{"action":"created","comment":{"url":"https://api.github.com/repos/mne-tools/mne-python/pulls/comments/71978442","id":71978442,"diff_hunk":"@@ -1191,3 +1191,342 @@ def plot_evoked_joint(evoked, times=\"peaks\", title='', picks=None,\n     # show and return it\n     plt_show(show)\n     return fig\n+\n+\n+def _ci(arr, ci):\n+    \"\"\"Calculate the `ci`% parametric confidence interval for `arr`\"\"\"\n+    from scipy import stats\n+    mean, sigma = arr.mean(0), stats.sem(arr, 0)\n+    # This is highly convoluted to support 17th century Scipy\n+    # XXX Fix when Python 2.6 support is dropped!\n+    return np.asarray([stats.t.interval(ci, arr.shape[0],\n+                       loc=mean_, scale=sigma_)\n+                       for mean_, sigma_ in zip(mean, sigma)]).T\n+\n+\n+def plot_compare_evokeds(evokeds, picks=None, conditions=None, ch_names=None,\n+                         colors=None, linestyles=['-'], styles=None,\n+                         vlines=[0], ci=0.95, truncate_yaxis=True, ymin=None,\n+                         ymax=None, invert_y=False, ax=None, title=None,\n+                         show=True):\n+    \"\"\" Plot, for multiple conditions, individual evoked time courses of one or\n+    the mean of multiple sensors\n+\n+    This function is useful for comparing ER[P/F]s at a specific location. It\n+    plots grand averages or, if supplied with a list/dict of lists of evoked\n+    instances, grand averages plus confidence intervals.\n+\n+    Parameters\n+    ----------\n+    evokeds : instance of mne.Evoked | list | dict\n+        If a single evoked instance, it is plotted as a time series.\n+        If a dict, the contents are plotted as single time series each and the\n+        keys are used as condition labels.\n+        If a [dict/list] of lists, the unweighted mean is plotted as a time\n+        series and the parametric confidence interval is plotted as a shaded\n+        area. All instances must have the same shape - channel numbers, time\n+        points etc.\n+    picks : int | list of int | str | list of str | None\n+        The indices (int) or names (str) of the sensors to plot. Must all\n+        have the same channel type.\n+        If the selected channels are gradiometers, the corresponding pairs\n+        will be selected.\n+        If None, the global field power is plotted instead.\n+    conditions : list of str | None\n+        If evokeds is a single instance or a list (but not a dictionary),\n+        this can be used for legend labels.\n+    colors : list | dict | None\n+        If a list, will be sequentially used for line colors.\n+        If a dict, can map evoked keys or HED-like tags to conditions. For\n+        example, if `evokeds` is a dict with the keys \"Aud/L\", \"Aud/R\",\n+        \"Vis/L\", \"Vis/R\", `colors` can be dict(Aud='r', Vis='b') to map both\n+        Aud/L and Aud/R to the color red.\n+        If None, a sequence of desaturated colors is used.\n+    linestyles : list | dict\n+        If a list, will be sequentially used for evoked plot linestyles.\n+        If a dict, can map the `evoked` keys or HED-like tags to conditions.\n+        For example, if evokeds is a dict with the keys \"Aud/L\", \"Aud/R\",\n+        \"Vis/L\", \"Vis/R\", `linestyles` can be dict(L='--', R='-') to map both\n+        Aud/L and Vis/L to dashed lines.\n+    styles : dict | None\n+        If a dict, keys must map to evoked keys or conditions, and values must\n+        be a dict of legal inputs to `matplotlib.pyplot.plot`. These\n+        parameters will be passed to the line plot call of the corresponding\n+        condition, overriding defaults.\n+    vlines : list\n+        A list of integers corresponding to the positions, in milliseconds,\n+        at which to plot dashed vertical lines.\n+    ci : float | None\n+        If not None and `evokeds` is a [list/dict] of lists, a confidence\n+        interval is drawn around the individual time series. This value\n+        determines the CI width. E.g., if this value is .95 (the default),\n+        the 95% parametric confidence interval is drawn.\n+        If None, no shaded error band is plotted.\n+    truncate_yaxis : bool\n+        If True, the left y axis is truncated to reduce visual clutter.\n+    invert_y : bool\n+        Should negative values be plotted up (as is sometimes done for ERPs\n+        out of tradition)? Defaults to False.\n+    ax : None | `matplotlib.pyplot.axes` instance\n+        What axes to plot to. If None, a new axes is created.\n+    ymin, ymax : float | None\n+        If not `None`, can be used to manually scale the y axis.\n+    title : None | str\n+        If str, will be plotted as figure title. If None, the channel\n+        names will be shown.\n+    show : bool\n+        Show the figure?