From a8dcea8f97b7b323f9109bf794a56b0c0fc4c35f Mon Sep 17 00:00:00 2001 From: Eric Denovellis Date: Tue, 16 Jan 2024 13:01:59 -0800 Subject: [PATCH 1/8] Comment out video section for mini-rec in Update 20_Position_Trodes.ipynb (#767) * Update 20_Position_Trodes.ipynb * Update 20_Position_Trodes.py --- notebooks/20_Position_Trodes.ipynb | 302 ++++++--------------- notebooks/py_scripts/20_Position_Trodes.py | 26 +- 2 files changed, 93 insertions(+), 235 deletions(-) diff --git a/notebooks/20_Position_Trodes.ipynb b/notebooks/20_Position_Trodes.ipynb index 0b8167710..c6775795a 100644 --- a/notebooks/20_Position_Trodes.ipynb +++ b/notebooks/20_Position_Trodes.ipynb @@ -66,10 +66,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-12 11:15:47,260][INFO]: Connecting sambray@lmf-db.cin.ucsf.edu:3306\n", - "[2024-01-12 11:15:47,307][INFO]: Connected sambray@lmf-db.cin.ucsf.edu:3306\n", - "WARNING:root:Populate: Entry in DataAcquisitionDeviceSystem with primary keys {'data_acquisition_device_system': 'SpikeGadgets'} already exists.\n", - "WARNING:root:Populate: Entry in DataAcquisitionDeviceAmplifier with primary keys {'data_acquisition_device_amplifier': 'Intan'} already exists.\n" + "[2024-01-12 13:47:50,578][INFO]: Connecting root@localhost:3306\n", + "[2024-01-12 13:47:50,652][INFO]: Connected root@localhost:3306\n" ] } ], @@ -368,27 +366,21 @@ " \n", " \n", " default\n", - "=BLOB=default_led0\n", - "=BLOB=max-sep_80\n", "=BLOB=single_led\n", "=BLOB=single_led_upsampled\n", - "=BLOB=upsample_1000_Hz\n", "=BLOB= \n", " \n", " \n", - "

Total: 6

\n", + "

Total: 3

\n", " " ], "text/plain": [ "*trodes_pos_pa params \n", "+------------+ +--------+\n", "default =BLOB= \n", - "default_led0 =BLOB= \n", - "max-sep_80 =BLOB= \n", "single_led =BLOB= \n", "single_led_ups =BLOB= \n", - "upsample_1000_ =BLOB= \n", - " (Total: 6)" + " (Total: 3)" ] }, "execution_count": 5, @@ -511,52 +503,32 @@ "
\n", "

valid_times

\n", " numpy array with start/end times for each interval\n", - "
\n", - "

pipeline

\n", - " type of interval list (e.g. 'position', 'spikesorting_recording_v1')\n", "
\n", " minirec20230622_.nwb\n", "01_s1\n", - "=BLOB=\n", - "minirec20230622_.nwb\n", - "01_s1_first9\n", - "=BLOB=\n", - "minirec20230622_.nwb\n", - "01_s1_first9 lfp band 100Hz\n", - "=BLOB=\n", - "lfp bandminirec20230622_.nwb\n", + "=BLOB=minirec20230622_.nwb\n", "02_s2\n", - "=BLOB=\n", - "minirec20230622_.nwb\n", - "lfp_test_01_s1_first9_valid times\n", - "=BLOB=\n", - "lfp_v1minirec20230622_.nwb\n", + "=BLOB=minirec20230622_.nwb\n", "pos 0 valid times\n", - "=BLOB=\n", - "minirec20230622_.nwb\n", + "=BLOB=minirec20230622_.nwb\n", "pos 1 valid times\n", - "=BLOB=\n", - "minirec20230622_.nwb\n", + "=BLOB=minirec20230622_.nwb\n", "raw data valid times\n", - "=BLOB=\n", - " \n", + "=BLOB= \n", " \n", " \n", - "

Total: 8

\n", + "

Total: 5

\n", " " ], "text/plain": [ - "*nwb_file_name *interval_list valid_time pipeline \n", - "+------------+ +------------+ +--------+ +----------+\n", - "minirec2023062 01_s1 =BLOB= \n", - "minirec2023062 01_s1_first9 =BLOB= \n", - "minirec2023062 01_s1_first9 l =BLOB= lfp band \n", - "minirec2023062 02_s2 =BLOB= \n", - "minirec2023062 lfp_test_01_s1 =BLOB= lfp_v1 \n", - "minirec2023062 pos 0 valid ti =BLOB= \n", - "minirec2023062 pos 1 valid ti =BLOB= \n", - "minirec2023062 raw data valid =BLOB= \n", - " (Total: 8)" + "*nwb_file_name *interval_list valid_time\n", + "+------------+ +------------+ +--------+\n", + "minirec2023062 01_s1 =BLOB= \n", + "minirec2023062 02_s2 =BLOB= \n", + "minirec2023062 pos 0 valid ti =BLOB= \n", + "minirec2023062 pos 1 valid ti =BLOB= \n", + "minirec2023062 raw data valid =BLOB= \n", + " (Total: 5)" ] }, "execution_count": 6, @@ -588,6 +560,14 @@ "id": "0de9bdcd-79d1-4099-9503-8bec1a3a4014", "metadata": {}, "outputs": [ + { + "name": "stderr", + 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" ] @@ -962,10 +942,10 @@ " minirec20230622_.nwb\n", "pos 0 valid times\n", "single_led\n", - "minirec20230622_69C4D0YICW.nwb\n", - "77139fd9-2744-4ffc-b39b-a60bfff916b9\n", - "761505ec-e15a-46b2-b414-6c1d5947515a\n", - "394f68c5-0930-476c-84f0-cc124b42ff41 \n", + "minirec20230622_AQQP7U6Y24.nwb\n", + "f519d1e4-0919-492a-85a4-6730cce26c10\n", + "6ae01b40-f5d9-4dd1-9203-76881d7bd339\n", + "002ce1f0-50a6-40b5-8b97-4b5a80140193 \n", " \n", " \n", "

Total: 1

\n", @@ -974,7 +954,7 @@ "text/plain": [ "*nwb_file_name *interval_list *trodes_pos_pa analysis_file_ position_objec orientation_ob velocity_objec\n", "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n", - "minirec2023062 pos 0 valid ti single_led minirec2023062 77139fd9-2744- 761505ec-e15a- 394f68c5-0930-\n", + "minirec2023062 pos 0 valid ti single_led minirec2023062 f519d1e4-0919- 6ae01b40-f5d9- 002ce1f0-50a6-\n", " (Total: 1)" ] }, @@ -1125,6 +1105,13 @@ "id": "998fda38-17ba-46ff-bb7b-656abb25b162", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2024-01-12 13:47:55,378][WARNING]: Skipped checksum for file with hash: c87c4027-855f-0181-d477-cf78242a7c20, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/minirec20230622/minirec20230622_AQQP7U6Y24.nwb\n" + ] + }, { "data": { "text/html": [ @@ -1416,7 +1403,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -1435,7 +1422,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 17, "id": "be8679cf-6f14-4726-af03-36430fb4b5a9", "metadata": {}, "outputs": [ @@ -1445,13 +1432,13 @@ "Text(0.5, 1.0, 'Velocity')" ] }, - "execution_count": 22, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -1470,7 +1457,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "93d440ae-e995-42af-88b3-c1146ccd7d45", "metadata": {}, "outputs": [ @@ -1480,13 +1467,13 @@ "(1687474800.1833298, 1687474810.565797)" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -1511,140 +1498,35 @@ "source": [ "### Video\n", "\n", - "To keep `minirec` small, the download link does not include videos by default.\n", - "\n", - "(Download links coming soon)\n", + "To keep `minirec` small, the download link does not include videos by default. \n", "\n", - "Full datasets can be further visualized by plotting the results on the video,\n", - "which will appear in the current working directory.\n" + "If it is available, you can uncomment the code, populate the `TrodesPosVideo` table, and plot the results on the video using the `make_video` function, which will appear in the current working directory.\n" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "id": "9bd9f843-4afd-438b-87fe-b09dee880282", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading position data...\n", - "Loading video data...\n", - "Found 0 videos for {'nwb_file_name': 'minirec20230622_.nwb', 'epoch': 1}\n" - ] - } - ], + "outputs": [], "source": [ - "sgp.v1.TrodesPosVideo().populate(\n", - " {\n", - " \"nwb_file_name\": nwb_copy_file_name,\n", - " \"interval_list_name\": interval_list_name,\n", - " \"position_info_param_name\": trodes_params_name,\n", - " }\n", - ")" + "# sgp.v1.TrodesPosVideo().populate(\n", + "# {\n", + "# \"nwb_file_name\": nwb_copy_file_name,\n", + "# \"interval_list_name\": interval_list_name,\n", + "# \"position_info_param_name\": trodes_params_name,\n", + "# }\n", + "# )" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "id": "d187fdb2", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "
\n", - "

nwb_file_name

\n", - " name of the NWB file\n", - "
\n", - "

interval_list_name

\n", - " descriptive name of this interval list\n", - "
\n", - "

trodes_pos_params_name

\n", - " name for this set of parameters\n", - "
\n", - "

has_video

\n", - " \n", - "
minirec20230622_.nwbpos 0 valid timessingle_led0
\n", - " \n", - "

Total: 1

\n", - " " - ], - "text/plain": [ - "*nwb_file_name *interval_list *trodes_pos_pa has_video \n", - "+------------+ +------------+ +------------+ +-----------+\n", - "minirec2023062 pos 0 valid ti single_led 0 \n", - " (Total: 1)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "sgp.v1.TrodesPosVideo()" + "# sgp.v1.TrodesPosVideo()" ] }, { @@ -1667,7 +1549,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "id": "a13cdc05-ea4c-4edb-83a6-bb52af28fa75", "metadata": {}, "outputs": [ @@ -1737,30 +1619,24 @@ " \n", " \n", " default\n", - "=BLOB=default_led0\n", - "=BLOB=max-sep_80\n", "=BLOB=single_led\n", "=BLOB=single_led_upsampled\n", - "=BLOB=upsample_1000_Hz\n", "=BLOB= \n", " \n", " \n", - "

Total: 6

\n", + "

