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@@ -38,6 +38,8 @@ for (year, data) in vars_by_year.copy().items():
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#print(year)
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#print(y_mat.shape)
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+# For all the datasets we assume that the higher the value the higher its region's attractiveness
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+# TODO: the above statement should be made optional via some (positive/negative) effect switch
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for (year, data) in vars_by_year.items():
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y_mat = np.matrix(data)
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# Save all pilot region names (can't be read elsewhere), as well as domain and scenario names
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@@ -49,17 +51,19 @@ for (year, data) in vars_by_year.items():
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domains = np.squeeze(y_mat[:, -2]).tolist()[0]
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# Scenario name is stored in the "SCENARIO" column which is 1st from the end
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scenarios = np.squeeze(y_mat[:, -1]).tolist()[0]
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+ # Drop last three columns (MODEL, DOMAIN, SCENARIO) and convert the value's datatype
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y_mat = y_mat[:, :-3].astype(float)
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#print(y_mat.dtype)
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+ # Maximum of each column (each dataset)
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maxs = y_mat.max(axis=0)
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- #print(maxs)
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+ # Minimum of each column (each dataset)
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mins = y_mat.min(axis=0)
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- #print(mins)
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+ # Value range of each column (each dataset)
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spans = maxs-mins
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- #print(spans)
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+ # Align the range of each column (dataset) so it starts at 0
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y_mat = y_mat - mins
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+ # Align the range of each column (dataset) into range 0 to 1
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y_mat = y_mat / spans
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- #print(y_mat)
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updated_by_year[year] = y_mat
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out = open(outname, 'w', encoding="utf-8", newline="")
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