backup

README.md

@@ -26,7 +26,7 @@
 If you are a computational biologist, chances are that you cursed one too many times about protein structure files. Yes, I am talking about ye Goode Olde Protein Data Bank format, aka "PDB files." Nothing against PDB, it's a neatly structured format (if deployed correctly); yet, it is a bit cumbersome to work with PDB files in "modern" programming languages -- I am pretty sure we all agree on this.
 
 As machine learning and "data science" person, I fell in love with [pandas](http://pandas.pydata.org) DataFrames for handling just about everything that can be loaded into memory.  
-So, why don't we take pandas to the structural biology world? Working with molecular structures of biological macromolecules in pandas DataFrames is what BioPandas is all about!
+So, why don't we take pandas to the structural biology world? Working with molecular structures of biological macromolecules (from PDB and MOL2 files) in pandas DataFrames is what BioPandas is all about!
 
 <br>
 

biopandas/mol2/__init__.py

@@ -10,5 +10,6 @@
 """
 
 from .pandas_mol2 import PandasMOL2
+from .mol2_io import split_multimol2
 
-__all__ = ["PandasMOL2"]
+__all__ = ["PandasMOL2", "split_multimol2"]

biopandas/mol2/pandas_mol2.py

@@ -5,6 +5,7 @@
 # Code Repository: https://github.com/rasbt/biopandas
 
 import pandas as pd
+import numpy as np
 from .mol2_io import split_multimol2
 
 
@@ -211,3 +212,23 @@ def rmsd(df1, df2, heavy_only=True):
                  (d1['z'] - d2['z'])**2)
         rmsd = round((total.sum() / df1.shape[0])**0.5, 4)
         return rmsd
+
+    def distance(self, xyz=(0.00, 0.00, 0.00)):
+        """Computes Euclidean distance between atoms and a 3D point.
+
+        Parameters
+        ----------
+        xyz : tuple (0.00, 0.00, 0.00)
+            X, Y, and Z coordinate of the reference center for the distance
+            computation
+
+        Returns
+        ---------
+        pandas.Series : Pandas Series object containing the Euclidean
+            distance between the atoms in the atom section and `xyz`.
+
+        """
+        return self.df.apply(lambda x: np.sqrt(np.sum(
+            ((x['x'] - xyz[0])**2,
+             (x['y'] - xyz[1])**2,
+             (x['z'] - xyz[2])**2))), axis=1)

biopandas/mol2/tests/test_pandas_mol2.py

@@ -47,3 +47,10 @@ def test_rmsd():
 
     assert pdmol_1.rmsd(pdmol_1.df, pdmol_2.df, heavy_only=False) == 1.5523
     assert pdmol_1.rmsd(pdmol_1.df, pdmol_2.df) == 1.1609
+
+
+def test_distance():
+    data_path = os.path.join(this_dir, 'data', '1b5e_1.mol2')
+
+    pdmol = PandasMOL2().read_mol2(data_path)
+    assert round(pdmol.distance().values[0], 3) == 31.185

docs/mkdocs.yml

@@ -24,7 +24,7 @@ extra_javascript:
 extra_css:
   - extra.css
 
-copyright: Copyright &copy; 2015-2016 <a href="http://sebastianraschka.com">Sebastian Raschka</a>
+copyright: Copyright &copy; 2015-2017 <a href="http://sebastianraschka.com">Sebastian Raschka</a>
 google_analytics: ['UA-38457794-3', 'rasbt.github.io/biopandas']
 
 pages:
@@ -33,6 +33,7 @@ pages:
   - tutorials/Working_with_PDB_Structures_in_DataFrames.md
 - API:
   - api_subpackages/biopandas.pdb.md
+  - api_subpackages/biopandas.mol2.md
 - Changelog: changelog.md
 - Installation: installation.md
 - Contributing: contributing.md

