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<h1>Source code for pymatgen.alchemy.transmuters</h1><div class="highlight"><pre>
<span></span><span class="c1"># coding: utf-8</span>
<span class="c1"># Copyright (c) Pymatgen Development Team.</span>
<span class="c1"># Distributed under the terms of the MIT License.</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">unicode_literals</span>
<span class="sd">"""</span>
<span class="sd">This module implements various transmuter classes.</span>
<span class="sd">Transmuters are essentially classes that generate TransformedStructures from</span>
<span class="sd">various data sources. They enable the high-throughput generation of new</span>
<span class="sd">structures and input files.</span>
<span class="sd">It also includes the helper function, batch_write_vasp_input to generate an</span>
<span class="sd">entire directory of vasp input files for running.</span>
<span class="sd">"""</span>
<span class="kn">from</span> <span class="nn">six.moves</span> <span class="k">import</span> <span class="nb">filter</span><span class="p">,</span> <span class="nb">map</span>
<span class="n">__author__</span> <span class="o">=</span> <span class="s2">"Shyue Ping Ong, Will Richards"</span>
<span class="n">__copyright__</span> <span class="o">=</span> <span class="s2">"Copyright 2012, The Materials Project"</span>
<span class="n">__version__</span> <span class="o">=</span> <span class="s2">"0.1"</span>
<span class="n">__maintainer__</span> <span class="o">=</span> <span class="s2">"Shyue Ping Ong"</span>
<span class="n">__email__</span> <span class="o">=</span> <span class="s2">"shyuep@gmail.com"</span>
<span class="n">__date__</span> <span class="o">=</span> <span class="s2">"Mar 4, 2012"</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">re</span>
<span class="kn">from</span> <span class="nn">multiprocessing</span> <span class="k">import</span> <span class="n">Pool</span>
<span class="kn">from</span> <span class="nn">pymatgen.alchemy.materials</span> <span class="k">import</span> <span class="n">TransformedStructure</span>
<span class="kn">from</span> <span class="nn">pymatgen.io.vasp.sets</span> <span class="k">import</span> <span class="n">MPRelaxSet</span>
<div class="viewcode-block" id="StandardTransmuter"><a class="viewcode-back" href="../../../pymatgen.alchemy.transmuters.html#pymatgen.alchemy.transmuters.StandardTransmuter">[docs]</a><span class="k">class</span> <span class="nc">StandardTransmuter</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> An example of a Transmuter object, which performs a sequence of</span>
<span class="sd"> transformations on many structures to generate TransformedStructures.</span>
<span class="sd"> .. attribute: transformed_structures</span>
<span class="sd"> List of all transformed structures.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transformed_structures</span><span class="p">,</span> <span class="n">transformations</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">extend_collection</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ncores</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Initializes a transmuter from an initial list of</span>
<span class="sd"> :class:`pymatgen.alchemy.materials.TransformedStructure`.</span>
<span class="sd"> Args:</span>
<span class="sd"> transformed_structures ([TransformedStructure]): Input transformed</span>
<span class="sd"> structures</span>
<span class="sd"> transformations ([Transformations]): New transformations to be</span>
<span class="sd"> applied to all structures.</span>
<span class="sd"> extend_collection (int): Whether to use more than one output</span>
<span class="sd"> structure from one-to-many transformations. extend_collection</span>
<span class="sd"> can be an int, which determines the maximum branching for each</span>
<span class="sd"> transformation.</span>
<span class="sd"> ncores (int): Number of cores to use for applying transformations.</span>
<span class="sd"> Uses multiprocessing.Pool. Default is None, which implies</span>
<span class="sd"> serial.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">transformed_structures</span> <span class="o">=</span> <span class="n">transformed_structures</span>
<span class="bp">self</span><span class="o">.</span><span class="n">ncores</span> <span class="o">=</span> <span class="n">ncores</span>
<span class="k">if</span> <span class="n">transformations</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">for</span> <span class="n">trans</span> <span class="ow">in</span> <span class="n">transformations</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="n">trans</span><span class="p">,</span>
<span class="n">extend_collection</span><span class="o">=</span><span class="n">extend_collection</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformed_structures</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">__getattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
<span class="k">return</span> <span class="p">[</span><span class="nb">getattr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformed_structures</span><span class="p">]</span>
<div class="viewcode-block" id="StandardTransmuter.undo_last_change"><a class="viewcode-back" href="../../../pymatgen.alchemy.transmuters.html#pymatgen.alchemy.transmuters.StandardTransmuter.undo_last_change">[docs]</a> <span class="k">def</span> <span class="nf">undo_last_change</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Undo the last transformation in the TransformedStructure.</span>
<span class="sd"> Raises:</span>
<span class="sd"> IndexError if already at the oldest change.</span>
<span class="sd"> """</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformed_structures</span><span class="p">:</span>
