70 lines
1.8 KiB
Python
70 lines
1.8 KiB
Python
import numpy
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from .models import DB
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def getTemps(datasets, community_id, minyear, maxyear):
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model, scenario = datasets.split(',')
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data = DB.getTemps(minyear, maxyear, community_id, model, scenario)
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return data
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def avg_air_temp(temps):
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year_counter, total = 0, 0
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for temp in temps:
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total += sum(temp[1])
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year_counter += 1
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return total / (year_counter * 12)
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def ann_air_indices(temps):
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ATI, AFI = 0.0, 0.0
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# TODO: drop numpy
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indices = numpy.zeros((len(temps), 2), dtype='int')
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months = [0.0 for m in range(12)]
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days = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
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i = 0
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for year in temps:
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j = 0
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for month in months:
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months[j] = days[j] * year[1][j]
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j += 1
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for ind in months:
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if ind >= 0.0:
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ATI = ATI + ind
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else:
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AFI = AFI + ind
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indices[i, 0], indices[i, 1] = int(ATI), int(AFI)
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ATI, AFI = 0.0, 0.0
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i += 1
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return indices
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def avg_air_indices(indices):
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# TODO: drop numpy
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temp = numpy.average(indices, axis=0)
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return (int(temp[0]), int(temp[1]))
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def des_air_indices(indices):
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if indices.shape[0] > 2:
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# TODO: drop numpy
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ati = numpy.sort(indices[:, 0])
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afi = numpy.sort(indices[:, 1])
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dti = (ati[-1] + ati[-2] + ati[-3]) / 3.0
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dfi = (afi[0] + afi[1] + afi[2]) / 3.0
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return (int(dti), int(dfi))
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else:
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return (None, None)
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def communitiesSelect():
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return [(c.id, c.name) for c in DB.getCommunities()]
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def datasetsSelect():
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return [("{0.modelname},{0.scenario}".format(d),
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"{x} ({d.resolution}) - {d.modelname} {d.scenario}".format(d=d,
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x=d.datatype.title()))
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for d in DB.getDatasets()]
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