prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  use bibtex in multi_comp.py
  convert references in `mne/stats/multi_comp.py` to use footcite / footbibliography
  
  
  </issue>
  <code>
  [start of mne/stats/multi_comp.py]
  1 # Authors: Josef Pktd and example from H Raja and rewrite from Vincent Davis
  2 #          Alexandre Gramfort <alexandre.gramfort@inria.fr>
  3 #
  4 # Code borrowed from statsmodels
  5 #
  6 # License: BSD (3-clause)
  7 
  8 import numpy as np
  9 
  10 
  11 def _ecdf(x):
  12     """No frills empirical cdf used in fdrcorrection."""
  13     nobs = len(x)
  14     return np.arange(1, nobs + 1) / float(nobs)
  15 
  16 
  17 def fdr_correction(pvals, alpha=0.05, method='indep'):
  18     """P-value correction with False Discovery Rate (FDR).
  19 
  20     Correction for multiple comparison using FDR [1]_.
  21 
  22     This covers Benjamini/Hochberg for independent or positively correlated and
  23     Benjamini/Yekutieli for general or negatively correlated tests.
  24 
  25     Parameters
  26     ----------
  27     pvals : array_like
  28         Set of p-values of the individual tests.
  29     alpha : float
  30         Error rate.
  31     method : 'indep' | 'negcorr'
  32         If 'indep' it implements Benjamini/Hochberg for independent or if
  33         'negcorr' it corresponds to Benjamini/Yekutieli.
  34 
  35     Returns
  36     -------
  37     reject : array, bool
  38         True if a hypothesis is rejected, False if not.
  39     pval_corrected : array
  40         P-values adjusted for multiple hypothesis testing to limit FDR.
  41 
  42     References
  43     ----------
  44     .. [1] Genovese CR, Lazar NA, Nichols T. Thresholding of statistical maps
  45            in functional neuroimaging using the false discovery rate.
  46            Neuroimage. 2002 Apr;15(4):870-8.
  47     """
  48     pvals = np.asarray(pvals)
  49     shape_init = pvals.shape
  50     pvals = pvals.ravel()
  51 
  52     pvals_sortind = np.argsort(pvals)
  53     pvals_sorted = pvals[pvals_sortind]
  54     sortrevind = pvals_sortind.argsort()
  55 
  56     if method in ['i', 'indep', 'p', 'poscorr']:
  57         ecdffactor = _ecdf(pvals_sorted)
  58     elif method in ['n', 'negcorr']:
  59         cm = np.sum(1. / np.arange(1, len(pvals_sorted) + 1))
  60         ecdffactor = _ecdf(pvals_sorted) / cm
  61     else:
  62         raise ValueError("Method should be 'indep' and 'negcorr'")
  63 
  64     reject = pvals_sorted < (ecdffactor * alpha)
  65     if reject.any():
  66         rejectmax = max(np.nonzero(reject)[0])
  67     else:
  68         rejectmax = 0
  69     reject[:rejectmax] = True
  70 
  71     pvals_corrected_raw = pvals_sorted / ecdffactor
  72     pvals_corrected = np.minimum.accumulate(pvals_corrected_raw[::-1])[::-1]
  73     pvals_corrected[pvals_corrected > 1.0] = 1.0
  74     pvals_corrected = pvals_corrected[sortrevind].reshape(shape_init)
  75     reject = reject[sortrevind].reshape(shape_init)
  76     return reject, pvals_corrected
  77 
  78 
  79 def bonferroni_correction(pval, alpha=0.05):
  80     """P-value correction with Bonferroni method.
  81 
  82     Parameters
  83     ----------
  84     pval : array_like
  85         Set of p-values of the individual tests.
  86     alpha : float
  87         Error rate.
  88 
  89     Returns
  90     -------
  91     reject : array, bool
  92         True if a hypothesis is rejected, False if not.
  93     pval_corrected : array
  94         P-values adjusted for multiple hypothesis testing to limit FDR.
  95     """
  96     pval = np.asarray(pval)
  97     pval_corrected = pval * float(pval.size)
  98     # p-values must not be larger than 1.
  99     pval_corrected = pval_corrected.clip(max=1.)
  100     reject = pval_corrected < alpha
  101     return reject, pval_corrected
  102
  [end of mne/stats/multi_comp.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/mne/stats/multi_comp.py b/mne/stats/multi_comp.py
  --- a/mne/stats/multi_comp.py
  +++ b/mne/stats/multi_comp.py
  @@ -17,7 +17,7 @@
   def fdr_correction(pvals, alpha=0.05, method='indep'):
       """P-value correction with False Discovery Rate (FDR).
   
  -    Correction for multiple comparison using FDR [1]_.
  +    Correction for multiple comparison using FDR :footcite:`GenoveseEtAl2002`.
   