<span class="n">x</span><span class="o">.</span><span class="n">undo_last_change</span><span class="p">()</span></div>
<div class="viewcode-block" id="StandardTransmuter.redo_next_change"><a class="viewcode-back" href="../../../pymatgen.alchemy.transmuters.html#pymatgen.alchemy.transmuters.StandardTransmuter.redo_next_change">[docs]</a> <span class="k">def</span> <span class="nf">redo_next_change</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Redo the last undone transformation in the TransformedStructure.</span>
<span class="sd"> Raises:</span>
<span class="sd"> IndexError if already at the latest change.</span>
<span class="sd"> """</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformed_structures</span><span class="p">:</span>
<span class="n">x</span><span class="o">.</span><span class="n">redo_next_change</span><span class="p">()</span></div>
<span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformed_structures</span><span class="p">)</span>
<div class="viewcode-block" id="StandardTransmuter.append_transformation"><a class="viewcode-back" href="../../../pymatgen.alchemy.transmuters.html#pymatgen.alchemy.transmuters.StandardTransmuter.append_transformation">[docs]</a> <span class="k">def</span> <span class="nf">append_transformation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transformation</span><span class="p">,</span> <span class="n">extend_collection</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">clear_redo</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Appends a transformation to all TransformedStructures.</span>
<span class="sd"> Args:</span>
<span class="sd"> transformation: Transformation to append</span>
<span class="sd"> extend_collection: Whether to use more than one output structure</span>
<span class="sd"> from one-to-many transformations. extend_collection can be a</span>
<span class="sd"> number, which determines the maximum branching for each</span>
<span class="sd"> transformation.</span>
<span class="sd"> clear_redo (bool): Whether to clear the redo list. By default,</span>
<span class="sd"> this is True, meaning any appends clears the history of</span>
<span class="sd"> undoing. However, when using append_transformation to do a</span>
<span class="sd"> redo, the redo list should not be cleared to allow multiple</span>
<span class="sd"> redos.</span>
<span class="sd"> Returns:</span>
<span class="sd"> List of booleans corresponding to initial transformed structures</span>
<span class="sd"> each boolean describes whether the transformation altered the</span>
<span class="sd"> structure</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ncores</span> <span class="ow">and</span> <span class="n">transformation</span><span class="o">.</span><span class="n">use_multiprocessing</span><span class="p">:</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">Pool</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ncores</span><span class="p">)</span>
<span class="c1"># need to condense arguments into single tuple to use map</span>
<span class="n">z</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span>
<span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">transformation</span><span class="p">,</span> <span class="n">extend_collection</span><span class="p">,</span> <span class="n">clear_redo</span><span class="p">),</span>
<span class="bp">self</span><span class="o">.</span><span class="n">transformed_structures</span><span class="p">)</span>
<span class="n">new_tstructs</span> <span class="o">=</span> <span class="n">p</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">_apply_transformation</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">transformed_structures</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">ts</span> <span class="ow">in</span> <span class="n">new_tstructs</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">transformed_structures</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">ts</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">new_structures</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformed_structures</span><span class="p">:</span>
<span class="n">new</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="n">transformation</span><span class="p">,</span>
<span class="n">extend_collection</span><span class="p">,</span>
<span class="n">clear_redo</span><span class="o">=</span><span class="n">clear_redo</span><span class="p">)</span>
<span class="k">if</span> <span class="n">new</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">new_structures</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">new</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">transformed_structures</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">new_structures</span><span class="p">)</span></div>
<div class="viewcode-block" id="StandardTransmuter.extend_transformations"><a class="viewcode-back" href="../../../pymatgen.alchemy.transmuters.html#pymatgen.alchemy.transmuters.StandardTransmuter.extend_transformations">[docs]</a> <span class="k">def</span> <span class="nf">extend_transformations</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transformations</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Extends a sequence of transformations to the TransformedStructure.</span>
<span class="sd"> Args:</span>
<span class="sd"> transformations: Sequence of Transformations</span>
<span class="sd"> """</span>
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">transformations</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="n">t</span><span class="p">)</span></div>