\n+\n+    Returns\n+    -------\n+    fig : Figure\n+        The figure on which the plot is drawn.\n+    \"\"\"\n+    from ..evoked import Evoked\n+    import matplotlib.pyplot as plt\n+\n+    # set up labels and instances\n+    if isinstance(evokeds, Evoked):\n+        evokeds = dict(Condition=evokeds)\n+\n+    if isinstance(evokeds, dict):\n+        conditions = sorted(list(evokeds.keys()))\n+    else:\n+        evokeds = dict((str(ii + 1), evoked)\n+                       for ii, evoked in enumerate(evokeds))\n+        conditions = sorted(list(evokeds.keys()))\n+\n+    if conditions is None:\n+        conditions = sorted([str(ii) for ii in evokeds])\n+    if isinstance(conditions, string_types):\n+        conditions = [conditions]\n+\n+    # get and set a few limits and variables (times, channels, units)\n+    example = (evokeds[conditions[0]]\n+               if isinstance(evokeds[conditions[0]], Evoked)\n+               else evokeds[conditions[0]][0])\n+    times = example.times\n+    tmin, tmax = times[0], times[-1]\n+\n+    # deal with picks: infer indices and names\n+    if picks is None:\n+        if ymin is None:\n+            ymin = 0\n+        ch_type = channel_type(example.info, 0)\n+        ch_names = ['GFP']\n+    else:\n+        if isinstance(picks, int):\n+            ch_names = [example.ch_names[picks]]\n+            picks = [picks]\n+        elif isinstance(picks, string_types):\n+            ch_names = [picks]\n+            picks = [example.ch_names.index(picks)]\n+        elif isinstance(picks[0], int):\n+            ch_names = [example.ch_names[pick] for pick in picks]\n+        elif isinstance(picks[0], string_types):\n+            ch_names = picks[:]\n+            picks = [example.ch_names.index(pick) for pick in picks]\n+        else:\n+            raise ValueError(\"`picks` must be int, a list of int, \"\n+                             \"str, a list of str, or None, not \" +\n+                             str(type(picks)))\n+        ch_type = channel_type(example.info, picks[0])\n+    scaling = _handle_default(\"scalings\")[ch_type]\n+\n+    if ch_type == 'grad' and picks is not None:\n+        from ..channels.layout import _merge_grad_data, _pair_grad_sensors\n+        picked_chans = []\n+        pairpicks = _pair_grad_sensors(example.info, topomap_coords=False)\n+        for pick in picks:\n+            picked_chans.append(pairpicks.index(pick))\n+            picked_chans.append(pairpicks.index(pick - 1)\n+                                if pick % 2 else pairpicks.index(pick + 1))\n+        picks = list(sorted(set(picked_chans)))\n+        ch_names = [example.ch_names[pick] for pick in\n+                    [pairpicks[p_] for p_ in picks]]\n+        if ymin is None:\n+            ymin = 0\n+\n+    # deal with dict/list of lists, and calculate the CI\n+    if not isinstance(evokeds[conditions[0]], Evoked):\n+        from ..evoked import combine_evoked\n+        if not isinstance(evokeds[conditions[0]][0], Evoked):\n+            raise ValueError(\"evokeds must be an `mne.Evoked` \"\n+                             \"or of a collection of `mne.Evoked`s\")\n+\n+        if ci and picks is not None:","path":"mne/viz/evoked.py","position":167,"original_position":167,"commit_id":"6a9d5866c4de111ba16d2d013bff985399d1ba57","original_commit_id":"6a9d5866c4de111ba16d2d013bff985399d1ba57","user":{"login":"mmagnuski","id":8452354,"avatar_url":"https://avatars.githubusercontent.com/u/8452354?v=3","gravatar_id":"","url":"https://api.github.com/users/mmagnuski","html_url":"https://github.com/mmagnuski","followers_url":"https://api.github.com/users/mmagnuski/followers","following_url":"https://api.github.com/users/mmagnuski/following{/other_user}","gists_url":"https://api.github.com/users/mmagnuski/gists{/gist_id}","starred_url":"https://api.github.com/users/mmagnuski/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mmagnuski/subscriptions","organizations_url":"https://api.github.com/users/mmagnuski/orgs","repos_url":"https://api.github.com/users/mmagnuski/repos","events_url":"https://api.github.com/users/mmagnuski/events{/privacy}","received_events_url":"https://api.github.com/users/mmagnuski/received_events","type":"User","site_admin":false},"body":"Yes - I think that would be