prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Memory consumption in plot_brainstorm_phantom_elekta
  Circle is failing because it's using so much memory.
  
  
  </issue>
  <code>
  [start of tutorials/plot_brainstorm_phantom_elekta.py]
  1 # -*- coding: utf-8 -*-
  2 """
  3 ==========================================
  4 Brainstorm Elekta phantom tutorial dataset
  5 ==========================================
  6 
  7 Here we compute the evoked from raw for the Brainstorm Elekta phantom
  8 tutorial dataset. For comparison, see [1]_ and:
  9 
  10     http://neuroimage.usc.edu/brainstorm/Tutorials/PhantomElekta
  11 
  12 References
  13 ----------
  14 .. [1] Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM.
  15        Brainstorm: A User-Friendly Application for MEG/EEG Analysis.
  16        Computational Intelligence and Neuroscience, vol. 2011, Article ID
  17        879716, 13 pages, 2011. doi:10.1155/2011/879716
  18 """
  19 
  20 # Authors: Eric Larson <larson.eric.d@gmail.com>
  21 #
  22 # License: BSD (3-clause)
  23 
  24 import os.path as op
  25 import numpy as np
  26 
  27 import mne
  28 from mne import find_events, fit_dipole
  29 from mne.datasets.brainstorm import bst_phantom_elekta
  30 from mne.io import read_raw_fif
  31 
  32 print(__doc__)
  33 
  34 ###############################################################################
  35 # The data were collected with an Elekta Neuromag VectorView system at 1000 Hz
  36 # and low-pass filtered at 330 Hz. Here the medium-amplitude (200 nAm) data
  37 # are read to construct instances of :class:`mne.io.Raw`.
  38 data_path = bst_phantom_elekta.data_path()
  39 
  40 raw_fname = op.join(data_path, 'kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif')
  41 raw = read_raw_fif(raw_fname, preload=True)
  42 
  43 ###############################################################################
  44 # Data channel array consisted of 204 MEG planor gradiometers,
  45 # 102 axial magnetometers, and 3 stimulus channels. Let's get the events
  46 # for the phantom, where each dipole (1-32) gets its own event:
  47 
  48 events = find_events(raw, 'STI201')
  49 raw.plot(events=events)
  50 raw.info['bads'] = ['MEG2421']
  51 
  52 ###############################################################################
  53 # The data have strong line frequency (60 Hz and harmonics) and cHPI coil
  54 # noise (five peaks around 300 Hz):
  55 
  56 raw.plot_psd()
  57 
  58 ###############################################################################
  59 # We know our phantom produces sinusoidal bursts below 25 Hz, so let's filter.
  60 
  61 raw.filter(None, 40., h_trans_bandwidth=10., filter_length='1s')
  62 raw.plot_psd()
  63 
  64 ###############################################################################
  65 # The data are still a bit noisy, so let's use Maxwell filtering to clean it.
  66 # Ideally we would have the fine calibration and cross-talk information
  67 # for the site of interest, but we don't, so we just do:
  68 
  69 raw.fix_mag_coil_types()
  70 raw = mne.preprocessing.maxwell_filter(raw, origin=(0., 0., 0.))
  71 raw.plot(events=events)
  72 
  73 ###############################################################################
  74 # Now we epoch our data, average it, and look at the first dipole response.
  75 # The first peak appears around 3 ms.
  76 
  77 tmin, tmax = -0.2, 0.2
  78 event_id = list(range(1, 33))
  79 epochs = mne.Epochs(raw, events, event_id, tmin, tmax, baseline=(None, -0.01))
  80 epochs['1'].average().plot()
  81 
  82 ###############################################################################
  83 # Let's do some dipole fits. The phantom is properly modeled by a single-shell
  84 # sphere with origin (0., 0., 0.). We compute covariance, then do the fits.
  85 
  86 t_peak = 0.061  # 3 MS at largest peak
  87 sphere = mne.make_sphere_model(r0=(0., 0., 0.), head_radius=None)
  88 cov = mne.compute_covariance(epochs, tmax=0)
  89 data = []
  90 for ii in range(1, 33):
  91     evoked = epochs[str(ii)].average().crop(t_peak, t_peak)
  92     data.append(evoked.data[:, 0])
  93 evoked = mne.EvokedArray(np.array(data).T, evoked.info, tmin=0.)
  94 dip = fit_dipole(evoked, cov, sphere, n_jobs=2)[0]
  95 
  96 ###############################################################################
  97 # Now we can compare to the actual locations, taking the difference in mm:
  98 
  99 actual_pos = mne.dipole.get_phantom_dipoles(kind='122')[0]
  100 diffs = 1000 * np.sqrt(np.sum((dip.pos - actual_pos) ** 2, axis=-1))
  101 print('Differences (mm):\n%s' % diffs[:, np.newaxis])
  102
  [end of tutorials/plot_brainstorm_phantom_elekta.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/tutorials/plot_brainstorm_phantom_elekta.py b/tutorials/plot_brainstorm_phantom_elekta.py
  --- a/tutorials/plot_brainstorm_phantom_elekta.py
  +++ b/tutorials/plot_brainstorm_phantom_elekta.py
  @@ -38,7 +38,7 @@
   data_path = bst_phantom_elekta.data_path()
   
   raw_fname = op.join(data_path, 'kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif')
  -raw = read_raw_fif(raw_fname, preload=True)
  +raw = read_raw_fif(raw_fname)
   