       This covers Benjamini/Hochberg for independent or positively correlated and
       Benjamini/Yekutieli for general or negatively correlated tests.
  @@ -41,9 +41,7 @@
   
       References
       ----------
  -    .. [1] Genovese CR, Lazar NA, Nichols T. Thresholding of statistical maps
  -           in functional neuroimaging using the false discovery rate.
  -           Neuroimage. 2002 Apr;15(4):870-8.
  +    .. footbibliography::
       """
       pvals = np.asarray(pvals)
       shape_init = pvals.shape
  
verification_info: {"golden_diff": "diff --git a/mne/stats/multi_comp.py b/mne/stats/multi_comp.py\n--- a/mne/stats/multi_comp.py\n+++ b/mne/stats/multi_comp.py\n@@ -17,7 +17,7 @@\n def fdr_correction(pvals, alpha=0.05, method='indep'):\n     \"\"\"P-value correction with False Discovery Rate (FDR).\n \n-    Correction for multiple comparison using FDR [1]_.\n+    Correction for multiple comparison using FDR :footcite:`GenoveseEtAl2002`.\n \n     This covers Benjamini/Hochberg for independent or positively correlated and\n     Benjamini/Yekutieli for general or negatively correlated tests.\n@@ -41,9 +41,7 @@\n \n     References\n     ----------\n-    .. [1] Genovese CR, Lazar NA, Nichols T. Thresholding of statistical maps\n-           in functional neuroimaging using the false discovery rate.\n-           Neuroimage. 2002 Apr;15(4):870-8.\n+    .. footbibliography::\n     \"\"\"\n     pvals = np.asarray(pvals)\n     shape_init = pvals.shape\n", "issue": "use bibtex in multi_comp.py\nconvert references in `mne/stats/multi_comp.py` to use footcite / footbibliography\r\n\n", "before_files": [{"content": "# Authors: Josef Pktd and example from H Raja and rewrite from Vincent Davis\n#          Alexandre Gramfort <alexandre.gramfort@inria.fr>\n#\n# Code borrowed from statsmodels\n#\n# License: BSD (3-clause)\n\nimport numpy as np\n\n\ndef _ecdf(x):\n    \"\"\"No frills empirical cdf used in fdrcorrection.\"\"\"\n    nobs = len(x)\n    return np.arange(1, nobs + 1) / float(nobs)\n\n\ndef fdr_correction(pvals, alpha=0.05, method='indep'):\n    \"\"\"P-value correction with False Discovery Rate (FDR).\n\n    Correction for multiple comparison using FDR [1]_.\n\n    This covers Benjamini/Hochberg for independent or positively correlated and\n    Benjamini/Yekutieli for general or negatively correlated tests.\n\n    Parameters\n    ----------\n    pvals : array_like\n        Set of p-values of the individual tests.\n    alpha : float\n        Error rate.\n    method : 'indep' | 'negcorr'\n        If 'indep' it implements Benjamini/Hochberg for independent or if\n        'negcorr' it corresponds to Benjamini/Yekutieli.\n\n    Returns\n    -------\n    reject : array, bool\n        True if a hypothesis is rejected, False if not.\n    pval_corrected : array\n        P-values adjusted for multiple hypothesis testing to limit FDR.\n\n    References\n    ----------\n    .. [1] Genovese CR, Lazar NA, Nichols T. Thresholding of statistical maps\n           in functional neuroimaging using the false discovery rate.\n           Neuroimage. 2002 Apr;15(4):870-8.\n    \"\"\"\n    pvals = np.asarray(pvals)\n    shape_init = pvals.shape\n    pvals = pvals.ravel()\n\n    pvals_sortind = np.argsort(pvals)\n    pvals_sorted = pvals[pvals_sortind]\n    sortrevind = pvals_sortind.argsort()\n\n    if method in ['i', 'indep', 'p', 'poscorr']:\n        ecdffactor = _ecdf(pvals_sorted)\n    elif method in ['n', 'negcorr']:\n        cm = np.sum(1. / np.arange(1, len(pvals_sorted) + 1))\n        ecdffactor = _ecdf(pvals_sorted) / cm\n    else:\n        raise ValueError(\"Method should be 'indep' and 'negcorr'\")\n\n    reject = pvals_sorted < (ecdffactor * alpha)\n    if reject.any():\n        rejectmax = max(np.nonzero(reject)[0])\n    else:\n        rejectmax = 0\n    reject[:rejectmax] = True\n\n    pvals_corrected_raw = pvals_sorted / ecdffactor\n    pvals_corrected = np.minimum.accumulate(pvals_corrected_raw[::-1])[::-1]\n    pvals_corrected[pvals_corrected > 1.0] = 1.0\n    pvals_corrected = pvals_corrected[sortrevind].reshape(shape_init)\n    reject = reject[sortrevind].reshape(shape_init)\n    return reject, pvals_corrected\n\n\ndef bonferroni_correction(pval, alpha=0.05):\n    \"\"\"P-value correction with Bonferroni method.\n\n    Parameters\n    ----------\n    pval : array_like\n        Set of p-values of the individual tests.\n    alpha : float\n        Error rate.\n\n    Returns\n    -------\n    reject : array, bool\n        True if a hypothesis is rejected, False if not.\n    pval_corrected : array\n        P-values adjusted for multiple hypothesis testing to limit FDR.\n    \"\"\"\n    pval = np.asarray(pval)\n    pval_corrected = pval * float(pval.size)\n    # p-values must not be larger than 1.\n    pval_corrected = pval_corrected.clip(max=1.)\n    reject = pval_corrected < alpha\n    return reject, pval_corrected\n", "path": "mne/stats/multi_comp.py"}]}