better.","created_at":"2016-07-23T21:21:21Z","updated_at":"2016-07-23T21:21:21Z","html_url":"https://github.com/mne-tools/mne-python/pull/3413#discussion_r71978442","pull_request_url":"https://api.github.com/repos/mne-tools/mne-python/pulls/3413","_links":{"self":{"href":"https://api.github.com/repos/mne-tools/mne-python/pulls/comments/71978442"},"html":{"href":"https://github.com/mne-tools/mne-python/pull/3413#discussion_r71978442"},"pull_request":{"href":"https://api.github.com/repos/mne-tools/mne-python/pulls/3413"}}},"pull_request":{"url":"https://api.github.com/repos/mne-tools/mne-python/pulls/3413","id":76886824,"html_url":"https://github.com/mne-tools/mne-python/pull/3413","diff_url":"https://github.com/mne-tools/mne-python/pull/3413.diff","patch_url":"https://github.com/mne-tools/mne-python/pull/3413.patch","issue_url":"https://api.github.com/repos/mne-tools/mne-python/issues/3413","number":3413,"state":"open","locked":false,"title":"plot_compare_evoked","user":{"login":"jona-sassenhagen","id":4321826,"avatar_url":"https://avatars.githubusercontent.com/u/4321826?v=3","gravatar_id":"","url":"https://api.github.com/users/jona-sassenhagen","html_url":"https://github.com/jona-sassenhagen","followers_url":"https://api.github.com/users/jona-sassenhagen/followers","following_url":"https://api.github.com/users/jona-sassenhagen/following{/other_user}","gists_url":"https://api.github.com/users/jona-sassenhagen/gists{/gist_id}","starred_url":"https://api.github.com/users/jona-sassenhagen/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jona-sassenhagen/subscriptions","organizations_url":"https://api.github.com/users/jona-sassenhagen/orgs","repos_url":"https://api.github.com/users/jona-sassenhagen/repos","events_url":"https://api.github.com/users/jona-sassenhagen/events{/privacy}","received_events_url":"https://api.github.com/users/jona-sassenhagen/received_events","type":"User","site_admin":false},"body":"Not ready for merge yet. Comments welcome.\r\n\r\nDoes stuff like this:\r\n\r\n```\r\ncolors = {\"control\":'Crimson', 'violation':'CornFlowerBlue'}\r\nplot_compare_evokeds(hard, picks='Cz', colors=colors);\r\n```\r\n\r\n![unknown-157](https://cloud.githubusercontent.com/assets/4321826/16716784/e10593aa-4706-11e6-93bf-086245006d6c.png)\r\n\r\n\r\nMissing:\r\n\r\n- [x] tests\r\n- [ ] more tests\r\n- [x] more input checks\r\n- [x] cleanup","created_at":"2016-07-10T23:32:17Z","updated_at":"2016-07-23T21:21:22Z","closed_at":null,"merged_at":null,"merge_commit_sha":"e8f77c4e6c7348a44644b1efe7dca2235b85b773","assignee":null,"assignees":[],"milestone":null,"commits_url":"https://api.github.com/repos/mne-tools/mne-python/pulls/3413/commits","review_comments_url":"https://api.github.com/repos/mne-tools/mne-python/pulls/3413/comments","review_comment_url":"https://api.github.com/repos/mne-tools/mne-python/pulls/comments{/number}","comments_url":"https://api.github.com/repos/mne-tools/mne-python/issues/3413/comments","statuses_url":"https://api.github.com/repos/mne-tools/mne-python/statuses/6a9d5866c4de111ba16d2d013bff985399d1ba57","head":{"label":"jona-sassenhagen:patch-24","ref":"patch-24","sha":"6a9d5866c4de111ba16d2d013bff985399d1ba57","user":{"login":"jona-sassenhagen","id":4321826,"avatar_url":"https://avatars.githubusercontent.com/u/4321826?v=3","gravatar_id":"","url":"https://api.github.com/users/jona-sassenhagen","html_url":"https://github.com/jona-sassenhagen","followers_url":"https://api.github.com/users/jona-sassenhagen/followers","following_url":"https://api.github.com/users/jona-sassenhagen/following{/other_user}","gists_url":"https://api.github.com/users/jona-sassenhagen/gists{/gist_id}","starred_url":"https://api.github.com/users/jona-sassenhagen/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jona-sassenhagen/subscriptions","organizations_url":"https://api.github.com/users/jona-sassenhagen/orgs","repos_url":"https://api.github.com/users/jona-sassenhagen/repos","events_url":"https://api.github.com/users/jona-sassenhagen/events{/privacy}","received_events_url":"https://api.github.com/users/jona-sassenhagen/received_events","type":"User","site_admin":false},"repo":{"id":26366770,"name":"mne-python","full_name":"jona-sassenhagen/mne-python","owner":{"login":"jona-sassenhagen","id":4321826,"avatar_url":"https://avatars.githubusercontent.com/u/4321826?v=3","gravatar_id":"","url":"https://api.github.com/users/jona-sassenhagen","html_url":"https://github.com/jona-sassenhagen","followers_url":"https://api.github.com/users/jona-sassenhagen/followers","following_url":"https://api.github.com/users/jona-sassenhagen/following{/other_user}","gists_url":"https://api.github.com/users/jona-sassenhagen/gists{/gist_id}","starred_url":"https://api.github.com/users/jona-sassenhagen/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jona-sassenhagen/subscriptions","organizations_url":"https://api.github.com/users/jona-sassenhagen/orgs","repos_url":"https://api.github.com/users/jona-sassenhagen/repos","events_url":"https://api.github.com/users/jona-sassenhagen/events{/privacy}","received_events_url":"https://api.github.com/users/jona-sassenhagen/received_events","type":"User","site_admin":false},"private":false,"html_url":"https://github.com/jona-sassenhagen/mne-python","description":"MNE 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