Versioning and some cleaning

Author: Wolfffff

docs/notebooks/naps_graphics.ipynb

@@ -465,13 +465,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "dset = sleap.load_file(\"example_1h.slp\")\n",
-        "track_occupancy = list(dset.get_track_occupancy(dset.video).values())\n",
-        "track_start_end = [[rangelist.start, rangelist.end] for rangelist in track_occupancy]\n",
-        "track_length =  [rangelist.end- rangelist.start for rangelist in track_occupancy]\n",
+        "# dset = sleap.load_file(\"example_1h.slp\")\n",
+        "# track_occupancy = list(dset.get_track_occupancy(dset.video).values())\n",
+        "# track_start_end = [[rangelist.start, rangelist.end] for rangelist in track_occupancy]\n",
+        "# track_length =  [rangelist.end- rangelist.start for rangelist in track_occupancy]\n",
         "\n",
-        "frame_rate = 20\n",
-        "data=[ l/frame_rate for l in track_length],\n"
+        "# frame_rate = 20\n",
+        "# data=[ l/frame_rate for l in track_length],\n"
       ]
     },
     {
@@ -482,10 +482,10 @@
       "source": [
         "from turtle import title\n",
         "import h5py\n",
-        "with h5py.File('example_1h.analysis.h5', 'r') as f:\n",
+        "with h5py.File('example_sleap_output.analysis.h5', 'r') as f:\n",
         "    occupancy_matrix_slp = f['track_occupancy'][:]\n",
         "    \n",
-        "with h5py.File('/Genomics/ayroleslab2/scott/bees/naps_data/example_1h_1130to1230pm_naps.analysis.h5', 'r') as f:\n",
+        "with h5py.File('example_naps_output.analysis.h5', 'r') as f:\n",
         "    occupancy_matrix_naps = f['track_occupancy'][:]\n",
         "    \n",
         "missing_freq_slp = np.sum(occupancy_matrix_slp.T == 1,axis=1) / occupancy_matrix_slp.shape[0]\n",
@@ -498,7 +498,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "aruco_only =  np.load(\"output_aruco_1h.npy\")\n",
+        "aruco_only =  np.load(\"example_aruco_output.npy\")\n",
         "aruco_only = aruco_only[0:72000,0,0,:]\n",
         "occupancy_matrix_aruco = ~np.isnan(aruco_only)\n",
         "missing_freq_aruco = np.sum(occupancy_matrix_aruco.T == 1,axis=1) / occupancy_matrix_aruco.shape[0]\n",
@@ -570,26 +570,31 @@
         "mpl.rcParams[\"font.family\"] = \"sans-serif\"\n",
         "mpl.rcParams[\"font.sans-serif\"] = [\"Arial\", \"DejaVu Sans\"]\n",
         "mpl.rcParams[\"axes.titlesize\"] = \"x-large\"  # medium, large, x-large, xx-large\n",
-        "\n",
+        "import palettable\n",
         "mpl.style.use(\"seaborn-deep\")\n",
         "# palette = sns.color_palette(cc.glasbey, n_colors=arr.shape[3])\n",
         "fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n",
         "# fig.suptitle(\"Missingness frequency of tracklets\")\n",
         "sns.set_style(\"ticks\")\n",
         "import itertools\n",
+        "from matplotlib.colors import to_rgba\n",
         "def patch_violinplot():\n",
         "     from matplotlib.collections import PolyCollection\n",
         "     ax = plt.gca()\n",
+        "     i = 0\n",
         "     for art in ax.get_children():\n",
-        "          if isinstance(art, PolyCollection):\n",
-        "              art.set_edgecolor('#424952')\n",
-        "              art.set_linewidth(1)\n",
-        "              art.set_alpha(0.5)\n",
+        "            if isinstance(art, PolyCollection):\n",
+        "              art.set_edgecolor(palettable.wesanderson.Moonrise7_5.mpl_colors[i])\n",
+        "              i=i+1\n",
+        "            #   art.set_linewidth(1)\n",
+        "            #   art.set_alpha(0.1)\n",
+        "# for violin, alpha in zip(ax.collections[::2], [0.2]):\n",
+        "#     violin.set_facecolor(to_rgba(violin.get_facecolor(), alpha=alpha))\n",
         "              \n",
-        "list_of_missing_freqs = [missing_freq_aruco, missing_freq_slp, missing_freq_naps]\n",
+        "list_of_missing_freqs = [missing_freq_slp, missing_freq_aruco, missing_freq_naps]\n",
         "data = pd.DataFrame(\n",
         "    (_ for _ in itertools.zip_longest(*list_of_missing_freqs)),\n",
-        "    columns=[\"ArUco\", \"SLEAP\", \"NAPS\"],\n",
+        "    columns=[\"SLEAP\", \"ArUco\", \"NAPS\"],\n",
         ")\n",
         "sns.violinplot(\n",
         "    ax=ax,\n",
@@ -598,18 +603,36 @@
         "    inner=\"quartile\",\n",
         "    bw=0.15,\n",
         "    palette=palettable.wesanderson.Moonrise7_5.mpl_colors,\n",
-        "    linewidth=1.5,\n",
-        "    \n",
+        "    linewidth=1.5,   \n",
+        "    # alpha=0.8\n",
         ")\n",
+        "for violin, alpha in zip(ax.collections, [0.2,0.2,0.2]):\n",
+        "    violin.set_facecolor(to_rgba(violin.get_facecolor(), alpha=alpha))\n",
+        "\n",
+        "for l in ax.lines[0::3]:\n",
+        "    l.set_linewidth(0)\n",
+        "for l in ax.lines[2::3]:\n",
+        "    l.set_linewidth(0)\n",
+        "    \n",
+        "# for c in data:\n",
+        "#     sns.pointplot(c,np.nanmean(data[c]))\n",
+        "\n",
         "patch_violinplot()\n",
         "plt.ylim(0, 100)\n",
         "sns.despine(offset=10, trim=False)\n",
         "plt.ylabel(\"Track Occupancy (%)\")\n",
         "plt.title(\"Track Occupancy Distribution by Source\", fontsize=16, y=1.025)\n",
         "plt.tight_layout()\n",
-        "plt.savefig(\"figures/track_length_violin.jpeg\", dpi=600)\n",
+        "plt.savefig(\"figures/track_length_violin.jpg\", dpi=600)\n",
         "# plt.legend(labels=[\"SLEAP\", \"NAPS\", \"ArUco\"], title = \"Source of Tracks\", loc=\"upper center\")\n"
       ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
     }
   ],
   "metadata": {

