{"action":"created","comment":{"url":"https://api.github.com/repos/HERA-Team/hera_pspec/pulls/comments/202538092","pull_request_review_id":137261763,"id":202538092,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDIwMjUzODA5Mg==","diff_hunk":"@@ -690,7 +699,269 @@ def bootstrap_average_blpairs(uvp_list, blpair_groups, time_avg=False,\n                                blpair_weights=blpair_wgts_list[i], \n                                time_avg=time_avg, inplace=False)\n         uvp_avg.append(_uvp)\n-    \n+\n     # Return list of averaged spectra for now\n-    return uvp_avg, blpair_wgts_list\n+    if single_uvp:\n+        return uvp_avg[0], blpair_wgts_list[0]\n+    else:\n+        return uvp_avg, blpair_wgts_list\n+\n+\n+def bootstrap_resampled_error(uvp, blpair_groups=None, time_avg=False, Nsamples=1000, seed=None,\n+                              normal_std=True, robust_std=True, conf_ints=None, bl_error_tol=1.0,\n+                              add_to_history='', verbose=False):\n+    \"\"\"\n+    Given a UVPSpec object, generate bootstrap resamples of its average\n+    and calculate their spread as an estimate of the errorbar on the\n+    uniformly averaged data.\n+\n+    Parameters:\n+    -----------\n+    uvp : UVPSpec object\n+        A UVPSpec object from which to bootstrap resample & average.\n+\n+    blpair_groups : list\n+        A list of baseline-pair groups to bootstrap resample over. Default\n+        behavior is to calculate and use redundant baseline groups.\n+\n+    time_avg : boolean\n+        If True, average spectra before bootstrap resampling\n+\n+    Nsamples : int\n+        Number of times to perform bootstrap resample in estimating errorbar.\n+\n+    seed : int\n+        Random seed to use in bootstrap resampling.\n+    \n+    normal_std : bool\n+        If True, calculate an error estimate from numpy.std\n+\n+    robust_std : bool\n+        If True, calculate an error estimate from astropy.stats.biweight_midvariance\n+\n+    conf_ints : list\n+        A list of integer confidence interval percentages (0 < conf_int < 100) to use\n+        as an error estimate, using numpy.percentile\n+\n+    bl_error_tol : float\n+        Redundancy error tolerance of redundant groups if blpair_groups is None.\n+\n+    add_to_history : str\n+        String to add to history of output uvp_avg object.\n+\n+    verbose : bool\n+        If True, report feedback to stdout.\n     \n+    Returns:\n+    --------\n+    uvp_avg : UVPSpec object\n+        A uvp holding the uniformaly averaged data in data_array, and the various error\n+        estimates calculated from the bootstrap resampling.\n+    \"\"\"\n+    from hera_pspec import UVPSpec\n+    # type check\n+    assert isinstance(uvp, (UVPSpec, str, np.str)), \"uvp must be fed as a UVPSpec object or filepath\"\n+    if isinstance(uvp, (str, np.str)):\n+        _uvp = UVPSpec()\n+        _uvp.read_hdf5(uvp)\n+        uvp = _uvp\n+\n+    # Check for blpair_groups\n+    if blpair_groups is None:\n+        blpair_groups, _, _, _ = utils.get_blvec_reds(uvp, bl_error_tol=bl_error_tol)\n+\n+    # Uniform average\n+    uvp_avg = average_spectra(uvp, blpair_groups=blpair_groups, time_avg=time_avg, inplace=False)\n+\n+    # initialize a seed\n+    if seed is not None: np.random.seed(seed)\n+\n+    # Iterate over Nsamples and create bootstrap resamples\n+    uvp_boots = []\n+    uvp_wgts = []\n+    for i in range(Nsamples):\n+        # resample\n+        boot, wgt = bootstrap_average_blpairs(uvp, blpair_groups=blpair_groups, time_avg=time_avg, seed=None)\n+        uvp_boots.append(boot)\n+        uvp_wgts.append(wgt)\n+\n+    # get all keys in uvp_avg and get data from each uvp_boot\n+    keys = uvp_avg.get_all_keys()\n+    uvp_boot_data = odict([(k, np.array(map(lambda u: u.get_data(k), uvp_boots))) for k in keys])\n+\n+    # calculate various error estimates\n+    stats_array = odict()\n+    if normal_std:\n+        stats_array[\"normal_std\"] = odict()\n+        for k in keys:\n+            stats_array[\"normal_std\"][k] = np.std(uvp_boot_data[k].real, axis=0) \\\n+                                            + 1j*np.std(uvp_boot_data[k].imag, axis=0)\n+    if robust_std:\n+        stats_array[\"robust_std\"] = odict()\n+        for k in keys:\n+            stats_array[\"robust_std\"][k] = np.sqrt(astats.biweight_midvariance(uvp_boot_data[k].real, axis=0)) \\\n+                                            + 1j*np.sqrt(astats.biweight_midvariance(uvp_boot_data[k].imag, axis=0))\n+    if conf_ints is not None:\n+        for ci in conf_ints:\n+            ci_tag = \"conf_int_{:05.2f}\".format(ci)\n+            stats_array[ci_tag] = odict()\n+            for k in keys:\n+                stats_array[ci_tag][k] = np.percentile(uvp_boot_data[k].real, ci, axis=0) \\\n+                                            + 1j*np.percentile(uvp_boot_data[k].imag, ci, axis=0)\n+\n+    # Set stats array in uvp_avg\n+\n+    # Update history\n+    uvp_avg.history = \"Bootstrap errors estimated w/ hera_pspec [{}], {} samples, {} seed\\n{}\\n{}\\n{}\" \\\n+                      \"\".format(version.git_hash[:15], Nsamples, seed, add_to_history, '-'*40, uvp_avg.history)\n+\n+    return uvp_avg, uvp_boots, uvp_wgts\n+\n+\n+def bootstrap_run(filename, spectra=None, blpair_groups=None, time_avg=False, Nsamples=1000, seed=0,\n+                  normal_std=True, robust_std=True, conf_ints=None, keep_samples=False,\n+                  bl_error_tol=1.0, overwrite=False, add_to_history='', verbose=True):\n+    \"\"\"\n+    Run bootstrap resampling on a PSpecContainer object to estimate errorbars.\n+    For each group/spectrum specified in the PSpecContainer, this function produces\n+        1. uniform average of UVPSpec objects in the group\n+        2. various error estimates from the bootstrap resamples\n+       (3.) series of bootstrap resamples of UVPSpec average (optional)\n+\n+    The output of 1. and 2. are placed in a *_avg spectrum, while the output of 3.\n+    is placed in *_bs0, *_bs1, *_bs2 etc. objects. \n+\n+    Parameters:\n+    -----------\n+    filename : str or PSpecContainer object\n+        PSpecContainer object to run bootstrapping on.\n+\n+    spectra : list\n+        A list of power spectra names (with group prefix) to run bootstrapping on.\n+        Default is all spectra in object. Ex. [\"group1/psname1\", \"group1/psname2\", ...]\n+\n+    blpair_groups : list\n+        A list of baseline-pair groups to bootstrap over. Default is to solve for and use\n+        redundant baseline groups. Ex: [ [((1, 2), (2, 3)), ((1, 2), (3, 4))], \n+                                         [((1, 3), (2, 4)), ((1, 3), (3, 5))],\n+                                         ...\n+                                        ]\n+\n+    time_avg : bool\n+        If True, perform time-average of power spectra in averaging step.\n+\n+    Nsamples : int\n+        The number of samples in bootstrap resampling to generate.\n+\n+    seed : int\n+        The random seed to initialize with before drwaing bootstrap samples.\n+\n+    normal_std : bool\n+        If True, use np.std to calculate a \"normal\" standard deviation of BS samples.\n+\n+    robust_std : bool\n+        If True, use astropy.stats.biweight_midvariance(..., c=9.0) to get a \"robust\"\n+        standard deviation of the BS samples.