__all__ = ["degree_days_accum"]
from dnppy import core
from enf_rastlist import enf_rastlist
from to_numpy import to_numpy
from from_numpy import from_numpy
import os
import numpy
[docs]def degree_days_accum(rasterlist, critical_values = None, outdir = None):
"""
Accumulates degree days in a time series rasterlist
This function is the logical successor to calc.degree_days. Input a list of rasters
containing daily data to be accumulated. Output raster for a given day will be the sum
total of the input raster for that day and all preceding days. The last output raster in
a years worth of data (image 356) would be the sum of all 365 images. The 25th output
raster would be a sum of the first 25 days.
Critical value rasters will also be created. Usefull for example: we wish to know on what day
of our 365 day sequence every pixel hits a value of 100. Input 100 as a critical value
and that output raster will be generated.
:param rasterlist: list of files, or directory containing rasters to accumulate
:param critical_values: Values at which the user wishes to know WHEN the total accumulation
value reaches this point. For every critical value, an output
raster will be created. This raster contains integer values denoting
the index number of the file at which the value was reached.
This input must be a list of ints or floats, not strings.
:param outdir: Desired output directory for all output files.
:return output_filelist: a list of all files created by this function.
"""
output_filelist = []
rasterlist = enf_rastlist(rasterlist)
if critical_values:
critical_values = core.enf_list(critical_values)
# critical values of zero are problematic, so replace it with a small value.
if 0 in critical_values:
critical_values.remove(0)
critical_values.append(0.000001)
if outdir is not None and not os.path.exists(outdir):
os.makedirs(outdir)
for i, rast in enumerate(rasterlist):
image, meta = to_numpy(rast,"float32")
xs, ys = image.shape
if i == 0:
Sum = numpy.zeros((xs,ys))
Crit = numpy.zeros((len(critical_values),xs,ys))
if image.shape == Sum.shape:
# only bother to proceed if at least one pixel is positive
if numpy.max(image) >= 0:
for x in range(xs):
for y in range(ys):
if image[x,y] >= 0:
Sum[x,y] = Sum[x,y]+image[x,y]
if critical_values is not None:
for z,critical_value in enumerate(critical_values):
if Sum[x,y] >= critical_value and Crit[z,x,y]==0:
Crit[z,x,y] = i
else:
print "Encountered an image of incorrect size! Skipping it!"
Sum = Sum.astype('float32')
outname = core.create_outname(outdir, rast, "Accum")
from_numpy(Sum, meta, outname)
output_filelist.append(outname)
del image
# output critical accumulation rasters using some data from the last raster in previous loop
Crit = Crit.astype('int16')
crit_meta = meta
crit_meta.NoData_Value = 0
head , tail = os.path.split(outname) # place these in the last raster output location
for z, critical_value in enumerate(critical_values):
outname = os.path.join(head, "Crit_Accum_Index_Val-{0}.tif".format(str(critical_value)))
print("Saving {0}".format(outname))
from_numpy(Crit[z,:,:], crit_meta, outname)
return output_filelist