   ###############################################################################
   # Data channel array consisted of 204 MEG planor gradiometers,
  @@ -51,39 +51,41 @@
   
   ###############################################################################
   # The data have strong line frequency (60 Hz and harmonics) and cHPI coil
  -# noise (five peaks around 300 Hz):
  +# noise (five peaks around 300 Hz). Here we plot only out to 60 seconds
  +# to save memory:
   
  -raw.plot_psd()
  +raw.plot_psd(tmax=60.)
   
   ###############################################################################
  -# We know our phantom produces sinusoidal bursts below 25 Hz, so let's filter.
  -
  -raw.filter(None, 40., h_trans_bandwidth=10., filter_length='1s')
  -raw.plot_psd()
  -
  -###############################################################################
  -# The data are still a bit noisy, so let's use Maxwell filtering to clean it.
  +# Let's use Maxwell filtering to clean the data a bit.
   # Ideally we would have the fine calibration and cross-talk information
   # for the site of interest, but we don't, so we just do:
   
   raw.fix_mag_coil_types()
   raw = mne.preprocessing.maxwell_filter(raw, origin=(0., 0., 0.))
  +
  +###############################################################################
  +# We know our phantom produces sinusoidal bursts below 25 Hz, so let's filter.
  +
  +raw.filter(None, 40., h_trans_bandwidth=10., filter_length='1s')
   raw.plot(events=events)
   
   ###############################################################################
   # Now we epoch our data, average it, and look at the first dipole response.
  -# The first peak appears around 3 ms.
  +# The first peak appears around 3 ms. Because we low-passed at 40 Hz,
  +# we can also decimate our data to save memory.
   
  -tmin, tmax = -0.2, 0.2
  +tmin, tmax = -0.1, 0.1
   event_id = list(range(1, 33))
  -epochs = mne.Epochs(raw, events, event_id, tmin, tmax, baseline=(None, -0.01))
  +epochs = mne.Epochs(raw, events, event_id, tmin, tmax, baseline=(None, -0.01),
  +                    decim=5)
   epochs['1'].average().plot()
   
   ###############################################################################
   # Let's do some dipole fits. The phantom is properly modeled by a single-shell
   # sphere with origin (0., 0., 0.). We compute covariance, then do the fits.
   
  -t_peak = 0.061  # 3 MS at largest peak
  +t_peak = 60e-3  # ~60 MS at largest peak
   sphere = mne.make_sphere_model(r0=(0., 0., 0.), head_radius=None)
   cov = mne.compute_covariance(epochs, tmax=0)
   data = []
  