\n+\n+    conf_ints : list","path":"hera_pspec/grouping.py","position":339,"original_position":339,"commit_id":"ccff2e4944861d133b60ddf0b946dcdbfe221b6d","original_commit_id":"ccff2e4944861d133b60ddf0b946dcdbfe221b6d","user":{"login":"nkern","id":4307358,"node_id":"MDQ6VXNlcjQzMDczNTg=","avatar_url":"https://avatars0.githubusercontent.com/u/4307358?v=4","gravatar_id":"","url":"https://api.github.com/users/nkern","html_url":"https://github.com/nkern","followers_url":"https://api.github.com/users/nkern/followers","following_url":"https://api.github.com/users/nkern/following{/other_user}","gists_url":"https://api.github.com/users/nkern/gists{/gist_id}","starred_url":"https://api.github.com/users/nkern/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/nkern/subscriptions","organizations_url":"https://api.github.com/users/nkern/orgs","repos_url":"https://api.github.com/users/nkern/repos","events_url":"https://api.github.com/users/nkern/events{/privacy}","received_events_url":"https://api.github.com/users/nkern/received_events","type":"User","site_admin":false},"body":"sure thing","created_at":"2018-07-15T10:31:51Z","updated_at":"2018-07-15T10:31:51Z","html_url":"https://github.com/HERA-Team/hera_pspec/pull/151#discussion_r202538092","pull_request_url":"https://api.github.com/repos/HERA-Team/hera_pspec/pulls/151","author_association":"MEMBER","_links":{"self":{"href":"https://api.github.com/repos/HERA-Team/hera_pspec/pulls/comments/202538092"},"html":{"href":"https://github.com/HERA-Team/hera_pspec/pull/151#discussion_r202538092"},"pull_request":{"href":"https://api.github.com/repos/HERA-Team/hera_pspec/pulls/151"}},"in_reply_to_id":202189120},"pull_request":{"url":"https://api.github.com/repos/HERA-Team/hera_pspec/pulls/151","id":199931515,"node_id":"MDExOlB1bGxSZXF1ZXN0MTk5OTMxNTE1","html_url":"https://github.com/HERA-Team/hera_pspec/pull/151","diff_url":"https://github.com/HERA-Team/hera_pspec/pull/151.diff","patch_url":"https://github.com/HERA-Team/hera_pspec/pull/151.patch","issue_url":"https://api.github.com/repos/HERA-Team/hera_pspec/issues/151","number":151,"state":"open","locked":false,"title":"power spectrum pipeline v1","user":{"login":"nkern","id":4307358,"node_id":"MDQ6VXNlcjQzMDczNTg=","avatar_url":"https://avatars0.githubusercontent.com/u/4307358?v=4","gravatar_id":"","url":"https://api.github.com/users/nkern","html_url":"https://github.com/nkern","followers_url":"https://api.github.com/users/nkern/followers","following_url":"https://api.github.com/users/nkern/following{/other_user}","gists_url":"https://api.github.com/users/nkern/gists{/gist_id}","starred_url":"https://api.github.com/users/nkern/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/nkern/subscriptions","organizations_url":"https://api.github.com/users/nkern/orgs","repos_url":"https://api.github.com/users/nkern/repos","events_url":"https://api.github.com/users/nkern/events{/privacy}","received_events_url":"https://api.github.com/users/nkern/received_events","type":"User","site_admin":false},"body":"This adds the `pipelines/pspec_pipeline/pspec_pipe.py` script as version 1 of the power spectrum pipeline, which goes through the analysis blocks in the following order:\r\n\r\n1. Visibility data difference (e.g. for jacknives) [optional]\r\n2. OQE pipeline\r\n3. Bootstrap error pipeline\r\n\r\nThis branch should be merged __after__ the `stats_array` branch is merged in.\r\n\r\nA statistical evaluation step seems appropriate for this script (after 3.) but due to circular dependency with `hera_stats` its not possible to add it in. We should make a `stats_pipe.py` script and `stats_pipe.yaml` script in `hera_stats/pipelines/stats_pipe` of similar format to perform this last step of the full power spectrum pipeline.\r\n\r\n[UPDATE]\r\nThe #145 PR and #148 PR were merged into this PR because they were all inter-dependent. 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