docs/notebooks/sorted_length_dist.ipynb

@@ -143,13 +143,6 @@
         "\n",
         "plt.savefig(\"figures/sorted_length_distribution.jpg\", dpi=600)"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
     }
   ],
   "metadata": {

docs/notebooks/t50_plot.ipynb

@@ -133,21 +133,15 @@
         "ax[2].set_title(\"NAPS\")\n",
         "ax[2].axvline(x=t50_naps, ymin=0,ymax=occupancy_cumsum_naps[t50_naps-1],color=\"black\",linewidth=2,linestyle=\"--\")\n",
         "plt.setp(ax,ylim=(0,1))\n",
+        "plt.setp(ax,xlim=(200,257))\n",
         "\n",
         "# plt.xlim(0, 16)\n",
         "sns.despine(offset=3, trim=False)\n",
         "# plt.legend(labels=[\"ArUco\", \"SLEAP\", \"NAPS\"], title = \"Source of Tracks\")\n",
         "fig.tight_layout(rect=[0.05, 0.05, 1, 1])\n",
         "\n",
-        "plt.savefig(\"figures/t50_distribution.jpg\", dpi=600)"
+        "plt.savefig(\"figures/t50_distribution_200through257.jpg\", dpi=600)"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
     }
   ],
   "metadata": {

naps/__init__.py

@@ -1,6 +1,6 @@
 # Basic Information
 __name__ = "naps-track"
-__version__ = "0.1.15"
+__version__ = "1.0.0"
 __summary__ = "NAPS (NAPS is ArUco Plus SLEAP)"
 __url__ = "https://github.com/kocherlab/naps"
 __code__ = "https://github.com/kocherlab/naps"
@@ -10,7 +10,7 @@
 __copyright__ = "2022"
 
