{"action":"created","comment":{"url":"https://api.github.com/repos/LCOGT/nres-pipe/pulls/comments/201090934","pull_request_review_id":135524850,"id":201090934,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDIwMTA5MDkzNA==","diff_hunk":"@@ -0,0 +1,1090 @@\n+\"\"\"\n+Written by Allen B. Davis (c) 2018.\n+Yale University.\n+\n+Last update: May 2, 2018.\n+\"\"\"\n+\n+import numpy as np\n+import matplotlib.pyplot as plt\n+from scipy.signal import medfilt\n+import collections\n+import warnings\n+import sys\n+import datetime\n+from astropy.io import fits\n+\n+\n+@app.task\n+def trace(in_file, out_file, n_orders=67, reference_row=3500, reference_column=2000, blind_search_column_length=300,\n+          guess_percentile=90., column_halfwidth=8, baffle_clip=150, degree=4, every=16,\n+          return_objects=False, debug_plots=False, verbose=False):\n+    \"\"\"\n+    Generate polynomial fits for two fibers across each order.\n+\n+    Parameters\n+    ----------\n+    in_file : str\n+        Full file path and name for the image fits file you want to trace. Image should have its higher row indices\n+        corresponding to bluer orders.\n+    out_file : str\n+        Full file path and name of the output ascii file.\n+    n_orders : int (opt)\n+        Number of orders to be traced. Effectively controls how far into the red will be traced.\n+    reference_row : int (opt)\n+        Central row of the initial columnar slice used to find the first science-order pixel. Must be near the\n+        blue end of the chip to ensure the orders are well-separated.\n+    reference_column : int (opt)\n+        Column index of the initial columnar slice used to find the first science-order pixel. This column must run\n+        through all valid orders/fibers.\n+    blind_search_column_length : int (opt)\n+        Length of the initial cut along the reference_column used to locate a position along the first order.\n+        Should be long enough to definitely hit a couple orders.\n+    guess_percentile : float (opt)\n+        Determines which point within a blind slice is guessed to be on an order. Should be close to 100 to obtain a\n+        bright pixel, but not quite 100 because then you could get a cosmic ray.\n+    column_halfwidth : int (opt)\n+        The halfwidth of targeted sliced through an order. Fullwidth will be 1 + (column_halfwidth * 2).\n+    baffle_clip : int (opt)\n+        Controls how many previously-fit centroids will be rejected once a stop condition is met. Currently, this must\n+        be >= 1.\n+    degree : int (opt)\n+        Degree of the polynomial fits to the order centroids.\n+    every : int (opt)\n+        In the ascii file, positions are output every `every` columns.\n+    return_objects : bool (opt)\n+        If true, returns the centroids and polynomial fits.\n+    debug_plots: bool or list (opt)\n+        If true, makes all plots useful for debugging. If False, none are made. If list, elements should be the\n+        numbers of the desired plots. E.g., [1,4,5] would create plots 1, 4, and 5.\n+        1 : plot_guess: position of the guess on the 1D column, and in the 2D image\n+        2 : plot_column: shows the column centered on the initial and second centroid.\n+        3 : plot_first_polynomial: shows first polynomial fit on top of the 2D image\n+        4 : plot_first_polynomial_residuals: shows residuals for the first fit's centroids-polynomial\n+        5 : plot_all_polynomials: shows all polynomial fits on top of the 2D image\n+        6 : plot_all_polynomial_residuals: shows residuals for the each fit's centroids-polynomial\n+    verbose: bool (opt)\n+        If true, reports progress as orders are traced.\n+\n+    Returns\n+    -------\n+    red_fiber_centroids: 1D array (opt)\n+        Sub-pixel row indices for centroids of the red fibers for each order.\n+    blue_fiber_centroids: 1D array (opt)\n+        Sub-pixel row indices for centroids of the blue fibers for each order.\n+    red_polynomials: 1D array (opt)\n+        Polynomial functions fitted to red_fiber_centroids(col).\n+    blue_polynomials: 1D array (opt)\n+        Polynomial functions fitted to blue_fiber_centroids(col).