<div class="viewcode-block" id="StandardTransmuter.apply_filter"><a class="viewcode-back" href="../../../pymatgen.alchemy.transmuters.html#pymatgen.alchemy.transmuters.StandardTransmuter.apply_filter">[docs]</a> <span class="k">def</span> <span class="nf">apply_filter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">structure_filter</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Applies a structure_filter to the list of TransformedStructures</span>
<span class="sd"> in the transmuter.</span>
<span class="sd"> Args:</span>
<span class="sd"> structure_filter: StructureFilter to apply.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="nf">test_transformed_structure</span><span class="p">(</span><span class="n">ts</span><span class="p">):</span>
<span class="k">return</span> <span class="n">structure_filter</span><span class="o">.</span><span class="n">test</span><span class="p">(</span><span class="n">ts</span><span class="o">.</span><span class="n">final_structure</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">transformed_structures</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="n">test_transformed_structure</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">transformed_structures</span><span class="p">))</span>
<span class="k">for</span> <span class="n">ts</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformed_structures</span><span class="p">:</span>
<span class="n">ts</span><span class="o">.</span><span class="n">append_filter</span><span class="p">(</span><span class="n">structure_filter</span><span class="p">)</span></div>
<div class="viewcode-block" id="StandardTransmuter.write_vasp_input"><a class="viewcode-back" href="../../../pymatgen.alchemy.transmuters.html#pymatgen.alchemy.transmuters.StandardTransmuter.write_vasp_input">[docs]</a> <span class="k">def</span> <span class="nf">write_vasp_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Batch write vasp input for a sequence of transformed structures to</span>
<span class="sd"> output_dir, following the format output_dir/{formula}_{number}.</span>
<span class="sd"> Args:</span>
<span class="sd"> \\*\\*kwargs: All kwargs supported by batch_write_vasp_input.</span>
<span class="sd"> """</span>
<span class="n">batch_write_vasp_input</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformed_structures</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="StandardTransmuter.set_parameter"><a class="viewcode-back" href="../../../pymatgen.alchemy.transmuters.html#pymatgen.alchemy.transmuters.StandardTransmuter.set_parameter">[docs]</a> <span class="k">def</span> <span class="nf">set_parameter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Add parameters to the transmuter. Additional parameters are stored in</span>
<span class="sd"> the as_dict() output.</span>
<span class="sd"> Args:</span>
<span class="sd"> key: The key for the parameter.</span>
<span class="sd"> value: The value for the parameter.</span>
<span class="sd"> """</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformed_structures</span><span class="p">:</span>
<span class="n">x</span><span class="o">.</span><span class="n">other_parameters</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span></div>
<div class="viewcode-block" id="StandardTransmuter.add_tags"><a class="viewcode-back" href="../../../pymatgen.alchemy.transmuters.html#pymatgen.alchemy.transmuters.StandardTransmuter.add_tags">[docs]</a> <span class="k">def</span> <span class="nf">add_tags</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tags</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Add tags for the structures generated by the transmuter.</span>
<span class="sd"> Args:</span>
<span class="sd"> tags: A sequence of tags. Note that this should be a sequence of</span>
<span class="sd"> strings, e.g., ["My awesome structures", "Project X"].</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">set_parameter</span><span class="p">(</span><span class="s2">"tags"</span><span class="p">,</span> <span class="n">tags</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">output</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"Current structures"</span><span class="p">,</span> <span class="s2">"------------"</span><span class="p">]</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformed_structures</span><span class="p">:</span>
<span class="n">output</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">final_structure</span><span class="p">))</span>
<span class="k">return</span> <span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
<div class="viewcode-block" id="StandardTransmuter.append_transformed_structures"><a class="viewcode-back" href="../../../pymatgen.alchemy.transmuters.html#pymatgen.alchemy.transmuters.StandardTransmuter.append_transformed_structures">[docs]</a> <span class="k">def</span> <span class="nf">append_transformed_structures</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tstructs_or_transmuter</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Method is overloaded to accept either a list of transformed structures</span>
<span class="sd"> or transmuter, it which case it appends the second transmuter"s</span>
<span class="sd"> structures.</span>
<span class="sd"> Args:</span>
<span class="sd"> tstructs_or_transmuter: A list of transformed structures or a</span>
<span class="sd"> transmuter.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tstructs_or_transmuter</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">transformed_structures</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tstructs_or_transmuter</span>