docs/sources/api_subpackages/biopandas.mol2.md

@@ -0,0 +1,178 @@
+biopandas version: 0.2.0.dev0
+## PandasMOL2
+
+*PandasMOL2()*
+
+Object for working with Tripos Mol2 structure files.
+
+**Attributes**
+
+- `df` : pandas.DataFrame
+
+    DataFrame of a Mol2's ATOM section
+
+
+- `mol2_text` : str
+
+    Mol2 file contents in string format
+
+
+- `code` : str
+
+    ID, code, or name of the molecule stored
+
+
+
+### Methods
+
+<hr>
+
+*read_mol2(path, columns=None)*
+
+Reads Mol2 files (unzipped or gzipped) from local drive
+
+    Note that if your mol2 file contains more than one molecule,
+    only the first molecule is loaded into the DataFrame
+
+**Attributes**
+
+- `path` : str
+
+    Path to the Mol2 file in .mol2 format or gzipped format (.mol2.gz)
+
+
+- `columns` : dict or None (default: None)
+
+    If None, this methods expects a 9-column ATOM section that contains
+    the following columns:
+
+    {0:('atom_id', int), 1:('atom_name', str),
+    2:('x', float), 3:('y', float), 4:('z', float),
+    5:('atom_type', str), 6:('subst_id', int),
+    7:('subst_name', str), 8:('charge', float)}
+
+    If your Mol2 files are formatted differently, you can provide your
+    own column_mapping dictionary in a format similar to the one above.
+    However, note that not all assert_raise_message methods
+    may be supported then.
+
+**Returns**
+
+self
+
+<hr>
+
+*read_mol2_from_list(mol2_lines, mol2_code, columns=None)*
+
+Reads Mol2 file from a list into DataFrames
+
+**Attributes**
+
+- `mol2_lines` : list
+
+    A list of lines containing the mol2 file contents. For example,
+    ['@<TRIPOS>MOLECULE
+    ',
+    'ZINC38611810
+    ',
+    '   65    68     0     0     0
+    ',
+    'SMALL
+    ',
+    'NO_CHARGES
+    ',
+    '
+    ',
+    '@<TRIPOS>ATOM
+    ',
+    '      1 C1  -1.1786  2.7011  -4.0323 C.3  1 <0>   -0.1537
+    ',
+    '      2 C2  -1.2950  1.2442  -3.5798 C.3  1 <0>   -0.1156
+    ',
+    ...]
+
+
+- `mol2_code` : str or None
+
+    Name or ID of the molecule.
+
+
+- `columns` : dict or None (default: None)
+
+    If None, this methods expects a 9-column ATOM section that contains
+    the following columns:
+    {0:('atom_id', int), 1:('atom_name', str),
+    2:('x', float), 3:('y', float), 4:('z', float),
+    5:('atom_type', str), 6:('subst_id', int),
+    7:('subst_name', str), 8:('charge', float)}
+    If your Mol2 files are formatted differently, you can provide your
+    own column_mapping dictionary in a format similar to the one above.
+    However, note that not all assert_raise_message methods may be
+    supported then.
+
+**Returns**
+
+self
+
+
+
+<hr>
+
+*rmsd(df1, df2, heavy_only=True)*
+
+Compute the Root Mean Square Deviation between molecules
+
+**Parameters**
+
+- `df1` : pandas.DataFrame
+
+    DataFrame with HETATM, ATOM, and/or ANISOU entries
+
+- `df2` : pandas.DataFrame
+
+    Second DataFrame for RMSD computation against df1. Must have the
+    same number of entries as df1
+
+- `heavy_only` : bool (default: True)
+
+    Which atoms to compare to compute the RMSD. If `True` (default),
+    computes the RMSD between non-hydrogen atoms only.
+
+**Returns**
+
+- `rmsd` : float
+
+    Root Mean Square Deviation between df1 and df2
+
+### Properties
+
+<hr>
+
+*df*
+
+Acccesses the pandas DataFrame
+
+## split_multimol2
+
+*split_multimol2(mol2_path)*
+
+Splits a multi-mol2 file into individual Mol2 file contents.
+
+**Parameters**
+
+- `mol2_path` : str
+
+    Path to the multi-mol2 file. Parses gzip files if the filepath
+    ends on .gz.
+
+**Returns**
+
+A generator object for lists for every extracted mol2-file. Lists contain
+    the molecule ID and the mol2 file contents.
+    e.g., ['ID1234', ['@<TRIPOS>MOLECULE
+    ', '...']]. Note that bytestrings
+    are returned (for reasons of efficieny) if the Mol2 content is read
+    from a gzip (.gz) file.
+
+
+

docs/sources/api_subpackages/biopandas.pdb.md

@@ -1,4 +1,4 @@
-biopandas version: 0.1.5.dev0
+biopandas version: 0.2.0.dev0
 ## PandasPDB
 
 *PandasPDB()*
@@ -33,6 +33,64 @@ Object for working with Protein Databank structure files.
 
