{"action":"created","comment":{"url":"https://api.github.com/repos/biocore/scikit-bio/pulls/comments/29644550","id":29644550,"diff_hunk":"@@ -870,49 +866,276 @@ def _compare_distributions(test, samples, counts=5, mode=\"ind\", num_iter=1000):\n \n     \"\"\"\n \n-    # Determines the number of groups\n-    num_groups = len(samples)\n+    # Prealocates the pvalue matrix\n+    p_values = np.zeros((num_p, num_iter))\n \n-    # Checks the mode\n-    if mode not in {'ind', 'matched'}:\n-        raise ValueError('Supported sample modes are \"ind\" and \"matched\".')\n+    # Determines the number of samples per group\n+    num_groups = len(samples)\n+    samp_lens = [len(sample) for sample in samples]\n \n-    # Handles the number of samples for later instances\n     if isinstance(counts, int):\n         counts = np.array([counts] * num_groups)\n \n-    if not len(counts) == num_groups:\n-        raise ValueError('If counts is a 1-D array, there must be a count to'\n-                         ' draw for each group.')\n-\n-    # Checks the group length\n-    samp_lens = [len(sample) for sample in samples]\n-    # Checks the group length\n-    if mode == 'matched' and np.array([samp_lens[i] != samp_lens[i+1] for i in\n-                                       range(num_groups-1)]).all():\n-        raise ValueError('In \"matched\" mode, each sample must have the same'\n-                         ' number of observations.')\n-    if np.array([samp_lens[i] < counts[i] for i in range(num_groups)]).any():\n-        raise ValueError('You cannot choose more observations that exist '\n-                         'in a sample.')\n-\n-    # Prealocates the pvalue matrix\n-    p_values = np.zeros((num_iter))\n-\n     for idx in range(num_iter):\n         if mode == \"matched\":\n             pos = np.random.choice(np.arange(0, samp_lens[0]), counts[0],\n                                    replace=False)\n             subs = [sample[pos] for sample in samples]\n+        elif mode == \"paired\":\n+            subs = _draw_paired_samples(*samples)\n         else:\n             subs = [np.random.choice(np.array(pop), counts[i], replace=False)\n                     for i, pop in enumerate(samples)]\n \n-        p_values[idx] = test(subs)\n+        p_values[:, idx] = test(subs)\n+\n+    if num_p == 1:\n+        p_values = p_values.squeeze()\n \n     return p_values\n \n \n+def _check_subsample_power_inputs(test, samples, draw_mode='ind', ratio=None,\n+                                  max_counts=50, counts_interval=10,\n+                                  min_counts=None):\n+    r\"\"\"Makes sure that everything is sane before power calculations\n+\n+    Parameters\n+    ----------\n+    test : function\n+        The statistical test which accepts a list of arrays of values\n+        (sample ids or numeric values) and returns a p value or one-dimensional\n+        array of p values.\n+    samples : array_like\n+        `samples` can be a list of lists or a list of arrays where each\n+        sublist or row in the array corresponds to a sampled group.\n+    draw_mode : {\"ind\", \"matched\"}, optional\n+        \"matched\" samples should be used when observations in\n+        samples have corresponding observations in other groups. For instance,\n+        this may be useful when working with regression data where\n+        :math:`x_{1}, x_{2}, ..., x_{n}` maps to\n+        :math:`y_{1}, y_{2}, ..., y_{n}`. Sample vectors must be the same\n+        length in \"matched\" mode.\n+        If there is no reciprocal relationship between samples, then\n+        \"ind\" mode should be used.\n+    ratio : 1-D array, optional\n+        The fraction of the sample counts which should be\n+        assigned to each group. If this is a 1-D array, it must be the same\n+        length as `samples`. If no value is supplied (`ratio` is None),\n+        then an equal number of observations will be drawn for each sample. In\n+        `matched` mode, this will be set to one.