[med-svn] [python-mne] 25/376: more on doc

Yaroslav Halchenko debian at onerussian.com
Fri Nov 27 17:21:59 UTC 2015


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yoh pushed a commit to annotated tag v0.1
in repository python-mne.

commit 3c98f6ec105c34bef231118edc00c5bd156fcb42
Author: Alexandre Gramfort <alexandre.gramfort at inria.fr>
Date:   Thu Dec 30 18:00:23 2010 -0500

    more on doc
---
 examples/read_stc.py | 23 ++++++++++-------------
 fiff/channels.py     | 14 ++++----------
 fiff/cov.py          | 52 +++++++++++++++++++++++++++++-----------------------
 fiff/ctf.py          |  6 +++---
 fiff/event.py        | 15 +++++++++------
 fiff/evoked.py       | 35 +++++++++++++++++++++++------------
 fiff/forward.py      | 16 ++++++++--------
 fiff/inverse.py      | 10 +++++-----
 fiff/matrix.py       | 14 ++++++--------
 fiff/meas_info.py    |  4 ++--
 10 files changed, 99 insertions(+), 90 deletions(-)

diff --git a/examples/read_stc.py b/examples/read_stc.py
index 96bd266..e3fd2ed 100644
--- a/examples/read_stc.py
+++ b/examples/read_stc.py
@@ -1,4 +1,7 @@
-"""Reading a raw file segment
+"""Reading an STC file
+
+STC files contain activations on cortex ie. source
+reconstructions
 """
 print __doc__
 
@@ -7,16 +10,10 @@ import fiff
 fname = 'MNE-sample-data/MEG/sample/sample_audvis-ave-7-meg-lh.stc'
 
 stc = fiff.read_stc(fname)
-fiff.write_stc("tmp.stc", stc)
-stc2 = fiff.read_stc("tmp.stc")
-
-from scipy import linalg
-print linalg.norm(stc['data'] - stc2['data'])
 
-# n_vertices, n_samples = stc['data'].shape
-# print "tmin : %s (s)" % stc['tmin']
-# print "tstep : %s" % stc['tstep']
-# print "tmax : %s (s)" % (stc['tmin'] + stc['tstep'] * n_samples)
-# print "stc data size: %s (nb of vertices) x %s (nb of samples)" % (
-#                                                     n_vertices, n_samples)
-# 
+n_vertices, n_samples = stc['data'].shape
+print "tmin : %s (s)" % stc['tmin']
+print "tstep : %s" % stc['tstep']
+print "tmax : %s (s)" % (stc['tmin'] + stc['tstep'] * n_samples)
+print "stc data size: %s (nb of vertices) x %s (nb of samples)" % (
+                                                    n_vertices, n_samples)
diff --git a/fiff/channels.py b/fiff/channels.py
index dae6504..01ec9ba 100644
--- a/fiff/channels.py
+++ b/fiff/channels.py
@@ -3,16 +3,10 @@ from .tag import find_tag
 from .constants import FIFF
 
 
-def read_bad_channels(fid, node):
-    """
-    %
-    % [bads] = fiff_read_bad_channels(fid,node)
-    %
-    % Reas the bad channel list from a node if it exists
-    %
-    % fid      - The file id
-    % node     - The node of interest
-    %
+def _read_bad_channels(fid, node):
+    """Read bad channels
+
+    It returns the list of channel's names.
     """
 
     nodes = dir_tree_find(node, FIFF.FIFFB_MNE_BAD_CHANNELS)
diff --git a/fiff/cov.py b/fiff/cov.py
index 237b9c3..2d4529d 100644
--- a/fiff/cov.py
+++ b/fiff/cov.py
@@ -5,22 +5,28 @@ from .constants import FIFF
 from .tag import find_tag
 from .tree import dir_tree_find
 from .proj import read_proj
-from .channels import read_bad_channels
+from .channels import _read_bad_channels
 
 
 def read_cov(fid, node, cov_kind):
-    """
-    %
-    % [cov] = mne_read_cov(fid, node, kind)
-    %
-    % Reads a covariance matrix from a fiff file
-    %
-    % fid       - an open file descriptor
-    % node      - look for the matrix in here
-    % cov_kind  - what kind of a covariance matrix do we want?
-    %
-    """
+    """Read a noise covariance matrix
+
+    Parameters
+    ----------
+    fid: file
+        The file descriptor
+
+    node: dict
+        The node in the FIF tree
+
+    cov_kind: int
+        The type of covariance. XXX : clarify
 
+    Returns
+    -------
+    data: dict
+        The noise covariance
+    """
     #   Find all covariance matrices
     covs = dir_tree_find(node, FIFF.FIFFB_MNE_COV)
     if len(covs) == 0:
@@ -99,7 +105,7 @@ def read_cov(fid, node, cov_kind):
             projs = read_proj(fid, this)
 
             #   Read the bad channel list
-            bads = read_bad_channels(fid, this)
+            bads = _read_bad_channels(fid, this)
 
