update doc of loss_functions

docs/build/_modules/molearn/data/pdb_data.html

@@ -213,7 +213,7 @@ <h1>Source code for molearn.data.pdb_data</h1><div class="highlight"><pre>
 <span class="sd">        :param valid_size: if not None, specify number of train structures to be returned</span>
 <span class="sd">        :param train_size: if not None, speficy number of valid structures to be returned</span>
 <span class="sd">        :param manual_seed: seed to initialise the random number generator used for splitting the dataset. Useful to replicate a specific split.</span>
-<span class="sd">        :Return: two `torch.Tensor`, for training and validation structures.</span>
+<span class="sd">        :return: two `torch.Tensor`, for training and validation structures.</span>
 <span class="sd">        &#39;&#39;&#39;</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;dataset&#39;</span><span class="p">):</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">prepare_dataset</span><span class="p">()</span>

docs/build/data.html

@@ -126,7 +126,7 @@ <h1>Data Loading<a class="headerlink" href="#data-loading" title="Permalink to t
 <li><p><strong>manual_seed</strong> – seed to initialise the random number generator used for splitting the dataset. Useful to replicate a specific split.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Return<span class="colon">:</span></dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p>two <cite>torch.Tensor</cite>, for training and validation structures.</p>
 </dd>
 </dl>