\n+    max_counts : positive int, optional\n+        The maximum number of samples per group to draw for effect size\n+        calculation.\n+    counts_interval : positive int, optional\n+        The difference between each subsampling count.\n+    min_counts : positive int, optional\n+        How many samples should be drawn for the smallest\n+        subsample. If this is None, the `counts_interval` will be used.\n+\n+    Returns\n+    -------\n+    ratio : 1-D array\n+        The fraction of the sample counts which should be assigned to each\n+        group.\n+    num_p : positive integer\n+        The number of p values returned by `test`.\n+    sample_counts : array\n+        The number of samples drawn at each power calculation.\n+\n+    Raises\n+    ------\n+    ValueError\n+        If the `mode` is \"matched\", an error will occur if the arrays in\n+        `samples` are not the same length.\n+    ValueError\n+        There is a ValueError if there are fewer samples than the minimum\n+        count.\n+    ValueError\n+        If the `counts_interval` is greater than the difference between the\n+        sample start and the max value, the function raises a ValueError.\n+    ValueError\n+        There are not an equal number of groups in `samples` and in `ratios`.\n+    TypeError\n+        `test` does not return a float or a 1-dimensional numpy array.\n+\n+    \"\"\"\n+\n+    if draw_mode not in {'ind', 'matched'}:\n+        raise ValueError('mode must be \"matched\" or \"ind\".')\n+\n+    # Determines the minimum number of ids in a category\n+    id_counts = np.array([len(id_) for id_ in samples])\n+    num_ids = id_counts.min()\n+    # Determines the number of groups\n+    num_groups = len(samples)\n+\n+    # Checks that \"matched\" mode is handled appropriately\n+    if draw_mode == \"matched\":\n+        for id_ in samples:\n+            if not len(id_) == num_ids:\n+                raise ValueError('Each vector in samples must be the same '\n+                                 'length in \"matched\" draw_mode.')\n+\n+    # Checks the number of counts is appropriate\n+    if min_counts is None:\n+        min_counts = counts_interval\n+    if (max_counts - min_counts) < counts_interval:\n+        raise ValueError(\"No subsamples of the specified size can be drawn.\")\n+\n+    # Checks the ratio argument is sane\n+    if ratio is None or draw_mode == 'matched':\n+        ratio = np.ones((num_groups))\n+    else:\n+        ratio = np.asarray(ratio)\n+    if not ratio.shape == (num_groups,):\n+        raise ValueError('There must be a ratio for each group.')\n+\n+    ratio_counts = np.array([id_counts[i] / ratio[i]\n+                             for i in range(num_groups)])\n+    min_pos = (ratio_counts == ratio_counts.min())\n+    count_pos = np.arange(0, num_groups)[min_pos][0]\n+    largest = ratio_counts[count_pos] * ratio[count_pos]\n+\n+    # Determines the number of p values returned by the test\n+    p_return = test(samples)\n+    if isinstance(p_return, float):\n+        num_p = 1\n+    elif isinstance(p_return, np.ndarray) and len(p_return.shape) == 1:\n+        num_p = p_return.shape[0]\n+    else:\n+        raise TypeError('test must return a float or one-dimensional array.')\n+\n+    # Calculates the same counts\n+    sample_counts = np.arange(min_counts,\n+                              min(max_counts, largest),\n+                              counts_interval)\n+\n+    return ratio, num_p, sample_counts\n+\n+\n+def _identify_sample_groups(meta, cat, control_cats, order, strict_match):\n+    \"\"\"Aggregates samples matches for `control_cats` that vary by `cat`\n+\n+    Parameters\n+    ----------\n+    meta : pandas.DataFrame\n+        The metadata associated with the samples.\n+    cat : str, list\n+        The metadata category (or a list of categories) for comparison.