verification_info: {"golden_diff": "diff --git a/tutorials/plot_brainstorm_phantom_elekta.py b/tutorials/plot_brainstorm_phantom_elekta.py\n--- a/tutorials/plot_brainstorm_phantom_elekta.py\n+++ b/tutorials/plot_brainstorm_phantom_elekta.py\n@@ -38,7 +38,7 @@\n data_path = bst_phantom_elekta.data_path()\n \n raw_fname = op.join(data_path, 'kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif')\n-raw = read_raw_fif(raw_fname, preload=True)\n+raw = read_raw_fif(raw_fname)\n \n ###############################################################################\n # Data channel array consisted of 204 MEG planor gradiometers,\n@@ -51,39 +51,41 @@\n \n ###############################################################################\n # The data have strong line frequency (60 Hz and harmonics) and cHPI coil\n-# noise (five peaks around 300 Hz):\n+# noise (five peaks around 300 Hz). Here we plot only out to 60 seconds\n+# to save memory:\n \n-raw.plot_psd()\n+raw.plot_psd(tmax=60.)\n \n ###############################################################################\n-# We know our phantom produces sinusoidal bursts below 25 Hz, so let's filter.\n-\n-raw.filter(None, 40., h_trans_bandwidth=10., filter_length='1s')\n-raw.plot_psd()\n-\n-###############################################################################\n-# The data are still a bit noisy, so let's use Maxwell filtering to clean it.\n+# Let's use Maxwell filtering to clean the data a bit.\n # Ideally we would have the fine calibration and cross-talk information\n # for the site of interest, but we don't, so we just do:\n \n raw.fix_mag_coil_types()\n raw = mne.preprocessing.maxwell_filter(raw, origin=(0., 0., 0.))\n+\n+###############################################################################\n+# We know our phantom produces sinusoidal bursts below 25 Hz, so let's filter.\n+\n+raw.filter(None, 40., h_trans_bandwidth=10., filter_length='1s')\n raw.plot(events=events)\n \n ###############################################################################\n # Now we epoch our data, average it, and look at the first dipole response.\n-# The first peak appears around 3 ms.\n+# The first peak appears around 3 ms. Because we low-passed at 40 Hz,\n+# we can also decimate our data to save memory.\n \n-tmin, tmax = -0.2, 0.2\n+tmin, tmax = -0.1, 0.1\n event_id = list(range(1, 33))\n-epochs = mne.Epochs(raw, events, event_id, tmin, tmax, baseline=(None, -0.01))\n+epochs = mne.Epochs(raw, events, event_id, tmin, tmax, baseline=(None, -0.01),\n+                    decim=5)\n epochs['1'].average().plot()\n \n ###############################################################################\n # Let's do some dipole fits. The phantom is properly modeled by a single-shell\n # sphere with origin (0., 0., 0.). We compute covariance, then do the fits.\n \n-t_peak = 0.061  # 3 MS at largest peak\n+t_peak = 60e-3  # ~60 MS at largest peak\n sphere = mne.make_sphere_model(r0=(0., 0., 0.), head_radius=None)\n cov = mne.compute_covariance(epochs, tmax=0)\n data = []\n", "issue": "Memory consumption in plot_brainstorm_phantom_elekta\nCircle is failing because it's using so much memory.\n\n", "before_files": [{"content": "# -*- coding: utf-8 -*-\n\"\"\"\n==========================================\nBrainstorm Elekta phantom tutorial dataset\n==========================================\n\nHere we compute the evoked from raw for the Brainstorm Elekta phantom\ntutorial dataset. For comparison, see [1]_ and:\n\n    http://neuroimage.usc.edu/brainstorm/Tutorials/PhantomElekta\n\nReferences\n----------\n.. [1] Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM.\n       Brainstorm: A User-Friendly Application for MEG/EEG Analysis.\n       Computational Intelligence and Neuroscience, vol. 2011, Article ID\n       879716, 13 pages, 2011. doi:10.1155/2011/879716\n\"\"\"\n\n# Authors: Eric Larson <larson.eric.d@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport os.path as op\nimport numpy as np\n\nimport mne\nfrom mne import find_events, fit_dipole\nfrom mne.datasets.brainstorm import bst_phantom_elekta\nfrom mne.io import read_raw_fif\n\nprint(__doc__)\n\n###############################################################################\n# The data were collected with an Elekta Neuromag VectorView system at 1000 Hz\n# and low-pass filtered at 330 Hz. Here the medium-amplitude (200 nAm) data\n# are read to construct instances of :class:`mne.io.Raw`.\ndata_path = bst_phantom_elekta.data_path()\n\nraw_fname = op.join(data_path, 'kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif')\nraw = read_raw_fif(raw_fname, preload=True)\n\n###############################################################################\n# Data channel array consisted of 204 MEG planor gradiometers,\n# 102 axial magnetometers, and 3 stimulus channels. Let's get the events\n# for the phantom, where each dipole (1-32) gets its own event:\n\nevents = find_events(raw, 'STI201')\nraw.plot(events=events)\nraw.info['bads'] = ['MEG2421']\n\n###############################################################################\n# The data have strong line frequency (60 Hz and harmonics) and cHPI coil\n# noise (five peaks around 300 Hz):\n\nraw.plot_psd()\n\n###############################################################################\n# We know our phantom produces sinusoidal bursts below 25 Hz, so let's filter.\n\nraw.filter(None, 40., h_trans_bandwidth=10., filter_length='1s')\nraw.plot_psd()\n\n###############################################################################\n# The data are still a bit noisy, so let's use Maxwell filtering to clean it.\n# Ideally we would have the fine calibration and cross-talk information\n# for the site of interest, but we don't, so we just do:\n\nraw.fix_mag_coil_types()\nraw = mne.preprocessing.maxwell_filter(raw, origin=(0., 0., 0.))\nraw.plot(events=events)\n\n###############################################################################\n# Now we epoch our data, average it, and look at the first dipole response.\n# The first peak appears around 3 ms.\n\ntmin, tmax = -0.2, 0.2\nevent_id = list(range(1, 33))\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, baseline=(None, -0.01))\nepochs['1'].average().plot()\n\n###############################################################################\n# Let's do some dipole fits. The phantom is properly modeled by a single-shell\n# sphere with origin (0., 0., 0.). We compute covariance, then do the fits.\n\nt_peak = 0.061  # 3 MS at largest peak\nsphere = mne.make_sphere_model(r0=(0., 0., 0.), head_radius=None)\ncov = mne.compute_covariance(epochs, tmax=0)\ndata = []\nfor ii in range(1, 33):\n    evoked = epochs[str(ii)].average().crop(t_peak, t_peak)\n    data.append(evoked.data[:, 0])\nevoked = mne.EvokedArray(np.array(data).T, evoked.info, tmin=0.)\ndip = fit_dipole(evoked, cov, sphere, n_jobs=2)[0]\n\n###############################################################################\n# Now we can compare to the actual locations, taking the difference in mm:\n\nactual_pos = mne.dipole.get_phantom_dipoles(kind='122')[0]\ndiffs = 1000 * np.sqrt(np.sum((dip.pos - actual_pos) ** 2, axis=-1))\nprint('Differences (mm):\\n%s' % diffs[:, np.newaxis])\n", "path": "tutorials/plot_brainstorm_phantom_elekta.py"}]}