docs/build/loss_functions.html

@@ -49,13 +49,10 @@ <h3>Navigation</h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="molearn.loss_functions.openmm_thread.ModifiedForceField">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">ModifiedForceField</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alternative_residue_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/molearn/loss_functions/openmm_thread.html#ModifiedForceField"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#molearn.loss_functions.openmm_thread.ModifiedForceField" title="Permalink to this definition">¶</a></dt>
-<dd><p>Load one or more XML files and create a ForceField object based on them.</p>
-<dl class="simple">
-<dt>files<span class="classifier">list</span></dt><dd><p>A list of XML files defining the force field.  Each entry may
-be an absolute file path, a path relative to the current working
-directory, a path relative to this module’s data subdirectory
-(for built in force fields), or an open file-like object with a
-read() method from which the forcefield XML data can be loaded.</p>
+<dd><p>Takes all <cite>*args</cite> and <cite>**kwargs</cite> of <cite>openmm.app.ForceField</cite>, plus an optional parameter described here.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>alternative_residue_names</strong> (<em>dict</em>) – aliases for resnames, e.g., <cite>{‘HIS’:’HIE’}</cite>.</p>
 </dd>
 </dl>
 </dd></dl>
@@ -66,11 +63,13 @@ <h3>Navigation</h3>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>mol</strong> – (biobox.Molecule, default None) If pldataloader is not given, then a biobox object will be taken from this parameter. If neither are given then an error  will be thrown.</p></li>
-<li><p><strong>data_dir</strong> – (string, default None) if pldataloader is not given then this will be used to find files such as ‘variants.npy’</p></li>
-<li><p><strong>xml_file</strong> – (string, default: “amber14-all.xml”) xml parameter file</p></li>
-<li><p><strong>platform</strong> – (string, default ‘CUDA’) either ‘CUDA’ or ‘Reference’.</p></li>
-<li><p><strong>remove_NB</strong> – (bool, default False) remove NonbondedForce, CustomGBForce, CMMotionRemover else just remove CustomGBForce</p></li>
+<li><p><strong>mol</strong> (<em>biobox.Molecule</em>) – if pldataloader is not given, then a biobox object will be taken from this parameter. If neither are given then an error  will be thrown.</p></li>
+<li><p><strong>xml_file</strong> (<em>str</em>) – xml parameter file</p></li>
+<li><p><strong>platform</strong> (<em>str</em>) – ‘CUDA’ or ‘Reference’.</p></li>
+<li><p><strong>remove_NB</strong> (<em>bool</em>) – if True remove NonbondedForce, CustomGBForce and CMMotionRemover, else just remove CustomGBForce</p></li>
+<li><p><strong>alternative_residue_names</strong> (<em>dict</em>) – aliases for resnames, e.g., <cite>{‘HIS’:’HIE’}</cite>.</p></li>
+<li><p><strong>atoms</strong> – </p></li>
+<li><p><strong>soft</strong> – </p></li>
 </ul>
 </dd>
 </dl>
@@ -79,16 +78,18 @@ <h3>Navigation</h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="molearn.loss_functions.openmm_thread.OpenmmPluginScore">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">OpenmmPluginScore</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xml_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['amber14-all.xml']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">platform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'CUDA'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_NB</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alternative_residue_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{'HIS':</span> <span class="pre">'HIE',</span> <span class="pre">'HSE':</span> <span class="pre">'HIE'}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atoms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['CA',</span> <span class="pre">'C',</span> <span class="pre">'N',</span> <span class="pre">'CB',</span> <span class="pre">'O']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">soft</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/molearn/loss_functions/openmm_thread.html#OpenmmPluginScore"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#molearn.loss_functions.openmm_thread.OpenmmPluginScore" title="Permalink to this definition">¶</a></dt>
-<dd><p>This will use the new Openmm Plugin to calculate forces and energy. The intention is that this will be fast enough to be able to calculate forces and energy during training.
-NB. The current torchintegratorplugin only supports float on GPU and double on CPU.</p>
+<dd><p>This will use the new OpenMM Plugin to calculate forces and energy. The intention is that this will be fast enough to be able to calculate forces and energy during training.
+N.B.: The current torchintegratorplugin only supports float on GPU and double on CPU.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>mol</strong> – (biobox.Molecule, default None) If pldataloader is not given, then a biobox object will be taken from this parameter. If neither are given then an error  will be thrown.</p></li>
-<li><p><strong>data_dir</strong> – (string, default None) if pldataloader is not given then this will be used to find files such as ‘variants.npy’</p></li>
-<li><p><strong>xml_file</strong> – (string, default: “amber14-all.xml”) xml parameter file</p></li>