             #   Put it together
             cov = dict(kind=cov_kind, diag=diagmat, dim=dim, names=names,
@@ -120,6 +126,7 @@ from .proj import write_proj
 
 
 def write_cov(fid, cov):
+    
     """
     %
     %
@@ -178,16 +185,15 @@ def write_cov(fid, cov):
 
 
 def write_cov_file(fname, cov):
-    """
-    %
-    %   function mne_write_cov_file(name,cov)
-    %
-    %   Write a complete fif file containing a covariance matrix
-    %
-    %   fname    filename
-    %   cov      the covariance matrix to write
-    %
-    %
+    """Write a noise covariance matrix
+
+    Parameters
+    ----------
+    fname: string
+        The name of the file
+
+    cov: dict
+        The noise covariance
     """
     fid = start_file(fname)
 
diff --git a/fiff/ctf.py b/fiff/ctf.py
index f5c7461..6fa9a96 100644
--- a/fiff/ctf.py
+++ b/fiff/ctf.py
@@ -9,10 +9,10 @@ def hex2dec(s):
     return int(s, 16)
 
 
-def read_named_matrix(fid, node, matkind):
+def _read_named_matrix(fid, node, matkind):
     """
     %
-    % [mat] = fiff_read_named_matrix(fid,node)
+    % [mat] = fiff__read_named_matrix(fid,node)
     %
     % Read named matrix from the given node
     %
@@ -96,7 +96,7 @@ def read_ctf_comp(fid, node, chs):
     for node in comps:
 
         #   Read the data we need
-        mat = read_named_matrix(fid, node, FIFF.FIFF_MNE_CTF_COMP_DATA)
+        mat = _read_named_matrix(fid, node, FIFF.FIFF_MNE_CTF_COMP_DATA)
         for p in range(node.nent):
             kind = node.dir[p].kind
             pos = node.dir[p].pos
diff --git a/fiff/event.py b/fiff/event.py
index fcac9a6..a4d6672 100644
--- a/fiff/event.py
+++ b/fiff/event.py
@@ -53,12 +53,15 @@ def read_events(filename):
 
 
 def write_events(filename, event_list):
-    """
-    %
-    % mne_write_events(filename,eventlist)
-    %
-    % Write an event list into a fif file
-    %
+    """Write events to file
+
+    Parameters
+    ----------
+    filename: string
+        name of the fif file
+
+    events: array, shape (n_events, 3)
+        The list of events
     """
     #   Start writing...
     fid = start_file(filename)
diff --git a/fiff/evoked.py b/fiff/evoked.py
index 013c481..5038f2d 100644
--- a/fiff/evoked.py
+++ b/fiff/evoked.py
@@ -8,13 +8,23 @@ from .meas_info import read_meas_info
 
 
 def read_evoked(fname, setno=0):
-    """
-    [data] = fiff_read_evoked(fname,setno)
+    """Read an evoked dataset
 
-    Read one evoked data set
+    Parameters
+    ----------
+    fname: string
+        The file name.
 
-    """
+    setno: int
+        The index of the evoked dataset to read. FIF
+        file can contain multiple datasets.
 
+    Returns
+    -------
+    data: dict
+        The evoked dataset
+
+    """
     if setno < 0:
         raise ValueError, 'Data set selector must be positive'
 
@@ -220,14 +230,15 @@ from .ctf import write_ctf_comp
 
 
 def write_evoked(name, data):
-    """
-    %
-    % function fiff_write_evoked(name,data)
-    %
-    % name     filename
-    % data     the data structure returned from fiff_read_evoked
-    %
-    %
+    """Write an evoked dataset to a file
+
+    Parameters
+    ----------
+    name: string
+        The file name.
+
+    data: dict
+        The evoked dataset obtained with read_evoked
     """
 
     #  Create the file and save the essentials
diff --git a/fiff/forward.py b/fiff/forward.py
index 456fdf5..87a014c 100644
--- a/fiff/forward.py
+++ b/fiff/forward.py
@@ -5,10 +5,10 @@ from scipy import linalg
 from .constants import FIFF
 from .open import fiff_open
 from .tree import dir_tree_find
-from .channels import read_bad_channels
+from .channels import _read_bad_channels
 from .tag import find_tag
 from .source_space import read_source_spaces, find_source_space_hemi
-from .matrix import read_named_matrix, transpose_named_matrix
+from .matrix import _read_named_matrix, _transpose_named_matrix
 
 
 def _block_diag(A, n):
@@ -152,17 +152,17 @@ def _read_one(fid, node):
 
     one['nchan'] = tag.data
     try:
-        one['sol'] = read_named_matrix(fid, node,
+        one['sol'] = _read_named_matrix(fid, node,
                                             FIFF.FIFF_MNE_FORWARD_SOLUTION)
-        one['sol'] = transpose_named_matrix(one['sol'])
+        one['sol'] = _transpose_named_matrix(one['sol'])
     except Exception as inst:
         fid.close()
         raise 'Forward solution data not found (%s)' % inst
 
     try:
-        one['sol_grad'] = read_named_matrix(fid, node,
+        one['sol_grad'] = _read_named_matrix(fid, node,
                                         FIFF.FIFF_MNE_FORWARD_SOLUTION_GRAD)
-        one['sol_grad'] = transpose_named_matrix(one['sol_grad'])
+        one['sol_grad'] = _transpose_named_matrix(one['sol_grad'])
     except Exception as inst:
         one['sol_grad'] = None
 
