Add a nicer visual example

Author: mobiusklein

docs/conf.py

@@ -33,7 +33,9 @@
     'sphinx.ext.autodoc',
     'sphinx.ext.todo',
     'sphinx.ext.viewcode',
-    'sphinx.ext.napoleon'
+    'sphinx.ext.napoleon',
+    'matplotlib.sphinxext.plot_directive',
+
 ]
 
 # Add any paths that contain templates here, relative to this directory.

docs/doc-requirements.txt

@@ -0,0 +1,2 @@
+sphinx>=1.3
+matplotlib

docs/index.rst

@@ -6,27 +6,48 @@
 Welcome to brainpy's documentation!
 ===================================
 
-:mod:`brainpy` is a small Python library implementing the *B*afflingly *R*ecursive
-*A*lgorithm for *I*sotopic Patter*N* generation. It includes three implementations,
+:mod:`brainpy` is a small Python library implementing the *B* afflingly *R* ecursive
+*A* lgorithm for *I* sotopic Patter *N* generation. It includes three implementations,
 a pure-Python object oriented implementation, a :title-reference:`Cython` accelerated
 version of the object oriented implementation, and a pure :title-reference:`C` implementation,
 listed in order of ascending speed. The C implementation is used by default when available.
 
 
 BRAIN takes an elemental composition represented by any :class:`Mapping`-like Python object
 and uses it to compute its aggregated isotopic distribution. All isotopic variants of the same
-number of neutrons are collapsed into a single peak, meaning it does not consider isotopic fine
-structure.
+number of neutrons are collapsed into a single centroid peak, meaning it does not consider
+isotopic fine structure.
 
-.. code-block:: python
+.. plot::
+    :include-source:
 
     from brainpy import isotopic_variants
 
-    leucine = dict(C=6, H=13, O=2, N=1)
-    theoretical_isotopic_cluster = isotopic_variants(leucine, n_peaks=6, charge=1)
+    peptide = {'H': 53, 'C': 34, 'O': 15, 'N': 7}
+    theoretical_isotopic_cluster = isotopic_variants(peptide, npeaks=5, charge=1)
     for peak in theoretical_isotopic_cluster:
         print(peak.mz, peak.intensity)
 
+    # produce a theoretical profile using a gaussian peak shape
+    import numpy as np
+    grid = np.arange(theoretical_isotopic_cluster[0].mz - 1,
+                     theoretical_isotopic_cluster[-1].mz + 1, 0.02)
+    intensity = np.zeros_like(grid)
+    sigma = 0.002
+    for i, mz in enumerate(grid):
+        for peak in theoretical_isotopic_cluster:
+            intensity[i] += peak.intensity * np.exp(-(mz - peak.mz) ** 2 / (2 * sigma)
+                    ) / (np.sqrt(2 * np.pi) * sigma)
+
+    intensity = (intensity / intensity.max()) * 100
+
+    # draw the profile
+    from matplotlib import pyplot as plt
+    plt.plot(grid, intensity)
+    plt.xlabel("m/z")
+    plt.ylabel("Relative intensity")
+
+
 .. automodule:: brainpy
 
     .. autofunction:: isotopic_variants