<span class="o">.</span><span class="n">transformed_structures</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">for</span> <span class="n">ts</span> <span class="ow">in</span> <span class="n">tstructs_or_transmuter</span><span class="p">:</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ts</span><span class="p">,</span> <span class="n">TransformedStructure</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">transformed_structures</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tstructs_or_transmuter</span><span class="p">)</span></div>
<div class="viewcode-block" id="StandardTransmuter.from_structures"><a class="viewcode-back" href="../../../pymatgen.alchemy.transmuters.html#pymatgen.alchemy.transmuters.StandardTransmuter.from_structures">[docs]</a> <span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">from_structures</span><span class="p">(</span><span class="n">structures</span><span class="p">,</span> <span class="n">transformations</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">extend_collection</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Alternative constructor from structures rather than</span>
<span class="sd"> TransformedStructures.</span>
<span class="sd"> Args:</span>
<span class="sd"> structures: Sequence of structures</span>
<span class="sd"> transformations: New transformations to be applied to all</span>
<span class="sd"> structures</span>
<span class="sd"> extend_collection: Whether to use more than one output structure</span>
<span class="sd"> from one-to-many transformations. extend_collection can be a</span>
<span class="sd"> number, which determines the maximum branching for each</span>
<span class="sd"> transformation.</span>
<span class="sd"> Returns:</span>
<span class="sd"> StandardTransmuter</span>
<span class="sd"> """</span>
<span class="n">tstruct</span> <span class="o">=</span> <span class="p">[</span><span class="n">TransformedStructure</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="p">[])</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">structures</span><span class="p">]</span>
<span class="k">return</span> <span class="n">StandardTransmuter</span><span class="p">(</span><span class="n">tstruct</span><span class="p">,</span> <span class="n">transformations</span><span class="p">,</span> <span class="n">extend_collection</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="CifTransmuter"><a class="viewcode-back" href="../../../pymatgen.alchemy.transmuters.html#pymatgen.alchemy.transmuters.CifTransmuter">[docs]</a><span class="k">class</span> <span class="nc">CifTransmuter</span><span class="p">(</span><span class="n">StandardTransmuter</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Generates a Transmuter from a cif string, possibly containing multiple</span>
<span class="sd"> structures.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cif_string</span><span class="p">,</span> <span class="n">transformations</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">primitive</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">extend_collection</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Generates a Transmuter from a cif string, possibly</span>
<span class="sd"> containing multiple structures.</span>
<span class="sd"> Args:</span>
<span class="sd"> cif_string: A string containing a cif or a series of cifs</span>
<span class="sd"> transformations: New transformations to be applied to all</span>
<span class="sd"> structures</span>
<span class="sd"> primitive: Whether to generate the primitive cell from the cif.</span>
<span class="sd"> extend_collection: Whether to use more than one output structure</span>
<span class="sd"> from one-to-many transformations. extend_collection can be a</span>
<span class="sd"> number, which determines the maximum branching for each</span>
<span class="sd"> transformation.</span>
<span class="sd"> """</span>
<span class="n">transformed_structures</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">lines</span> <span class="o">=</span> <span class="n">cif_string</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
<span class="n">structure_data</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">read_data</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
<span class="k">if</span> <span class="n">re</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="sa">r</span><span class="s2">"^\s*data"</span><span class="p">,</span> <span class="n">line</span><span class="p">):</span>
<span class="n">structure_data</span><span class="o">.</span><span class="n">append</span><span class="p">([])</span>
<span class="n">read_data</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">if</span> <span class="n">read_data</span><span class="p">:</span>
<span class="n">structure_data</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
<span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">structure_data</span><span class="p">:</span>
<span class="n">tstruct</span> <span class="o">=</span> <span class="n">TransformedStructure</span><span class="o">.</span><span class="n">from_cif_string</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="p">[],</span>
<span class="n">primitive</span><span class="p">)</span>
<span class="n">transformed_structures</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tstruct</span><span class="p">)</span>
<span class="nb">super</span><span class="p">(</span><span class="n">CifTransmuter</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">transformed_structures</span><span class="p">,</span>
<span class="n">transformations</span><span class="p">,</span> <span class="n">extend_collection</span><span class="p">)</span>
