Merge 4f570f0 into 1292ce4

CHANGELOG.rst

@@ -2,6 +2,12 @@
 Changelog
 =========
 
+Version 0.4
+===========
+- Tidy up the command line interface
+- Convert to/from SQLite database from/to datapackage
+- Remove rotten pygraphviz dependency
+
 Version 0.3
 ===========
 

README.rst

@@ -39,9 +39,11 @@ which can be difficult to install on Windows.
 
 The easiest way to install the dependencies is to use miniconda.
 
-1. Obtain the .. _miniconda package: https://docs.conda.io/en/latest/miniconda.html
+1. Obtain the `miniconda package: <https://docs.conda.io/en/latest/miniconda.html>`_
 2. Add the **conda-forge** channel ``conda config --add channels conda-forge``
-3. Create a new Python environment ``conda create -n myenv python=3.7 networkx datapackage pandas pulp pygraphviz xlrd``
+3. Create a new Python environment
+   ``conda create -n myenv python=3.7 networkx datapackage
+   pandas pulp graphviz xlrd``
 4. Activate the new environment ``conda activate myenv``
 5. Use pip to install otoole ``pip install otoole``
 

docs/index.rst

@@ -13,15 +13,15 @@ Check the version installed::
 
 Download an OSeMOSYS datapackage and convert it to a modelfile::
 
-    otoole prep datafile https://zenodo.org/record/3479823/files/KTH-dESA/simplicity-v0.1a0.zip ./simplicity.txt
+    otoole convert datapackage datafile https://zenodo.org/record/3479823/files/KTH-dESA/simplicity-v0.1a0.zip simplicity.txt
 
 Visualise the Reference Energy System::
 
     otoole viz res https://zenodo.org/record/3479823/files/KTH-dESA/simplicity-v0.1a0.zip res.png && open res.png
 
 Alternatively, convert an OSeMOSYS datafile to a datapackage::
 
-    otoole convert --convert_from datafile --convert_to datapackage --from_file simplicity.txt --to_file simplicity
+    otoole convert datafile datapackage simplicity.txt simplicity
 
 
 Contents

requirements.txt

@@ -4,6 +4,6 @@ importlib_resources; python_version<'3.7'
 networkx
 pandas
 pulp
-pygraphviz
+pydot
 pyyaml
 xlrd

setup.cfg

@@ -33,7 +33,7 @@ setup_requires = pyscaffold>=3.2a0,<3.3a0
 install_requires =
     xlrd
     pyyaml
-    pygraphviz
+    pydot
     importlib_resources; python_version<'3.7'
     datapackage
     pandas

src/otoole/visualise/res.py

@@ -1,10 +1,11 @@
 """Visualise the reference energy system
 """
 import logging
+import os
 import sys
 from typing import Dict, List, Tuple
 
-import networkx as nx
+import networkx as nx  # mypy: ignore
 from datapackage import Package
 
 logger = logging.getLogger(__name__)
@@ -25,7 +26,7 @@ def create_res(path_to_datapackage: str, path_to_resfile: str):
     path_to_datapackage : str
         The path to the ``datapackage.json``
     path_to_resfile : str
-        The path to the PNG file to be created
+        The path to the image file to be created
     """
     logger.debug(path_to_resfile, path_to_resfile)
     package = load_datapackage(path_to_datapackage)
@@ -36,8 +37,8 @@ def create_res(path_to_datapackage: str, path_to_resfile: str):
     emission = package.get_resource('EMISSION').read()
 
     def extract_nodes(package_rows: List[List], node_type='technology',
-                      color='red', shape='circle') -> List:
-        nodes = [(x[0], {'type': node_type, 'name': x,
+                      color='red', shape='circle') -> List[Tuple[str, Dict]]:
+        nodes = [(x[0], {'type': node_type,
                          'fillcolor': color, 'shape': shape,
                          'style': 'filled'}
                   )
@@ -101,7 +102,7 @@ def extract_edges(package_rows: List[Dict], from_column: str,
     nodes += extract_nodes(emission, node_type='emission', color='grey')
     nodes += [('AnnualDemand',
                {'type': 'demand', 'fillcolor': 'green',
-                'name': 'AccumulatedAnnualDemand',
+                'label': 'AccumulatedAnnualDemand',
                 'style': 'filled'}
                )
               ]
@@ -146,17 +147,27 @@ def draw_graph(graph, path_to_resfile):
     path_to_resfile : str
         The file path of the PNG image file that will be created
     """
+    for node, attributes in graph.nodes.data():
+        logger.debug("%s: %s", node, attributes)
+    for source, sink, attributes in graph.edges.data():
+        logger.debug("%s-%s: %s", source, sink, attributes)
 
-    pygraph = nx.nx_agraph.to_agraph(graph)
-    pygraph.graph_attr['rankdir'] = 'LR'
-    pygraph.graph_attr['splines'] = 'ortho'
-    pygraph.graph_attr['concentrate'] = 'true'
+    filename, ext = os.path.splitext(path_to_resfile)
+    nx.write_graphml(graph, filename + '.graphml')
+    dot_graph = nx.nx_pydot.to_pydot(graph)
 
-    pygraph.layout(prog='dot')
-    pygraph.draw(path_to_resfile)
+    dot_graph.set('rankdir', 'LR')
+    dot_graph.set('splines', 'ortho')
+    dot_graph.set('concentrate', 'true')
 
+    image_format = ext.strip(".")
 
-def build_graph(nodes: List[Tuple], edges: List[Tuple[str, str, Dict]]):
+    image = dot_graph.create(prog='dot', format=image_format)
+    with open(path_to_resfile, 'wb') as image_file:
+        image_file.write(image)
+
+
+def build_graph(nodes: List[Tuple[str, Dict]], edges: List[Tuple[str, str, Dict]]) -> nx.DiGraph:
     """Builds the graph using networkx
 
     Arguments

tests/test_visualise.py

@@ -0,0 +1,10 @@
+import tempfile
+
+from otoole.visualise import create_res
+
+
+def test_create_res():
+
+    url = 'https://raw.githubusercontent.com/OSeMOSYS/simplicity/master/datapackage.json'
+    _, path_to_resfile = tempfile.mkstemp(suffix='.pdf')
+    create_res(url, path_to_resfile)

tests/travis_install.sh

@@ -36,7 +36,8 @@ if [[ "$DISTRIB" == "conda" ]]; then
     # Configure the conda environment and put it in the path using the
     # provided versions
     # (prefer local venv, since the miniconda folder is cached)
-    conda create -p ./.venv --yes python=${PYTHON_VERSION} pip virtualenv pandas xlrd pyyaml
+    conda config --add channels conda-forge
+    conda create -p ./.venv --yes python=${PYTHON_VERSION} pip virtualenv pandas xlrd pyyaml graphviz networkx pulp
     source activate ./.venv
 fi