\n+\n+    Notes\n+    -----\n+    Identifies an initial pixel on an order. Follows the centroid of slices through that order along the dispersion\n+    direction, terminating when it reaches the edge of the chip or gets too faint. Then it searches for neighbor fiber\n+    to the initial fiber, following it across the dispersion direction. Polynomials are fit to the identified centroids.\n+    Then the next blueward fiber/order is found and traced, along with its neighbor. This continues until it hits the\n+    blue edge of the chip. It returns to the first order, and then proceeds redward. Continues until the desired number\n+    of orders are found.\n+    \"\"\"\n+\n+    image, header = get_data(in_file)\n+\n+    initial_row_indices, initial_column_intensities = get_column(\n+        image, reference_column, center=reference_row, halfwidth=blind_search_column_length // 2, smooth=7)\n+    guess_row_index, guess_column_intensity = guess_point(initial_row_indices, initial_column_intensities,\n+                                                          percentile=guess_percentile)\n+    plot_guess(image, initial_row_indices, initial_column_intensities, guess_row_index, guess_column_intensity,\n+               reference_column, blind_search_column_length, debug_plots=debug_plots)\n+\n+    row_indices, intensities = get_column(image, reference_column, center=guess_row_index,\n+                                          halfwidth=column_halfwidth)\n+    centroid = get_centroid(row_indices, intensities)\n+    fig, ax = plot_column(row_indices, intensities, guess_row_index, guess_column_intensity, centroid,\n+                          debug_plots=debug_plots)\n+    row_indices, intensities = get_column(image, reference_column, center=centroid, halfwidth=column_halfwidth)\n+    centroid = get_centroid(row_indices, intensities)\n+    plot_column(row_indices, intensities, guess_row_index, guess_column_intensity, centroid, fig=fig, ax=ax,\n+                debug_plots=debug_plots)\n+\n+    centroids_initial = follow_fiber(image, centroid, reference_column, column_halfwidth, baffle_clip)\n+    polynomial_initial = fit_polynomial(np.arange(image.shape[1]), centroids_initial, degree=degree)\n+\n+    neighbor_fiber_row_indices, neighbor_fiber_intensities = find_first_neighbor_fiber(\n+        image, centroids_initial, reference_column, column_halfwidth)\n+    guess_row_index, guess_column_intensity = guess_point(neighbor_fiber_row_indices, neighbor_fiber_intensities,\n+                                                          percentile=guess_percentile)\n+\n+    row_indices, intensities = get_column(image, reference_column,\n+                                          center=guess_row_index, halfwidth=column_halfwidth)\n+    centroid = get_centroid(row_indices, intensities)\n+    row_indices, intensities = get_column(image, reference_column,\n+                                          center=centroid, halfwidth=column_halfwidth)\n+    centroid = get_centroid(row_indices, intensities)\n+\n+    centroids_neighbor = follow_fiber(image, centroid, reference_column, column_halfwidth, baffle_clip=baffle_clip)\n+    polynomial_neighbor = fit_polynomial(np.arange(image.shape[1]), centroids_neighbor, degree=degree)\n+\n+    red_fiber_centroids, blue_fiber_centroids, red_polynomials, blue_polynomials = assign_fiber_colors(\n+        polynomial_initial, polynomial_neighbor, centroids_initial, centroids_neighbor, reference_column)\n+\n+    plot_first_polynomial(image, red_fiber_centroids, red_polynomials, blue_fiber_centroids, blue_polynomials,\n+                          debug_plots=debug_plots)\n+    plot_first_polynomial_residuals(image, red_fiber_centroids, red_polynomials, blue_fiber_centroids, blue_polynomials,\n+                                    debug_plots=debug_plots)\n+\n+    red_peak = get_peak_flux(image, red_polynomials, reference_column, column_halfwidth)\n+    order_separation_initial = get_initial_order_separation(image, red_peak, blue_polynomials, reference_column,\n+                                                            column_halfwidth)\n+\n+    