\n+    control_cats : list\n+        The metadata categories to be used as controls. For example, if you\n+        wanted to vary age (`cat` = \"AGE\"), you might want to control for\n+        gender and health status (i.e. `control_cats` = [\"SEX\", \"HEALTHY\"])\n+    order : list\n+        The order of groups in the category. This can be used\n+        to limit the groups selected. For example, if there's a category with\n+        groups 'A', 'B' and 'C', and you only want to look at A vs B, `order`\n+        would be set to ['A', 'B'].\n+    ctrl_pos : int\n+        The location of the smallest group in `order`.\n+    strict_match: bool, optional\n+        This determines how data is grouped using `control_cats`. If a sample\n+        within `meta` has an undefined value (`NaN`) for any of the columns in\n+        `control_cats`, the sample will not be considered as having a match and\n+        will be ignored when `strict_match` is True. If `strict_match` is\n+        False, missing values (NaN) in the `control_cats` can be considered\n+        matches.\n+\n+    Returns\n+    -------\n+    meta_pairs : dict\n+        Describes the categories matched for metadata. The\n+        `control_cat`-grouped samples are numbered, correspoinding to the\n+        second list in `index`. The group is keyed to the list of sample arrays\n+        with the same length of `order`.\n+    index : list\n+        A list of numpy arrays describing the positions of samples to be drawn.\n+        The first array is an index array. The second gives an integer\n+        corresponding to the `control_cat`-group, and the third lists the\n+        position of the reference group sample in the list of samples.\n+\n+    \"\"\"\n+\n+    # Sets up variables to be filled\n+    meta_pairs = {}\n+    index1 = []\n+    index2 = []\n+    i1 = 0\n+    i2 = 0\n+\n+    # Groups the data by the control groups\n+    ctrl_groups = meta.groupby(control_cats).groups\n+    # Identifies the samples that satisfy the control pairs\n+    for (g, ids) in viewitems(ctrl_groups):\n+        if not _check_nans(g, switch=True) and strict_match:\n+            continue\n+        try:","path":"skbio/stats/power.py","position":911,"original_position":911,"commit_id":"88a5079c7f24b58ed65158c58cf1c6ced60a0c9e","original_commit_id":"88a5079c7f24b58ed65158c58cf1c6ced60a0c9e","user":{"login":"mortonjt","id":4184797,"avatar_url":"https://avatars.githubusercontent.com/u/4184797?v=3","gravatar_id":"","url":"https://api.github.com/users/mortonjt","html_url":"https://github.com/mortonjt","followers_url":"https://api.github.com/users/mortonjt/followers","following_url":"https://api.github.com/users/mortonjt/following{/other_user}","gists_url":"https://api.github.com/users/mortonjt/gists{/gist_id}","starred_url":"https://api.github.com/users/mortonjt/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mortonjt/subscriptions","organizations_url":"https://api.github.com/users/mortonjt/orgs","repos_url":"https://api.github.com/users/mortonjt/repos","events_url":"https://api.github.com/users/mortonjt/events{/privacy}","received_events_url":"https://api.github.com/users/mortonjt/received_events","type":"User","site_admin":false},"body":"Just out of curiosity, what sort of errors do you anticipate catching?\r\n\r\nIf possible, may want to replace with an if/else statement.  It could make debugging in the future less painful.","created_at":"2015-05-05T05:17:58Z","updated_at":"2015-05-05T05:17:58Z","html_url":"https://github.com/biocore/scikit-bio/pull/911#discussion_r29644550","pull_request_url":"https://api.github.com/repos/biocore/scikit-bio/pulls/911","_links":{"self":{"href":"https://api.github.com/repos/biocore/scikit-bio/pulls/comments/29644550"},"html":{"href":"https://github.com/biocore/scikit-bio/pull/911#discussion_r29644550"},"pull_request":{"href":"https://api.github.com/repos/biocore/scikit-bio/pulls/911"}}},"pull_request":{"url":"https://api.github.com/repos/biocore/scikit-bio/pulls/911","id":34581344,"html_url":"https://github.com/biocore/scikit-bio/pull/911","diff_url":"https://github.com/biocore/scikit-bio/pull/911.diff","patch_url":"https://github.com/biocore/scikit-bio/pull/911.patch","issue_url":"https://api.github.com/repos/biocore/scikit-bio/issues/911","number":911,"state":"open","locked":false,"title":"Fixes