-<li><p><strong>platform</strong> – (string, default ‘CUDA’) either ‘CUDA’ or ‘Reference’.</p></li>
-<li><p><strong>remove_NB</strong> – (bool, default False) remove NonbondedForce, CustomGBForce, CMMotionRemover else just remove CustomGBForce</p></li>
+<li><p><strong>mol</strong> (<em>biobox.Molecule</em>) – if pldataloader is not given, then a biobox object will be taken from this parameter. If neither are given then an error  will be thrown.</p></li>
+<li><p><strong>xml_file</strong> (<em>str</em>) – xml parameter file</p></li>
+<li><p><strong>platform</strong> (<em>str</em>) – ‘CUDA’ or ‘Reference’.</p></li>
+<li><p><strong>remove_NB</strong> (<em>bool</em>) – if True remove NonbondedForce, CustomGBForce and CMMotionRemover, else just remove CustomGBForce</p></li>
+<li><p><strong>alternative_residue_names</strong> (<em>dict</em>) – aliases for resnames, e.g., <cite>{‘HIS’:’HIE’}</cite>.</p></li>
+<li><p><strong>atoms</strong> – </p></li>
+<li><p><strong>soft</strong> – </p></li>
 </ul>
 </dd>
 </dl>
@@ -97,7 +98,7 @@ <h3>Navigation</h3>
 <span class="sig-name descname"><span class="pre">execute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/molearn/loss_functions/openmm_thread.html#OpenmmPluginScore.execute"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#molearn.loss_functions.openmm_thread.OpenmmPluginScore.execute" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>x</strong> – torch tensor shape [b, N, 3]. dtype=float. device = gpu</p>
+<dd class="field-odd"><p><strong>x</strong> (<em>torch.Tensor</em>) – shape [b, N, 3]. dtype=float. device = gpu</p>
 </dd>
 </dl>
 </dd></dl>
@@ -111,8 +112,8 @@ <h3>Navigation</h3>
 <li><p><strong>pos_ptr</strong> – tensor.data_ptr()</p></li>
 <li><p><strong>force_ptr</strong> – tensor.data_ptr()</p></li>
 <li><p><strong>energy_ptr</strong> – tensor.data_ptr()</p></li>
-<li><p><strong>n_particles</strong> – int</p></li>
-<li><p><strong>batch_size</strong> – int</p></li>
+<li><p><strong>n_particles</strong> (<em>int</em>) – number of particles</p></li>
+<li><p><strong>batch_size</strong> (<em>int</em>) – batch size</p></li>
 </ul>
 </dd>
 </dl>
@@ -126,11 +127,13 @@ <h3>Navigation</h3>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>mol</strong> – (biobox.Molecule, default None) If pldataloader is not given, then a biobox object will be taken from this parameter. If neither are given then an error  will be thrown.</p></li>
-<li><p><strong>data_dir</strong> – (string, default None) if pldataloader is not given then this will be used to find files such as ‘variants.npy’</p></li>
-<li><p><strong>xml_file</strong> – (string, default: “amber14-all.xml”) xml parameter file</p></li>
-<li><p><strong>platform</strong> – (string, default ‘CUDA’) either ‘CUDA’ or ‘Reference’.</p></li>
-<li><p><strong>remove_NB</strong> – (bool, default False) remove NonbondedForce, CustomGBForce, CMMotionRemover else just remove CustomGBForce</p></li>
+<li><p><strong>mol</strong> (<em>biobox.Molecule</em>) – if pldataloader is not given, then a biobox object will be taken from this parameter. If neither are given then an error  will be thrown.</p></li>
+<li><p><strong>xml_file</strong> (<em>str</em>) – xml parameter file</p></li>
+<li><p><strong>platform</strong> (<em>str</em>) – ‘CUDA’ or ‘Reference’.</p></li>
+<li><p><strong>remove_NB</strong> (<em>bool</em>) – if True remove NonbondedForce, CustomGBForce and CMMotionRemover, else just remove CustomGBForce</p></li>
+<li><p><strong>alternative_residue_names</strong> (<em>dict</em>) – aliases for resnames, e.g., <cite>{‘HIS’:’HIE’}</cite>.</p></li>
+<li><p><strong>atoms</strong> – </p></li>
+<li><p><strong>soft</strong> – </p></li>
 </ul>
 </dd>
 </dl>
@@ -167,8 +170,8 @@ <h3>Navigation</h3>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>plugin</strong> – # OpenmmPluginScore instance</p></li>
-<li><p><strong>x</strong> – torch tensor, dtype = float, shape = [B, N, 3], device = Cuda</p></li>
+<li><p><strong>plugin</strong> – OpenmmPluginScore instance</p></li>
+<li><p><strong>x</strong> (<em>torch.Tensor</em>) – dtype = float, shape = [B, N, 3], device = cuda</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -216,8 +219,8 @@ <h3>Navigation</h3>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>plugin</strong> – # OpenmmPluginScore instance</p></li>
-<li><p><strong>x</strong> – torch tensor, dtype = float, shape = [B, N, 3], device = any</p></li>
+<li><p><strong>plugin</strong> – OpenmmPluginScore instance</p></li>
+<li><p><strong>x</strong> (<em>torch.Tensor</em>) – dtype = float, shape = [B, N, 3], device = any</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>

src/molearn/data/pdb_data.py

@@ -174,7 +174,7 @@ def get_datasets(self, validation_split=0.1, valid_size=None, train_size=None, m
         :param valid_size: if not None, specify number of train structures to be returned
         :param train_size: if not None, speficy number of valid structures to be returned
         :param manual_seed: seed to initialise the random number generator used for splitting the dataset. Useful to replicate a specific split.
-        :Return: two `torch.Tensor`, for training and validation structures.
+        :return: two `torch.Tensor`, for training and validation structures.
         '''
         if not hasattr(self, 'dataset'):
             self.prepare_dataset()