<div class="viewcode-block" id="CifTransmuter.from_filenames"><a class="viewcode-back" href="../../../pymatgen.alchemy.transmuters.html#pymatgen.alchemy.transmuters.CifTransmuter.from_filenames">[docs]</a> <span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">from_filenames</span><span class="p">(</span><span class="n">filenames</span><span class="p">,</span> <span class="n">transformations</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">primitive</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">extend_collection</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Generates a TransformedStructureCollection from a cif, possibly</span>
<span class="sd"> containing multiple structures.</span>
<span class="sd"> Args:</span>
<span class="sd"> filenames: List of strings of the cif files</span>
<span class="sd"> transformations: New transformations to be applied to all</span>
<span class="sd"> structures</span>
<span class="sd"> primitive: Same meaning as in __init__.</span>
<span class="sd"> extend_collection: Same meaning as in __init__.</span>
<span class="sd"> """</span>
<span class="n">allcifs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">fname</span> <span class="ow">in</span> <span class="n">filenames</span><span class="p">:</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fname</span><span class="p">,</span> <span class="s2">"r"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">allcifs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">())</span>
<span class="k">return</span> <span class="n">CifTransmuter</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">allcifs</span><span class="p">),</span> <span class="n">transformations</span><span class="p">,</span>
<span class="n">primitive</span><span class="o">=</span><span class="n">primitive</span><span class="p">,</span>
<span class="n">extend_collection</span><span class="o">=</span><span class="n">extend_collection</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="PoscarTransmuter"><a class="viewcode-back" href="../../../pymatgen.alchemy.transmuters.html#pymatgen.alchemy.transmuters.PoscarTransmuter">[docs]</a><span class="k">class</span> <span class="nc">PoscarTransmuter</span><span class="p">(</span><span class="n">StandardTransmuter</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Generates a transmuter from a sequence of POSCARs.</span>
<span class="sd"> Args:</span>
<span class="sd"> poscar_string: List of POSCAR strings</span>
<span class="sd"> transformations: New transformations to be applied to all</span>
<span class="sd"> structures.</span>
<span class="sd"> extend_collection: Whether to use more than one output structure</span>
<span class="sd"> from one-to-many transformations.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">poscar_string</span><span class="p">,</span> <span class="n">transformations</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">extend_collection</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="n">tstruct</span> <span class="o">=</span> <span class="n">TransformedStructure</span><span class="o">.</span><span class="n">from_poscar_string</span><span class="p">(</span><span class="n">poscar_string</span><span class="p">,</span> <span class="p">[])</span>
<span class="nb">super</span><span class="p">(</span><span class="n">PoscarTransmuter</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">([</span><span class="n">tstruct</span><span class="p">],</span> <span class="n">transformations</span><span class="p">,</span>
<span class="n">extend_collection</span><span class="o">=</span><span class="n">extend_collection</span><span class="p">)</span>
<div class="viewcode-block" id="PoscarTransmuter.from_filenames"><a class="viewcode-back" href="../../../pymatgen.alchemy.transmuters.html#pymatgen.alchemy.transmuters.PoscarTransmuter.from_filenames">[docs]</a> <span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">from_filenames</span><span class="p">(</span><span class="n">poscar_filenames</span><span class="p">,</span> <span class="n">transformations</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">extend_collection</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Convenient constructor to generates a POSCAR transmuter from a list of</span>
<span class="sd"> POSCAR filenames.</span>
<span class="sd"> Args:</span>
<span class="sd"> poscar_filenames: List of POSCAR filenames</span>
<span class="sd"> transformations: New transformations to be applied to all</span>
<span class="sd"> structures.</span>
<span class="sd"> extend_collection:</span>
<span class="sd"> Same meaning as in __init__.</span>
<span class="sd"> """</span>
<span class="n">tstructs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">filename</span> <span class="ow">in</span> <span class="n">poscar_filenames</span><span class="p">:</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s2">"r"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">tstructs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">TransformedStructure</span>
<span class="o">.</span><span class="n">from_poscar_string</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">(),</span> <span class="p">[]))</span>
<span class="k">return</span> <span class="n">StandardTransmuter</span><span class="p">(</span><span class="n">tstructs</span><span class="p">,</span> <span class="n">transformations</span><span class="p">,</span>
<span class="n">extend_collection</span><span class="o">=</span><span class="n">extend_collection</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="batch_write_vasp_input"><a class="viewcode-back" href="../../../pymatgen.alchemy.transmuters.html#pymatgen.alchemy.transmuters.batch_write_vasp_input">[docs]</a><span class="k">def</span> <span class="nf">batch_write_vasp_input</span><span class="p">(</span><span class="n">transformed_structures</span><span class="p">,</span> <span class="n">vasp_input_set</span><span class="o">=</span><span class="n">MPRelaxSet</span><span class="p">,</span>