red_fiber_centroids, blue_fiber_centroids, red_polynomials, blue_polynomials = find_other_orders(\n+        image, order_separation_initial, red_fiber_centroids, blue_fiber_centroids, red_polynomials,\n+        blue_polynomials, reference_column, column_halfwidth, baffle_clip, degree, n_orders, verbose)\n+\n+    plot_all_polynomials(image, red_fiber_centroids, blue_fiber_centroids, red_polynomials, blue_polynomials,\n+                         debug_plots=debug_plots)\n+    plot_all_polynomial_residuals(image, red_fiber_centroids, red_polynomials, blue_fiber_centroids, blue_polynomials,\n+                                  debug_plots=debug_plots)\n+\n+    make_ascii(out_file, in_file, header, red_polynomials, blue_polynomials, every)\n+\n+    if return_objects:\n+        return red_fiber_centroids, blue_fiber_centroids, red_polynomials, blue_polynomials\n+\n+\n+def find_first_neighbor_fiber(image, centroids_initial, reference_column, column_halfwidth):\n+    \"\"\"\n+    Executes a blind searches for the neighbor fiber (i.e., within the same order) to the initial fiber.\n+\n+    Parameters\n+    ----------\n+    image : 2D array\n+    centroids_initial : 1D array\n+    reference_column : int\n+    column_halfwidth : int\n+\n+    Returns\n+    -------\n+    neighbor_fiber_row_indices : 1D array\n+    neighbor_fiber_intensities : 1D array\n+    \"\"\"\n+    fiber_hunt_offset = 2 + column_halfwidth\n+    fiber_hunt_column_length = 20\n+    centroid_initial = centroids_initial[reference_column]\n+\n+    candidate_1_indices, candidate_1_intensities = get_column(","path":"nrespipe/trace_orders.py","position":174,"original_position":174,"commit_id":"d252066fd3139e45430f31c358f49353fb54bdd1","original_commit_id":"d3a67dad219314b57019dfd2de03bf0ae6f3344e","user":{"login":"allenbdavis","id":10965404,"node_id":"MDQ6VXNlcjEwOTY1NDA0","avatar_url":"https://avatars0.githubusercontent.com/u/10965404?v=4","gravatar_id":"","url":"https://api.github.com/users/allenbdavis","html_url":"https://github.com/allenbdavis","followers_url":"https://api.github.com/users/allenbdavis/followers","following_url":"https://api.github.com/users/allenbdavis/following{/other_user}","gists_url":"https://api.github.com/users/allenbdavis/gists{/gist_id}","starred_url":"https://api.github.com/users/allenbdavis/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/allenbdavis/subscriptions","organizations_url":"https://api.github.com/users/allenbdavis/orgs","repos_url":"https://api.github.com/users/allenbdavis/repos","events_url":"https://api.github.com/users/allenbdavis/events{/privacy}","received_events_url":"https://api.github.com/users/allenbdavis/received_events","type":"User","site_admin":false},"body":"I can't find it. Here's the search criteria I used. https://archive.lco.global/?q=a&RLEVEL=&PROPID=&INSTRUME=&OBJECT=&SITEID=elp&TELID=&FILTER=&OBSTYPE=LAMPFLAT&EXPTIME=&BLKUID=&REQNUM=&basename=&start=2018-06-19%2000%3A00&end=2018-06-21%2023%3A00&id=&public=true","created_at":"2018-07-09T17:49:07Z","updated_at":"2018-07-09T17:49:08Z","html_url":"https://github.com/LCOGT/nres-pipe/pull/3#discussion_r201090934","pull_request_url":"https://api.github.com/repos/LCOGT/nres-pipe/pulls/3","author_association":"COLLABORATOR","_links":{"self":{"href":"https://api.github.com/repos/LCOGT/nres-pipe/pulls/comments/201090934"},"html":{"href":"https://github.com/LCOGT/nres-pipe/pull/3#discussion_r201090934"},"pull_request":{"href":"https://api.github.com/repos/LCOGT/nres-pipe/pulls/3"}},"in_reply_to_id":201060005},"pull_request":{"url":"https://api.github.com/repos/LCOGT/nres-pipe/pulls/3","id":185866735,"node_id":"MDExOlB1bGxSZXF1ZXN0MTg1ODY2NzM1","html_url":"https://github.com/LCOGT/nres-pipe/pull/3","diff_url":"https://github.com/LCOGT/nres-pipe/pull/3.diff","patch_url":"https://github.com/LCOGT/nres-pipe/pull/3.patch","issue_url":"https://api.github.com/repos/LCOGT/nres-pipe/issues/3","number":3,"state":"open","locked":false,"title":"Allen","user":{"login":"allenbdavis","id":10965404,"node_id":"MDQ6VXNlcjEwOTY1NDA0","avatar_url":"https://avatars0.githubusercontent.com/u/10965404?v=4","gravatar_id":"","url":"https://api.github