in spanList:
index = x-1
adder = combiner[index]
meanAdder = meanCombiner[index]
meanLine.append(meanAdder)
percLine.append(adder[0])
perc2Line.append(adder[1])
perc3Line.append(adder[2])
perc4Line.append(adder[3])
perc5Line.append(adder[4])
perc6Line.append(adder[5])
perc7Line.append(adder[6])
# Collect for all 7 lines
for x in spanList:
index = x-1
adder = combiner[index]
meanAdder = meanCombiner[index]
meanLine.append(meanAdder)
percLine.append(adder[0])
perc2Line.append(adder[1])
perc3Line.append(adder[2])
perc4Line.append(adder[3])
perc5Line.append(adder[4])
perc6Line.append(adder[5])
perc7Line.append(adder[6])
# Start a list to send to Django template
newList = ['Month,Year,Value,Mean,5th,95th,10th,90th,25th,75th']
# Loop through data and send it to screen in a csv format
zeroValue =1
#print monthDates
#print yearDates
#print lastMonthY
#print len(percLine)
#print len(perc2Line)
#print len(perc3Line)
#print len(perc4Line)
#print len(perc5Line)
#print len(perc6Line)
while zeroValue < len(monthDates):
#print monthDates[zeroValue], yearDates[zeroValue], lastMonthY[zeroValue], meanLine[zeroValue]
newList.append(monthDates[zeroValue]+','+yearDates[zeroValue]+','+'%4.2f'%lastMonthY[zeroValue]+','+'%4.2f'%meanLine[zeroValue]+','+'%4.2f'%percLine[zeroValue]+','+'%4.2f'%perc7Line[zeroValue]+','+'%4.2f'%perc2Line[zeroValue]+','+'%4.2f'%perc6Line[zeroValue]+','+'%4.2f'%perc3Line[zeroValue]+','+'%4.2f'%perc4Line[zeroValue])
#newList.append(monthDates[zeroValue]+','+yearDates[zeroValue]+','+'%4.2f'%lastMonthY[zeroValue])
zeroValue+=1
return newList[:]
#run = Climatology(region=131170803712, variable='pdsi', monthSpan=6)
#run.getData()
#run.getText()
class Plot():
'''This class accesses and delineates climate data '''
def __init__(self, region, startYear, endYear, variable, month, span, runavg, data=None):