subsample power","user":{"login":"JWDebelius","id":5534846,"avatar_url":"https://avatars.githubusercontent.com/u/5534846?v=3","gravatar_id":"","url":"https://api.github.com/users/JWDebelius","html_url":"https://github.com/JWDebelius","followers_url":"https://api.github.com/users/JWDebelius/followers","following_url":"https://api.github.com/users/JWDebelius/following{/other_user}","gists_url":"https://api.github.com/users/JWDebelius/gists{/gist_id}","starred_url":"https://api.github.com/users/JWDebelius/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/JWDebelius/subscriptions","organizations_url":"https://api.github.com/users/JWDebelius/orgs","repos_url":"https://api.github.com/users/JWDebelius/repos","events_url":"https://api.github.com/users/JWDebelius/events{/privacy}","received_events_url":"https://api.github.com/users/JWDebelius/received_events","type":"User","site_admin":false},"body":"* Addresses the issue raised in #910. \r\n* Includes features and commentary from #880 \r\n\r\n\r\n","created_at":"2015-05-02T05:30:31Z","updated_at":"2015-05-05T05:17:58Z","closed_at":null,"merged_at":null,"merge_commit_sha":"012f0c8928a77600cffce4b09895bfcd4ac2a2d1","assignee":null,"milestone":null,"commits_url":"https://api.github.com/repos/biocore/scikit-bio/pulls/911/commits","review_comments_url":"https://api.github.com/repos/biocore/scikit-bio/pulls/911/comments","review_comment_url":"https://api.github.com/repos/biocore/scikit-bio/pulls/comments{/number}","comments_url":"https://api.github.com/repos/biocore/scikit-bio/issues/911/comments","statuses_url":"https://api.github.com/repos/biocore/scikit-bio/statuses/88a5079c7f24b58ed65158c58cf1c6ced60a0c9e","head":{"label":"JWDebelius:power_update","ref":"power_update","sha":"88a5079c7f24b58ed65158c58cf1c6ced60a0c9e","user":{"login":"JWDebelius","id":5534846,"avatar_url":"https://avatars.githubusercontent.com/u/5534846?v=3","gravatar_id":"","url":"https://api.github.com/users/JWDebelius","html_url":"https://github.com/JWDebelius","followers_url":"https://api.github.com/users/JWDebelius/followers","following_url":"https://api.github.com/users/JWDebelius/following{/other_user}","gists_url":"https://api.github.com/users/JWDebelius/gists{/gist_id}","starred_url":"https://api.github.com/users/JWDebelius/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/JWDebelius/subscriptions","organizations_url":"https://api.github.com/users/JWDebelius/orgs","repos_url":"https://api.github.com/users/JWDebelius/repos","events_url":"https://api.github.com/users/JWDebelius/events{/privacy}","received_events_url":"https://api.github.com/users/JWDebelius/received_events","type":"User","site_admin":false},"repo":{"id":23196783,"name":"scikit-bio","full_name":"JWDebelius/scikit-bio","owner":{"login":"JWDebelius","id":5534846,"avatar_url":"https://avatars.githubusercontent.com/u/5534846?v=3","gravatar_id":"","url":"https://api.github.com/users/JWDebelius","html_url":"https://github.com/JWDebelius","followers_url":"https://api.github.com/users/JWDebelius/followers","following_url":"https://api.github.com/users/JWDebelius/following{/other_user}","gists_url":"https://api.github.com/users/JWDebelius/gists{/gist_id}","starred_url":"https://api.github.com/users/JWDebelius/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/JWDebelius/subscriptions","organizations_url":"https://api.github.com/users/JWDebelius/orgs","repos_url":"https://api.github.com/users/JWDebelius/repos","events_url":"https://api.github.com/users/JWDebelius/events{/privacy}","received_events_url":"https://api.github.com/users/JWDebelius/received_events","type":"User","site_admin":false},"private":false,"html_url":"https://github.com/JWDebelius/scikit-bio","description":"Core 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