.com/users/allenbdavis","html_url":"https://github.com/allenbdavis","followers_url":"https://api.github.com/users/allenbdavis/followers","following_url":"https://api.github.com/users/allenbdavis/following{/other_user}","gists_url":"https://api.github.com/users/allenbdavis/gists{/gist_id}","starred_url":"https://api.github.com/users/allenbdavis/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/allenbdavis/subscriptions","organizations_url":"https://api.github.com/users/allenbdavis/orgs","repos_url":"https://api.github.com/users/allenbdavis/repos","events_url":"https://api.github.com/users/allenbdavis/events{/privacy}","received_events_url":"https://api.github.com/users/allenbdavis/received_events","type":"User","site_admin":false},"body":"Moved trace_orders.py into the nrespipe folder. Function now takes infile and outfile names directory. Return objects optional. Attempted to integrate function into the rest of the pipeline in: main.py, tasks.py, and setup.py. Probably not working quite yet. \r\n\r\nSome questions:\r\nIn tasks.py, when the function is called, what do I get it for an output file?\r\n\r\nIn tasks.py, should I import trace_orders and then call trace, or do I call some sort of \"run_trace\" thing based elsewhere?\r\n\r\nPython doesn't seem like that I just stuck the @app line about my function. Did you mean for me to do that somewhere other than in trace_orders.py? Or should I import celery in that file before calling @app?\r\n\r\nIn setup.py, I put in: run_nres_trace_orders=nrespipe.main:run_trace_orders. Are these names consistent and good? the right hand side matches the name of the function I put in main.py.\r\n\r\nShould I add more optional arguments to the parser, like the many that are available for the trace function? Or is it sufficient that only the in- and out-put files be specified from the cmd line?","created_at":"2018-05-03T23:55:12Z","updated_at":"2018-07-09T17:49:08Z","closed_at":null,"merged_at":null,"merge_commit_sha":"c33354ee3d3095f2ec53ec1453bfa581fac9b467","assignee":{"login":"allenbdavis","id":10965404,"node_id":"MDQ6VXNlcjEwOTY1NDA0","avatar_url":"https://avatars0.githubusercontent.com/u/10965404?v=4","gravatar_id":"","url":"https://api.github.com/users/allenbdavis","html_url":"https://github.com/allenbdavis","followers_url":"https://api.github.com/users/allenbdavis/followers","following_url":"https://api.github.com/users/allenbdavis/following{/other_user}","gists_url":"https://api.github.com/users/allenbdavis/gists{/gist_id}","starred_url":"https://api.github.com/users/allenbdavis/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/allenbdavis/subscriptions","organizations_url":"https://api.github.com/users/allenbdavis/orgs","repos_url":"https://api.github.com/users/allenbdavis/repos","events_url":"https://api.github.com/users/allenbdavis/events{/privacy}","received_events_url":"https://api.github.com/users/allenbdavis/received_events","type":"User","site_admin":false},"assignees":[{"login":"allenbdavis","id":10965404,"node_id":"MDQ6VXNlcjEwOTY1NDA0","avatar_url":"https://avatars0.githubusercontent.com/u/10965404?v=4","gravatar_id":"","url":"https://api.github.com/users/allenbdavis","html_url":"https://github.com/allenbdavis","followers_url":"https://api.github.com/users/allenbdavis/followers","following_url":"https://api.github.com/users/allenbdavis/following{/other_user}","gists_url":"https://api.github.com/users/allenbdavis/gists{/gist_id}","starred_url":"https://api.github.com/users/allenbdavis/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/allenbdavis/subscriptions","organizations_url":"https://api.github.com/users/allenbdavis/orgs","repos_url":"https://api.github.com/users/allenbdavis/repos","events_url":"https://api.github.com/users/allenbdavis/events{/privacy}","received_events_url":"https://api.github.com/users/allenbdavis/received_events","type":"User","site_admin":false}],"requested_reviewers":[],"requested_teams":[],"labels":[{"id":419594300,"node_id":"MDU6TGFiZWw0MTk1OTQzMDA=","url":"https://api.github.com/repos/LCOGT/nres-pipe/labels/help%20wanted","name":"help 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