<span class="n">output_dir</span><span class="o">=</span><span class="s2">"."</span><span class="p">,</span> <span class="n">create_directory</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">subfolder</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">include_cif</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Batch write vasp input for a sequence of transformed structures to</span>
<span class="sd"> output_dir, following the format output_dir/{group}/{formula}_{number}.</span>
<span class="sd"> Args:</span>
<span class="sd"> transformed_structures: Sequence of TransformedStructures.</span>
<span class="sd"> vasp_input_set: pymatgen.io.vaspio_set.VaspInputSet to creates</span>
<span class="sd"> vasp input files from structures.</span>
<span class="sd"> output_dir: Directory to output files</span>
<span class="sd"> create_directory (bool): Create the directory if not present.</span>
<span class="sd"> Defaults to True.</span>
<span class="sd"> subfolder: Function to create subdirectory name from</span>
<span class="sd"> transformed_structure.</span>
<span class="sd"> e.g., lambda x: x.other_parameters["tags"][0] to use the first</span>
<span class="sd"> tag.</span>
<span class="sd"> include_cif (bool): Boolean indication whether to output a CIF as</span>
<span class="sd"> well. CIF files are generally better supported in visualization</span>
<span class="sd"> programs.</span>
<span class="sd"> """</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">transformed_structures</span><span class="p">):</span>
<span class="n">formula</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="sa">r</span><span class="s2">"\s+"</span><span class="p">,</span> <span class="s2">""</span><span class="p">,</span> <span class="n">s</span><span class="o">.</span><span class="n">final_structure</span><span class="o">.</span><span class="n">formula</span><span class="p">)</span>
<span class="k">if</span> <span class="n">subfolder</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">subdir</span> <span class="o">=</span> <span class="n">subfolder</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
<span class="n">dirname</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">subdir</span><span class="p">,</span>
<span class="s2">"</span><span class="si">{}</span><span class="s2">_</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">formula</span><span class="p">,</span> <span class="n">i</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">dirname</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="s2">"</span><span class="si">{}</span><span class="s2">_</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">formula</span><span class="p">,</span> <span class="n">i</span><span class="p">))</span>
<span class="n">s</span><span class="o">.</span><span class="n">write_vasp_input</span><span class="p">(</span><span class="n">vasp_input_set</span><span class="p">,</span> <span class="n">dirname</span><span class="p">,</span>
<span class="n">create_directory</span><span class="o">=</span><span class="n">create_directory</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">if</span> <span class="n">include_cif</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">pymatgen.io.cif</span> <span class="k">import</span> <span class="n">CifWriter</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">CifWriter</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">final_structure</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">write_file</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dirname</span><span class="p">,</span> <span class="s2">"</span><span class="si">{}</span><span class="s2">.cif"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">formula</span><span class="p">)))</span></div>
<span class="k">def</span> <span class="nf">_apply_transformation</span><span class="p">(</span><span class="n">inputs</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Helper method for multiprocessing of apply_transformation. Must not be</span>
<span class="sd"> in the class so that it can be pickled.</span>
<span class="sd"> Args:</span>
<span class="sd"> inputs: Tuple containing the transformed structure, the transformation</span>
<span class="sd"> to be applied, a boolean indicating whether to extend the</span>
<span class="sd"> collection, and a boolean indicating whether to clear the redo</span>
<span class="sd"> Returns:</span>
<span class="sd"> List of output structures (the modified initial structure, plus</span>
<span class="sd"> any new structures created by a one-to-many transformation)</span>
<span class="sd"> """</span>
<span class="n">ts</span><span class="p">,</span> <span class="n">transformation</span><span class="p">,</span> <span class="n">extend_collection</span><span class="p">,</span> <span class="n">clear_redo</span> <span class="o">=</span> <span class="n">inputs</span>
<span class="n">new</span> <span class="o">=</span> <span class="n">ts</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="n">transformation</span><span class="p">,</span> <span class="n">extend_collection</span><span class="p">,</span>
<span class="n">clear_redo</span><span class="o">=</span><span class="n">clear_redo</span><span class="p">)</span>
<span class="n">o</span> <span class="o">=</span> <span class="p">[</span><span class="n">ts</span><span class="p">]</span>
<span class="k">if</span> <span class="n">new</span><span class="p">:</span>
<span class="n">o</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">new</span><span class="p">)</span>
<span class="k">return</span> <span class="n">o</span>
</pre></div>
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