 # Author Information
-__authors__ = "Scott Wolf, Dee Ruttenberg, Daniel Knapp, Andrew Webb, Ian Traniello Grace McKenzie-Smith, Joshua Shaevitz and Sarah Kocher"
+__authors__ = "Scott Wolf, Dee Ruttenberg, Daniel Knapp, Andrew Webb, Ian Traniello, Grace McKenzie-Smith, Joshua Shaevitz, and Sarah Kocher"
 __email__ = "[email protected], [email protected]"
 
 import logging

naps/matching.py

@@ -1,12 +1,11 @@
 #!/usr/bin/env python
-import os
-import cv2
 import math
-
-import numpy as np
-
+import os
 from collections import defaultdict
 from typing import Iterable, Tuple
+
+import cv2
+import numpy as np
 from ray.util.multiprocessing import Pool
 
 from naps.aruco import ArUcoModel
@@ -25,11 +24,12 @@ def __init__(
         tag_node_matrix: np.ndarray,
         threads: int,
         min_sleap_score: float = 0.1,
-        **kwargs
-    ):  
+        **kwargs,
+    ):
 
         # Confirm the video file exists
-        if not os.path.isfile(video_filename): raise Exception (f'{video_filename} does not exist')
+        if not os.path.isfile(video_filename):
+            raise Exception(f"{video_filename} does not exist")
 
         # Video arguments
         self.video_filename = video_filename

tests/test_aruco.py

@@ -48,6 +48,7 @@ def test_ArUcoModel_tag_sets(tag_set):
     except Exception as exc:
         assert False, f"Tag set {tag_set} raised an exception {exc}"
 
+
 def test_ArUcoModel_tag_set_dict_error():
 
     param_dict = {
@@ -63,6 +64,7 @@ def test_ArUcoModel_tag_set_dict_error():
         test_model = ArUcoModel.withTagSet("DICT_4X4_FAIL", **param_dict)
         test_model.buildModel()
 
+
 def test_ArUcoModel_no_tag_set_error():
 
     param_dict = {
@@ -77,6 +79,7 @@ def test_ArUcoModel_no_tag_set_error():
     with pytest.raises(Exception) as e_info:
         test_model = ArUcoModel.withTagSet(**param_dict)
 
+
 @pytest.mark.parametrize(
     "param, value",
     [
@@ -106,14 +109,15 @@ def test_ArUcoModel_params_type_error(param, value):
         test_model = ArUcoModel.withTagSet("DICT_4X4_100", **param_dict)
         test_model.buildModel()
 
+
 @pytest.mark.parametrize(
     "coords, tag",
     [
-        ([100, 350,100, 350], 1),
-        ([100, 350,800,1050], 2),
-        ([450, 700,450, 700], 3),
-        ([800,1050,100, 350], 4),
-        ([800,1050,800,1050], 5),
+        ([100, 350, 100, 350], 1),
+        ([100, 350, 800, 1050], 2),
+        ([450, 700, 450, 700], 3),
+        ([800, 1050, 100, 350], 4),
+        ([800, 1050, 800, 1050], 5),
     ],
 )
 def test_ArUcoModel_detect(coords, tag):
@@ -136,8 +140,10 @@ def test_ArUcoModel_detect(coords, tag):
 
     # Open the aruco image
     aruco_image = cv2.imread("tests/data/example_ArUco_image.jpg", 0)
-    tag_image = aruco_image[coords[0] - 100:coords[1] + 100, coords[2] - 100:coords[3] + 100]
+    tag_image = aruco_image[
+        coords[0] - 100 : coords[1] + 100, coords[2] - 100 : coords[3] + 100
+    ]
 
     # Detect ArUco tags
     _, tags, _ = test_model.detect(tag_image)
-    assert tags[0][0] == tag
+    assert tags[0][0] == tag

tests/test_cost_matrix.py

@@ -1,5 +1,4 @@
 import pandas as pd
-import pytest
 
 from naps.cost_matrix import CostMatrix
 
@@ -13,7 +12,7 @@ def test_CostMatrix():
         2: {"a": [3], "b": [1, 2, 3], "c": [3]},
         3: {"a": [2], "b": [1, 2, 3], "c": []},
         4: {"a": [2], "b": [1, 2, 3], "c": []},
-        5: {"a": [1, 2], "b": [2], "c": [1, 3]}
+        5: {"a": [1, 2], "b": [2], "c": [1, 3]},
     }
 
     # Create the cost matrix, the assign the track/tag pairs