 <hr>
 
+*amino3to1(record='ATOM', residue_col='residue_name', fillna='?')*
+
+Creates 1-letter amino acid codes from DataFrame
+
+    Non-canonical amino-acids are converted as follows:
+    ASH (protonated ASP) => D
+    CYX (disulfide-bonded CYS) => C
+    GLH (protonated GLU) => E
+    HID/HIE/HIP (different protonation states of HIS) = H
+    HYP (hydroxyproline) => P
+    MSE (selenomethionine) => M
+
+**Parameters**
+
+- `record` : str (default: 'ATOM')
+
+    Specfies the record DataFrame
+
+- `residue_col` : str (default: 'residue_name')
+
+    Column in `record` DataFrame to look for 3-letter amino acid
+    codes for the conversion
+
+- `fillna` : str (default: '?')
+
+    Placeholder string to use for unknown amino acids
+
+**Returns**
+
+- `pandas.Series` : Pandas Series object containing the 1-letter amino
+
+    acid codes after conversion
+
+<hr>
+
+*distance(xyz=(0.0, 0.0, 0.0), record='ATOM')*
+
+Computes Euclidean distance between atoms and a 3D point.
+
+**Parameters**
+
+- `xyz` : tuple (0.00, 0.00, 0.00)
+
+    X, Y, and Z coordinate of the reference center for the distance
+    computation
+
+- `record` : str (default: 'ATOM')
+
+    Specfies the record DataFrame
+
+**Returns**
+
+- `pandas.Series` : Pandas Series object containing the Euclidean
+
+    distance between the atoms in the record section and `xyz`.
+
+<hr>
+
 *fetch_pdb(pdb_code)*
 
 Fetches PDB file contents from the Protein Databank at rcsb.org.

docs/sources/api_subpackages/biopandas.testutils.md

@@ -1 +1 @@
-biopandas version: 0.1.5.dev0
+biopandas version: 0.2.0.dev0

docs/sources/index.md

@@ -8,7 +8,7 @@
 [![PyPI Version](https://img.shields.io/pypi/v/biopandas.svg)](https://pypi.python.org/pypi/biopandas/)
 [![License](https://img.shields.io/badge/license-new%20BSD-blue.svg)](https://github.com/rasbt/biopandas/blob/master/LICENSE)
 ![Python 2.7](https://img.shields.io/badge/python-2.7-blue.svg)
-![Python 3.5](https://img.shields.io/badge/python-3.5-blue.svg)
+![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)
 
 <br>
 
@@ -28,7 +28,8 @@
 If you are a computational biologist, chances are that you cursed one too many times about protein structure files. Yes, I am talking about ye Goode Olde Protein Data Bank format, aka "PDB files." Nothing against PDB, it's a neatly structured format (if deployed correctly); yet, it is a bit cumbersome to work with PDB files in "modern" programming languages -- I am pretty sure we all agree on this.
 
 As machine learning and "data science" person, I fell in love with [pandas](http://pandas.pydata.org) DataFrames for handling just about everything that can be loaded into memory.  
-So, why don't we take pandas to the structural biology world? Working with molecular structures of biological macromolecules in pandas DataFrames is what BioPandas is all about!
+So, why don't we take pandas to the structural biology world? Working with molecular structures of biological macromolecules (from PDB and MOL2 files) in pandas DataFrames is what BioPandas is all about!
+
 
 
 

docs/sources/installation.md

@@ -12,7 +12,7 @@ pip install biopandas
 
 ## Conda-forge
 
-The latest stable release of `biopandas` is now also available via [conda-forge](https://github.com/conda-forge/biopandas-feedstock); you can install it via
+Versions of `biopandas` are now also available via [conda-forge](https://github.com/conda-forge/biopandas-feedstock); you can install it via
 
 
 ```bash
@@ -21,9 +21,10 @@ conda install biopandas -c conda-forge
 
  or simply
 
- ```bash
+```bash
 conda install biopandas
 ```
+
 if you have `conda-forge` already [added to your channels](https://github.com/conda-forge/biopandas-feedstock).