@@ -206,7 +206,7 @@ def read_forward_solution(fname, force_fixed=False, surf_ori=False,
 
     Returns
     -------
-    fwt: dict
+    fwd: dict
         The forward solution
 
     """
@@ -240,7 +240,7 @@ def read_forward_solution(fname, force_fixed=False, surf_ori=False,
     fwd = None
 
     #   Bad channel list
-    bads = read_bad_channels(fid, tree)
+    bads = _read_bad_channels(fid, tree)
 
     print '\t%d bad channels read' % len(bads)
 
diff --git a/fiff/inverse.py b/fiff/inverse.py
index 6ce1fac..7ed16e6 100644
--- a/fiff/inverse.py
+++ b/fiff/inverse.py
@@ -1,7 +1,7 @@
 from .constants import FIFF
 from .open import fiff_open
 from .tag import find_tag
-from .matrix import read_named_matrix, transpose_named_matrix
+from .matrix import _read_named_matrix, _transpose_named_matrix
 from .cov import read_cov
 from .proj import read_proj
 from .tree import dir_tree_find
@@ -103,19 +103,19 @@ def read_inverse_operator(fname):
     #
     inv['eigen_leads_weighted'] = False
     try:
-       inv['eigen_leads'] = read_named_matrix(fid, invs, FIFF.FIFF_MNE_INVERSE_LEADS)
+       inv['eigen_leads'] = _read_named_matrix(fid, invs, FIFF.FIFF_MNE_INVERSE_LEADS)
     except:
        inv['eigen_leads_weighted'] = True
        try:
-          inv.eigen_leads = read_named_matrix(fid,invs,FIFF.FIFF_MNE_INVERSE_LEADS_WEIGHTED);
+          inv.eigen_leads = _read_named_matrix(fid,invs,FIFF.FIFF_MNE_INVERSE_LEADS_WEIGHTED);
        except Exception as inst:
           raise ValueError, '%s' % inst
     #
     #   Having the eigenleads as columns is better for the inverse calculations
     #
-    inv['eigen_leads'] = transpose_named_matrix(inv['eigen_leads'])
+    inv['eigen_leads'] = _transpose_named_matrix(inv['eigen_leads'])
     try:
-        inv['eigen_fields'] = read_named_matrix(fid, invs, FIFF.FIFF_MNE_INVERSE_FIELDS)
+        inv['eigen_fields'] = _read_named_matrix(fid, invs, FIFF.FIFF_MNE_INVERSE_FIELDS)
     except Exception as inst:
         raise ValueError, '%s' % inst
 
diff --git a/fiff/matrix.py b/fiff/matrix.py
index ce51a4c..3eb82d0 100644
--- a/fiff/matrix.py
+++ b/fiff/matrix.py
@@ -2,7 +2,7 @@ from .constants import FIFF
 from .tag import find_tag, has_tag
 
 
-def transpose_named_matrix(mat):
+def _transpose_named_matrix(mat):
     """Transpose mat inplace (no copy)
     """
     mat['nrow'] = mat['ncol']
@@ -13,13 +13,11 @@ def transpose_named_matrix(mat):
     return mat
 
 
-def read_named_matrix(fid, node, matkind):
-    """
-    %
-    % [mat] = fiff_read_named_matrix(fid,node)
-    %
-    % Read named matrix from the given node
-    %
+def _read_named_matrix(fid, node, matkind):
+    """Read named matrix from the given node
+
+    XXX
+
     """
 
     #   Descend one level if necessary
diff --git a/fiff/meas_info.py b/fiff/meas_info.py
index 9858fed..1e7d6e8 100644
--- a/fiff/meas_info.py
+++ b/fiff/meas_info.py
@@ -6,7 +6,7 @@ from .constants import FIFF
 from .tag import read_tag
 from .proj import read_proj
 from .ctf import read_ctf_comp
-from .channels import read_bad_channels
+from .channels import _read_bad_channels
 
 
 def read_meas_info(source, tree=None):
@@ -187,7 +187,7 @@ def read_meas_info(source, tree=None):
     comps = read_ctf_comp(fid, meas_info, chs)
 
     #   Load the bad channel list
-    bads = read_bad_channels(fid, meas_info)
+    bads = _read_bad_channels(fid, meas_info)
 
     #
     #   Put the data together

-- 
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