Chris Mutel avatar Chris Mutel committed a3b95f1

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+syntax:glob
+dist
+*.pyc
+*~
+MANIFEST
+*.sublime*
+Copyright (c) 2012, Chris Mutel and ETH Zürich
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+
+Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+Neither the name of ETH Zürich nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+include *.txt
+include bw2analyzer/*.py
+This is a set of tools to analyze results of LCA calculations for the the Brightway2 LCA software.

Empty file added.

bw2analyzer/__init__.py

+# encoding: utf-8
+from contribution import ContributionAnalysis

bw2analyzer/contribution.py

+# encoding: utf-8
+import numpy as np
+from brightway2 import Database
+
+
+class ContributionAnalysis(object):
+    def __init__(self):
+        self.db_names = {}
+
+    def sort_array(self, data, limit=25, limit_type="number", total=None):
+        """Common sorting function for all top methods."""
+        total = total or np.abs(data).sum()
+        if limit_type not in ("number", "percent"):
+            raise ValueError("limit_type must be either 'percent' or 'index'.")
+        if limit_type == "percent":
+            if 0 <= limit >= 1:
+                raise ValueError("Percentage limits must be between 0 and 1.")
+            limit = (data > (total * limit)).sum()
+
+        results = np.hstack((data.reshape((-1, 1)),
+            np.arange(data.shape[0]).reshape((-1, 1))))
+        return results[np.argsort(np.abs(data))[::-1]][:limit, :]
+
+    def annotate(self, sorted_data, rev_mapping):
+        """Reverse the mapping from database ids to array indices"""
+        return [(row[0], rev_mapping[row[1]]) for row in sorted_data]
+
+    def top_processes(self, matrix, **kwargs):
+        """Return an array of [value, index] technosphere processes."""
+        return self.sort_array(np.array(matrix.sum(axis=0)).ravel(), **kwargs)
+
+    def top_emissions(self, matrix, **kwargs):
+        """Return an array of [value, index] biosphere emissions."""
+        return self.sort_array(np.array(matrix.sum(axis=1)).ravel(), **kwargs)
+
+    def annotated_top_processes(self, matrix, rev, **kwargs):
+        return [(score, self.get_name(rev[index])) for score, index in \
+            self.top_processes(matrix)]
+
+    def annotated_top_emissions(self, matrix, rev, **kwargs):
+        return [(score, self.get_name(rev[index])) for score, index in \
+            self.top_emissions(matrix)]
+
+    def get_name(self, name):
+        if name[0] not in self.db_names:
+            self.db_names[name[0]] = Database(name[0]).load()
+        return self.db_names[name[0]][name]["name"]
+
+    def d3_treemap(self, matrix, rev_bio, rev_techno, limit=0.025,
+            limit_type="percent"):
+        """
+Construct treemap input data structure for LCA result. Output like:
+
+    {
+    "name": "LCA result",
+    "children": [{
+        "name": process 1,
+        "children": [
+            {"name": emission 1, "size": score},
+            {"name": emission 2, "size": score},
+            ],
+        }]
+    }
+
+        """
+        total = np.abs(matrix).sum()
+        processes = self.top_processes(matrix, limit=limit,
+            limit_type=limit_type)
+        data = {"name": "LCA result", "children": []}
+        for dummy, tech_index in processes:
+            name = self.get_name(rev_techno[tech_index])
+            this_score = np.abs(matrix[:, tech_index].toarray().ravel()).sum()
+            children = []
+            for score, bio_index in self.sort_array(matrix[:, tech_index
+                    ].toarray().ravel(), limit=limit, limit_type=limit_type,
+                    total=total):
+                children.append({"name": self.get_name(rev_bio[bio_index]),
+                    "size": float(abs(matrix[bio_index, tech_index]))})
+            children_score = sum([x["size"] for x in children])
+            if children_score < (0.95 * this_score):
+                children.append({"name": "Others", "size":
+                    this_score - children_score})
+            data["children"].append({
+                "name": name,
+                "children": children
+                })
+        return data
+
+    # def top_emissions_for_process(self, process, **kwargs):
+    #     if hasattr(process, "id"):
+    #         process = process.id
+    #     if not hasattr(self.dicts, 'reverse'):
+    #         self.construct_reverse_dicts()
+    #     return self._top(array(self.weighted_biosphere[:,process].todense( 
+    #         )).ravel(), self.dicts.reverse.biosphere, **kwargs)
+
+    # def top_processes_for_emission(self, biosphere_flow, **kwargs):
+    #     if hasattr(biosphere_flow, "id"):
+    #         biosphere_flow = biosphere_flow.id
+    #     if not hasattr(self.dicts, 'reverse'):
+    #         self.construct_reverse_dicts()
+    #     return self._top(array(self.weighted_biosphere[biosphere_flow,: 
+    #         ].todense()).ravel(), self.dicts.reverse.technosphere, **kwargs)
+
+    # def top_processes_for_emission_inventory(self, emission, **kwargs):
+    #     """Get the most important inventory processes for an emission"""
+    #     if hasattr(emission, "id"):
+    #         emission = emission.id
+    #     if not hasattr(self.dicts, 'reverse'):
+    #         self.construct_reverse_dicts()
+    #     return self._top(array(self.calculated_biosphere[emission,:].todense( 
+    #         )).ravel(), self.dicts.reverse.technosphere, **kwargs)
+from distutils.core import setup
+
+setup(
+  name='bw2analyzer',
+  version="0.1",
+  packages=["bw2analyzer"],
+  author="Chris Mutel",
+  author_email="cmutel@gmail.com",
+  license=open('LICENSE.txt').read(),
+  requires=["brightway2"],
+  long_description=open('README.txt').read(),
+)
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