Total: 3

\n", " " ], "text/plain": [ "*trodes_pos_pa params \n", "+------------+ +--------+\n", "default =BLOB= \n", - "default_led0 =BLOB= \n", - "max-sep_80 =BLOB= \n", "single_led =BLOB= \n", "single_led_ups =BLOB= \n", - "upsample_1000_ =BLOB= \n", - " (Total: 6)" + " (Total: 3)" ] }, - "execution_count": 18, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1785,28 +1661,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "id": "2b78e50a-d7b1-4b88-a449-ea3cb4ff8a9a", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computing position for: {'nwb_file_name': 'minirec20230622_.nwb', 'interval_list_name': 'pos 0 valid times', 'trodes_pos_params_name': 'single_led_upsampled'}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[11:17:06][INFO] Spyglass: Writing new NWB file minirec20230622_Q7OD9FOR3T.nwb\n", - "INFO:spyglass:Writing new NWB file minirec20230622_Q7OD9FOR3T.nwb\n", - "[11:17:07][INFO] Spyglass: No video frame index found. Assuming all camera frames are present.\n", - "INFO:spyglass:No video frame index found. Assuming all camera frames are present.\n" - ] - } - ], + "outputs": [], "source": [ "trodes_s_up_key = {\n", " \"nwb_file_name\": nwb_copy_file_name,\n", @@ -1822,7 +1680,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "id": "ab65c431-9977-43e7-a0f9-f95f73c309b0", "metadata": {}, "outputs": [ @@ -1830,8 +1688,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[11:19:17][WARNING] Spyglass: Upsampled position data, frame indices are invalid. Setting add_frame_ind=False\n", - "WARNING:spyglass:Upsampled position data, frame indices are invalid. Setting add_frame_ind=False\n" + "[13:47:56][WARNING] Spyglass: Upsampled position data, frame indices are invalid. Setting add_frame_ind=False\n", + "[2024-01-12 13:47:56,476][WARNING]: Skipped checksum for file with hash: 119a4889-1117-30a9-c774-3c7db7048f02, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/minirec20230622/minirec20230622_PBPM9HN98Y.nwb\n" ] }, { @@ -2009,7 +1867,7 @@ "[5193 rows x 6 columns]" ] }, - "execution_count": 25, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -2022,7 +1880,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "id": "1a1f6d12-3199-4136-ab66-9ac80241d4b5", "metadata": {}, "outputs": [ @@ -2032,13 +1890,13 @@ "Text(0.5, 1.0, 'Upsampled Position')" ] }, - "execution_count": 26, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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PSa6//np8fHxKvObu7m5d7J48ebKqUxYRERGRWjBq1ChcXFxKvObm5saoUaOA/Gffwn/8L3gmTkxMtG5QqirDMIiNjeXo0aPs37/f+lVwZNOePXvK7D9y5MhS//jfsWNH6/d33HFHqWMUHPGUmJhIUlJSqe06dOhQ7OirwgqevfPy8so9Pqywbdu2WT/fgs+8NIWDAwXHOwHs2LGDxMREAMaMGVPqxi0/Pz8GDx5c4blVROH1mK2tLb6+vkyePNk6n5tvvplXX33V2r5g7REUFFSkAPyFJkyYUKwPQN26da0btr755pvLsrB5dHQ0hw4dAvL/m7q7u5fYztbW1hroSExMZOfOnaWOec8995R6rfDvZVhYWFWmLCKXGQVLRESk2vj7+zN16lT27dvHoEGDANi/f7/1eo8ePcrsX3A9IyOjSBDgrrvuwt7enuzsbPr06cPw4cP57LPPOHDgAIZhXIJ3UnkF7zMgIKDUIAeAg4ODdcde4c/mQgU7nUpTsFsrNTW1slMVERERkVrUrVu3Mq93797d+n3h58Wbb77Zmg0wYsQIBg4cyPvvv8+OHTuKbDQqy8qVKxk2bBienp40atSI1q1b06FDB+vXypUrgfyskLK0atWq1GuFMxYq2q6sZ9qqfl7l2b59u/X7Xr16FQk+XPhVOKulIMMHKFKHpDLzvFScnJzo168f3333HT/99JM1oJWdnc2xY8eA8tdkwcHB1n6FP09HR0dr8GvRokW0aNGCp59+mp9//vmyyaqoytrzwn4XKmtdVjiDRusykX8GFXgXEZFKmzRpEpMnT7b+7OTkRN26dalTp06xtgkJCdbvyzpCC/LT8Uvq16ZNG+bNm8eECRNITExkxYoVrFixAshPr77xxht58MEHueaaa6r8ni5WwXzLe4/w9/ss/B4vVNpuwwIFu9YqujAWERERkYor6eie8hRs4imvb1kba6Do82Th58W6deuybNky7rrrLqKioli3bh3r1q0D8jO7Bw0axLhx4xg2bFiJc5swYQJfffVVhd5LZmZmmdfLelYtnF1R0XZlPdNW9fMqz9mzZyvctrCMjAzr9wVZHFC5eVaHwoEaW1tb3N3dadiwofVo48IKz7O8edjb21O3bl1iY2OLfZ7Tp08nKSmJ5cuXc+rUKd5++23efvttbG1tCQkJYdSoUTz44IN4eHhc5LurmupYe16oOn6HReTKoWCJiIhUmo+PT5nnJFdVWVkit956K4MGDeKHH35gzZo1/PHHH8TFxREfH8+3337Lt99+y5gxY5g1a1at1S2Bii2sL5dsGBEREREpWeGaH4X/OF6W9PR0AFxdXctsV97zYlnPitdccw3Hjx9n8eLF/Pzzz2zcuJHIyEhSUlJYsmQJS5Ys4YYbbmDJkiVF/sg7a9Ysa6Ckc+fOPPbYY/To0QNfX19cXFywtbUF4L777uObb765rJ5XqxK4qojCf9xev349devWrVC/wkGRwp/Txfx3rYqqrscuZr3i4eHBsmXL2LZtGwsWLGDdunXs2bMHs9lMaGgooaGhvP322/z444/06tWrSvOrLjX930NE/hkULBERkUuqcGrymTNnaNKkSaltz5w5U2K/Ap6enjz44IM8+OCDQP45zsuWLePjjz8mOjqauXPnEhwcXGIRyUutYL6F0/JLU/A+a7PwoYiIiIiUrvBzWmxsLEFBQWW2z87OttbdKO8Zr/Azb0kKZzyUNJaTkxP33HOPtZbCyZMnWblyJdOnT+fo0aOsWbOG//znP0WKyH/xxRcANG/enC1btpRYAB6KZiBcLsr7vMpbQ5SmcHDEwcGhSsGHC9c6ZR07VtVMlupQ+ASA8tYreXl51kyL0j7P7t27W48VS01NZf369cyePZulS5dy9uxZbr31Vk6cOFHq79mlcuG/27JU9fdGRP7ZVLNEREQuqcKLjr/++qvMtgXF211cXAgMDCx37KCgIJ599ln+/PNP6w6+BQsWVGp+1bVTreB9hoeHl7kQys3NZdeuXUX6iIiIiMjlpXCR8rKKPxco2F1/Yd+ShIaGVvh6RZ4XmzVrxtSpUwkNDcXPzw8o/kx84MABAG655ZZS/4BtGEaF3mtNq+7Pq0BBHUGAX375pfITI7/4fEnzKEl51y8lR0dHWrZsCZS/Jtu1axe5ublAxT5Pd3d3hg8fzpIlS3jkkUcAiImJYdOmTZWaY3Wsy6qy9rywn4hc3RQsERGRS6p///7WtP6yzkg+ffo0v/76q7VPSWftlsbf39+6i6u8YpQXcnJysn6fnZ1dqb6FFRS0NwyDWbNmldpu0aJF1gKIBX1ERERE5PJy7bXXWp9H582bV+6RPd9++631++uuu67MtgsXLiy1Jkh6ero10BEUFESjRo0qPGcPDw9rkfELn4nz8vKAso8UW7ZsGdHR0RW+X03Zt2+fdbNRSQqevW1tbenfv3+Fx+3bt681o+Czzz4jJSWl0nPr0qWLNWujrOPLoqKiqhyQqS4Fa4+DBw/y559/ltruyy+/LNanogr/7ld1XXYxa7LGjRvTtm1bIP/fWWlF181mM3PmzAHys25CQkKqfE8R+WdRsERERC6pxo0bM2LECADWrFlTYiAhJyeH8ePHW3cwPfzww0Wu//jjj9ZjDUoSERHB4cOHASqUkVJY4QXoiRMnKtW3sBEjRtC4cWMA3njjDfbs2VPiPJ988kkgP3tm3LhxVb6fiIiIiFw6DRs25LbbbgPyM0veeuutUtv+/vvvfPbZZwA0bdqU4cOHlzl2bGwsTzzxRInX/v3vf1uzlCdNmlTk2po1a4iJiSl13OTkZOtu+QufiQuyCpYvX17iUVsnTpxg8uTJZc67Nj344IPWmjCFff/99/z8888A/Otf/6pUcMnJycn6bB4bG8udd95Z4j0KpKamMn369CKvOTo6Wp/pd+/ezdtvv12sX15eHhMmTCAnJ6fCc7sUJk2aZK3t+OCDD1o3cBX2yy+/WDe4de/e3Rp8g/zj3jZs2FDmPQoHhKq6LruYNRnAlClTAIiLi2Pq1KklBrBeeeUVDh48CMCECRNwdHS8qHuKyD+HapaIiMgl9/7777N27VoSExN54IEH2Lx5M3feeSfe3t4cPnyYd955h927dwMwatQohgwZUqT/Bx98wD333MPQoUMZOHAgbdu2xdPTk8TERLZv387HH39s3Z134aKyPMHBwTg5OZGVlcWLL76InZ0dAQEB1oWEr69vhc7atbe35/PPP2f48OGkpqbSt29fnnrqKa677jrs7OzYsmULb731lnXx+84771CvXr1KzVVEREREas57773H77//ztmzZ3n++edZv3499957L61atcLOzo7IyEiWL1/O3LlzycvLw8bGhtmzZ5ebId21a1dmzJhBWFgYEydOxN/fn4iICGbMmMGaNWuA/GfUiRMnFuk3b948hg8fzvXXX8/gwYNp37493t7epKamsn//fqZPn05UVBRQ/Jn4vvvu46mnniIqKorevXvz9NNP065dO7Kysvj999/54IMPyM7OJiQk5LI7iqtr165s376drl278swzz9ChQweSk5NZtGgRM2fOBPKPgnrnnXcqPfbTTz/N2rVrWbt2LatWrSIoKIiJEyfSq1cvvLy8SE1N5ciRI6xfv54ff/wRJyenYhu7XnrpJRYsWEBkZCTPPPMMu3fv5r777sPHx4ejR4/y3nvvERoaSrdu3Wr1KK4OHTrwxBNP8Pbbb7Nv3z5CQkJ45plnCA4OJiMjg+XLl/PRRx9hNptxcHCwfrYFTp8+zYABAwgKCmLEiBF07doVX19fIH9T2A8//GDNigoODqZHjx6Vml/v3r1Zt24doaGhvPXWWwwZMsR61LKzs7P1XuWZOHEi3333HVu3bmXu3LmcOnWKKVOm0KxZM2JiYpg1axZLliwB8mv4vPjii5Wap4j8wxkiIiIVsG7dOgMwAOPll1+udP+dO3cajRs3to5R0tfIkSONzMzMYn379etXZj/AsLW1Nd54441ifcPCwqxtZs+eXeLcnn766VLHXbdunbXdyy+/bH29NHPmzDEcHR0rPc8CFf2MCz6Tfv36ldlORERERKru8OHDRtu2bct9FvXy8jJWrFhR6jiFn6XXrFljDB48uNSx2rRpY0RFRRUbY8yYMeXOAzCmTJlimM3mIn1zcnLKvKezs7OxYMEC6z2aNm1a7P4Vea6+8L0Wfpa+0OzZs63twsLCil0v/Fxc+Dn8wi8PDw9j/fr1VZ5LRkaGcd9991Xosw0MDCxxjP379xsNGzYstd+4cePKfb8VUXhdVBVms9mYPHlyme/R09PTWLNmTbG+hT/Lsr7atm1b4vsr63fLMAwjMjLS8Pb2LnHMC9c85a2Zzp07Z/Tp06fceYaHh5fYvyLrvgs/k7J+10XkyqFjuEREpEYEBwdz5MgR3nzzTXr06IGXlxcODg40btyYkSNHsmzZMhYvXlykhkiBBQsW8N133zF27Fg6d+5Mw4YNsbOzw83Njfbt2zN58mR27drFc889V6W5vfXWW3zxxRdcc801eHt7W2usVMWYMWM4fPgwjz76KG3btsXV1RVnZ2eaN2/OhAkTLmqeIiIiIlKzWrduzd69e/n222+57bbbaNq0KS4uLjg4ONCwYUOuu+463n77bcLDwxk6dGiFxnRwcGDVqlV8+umn9OzZEy8vL1xcXOjQoQP//e9/2blzp/V418I++OADFi9ezMSJE627+h0cHHB2dqZVq1aMHTuWTZs2MX36dGuWdAF7e3tWrlzJRx99RNeuXXFxccHZ2ZkWLVowceJEdu7cye23314tn9mlMG3aNFavXs3QoUNp0KABDg4OBAQEMHnyZA4cOEC/fv2qPLazszNz585l+/btTJo0iXbt2uHp6YmdnR1eXl507tyZ+++/n0WLFnHo0KESx2jXrh0HDhzg6aefpmXLljg6OlKvXj0GDBjA999/X2ZNw5pkY2PDJ598wsaNG7nnnnto0qQJjo6OeHh40LlzZ55//nmOHTvG4MGDi/W95ppr2Lp1K6+++ioDBw6kRYsWuLu7Y29vT4MGDRg8eDAzZ85k9+7dBAQEVHpuvr6+bNu2jfvvv58WLVqUuC6sKG9vbzZu3Mg333zDjTfeSIMGDbC3t6du3br079+f6dOns3v3bpo2bVrle4jIP5PJMMqpUiYiIiIiIiIiIlW2fv16BgwYAMC6desqVYj8amUymQB4+eWXmTZtWu1ORkRErgrKLBERERERERERERERkauagiUiIiIiIiIiIiIiInJVU7BERERERERERERERESuagqWiIiIiIiIiIiIiIjIVU3BEhERERERERERERERuaqZDMMwansSIiIiIiIiIiIiIiIitcWutidwpbJYLERHR+Pu7o7JZKrt6YiIiIiI/OMZhkFqaiqNGzfGxubKTJLXOkJEREREpGZVdB2hYEkVRUdH4+/vX9vTEBERERG56kRERODn51fb06gSrSNERERERGpHeesIBUuqyN3dHcj/gD08PGp5NiIiIiIi/3wpKSn4+/tbn8WvRFpHiIiIiIjUrIquIxQsqaKClHkPDw8tckREREREatCVfHyV1hEiIiIiIrWjvHXElXnQr4iIiIiIiIiIiIiISDVRsERERERERERERERERK5qCpaIiIiIiIiIiIiIiMhVTcESERERERERERERERG5qilYIiIiIiIiIiIiIiIiVzUFS0RERERERERERERE5KqmYImIiIiIiIiIiIiIiFzVFCwREREREREREREREZGrmoIlIiIiIiIiIiIiIiJyVVOwRERERERERERERERErmoKloiIiIiIiIiIiIiIyFVNwRIREREREREREREREbmqKVgiIiIiIiIiIiIiIiJXNQVLRERERERERERERETkqqZgiYiIiIiIiIiIiIiIXNUULBERERERERERERERkauagiUiIiIiIiIiIiIiInJVU7BERERERERERERERESuagqWiIiIiIiIiIiIiIjIVU3BEhERERERERERERERuaopWCIiIiIiIiIiIiIiIlc1BUtEREREREREREREROSqpmCJiIiIiIiIiIiIiIhc1RQsERERERERERERERGRq5qCJSIiIiIiIiIiIiIiclVTsERERERERERERERERK5qCpaIiIiIiIiIiIiIiMhVTcESkRKkZOVeVP+MnLxqmomIiIiIiIiIiIiIXGoKlogUkp1n5rH5u+g47RemLTuA2WJUqn+e2cILP+6j/ctr+PKPk5doliIiIiIicjlJzcrlsfm76Pf2Oj7bcALDqNw6IivXzFML9zDy0838dfLcJZqliIiIiJRFwRKR85Iychj95TZ+3B0NwJwt4Tz0zY4KZ4mkZefxwNfb+fbP01gMeOeXI0QnZV7KKYuIiIiISC2LSc7k9s+28uPuaE6dy+CtVYd5etFecs2WCvU/l5bN3V/8ycIdkew8ncTk73ZedKa7iIiIiFSegiUiwOlzGYycsYVt4Qm4O9rx6HUtcbCz4bdDZ7jr8z+JS80us/+ZlCzumLmV9UficLK3oXl9V7JyLfzf6sM19A5ERERERKSmHYxOYcQnWzgcm0p9d0du7+KHjQkW7ohk/JxQUssJeoTFpzNyxhZ2nk7CwS5/eX4uPYeP1x6riemLiIiISCEKlshVb3dEEiNnbOZkXDqNPZ1YNKk3j1/finkTelDHxZ49kcmM+HQzx8+mldj/SGwqIz7ZzIHoFOq5OTD/wV58cEcwJhP8uDuanacTa/gdiYiIiIjIpbbxaByjZm4lNiWLFj5uLJ3cm7dv78SXY7ri4mDLH8fiuf2zrcQkl5xtvuNUAiM/3cypcxn41XHm50euYfa4bkB+lntYfHpNvh0RERGRq56CJXJV+/3wGe78fCvxaTm0a+zB0il9aN3QHYAuTb1ZMrkPTeu6EJmYya0zthQ7P/ivk+e4bcYWopOzaFbflSWT+tDZ34sOfp7cFuIHwCvLD2KpZO0TERERERG5fC3fE824OaGkZefRs5k3iyf2xq+OCwAD2zTghwd7Ud/dkcOxqYz4ZAsHo1OK9F935Cx3ffEXiRm5dPLzZOnkPrTwcWNAax/6t65Prtng9ZWHauOtiYiIiFy1FCyRq9qLPx4gK9dCv1b1+eGhXjTwcCpyPbCeK0sm9SakiRfJmbmM/mobP+2Osl5/dcVBUrPz6B7gzZJJvWlS18V67akbW+PqYMueiCR+LNRHRERERESuXGaLwYs/7cdsMbi5U2Pmju+Op4t9kTYd/DxZOrk3LX3ciE3JYtTMrWw8Gme9/tJP+8nJs3BdGx/mPdiT+u6O1msvDA3C1sbEb4fOsOlYfI29LxEREZGrnYIlclVLzsw/Q3jaze1wc7QrsU1dN0e+n9CTIe0bkmO28Oj83Xy6/jiGYZCUkd//+aFt8XJxKNLPx92JKQNbAPC/1YdJz65YoXgREREREbl85eRZrOuA10e0x9HOtsR2fnVcWDSpN72a1SUtO49xc0JZEBoBwJnk/JqI025uh4tD0XVICx83RvdsCsBrKw6SV8FC8SIiIiJycRQsEQFM5Vx3srflk7tDeKBvIAD/t/oIzy/N301WVv/xfQLx93bmTEo2n204UX0TFhERERGRWmdrU/ZKwtPZnrnjuzMy2BezxeDpxXt5Z80RDPLXEXa2Jfd/bFBLvFzsOXImlXnnAywiIiIicmkpWCJSQTY2Jl4YFsS04UGYTDBv22liU7LK7ONkb8t/bmoLwOcbTxKZmFETUxURERERkcuEg50N747qxCPns86nrztOrrnsmoZeLg78+/pWALz3yxGSz2eyiIiIiMilo2CJXNUMo/KF18f2CWTmvV1wsq/YP58b2jWkZzNvsvMs/G/1kUrfT0RERERELh95lsofi2Uymfj34Nb8360dsSsnG6XA3d2b0NLHjcSMXL7cdLLS9xQRERGRylGwRK5aW47Hk55jxtbGhKezffkdChncriHzH+xFPTcHbEwUKwxfmMlk4sVhQQCs3BtNbHLZ2SgiIiIiInL5Wrk3BoDGnk4425dcr6Q0o7r5M2tsN9wc7XBxsC1zHWJna8Ojg1oCsGB7hGqXiIiIiFxiJVe0FvmHM1sMXl1xEIB7ejShjqtDOT2K6+zvxdon+pOYnkNDz9KDJQDtGnvSPdCbbWEJLN4ZyZQBLao0bxERERERqT0ZOXl8uPYYAOP7BmIyVSxLpLBrW9Vnw1P9ycqzFCvufqHrgxrg7erAmZRsNhyN47q2Dao0bxEREREpnzJL5Ko0P/Q0h2NT8XS25/FBrao8jqezPQH1XCvUdlRXfwAWbo+o0vFfIiIiIiJSu2ZuOElMcha+Xs7c27Nplcep6+aIr5dzue0c7WwZEewLwA8q9C4iIiJySSlYIled5Mxc3v3lKACPDWpZpaySqripQ0NcHWwJP5dBaHhijdxTRERERESqR3RSJjM3ngDg+Zva4lTJI7iq6o5u+Zuu1h4+y9lUHekrIiIicqkoWCJXnem/HyMhPYcWPm4XtRusslwc7BjWsTGQf+awiIiIiIhcOf63+jBZuRa6B3hzU4eGNXbfVg3cCWnihdlisHhHVI3dV0RERORqo2CJXFXC4tOZsyUcgBeGtsXetmb/CYzq5gfkF4VMy86r0XuLiIiIiEjV7DiVyE+7ozGZ4KXhQVWqVXIxCrJLFuhIXxEREZFLRsESuaq8vvIguWaDAa3r07+1T43fP6RJHZrVdyUz18zKvdE1fn8REREREakci8Xg1eUHALi9ix/tfT1rfA7DOjbG1cGWsPh0toUl1Pj9RURERK4GCpbIVeOPY3H8dugsdjYm/jM0qFbmYDKZChV6j6yVOYiIiIiISMUt3RXFnshk3BztePKG1rUyB1dHO4Z3yj/S9wcd6SsiIiJySShYIleF5Mxc/rviEACjezWlhY9brc1lZLAvtjYmtp9K5ERcWq3NQ0REREREynYyLo23Vh8GYMqAFvi4O9XaXEadP4rr530xJGfm1to8RERERP6pFCyRf7STcWm89NN+er25liNnUqnjYs9j17Wq1Tn5eDgxoHV9QNklIiIiIiKXowPRyTy5cA83fvAHcanZtPBxY3zfgFqdU7C/F60auJGVa2HZHh3pKyIiIlLdFCyRfxzDMNh4NI5xs7cx8N0NfL31FBk5Zto0dOfTe7rg6WJf21Pkti75u8IW74wkz2yp5dmIiIiIiIjZYrDmQCx3zNzK0I82sWhHJDlmCz2beTP/wZ442tnW6vxMJhN3dGsCwIJQHcUlIiIiUt3sansCItUlM8fMkl2RzNkczrGz+cdbmUxwXRsfxvcJpFfzuphMplqeZb6BbXyo6+pAXGo2G47GcV3bBrU9JRERERGRq1JKVi4LQiOYuzWciIRMAOxsTAzp0IhxfQIIaVKnlmf4txHBvry16hD7opLZH5VcK8XmRURERP6pFCyRK15MciZfbz3FvG2nScrIP7vX1cGW27v6M7Z3AAH1XGt5hsU52NkwItiXLzeFsWB7hIIlIiIiIiI1LCw+nblbwlm4PYL0HDMAXi723N29CaN7NaWRp3Mtz7A4b1cHBrdryMq9MSzYHqFgiYiIiEg1UrBErlhZuWamLTvAwh2RmC0GAP7ezoztHcjtXf3wcCr7uK39Ucl8vTWcI7GpRV7fE5ls/b6TX6HFh8nEDe0aMPHa5tjYXHyGyu1d/flyUxhrD50lPi2bem6OFz2miIiIiIiULTvPzNurj/DV5jCM/GUELX3cGN83kH919sXZofTjtgzDYPupRFbsieZgTAo5eX8fqVuwjnBztKN5/b83bNV1c+TmTo25pXPjasl0v72LHyv3xvDLgTO8ekv7ix5PRERERPIpWCJXpIT0HCZ8vZ0dpxIB6NnMm/F9ArmubQNsywhkmC0Gvx48w6zNYWwLSyj3PoUDJwB7IpI4FJPKO7d3vOgzi1s3dKdZfVdOxqVzMDqFa1vVv6jxRERERESkbFFJmTz0zXb2R6UAMKB1fe7v24w+Lco+sjcnz8LKfdHM2hTOvqjkUtsBpGXnFVtH/H74LLtOJ/LS8HZlrlcqIqRp/rFgsSlZJGfm4ulc+zUZRURERP4JFCyRK054fDrj5oQSFp+Ou5MdM+7pQt+W9crsk5yZy8LtEczZEk5k4t/nEN/UoRE3dWiErY2JN34+RFh8erG+XZrWYXL/5oTFp/PWqsMs3xPNmeQsPr+vC14uDhf1Xpzt8wMuxkWNIiIiIiIi5dkflcz4OaGcTc2mjos9b9/WiUFBZR+HG5+Wzfd/neabP08Rl5oNgKOdDTd3akzflvVwtLPh6UV7ScnKK9Z3cFAD7ujmz7bwBGZuOMncraeISsrio7s64+JQ9aW4h5M93q4OJKTnEJOcqWCJiIiISDVRsESuKDtOJTLh6+0kpOfg6+XMnHHdaNnAvdT2YfHpzNkcxsIdkWScP4e4jos9d/dowuieATT0dCIzx8xjP+yyBkqeHdKGB69pxpurDvHFH2HsOJXIrwfP8Nq/2tO2kQcTv9nBtvAERs7Ywpyx3WlS16VG3ruIiIiIiFTNuiNnmfLdTjJyzLRu4M6scd3w9Sq9JsnB6BRmbw7jpz3R1qO2Gng4cl+vAO7q3gRvVweSM3J56NvtpGTlYWdj4o0RHfhXsC/PLt7Lkl1R/HLwDK0auPPsjW3o5OfFYz/s5rdDZ7jr8z/5ckw36rtX/RjeguwUQ7uuRERERKqNgiVyxVi9P4ZH5+8mO89CB19PvhrbFR93p2LtDMNg8/FzzNocxu+Hz1pfb9XAjfF9AvlXsC9O5zM6zqVlc//c7eyOSMLB1oZ3RnXi5k6NAfjP0CD86rjwyvIDzA+NIDo5i0/vCWHRpN6Mm72Nk3HpjPh0M1+N7UZnf68a+QxERERERKRyvv/rNC/+tB+zxaBvi3p8em9IqfUNNxyNY8b64/x58u8jezv5eTK+byBD2jfCwc4GyD/Oa8ysbRw/m4abox2f3hNiPVb33VGd8PN24aO1x5i+7jgRiRm8fVsnvn+gBxO+3s6eyGRGfLqZOeO608LH7dJ/ACIiIiJSIQqWyBXhq01h/HflQQwDrmvjw0d3BePqWPKv7yfrjvPOL0etP1/XxofxfQPp3bz4OcQPfrOD3RFJeDrb88V9Xeke6F3k+pjeATT2cmbqvJ1sPBrHlO92Mnd8d5ZO6cO42aEcjEnh3i//YvMzA/F0Ufq7iIiIiMjlwmIxePuXI8xYfwKAW0P8eHNkB2vA40JLd0Xy+A97gPzMjSHtGzKuTyAhTbyKrSP+b/Vhjp9Nw9vVgW/v70FQYw/rNZPJxL+vb4VfHWeeX7KPn3ZH41/HhSdvaM2SyX0YO3sbp85lcO+Xf7Hx6QGlzkdEREREapaeyuSyZrYYTFt2gNdW5AdK7u3ZhJmju5QaKMnOM/PlpjAA7ujqz7on+/PV2G70aVGvxIKNeZb8vPUHr21WLFBS4PqgBsyb0BMbU/5Os5NxaTTwcGLBxF54uzqQlp1H+LnitU5ERERERKR2ZOeZefSH3dZAyWODWvLO7R1LDUwkpOfw7OJ9QH5Q5Y+nBzD97hC6NK1T4jqioH7Jg9c2KxIoKWxUV3/eu6MzAJ//cZKE9BwC67myZFJvbG1MxKZkcS49+2LfqoiIiIhUEwVL5LKVmWNm4rc7mLMlHIDnhrThtVvaY2db+q/tbwfPkpSRSyNPJ94Y2YHAeq5l3uPWEF8Alu+JxijjwN/gJnXodz6tfuGOSADcHO2sBdpFREREROTykJSRw+ivtrF8TzR2Nibeub0Tjw1qVWLQo8CSnZFk51kIauTB27d1pHEZ9UwAQprUAWDriXNlthvesRHtGnuQk2fhp91RANR1c8S2jLmIiIiISO1QsEQuS/Fp2dz5xZ/8evAMDnY2TL87mIf6NS9zgQOwYHsEkL8brKDoYVlu7tQYBzsbDsemsj8qpcy2o7r6A7B4RyR5ZksF34mIiIiIiNSU0+cyGDljC9vCEnB3tGPOuO7c1sWvzD6GYfBDaP464u4eTbCpwDqiYMyNx+KITsostZ3JZLKuI34IjShzg5aIiIiI1C4FS+SyczIujRGfbmZPRBJeLvZ890APhnVsXG6/6KRMNh6LAyh3QVTAy8WBG9o1BGDhjogy217XtgHerg6cTc223kdERERERC4P4fHpjJyxmZNx6TTydGLhpF70bVmv3H67IpI4djYNJ3sbbu5c/roDIKCeKz0CvTGM/KyUstzSuTEOtvkbtA5El71BS0RERERqj4IlclnJM1uY/N1OIhIyaeLtwpJJvekWUHItkQst2RmJYUCPQG8Cyjl+q7BRXfMDKz/uiiIr11xqOwc7G/7VOf/YrgWhZS+IRERERESk5mTk5DF+TijxaTm0aejO0sl9aNOw5FoiF/phW/6mqZs6NMLDyb7C9yzIGFmwPRKLpfSMES8XBwa3a3C+bdkbtERERESk9ihYIpeV+aERHI5NxdPZnkWTetGsvluF+lksBgu25wcwChYtFdW7eT18vZxJycrjl4Nnymw7qlt+YOW3Q2c4l6ZijCIiIiIil4PPNpzkZHw6DT2c+Pr+7jT0dKpQv7TsPJbvjQbgzm5NKnXPIR0a4uZox+mEDLaFJ5TZtmCNUt4GLRERERGpPQqWyGUjOTOX9349CsDjg1ri416xBQ7AtvAETidk4OZox5AODSt1X1sbE7eeP7ZrYTk7vdo09KCjnyd5FoOlu6IqdR8REREREal+UUmZzNxwAoCXhgdVah2xYk80GTlmmtVzpVtAnUrd18XBjmEdGwHlZ4z0aVGPxp5OFdqgJSIiIiK1Q8ESuWx8tPYYCek5tPBx456eTSvVt2BxMqxjI1wc7Cp979vPB0s2HY8nMjGj7Lbnd4Ut2qGjuEREREREattbqw6TnWehe6A3Q9pXbuPU/POF3e/o5o/JVH5h9wsVrA1+3hdDalZuqe1sbUzWuorlbdASERERkdqhYIlcFk7GpTF3SzgALw4Lwt624r+aqVm5rNoXC/y9WKksf28XejWri2HA4h1lZ4zc3Kkxjnb5BRqjkjKrdD8REREREbl428MTWL4nGpMJXhoWVKmAx5HYVHZHJGFnY2JkiF+V7h/SxIvm9V3JyrWwcm9MmW1v65K/Vtl0PJ4cs6VK9xMRERGRS0fBErksvL7yEHkWgwGt69OvVf1K9V25N4bMXDPN67sS0sSrynMoqEeyaGdEmQUaPZ3tuaFd5XasiYiIiIhI9bJYDF5ZfhCAUV38ae/rWan+P5zPKhnUtgH13R2rNAeTyVSo0HvZGSNN6v69QUtERERELj8Klkit23g0jrWHz2JnY+KFYUGV7l+wKBnVtWqp8wVubNcId0c7IhIy+TPsXJltK1tEXkREREREqteSXVHsi0rGzdGOJ29oXam+2XlmluzKP1b3ju4X92w/IsQXWxsTO08ncfxsapltCzZoiYiIiMjlR8ESqVV5ZguvrcjfDXZfrwCa13erVP/jZ1PZeToJWxsTI0J8L2ouzg62DO/cGICF28uuR9K7eV18vZwv6n4iIiIiIlI16dl5/N/qwwA8PLBFpTNDfjlwhqSMXBp5OnFty8pltl/Ix92JAa19AFhQzjqiYIOWiIiIiFx+FCyRWvX9ttMcO5tGHRd7Hr2uZaX7FwQ1BrSuj4+700XPp6DQ+8/7Ykgpo0CjTaECjSIiIiIiUrNmrD/B2dRsmni7MK5PQKX7F2Sn397FD1ubqmenFxjV9fyRvjsiyco1l9rO2cGWYZ0aX/T9RERERKT6KVgitSbXbOH9X48C8O/rW+HpYl+p/pk5ZhbvzA+WVLWw+4U6+3vR0seN7DwLi3eUvSuscLAkNiWrWu4vIiIiIiJlMwyD+efrjTw7pA2OdraV6h+RkMEfx+IxmapvHTGwjQ++Xs4kpOewbE90mW1HBP+dEa/6JSIiIiKXDwVLpNakZOaSmJGfvXFn9yaV7j9z4wni03Lw9XJmYBufapmTyWTivt4BAHz8+3GSM0vPLvH3drF+f1bBEhERERGRGnEmJZv4tGxsbUxVWgfM2HACgL4t6hV5pr8YdrY2jO7VFIBP1h0nO6/07JJO/n8Xoo9Lza6W+4uIiIjIxVOwRC4LdpVMfY9OyuSz84uc529qi71t9f0q39nNnxY+biSk5/Dx2mPVNq6IiIiIiFy8xIwcAOq42ONkX7mskkMxKczfdhqAqQMrfwxwWe7t2ZT67o6cOpfB3C3hpbYrnAljVmqJiIiIyGVDwRK5Iv1v9WGyci10D/Dmpg4Nq3Vse1sbXhjaFoA5W8I5GZdWreOLiIiIiEh1qNyGK8MweHX5QSwGDO3YiO6B3tU6GzdHO56+oTUAH689TnyaskZEREREriQKlsgVZ8epRH7aHY3JBC8ND8JkuviCjBfq39qHAa3rk2cxeH3loXLbaz+YiIiIiMjlbc2BM2w9eQ4HOxueG9Lmktzj1hA/Ovh6kpqdx7u/HCmxjcXy9+rB5hKsZURERESkahQskSuKxWLw6vIDANzexY/2vp7l9Ki6F4YFYWdjYu3hs2w8GlfsulEoZf6lnw6Qa7ZcsrmIiIiIiEjVZeeZeePn/E1QD17TDL861VOr5EI2NiZeGh4EwPzQCA5EJxdrE52caf3+q01hRYInIiIiIlJ7FCyRK8qPu6PYE5mMq4MtT55Pcb9Umtd3475eAQC8tuIgeRcEQ46cSS3y8/g5oaRmlV4QXkREREREasfszeGcTsjAx92RSf2bX9J7dQvwZljHRhhG/jrCuKAuybEzfx/zu3xPNI/+sLvMgvAiIiIiUjMULJErRnp2Hv9bfRiAKQNb4OPudMnv+eh1LanjYs+xs2l8f74QZIH1R4pmm/xxLJ7bP9tKTKGdYiIiIiIiUrviUrOZ/vtxAJ65sQ2ujnaX/J7P3dQWRzsb/jyZwJoDsUWubTxWdB2xfE80o7/cRtL5wvUiIiIiUjsULJErxmcbTnAmJRt/b2fG9wmskXt6utjz7+tbAfDer0eLLGDWHT5r/f72Ln7Uc3PkcGwqIz7ZwsHolBqZn4iIiIiIlO2dNUdIy86jk78XI4J9a+Sevl7OPHRtMwBe//kQWbl/Z45sKLTp6onrW+HuaMe28ARGztjC6XMZNTI/ERERESlOwRK5Ihw7k8rnG08C8J+b2uJkb1tj976rexNaN3AnKSOXkZ9u4bYZW7jh/Y38FZZgbdPRz5Olk3vTwseN2JQsRs3cyo5TiTU2RxERERERKe63g2dYsCMCgJeGBWFjU3MF1Sf2b04DD0ciEjLp//Z6bpuxhW6v/8bJ+HRrm2ta1WfhpF409nTiZFw6d3y+lTMpWTU2RxERERH5m4Ilctn76+Q5bvtsK9l5Fno1q8sN7RrW6P3tbG2sRRpPxqez/VRisXol7X098fd2YfHE3vRs5k1adh7PLt5brM6JiIiIiIjUjF8OxPLwvJ0YBtzbswldmtap0fu7ONjx1q0dcbC1ITYli+2nEolLzS7Spl1jD9o09GDplD40q+9KTHIWj8zbVazOiYiIiIhcegqWyGXtp91RjP5qG8mZuQQ38WL63cGYTDW3G6xAnxb1+HFKHz67N4S3RnYocm3CNYEEN8lfeHm62DNzdFe8XR04djaN7/46XdJwIiIiIiJyCc3ZHMZD3+4gK9fCdW18eHl4u1qZx4DWPqx9oh+f3RvCy+c3YBWYNbYr9rb5S/IGHk7MGtMNRzsb/gpLYNX+2JKGExEREZFLSMESuSwZhsGn64/z6Pzd5Jgt3NiuIfMm9KSum2OtzamzvxdBjTytx4EBPNSvGf8ZWnTR4+n8d52T9387qkKNIiIiIiI1xGIxeG3FQaYtP4hhwN09mjBzdBdrUKI2+Hu70LSuKx+tPWZ97f9u68jANg2KtAuo52qtc/LGBXVOREREROTSs6vtCYhcKM9s4cWfDjBvW35Wxv19A3n+prbY1uD5wiXZE5HE/XNDiU/7O/jx+KBWxKVmY1A0Tf66tj68/6sD59Jz+M/S/bx8c9CFwwGQZ1Z6vYiIiIhIdcjKNfPY/N2sPpCflfHMjW2Y2K9ZrWSmF7bxaByTv9tJWnae9bVbQ/w4m1q8NsmtXfz4ZP0JIhMzeWvVYSYPaF7imMkZuZdsviIiIiJXKwVLpNbsjUy2fm8YYDJBWnYeD3+/k/VH4jCZ8oswjusTWIuzzPfLgVgemb+LrNy/a5C09/Xgjs//ZE9EUpl9V+6LYeW+mEs8QxERERGRq8Pm4/EAxKf9Xf/jXFo2D3y9nV2nk3CwteHt2ztyS2ff2pqi1YLQCJ5fuo88y9+bpHo282bw+xs4EZdeRk+YsyWcOVvCy2xjb1u7gSARERGRfxIFS6RW/HrwDBO+3l7ktTMpWYyfE8qB6BSc7G348M7gGi/mXpK5W8KZtvwAhgH9WtUnLjWbgzEp7I9KsbYpbbNa4bqMpbXx9XKmg69nNc5YREREROSfacH2CP678lCR18Li0xk7exunzmXg6WzP56O70KNZ3VqaYT7DMHj/16N89PtxAG7p3Jjt4YlEJWXy58kEa7uS1ggX1nYvqY0JGNKhEc3ru1XjrEVERESubgqWSI37ems405YdKPLakTOp3D8nlOjkLOq6OvDV2G509veqnQmeZ7EYvPHzIb7cFAbAXd39eeqGNoS89qu1TbP6rswZ250mdV1KHCM8Pp3r399Artlg1phuDGjjUyNzFxERERH5J7kw+FBgx6kEHpi7ncSMXPy9nZk9tjstfGo3gJCTZ+HZxXtZsisKgIcHtGBkiC8D391gbdPJz5Mvx3SjvnvJNRl3nk5k5KdbAPhpSh86+nld8nmLiIiIXO1U4F1qjMVi8PrKg7z00wEsBvQI9LZeu23GFqKTs2hW35Wlk/vUeqAkK9fMlO93WgMlT93QmjdGdOCphXusbboHeLNkUu9SAyWQX6Rx/PljxF5beZBcs6XUtiIiIiIiUlxOnoUnFuyxBkp8vZyt1+764i8SM3Lp5OfJkkl9aj1QkpyZy5hZ21iyKwpbGxNvjezAE4NbMfqrbdY2g9o2YN6DPUsNlACENKnDvzo3BuDV5QcxLkw3EREREZFqp2CJ1IisXDMPz9vJF3/8HXx47V/trdfTc8x0C6hTbvChJpxNzeLuL/5k1f5YHGxt+PDOzkzs15wPfjvG2sNnre2+vr87Xi4O5Y738MAW1HNz4GRcOl9vPXUppy4iIiIi8o+SlWtm7Oy/gw9vjuzAkze0sl7PybNUKPhQE3adTmTEp5vZevIcrg62zBrbjZs6NuL5pfuISsq0tps5ugsuDuUf8vDMkDY429uy/VQiK/aqBqKIiIjIpaZgidSID347xs/7/g4+TBnQgqxcs/X60A6N+Ob+HhUKPlwqOXkWPt94goHvbGDn6SQ8nOz4+v7uNPBwYuhHf/Dh2mPWtl+P746TvW2FxnV3sueJwa0B+PC3oySk51yS+YuIiIiI/NP83+ojbDmRH3z4ckxX7urehIycv9cRo3s2rXDw4VLJyMnjjZ8PceuMLZyMS6eBhyM/PNSLsylZDHxnA/O2RVjbrnnsWmxtKlaUvZGnMxP7NQfgrVWHi6yfRERERKT6qWaJXHKnzqUz6/xxVh/e2ZkhHRoBFFnkfHBnZ+xtay929/vhM7y24hBh8ekAdPTz5InBrflm6ylW7iu6i8vZ3pYezbxLGqZUo7r68/XWUxyKSeH9X48WyaoREREREZHi9kQkMWtz/jri47uDGdA6v/7f6XMZ1jav3tIOU0kV0GuAYRgs3xvDmz8fIiY5C4CRwb7c3Lkx//lxP3sikoq0b+zpRKsGlTsm7MFrm/FD6GmikjL5fONJHrmuZXVNX0REREQuoMwSueTe+PkQOWYL17Ssx43tG5bYxqaWFjgn4tIYO3sb4+dsJyw+nXpujrx2Szv6t6rPg19vZ+W+GGxMcG/PJtzfN7/2SJ8WdXG0q1hWSQFbGxMvDw8C4Lu/TnEkNrXa34uIiIiIyD+FYRi8uuIgkB+AGNimQYntaitQsj8qmVEzt/LIvF3EJGfhV8eZ/7u1I3a2JsbODmVPRBKuDrY8f1Mb7ureBIB+rX0qPV9nB1uevaktADPWnyD2fFBGRERERKqfMkvkktpyIp41B85gY4IXhwXV2mLmQilZuXy89hizN4eTZzGwtzUxvk8ggfVc+WjtMaLPL0J6BHrz8vB2BDX24LYZWwDof35HW2X1bFaXIe0bsmp/LK+tOMg393e/bD4PEREREZHLybI90ew4lYiLgy3PDGlT29OxOpeWzTu/HGF+aASGkZ91PuHaZjja2fDaioOkZucBMDLEl2dvbEN9d0f6vb0egAGt61fpnsM7NuLrLeFsP5XI/60+zHt3dK6mdyMiIiIihSlYIpeM2WLw6vL83WD39GhKqwbutTwjsFgMFu2I5P/WHCY+Lb92yMA2Ptwa4sfcreHM3HgSAF8vZ/4ztC1D2jfEZDKRnJHLztOJAPSv4iIH4Pmb2rL20Fk2HY/nt0NnuT6o5B1yIiIiIiJXq8wcM2+tOgzA5P7NaeDhVMszglyzha+3nuKD346SmpUfELmlc2P6tqjHzI0nOX42DYAOvp5Mu7kdXZrWAWBvZBKnEzJwsLOhd4t6Vbq3yWTipeFB3Dx9M0t2RTG6V1OCm9SpnjcmIiIiIlYKlsgl80NoBIdjU/FwsuPx61vV9nTYcSqBacsOsi8qGYBm9Vx5eGALtp9KZOq8nVgMcLK3YVK/FjzUr1mRAu6rD8RgMaBVAzf86rhUeQ7+3i7cf00gM9af4PWVB7m2Vb1KH+klIiIiIvJPNnPjCWKSs/D1cuaBa5rV9nTYcDSOV5cf4ERcfn3D9r4ePNC3Gav3x/LUor0AeLs68PQNrbm9q3+RAu4Lt0cCcEO7hrg5Vn353dHPi1tD/Fi8M5JXVxxkyaTeylIXERERqWYKlsglkZKVy7u/HAHgsUGt8HZ1qLW5xCZn8daqQ/y4OxoAd0c7Jg9ogZ2NiWnLDpByfmfYsI6NeO6mtvh6ORcbY8H5Rc6IYL+Lns+UAS1YtCOS8HMZfL3lFBOurf0FoIiIiIjI5SAmOZPPNpwA4Lmb2hTZwFTTwuPT+e/Kg/x26CwAdV0dmDqwBefSc3hm8V6y8yzY2pgY3bMpjw9qhaeLfZH+Wblmlu3JX4Pc3uXi1xFP39iaVftj2HU6iZ92R/OvYN+LHlNERERE/qZgiVwSH689xrn0HJrVd2V0r6a1MoesXDNfbQrjk3XHycgxYzLBqC7+9Gpel0/WHefY+VT5oEYeTLu5Hd0DvUsc5/jZNHacSsTWxsStIRe/IHFztOOpG1rz9KK9fLT2GCNCfKnn5njR44qIiIiIXOn+t+owWbkWugd4M7RDo1qZQ1p2Hh//foxZm8LINRvY2ZgY0zuAVg3c+GjtcaKSMgHo1awu025uR+uGJR83/NuhMyRn5tLI04k+VTyCq7AGHk5MGdCCt9cc4a1VhxncrgEuDlrSi4iIiFQXPVlJtQuLT2fOlnAAXhwahL2tTY3e3zAM1hw4w+s/HyQiIX8h06VpHcb1CeCn3dE89sNuID9V/snBrbmjW9FU+Qst3BEBQP9W9fGppvOSbwvx4+ut4eyPSuHdX47y5sgO1TKuiIiIiMiVaufpRH7cHY3JBC8OC6rxY6YsFoMlu6L43+rDxKVmA9CvVX0e6teMr/4I46tNYUDx+oalKTiC69YQvzLXG5Vxf99A5m07TWRiJjM3nLwsjjsWERER+adQsESqlcVi8NJP+8k1G/RrVZ8BbXwq1O9kXBotq6EA/NEzqbyy/ACbj58DoIGHI88NaYvJBP9esIec86ny9/VqymPXFU+Vv1Cu2cLiHVEA3N7V/6LnV8DGxsRLw9oxauZW5oee5v6+AbTwufj3LyIiIiJyJcrIyePJhXuA/I1FHfw8K9QvJjmTRp7Fj9GtrF2nE3ll+UF2RyQBEFDXhReHBZGWncekb3eSnJmLg60NE/s1Y1L/Fjg7lH08WGxyFn8ciwPgtmo4gquAk70tz9/Ulsnf7WTW5jAm9mte7lxEREREpGJqdsu//OPN2HCCP47F42hnw4vDgspsW8fl7zomI2dsYVtYQpXvm5yRy7RlBxjy4R9sPn4OBzsbpgxozu9P9Cc6OZNH5+8mJ89C7+Z1Wf3oNbw8vF25gRKADUfiiE/Lpq6rAwMrGPipqO6B3gxq2wDDgHnbIqp1bBERERGRK8nLPx3gZFx6/manm9qW2Tawnqv1+xGfbOFQTEqV73s2JYsnFuxhxKdb2B2RhKuDLc8OacPqx65ly4lzPDp/N8mZubT39WD51L78e3DrCgUnFu+MxGJA9wBvAgrNtzrc2K4h/t7OpGblsWp/TLWOLSIiInI1U7BEqs2W4/HWou6v/as9LXzcymxfOBM9NSuPe7/8i+XnCyBWlNli8M2fp+j/zjrmbAnHbDG4oV0Dfnu8H48PasV/Vx7i/1bnz+mBvoF8e3+PSmWwFBzBNSLYFwe76v/ncneP/GyVpbuiyMmzVPv4IiIiIiKXu592R7FwRyQmE3xwRzDerg5ltne0//u5PDYli9s/28rGo3GVumd2npkZ608w4J31LN6Zf1zWbV38WPdUf8b2DuCx+butx249cl1Llk7uU2ptkgsZhsGiHefH7Fp9WSUFbGxM3HE+631+qDZdiYiIiFQXBUukWpxJyeKR+buwGHB7Fz9GVfLIquuDGpBjtjB13i5mrD+BYRjl9vnz5DmGfvQHL/64n8SMXFo1cOO7B3owc3RXvN0ceODr7czbdhqTCaYND+KFYUHYVOKs4Pi0bNYeOgtU7xFchV3bsj4+7o4kpOfw++Gzl+QeIiIiIiKXqxNxaTy/ZB8AUwe0oFfzupXq37OZN2nZeYyfE8qC7eUHDgzD4LeDZ7jh/Y38b/Vh0nPMdPb34scpfXjn9k7Ymkzc/cWfrD4Qi4OtDR/dFcy/r29VqTqMO04lEhafjouD7SUrUn9bF39sTLAtLIGTcWmX5B4iIiIiVxsFS+Si5ZktTP1+F/FpObRp6M6rt7Sv9Bif3hPC+D6BAPxv9WFe+HE/eeaSMy0iEzOY8t1O7vz8Tw7HpuLhZMe04UH8/Mg19GlRjzMpWdwxcyvrj8ThZG/DzHu7MPb82JXx464o8iwGnfw8K7yLrLLsbG249fwZxgsrsLgTEREREfmnyMwxM+W7naTnmOkR6M0j17Ws9Bhzx3fnX50bk2cxeHrRXt775UipG6+On01lzOxQHvh6O+HnMqjv7si7t3diyaTedPb3Iiw+nZEztrDzdBKezvZ8c393bu7UuNJzKijsflOHRrg6XpoyoQ09nejfOv+Y4AXn7yciIiIiF0cF3uWivf3LEbaFJ+DmaMeMe7tUqcCgrcnES8OD8Pd25tUVB/nur9NEJ2Uy/e4Q6wIjM8fMZxtO8NmGE2TnWbAxwV3dm/DE4NbWVP0jsamMm72N6OQs6rk58OWYbnT296r0fAzD4IfzKe2XKqukwO1d/Jix/gTrjpzlTEoWDTycLun9REREREQuB9OWHeBwbCr13Bz4+K5g7CqRvVHA0c6W9+/ojF8dF6avO85Hvx8nMjGTt27taD1GNzkzlw9/O8bXW8PJsxg42Nowvm8gDw9sgdv5tcaOUwk8MHc7iRm5+Hs7M3ts93KPFS5JRk4eK/bmHy1cnYXdS3JHN39+P3yWRTsieWJw5bJfRERERKQ4BUvkovx68AwzN5wE4O3bOhYptlgV4/oE0tjLmUfn72LdkTju+HwrX43pxrawBN78+RDRyVkA9Aj05uXh7Qhq7GHtu/l4PBO/2UFqdh7N6rsyZ2x3mtR1qdI89kQmc+xsGo52Ngyvwm6yymhW342uTeuw/VQiS3ZGMal/80t6PxERERGR2rZ4RyQ/bI/AZIIP7wzG5yI2DJlMJp68oTV+dZz5z4/7WbIritiULD69J4RV+2N5Z80RzqXnADCobQNeGNq2SNH1lXtjeHzBbnLyLHTy8+TLMd2o7+5Ypbms2hdLeo6ZJt4u9Aj0rvJ7qoiBbXyo5+ZIfFo2vx8+yw3tGl7S+4mIiIj80ylYIlV2+lwGTyzYDcD4PoEMqabzeG9o15B5E3rywNzt7I9Koccba63XfL2cef6mttzUoSEm09/1RxbviOSZxXvJsxh0D/Dm8/u64OVSdmHIshScdzykfUM8ne2r/mYqaFRXf7afSmTh9ggm9mtW5L2JiIiIiPyTHD2Tygs/7gfgseta0adFvWoZ987uTWjk5czkb3ew5cQ5Or/6q/VaCx83XhoWxLWt6ltfMwyDL/8I4/WfDwH5gZSP7uqMi0PVl8kLd+SvI27r4nfJn+ntbW24rYsfn204wQ+hEQqWiIiIiFwk5elKlWTlmpn8/Q5SsvIIaeLFs0PaVOv4Teu6Fskagfxskt/+3Y+hHRtZFx6GYfDhb8d4YuEe8iwGwzs15uv7u19UoCQzx8zy3fmp85UtVF9VN3VshIuDLSfj09lxKrFG7ikiIiIiUtPSs/OY/N1OMnPN9G1Rj4cHtqjW8Vs1cKORl3OR10aG+LLq0WuKBErMFoOXlx2wBkrG9GrKzNFdLipQEpGQwZ8nEzCZsNYlvNRGdc2/z/ojZ4k9n4UvIiIiIlWjYIlUyasrDrI/KoU6LvZMvzvEeh5wdVi9P4b+b6/jj2PxRV7feTqRv8LOWX/ONVt4atFe3v/tKAAT+zXnwzs642Rf+Zopha05EEtqdh5+dZzp2azuRY1VUW6Odgw9n5mzQIXeRUREROQfyDAMXvxxP8fPpuHj7sgHd3bG1qb6si92nErgpg//4PjZtCKvr94fy8m4dOvPGTl5PPTNdr7eegqTCV4Y2pZpN7e76Lks2pFfaL1P83r4XhCwuVSa1Xeje6A3FgMW7dA6QkRERORiKFgilbZ0VyTf/3Uakwk+uDOYxtW4EDgYncKj83eTkpVH20Ye/PBgTw6/diPXBzUg12zw7Z+nAEjJymXc7FAW7YjExgSvj2jPs0PaYFMNi62CYMXtXfyrZbyKKigkv2JvDOnZeTV2XxERERGRmvBDaARLdkVhY4KP7wqmnlvV6oKUZOuJc9z9xV8kZuTS3teDFVP7snfaYDr7e5GRY+a7v/LXEWdTs7jz8z/57dBZHOxs+OTuEB645uKPwbVYDGuw5PauNZNVUuDObvnriB+2R2CxGDV6bxEREZF/EgVLpFKOnknl+SX55ws/MrAl/Qqlsl+slKxcJn+3g+w8C/1b12fF1L70aFYXJ3tbbjx//m6u2SAmOZNRn21l0/F4XBxs+WpMN+7p0bRa5hCRkMGWE+fOp877VsuYFdUtoA4BdV3IyDGzcl9Mjd5bRERERORSOhSTwsvLDgDw5A2t6VGNGdxnUrKYOm8n2XkWBrbxYcFDvWjv64mHkz2D2zUA8o/aPX42lZGfbmFvZDJ1XOyZN6EHN1VT3cU/T54jKikTdye7Gq8dMqR9I9yd7IhIyGTryXPldxARERGREilYIhWWlp3HxG93WM8XfuS6ltU2tgE8vXAv4ecy8PVy5v1RJafkH4xJ4V+fbOZwbCr13R1Z8FAvBrTxqbZ5LCyUOu9Xx6Xaxq0Ik8lkzS5ZqKO4REREROQfIjUrl8nf5QczBrSuz8Rrm1fb2HlmC1O/30V8Wg5tGrrz6T0hReqOmMhfU4SGJzDy0y1EJmYSUNeFJZP70KWpd7XNo2AdMbxT44s+FriynB1suaVzYwDmh2odISIiIlJVCpZIhRiGwXNL9nEyLp2GHk58WM3nC8/aFMbqA7HY25r45J4QcswWTp/LsH7FpuQXK4xLzeZMSjYtfdxYOrk37X09q20OFovB4lpKnS9wa4gfNiYIDU/kZFxa+R1ERERERC5jBeuIsPh0Gns68d6oztV61O3bvxxhW3gCbo52zLi3C+fSc4qsI04n5NcqCT+XQUpWHiFNvFg8qTeB9VyrbQ4pWbms2p+fGX57DRV2v9Cd3ZoAsGZ/LGdSVOhdREREpCrsym8ikr9TavmeaOxsTEy/O5i61Xi+MMCbqw4B8MLQIO7+4k8ycsyltu3VrC6fje6Cp7N9tc5hy4n81HmPWkidL9DQ04l+reqz7kgcU+ftYnL/FtzQrgF2toprioiIiMiV59u/TrNibwx2NiY+vjuEOq4O1Tr+zA0nAXj7to4MeGd9mW37tarPzNFdqj3zY+XeGLJyLbTwcaOzv1e1jl1R7X096eTvxZ6IJMbM2sYzN7ahX6v6NVqDUURERORKp7/ASrlSsnL536rDADwxuDVdA6ovXb2AxYBhHRtxX6+meJUTBHlmSJtqD5TA34Xdb+nsW+Op84VNHtACBzsbDkSnMOX7nVz7f+v4bMMJkjNya21OIiIiIiKVFZOcyRsr8zdFPTukDV2a1rkk97m/byBDKlB75JWb212S5/yCI3Rv7+J30YXiL8aHd3TG29WBw7GpjJsTyqD3NvDN1nDSs/NqbU4iIiIiVxIFS6Rc038/zrn0HJrVd+WBawKrbVyzYVi/D6znylu3dsRkMrHlues4+t8hHH7tRvZNG0zbRh5F+r358yGMQn2rQ3JGLqsPxAIw6nzdkNrSLcCbTU8P4JGBLajr6kB0chZvrTpMzzfX8sKP+zh+VsdziYiIiMjl73+rDpOZa6ZbQB3u71t964jsXIv1+5AmXjw7pA0A4W8N5ch/b+Twazey/YVBNPZ0KtLvvV+PVtscChw/m8bO00nY2pgYEeJb7eNXRkA9V1Y+0pcJ1wTi7mTHyfh0XvzpAD3fXMsbPx8iMjGjVucnIiIicrlTsETKFB6fzuzNYQC8ODQI+2o8Durjtcet38+4NwQ3x79PhXOws8HJ3pZ3fznKoZgUPJ3t+eHBnjja2fBXWAKr98dW2zwAlu2JIifPQpuG7rT39Si/wyXm4+HEvwe3ZvOzA/m/2zrSpqE7mblmvv3zNIPe28CYWdvYcDSu2oNGIiIiIiLVYefpRH7cHY3JBC8Na1etGRevrjho/X763SFF1iiOdrY42tnw0k/7iU7OorGnE9890AOTCZbtiWbHqYRqmwfAovM1D/u3qo+Pu1M5rS+9Rp7O/GdoEH8+dx2v3tKOwHqupGbl8fnGk1z7f+uY9O0OQsMTtI4QERERKYFqlkiZXv/5ELlmg36t6jOgjU+1jbvxaBwr98VYf27l416szYq90czZEg7Ae6M60aNZXR66thkf/X6cN1YdYkAbn2pLo1+wPX+RM6qr/0Uv5HLyLPx26AzJmcWPzeoe6E3z+m7Wn8+kZLH+yFksF6xVHGxtGNS2AZ4u9ozq6s/tXfz482QCszaH8duhM2w4GseGo3E0r+/KqK7+eFTzsWQ2JujXyoeGnrW/4BMRERGRK4vFYvDq8vyAxm0hfnTw86y2sZfsjCxS37Cxl3OxNrM3h/PzvljsbU18ck8IwU3qcEdXf+aHRvDK8oP8OLlPtdTyyDNbWLIzfx1xe9eLL+xusRisP3qWMynZRV53sLWhf+v6RepGpmTlsvbQGbIKZdkAeLs6MKC1D66OdtzXK4B7ezRl/dGzzN4czh/H4lm1P5ZV+2Pp4OvJoLYN8PGo3lqUzeq50qNZ3WodU0RERKSmKFgipdp8PJ5fD57B1sbEi8PaVtu40UmZPDp/V5ltTsSl8cyivQBM7t+c69o2AOChfs35YXsEEQmZzNocxuT+LS56PodiUtgXlYy9rYl/BV9c6rxhGEydt5M1B86UeN3Bzob3R3VmaMf8M5WfXLiHP47Fl9i2ibcLs8d1o3l9N0wmE72a16VX87qcOpfO3C2nWLA9ghNx6bx5vp5MdWvVwI2fH7lGxeVFREREpFJ+2hPF7ogkXB1seerG1tU27tEzqfxn6f4y2+w4lcgbP+fXSfnPTW0JbpJfJ+WJwa1ZsTeGvZHJLN0Vxa1dLj648cexeM6mZlPHxZ6BbRpc1Fi5ZguP/bCblXtjSrzu7mTHZ/d2oU+LegA8Mm8X64/Eldg2qJEHs8Z2o6GnEzY2Jga2acDANg04EpvKnC1hLNkZxb6oZPZFJV/UnEszMtiXd0d1qtX6LSIiIiJVoWCJlCjPbLHuBhvdsyktSsj8qIpcs4WHv99J4gXFygs/R2fk5DHp2x2k55jpEejNv69vZb3m6mjHMze24d8L9vDJ78e5LcQPH4+Ly35YeD6rZFDbBni7OlzUWF9tCmPNgTPY25ro18qnyPs6k5LF3shkpny/k6ikNky4phkJ6TkABDfxol6hnWIHopI5nZDBrTO28MV9XekW4G291rSuKy8ND+Lx61uycHskf4WdK5aZcrH+OnmOo2fS+H7bae7rFVC9g4uIiIjIP1ZGTh7/W3UEgCkDW1Tb0VRp2XlM/HYHmbnmUtskpOfw8Pc7ybMYDO3YiDG9A6zX6rs78vDAFry16jD/W32YG9s3xNXx4pbDBUdw3dLZFwe7i9tg9M6aI6zcG3N+HVG/SKDhRFwaJ+PSGTNrG2/d2pHbuvhxIDoFyK/92MInP3PdMAxCwxM5GJPCiE83M3tcN9o0/PuI4dYN3XlzZEeeuqENK/ZGs/t0EqnVWPw9K9fMH8fiWbIrisHtGnBj+0bVNraIiIhITVCwREo0LzSCI2dS8XKx57FBLatt3LdWHWbn6STcHe1KfDA3DIMXftzP0TNp1Hd35OO7g4tlNvyrsy9zt55iT0QSb685wtu3d6ryfHLyLCzd9fcRXBdje3iCNcvjxWFBxYIMZovBaysOMmdLOG/8fJjIxEzM56Mcjw1qRb9W9a1t49OyuX/udvZEJHHPl3/x3qhODOvYuMh47k72jO8byPhqLJZZ4Jut4bz40wHe+/UoN3dqjJfLxQWRREREROTq8Nn6E8SmZOHv7cz4PtXznGoYBs8t2cfJuHTquNgX23gF+c/aj87fRUxyFs3qu/K/WzsWy2wY1yeAedtOc+pcBjPWn+DJG6qe9ZKUkcOvB/OzyS/2CK41B2KZufEkAB/dGcyQDkWDDFm5Zp5atJfle6J5cuEeohIzKSg58uk9IbRt9HdAJCIhg7Gzt3EiLp3bZ2xlxr1d6NuyXpHxvF0duK9XAPf1uqhpl+i9X47w0e/Hef3nQ/RvXX3HJouIiIjUBJ2vI8UkZ+by3i/5u8EeH9Sq2v5QvmpfDF9tyi8Wf3spgYkfQiNYsjMKGxN8fFdwiTvRbGxMvDw8CIBFOyPZF1n19PG1h86QmJFLAw9HrrlgEVEZ8WnZPPz9LswWg+GdGjO6Z9NibWzPz/uFoW0xmeDrrac4HJta4nj13ByZP6Eng4MakJNn4eHvd/HZhhM1Vojxru5NaN3AnaSMXD747ViN3FNERERErmxRSZnWP/o/P6Rttf2hfO6WcJbvicbOxsQtnf8+NrdwVvj034/zx7F4nOxtmHFPF9xKyBpxtLPl+Zvyjxf+/I+TRCRkVHlOy/dEk2O2ENTIg3aNq16T5fS5DJ5cuAeAB/oGFguUADjZ2/LhHZ2Z1L85AO//dpT4tOxi7QD8vV1YMqkP3QO9Sc3OY+zsbSzcHlHl+VXWxP7NaejhZD02WURERORKomCJFGEYBv+3+jCJGbm09HHjnh5NLnrMXLOFZXuieep8DZKHrm1GUkZOsXb7o5J5adkBAJ68oTU9yygMGNKkDrd0boxhwGsrDlZ5bgvOLxxuDfGrcm0Os8Xgsfm7iU3Jonl9V94c2aHU83lNJhMPXNOMT+8OwbGcVH1nB1tm3NuFseePD3hr1WFe/Gk/eWZLmf2qg52tDS8Oyw9IffPnKY6fLTmoIyIiIiIC+c/8Ty/aQ3aehR6B3tzYvuFFj5mVa2b678eYdv544GeHtOFEXJr1us35Z+4/jsXxwdqjALz+rw60blj6EcKDgxrQq1ldcvIsfPnHySrPbeW+/NoiIy6i5mFWrplJ3+0gNSuPLk3r8MyQNqW2tbEx8cyNbXh9RHvKq03v6WLPN/d355bOjcmzGDy1aC/v/3q0RjZeuTjY8cyQ/IydT34/ztmUrEt+TxEREZHqomCJWOWZLTy/dB/f/XUayD9K6mKKeyem5/Dp+uNc8791PDJvF2nZeXQLqMOk/s35eX/RwoUpmXlM/m4nOXkWrmvjw8Rrm5c7/vVB+UUUd0ckkVvFAML2U4kA3FTCDq6K+nDtMTYdj8fZPj+4UdIutgsN6dCI7yf0tO6Gq1+oXklhtjYmpt3cjheHBWEywbd/nuahb3aQkVN9ZwuXpm/Legxq2wCzxeC/Kw9d8vuJiIiIyJUpM8fMlO92svn4OVwcbHn1lvYXVdz7TEoW7/5yhN5v/c47v+QHQcb1CeDG9g3ZdDy+SNuY5Ewenb8bw4C7uvuXW7jdZDLRLTC/HmBcKdkZ5cnJs7AtLAGAwe2qXtj9leUHORCdgrerA9PvDsa+Amuve3o05asx3XBxsMXGBHVLqbnoaGfL+6M6M2VA/rrqw7XHeHLhXnLyLv3Gq1s6+dLJ34v0HDPvnD+xQERERORKcNkHS9588026deuGu7s7Pj4+/Otf/+LIkaIPXGlpaTz88MP4+fnh7OxM27ZtmTFjRpnjzpkzB5PJVOwrK+vq3PmSlp3H/XO3M29bBDYmePWWdlxbqIZGZRw9k8pzS/bR6621/N/qI8SmZFHPzYFHr2vJrLHdWLU/lqxcS5G0+ScX7eF0QgZ+dZx5d1QnbMrZLpVrtvD+r/kLp/t6Na3QwqIkBZurKhLgKMmGo3F8/Hv+MVVvjGxPqwal72K7UJemdfjl8WtZPKkXQY09ymx7f99AZtyTn42y9vBZ7pj5J2dTL/3v6n+GtsXe1sT6I3GsO3L2kt9PRERERK4s8WnZ3PXFn/xy8AwOtjZMvzu4zMyOsuyNTOKx+bvo+7/f+fj34ySk5+Dr5cxbIzvw8vB2LN4RhWH8ffxWniX/uNqE9ByCGnnw8vB25d4jLTuPedvyN4d1aepdpXmaLQbnSw9S373kTU/lWbwjknnbTmMywYd3dqaRp3OF+w5o48Nv/+7HT1P64uNR/NjiAjY2Jp66oQ1vjOiArY2JxTsjGTdnGylZxWu+VKfCxyYv3BHJ/qiqH5ssIiIiUpMu+2DJhg0bmDJlCn/++Se//voreXl5DB48mPT0dGubxx9/nNWrV/Ptt99y6NAhHn/8caZOncpPP/1U5tgeHh7ExMQU+XJyKv1h858qNjmLUZ9tZcPROJztbfl8dNdixcnLY7EYrDt8ltFf/cXg9zcyb9tpsnLzz/B95/ZObH52II9f3wp3J3vr0VeFCyH+en5x9ek9IRWqkfLdn6c4EZeOt6sDU6+rvgL0lRGdlMlj83dhGHB3jyaMCK58Ycd6bo4VXqTd2L4R8x7Mz0bZF5XMiE+2cOzMpT0eK7Ceq/UYsP+uOFjlDB4RERER+ec5EZfGyE+3sDsiCU9ne759oAcD21Qu0yLPbGHl3hhum7GFm6dv5sfd0eSaDboF1GHGPSFseKo/d3ZvgsVisHBH0XVEUkYuO04l4u5kx4x7QypUI+XTdceJS82maV0X7u158UcOV8WR2FT+8+M+AB67rhXXtKz8JrXGXs508KtYrZS7ezThyzFdcXWwZfPxc9w2YwtRSZmVvmdlhDSpw7/OH5v8yvIDNVZ7UURERORiVG07fQ1avXp1kZ9nz56Nj48PO3bs4NprrwVg69atjBkzhv79+wPw4IMPMnPmTLZv384tt9xS6tgmk4mGDS/+LN0rRVauudgfu0+dy2DC19uJSc7P/pg1thsd/bwqPGZ6dh6Ld0YyZ3M4J+PzA1g2pvwjssb3CaR7oHeRFPxjZ1LZdToJWxsTI4J9mbnh73OCH7++FYH1XEktZ6dTUkYur50/FurBa5thY6LcPqWp6kN7Tp6FKd/vJDEjl/a+Hrx0vr7HpRbSpA5LJvVm3JxQwuLTuXXGFmaO7kqv5qXXd7lYU69ryZKdUZyIS+ebracY3zfwkt1LRERERC4/mTlm8ixF1xH7o1KY9N0OkjJy8fd2Zs647jSv71bhMZMzcpkXepqvt4QTnZyfMW1va2J4x8aM6xNYLBDw58lzRCZm4u5oxw3tGhZZR7w4LAhvV4dy1wQRCZl8uv4EAI9e15KcPEuVjqXKzDVXuk+BtOw8Jn23g6xcC9e0rMfUgS2qPFZlDGjtw4KJvRg3O5SjZ9IY8clmvhrTrcIBl6p4ZkgbVh+IJTQ8kZX7YhjWsfElu5eIiIhIdbjsgyUXSk7OT+H19v57N37fvn1ZtmwZ48ePp3Hjxqxfv56jR4/y4YcfljlWWloaTZs2xWw207lzZ1577TWCg4NLbJudnU129t9n2qakpFTDu6k5K/ZG89j83eRZSg4ONK/vypxx3fH3dqnQeBEJGXy9NZz5oRGkZuXXz3B3tOOObv6M6R1Q6jgLd0QC0K6xB5+sO1Hk2v9WH+Z/qw9X9C0B+UXP31pVuT7V4a1Vh9l1OgkPJztm3NOlQrvYqktAPVcWT+rNhK+3s+NUIqO/+oubOzdmfJ9A2vtW/2LHw8meSf2b89+Vh/jgt6OMvohjz0RERESuNlf6OmLulnCmLT9AaXuMOvl78dWYrtQrpQbfhY6fTWPOljAW74iyBh3qujpwT48m3NuzaanHShVkp3du4sVzi/cVufb0or08vWhvBd9Rvn8v2FOp9qWxqURtFsMweGbxXk7GpdPI04kP7uhc7vHD1aldY0+WTunD+NmhHDmTyi2fbGJkiB8T+zWnhU/FA10V1cjTmZs6NGLJziiW7oxSsEREREQue1dUsMQwDP7973/Tt29f2rdvb339o48+YsKECfj5+WFnZ4eNjQ1ffvklffv2LXWsNm3aMGfOHDp06EBKSgoffvghffr0Yc+ePbRsWfxYpzfffJNXXnnlkryvmhAallBqoKRfq/p8dGcwni72ZY5hGAah4YnM2hTGLwdjref0FhzVdGsXvzJrf+SaLXy+MX8H2N7IZPZGXh5n17b0caOxV8XPCP55XwyzNocB8O6ozhUOMFUnb1cHvnugB08t2svyPdEs2RnFkp1RdA/wZnzfAK4PaohtNS289kclW/+7eTjbY1EKvYiIiEiFXenriD9Pnis1UDKsYyPevq0Tzg5lbxwyDIONx+KZtSmMDUfjrK+3aejO+L6B3NypcZmbj5Izc/lxdzQAfxyLL7VdTXKwteHenk0rtWnq662nWLk3BjsbE9PvDqFuBQNM1cnXy5mFk3rx7OK9/LwvlkU7Ilm0I5J+reozrk8A17asX20BnC0n4lmzPxagzNoqIiIiIpcLk3EFHR46ZcoUVq5cyaZNm/Dz+7s+xDvvvMMXX3zBO++8Q9OmTdm4cSPPPfccS5cuZdCgQRUa22KxEBISwrXXXstHH31U7HpJO8L8/f1JTk7Gw6Ps4tyXg5d/2s/craeY3L85jxSq8WEygaNd2Q/42XlmVuyJYfaWMPZH/b0Trm+LeozvG0D/Vj5lPlDnmS2sPhDLw9/vKrXN4ddurND7uG/WNraFJdCvVX1mju5SoT7lcbC1qfCC4GRcGjdP30xadh4P9WvGc0PaVsscLsbuiCRmbw5j5d4Ya0DM18uZsb0DGNXNH0/nsoNgZVl3+CxTvt9JRo6Z1g3cmT2uW6UCSyIiIiLVKSUlBU9PzyvmGRyu/HXEpG93sGp/LC8PD+Ku7n/X+KjIOiIjJ48lO6OYvTmME3Hp1n6D2uYf2duzWdEjey+UlWtm2Z7oMrNGKrKOsBgGwz/exIm4dG7r4sd//9W+3D7lsbMxYVeJbOtdpxMZNXMruWaDF4cFcf9lcLTtrtOJfLr+BL8dOmMNiDWv78q4PoGMDPHFxaHqeyuX7ork6UV7yTUbdG1ahy/u60od1/JrU4qIiIhcChVdR1wxmSVTp05l2bJlbNy4sUigJDMzk+eff56lS5cydOhQADp27Mju3bt55513KhwssbGxoVu3bhw7dqzE646Ojjg61vzOn+pmZ2Oq8O6n+LRsvvvzNN/8eYr4tPwFnqOdDSNDfBnbO5DWDd3L7J+UkcO8bRF8s/Xvc4gLNPZ0KvKao51NmQslgN8Pn2FbWAL2tiZeubldjR59BflnNU/+bidp2Xl0D/TmqcGta/T+pens78WHdwbz3JC2fPvnKb776xRRSZm8/vMh3v/tKLd18WNs7wCaVeIMaYDv/zrNiz/tx2wx6NuiHp/eG4KHU9UDLyIiIiJXo3/MOsLWpsLP31FJmflH9m6LIDkzv46Im6Mdo7r6M6Z3U5rWdS2z/9nULL798zTf/XmKc+k5Ra65O9lZjwGu5+ZYoTnN33aaE3HpuDvZ8dyQNjW+johLzebh73eRazYY0r4h4/sE1Oj9SxPcJD+IcepcOnO3nGLB9ghOxKXzwo/7+b/Vh7mrRxPu6xWAbyU2SxmGwce/H+e9X48CMLRDI94d1anGP3MRERGRqrjsgyWGYTB16lSWLl3K+vXrCQwsugMnNzeX3NxcbGyK7uqxtbXFYql4sT7DMNi9ezcdOnSolnlfrg5Ep7AgNML6819hCZw6l16kjQHsOJVYYn/fOs4cO5PGf5b+fU5wz2Z1aXLBUVR7IpNYvDOSrNzi/w1eubkdLy87UKl55+RZ+O+K/KLu4/sEElCv7AVWdTMMg5d+2s/h2FTquTkw/a7gSu0kqwkNPZ148obWPDywBT/uimL25nCOnEnl662n+HrrKQa0rs/1QQ2xq0AWzYHoZOZuPQXArSF+vDmyAw52l9f7FREREZGa892fp3As9Px7ODaVvZFJRdqk55g5FFNyTRa/Os7sjUziiQX5fVwd7RjU1qdIdkpWnpnQ8ERW748h11z8AIQXhwXx2oqDlZp3alYu7/xyBMgv6l7TR19l5ZqZ8PV2opIyCaznyv9u61juJrGa1rSuKy8ND+Lx61uycHskc7aEczohg5kbTvLlH2EMautDz2Z1cS0n08RiGKw/EsfqA/lHbz10bTOeubFNjdZlEREREbkYl32wZMqUKXz//ff89NNPuLu7Exub/+Dl6emJs7MzHh4e9OvXj6eeegpnZ2eaNm3Khg0b+Prrr3nvvfes49x33334+vry5ptvAvDKK6/Qs2dPWrZsSUpKCh999BG7d+/mk08+qZX3eakVFORee/gsaw+frfI4J+PSOUnR4Mr2UgIrAG0beTC+TwDHz6Yxc+NJOvp5Mm/baSD/jOMVe2MqdN+vt4ZzMj6dem4OPDywRZXnXxV5ZgvTlh9g4Y5IbEzw0V3Bl/WZu072ttzZvQl3dPNny4lzzN4cxtrDZ1l3JI51R+LKH6CQxwa15NHrWl52CzoRERERqRnuTvlLxsOxqTy9uHJF1As7HJta7LXC9Usu1KVpHcb1CWDB9kg2Ho3j+qAGfHG+jl5l1hHTfz9OfFoOzeq5cl+vgCrNvaqycs1MnbeL3RFJeLnYM2tst8s6U9vdyZ7xfQMZ0zuA3w+fZdamMLaePMeaA2dYc+BMhcexMcErt7RndM+ml3C2IiIiItXvsg+WzJgxA4D+/fsXeX327NmMHTsWgPnz5/Pcc89xzz33kJCQQNOmTXn99deZOHGitf3p06eLZJ8kJSXx4IMPEhsbi6enJ8HBwWzcuJHu3btf8vdUG+7s3oTYlCwyc8zW13ZFJJFwQVp7Yd6uDng62xMWn15qmwLXtfEp8rOniz2juvrTI9CbqKRMXvhxf/7rzvb8cSweT2d7HhvUqkKLnIT0HD5cm3882pODW+NegwuM9Ow8ps7bxe+Hz2Iy5WfF9G5er8bufzFMJhN9WtSjT4t6hMWn8+2fpwivwH9LABsbE//q7MvQjo0u8SxFRERE5HL2zI1taODhxKGYFGtdi/I2X/l6OZOdZ7Ee5VsaZ3tbejeva/3ZxsZEm4buDGrbgE7+Xuw4lcDGo3HY2phwtrclNiULvzrOPHBNswqtI8Lj05m1OQyA/wxtW6OZ0onpOUz4ejvbTyXiYGvDzHu7EFjD2fFVZWtj4vqgBlwf1ICD0SmsPhDL4ZgUzJbyy536eDhyWxc/ujT1roGZioiIiFSvyz5YUpH68w0bNmT27Nlltlm/fn2Rn99//33ef//9i5naFaWFjxvT7w4p8trO04mM/HQLTbxd2Pj0gGJ9toUl8OA32wFo4u3CnHHdqOvqyEPfbufPkwnY2ph4Y0R77ujWpFjfwt5adZjsPAtBjTw4EJ2flv/YoJbUcalY0OO9X4+QmpVHUCMPbu/qX6E+1eFsahbj54SyPyoFRzsbPrijM0M6XJnBg8B6rrw4LKi2pyEiIiIiV5i6bo48cUGtvtX7Y5n47Q66Nq3Dokm9i/X59eAZps7bCUBQIw9mj+sGwLjZoRyMScHJ3oaP7gxmcLuGpd7XMAz+tyr/+Kzezevyy8H8Ewaev6ktjhUMerzx8yFyzQbXtKzHwAs2d11Kp86lM3Z2KGHx6Xg42TFzdFd6NKtbfsfLUFBjD4Ial14EVUREROSf5LIPlkjtWL4nmicW7CHHbCG4iRdf3teVjBwzt362heNn03BztOPTe0K4tlX9MscJDU9gxd4YTCao42rPwZgUWvi4cW/PpqScL/ZYlsOxKXz/V/6xXS8ND8K2hs67PXYmlbGzQ4lKysTb1YEv7utKl6Z1auTeIiIiIiJXqrlbwnll+QEsBvRrVZ9P7gkhKjGTcbO3EZ2cRT03B74c043O/l5ljrNqfyzbwhNwtLMhM8dMVq6F7oHeDGnfsMQjvS605Xg8vxw8g62NiZeGBdXYsbI7TyfywNztJKTn4OvlzJxx3WjZwL1G7i0iIiIiF0fBEinCMAw+23CS/60+DMAN7Rrw4Z3BHDuTxrg5ocSnZdPQw4lZY7uVu8PIYjF4dXl+AcbO/l78dTIBgBeGtrXWUClvLq+tOIjFgCHtG9KzhnZjbT1xjge/2U5qVh6B9VyZPbZbjReUFxERERG5klgsBm/8fIgvN+Ufe3VXd39eu6U9f4UlMPGbHaRm59GsvitzxnanSV2XMsfKyjXzxs+HgPwM6e2nErExUeGgR57ZwqvnC8Hf26NJjQUrVu+P5dH5u8jOs9DB15OvxnbFx/3yrXUoIiIiIkUpWCJWeWYLLy87wHfnMznG9wnkP0Pbsv7IWR7+fheZuWbaNHRn9rhuNPJ0Lne8xTsj2ReVDMCu00kADGhdn/6tK5YC/+vBM2w+fg4HOxuev6lt1d5UJS3dFcnTi/aSazbo0rQOX9zXFW9Xhxq5t4iIiIjIlSgr18zjP+xm1f78o7KeuqE1k/s3Z/HOKJ5dvJc8i0H3AG8+v68LXi7lP1t/tSmMyMRM4O/C8A9e25z2vp4Vms8P2yM4HJtqrZNYE77aFMZ/Vx7EMPLrOX50VzCujlpui4iIiFxJ9PQmQH4h84e/38m6I3GYzu/aGtcnkG/+PMXLP+3HYsA1Levx6T0hFSqwnpadx/+tOVLktS5N6/DeqM4Vmk92npnXz+8me6BvIP7eZe8+u1iGYfDJuuO888tRAIZ2aMS7ozrhZG97Se8rIiIiInIlO5eWzYSvt7PzdBIOtja8fXtHbu7UmA/XHuOD344BMLxTY96+rWOFnq3PpmTxybrjRV4b2MaHx69vWaH5JGfm8u75Z/rHBrWkziXe+GS25GfDz9kSDsC9PZswbXg77CqQSS8iIiIilxcFS4T07Dzu+Hwr+6Pyiy1+eGcw17dtwJs/H2LmxpMA3N7FjzdGdqjQ8VkAn647TlxqtvXnmzo05L1RnSscfJi7JZxT5zKo7+7I5AEtKv+mKiHXbOHFH/czPzQCgIeubcYzN7bBpobqo4iIiIiIXIni0rK5dcYWws9l4OFkx+f3dSWkSR2eWrSXRTsiAZjUvzlPDW5d4Wfrt1YfJiPHbP35nh5NeOXmigcfPl57jIT0HGudxEspM8fMo/N38cvBMwA8N6QND17brMbqo4iIiIhI9VKwRDiXnsO59Bzqujrw5ZiutG3kwdT5u1i5NwaAJ65vxcMDW5T40G+xGGTlmYu8dupcBp+uP2H9+cFrm/FsOcGHjBwzBcMnZuTy8dr83WRP39AatzLS1w3DAKjygiQ1K5cp3+9i49E4bEzwys3tGN0roEpjiYiIiIhcTU6dywDAr05+IXMfDyfGzwll0/F4bEzw2r/ac0+PkgMWZotB9gXriL9OJrBkZ5T152eHtOGhMoIPmTl5ZOTkWX8+fjbNmuFR0TqJVRWTnMmkb3eyOyIJBzsb3hvViWEdG1+y+4mIiIjIpadgyVVs64lz1u+b1XNlzrjuuDvZce+Xf7H9VCL2tib+d2tHRob4ldh/d0QSU77bSVRSZqn3ePWWdtxXQvDhbGoWn60/af253ctrirXp4OvJraXcG+DUuXQe+mYHOWYLn93bhVaVKNyYkpXLgtAIZm8OJyopE2d7Wz6+K5hBQQ0qPIaIiIiIyNXot0NnrN939PPkyzFdyTMb3D5jK0fOpOLiYMsnd4cwoE3JtQo3HI3j3z/s5lx6Tqn3+PiuYIZ3Kh58iEjIsBZ/T88xE/RS8XVEeXUSD0Qn886aIzg72PKfoUH4epVfj7HAubRsvv/rNDM3niQtOw8vF3u+uK8r3QK8KzyGiIiIiFyeFCy5Si3eEcnbhWqKLJ7Um5SsXG6dsYWT8em4O9kx894u9G5Rr8T+vxyI5ZH5u8jKtZR6j6duaF0sULI/KplZm8NYvieaXLNRal9ne1tevaVdqdkoO08n8sDc7SScX2DdOmMLM0d3oXfzkudbICw+nblbwlm4PYL08+n9Pu6OfDmmKx39vMrsKyIiIiJyNTMMg682hVmP2AKY/2BPwuLTGT8nlDMp2dR3d2T22G6lFmP/IfQ0zy/dj9lS+lrgowsCJYZhEBqeyKxNYfxyMJYyutLJ34u3bu1Y6vX1R84y5bud1rXA9vBEZpUx3wKHYlKYvTmMH3dHk5OXvwYKbuLFe6M6E1jPtcy+IiIiInJlULDkKmMYBh+tPc77vx21vtbE24Xwc+k8MHc759Jz8PVyZva4bqVmaszeHMarKw5iGNC/df3ztUhsMFsMbp6+mbD4dO7q7s+U87VGzBaDXw/GMmtTONvCE0ocs4m3C6cTMqw/BzfxIikzF4vFKBYwWb0/hkfn7yY7z0J7Xw+c7GzZfiqRMbO28X+3dWREcNFsFMMw2HLiHLM2hfH7kbOcP7mLlj5ujOsTyIhgX5wdVMhdRERERKQ0FxYyB+jStA7bwhKswYdWDdyYPa57iZkahmHw7i9HmX6+ePvIYF+m3dIOOxsTWbkWrnt3PYkZuTw+qBU3nw+UZOeZWbEnhtlbwtgflVLivBp6OBGbkmX9uYG7I2Hx6fi4OxY7vmvettO88GN+oKZNQ3cyc82cOpfBqJlb+eSeEAZckI1ithj8fvgsszaFsfXk31n5Hf08ub9vIMM6NsZWdQ5FRERE/jFMRkHRB6mUlJQUPD09SU5OxsPDo7anUyG5ZgvPL9nHwvM7wfq1qs+Go3EAONnbkJWbH3yYNSb/vOELmS0Gr688xKzNYQDc3aMJrxYqtjh/22meXbIPdyc71j/ZHztbGxaERjBnS7j1qC47GxNDOzZiXJ9AopMymfzdTgCOvT6EnacSmbU5jF8PnrHuFmtW35VxvQMYGeKHq6MdX20K478r8wM1A9v48PFdwdjamHhi4R5rjZV/X9+KqQNbkJ1n4cddUczeHM6RM6nW9zGwjQ/j+gTQt0U9FV8UERERuYJcic/gF7oS30NGTh6PzNttPX6rW0AdQsMTAbC1MWG2GPRqVpfPRnfB09m+WP/sPDPPLNrLj7ujAXhkYAsev76V9Vn8o7XHeO/XozT2dGLtE/1Jy87ju79O8e2fp4lPywbA0c6GkSG+jO0dyOs/H2Lj+XXMyTduYsOxOGZtCuOPY/HWewY18mB830CGd2qEg60N7/xyhE/W5ddVHBnsy1u3diQrz8ykb3ew+fg5bG1MvHZLe+7u0YTUrFwWbo9k7tZwa10WWxsTN7ZvyPg+AYQ0qaN1hIiIiMgVpKLP4AqWVNGVtshJzcpl8nc7+eNYfrHFV25pT7vGHoz8dIu1zYDW9Zl+dwiuJRRUz8o189j83aw+EAvAMze2YWK/v4stpmblMuCd9cSn5TAi2BcPJzsW7ogk43x6ex0Xe+7p0ZR7ezaloWd+IObnfTHWYMnx14dYgy4RCRnM3RLOD6ERpGbnF2x0c7QjLfvv4o339GjCK4UCNRaLwf9WH2bmxvw6KH1b1ONAdDKJGbkAuDjYcnsXP8b0DqBZfbdq+lRFREREpCZdac/gJbnS3kNcajYPzA1lT2QyDnY2fHBHZ2xMMPHbndY2BcEHB7viBdWTM3J56Nvt/HkyATsbE2+M6MCobv7W67HJWQx4Zz2ZuWZGBPtiAlbsjSHHnH/UVQMPR+7rFcBd3Zvg7eoAwOiv/rIGRsLfGmod69iZVGZvCWfJzkjrccHuTnakZv29jnjkupY8PqildR2Tk2fhuSX7WLwzf0NZJ38vTpxNs649PJ3tubO7P/f1CqhUbRMRERERuXxU9Blcx3BdBWKSMxk3O5TDsak429sy/e5g+rf24dYZfwdKLgw+FHYuLZsHvt7OrtNJONja8PbtHbmls6/1ekpWLsM/3kR8Wn79kKW7oqzXWjdwZ3zfAG7p7IuTfdGjrmKTsyiJv7cLLwwL4rHrW7F4RyQz1p8okloPcEtn3yIp7zY2Jp67qS1+dZx5edkBNh3PXzz5ejkzrk8At3f1L3GXm4iIiIiIlOz42TTGzdlGREImdVzs+XJMV9r7etLplV+sbS4MPhQWkZDBuDmhHD+bhpujHZ/eE8K1repbryek59Dnf79b65cUXkd08vdifJ8AburQCPsL1iiFM0gKa9nAnTdGdOCpwa2ZF3qa6b8fLxIoARgc1KDIXB3sbHjn9o74ezvzwW/H2BORBEDz+q6M6xPIyBBfXBy0bBYRERG5Guip7x/uYHQK4+ZssxZbnDWmGy183Jj83Q52n18IAPz3X+1LXOCcjEtj3JxQTp3LwNPZns9Hd6F7oDfHzqSy7shZ1h2OK3J+L+RncfRrVZ/RPZvSq3ndEsc1jPxMkAIltXFztGNox0Ys2RlZLFgyauZWOvh6Mq5PAMM6NrbuYhvdKwB/bxeW7opiSPuGDGrboMQAkIiIiIiIlO6vk+d48JsdJGfm0rSuC3PGdcfbxYH7vtpmzdqA/CNwS7IvMplxc0KJT8umoYcTs8Z2o01Dd/ZGJrHucBzrj55l1+mkIn0aeDgyOKghI0J8CWlSp8RxyyoMX6COqwPDOzZmQWgE4ecyilwb9vEmugd6M75PANcHNcTW5v/Zu+/wqOq0jeP3THoPSUgIJAEChF5DkSJNRaxIU2wodinW1cW2q+u6dl8VBTuIhXWlCIggFnrvLUDoKYQklPQ+M+8fwQjSwpDJmUy+n+vyWpicnHMfV8nv8Tnn+ZlkMpn02JVx6tY4VDsOZ6t5vQD1bBJ2xt6JAAAAcG00S1zYksRMjflmo/KKy9Q03F9TRnWRt4ebbv109WmNkpgQ37M2K9YfPK77p67XiYJS1Q3w0oO9Y/Xj1jQ9+f0WpZwoPOs1v763m7o0riMv9/NvmL5od4aKy6znPWZfZp5GTV6npOPljZpPR3ZWkI+Hpqw8oJkbU7UtNVtP/G+LXp2/S3de1lC3dYtRmL+X+jYPV9+/bM4IAAAAoHLmbDmsv/1vi0osVnWMCdZnIzuroMSioR+t1N6MvIrjOjc8e0Pjt53pGvvtJhWWWlQ/yFujejbWZ8v3a2liZsXb6H81a3QPtY8KvmCDYvqG5Avm35qSpXumrNPRvBLVC/TW5FFdVFRq0eQVB/XTtjStPXBcaw8cV1QdH93VvZFu7lL+Fnr3JqHq3iT0gucHAACAa2LPEjs5+6zh79Yl6dlZ22Wx2nRZbIg+vqOzjuUX6+5Tmg+j+zbRq/N3KSbEV0uf7nfa98/bmqYx3248x9nLX1e/LDZU3u5mLUxIl5vZpAWPXq5mEQEXzFZSZtXAd5dq/9H8is/2/efa08ZqrTvZqMkqKFV0iI+mjOqqJqfsNXI8v0TT1iZp6qqDSs8prsg0qH19jerZWK3qO9//JwAAALg0zr4GrwxnvgebzaZJS/bpjQW7JUkDW9fTuyM6KDE9V/dMWV/xlsiIrtF699c96tywjqY/3OO0c3y16qBemL3jnNfw93JXr6ZhOlFQojUHjsvX002L/tZXEYHeF8x36j6Jfzh1zxJJ+jUhXeOmlTdqWkYGavLdXSr2TJTKRwF/tfqgvl2TdMb+hnf3bKzGYX4X/hsFAACAGoUN3h3MWYscm82mtxcm6oNFeyVJN3Wor9eHtdPUlYf0yk87K45rHOan7MJSHc8/+5NdZ1M/yFv9WoSrf4vwiieuhkxcqV1HcnVX94Z6aVCbSp3ns2X79e95O0/77NRmydwth/Xk91tUUmZV++hgfX5XZ4X5e531XKUWq37alqYvVhysmC8sSZfFhuipq1so/hxPuwEAAKDmcdY1+MVw1nsos1j1wuwdmrY2SZJ0b6/Gevbalnpr4W5NWryv4rhWkYFKSMu5qHPHRfir38m3v+Mb1tHhrELdMGG5covL9NTVzTWmX9NKnefV+Tv18ZL9p312arNk6qqDenHODlltUu+4uvrwto4K8D77voVFpRb9sClVX6w4oMT0P9+Wub5dpMZf00JRdXwv6h4BAADgvNjgvRYqKbPq7zO2VmyMOK5/Uz1xVZxGf7NR87cfOe3YA6e81XE+nRvW0ZWtItSvebjiIvwrxnVl5hbr3i/LN40P8vHQY1eefVbxXx3PL9F7v+2RJA3u2OC0TRxtNps+Xrpfr80v38vkqlYRen9ER/l4nnukl4ebWYM6NNCgDg20MemEvlh+QPO3H9Hq/cc1dNJKDe7YQOOvaVGpJ9UAAACA2iivuExjv92oxbszZTJJ/7i+lUb1bKwB/7fktEaCpEo3Sq5sGaF+Leqqb/NwNQj2qfg86ViB7pmyTrnFZercsI4e6B1bqfMdOpavycsPnvVrVqtNr87fqU+XHZAk3dI5Wv8e3OaMjeFP5e3hphFdY3RLl2it2HtMk1cc0G+7MvTj1jT9kpCuB/s00UN9YtncHQAAoBZh5ecisgtK9eDX67V6/3G5mU36z+A2urlztDr/+1cdO+XtkX9c30qJJzdn/2N81blMHtVF/c6y98fejFzdPXmdUk4Uqo6vhz67q7Pq+HlWKuc7v+xWblGZWkYGanh8VEWzpMxq1b/mJuibNeVPst3do5FeuL7VaaO5LqRTTB11uq38SbV3f03U/9anaNamVP2844jG9Guqe3s1lrfH+fdSAQAAAGqT9Jwi3TNlnXYczpG3h1nvjeioK1qEq9H4eacd9+y1LbT2wHEt2p15wU3Wf3uyz2kjdP+wOTlL905Zp2P5Jaof5K0Jt3U8b0PjVK/M26kSi1WXNwvTriO5yswtr2WKSi164n+b9dO28ofD/jYgTmP6NT3rnoxnYzKZ1KtZmHo1C9OOw9n619wErTlwXO//tkf/W5es8de00KAO9St9PgAAANRcjOGykzO9Pp9yokB3T16nvRl58vN008Q74nVZbIiaP7/gtOOuaxeplXuPVszm/UP7qCD1axGufs3D1bZBkPq+tVhJxws04+EeZ4yxWrP/mO6ful45RWVqGOqrKaO6Vnqu764jObr2vWWy2qT/PnCZJGnEJ6slSX3i6mpJYvmTbM9f10r39mps79+OCluSs/TS3B3amJQlqXwj++eua6kBrSIodgAAAGogZ1qD28uZ7mH3kVyNmrxWh7OLFOrnqc/v7qLGYX5q/9LC047rHhuq9YeOq9TyZ+no7WFWjyZh6tu8rvrGhSsm1FctXpivolKrlj3dT9Ehp4+xWrjjiB757yYVlVrVun6gvri7S6Xf/l6596hu+2xNxT6Jt3+2RhknmyXxDetow6ET8nAz6Y1h7TS4Y9Ql/T2x2Wyav/2IXpm3U6lZhZKkTjHB+ucNrdU+OviSzg0AAABjMIarltiUdEK3fbpGhaUWeXuY9dGd8aof7HNGo0Qq37RdkgK83dU7rq76Nw/X5c3CFOT75xzfUqtVNp29fzZ7c6qe+n6rSixWdYwJ1mcjOyv0HHuJ/JXNZtPLPybIapOuaVNPl8WGavX+YxVfX5KYKS93s94b0UED20RezN+Cc2ofHawZD/fQD5tT9dr8XUo6XqAHv9qgy5uF6R/Xt6rUZvQAAACAK1q+56ju+HyNJCnEz1Of3dVZuUWlZzRKJGnVyXV7w1Dfk3uP1FXXxiGnvQVeXGY5rZlyqikrDuilHxNks0l9m9fVB7d1kr9X5UpRi9Wmf/2YIEm6o1vMGWv4DYdOKNDbXR/f2bliX8VLYTKZdG3bSPVvEa7Plu3XxMX7tDEpS4M+XKGhnaL094HNFc6IXwAAAJdEs6QGW7QrQ6OmrKv4fVGpVXd+vvasxzaPCDj59khdxTesI3c3sxLTc9X1P79d8Do2m02TluzTGwt2S5IGtq6nd0d0uKiRViv2HtOKvcfk6WbWs9e2lFS+x8ofgnw8NHlUF3WKqdoN2U0mkwZ3jNKAVvX04aK9+mzZAS3bc1QD31umOy9rqMevjDutWQQAAAC4uoTDORWNEql8X8HBE1eecZynu1mXxYaqb1xd9WsRXvFG+aakE2r1j5/Pef4/mihWq02v/LRTny8v30vk1q4xenlQa7lXcvSWJK3Ye1S7juQq0Nu9Yp/EghJLxdcbBPtoyqguVf4glLeHm8b2b6Zh8dF6Y8EuzdyUqhkbU7Rge5pGM+IXAADAJdEsqcFKLdYLHvPK4DZnbKr4h/dPbrR+NhGBXmoa7q8yi1UvzN6haWvL9xK5t1djPXtty4vaS0SSdqfnSpKuaBle8Ur+7M2HK74+e0xPNarkOC97+Hm56+mBLXRLl2i9Mm+nFiaka8rKg5qz5bCeHBCnEV1iLvqeAAAAgJrIeoFJzKF+nnpjWDt1bxJ61g3O7/1y/Tm/9+rWEYoM8lZRqUWP/XezFuwo30vk6YHN9XCfJhc9DnfdweMnz1uvYp/EyCBv7cko33h+1ugeDn3To16Qt965pYPu7N5QL81N0ObkLL358279d12Snru2pa5uXY8RvwAAAC6CZkkNNqB1PW17cYCOZBed8bWoOr7y8az8k07bXhxw2u99PNxUVGbVfVPXa/Hu8r1E/nl9K93d89L2EvF0L3+KLPl4gWZsTKn4vGGo77m+pUo1DPXTJyM7a/meo3pp7g7tycjTc7O265vVSfrnDa3ULfbSX90HAAAAnFmbBkHa9fJAJR8vOO1zdzezGoX6XvA//heX/vlmx6l1hMlkkr+Xu47lFev+qeu1MSlLnm5mvTm8nQZ1aGBX1pKTD4gFn3wbfFtKdkWjRFK1jcTqGFNHMx/uodlbykf8Jh8v1ENfb1T32FD944ZWahlZM/fQAQAAwJ9oltRwAd4eCvC+9DFSfz1Hek6R7pmyTjsO58jbw6z3R3TUgNb1Lvk6f/jPTzur7Fz26NUsTD89erm+Xn1I//dLohLScnTLJ6t1XbtIPXtty7O+iQMAAAC4Cm8PtyoZXfXXOuLA0XzdPXmtDh0rUJCPhz65M77KHkiy2Wx6ae6Oit+HVXL/xKpiNv854vejJfv0ydL9WrX/mK57f5lGdI3Rk1fFVXpPRwAAADgfmiWQJP2wKbXi1yUWq979JVGHs4sU5u+pz+7qog7RwVV2rdX7j2n+9iNVdj57ebiZNapnY93Yvr7e+SVR09Ymad7WNP22M10P9Wmih/s2kZc7c4gBAACAczm1jsjMLdbExXt1oqBUUXV8NGVUVzUN96+ya/24NU3rD52osvPZy8/LXU8OaK6bO0frtfm7NG9bmr5dk6S5Ww7rmWta6rZuMUZHBAAAgB1oltRiHqdsrPjYd5vP+HpsXT9NuburYqpwRJbFatO/5iZIkq5pU88pmiah/l56ZXBb3dYtRi/NTdDaA8f17q97tGLvUX1yZ+eK2cgAAAAApGBfT+WXFEo6ex3RPipIn93VRXUDqu4ti6JSq16bv0uS89QR0SG++vD2Thq5/5hempughLQcPTtrmxLTc/XC9a3YExEAAKCGMV/4ELiqVqfM1b28Wdhpf93aNUYzH+5RpY0SqfxpsIS0HAV4u+uJq+Kq9NyXqnX9IH33wGWacGtHBXi5a93BExo6aaWSjhVc+JsBAACAWqJr45CKX/+1jhh/TQt992D3Km2USNJXqw8pNatQ9YO8dX/v2Co996XqFhuqueN66amrm0uSpqw8qAemrld+cZnByQAAAHAxeLOkFjOf8qTTV/d2q9ZrP3pFM4U44RsbJpNJN7Svr7iIAI2avFb7j+Zr8MQV+vzuqh1FBgAAANRUp74vUd11xPhrW8rHw/lG5bqZTRrTr6kah/np8e8267ddGbr541X6/K4uqhdUPZvQAwAA4NLwZgmqXWyYn0Z2b2R0jPNqXi9As8b0VOv6gTqWX6IRn6zSwh3Gv+oPAAAA1FadG9bRDe0ijY5xXte2jdS0By5TqJ+ndhzO0U0frlDC4RyjYwEAAKASeLOkFrPZbBW/vn/qervP07JegG6/rKEiAs/+xJTVajut0fDcdS3l6e78fbqIQG9992B3jf12oxbvztSDX2/QP65vpVE9GxsdDQAAADCM7ZRf21tH+Hu5q0N0sG5oX/+cb5yXWqz6YvmBit//44ZWMpmcfx+QTjF19MOYnrp78lrty8zX8I9W6oPbOqlfi3CjowEAAOA8aJbUYqc+4fRLQrrd5/klIV0TF+/Tde0iNapn44pxVXnFZfp+fbKmrDyoQ6fs+9G/BhUJ/l7u+mxkZ70we4emrU3SS3MTlHKiUM9d2/K0MWYAAABAbbHh0ImKX19KHTFrU6r+89NODenUQKN6NlZcRIAk6UR+ib5dm6SvVh1SqeXP1ky7qGC7r1XdokN8NfPhnnr4mw1aue+Y7v1ynV66sbXudPI37AEAAGozmiW1WInFWvHr/wxua9c5Si1WzduaprUHj2v25sOavfmwwvy95GaWcgrLVFhqOe34fs3r1oinwU7l7mbWfwa3UXSIj95YsFufLz+g1BOFendEB3k74bxkAAAAwJGO5RVX/NreOuJoXrEWJhzR9tQcTVubrGlrkxUe4CWTSTpRUKqSMutpx9/Xq+a93R3k66Epo7rquVnb9P2GFL0we4cOHivQs9e2lBsPXgEAADgdmiWQJN3WLcbu772rRyNtT83Wg19tUGpWoY6eUjxJ0lNXN5fNZtNbCxMV6ONxqVENYTKZNLpvUzUI9tFT32/Vgh1HdOunq/XZyM4K9fcyOh4AAABgiEupI8b1b6q1B45r5BdrVVxmVUbu6XXEy4Naa//RfE1ecVA17HmrCp7uZr0xrJ0ahfnpzZ/LH7xKOl6g90Z0kK8n5TgAAIAzYXWGsyous8hmO/0zk0ly+0uVUmqxae7Ww/pi+QGlZhWe9VwTft+jotLyJ8PKrDaVnXyjxWK1nfV4ZzaoQwPVC/TWA19t0KakLA2ZtFJTRnVV4zA/o6MBAAAAhiv6y5vlkuRmNumvvY6M3GL9kpCuKSsPqvgvb5H84Y0Fu5VbXCapvO6oqXWEyWTSmH5NFRPiqye/36JfEtJ1y8er9fldnRV+jn0fAQAAUP1oluAMjcbPs+v7fDzcNDS+ge7u0VhRdXw0d8thTV5xUAlpf+6NMm9rmuZtTauqqIboFhuqGQ/30N2T1+rQsQIN/2iVlj7dlyfDAAAAUKvZW0cEeLtrRJdojezeSCF+npq5MUWTVxzU/qP5FcdMWXlQU1YerKKkxrihfX3VD/bW/VM3aFtqtm79dLUWPt6HkVwAAABOwmx0ABjnqlYRVXKe+kHeeuaaFlr9zBX6901t1TTcX94ebhreOVrzHuml7x647Lzf37VxSJXkqE5Nw/01a3RPSeXzlvek5xmcCAAAAKgeQ+OjLvkcbmaT2jYI0r8GtdbqZ67Qc9e1UnSIr/y83HVn90b69Yk+mnx3l/Oe47LYmldHxDcM0azRPSRJ+zLzlZFbZHAiAAAA/IFH4Wux6BBfSVLMyf/9w8HXrlPKiQJZT3kb/sNFe/Xd+mQN6lBf/7qxzWnHB3i7y3yOp6FMJpO6xYbqhetb6eUfE9S3eV29d0vH044J9HGvcZu+S1LdAC9tfXGAbFbJz4uN3gEAAFA79GgSpqmrDqlzwzqnfX7g1WuVfPz00bx/n7FVq/Yf0/2XN9bYfs0qPvfyMMvb49xraLPZpH4twvVgn1h9vGS/bu4cpeeubfXnASYp0LtmlrMNQ/205R8DJJXXUgAAAHAOrMxwVlF1Tm+gRASWb2Ie5OOhIF/7N2m/1O93NoHernMvAAAAwKUwmUyKCT29jmgY6qtV+49RR/yFK90LAACAq2AMFwAAAADgolltF95ovRKHVEpVnQcAAAA4F5oltZiHufz//vScIq3ad+ycx2UVlGhJYqYkyd1s3z8yHm7lY7Y2Jp1Q8vECu84BAAAAwHih/p6SpIS0HG1PzT7ncWnZhVqx76gknXfk1vnUC/SWJC3YcURH84rtOgcAAABQGTRLarFW9QPVtVGIisusGvnFGv2wKfWMY5KOFWjIpJXakpKtAC93De9s32aOA1vXU71AbyUfL9TgiSu0NSXrEtMDAAAAMEJ8TB21jwpSUalVt3y8Sot2Z5xxTMLhHA3+cKVSThQqPMBLgzo0sOtat3SJVoNgH6WcKNSQiSu1PzPvUuMDAAAAZ0WzpBZzM5s09d6uurZtPZVabHrsu836cNFe2U6+4745OUuDJ67Q/sx81Q/y1vSHe6hlZKBd1woP9NYPY3qqZWSgjuaV6JaPV+vXhPSqvB0AAAAA1cBsNmnqvd3Uo0mo8kssuu/L9fp2TVLF15cmZurmj1fpSE6RmoX7a+boHqob4GXXtXw93fXVvV0VE+KrpOPlD3KtO3i8qm4FAAAAqGCy2Zj+ao+cnBwFBQUpOztbgYH2NRCchdVq02sLdumTpfslSSO6RKtPXF09/r/NKiq1qnX9QH1xdxdFnHwF/lLkFpVqzLebtDQxU2aT9OKNrTWye6NLPi8AAABcnyuswV3hHv5QUmbV+JlbNXNj+Rvqo/s2UcNQXz07a7ssVpsuiw3Rx3d2VpDPpW9mfjSvWPd9uV6bk7Pk6W7WOze31/Xt6l/yeQEAAOD6KrsGp1liJ1cqcv7w1aqD+uecHbKe8k9E3+Z19cFtneTv5V5l1ym1WPX8rO36bn2yJOmB3rEaP7CFzGZTlV0DAAAArscV1uCucA+nstlsevfXPXrvtz2nfT64YwO9NrStvNzt26vkbApLLHr0v5u08OQb6uOvaaEHe8fKZKKOAAAAwLlVdg3OGC5UuLN7I31yZ2f5nNx88bZuMfpsZOcqbZRIkoebWa8Nbau/DYiTJH2ydL/GTdukolJLlV4HAAAAgGOZTCY9flWc3hzWTp7uZplM0rj+TfXOze2rtFEiST6ebpp0R7xG9WwkSXpt/i69MHu7yizWKr0OAAAAaifeLLGTqz0RdqqDR/OVdLxAlzcLc/hTWrM2pejp6VtVarEpvmEdfTqys0L8PB16TQAAANRMrrAGd4V7OJfswlKVWawK9bdvf5KL8cXyA3p5XoJsNql/i3BNuLWj/Kr4IS8AAAC4Bt4sgd0ahfmpd1zdanmdfXDHKE29p5sCvd214dAJDZ20UoeO5Tv8ugAAAACqVpCPR7U0SiTpnl6NNen2eHm5m/X7rgzd8skqZeQWVcu1AQAA4JpolsBw3ZuEasbDPdQg2EcHjuZr8MSV2ph0wuhYAAAAAJzYwDb1NO2ByxTi56ntqTka/OFK7UnPNToWAAAAaiiaJXAKzSICNGtMD7VtEKTj+SW69ZPVWrA9zehYAAAAAJxYp5g6mjW6hxqH+Sk1q1BDJq3Uqn3HjI4FAACAGohmCZxGeIC3/vvAZbqiRbiKy6x6+JuN+nz5AaNjAQAAAHBiDUP9NPPhHurcsI5yi8o08os1mrUpxehYAAAAqGFolsCp+Hm56+M743XHZTGy2aSXf0zQi3N2yGK1GR0NAAAAgJOq4+epr+/rpuvaRqrUYtPj323RhN/2yGajjgAAAEDl0CyB03F3M+vlQW30zDUtJElTVh7Uw19vUGGJxeBkAAAAAJyVt4ebJtzaUQ/2jpUkvf1LosbP2KZSi9XgZAAAAKgJaJbAKZlMJj3Yp4k+uK2jPN3NWpiQrhGfrtbRvGKjowEAAABwUmazSc9c21IvD2ots0n6bn2y7v1yvXKLSo2OBgAAACdHswRO7fp29fXNfd0U7OuhLclZGjJxpfZl5hkdCwAAAIATu7N7I306srN8PNy0NDFTwz9apSPZRUbHAgAAgBOjWQKn16VRiGY+3EMxIb5KOl6goZNWau2B40bHAgAAAODErmgZoe8evExh/l7adSRXgyeu0M60HKNjAQAAwEnRLEGNEFvXXzNH91CH6GBlFZTqjs/WaO6Ww0bHAgAAAODE2kUFa9boHmoa7q+07CIN/2iVlu3JNDoWAAAAnBDNEtQYYf5emnb/Zbq6dYRKLFaNm7ZJkxbvk81mMzoaAAAAACcVHeKrGQ/10GWxIcorLtOoyev0v/XJRscCAACAk6FZghrFx9NNE2+P1z09G0uSXl+wS8//sF1lFqvByQAAAAA4qyBfD315T1fd1KG+yqw2PT19q975JZEHrwAAAFCBZglqHDezSf+4oZX+cX0rmUzSN2uS9MBXG5RfXGZ0NAAAAABOysvdTf93SweN7ddUkvT+b3v05PdbVFLGg1cAAACgWYIa7J5ejfXRHfHy9jDr910ZuuWTVcrIKTI6FgAAAAAnZTKZ9Lerm+u1IW3lZjZp5sZU3T15rbILS42OBgAAAIPRLEGNdnXrepp2/2UK9fPU9tQcDZ64UonpuUbHAgAAAODERnSN0Rd3d5Gfp5tW7jum4R+tVGpWodGxAAAAYCCaJajxOsbU0czRPRQb5qfUrEINnbRSK/cdNToWAAAAACfWJ66u/vdQd0UEeikxPU83fbhC21OzjY4FAAAAg9AsgUtoGOqnGQ/3UJdGdZRbVKa7vlirWZtSjI4FAAAAwIm1rh+kWaN7qkW9AGXmFuvmj1dp0a4Mo2MBAADAADRL4DLq+Hnqq3u76bp2kSq12PT4d1s04bc9stlsRkcDAAAA4KTqB/vofw91V6+mYSoosei+qev1zZpDRscCAABANaNZApfi7eGmCSM66sE+sZKkt39J1PgZ21RqsRqcDAAAAICzCvT20ORRXTQsPkoWq03Pzdqu1xfsktXKg1cAAAC1Bc0SuByz2aRnrmmpf9/URmaT9N36ZN0zZZ1yi0qNjgYAAADASXm4mfXmsHZ64qo4SdKkxfv06HebVVRqMTgZAAAAqgPNErisOy5rqM/u6ixfTzct23NUwz9apdSsQqNjAQAAAHBSJpNJj1zRTG8Pby93s0lztxzW9ROWa0tyltHRAAAA4GA0S+DS+reI0HcPdFfdAC/tOpKrK95erNfm71JWQYnR0QAAAAA4qaHxUZp6T1fVDfDS3ow8DZm0Um/+vEt5xWVGRwMAAICD0CyBy2sbFaRZo3uoc8M6Kiq16qMl+3T564s04bc9FDsAAAAAzqpH0zAtfKy3bmhfXxarTR8u2qc+byzS58sPMJoLAADABZlsNhs71tkhJydHQUFBys7OVmBgoNFxUAk2m02LdmfozZ8TtTMtR5IU6uep0f2a6vZuMfL2cDM4IQAAAM7HFdbgrnAPtdGC7Wl6bf4uHTxWIEmKDPLWo1c007D4KLm78QwiAACAM6vsGpxmiZ0ocmouq9WmedvS9M4viTpwNF/Sn8XO0PgoeVDsAAAAOCVXWIO7wj3UVqUWq6ZvSNF7v+7RkZwiSVLjMD89flWcrm8bKbPZZHBCAAAAnA3NEgejyKn5yixWzdhYXuwczqbYAQAAcHausAZ3hXuo7YpKLfp69SFNXLxPx/PL90JsUS9AT13dXP1bhMtkoo4AAABwJjRLHIwix3UUlVr07Zokfbhor45R7AAAADgtV1iDu8I9oFxecZm+WH5Any7dr9yTeyF2ignWU1e3UPcmoQanAwAAwB9oljgYRY7ryS8u0+QVB/Tx0v3KLaLYAQAAcDausAZ3hXvA6U7kl+ijpfv05cqDKiq1SpIubxamvw1orvbRwcaGAwAAAM0SR6PIcV1ZBSX6eOl+TV5xoKLY6dU0TH+7urk6UOwAAAAYxhXW4K5wDzi7jJwifbBor6atTVKppbzMvrp1hJ4c0FxxEQEGpwMAAKi9aJY4GEWO68vIKdKHi/bq21OKnQGtyoud5vUodgAAAKqbK6zBXeEecH7Jxwv07q97NGtTiqw2yWSSburQQI9d2UwNQ/2MjgcAAFDr0CxxMIqc2iP5eIHe+22PZm6k2AEAADCSK6zBXeEeUDl70nP1zi+Jmr/9iCTJ3WzSLV2iNa5/M9UL8jY4HQAAQO1Bs8TBKHJqn70Z5cXOT9sodgAAAIzgCmtwV7gHXJytKVl6a2GiliZmSpK83M26q0cjPdSniUL8PA1OBwAA4PpoljgYRU7ttS0lW28t3K0lFDsAAADVyhXW4K5wD7DP6v3H9NbPu7X+0AlJkr+Xu+67vLHu7dVYAd4eBqcDAABwXTRLHIwiB2v2H9NbC3dr3UGKHQAAgOrgCmtwV7gH2M9ms2nx7ky9+fNuJaTlSJLq+HpodN+murN7Q3l7uBmcEAAAwPXQLHEwihxIJ4udxEy99fNu7Tj8Z7HzcN8murVrDE0TAACAKuQKa3BXuAdcOqvVpvnbj+jtX3Zrf2a+JCnM31O3dWuoOy6LUXgAY34BAACqCs0SB6PIwanOVux4e5h1TZtIDe0UpR5NQmU2mwxOCQAAULO5whrcFe4BVafMYtXMTal679c9Ss0qlCR5uJl0fbv6ur1bjOIb1pHJRB0BAABwKWiWOBhFDs6mzGLVrE2p+mjJPu072TSRpPpB3hrSKUpD46PUOMzPwIQAAAA1lyuswV3hHlD1Si1WLdh+RJNXHNDGpKyKzxuH+WlopwYa3ClKDYJ9jAsIAABQg9EscTCKHJyPzWbTlpRsTd+QrDmbDyunqKzia/EN62hYfJSuaxepQMZ0AQAAVJorrMFd4R7gWFuSszR11SHN356mghKLJMlkkno0CdWw+Chd3bqefD3dDU4JAABQc9AscTCKHFRWUalFv+3M0PQNyVqSmCnryX/jvNzNurp1PQ2Lj1LPpmFyY0wXAADAebnCGtwV7gHVI7+4TPO3H9H0Dclavf94xed+nm66rl2khsVHq0sjxnQBAABcCM0SB6PIgT0ycor0w+ZUTd+QosT0vIrP6wV6a3CnBhraKUpNw/0NTAgAAOC8XGEN7gr3gOqXfLxAMzemasbGFCUdL6j4PCbEV0M7RWlIpwaKDvE1MCEAAIDzolniYBQ5uBQ2m03bUrM1Y0OKZm85rKyC0oqvdYwJ1tBOUbqhXX0F+TKmCwAA4A+usAZ3hXuAcWw2m9YdPKHpG5I1b2ua8k+O6ZKk7rGhGhofpWva1JOfF2O6AAAA/kCzxMEoclBVisss+n1nhmZsTNGi3ZmynJzT5elu1oBWERoaH6XLm4bJ3c1scFIAAABjucIa3BXuAc6hoKRMP+84oukbUrRy3zH9Udn7errp2raRGhYfpa6NQmRm3C8AAKjlaJY4GEUOHCEzt1izT47p2nUkt+Lz8AAvDe7YQEPjoxQXEWBgQgAAAOO4whrcFe4Bzic1q1CzNqZo+oYUHTz255iuqDo+GtopSkM7RSkmlDFdAACgdqJZ4mAUOXAkm82mHYdzNH1DimZvTtWJU8Z0tY8K0tD4KN3Yvr6CfT0NTAkAAFC9XGEN7gr3AOdls9m04dAJTd+Qoh+3pimvuKzia10bh2hYfJSubRspf8Z0AQCAWoRmiYNR5KC6lJRZtWh3hqZvSNGiXRkq+2NMl5tZV7YK19BOUeoTV5cxXQAAwOW5whrcFe4BNUNhiUULE8rHdC3fe7RiTJePh5uuaVNPw+KjdFlsKGO6AACAy6NZ4mAUOTDC0bxizdl8WNM3pCghLafi8zB/Lw3uWF9D46PUoh7/PAIAANfkCmtwV7gH1DyHswo1a1OqZmxI0f6j+RWfNwj20ZBODTS0U5QahfkZmBAAAMBxaJY4GEUOjJZwOEczNqboh02pOpZfUvF5mwaBGtYpSjd2aKAQP8Z0AQAA1+EKa3BXuAfUXDabTZuSszR9Q4rmbjms3KI/x3R1aVRHQztF6bp2kQrw9jAwJQAAQNWiWeJgFDlwFqUWqxbvztT0Dcn6fVeGSi3l/0p7uJnUv0W4hsVHq2/zuvJgTBcAAKjhXGEN7gr3ANdQVGrRLwnpmr4hRcv2ZOrktF95e5g1sHU9DY2PUvfYUMb9AgCAGo9miYNR5MAZHc8v0ZzNqZq+MUXbU/8c0xXq56lBHRqoe5NQGVnrBHh7qHX9QPl6sqEkAAC4eK6wBneFe4DrOZJdVD6ma2OK9mbkVXzu5W5W6/qBahcVrHZRQQr2rd43Tjzd3FQ/2Fv1g33k7eFWrdcGAACug2aJg1HkwNntOpKjGRtSNGvTYR3NKzY6TgU3s0nNIwLUMSZYHWPqqGNMsBqH+rGxJAAAuCBXWIO7wj3AddlsNm1Jydb0Dcn6cWuasgpKjY5UoW6Al2JCfNW2QVB5LRFdR9EhPjKZqCMAAMD50SxxMIoc1BSlFquWJmZq5qZUpRwvMDRLek6xjuQUnfF5oLe7OsTUUcfoYHWMCVaH6GAF+7LfCgAAOJ0rrMFd4R5QO1itNh04lq9tKdnakpKlHYdzVFxqqdYMhaUWpZwoVEHJ2a8b6udZ8RBWh+jyt1/YbwUAAPwVzRIHo8gB7JOWXajNSVnalJylTUkntC01W0Wl1jOOiw3zU4c/3j6JDlaLegHMSwYAoJZzhTW4K9wDUJ1sNpuyCkqVcqJQ+4/madPJWiLhcHbFfo1/MJmkuPA/3mIPVofoOmoa7i833mIHAKBWo1niYBQ5QNUotVi1+0iuNiWdqCh8DhzNP+M4Hw+3P1+5P9lEiQj0NiAxAAAwiiuswV3hHgBnUFRqUUJaTnkNcbKWSM0qPOM4fy93tY8OUsfoOhVvsYf6exmQGAAAGIVmiYNR5ACOcyK/RJtTsioKn83JWcotKjvjuPpB3hWv3HeMCVabBkFs/AgAgAtzhTW4K9wD4KwycotOe4t9a0r2WUd4xYT4ntz3pPwhrJaRgfJ05y12AABcFc0SB6PIAaqP1Wo77ZX7TUlZ2n0kR9a//OnlbjapVf1AdYwOLh/hFV1HDUN92fQRAAAX4QprcFe4B6CmKLNYtScj78+3T5KztDcj74zjPN3NatsgqOIhrI4xdVQ/yJs6AgAAF0GzxMEocgBj5ReXaWtKtjYln9DmpCxtTMrS0bziM46r4+tRse9Jh5hgtY8OViCbPgIAUCO5whrcFe4BqMmyC0u1JTlLm5P/bKBkFZSecVx4gFfFvicdY8o3j/f1dDcgMQAAuFQ0SxyMIgdwLjabTalZhSefGsvS5uQT2p6aoxLL6ZvHm0xS07r+FU+MdYgOVlxEAJs+AgBQA7jCGtwV7gFwJTabTQePFVSM/92UlKWdaTkq+8tr7G5mk5pHBFTse9Ixpo5iw/xkpo4AAMDp0SxxMIocwPkVl1m0M+3UzeNPKPn4mZs++nm6qV1U8GkNlLoBbPoIAICzcYU1uCvcA+DqCkss2n44+7QGSlp20RnHBXq7q8Mpb7F3jA5WsK+nAYkBAMD50CxxMIocoGY6mld8ctPH8gbKluQs5Z9l08eoOj4V47s6xgSrVf1AebmzeTwAAEZyhTW4K9wDUBulZReetnn8ttRsFZVazzguNsyvvHFyspZoUS9A7m5sHg8AgJFoljgYRQ7gGixWm/Zk5JYXPiebKHsy8vTXPxk93czlm8efUvhE1fFh00cAAKqRK6zBXeEeAEilFqt2H/nzLfbNyVnafzT/jOO8Pcxq1+CPt9jLa4mIQG8DEgMAUHvRLHEwihzAdeUUlWprcrY2J/8xvitLx/NLzjguzN/r5Lzi4JObPgbL34tNHwEAcBRXWIO7wj0AOLsT+SXanHLyIayTI7xyi8rOOK5+kPfJsV3lm8e3aRAkbw/eYgcAwFFoljgYRQ5Qe9hsNiUdLzit6Nlx+MxNH80mKe7kpo9/FD5N6vqz6SMAAFXEFdbgrnAPACrHarVp/9H88rdPTu59svtIjv5SRsjdbFKr+oF/PogVXUcNQ315ix0AgCpCs8TBKHKA2q2o1KIdh7NPNlDKX7tPzTpz8/gAL3d1iAmuKHw6RNdRiB+bPgIAYA9XWIO7wj0AsF9+cZm2pWZXPIi1KTlLmbnFZxxXx9ejYvxvh5hgtY8OVqC3hwGJAQCo+WiWOBhFDoC/Ss8pqtj3ZFNSlramZJ1108dGob7lhc/JJkrLyEB5sOkjAAAX5AprcFe4BwBVx2azKTWrUJuT/xzftT01RyWW0+sIk0lqWte/Yt+TDtHBiosIkBtvsQMAcEE0SxyMIgfAhZRZrNqdnlvx9smm5BPan3nmpo9e7ma1bRB0WuETGeTNa/cAAPyFK6zBXeEeADhWcZlFO9NytfmU8V1JxwvOOM7P003tooJPqyPqBngZkBgAAOdGs8TBKHIA2CO7oPTkpo8nKsZ3ZReWnnFcRKBXxb4nHWPqqG2DIPl4sukjAKB2c4U1uCvcA4DqdzSvWJtPPoC1OTlLW5KzlVd85ubxUXV8KsZ3dYwJVqv6gfJyp44AANRuNEscjCIHQFWwWm06cCy/ovDZlJSlXUdyZfnLro9uZpNa1Dt98/jGYX68fQIAqFVcYQ3uCvcAwHgWq017M/JOewgrMSNXf/0vPJ5uZrWqH1jxEFbnhnVUP9jHmNAAABiEZomDUeQAcJSCkjJtS8nWpuQsbU7K0sakE8o4y6aPQT4e6hAdrKtaRWhYfJS8PXhiDADg2lxhDe4K9wDAOeUWlWprSnZFA2VTcpaO55eccVzHmGDd1KGBrm8XqVB/xnYBAFwfzRIHo8gBUF1sNpvSsosqNnzcnJylranZKin7c9PH8AAvPdA7Vrd1i5Gvp7uBaQEAcBxXWIO7wj0AqBlsNpuSjxdWvMG+KemEtqVm64+X2N3MJl3eLEyDOzbQwDb1GNcFAHBZNEscjCIHgJFKyqzadSRHq/Yd05crD+pwdpEkKcTPU/f2aqw7uzdUoLeHwSkBAKharrAGd4V7AFBzZeQUae7WNM3enKqtKdkVn9cL9NaDfWI1oksMeyUCAFwOzRIHo8gB4CxKyqyatSlFHy7ap6TjBZKkAG93jerRSKN6NlYdP0+DEwIAUDVcYQ3uCvcAwDXsy8zT7M2H9d26JKXnlI/9DfP31H2Xx+qOyxrK34s31gEAroFmiYNR5ABwNmUWq+ZuPawPF+3T3ow8SZKfp5vu6N5Q9/WKVd0A5hEDAGo2V1iDu8I9AHAtxWUWTd+QokmL9ynlRKGk8v0R7+nZWHf3aKQgX95YBwDUbDRLHIwiB4Czslpt+nnHEU34fa8S0nIkSV7uZt3aNUYP9olVZJCPwQkBALCPK6zBXeEeALimUotVszcf1sRFe7X/aL4kyd/LXSO7N9S9vRqzGTwAoMaiWeJgFDkAnJ3NZtPvuzI04fe92pycJUnycDNpWHy0Hu7TRDGhvsYGBADgIrnCGtwV7gGAa7NYbfppW5o++H2vdqfnSpJ8PNx0W7cYPdA7VhGB3gYnBADg4tAscTCKHAA1hc1m08p9x/T+b3u05sBxSZKb2aRBHeprdN+mahrub3BCAAAqxxXW4K5wDwBqB6vVpl93puuDRXsrNoP3dDfrls7RerBPrKLq8PAVAKBmoFniYBQ5AGqidQeP64Pf92pJYqYkyWSSrm0bqbH9mqplJH+WAQCcmyuswV3hHgDULjabTUv3HNWE3/Zo/aETkiR3s0lDOjXQw32bqnGYn8EJAQA4P5olDkaRA6Am25qSpQm/79UvCekVn13ZMkJj+zdVh+hg44IBAHAerrAGd4V7AFA72Ww2rTlQ/vDV8r1HJUlmk3RD+/oa06+p4iICDE4IAMDZ0SxxMIocAK5g15Ecfbhon37celh//DS4vFmYxvVvpq6NQ4wNBwDAX7jCGtwV7gEANiad0Ie/79VvuzIqPhvYup7G9m+qNg2CDEwGAMCZaJY4GEUOAFeyLzNPExft0w+bU2Wxlv9Y6No4ROP6N1WvpmEymUwGJwQAwDXW4K5wDwDwh+2p2fpw0V7N336k4rN+zetqbP9mim9Yx8BkAAD8iWaJg1HkAHBFyccLNGnJPk1fn6ISi1WS1D46WOP6NdUVLcNpmgAADOUKa3BXuAcA+KvE9FxNXLRXc7Yc1slnr9SjSajG9m+q7rGh1BEAAEPRLHEwihwAruxIdpE+XrpP09Ymqai0vGnSMjJQY/s11cA29eRmptgBAFQ/V1iDu8I9AMC5HDyar0mL92nGxhSVneyaxDeso7H9m6pvXF2aJgAAQ9AscTCKHAC1QWZusT5ffkBfrTqo/BKLJKlJXT+N6ddUN7avL3c3s8EJAQC1iSuswV3hHgDgQlKzCvXxkn3677pklZSVP3zVpkGgxvZrpgGtImTm4SsAQDWiWeJgFDkAapOsghJNXnFQk1ccUE5RmSQpJsRXD/dtoiGdGsjL3c3ghACA2sAV1uCucA8AUFkZOUX6dNl+fb06SYWl5Q9fxUX4a0y/prq+XX3eWAcAVIvKrsGd/pHgV199VV26dFFAQIDCw8N10003affu3acdk5eXp7FjxyoqKko+Pj5q2bKlJk2adMFzz5gxQ61atZKXl5datWqlWbNmOeo2AKBGC/b11ONXxWnF+P56emBzhfh5Kul4gZ6ZuU1931ysKSsOqOhk8QMAAAAAkhQe6K3nrmulFeP7a2y/pgrwcldiep4e/e9mXfnOEv1vfbJKT+6VCACA0Zy+WbJkyRKNGTNGq1ev1i+//KKysjINGDBA+fn5Fcc8/vjjWrBggb7++mvt3LlTjz/+uMaNG6fZs2ef87yrVq3SLbfcojvvvFNbtmzRnXfeqZtvvllr1qypjtsCgBopwNtDo/s21fK/99ML17dSRKCX0rKL9OLcBPV6fZE+XrJPecVlRscEAAAA4ERC/Dz1t6uba/n4/nryqjgF+3rowNF8PT19q/q+uVhfrT7Ew1cAAMPVuDFcmZmZCg8P15IlS9S7d29JUps2bXTLLbfohRdeqDguPj5e1157rV5++eWznueWW25RTk6O5s+fX/HZwIEDVadOHU2bNu2COXh9HgCkolKLpm9I0aTF+5SaVShJCvb10D09G+uuHo0U5ONhcEIAgCtxhTW4K9wDAFyq/OIyfbPmkD5ZekBH84olSeEBXnqgd6xu6xYjX093gxMCAFyJy4zh+qvs7GxJUkhISMVnvXr10pw5c5SamiqbzaZFixYpMTFRV1999TnPs2rVKg0YMOC0z66++mqtXLnyrMcXFxcrJyfntL8AoLbz9nDTHZc11OKn+urNYe3UOMxPWQWleueXRPV67Xe99fNuHc8vMTomAACGoY4AgDP5ebnrgd5NtPzv/fTSja0VGeStjNxi/XveTvV6fZE+XLRXuUWlRscEANQyNapZYrPZ9MQTT6hXr15q06ZNxefvv/++WrVqpaioKHl6emrgwIGaOHGievXqdc5zHTlyRBEREad9FhERoSNHjpz1+FdffVVBQUEVf0VHR1fNTQGAC/BwM2t452j9+kQfvX9rRzWPCFBucZk+WLRXPV/7Xa/MS1BGTpHRMQEAqHbUEQBwbt4ebrqrRyMteaqfXhvSVjEhvjqeX6I3f96tnq/9rnd+SVRWAQ9fAQCqR41qlowdO1Zbt249Y0zW+++/r9WrV2vOnDnasGGD3n77bY0ePVq//vrrec9nMplO+73NZjvjsz8888wzys7OrvgrOTn50m4GAFyQm9mkG9vX1/xHL9fHd8arbYMgFZZa9OmyA+r1xiL9Y/b2inFdAADUBtQRAHBhnu5mjegao9+f7KP/u6W9mtT1U05Rmd7/bY96vva7Xp2/U5m5xUbHBAC4uBozBHLcuHGaM2eOli5dqqioqIrPCwsL9eyzz2rWrFm67rrrJEnt2rXT5s2b9dZbb+nKK6886/nq1at3xlskGRkZZ7xt8gcvLy95eXlV0d0AgGszm026unU9DWgVoSWJmZrw+15tOHRCU1cd0rdrkjS0U5Qe7ttEjcL8jI4KAIBDUUcAQOW5u5k1uGOUbmzfQD/vOKIJv+/VzrQcfbxkv6asOKhbu8ZoXP+mCvXnz1UAQNVz+jdLbDabxo4dq5kzZ+r3339X48aNT/t6aWmpSktLZTaffitubm6yWq3nPG/37t31yy+/nPbZwoUL1aNHj6oLDwC1nMlkUt/m4Zr+UHdNu/8y9WwaqjKrTd+tT1b/txfrsf9u0t6MXKNjAgAAAHAibmaTrm0bqZ8e6aXPRnZW++hgFZdZNWXlQfV9c7E+XrJPxWUWo2MCAFxMpd4s+de//lXlF/7HP/5RqePGjBmjb7/9VrNnz1ZAQEDF2yBBQUHy8fFRYGCg+vTpo6eeeko+Pj5q2LChlixZoqlTp+qdd96pOM/IkSPVoEEDvfrqq5KkRx99VL1799brr7+uQYMGafbs2fr111+1fPnyKr9XAKjtTCaTujcJVfcmodpw6IQ+XLRXv+/K0A+bD2vu1jQ93KeJxl3RVF7ubkZHBQC4MCPrGgDAxTOZTLqyVYSuaBmu5XuP6rX5u7TjcI5enb9LX685pPEDW+ratvXOOVIdAICLYbLZbLYLHWQ2m2UymVSJQ89/sVN+eFkslXsC4Fw/8CZPnqy7775bUvlm7c8884wWLlyo48ePq2HDhnrggQf0+OOPV3x/37591ahRI02ZMqXiHNOnT9fzzz+v/fv3q0mTJnrllVc0ZMiQSuXKyclRUFCQsrOzFRgYWKnvAQD8aXtqtt79dY9+3ZkuSWoa7q83hrVTp5g6BicDADirS12DG1nX/IE6AgDsZ7XaNGNjit78ebcyTu5h0rlhHf17cBu1qMefqQCAs6vsGrzSzZKYmBiNGjXqkoN98cUXSklJueiiwtlQ5ABA1ViwPU3P/7BDR/OKZTJJ9/RsrL8NaC4fT94yAQCcriqaJUbXNdQRAHDpCkrK9PGS/fp46T4VlVrl4WbS2H7N9HDfJvJ0d/qJ8wCAalblzZJevXpp6dKllxzs8ssv18qVK2mWAAAqZBWU6F8/JmjmxlRJUkyIr14b2lY9moQZnAwA4EyqollidF1DHQEAVSctu1Av/LBdv+7MkCS1qBegN4e1V9uoIIOTAQCcSWXX4NXebr/UV94BAK4n2NdT79zcQZNHdVFkkLeSjhfotk/X6NlZ25RbVGp0PAAAzkBdAwDGiwzy0acjO+u9ER0U4uepXUdyddPEFXpt/i4Vldbsh3QBANWvUm+WZGdny93dXX5+fpd8wfz8fJWVlSkoqGZ3+XkiDAAcI7eoVK/N36Vv1iRJkiKDvPWfIW3Vr3m4wckAAEa71DW4M9Q11BEA4BjH8or14twEzd1yWJIUG+anN4a1U+dGIQYnAwAYrUrHcOFMFDkA4Fir9h3T+JlbdehYgSRpSMcG+scNrRTs62lwMgCAUVxhDe4K9wAAzuznHUf0/A/blZlbvifiXd0b6amrm8vPy93oaAAAgzjtGC4AACqje5NQLXi0t+7r1VgmkzRzU6qufGep5m9LMzoaAAAAACd1det6+vXxPhoeHyWbTZqy8qCufneplu85anQ0AICTo1kCAHBaPp5uev76VprxcA81DffX0bxiPfzNRo3+ZoMyc4uNjgcAAADACQX5eujN4e019Z6uahDso5QThbrj8zUaP2OrctgTEQBwDpc0hqukpESTJ0/WggULtH//fuXl5Z1zo0OTyaR9+/bZHdTZ8Po8AFSv4jKLJvy2V5OW7JPFalOwr4f+eUMr3dShgUwmk9HxAADVwFFr8Oqsa6gjAKB65RWX6Y0FuzR11SFJUkSgl165qa2ubBVhcDIAQHVx+J4laWlpuuKKK7R79+5zFhKnXchkksVisedSTokiBwCMsT01W09P36qEtBxJUr/mdfWfIW0VGeRjcDIAgKM5Yg1e3XUNdQQAGGPN/mP6+4ytOnhyT8RBHerrnze0VogfeyICgKtzeLPktttu03//+1/Fxsbq6aefVseOHVW3bt3zPt3bsGFDey7llChyAMA4pRarPlm6X+/9ukclFqsCvNz1zLUtdWvXaN4yAQAX5og1eHXXNdQRAGCcolKL/u+XRH26bL+sNinUz1MvDWqt69pGUkcAgAtzeLMkJCRERUVF2rNnjxo0aGB30JqKIgcAjLcnPVdPz9iqTUlZkqTusaF6fWg7xYT6GhsMAOAQjliDV3ddQx0BAMbbnJylp6dvUWJ6niRpQKsI/fumNgoP9DY4GQDAESq7Brd7g3eLxaIWLVrUykYJAMA5NIsI0PSHeuiF61vJ28OsVfuP6ep3l+qL5Qdksdq9JRcAoBahrgGA2qdDdLDmjuulR65oJnezSQsT0nXlO0v0/frkSo1kBAC4JrubJe3atdOxY8eqMgsAABfNzWzSvb0a6+fHeuuy2BAVllr0rx8TNPyjldqbkWt0PACAk6OuAYDaycvdTU9cFac5Y3upbYMg5RSV6anpW3XX5HVKzSo0Oh4AwAB2N0ueeuopJScn67vvvqvKPAAA2KVhqJ++ve8yvTK4jfy93LUxKUvXvrdcHy7aq1KL1eh4AAAnRV0DALVbq/qBmjW6h/4+sIU83c1ampipAe8s0VerD8nK2+oAUKvY3Sy58cYb9fbbb+u+++7Tk08+qR07dqioqKgqswEAcFHMZpNu79ZQCx/vrb7N66rEYtWbP+/WTR+u0I7D2UbHAwA4IeoaAIC7m1kP922i+Y9erviGdZRfYtELP2zXiE9X6+DRfKPjAQCqid0bvEtSenq67rvvPv30008XvpDJpLKyMnsv5XTYmBEAnJvNZtOsTal6aW6CsgtL5W426eG+TTS2f1N5ubsZHQ8AYAdHrcGrs66hjgAA52ax2jR11UG9sWC3Ckst8vYw68mrmuueXo3lZjYZHQ8AYIfKrsHtbpbs3btXffv2VVpaWqU3v7JaXWcMCkUOANQMGblF+ufsHZq//YgkqVm4v94Y1k4dY+oYnAwAcLEcsQav7rqGOgIAaobk4wUaP3OrVuwt39eqfXSw3hzWTnERAQYnAwBcrMquwe0ew/X000/r8OHD6tatm37++Welp6fLarWe9y8AAKpbeIC3Jt0Rr4m3d1KYv6f2ZORp6KSV+vePCSossRgdDwBgMOoaAMDZRIf46ut7u+m1IW0V4OWuLclZuu79ZZrw2x72RAQAF2X3myWhoaEqLS1VSkpKrXwiiifCAKDmOZFfon/9mKBZm1IlSQ1DffX60Ha6LDbU4GQAgMpwxBq8uusa6ggAqHnSsgv1/Kzt+m1XhiSpZWSg3hzWTm0aBBmcDABQGQ5/s8Rqtap58+Ys8AEANUYdP0/93y0dNPnuLooM8tahYwUa8clqPf/DNuUWlRodDwBgAOoaAMCFRAb56LO7Ouu9ER1Ux9dDO9NyNOjDFXpjwS4VlfK2OgC4CrubJfHx8UpJSanKLAAAVIt+LcL18+O9dWvXGEnS16uTdPX/LdXi3RkGJwMAVDfqGgBAZZhMJg3q0EC/PNFH17WLlMVq08TF+3Td+8u04dBxo+MBAKqA3c2SF154QUePHtV7771XlXkAAKgWgd4eenVIW317XzdFh/jocHaR7p68Tk/+b4uyCkqMjgcAqCbUNQCAixHm76UPb+ukj+6IV90AL+3LzNewj1bppbk7VFBSZnQ8AMAlsHvPkqSkJM2cOVPjx4/Xddddp3vuuUdNmjSRr6/vOb8nJibG7qDOhlnDAOA6CkrK9NbPiZq88oBstvIC6N83tdHANvWMjgYAOIUj1uDVXddQRwCA68guKNXL8xI0fUP5G4rRIT56fUg79WgaZnAyAMCpKrsGt7tZYjabZTKZZLPZZDKZLni8yWRSWZnrdNgpcgDA9Ww4dFxPT9+qfZn5kqTr2kbqpUGtFebvZXAyAIDkmDV4ddc11BEA4HoW787QszO36XB2kSTp1q7Reubalgr09jA4GQBAqoZmSaNGjSpVTJzqwIED9lzKKVHkAIBrKiq1aMLve/TRkv2yWG2q4+uhf97QWoM61L/on3sAgKrliDV4ddc11BEA4Jpyi0r1+oJd+np1kiSpXqC33hzeTpc3q2twMgCAw5sltR1FDgC4tu2p2Xpq+lbtTMuRJPVvEa5XBrdRZJCPwckAoPZyhTW4K9wDAODcVu8/pvEzturgsQJJ0gO9Y/W3Ac3l6W73tsEAgEtU2TU4f1IDAHAWbRoEac7Ynnryqjh5upn1+64MXfPeMi3enWF0NAAAAABO6rLYUM1/tLdu71a+v9UnS/dr6KSV2p+ZZ3AyAMCF0CwBAOAcPNzMGndFM/34SC+1bRCkrIJSjZqyTu/+miirlRczAQAAAJzJx9NNrwxuq4/vjFewr4e2pWbruveX63/rksWAFwBwXnY3S5YuXar+/fvr448/Pu9xH330kfr3768VK1bYeykAAAwVFxGg6Q931+3dYmSzSe/+ukejpqzTifwSo6MBAC4RdQ0AwFGubl1PCx7tre6xoSostejpGVv16H83K7+4zOhoAICzsLtZ8tlnn2nJkiXq3r37eY/r3r27Fi9erC+++MLeSwEAYDgv9/Knw94e3l5e7mYtSczU9ROWa2tKltHRAACXgLoGAOBI9YK89fV93fT0wOZyM5s0Z8th3fThCu3NYCwXADgbuzd4j4uL04kTJ5SZmXnBY+vWravQ0FDt2rXLnks5JTZmBIDaK+Fwjh7+ZoMOHSuQp5tZL97YWrd2jZbJZDI6GgC4NEeswau7rqGOAIDaa93B4xrzzUZl5BbLz9NNbw5vr2vbRhodCwBcnsM3eE9NTVWjRo0qdWyjRo2Umppq76UAAHAqreoHas7YXrqyZYRKLFY9O2ubnpq+VYUlFqOjAQAuEnUNAKC6dGkUoh8f6aVujUOUX2LR6G826pV5CSq1WI2OBgDQJTRLPD09lZubW6ljc3NzZTazlzwAwHUE+Xjokzvj9feBLWQ2SdM3pGjIpJU6dCzf6GgAgItAXQMAqE7hAd765r5uerB3rCTp02UHdPuna5SRU2RwMgCA3Sv9Fi1aaM+ePUpMTDzvcYmJiUpMTFRcXJy9lwIAwCmZzSY93LeJvr63m0L9PLUzLUfXT1iuXxPSjY4GAKgk6hoAQHVzdzPrmWtb6qM7Osnfy11rDx7XdROWa83+Y0ZHA4Baze5mydChQ2Wz2TRy5EhlZWWd9ZisrCzdddddMplMGj58uL2XAgDAqfVoGqZ5j1yuTjHByi0q031T1+vNn3fJYrVrWzAAQDWirgEAGGVgm0jNGdtTzSMClJlbrNs+W6NPl+6XndsLAwAukd0bvBcWFio+Pl67d+9WeHi47r33XnXr1k3BwcHKysrS6tWr9cUXXyg9PV0tWrTQhg0b5OPjU9X5DcPGjACAvyops+o/P+3UlJUHJUk9m4bqvREdFebvZWwwAHARjliDV3ddQx0BAPirgpIyPTNzm2ZvPixJuqZNPb0xrJ0CvD0MTgYArqGya3C7myWSlJycrMGDB2vjxo0ymUxnfN1ms6lz586aMWOGoqOj7b2MU6LIAQCcy5wthzV+xlYVlFhUL9BbE+/opE4xdYyOBQA1nqPW4NVZ11BHAADOxmaz6evVh/SvHxNUarEptq6fProjXnERAUZHA4Aar1qaJZJktVo1c+ZMzZ49Wzt37lROTo4CAgLUunVr3XTTTbrppptcchNEihwAwPkkpufqoa83aH9mvjzcTHr+ulYa2b3hWf8jHACgchy5Bq+uuoY6AgBwPhuTTmjMNxuVll0kHw83vTa0rQZ1aGB0LACo0aqtWVJbUeQAAC4kt6hUf5+xVT9tOyJJGtShvl4d0la+nu4GJwOAmskV1uCucA8AAMc6llesR/+7Wcv3HpUk3dW9oZ67rpU83V3vYWQAqA6VXYPzpywAAA4S4O2hD2/rpOevayk3s0mzNx/WTR+u0L7MPKOjAQAAAHBSof5e+vKerhrbr6kk6ctVhzTik1VKyy40OBkAuDaaJQAAOJDJZNJ9l8dq2v2XqW6AlxLT8zTogxWavy3N6GgAAAAAnJSb2aS/Xd1cn43srABvd21MytL17y/XipNvmwAAql6lmiX9+/fXY489ViUXfOSRR3TFFVdUybkAAKgpujYO0bxHeqlr4xDlFZfp4W826pV5CSqzWI2OBgC1BnUNAKCmubJVhOaNu1ytIgN1LL9Ed36+Rh8u2iurlan6AFDVKtUsWbx4sTZu3FglF9y0aZMWL15cJecCAKAmCQ/w1jf3ddMDvWMlSZ8uO6DbPlujjNwig5MBQO1AXQMAqIliQn01c3QPDY+PktUmvfnzbj3w1QZlF5YaHQ0AXEqld5gtLi5WcnKyLnU/+OLi4kv6fgAAajIPN7OevbalOsUE62/fb9XaA8d13fvL9eFtndS1cYjR8QDA5VHXAABqIm8PN70xrJ06Nayjf87eoV93puvGD5Zr0u3xalX/3JsVAwAqz2SrRJVgNptlMpmq5II2m00mk0kWi6VKzmeUnJwcBQUFKTs7W4GB/FACAFy8fZl5evjrDUpMz5Ob2aRnrmmhe3s1rrKfuQDgai51De4MdQ11BADgUm1NydLDX29UalahvNzNemVwWw2LjzI6FgA4rcquwSvVLGnUqFGV/4ebAwcOVOn5qhtFDgCgKhSUlOmZmds0e/NhSdK1bevpjWHt5e9V6Zc/AaDWuNQ1uDPUNdQRAICqkFVQose+26zFuzMlSbd2jdE/b2glbw83g5MBgPOp0mYJzkSRAwCoKjabTV+tPqSXf0xQqcWm2Lp++viOeDWLCDA6GgA4FVdYg7vCPQAAnIPVatOE3/fq3d8SZbNJ7aKCNPH2Toqq42t0NABwKpVdg1dqg3cAAOA4JpNJI7s30ncPdldkkLf2Z+Zr0IcrNHtzqtHRAAAAADgps9mkR69spsl3d1Gwr4e2pmTr+gnLtSQx0+hoAFAj0SwBAMBJdIqpox/H9VLPpqEqKLHo0f9u1otzdqikzGp0NAAAAABOqm/zcP04rpfaRQUpq6BUd09eq/d+3SOrlWEyAHAxaJYAAOBEQv29NPWebhrTr4kkacrKgxrxySqlZRcanAwAAACAs4qq46vvH+qu27rFyGaT/u/XRN3z5TqdyC8xOhoA1Bg0SwAAcDJuZpOeurqFPhvZWQHe7tqYlKXr31+ulXuPGh0NAAAAgJPycnfTfwa31VvD28vL3azFuzN1/YTl2paSbXQ0AKgRaJYAAOCkrmwVoR/H9VLLyEAdyy/RHZ+v0cTFe3mdHgAAAMA5DYuP0qzRPdUw1FepWYUaOmmlpq1Nks1GHQEA50OzBAAAJ9Yw1E+zRvfQsPgoWW3SGwt264GvNii7sNToaAAAAACcVKv6gZoztpeubBmhEotVz8zcpqenb1VRqcXoaADgtGiWAADg5Lw93PTmsHZ6dUhbebqZ9evOdN34wXLtTMsxOhoAAAAAJxXk46FP7ozX0wOby2ySvt+QoiETV+rQsXyjowGAU6JZAgBADWAymXRr1xhNf7i7GgT76NCxAg2euEIzNqQYHQ0AAACAkzKbTRrdt6m+urebQv08lZCWo+snLNevCelGRwMAp0OzBACAGqRdVLB+HNdLfeLqqqjUqie/36LnZm1TcRmv0wMAAAA4u55Nw/TjI73UMSZYuUVlum/qer358y5Z2A8RACqYbFWwu1N2drb279+vvLy8824W1bt370u9lNPIyclRUFCQsrOzFRgYaHQcAEAtY7Xa9P7ve/Teb3tks0ntooI08fZOiqrja3Q0AHAYR6/Bq6OuoY4AABippMyq//y0U1NWHpQk9WoapvdGdFCov5exwQDAgSq7Br+kZsnSpUs1fvx4rVmz5oLHmkwmlZWV2Xspp0ORAwBwBot3Z+ix7zYrq6BUwb4eem9ER/WJq2t0LABwCEetwauzrqGOAAA4g9mbUzV+xjYVlloUGeStD2/vpE4xdYyOBQAOUdk1uLu9F1i0aJEGDhyo0tJSeXl5qVGjRgoPD5fZzGQvAACqS9/m4fpxXC+N/majtqZk6+7Ja/XYFXEa17+pzGaT0fEAwOlR1wAAaqNBHRqoZWSgHvpqg/YfzdctH6/SC9e30p2XNZTJRB0BoHay+82S3r17a/ny5br99tv17rvvKjQ0tKqzOTWeCAMAOJOiUotempugaWuTJEl9m9fVu7d0ULCvp8HJAKDqOGINXt11DXUEAMCZ5BaV6unpWzV/+xFJ0k0d6us/Q9rK19Pu56sBwOk4fAyXv7+/PD09lZmZKTc3N7uD1lQUOQAAZ/T9+mQ9/8N2FZdZ1SDYRx/dEa+2UUFGxwKAKuGINXh11zXUEQAAZ2Oz2fT58gN6dX75hu/NIwI06Y5Oiq3rb3Q0AKgSlV2D2/1uuaenp5o2bVorGyUAADir4Z2jNWt0TzUM9VVqVqGGfrRS/z35tgkA4EzUNQCA2s5kMum+y2M17f7LVDfAS7vTc3XjByu0YHua0dEAoFrZ3Szp1q2bDh48qEvYHx4AADhAq/qBmjO2l65sGa6SMqvGz9ymp6dvUVGpxehoAOB0qGsAACjXtXGI5o3rpa6NQpRXXKaHvt6o//y0U2UWq9HRAKBa2N0s+ec//6ns7Gy99dZbVZkHAABUgSAfD31yZ2c9dXVzmU3S/9anaMjElUo6VmB0NABwKtQ1AAD8KTzQW9/c3033X95YkvTJ0v267bM1ysgtMjgZADie3XuWJCUlae7cuXryySd1zTXX6N5771WTJk3k5+d3zu+JiYmxO6izYdYwAKCmWLH3qB6ZtknH8ksU6O2u/7ulg65oGWF0LAC4aI5Yg1d3XUMdAQCoKX7alqanp29VXnGZwgO89OHtndSlUYjRsQDgojl8g3ez2SyTySSbzSaTyXTB400mk8rKyuy5lFOiyAEA1CRp2YUa/c1GbUrKkiSN7ddUj18VJzfzhX+GA4CzcMQavLrrGuoIAEBNsi8zTw99tUF7MvLkZjbpmWta6N5ejSv1MxMAnIXDmyWNGjW66D8YDxw4YM+lnBJFDgCgpikps+o/P+3UlJUHJUm9mobpvREdFOrvZWwwAKgkR6zBq7uuoY4AANQ0+cVlembmNs3ZcliSdF3bSL0+rJ38vdwNTgYAlePwZkltR5EDAKipZm9O1fgZ21RYalFkkLcm3t5JHWPqGB0LAC7IFdbgrnAPAIDax2azaeqqQ3r5xwSVWW1qUtdPH90Rr2YRAUZHA4ALquwa3O4N3gEAQM00qEMDzR7bU7FhfkrLLtLNH6/SV6sOiucnAAAAAJyNyWTSXT0a6bsHu6teoLf2ZeZr0IcrNPfk2yYA4ApolgAAUAvFRQRo9tieuqZNPZVabHph9g498b8tKihxnf3FAAAAAFSt+IZ19OMjvdSjSagKSiwaN22TXpq7QyVlVqOjAcAlu+QxXMXFxZo2bZoWLlyoxMRE5ebmKiAgQHFxcbr66qs1YsQIeXm53ix0Xp8HALgCm82mz5Yd0GsLdslital5RIAm3dFJsXX9jY4GAGdw5Bq8uuoa6ggAgCuwWG16e+FuTVy8T1J5E+XD2zqpXpC3wckA4EzVsmfJxo0bNXz4cB08ePbRHSaTSY0bN9b//vc/derUyd7LOCWKHACAK1mz/5jGTtukzNxi+Xu5663h7TSwTaTRsQDgNI5ag1dnXUMdAQBwJQt3HNGT329RblGZwvw99f6tHdWjSZjRsQDgNA5vlqSkpKh9+/Y6ceKEwsLCdP/996t169aKiIhQenq6duzYoc8++0yZmZkKDQ3V5s2b1aBBA7tvyNlQ5AAAXE1GTpHGfrtJaw8elyQ92DtWT13dXO5uTO0E4BwcsQav7rqGOgIA4GoOHs3XQ19v0K4juTKbpKeubqGH+sTKZDIZHQ0AJFVDs2TMmDGaNGmShgwZoq+++ko+Pj5nHFNUVKQ777xTM2bM0OjRo/XBBx/YcymnRJEDAHBFpRar3liwS58uOyBJ6tY4RBNu66jwAF6nB2A8R6zBq7uuoY4AALiiwhKLnv9hu2ZsTJEkDWgVobdubq9Abw+DkwFANTRLYmNjlZmZqbS0NPn7n3uueV5eniIjI1W3bl3t37/fnks5JYocAIAr+2lbmp76fovySywKD/DSh7d3UpdGIUbHAlDLOWINXt11DXUEAMBV2Ww2TVubrBfn7FCJxapGob6adEe8Wkby8w6AsSq7Brd7rsbhw4fVsmXL8xYUkuTv76+WLVsqLS3N3ksBAIBqdm3bSM0Z10vNwv2VkVusEZ+s1mfL9p91lj8A1GTUNQAAVA2TyaTbusXo+4e6q0Gwjw4eK9DgiSs08+TbJgDg7OxulgQEBCg9Pb1Sx6anp8vPz8/eSwEAAAM0qeuvH8b01I3t68titenf83Zq7LRNyisuMzoaAFQZ6hoAAKpW++hg/Tiul3rH1VVRqVVP/G+Lnpu1TcVlFqOjAcB52d0siY+PV0pKiv773/+e97hp06YpOTlZnTt3tvdSAADAIH5e7npvRAe9dGNruZtNmrc1TYM+WK69GblGRwOAKkFdAwBA1avj56nJd3fRo1c0k8kkfbMmSTd/vFqpWYVGRwOAc7K7WTJu3DjZbDbdddddevLJJ3XgwIHTvn7gwAE98cQTGjVqlEwmkx555JFLDgsAAKqfyWTSXT0a6bsHu6teoLf2Zebrxg9WaO6Ww0ZHA4BLRl0DAIBjuJlNevyqOH1xdxcF+XhoS3KWrn9/mZYmZhodDQDOyu4N3iXpmWee0euvvy6TySRJ8vLyUt26dZWZmani4mJJ5Zs7PfPMM3rllVeqJrGTYGNGAEBtdDSvWI9M26SV+45Jkkb1bKRnrmkpT3e7n78AgEpz1Bq8Ousa6ggAQG2UfLxAo7/ZqG2p2TKZpMevjNPYfk1lNpuMjgagFnD4Bu+S9Oqrr2rOnDnq3r27TCaTioqKlJycrKKiIplMJvXs2VNz5851uUYJAAC1VZi/l766t5tG920iSZq84qBu/XS1jmQXGZwMAOxHXQMAgGNFh/jq+4e669auMbLZpHd+SdS9X65TVkGJ0dEAoMIlvVlyqvz8fO3du1d5eXny9/dX06ZNXXrzQ54IAwDUdgt3HNGT329RblGZwvw99f6tHdWjSZjRsQC4sOpYgzu6rqGOAADUdt+vT9bzP2xXcZlVUXV89NEd8WrTIMjoWABcWGXX4FXWLKltKHIAAJAOHs3XQ19v0K4juTKbpKcHttCDvWMrRtkAQFVyhTW4K9wDAACXasfhbD389UYlHS+Qp7tZLw9qrVu6xBgdC4CLqpYxXAAAoHZrFOanWaN7aminKFlt0mvzd+nBrzYop6jU6GgAAAAAnFTr+kGaO7aXrmgRrpIyq/4+Y5uenr5FRaUWo6MBqMUq9WbJ1KlTJUlBQUEaNGjQaZ9djJEjR1709zgrnggDAOBPNptN09Ym68U5O1RisapRqK8m3RGvlpH8jARQdS51De4MdQ11BAAAf7JabZq0ZJ/eXrhbVpvUun6gJt0er5hQX6OjAXAhVTqGy2w2y2QyqXnz5kpISDjts4thsbhOd5giBwCAM21NydLDX29UalahvD3MenVIWw3uGGV0LAAu4lLX4M5Q11BHAABwpuV7juqR/27S8fwSBXq7690RHdS/RYTRsQC4iMquwd0rc7KRI0fKZDIpMjLyjM8AAAD+0C4qWD+O66VHv9uspYmZevy7Ldpw6IReuL6VvNzdjI4HoJajrgEAwDn1ahamH8f10uhvNmpzcpbumbJej/RvqkevjJObmZ/TAKoHG7zbiSfCAAA4N4vVpvd/26P3f98jm01qHx2sibd3UoNgH6OjAajBXGEN7gr3AACAo5SUWfXveQmauuqQJOnyZmF6b0RHhfh5GpwMQE3GBu8AAMAwbmaTHr8qTl/c3UVBPh7akpyl699fpqWJmUZHAwAAAOCkPN3N+tegNnr3lg7y8XDTsj1Hdf37y7Q5OcvoaABqAbubJf3799djjz1WqWMff/xxXXHFFfZeCgAA1FD9mofrx3G91LZBkE4UlOquyWs14bc9slp5sRWAc6CuAQDA+dzUsYF+GNNTsWF+OpxdpOEfrdRXqw+JATkAHMnuZsnixYu1cePGSh27efNmLV682N5LAQCAGiw6xFffP9Rdt3aNkc0mvf1Lou6bul7ZBaVGRwMA6hoAAJxU83oBmj22pwa2rqdSi00v/LBdT/5viwpLLEZHA+CiqmUMV0lJidzc2NQVAIDaytvDTa8Oaas3h7WTl7tZv+/K0HUTlml7arbR0QCg0qhrAACoXgHeHpp0Ryc9e20LuZlNmrkpVYMnrtCBo/lGRwPgghzeLCksLFRiYqJCQ0MdfSkAAODkhneO1szRPRQT4quUE4UaMmmlvluXZHQsALgg6hoAAIxhMpn0QO8m+ua+bgrz99KuI7m6ccJy/bzjiNHRALgY98oeOHv2bM2ePfu0z/bs2aN77rnnnN9TWFiodevW6fjx4xo2bJj9KQEAgMtoXT9Ic8f20pPfb9avOzP09xnbtPFQll4a1FreHjyxDcCxqGsAAKiZLosN1bxHemnstxu17uAJPfjVBj3YJ1ZPDWgud7dqGZ4DwMWZbJXcGemll17SSy+99Oc3mkyV3lSpWbNm+umnn9SkSRP7UjqhnJwcBQUFKTs7W4GBgUbHAQCgxrFabZq0ZJ/eXrhbVpvUpkGgJt0er+gQX6OjAXBSVbEGN7quoY4AAODSlFqsem3+Ln2+/IAk6bLYEE24tZPqBngZnAyAs6rsGrzSzZItW7Zo8+bNkiSbzaZ77rlHcXFxeuaZZ85+YpNJPj4+io2NVadOnWQymS7+LpwYRQ4AAFVj+Z6jeuS/m3Q8v0SB3u56d0QH9W8RYXQsAE6oKtbgRtc11BEAAFSNeVvT9PT0LcovsSg8wEsTb++kzo1CjI4FwAlVebPkrxo1aqRu3brpu+++sztkTUaRAwBA1TmcVagx327UpqQsSdIj/Zvq0Svj5GZ2rYctAFwaR6zBq7uuoY4AAKDq7M3I1UNfb9TejDy5m0169tqWGtWzkcs9tA3g0ji8WVLbUeQAAFC1SsqsemVegr5cdUiSdHmzML03oqNC/DwNTgbAWbjCGtwV7gEAAGeSX1ymv8/Yqh+3pkmSrm8XqdeHtpOfV6W3agbg4iq7Bmf3IwAA4BQ83c16aVAbvXtLB/l4uGnZnqO6/v1l2pycZXQ0AAAAAE7Kz8tdE27tqH/e0EruZpN+3JqmQR+uUGJ6rtHRANQwlXqzZOrUqZKkoKAgDRo06LTPLsbIkSMv+nucFU+EAQDgOLuP5Orhrzdo/9F8ebiZ9I8bWuuObjG8Tg/Ucpe6BneGuoY6AgAAx1l/8LjGfLtR6TnF8nI369lrW2pk94bUEUAtV6VjuMxms0wmk5o3b66EhITTPrsYFovloo53ZhQ5AAA4Vm5RqZ76fqsW7DgiSRrSsYFeGtRaAd4eBicDYJRLXYM7Q11DHQEAgGNl5hbrb99v0ZLETElSv+Z19caw9qob4GVwMgBGqewavFLD+0aOHCmTyaTIyMgzPgMAAHCEAG8PTbqjkz5bdkCvLdilmZtStWLfUb14Q2sNbFOPdQiAi0ZdAwCA66sb4KUpo7roy5UH9Z/5u7Rod6YGvrtUbwxrpytaRhgdD4ATY4N3O/FEGAAA1WftgeN6evoWHTxWIEnq3yJcL93YWtEhvgYnA1CdXGEN7gr3AABATZGYnqtHpm3SriPl+5fc1KG+nruuFW+ZALUMG7wDAACX0bVxiBY81lvj+jeVh5tJv+/K0ID/W6qPl+xTqcVqdDwAAAAATiguIkCzx/bU/Zc3lskk/bD5sK54e7G+WXNIVivPjwM4nUObJSdOnHDk6QEAQC3i7eGmJwc01/xHL1fXxiEqLLXo1fm7dMOE5dqYxJoDgONQ1wAAUHN5ubvpuetaafaYnmrTIFA5RWV6btZ2Df1opXam5RgdD4ATsbtZsnv3br3//vtavnz5aZ+XlJTokUcekb+/v8LCwtSkSRMtXLjwkoMCAABIUtPwAH33wGV6Y1g7Bft6aNeRXA2dtFLP/7BN2YWlRscDUMNQ1wAAUDu0iwrW7DG99M8bWsnfy12bkrJ0/YTl+s9PO1VQUmZ0PABOwO5myYcffqjHH39cOTmnd2BffPFFffDBByooKJDNZtOBAwc0aNAgHThw4JLDAgAASJLJZNLNnaP12xN9NLRTlGw26evVSbrynSWau+Ww2JINQGVR1wAAUHu4mU0a1bOxfn2ij65pU08Wq02fLN2vq95Zql8S0o2OB8BgdjdLlixZIm9vbw0cOLDis+LiYk2cOFFeXl76+eeflZWVpb/97W8qLi7W22+/XSWBAQAA/hDq76W3b26vb+/vptgwP2XmFmvctE26a/I6JZ3cDB4Azoe6BgCA2qdekLcm3RGvL+7urKg6PkrNKtT9U9frganrdTir0Oh4AAxid7MkLS1N0dHRMpv/PMXy5cuVk5OjIUOG6KqrrlJgYKD+/e9/KygoSEuWLKmSwAAAAH/Vo0mY5j92uR6/Mk6ebmYtTczUVf+3RB8u2quSMjaAB3Bu1DUAANRe/VtE6JfH++ihPk3kbjZpYUK6rnxniT5btl9lFuoIoLaxu1mSlZWloKCg0z5btmyZTCaTrrnmmorPPD09FRsbq6SkJPtTAgAAXICXu5sevbKZFjx2uXo0CVVxmVVv/rxb109YpnUHjxsdD4CToq4BAKB28/F00/hrWmjeI5erc8M6Kiix6N/zdurGD1Zoc3KW0fEAVCO7myVBQUFKSUk57bNFixZJknr37n3a5yaTyd7LAAAAXJTYuv765r5ueufm9grx81Riep6Gf7RK42dsVVZBidHxADgZ6hoAACBJzesF6H8PdtdrQ9oqyMdDCWk5GjxxhZ7/YZuyC0uNjgegGtjdLOnUqZOOHDmiuXPnSpK2bt2qFStWqFmzZoqJiTnt2P379ysyMvLSkgIAAFSSyWTSkE5R+v3JPhrRJVqS9N91ybri7SWatSmFDeABVKCuAQAAfzCbTRrRNUa/P9lHQzo1kM0mfb06SVe+s0RzthymjgBcnN3NkrFjx8pms2nYsGHq3LmzevXqJZvNpjFjxpx23Pr165WVlaUOHTpcalYAAICLEuzrqdeGttP/HuyuZuH+OpZfose/26I7Pl+jA0fzjY4HwAlQ1wAAgL8K9ffSOzd30Lf3d1NsXT9l5hbrkWmbNPKLtTpIHQG4LLubJTfccIPef/99+fv7a+PGjSotLdXf/vY3jRs37rTjPvvsM0nSgAEDLi0pAACAnbo2DtG8Ry7XU1c3l5e7WSv2HtPV7y7V+7/tUXGZxeh4AAxEXQMAAM6lR5MwzX/0cj1xVZw83c1atueoBry7VBOoIwCXZLJd4vtjFotFR48eVd26dWU2n9l72blzp0pKStSsWTP5+vpeyqWcSk5OjoKCgpSdna3AwECj4wAAgEo6dCxfz/+wXcv2HJUkxdb1038Gt9VlsaEGJwNwIY5cg1dXXUMdAQBAzXTgaL5e+GG7lu8tryOa1PXTv29qq+5NqCMAZ1fZNfglN0tqK4ocAABqLpvNprlb0/SvuQk6mlcsSRoWH6Vnr22pED9Pg9MBOBdXWIO7wj0AAFBb2Ww2zdlyWC//uLOijhjaKUrPXttCof5eBqcDcC7V3ixJTExUYmKicnNzFRAQoLi4OMXFxVXFqZ0SRQ4AADVfdmGp3liwS9+sSZIk1fH10LPXttSw+CiZTCaD0wH4q+pYgzu6rqGOAACg5ssuLNWbP5fXETabFOzroWeuaaHh8dEym6kjAGdTbc2Sjz/+WK+//roOHTp0xtcaNWqk8ePH6/7777+USzglihwAAFzHhkMn9Nysbdp1JFeS1K1xiF4Z3FZNw/0NTgbgVI5cg1dXXUMdAQCA69iYdELPzdqunWk5kqQujerolcFtFRcRYHAyAKeqlmbJqFGjNHXqVNlsNnl5eSk6OloRERFKT09XcnKyiouLZTKZNHLkSE2ePNneyzglihwAAFxLqcWqL5Yf0P/9mqiiUqs83Ex6uE8Tje7XVN4ebkbHAyDHrcGrs66hjgAAwLWUWayavOKg/u/XRBWUWORuNun+3rF6pH8z+XhSRwDOoLJr8DN3Lqykb7/9Vl9++aV8fX31xhtvKDMzU4mJiVq2bJkSExOVmZmpN954Q35+fpo6daqmTZtm76UAAAAczsPNrAf7NNEvj/dRv+Z1VWqx6f3f9+qa95ZpxclNHAG4HuoaAABwKdzdzLq/d6x+eaKPrmoVoTKrTZMW79NV/7dEi3ZlGB0PwEWw+82Sfv36aenSpZo/f74GDBhwzuMWLlyogQMHqm/fvvr999/tDupseCIMAADXZbPZNH/7Eb04Z4cycss3bhzcsYGeu66lwti4ETCMI9bg1V3XUEcAAODaFu4oryMOZxdJkq5tW0//uL616gV5G5wMqL0cPoYrJCREoaGh2rNnzwWPjYuLU2Zmpk6cOGHPpZwSRQ4AAK4vp6hUb/+8W1NXH5LNJgX5lG/ceHNnNm4EjOCINXh11zXUEQAAuL784jK999sefb78gCxWm/y93PXkgDiN7N5IbtQRQLVz+BiuoqIiBQcHV+rYwMBAFRcX23spAAAAQwR6e+ilQW00a3RPtYoMVHZhqcbP3KabP16lxPRco+MBqALUNQAAoKr5ebnr2Wtbau7YXuoYE6y84jK9NDdBN324QltTsoyOB+Ac7G6WxMTEaPv27Tp69PwzvDMzM7Vjxw7FxMTYeykAAABDdYgO1pyxPfX8dS3l6+mm9YdO6Nr3lumNBbtUWGIxOh6AS0BdAwAAHKVV/UDNeKiHXhncRoHe7tqWmq2bPlyhF+fsUG5RqdHxAPyF3c2SG2+8UcXFxbrllluUmZl51mMyMjJ0yy23qKSkRIMGDbI7JAAAgNHc3cy67/LTN26cuHifrnxniX7aliY7J5sCMBh1DQAAcCSz2aTbuzXUb0/21aAO9WW1SVNWHtQVby/RvK3UEYAzsXvPkuPHj6tDhw5KTU2Vl5eXhg8frlatWik8PFwZGRlKSEjQ999/r6KiIkVHR2vTpk0KCQmp6vyGYdYwAAC1288nN25MO7lxY/fYUP3zxlZqUY91AeAojliDV3ddQx0BAEDttnzPUT3/wzYdPFYgSerbvK5eurG1Gob6GZwMcF0O3+Bdkvbu3atbb71VGzZsKD+Z6c8Niv44bZcuXfTtt9+qSZMm9l7GKVHkAACAgpIyfbR4nz5eul/FZVaZTdLt3RrqiaviVMfP0+h4gMtx1Bq8Ousa6ggAAFBUatHExfv00eJ9KrFY5elm1n2XN9aYfk3l5+VudDzA5VRLs+QPv/32mxYuXKjExETl5eXJ399fcXFxuvrqq9W/f/9LPb1TosgBAAB/SD5eoP/8tFPztx+RJAX7euiJq+J0W9cYubvZPfUUwF84eg1eHXUNdQQAAPjDvsw8vThnh5btKd87LTzAS+OvaaGbOjSQ2Wy6wHcDqKxqbZbURhQ5AADgr1buO6p/zU3QriO5kqQW9QL0jxtaqUeTMIOTAa7BFdbgrnAPAACg6thsNv2SkK5/z9uppOPlo7k6xgTrxRtaq310sLHhABdBs8TBKHIAAMDZlFmsmrY2SW//kqisglJJ0sDW9fTcdS0VHeJrcDqgZnOFNbgr3AMAAKh6xWUWfb78gD74fa8KSiySpOHxUXpqYHOFB3gbnA6o2aq1WbJkyRL9/PPPSkxMVG5urgICAhQXF6cBAwaob9++l3TuV199VTNnztSuXbvk4+OjHj166PXXX1fz5s0rjjl1pvCp3njjDT311FNn/dqUKVM0atSoMz4vLCyUt/eF/wCiyAEAAOeTVVCid35J1NerD8lqkzzdzXqwd6we7ttEvp7MIQbs4eg1uCPrmj9QRwAAgPNJzynS6/N3aeamVEmSv5e7Hrmiqe7u0Vie7oz4BexRLc2SPXv2aOTIkVq7dq2kPzc/lP5sYHTp0kVTp05VXFycXdcYOHCgRowYoS5duqisrEzPPfectm3bpoSEBPn5+UmSjhw5ctr3zJ8/X/fee6/27t2r2NjYs553ypQpevTRR7V79+7TPq9Xr16lclHkAACAyth1JEf/mpuglfuOSZIig7w1/poWurF9/XM+8AHg7By1Bq+OuuYP1BEAAKAyNiad0EtzdmhLSrYkqXGYn164vqX6t4gwOBlQ8zi8WZKcnKzOnTsrMzNT3t7eGjZsmFq2bKmIiAhlZGRo586d+v7771VUVKS6detq/fr1io6OtvuG/pCZmanw8HAtWbJEvXv3PusxN910k3Jzc/Xbb7+d8zxTpkzRY489pqysLLtyUOQAAIDKstls+nnHEf173k6lnCiUJHVpVEf/vKG12jQIMjgdUHM4Yg1e3XUNdQQAAKgsq9Wm6RtT9MaC3TqaVyxJ6tu8rl68obUahfkZnA6oOSq7Brd7BsRzzz2nzMxMXXXVVfr6669Vt27dM455++23dfvtt+uXX37R888/ry+//NLey1XIzi7vpoaEhJz16+np6Zo3b16lrpWXl6eGDRvKYrGoQ4cOevnll9WxY8ezHltcXKzi4uKK3+fk5NiRHgAA1EYmk0kD20Sqb/Nwfbp0vyYu3qd1B0/ohg+W6+E+TfT0wBZGRwRqLUfXNdQRAADAXmazSTd3jtY1berpg9/36osVB7R4d6aGpqzU7LE9FVWHPRGBqmT3myURERHKy8tTSkqK6tSpc87jTpw4oaioKPn7+ys9Pd3uoFL5U5mDBg3SiRMntGzZsrMe88Ybb+i1117T4cOHz7v3yOrVq7V37161bdtWOTk5eu+99/TTTz9py5Ytatas2RnHv/jii3rppZfO+JwnwgAAwMVKyy7Uqz/t0pwthyVJH98Zr6tbV24UKFCbOeKtDEfXNdQRAACgquzPzNOYbzdpZ1qOWtQL0IyHe8jPi/0QgQtx+BguPz8/tW7dumKu7/l07dpVO3bsUH5+vj2XqjBmzBjNmzdPy5cvV1RU1FmPadGiha666ipNmDDhos5ttVrVqVMn9e7dW++///4ZXz/bE2HR0dEUOQAAwG6vzd+lj5bsU5i/l355vLfq+HkaHQlwao5olji6rqGOAAAAVSk1q1CDPliho3nFGtAqQh/dES+zmb0QgfOpbB1htvcCTZo0UWZmZqWOzczMVNOmTe29lCRp3LhxmjNnjhYtWnTORsmyZcu0e/du3XfffRd9frPZrC5dumjPnj1n/bqXl5cCAwNP+wsAAOBSPHZlMzUL99fRvGL9c84Oo+MAtZKj6xrqCAAAUJUaBPvo4zvj5elm1sKEdL37a6LRkQCXYXez5P7779ehQ4f0/fffn/e46dOn69ChQ7r//vvtuo7NZtPYsWM1c+ZM/f7772rcuPE5j/38888VHx+v9u3b23WdzZs3KzIy0q6cAAAAF8vbw01vDW8vN7NJc7Yc1oLtR4yOBNQ61VXXAAAAVJX4hnX0nyFtJUkTFu3V+oPHDU4EuAa7myXjxo3Tww8/rDvvvFNPPPGE9u7de9rX9+3bpyeffFJ33nmnRo8erbFjx9p1nTFjxujrr7/Wt99+q4CAAB05ckRHjhxRYWHhacfl5OTo+++/P+dbJSNHjtQzzzxT8fuXXnpJP//8s/bv36/Nmzfr3nvv1ebNm/XQQw/ZlRMAAMAe7aOD9WDvWEnS8z9s0/H8EoMTAbVLddU1AAAAVWlYfJSGdoqSzSY9NX2rCkssRkcCajy79yyJjS0v6lNSUmSxlP/L6OHhodDQUB07dkylpaWSJHd3dzVo0ODsFzeZtG/fvvMHNJ195t7kyZN19913V/z+k08+0WOPPaa0tDQFBQWdcXzfvn3VqFEjTZkyRZL0+OOPa+bMmTpy5IiCgoLUsWNHvfjii+revft58/zBEfOSAQBA7VRcZtENE5YrMT1P17eL1Ae3dTI6EuCUHLEGr6665g/UEQAAoKpkF5ZqwP8tUXpOse7t1VgvXN/K6EiAU3L4Bu9ms90vpfx5cZOpoiCpaShyAABAVdqakqXBE1fKYrVp0u2ddE1bRoMCf+WINXh11zXUEQAAoCot2p2hUZPXyWSSvnugu7o2DjE6EuB0KrsGd7f3AgcOHLD3WwEAAPAX7aKC9VCfWH24aJ+e/2G7ujYOUai/l9GxAJdHXQMAAGqyfs3DNTw+St9vSNHT07do/qO95ePpZnQsoEayu1nSsGHDqswBAABQ6z1yRTP9mpCh3em5+secHfqQcVyAw1HXAACAmu7561tp2Z6jOnisQG/8vEv/vKG10ZGAGunS3zkHAABAlfByd9Nbw9vLzWzSvK1pmrc1zehIAAAAAJxckI+HXhvaVpI0ZeVBrdl/zOBEQM1U5c2SRx55RFdccUVVnxYAAKBWaBsVpNF9m0iSXpi9XUfzig1OBNRO1DUAAKAm6ds8XLd0jpbNJj01fasKSsqMjgTUOFXeLNm0aZMWL15c1acFAACoNcb1b6YW9QJ0PL9E/5i93eg4QK1EXQMAAGqa565vqcggbyUdL9AbC3YbHQeocRjDBQAA4GQ83c16a3h7uZtN+mnbEf249bDRkQAAAAA4uUBvD702tJ2k8nFcqxnHBVwUmiUAAABOqE2DII3u11SS9MIP25WZyzguAAAAAOfXJ66ubu0aLUl6evpW5RczjguoLJolAAAATmpsv6ZqGRmoEwWleuGH7bLZbEZHAgAAAODknr22pRoE+yjpeIFeX7DL6DhAjVHlzRKKeAAAgKpRPo6rndzNJi3YcURzt6YZHQmoNahrAABATRXg7aHXhraVJE1ddUgr9x01OBFQM1R5s2TGjBnav39/VZ8WAACgVmpdP0hj+5eP4/rH7O3KyC0yOBFQO1DXAACAmuzyZnV1a9cYSYzjAirL7mbJ/Pnzz/q0VUREhBo2bHhJoQAAAPCnMf2aqlVkoLIKSvX8LMZxAVWJugYAALiq564rH8eVcqJQr81nHBdwIXY3S6677jpFR0dr/Pjx2rlzZ1VmAgAAwCk83Mx6a3h7uZtNWpiQrjlbDhsdCXAZ1DUAAMBV+Xu5641h7SRJX60+pJV7GccFnI/dzZLWrVvr8OHDevPNN9WmTRtddtll+uijj5SVlVWF8QAAACBJreoHalz/ZpKkf87ZwTguoIpQ1wAAAFfWs2mYbu9WPo7rqelblcc4LuCc7G6WbNu2TevXr9eYMWMUGhqqtWvXasyYMYqMjNSIESO0YMECRkQAAABUodH9mqh1/fJxXM8xjguoEtQ1AADA1T1zbfk4rtSsQr36E2/SAudislXByr+srEw//vijvvzyS/30008qLS2VyWRSvXr1dOedd+quu+5Sy5YtqyKv08jJyVFQUJCys7MVGBhodBwAAFBL7EzL0Y0fLFepxaZ3b+mgmzo2MDoSUG0cvQavjrqGOgIAABhh5d6juu2zNZKkb+7rpp5NwwxOBFSfyq7Bq6RZcqpjx47p22+/1ZQpU7Rp0yaZTCZJUufOnTVq1CiNGDFCwcHBVXlJQ1DkAAAAo0z4bY/e/iVRQT4e+uXx3goP9DY6ElAtqnMN7qi6hjoCAAAY5YUftuur1YfUINhHCx67XAHeHkZHAqpFZdfgdo/hOpfQ0FCNGzdOa9eu1WuvvSY3NzfZbDatW7dOY8aMUf369XXvvffqwIEDVX1pAACAWuGhvk3UpkGgsgtL9eysbYwIAhyAugYAALia8de0UHRI+Tiu//y0y+g4gNOp8mbJjh079PTTTysmJkbPPPOMysrKFBYWpkceeUQ333yzJGny5Mlq27atli1bVtWXBwAAcHkebma9Nby9PNxM+nVnhmZtSjU6EuByqGsAAICr8fNy1xtD20uSpq1N0rI9mQYnApxLlTRLjh07pgkTJqhz585q166d3nrrLWVkZGjgwIH6/vvvlZqaqnfffVfTpk1TSkqKxowZo4KCAj399NNVcXkAAIBap0W9QD12ZZwk6cU5O5SeU2RwIqDmo64BAACurnuTUN3VvaEk6e/Ttyq3qNTgRIDzsHvPklM3P5w/f75KS0tls9nUrFkz3X333br77rsVGRl5zu9v3ry5kpOTVVBQYHd4IzFrGAAAGK3MYtWQSSu1NSVb/VuE6/O7OlfsqwC4Ikeswau7rqGOAAAARisoKdPAd5cp6XiBbu0arVeHtDM6EuBQlV2Du9t7gfr16+vYsWOy2Wzy8/PTrbfeqnvuuUeXX355pb4/MjJSe/futffyAAAAtZ77yXFc17+/XL/vytCMjakaFh9ldCygRqGuAQAAtY2vp7veGNZOIz5ZrWlrkzWwTaT6xNU1OhZgOLubJUePHlX37t11zz336JZbbpG/v/9Fff97772nrKwsey8PAAAASXERAXr0ymZ68+fdemnuDvVqGqZ6Qd5GxwJqDOoaAABQG10WG6q7ezTSlJUHNX7GVv38eG8FensYHQswlN3Nkl27dikuLs7uC7dv397u7wUAAMCfHuwdq4U7jmhLSraemblVX9zdhXFcQCVR1wAAgNrq6YHNtWh3hg4dK9ArP+7U68MYx4Xaze4N3i+loAAAAEDV+WMcl6ebWYt2Z+r7DSlGRwJqDOoaAABQW/l6uuvNYe1lMknfrU/W4t0ZRkcCDGV3swQAAADOo1lEgB6/qvw/+r48N0Fp2YUGJwIAAADg7Lo2DtHdPRpJksbP2KbswlJjAwEGolkCAADgIu6/vLHaRwcrt7hM42dsk81mMzoSAAAAACf39NUt1CjUV0dyivTvHxOMjgMYhmYJAACAi3B3M+vt4e3k6W7WksRMfb+ecVwAAAAAzs/H001vDi8fx/X9hhQt2sU4LtRONEsAAABcSNPwAD35xziuHxN0OItxXAAAAADOr0ujEN3Ts7EkafzMrYzjQq1EswQAAMDF3Hd5rDrGnBzHNZNxXAAAAAAu7G8DmqtxmJ/Sc4r1MuO4UAvRLAEAAHAxbmaT3hzWXp7uZi1NzNR365KNjgQAAADAyfl4uunNYe1kMknTN6To913pRkcCqhXNEgAAABfUNNxffxtQPo7r3/N2KpVxXAAAAAAuoHOjEN3X6+Q4rhnblF3AOC7UHjRLAAAAXNS9vWLVKSZYecVlGj9jK+O4AAAAAFzQkwOaK7aunzJyi/XSjzuMjgNUG5olAAAALsrNbNJbw9vLy92sZXuOatpaxnEBAAAAOD9vDze9Oay9zCZp5sZU/ZrAOC7UDjRLAAAAXFhsXX89dXVzSdIr8xKUcqLA4EQAAAAAnF18wzq67/JYSdIzs7Ypq6DE4ESA49EsAQAAcHGjejZW54Z1lF9i0d8ZxwUAAACgEp64Kk5N6vopM7dYL81NMDoO4HA0SwAAAFycm9mkN4a1k7eHWSv2HtM3a5KMjgQAAADAyXl7uOmt4eXjuGZtStUvjOOCi6NZAgAAUAuUj+NqIUl69aedSj7OOC4AAAAA59cxpo7u710+juvZWdt0Ip9xXHBdNEsAAABqiVE9GqlLoz/HcVmtjOMCAAAAcH6PXxmnpuH+yswt1otzdxgdB3AYmiUAAAC1hNls0pvD2svbw6yV+47pm7WM4wIAAABwfqeO45q9+bB+3nHE6EiAQ9AsAQAAqEUahfnp7wMZxwUAAACg8jpEB+vBPk0kSc/N2s44LrgkmiUAAAC1zF3dG6lr4xAVlFj01PQtjOMCAAAAcEGPXdlMzcL9dTSvWP+cwzguuB6aJQAAALVM+TiudvLxcNPq/cf19ZpDRkcCAAAA4OS83MvHcbmZTZqz5bAWbE8zOhJQpWiWAAAA1EINQ/3094HNJUmv/rRLSccYxwUAAADg/NpHB+uhPrGSpOd/2K7jjOOCC6FZAgD4//buPDqq+n7j+HMnKwlJWBMSkkCQNSwJIIuCLFZBKqWKytaibbX1p2xKS9VaBWsr7ghutdZqrcomqCgugLIooFYgCfu+BkIISzbIOvf3B5IaSSBAku+dmffrnDmezNy5eQbvuSef88z9XgA+6tYrmqtHQgOdKi7VH1iOCwAAAEAVjP9JK7WJClNWXpEe/mCD6ThAtaEsAQAA8FGnl+NKUkign77dfUxvrt5jOhIAAAAAh/vhclwfpR3SJ+tZjgvegbIEAADAh8U3DNH9g9pKkp74dKv2Hs03nAgAAACA03WMjdBdfS+TdHo5rqy8QsOJgEtHWQIAAODjftmjmXq2OL0c16S5aSzHBQAAAOC8xv2kpdo2CdPR/CJNmpsq22aOgGejLAEAAPBx5Zbj2nNMb6zaYzoSAAAAAIcL8vfTcyOSFejv0tKtg95GCgAARn1JREFUR/Tm6r2mIwGXhLIEAAAAimsQogd+2k6S9ORnW7TrSJ7hRAAAAACcrm2TcP3p+2V9//bxZm3JyDGcCLh4lCUAAACQJP2ie7yuvKyhCordGj9rnQpLSk1HAgAAAOBwt13ZXP3bNFZRiVsTZqaooJg5Ap6JsgQAAACSTi/H9cywJNULCdCG9Bw9/skW05EAAAAAOJxlWXrqliQ1qhukrYdzmSPgsShLAAAAUCY6oo6evjlJkvT6yj1avOmw4UQAAAAAnK5R3SA9fUsnSdIbq/boiy3MEfA8lCUAAAAo55rEKP2mV4IkadK7qTp44pThRAAAAACcrl+byP/NEXPTlJlbYDgRcGEoSwAAAHCW+wa1UcemETpxslgTZq1TSanbdCQAAAAADnffoDZqFx2uo/lF+sPcNLndtulIQJVRlgAAAOAsQf5+en5kZ9UN8td/9xzX9M+3m44EAAAAwOGC/P00Y0SygvxdWrHtiF5ftcd0JKDKKEsAAABQoeaNQvW3GztIkl5YukOrdmQZTgQAAADA6VpFhemhwYmSpCc+2aKNB7MNJwKqhrIEAAAAlfp5clMNvzxOti1NmJ2irLxC05EAAAAAONwvesTr2sQoFZW6NX7mOp0sKjEdCTgvyhIAAACc05Qh7dUysq6O5BZq4pxU1h0GAAAAcE6WZemJmzqpSXiwdh7J1+QPNpqOBJwXZQkAAADOqU6gn14c1aVs3eFXv9xlOhIAAAAAh2sQGqjnRiTLZUlz1xzQ++vSTUcCzomyBAAAAOfVpkmYJv+svSTpqc+2au2+44YTAQAAAHC6ni0aavxPWkmSHnxvvXZn5RtOBFSOsgQAAABVMrJ7nK7vFK0St63xM9cp+1Sx6UgAAAAAHG7c1a3UI6GB8otKNW7mWhWWlJqOBFSIsgQAAABVYlmWpg7tqLgGdXTg+CndPy9Nts39SwAAAABUzs9lafqIzqofEqAN6Tl64pOtpiMBFaIsAQAAQJWFBwfo+ZFd5O+y9MmGDL39zT7TkQAAAAA4XJOIYD0zLEmS9K+Vu7Vk02HDiYCzUZYAAADggiTH1dN917WVJP3lo03afCjHcCIAAAAATnd12yjd3jtBkvSHd1N1KPuU4URAeZQlAAAAuGC3905Q/zaNVVTi1th31upkUYnpSAAAAAAc7r7r2qpj0widOFmsCTNTVFLqNh0JKENZAgAAgAvmcll6+pYkRYUHaeeRfE3+YKPpSAAAAAAcLtDfpRdGdVbdIH99u+eYZnyxw3QkoAxlCQAAAC5Kw7pBem54Z7ksae6aA3p/XbrpSAAAAAAcrlnDUD02tKMk6fkvtmvVzizDiYDTKEsAAABw0a64rKHGXd1KkvTge+u1OyvfcCIAAAAATjckKUbDL4+TbUv3zErR0bxC05EAyhIAAABcmvE/aaUeCQ2UX1Sqse+sVWFJqelIAAAAABxu8pBEtYysq8zcQv1+bqrcbtt0JPg4yhIAAABcEj+XpekjOqt+SIA2HszR1I+3mI4EAAAAwOFCAv31wqjOCvJ3adnWI/rXyt2mI8HHUZYAAADgkjWJCNYzw5IkSW+s2qNFGzMMJwIAAADgdG2bhOvhnyVKkp74dItS958wGwg+jbIEAAAA1eLqtlG6o3eCJGnSu2lKP3HKcCIAAAAATjeqe7x+2rGJikttjZu5TjkFxaYjwUdRlgAAAKDa/PG6tuoUG6HsU8WaMHOdSkrdpiMBAAAAcDDLsjR1aCfF1q+jfcdO6k/z18u2uX8Jah9lCQAAAKpNoL9Lz4/srLpB/vpu73E9t2S76UgAAAAAHC6iToBmjOwsf5elj9IOafZ/95uOBB9EWQIAAIBq1axhqKYO7ShJenHZDn21PctwIgAAAABO1yW+vv4wsI0kacqHG7XtcK7hRPA1lCUAAACodj9LitHI7nGybeme2Sk6kltoOhIAAAAAh/vdVS3Up3VjFRS7NfadtTpVVGo6EnwIZQkAAABqxMOD26t1VF1l5RVq4pwUud2sOwwAAACgci6XpWeHJalxWJC2Hc7TXz7aZDoSfAhlCQAAAGpEnUA/vTCqi4IDXPpye5ZeWbHLdCQAAAAADteobpCeG54sy5JmfrtPH6UdNB0JPoKyBAAAADWmdVSYpvysvSTp6UVbtWbvccOJAAAAADhdr5aNNKZfS0nSA/PWa9/Rk4YTwRdQlgAAAKBGDe8Wp58lxajUbWv8zHXKPllsOhIAAAAAh7vnmla6vFl95RaWaNysdSoqcZuOBC9HWQIAAIAaZVmWHruxg+IbhCj9xCndNy9Nts39SwAAAABUzt/PpekjOyuiToBS95/QM4u2mo4EL0dZAgAAgBoXFhygF0Z1VoCfpU83Zuitr/eajgQAAADA4ZrWq6Mnb+4kSXplxS4t3ZppOBG8GWUJAAAAakWn2Hq677q2kqRHF27WxoPZhhMBAAAAcLqB7ZvotiuaSZJ+PydVh3MKDCeCt6IsAQAAQK25vXeCftI2UkUlbo2buU75hSWmIwEAAABwuAd+2k6J0eE6ll+ke2enqNTNsr6ofpQlAAAAqDWWZempW5LUJDxYu47k6+EPNpqOBAAAAMDhggP89PyozgoJ9NOqnUf10tIdpiPBC1GWAAAAoFY1CA3U9BHJclnSvLUHNH/tAdORAAAAADjcZY3r6tGfd5AkTVuyTd/uPmY4EbwNZQkAAABqXY8WDTXhJ60lSX9+f4N2HckznAgAAACA093UNVZDOzeV25YmzFqn4/lFpiPBi1CWAAAAwIixV7dUzxYNdLKoVGPfWaeC4lLTkQAAAAA43KM3dFBCo1Adyi7QpHfTZNvcvwTVg7IEAAAARvi5LE0f0VkNQgO16VCOpn682XQkAAAAAA4XGuSv50d2VqCfS0s2H9a/V+0xHQlegrIEAAAAxkSFB+uZYUmSpH+v3qtPN2QYTgQAAADA6To0jdCfftpWkvTYx1u0IT3bcCJ4A8oSAAAAGNW/TaR+16eFJOmP76bqwPGThhMBAAAAcLrbrmyuaxOjVFTq1riZ65RXWGI6EjwcZQkAAACM+8OANkqKq6ecghJNmJWi4lK36UgAAAAAHMyyLD11cyfFRARrd1a+Hn5/g+lI8HCUJQAAADAu0N+l50d0VliQv9bsPa5pi7eZjgQAAADA4eqFBGr6yM7yc1mavy5d7645YDoSPBhlCQAAABwhvmGIHr+pkyTp5eU79eX2I4YTAQAAAHC6bs0b6N5rWkmSHnp/g3YeyTOcCJ6KsgQAAACOcX2naI3qES/blu6dnaojuYWmIwEAAABwuLv6tdSVlzXUqeJSjX1nnQqKS01HggeiLAEAAICjPDw4UW2iwpSVV6iJc1LkdtumIwEAAABwMD+XpeeGJ6thaKA2H8rRYx9vNh0JHoiyBAAAAI4SHOCnF0Z1VnCAS19uz9LLy3eajgQAAADA4SLDg/XMsCRJ0pur9+rTDRmGE8HTUJYAAADAcVpFhekvQzpIkp5dvE1r9h4znAgAAACA0/VrE6k7+7SQJP3x3VQdOH7ScCJ4EsoSAAAAONItl8fq58kxKnXbGj8zRSdOFpmOBAAAAMDh/jCwjZLj6imnoETjZ65TcanbdCR4CMoSAAAAOJJlWfrrDR3UrGGI0k+c0h/fTZNtc/8SAAAAAJUL8HPp+ZGdFRbkr7X7Tui5JdtMR4KHoCwBAACAY4UFB+iFkV0U4Gdp0abD+s/Xe01HAgAAAOBwcQ1C9PhNnSRJLy3bqa+2ZxlOBE9AWQIAAABH6xgboQcGtZMk/fWjzdp4MNtwIgAAAABOd32naI3qES/blu6ZnaIjuYWmI8HhKEsAAADgeL/u1VzXtItUUalb495Zp/zCEtORAAAAADjcw4MT1SYqTFl5hZo4J0VuN8v6onKUJQAAAHA8y7L01M1Jio4I1q6sfD30/gbTkQAAAAA4XHCAn14Y1VnBAS59uT1Lr6zYZToSHIyyBAAAAB6hfmigpo/oLJclzV+XrnlrDpiOBAAAAMDhWkWF6ZEh7SVJTy/aqjV7jxtOBKeiLAEAAIDH6J7QQPde01qS9NAHG7TzSJ7hRAAAAACcbtjlcfpZUoxK3bbGz1yn7FPFpiPBgShLAAAA4FHu7t9SV17WUCeLSjXm7bUqKC41HQkAAACAg1mWpcdu7KD4BiFKP3FK989Lk21z/xKUR1kCAAAAj+LnsvTc8GQ1DA3UloxcPfbxZtORAAAAADhcWHCAXhjVWQF+lj7ZkKG3v9lnOhIchrIEAAAAHicyPFjPDEuSJL25eq8+3XDIcCIAAAAATtcptp7uu66tJOkvH23S5kM5hhPBSShLAAAA4JH6tYnUnX1bSJL++G6a9h87aTgRAAAAAKf7Ta8E9W/TWEUlbo19Z61OFpWYjgSHoCwBAACAx/rDgDZKjqunnIISjZ+1TsWlbtORAAAAADiYy2Xp6VuSFBUepJ1H8jVlwUbTkeAQlCUAAADwWAF+Lj0/srPCgv21bt8JPbt4m+lIAAAAAByuYd0gPTe8syxLmvPdAX2Qkm46EhyAsgQAAAAeLa5BiJ68qZMk6eVlO7Vi2xHDiQAAAAA43RWXNdS4q1tJkv40f732ZOUbTgTTKEsAAADg8QZ1jNYve8ZLkibOSVFmboHhRAAAAACcbvzVLdU9oYHyi0o1duZaFZaUmo4EgyhLAAAA4BX+fH2i2jYJU1ZekSbOTpXbbZuOBAAAAMDB/P1cmj4iWfVCArQhPUdPfLLVdCQYRFkCAAAArxAc4KcXRnVWcIBLX+3I0mtf7TYdCQAAAIDDRUfU0dM3J0mS/rVyt5ZsOmw4EUyhLAEAAIDXaBkZpocHt5ckPfnZFm1IzzacCAAAAIDTXZMYpd/0SpAkTXo3VYeyTxlOBBMoSwAAAOBVRnaP08D2USoutTV+1jqdLCoxHQkAAACAw903qI06NA3X8ZPFmjArRSWlbtORUMsoSwAAAOBVLMvS40M7KSo8SLuO5OvRjzabjgQAAADA4YL8/fT8yC4KDfTTt7uP6fkvdpiOhFpGWQIAAACvUz80UM8OS5ZlSTO/3adPN2SYjgQAAADA4RIaheqxoR0lSc9/sV3f7TlmOBFqE2UJAAAAvFKvlo30uz4tJEn3z09TRnaB4UQAAAAAnO7nyU01tHNTuW1pwqwU5RQUm46EWkJZAgAAAK/1+2vbqGPTCJ04Wax7Z6eo1G2bjgQAAADA4R75eXvFNwhR+olT+vN7G2TbzBG+gLIEAAAAXivQ36XpI5JVJ8BPq3cd1T9W7DIdCQAAAIDDhQUH6LkRyfJzWVqQelDvrUs3HQm1gLIEAAAAXq1F47p6ZEh7SdIzi7Yq7cAJs4EAAAAAOF6X+Pqa8JNWkqSHP9iofUdPGk6EmkZZAgAAAK93y+Wx+mnHJipx25owK0X5hSWmIwEAAABwuDH9W6pb8/rKKyzRhNnrVFzqNh0JNYiyBAAAAF7PsixNvbGTYiKCtTsrX498uNF0JAAAAAAO5+eyNG14ssKC/bVu3wk9//l205FQgyhLAAAA4BMiQgL07PBkWZY057sDWph2yHQkAAAAAA4XWz9Ef7uxoyTphaU79O3uY4YToaZQlgAAAMBn9GzRUGP6tZQkPTA/TeknThlOBAAAAMDphiTFaGiXpnLb0r2zU5R9qth0JNQAyhIAAAD4lAnXtFJyXD3lFJTo3lkpKnXbpiMBAAAAcLi//LyD4huEKP3EKf35/Q2ybeYIb0NZAgAAAJ8S4OfS9BHJCg3007d7junlZTtMRwIAAADgcHWD/DV9RLL8XJY+TD2o+WvTTUdCNXN8WTJ16lR169ZNYWFhioyM1A033KCtW7eW28ayrAofTz311Dn3PW/ePCUmJiooKEiJiYl67733avKjAAAAwCGaNQzVX37eQZI0bcl2rd133HAiAAAAAE7XOb6+7r2mlSTp4Q82aO/RfMOJUJ0cX5YsX75cY8aM0ddff63FixerpKREAwYMUH7+/w7EQ4cOlXv861//kmVZuummmyrd7+rVqzV8+HCNHj1aqampGj16tIYNG6ZvvvmmNj4WAAAADBvapamGJMWo1G3rnlkpyi1g3WEAAAAA53ZXv5bqntBA+UWlmjArRcWlbtORUE0s28MWVzty5IgiIyO1fPly9enTp8JtbrjhBuXm5urzzz+vdD/Dhw9XTk6OPvnkk7LnrrvuOtWvX18zZ848a/vCwkIVFhaW/ZyTk6O4uDhlZ2crPDz8Ej4RAAAATMkpKNag575U+olTGtq5qZ4dnmw6Es4hJydHERERHvU3OHMEAACA90k/cUqDnluhnIISjbu6pX4/oI3pSDiHqs4Rjr+y5Meys7MlSQ0aNKjw9cOHD2vhwoW6/fbbz7mf1atXa8CAAeWeGzhwoFatWlXh9lOnTlVERETZIy4u7iLSAwAAwEnCgwM0fUSyXJY0f126Pkhh3WFUL+YIAAAA79O0Xh09NrSjJOnFpTv0za6jhhOhOnhUWWLbtiZOnKjevXurQ4cOFW7z73//W2FhYRo6dOg595WRkaGoqKhyz0VFRSkjI6PC7R944AFlZ2eXPfbv339xHwIAAACOcnnzBhp39el1h//83gbtP3bScCJ4E+YIAAAA7zS4U4xu7horty3dOztF2SdZ1tfTeVRZMnbsWKWlpVW4TNYZ//rXv/SLX/xCwcHB592fZVnlfrZt+6znzggKClJ4eHi5BwAAALzDuKtbqmuz+sotLNE9s1NUwrrDqCbMEQAAAN5rypD2atYwRAezC/Sn99fLw+54gR/xmLJk3LhxWrBggZYuXarY2NgKt/nyyy+1detW3XHHHefdX5MmTc66iiQzM/Osq00AAADg/fz9XHpueLLCgvy1Zu9xvbB0h+lIAAAAAByubpC/po/oLH+XpYVph/TumgOmI+ESOL4ssW1bY8eO1fz58/XFF18oISGh0m1fe+01de3aVUlJSefd7xVXXKHFixeXe27RokW68sorLzkzAAAAPE9cgxD99cbTS73O+Hy7vttzzHAiAAAAAE6XHFdP917bWpI0ecFG7cnKN5wIF8vxZcmYMWP01ltv6Z133lFYWJgyMjKUkZGhU6dOldsuJydHc+fOrfSqkltvvVUPPPBA2c8TJkzQokWL9MQTT2jLli164okntGTJEt1zzz01+XEAAADgYD9PbqqhnZvKbUsTZqUop4B1hwEAAACc2//1vUw9EhroZFGpJsxap2KW9fVIji9LXn75ZWVnZ6tfv36Kjo4ue8yePbvcdrNmzZJt2xo5cmSF+9m3b58OHTpU9vOVV16pWbNm6fXXX1enTp30xhtvaPbs2erRo0eNfh4AAAA42yM/b6/4BiFKP3FKf35vA+sOAwAAADgnP5elacOTFR7sr9QD2XpuyTbTkXARLJvp76Lk5OQoIiJC2dnZ3KQRAADAy6zdd1y3/H2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" ] @@ -2067,7 +1925,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "id": "7e7ee409-a3f3-4692-b07f-bbfb3a8697e7", "metadata": {}, "outputs": [ @@ -2077,13 +1935,13 @@ "Text(0.5, 1.0, 'Upsampled Speed')" ] }, - "execution_count": 27, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -2110,7 +1968,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "id": "68ae8cf4-cb14-4ce2-9a16-b2faafc73d26", "metadata": {}, "outputs": [ @@ -2120,13 +1978,13 @@ "Text(0.5, 1.0, 'Upsampled Velocity')" ] }, - "execution_count": 28, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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" ] @@ -2186,7 +2044,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.18" }, "vscode": { "interpreter": { diff --git a/notebooks/py_scripts/20_Position_Trodes.py b/notebooks/py_scripts/20_Position_Trodes.py index 45ac5c6d7..3bed7e4f6 100644 --- a/notebooks/py_scripts/20_Position_Trodes.py +++ b/notebooks/py_scripts/20_Position_Trodes.py @@ -5,7 +5,7 @@ # extension: .py # format_name: light # format_version: '1.5' -# jupytext_version: 1.15.2 +# jupytext_version: 1.16.0 # kernelspec: # display_name: Python 3 (ipykernel) # language: python @@ -292,21 +292,21 @@ # # To keep `minirec` small, the download link does not include videos by default. # -# (Download links coming soon) -# -# Full datasets can be further visualized by plotting the results on the video, -# which will appear in the current working directory. +# If it is available, you can uncomment the code, populate the `TrodesPosVideo` table, and plot the results on the video using the `make_video` function, which will appear in the current working directory. # -sgp.v1.TrodesPosVideo().populate( - { - "nwb_file_name": nwb_copy_file_name, - "interval_list_name": interval_list_name, - "position_info_param_name": trodes_params_name, - } -) +# + +# sgp.v1.TrodesPosVideo().populate( +# { +# "nwb_file_name": nwb_copy_file_name, +# "interval_list_name": interval_list_name, +# "position_info_param_name": trodes_params_name, +# } +# ) -sgp.v1.TrodesPosVideo() +# + +# sgp.v1.TrodesPosVideo() +# - # ## Upsampling position # From 6705ee04538e49a995649008276a295b4f7cb649 Mon Sep 17 00:00:00 2001 From: Eric Denovellis Date: Tue, 16 Jan 2024 13:02:08 -0800 Subject: [PATCH 2/8] Handle numpy arrays (#766) --- src/spyglass/lfp/lfp_electrode.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/spyglass/lfp/lfp_electrode.py b/src/spyglass/lfp/lfp_electrode.py index 4d2d75269..0b683a3da 100644 --- a/src/spyglass/lfp/lfp_electrode.py +++ b/src/spyglass/lfp/lfp_electrode.py @@ -1,4 +1,5 @@ import datajoint as dj +from numpy import ndarray from spyglass.common.common_ephys import Electrode from spyglass.common.common_session import Session # noqa: F401 @@ -48,6 +49,9 @@ def create_lfp_electrode_group( as_dict=True ) primary_key = Electrode.primary_key + if isinstance(electrode_list, ndarray): + # convert to list if it is an numpy array + electrode_list = list(electrode_list.astype(int).reshape(-1)) for e in all_electrodes: # create a dictionary so we can insert the electrodes if e["electrode_id"] in electrode_list: From 0089d5eb1fedf52d06291d93526f513bda354d0a Mon Sep 17 00:00:00 2001 From: Chris Brozdowski Date: Fri, 19 Jan 2024 13:15:44 -0600 Subject: [PATCH 3/8] Pytest revamp (#743) * WIP: Pull from old stash, resolve conflicts * Pytest WIP. Position centriod fix. Centralize device prompt logic * Add tests for all tables in * WIP: Improve coverage behav, dio * WIP: Add coverage, see details: - Add `return_fig` param to plotting helper functions to permit tests - `common_filter` - `common_interval` - Add coverage for ~1/2 of `common` - `common_behav` - `common_device` - `common_ephys` - `common_filter` - `common_interval` - with helper funcs tested seperately - `common_lab` - `common_nwbfile` - partial * WIP pytest common 2nd half, start lfp * WIP lfp tests, ahead of fetch upstream * Add lfp pipeline tests * Run pre-commit checks * Fix bug * Unpin position_tools for CI * Change download data dir * Change download data dir 2 * Fix teardown. Coverage 67% * Update changelog * logger.warn -> logger.warning --- .github/workflows/test-conda.yml | 21 +- CHANGELOG.md | 2 +- pyproject.toml | 39 ++- src/spyglass/common/common_device.py | 158 ++++----- src/spyglass/common/common_dio.py | 5 +- src/spyglass/common/common_filter.py | 12 +- src/spyglass/common/common_interval.py | 8 +- src/spyglass/common/common_position.py | 9 +- src/spyglass/common/common_session.py | 26 +- src/spyglass/data_import/__init__.py | 1 + src/spyglass/data_import/insert_sessions.py | 2 +- src/spyglass/decoding/decoding_merge.py | 4 +- src/spyglass/settings.py | 18 +- src/spyglass/utils/dj_merge_tables.py | 2 +- src/spyglass/utils/dj_mixin.py | 10 +- tests/README.md | 47 +++ tests/ci_config.py | 27 -- tests/{datajoint => common}/__init__.py | 0 tests/common/conftest.py | 48 +++ tests/common/test_behav.py | 73 +++++ tests/common/test_common_interval.py | 62 ---- tests/common/test_device.py | 40 +++ tests/common/test_dio.py | 31 ++ tests/common/test_ephys.py | 33 ++ tests/common/test_filter.py | 79 +++++ tests/common/test_insert.py | 220 +++++++++++++ tests/common/test_interval.py | 27 ++ tests/common/test_interval_helpers.py | 272 ++++++++++++++++ tests/common/test_lab.py | 110 +++++++ tests/common/test_nwbfile.py | 41 +++ tests/common/test_position.py | 151 +++++++++ tests/common/test_region.py | 29 ++ tests/common/test_ripple.py | 6 + tests/common/test_sensors.py | 21 ++ tests/common/test_session.py | 81 +++++ tests/conftest.py | 342 +++++++++++++++++--- tests/container.py | 216 +++++++++++++ tests/data_import/__init__.py | 3 + tests/data_import/test_insert_sessions.py | 115 ++----- tests/datajoint/_config.py | 1 - tests/datajoint/_datajoint_server.py | 110 ------- tests/lfp/conftest.py | 215 ++++++++++++ tests/lfp/test_pipeline.py | 25 ++ tests/test_insert_beans.py | 97 ------ tests/trim_beans.py | 73 ----- tests/{ => utils}/test_nwb_helper_fn.py | 10 +- 46 files changed, 2283 insertions(+), 639 deletions(-) create mode 100644 tests/README.md delete mode 100644 tests/ci_config.py rename tests/{datajoint => common}/__init__.py (100%) create mode 100644 tests/common/conftest.py create mode 100644 tests/common/test_behav.py delete mode 100644 tests/common/test_common_interval.py create mode 100644 tests/common/test_device.py create mode 100644 tests/common/test_dio.py create mode 100644 tests/common/test_ephys.py create mode 100644 tests/common/test_filter.py create mode 100644 tests/common/test_insert.py create mode 100644 tests/common/test_interval.py create mode 100644 tests/common/test_interval_helpers.py create mode 100644 tests/common/test_lab.py create mode 100644 tests/common/test_nwbfile.py create mode 100644 tests/common/test_position.py create mode 100644 tests/common/test_region.py create mode 100644 tests/common/test_ripple.py create mode 100644 tests/common/test_sensors.py create mode 100644 tests/common/test_session.py create mode 100644 tests/container.py delete mode 100644 tests/datajoint/_config.py delete mode 100644 tests/datajoint/_datajoint_server.py create mode 100644 tests/lfp/conftest.py create mode 100644 tests/lfp/test_pipeline.py delete mode 100644 tests/test_insert_beans.py delete mode 100644 tests/trim_beans.py rename tests/{ => utils}/test_nwb_helper_fn.py (86%) diff --git a/.github/workflows/test-conda.yml b/.github/workflows/test-conda.yml index cd793a480..594a7b2b8 100644 --- a/.github/workflows/test-conda.yml +++ b/.github/workflows/test-conda.yml @@ -17,16 +17,6 @@ jobs: env: OS: ${{ matrix.os }} PYTHON: '3.8' - # SPYGLASS_BASE_DIR: ./data - # KACHERY_STORAGE_DIR: ./data/kachery-storage - # DJ_SUPPORT_FILEPATH_MANAGEMENT: True - # services: - # datajoint_test_server: - # image: datajoint/mysql - # ports: - # - 3306:3306 - # options: >- - # -e MYSQL_ROOT_PASSWORD=tutorial steps: - name: Cancel Workflow Action uses: styfle/cancel-workflow-action@0.11.0 @@ -49,6 +39,17 @@ jobs: - name: Install spyglass run: | pip install -e .[test] + - name: Download data + env: + UCSF_BOX_TOKEN: ${{ secrets.UCSF_BOX_TOKEN }} + UCSF_BOX_USER: ${{ secrets.UCSF_BOX_USER }} + WEBSITE: ftps://ftp.box.com/trodes_to_nwb_test_data/minirec20230622.nwb + RAW_DIR: /home/runner/work/spyglass/spyglass/tests/_data/raw/ + run: | + mkdir -p $RAW_DIR + wget --recursive --no-verbose --no-host-directories --no-directories \ + --user $UCSF_BOX_USER --password $UCSF_BOX_TOKEN \ + -P $RAW_DIR $WEBSITE - name: Run tests run: | pytest -rP # env vars are set within certain tests diff --git a/CHANGELOG.md b/CHANGELOG.md index 895702b43..302c116d3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -12,6 +12,7 @@ - Add `deprecation_factory` to facilitate table migration. #717 - Add Spyglass logger. #730 - IntervalList: Add secondary key `pipeline` #742 +- Increase pytest coverage for `common`, `lfp`, and `utils`. #743 ### Pipelines @@ -31,7 +32,6 @@ - Allow multiple spike waveform features for clusterelss decoding #731 - Reorder notebooks #731 - ## [0.4.3] (November 7, 2023) - Migrate `config` helper scripts to Spyglass codebase. #662 diff --git a/pyproject.toml b/pyproject.toml index 33a7df931..521224737 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -70,6 +70,7 @@ spyglass_cli = "spyglass.cli:cli" [project.optional-dependencies] position = ["ffmpeg", "numba>=0.54", "deeplabcut<2.3.0"] test = [ + "docker", # for tests in a container "pytest", # unit testing "pytest-cov", # code coverage "kachery", # database access @@ -109,5 +110,41 @@ line-length = 80 [tool.codespell] skip = '.git,*.pdf,*.svg,*.ipynb,./docs/site/**,temp*' -# Nevers - name in Citation ignore-words-list = 'nevers' +# Nevers - name in Citation + +[tool.pytest.ini_options] +minversion = "7.0" +addopts = [ + "-sv", + "-p no:warnings", + # "--no-teardown", # don't teardown the database after tests + # "--quiet-spy", # don't show logging from spyglass + "--show-capture=no", + "--pdbcls=IPython.terminal.debugger:TerminalPdb", # use ipython debugger + "--cov=spyglass", + "--cov-report=term-missing", + "--no-cov-on-fail", +] +testpaths = ["tests"] +log_level = "INFO" + +[tool.coverage.run] +source = ["*/src/spyglass/*"] +omit = [ # which submodules have no tests + "*/__init__.py", + "*/_version.py", + "*/cli/*", + # "*/common/*", + "*/data_import/*", + "*/decoding/*", + "*/figurl_views/*", + # "*/lfp/*", + "*/linearization/*", + "*/lock/*", + "*/position/*", + "*/ripple/*", + "*/sharing/*", + "*/spikesorting/*", + # "*/utils/*", +] diff --git a/src/spyglass/common/common_device.py b/src/spyglass/common/common_device.py index 223862c81..2dd03c822 100644 --- a/src/spyglass/common/common_device.py +++ b/src/spyglass/common/common_device.py @@ -2,8 +2,8 @@ import ndx_franklab_novela from spyglass.common.errors import PopulateException -from spyglass.utils.dj_mixin import SpyglassMixin -from spyglass.utils.logging import logger +from spyglass.settings import test_mode +from spyglass.utils import SpyglassMixin, logger from spyglass.utils.nwb_helper_fn import get_nwb_file schema = dj.schema("common_device") @@ -154,25 +154,9 @@ def _add_device(cls, new_device_dict): all_values = DataAcquisitionDevice.fetch( "data_acquisition_device_name" ).tolist() - if name not in all_values: - # no entry with the same name exists, prompt user to add a new entry - logger.info( - f"\nData acquisition device '{name}' was not found in the " - f"database. The current values are: {all_values}. " - "Please ensure that the device you want to add does not already" - " exist in the database under a different name or spelling. " - "If you want to use an existing device in the database, " - "please change the corresponding Device object in the NWB file." - " Entering 'N' will raise an exception." - ) - to_db = " to the database" - val = input(f"Add data acquisition device '{name}'{to_db}? (y/N)") - if val.lower() in ["y", "yes"]: - cls.insert1(new_device_dict, skip_duplicates=True) - return - raise PopulateException( - f"User chose not to add device '{name}'{to_db}." - ) + if prompt_insert(name=name, all_values=all_values): + cls.insert1(new_device_dict, skip_duplicates=True) + return # Check if values provided match the values stored in the database db_dict = ( @@ -213,28 +197,11 @@ def _add_system(cls, system): all_values = DataAcquisitionDeviceSystem.fetch( "data_acquisition_device_system" ).tolist() - if system not in all_values: - logger.info( - f"\nData acquisition device system '{system}' was not found in" - f" the database. The current values are: {all_values}. " - "Please ensure that the system you want to add does not already" - " exist in the database under a different name or spelling. " - "If you want to use an existing system in the database, " - "please change the corresponding Device object in the NWB file." - " Entering 'N' will raise an exception." - ) - val = input( - f"Do you want to add data acquisition device system '{system}'" - + " to the database? (y/N)" - ) - if val.lower() in ["y", "yes"]: - key = {"data_acquisition_device_system": system} - DataAcquisitionDeviceSystem.insert1(key, skip_duplicates=True) - else: - raise PopulateException( - "User chose not to add data acquisition device system " - + f"'{system}' to the database." - ) + if prompt_insert( + name=system, all_values=all_values, table_type="system" + ): + key = {"data_acquisition_device_system": system} + DataAcquisitionDeviceSystem.insert1(key, skip_duplicates=True) return system @classmethod @@ -264,30 +231,11 @@ def _add_amplifier(cls, amplifier): all_values = DataAcquisitionDeviceAmplifier.fetch( "data_acquisition_device_amplifier" ).tolist() - if amplifier not in all_values: - logger.info( - f"\nData acquisition device amplifier '{amplifier}' was not " - f"found in the database. The current values are: {all_values}. " - "Please ensure that the amplifier you want to add does not " - "already exist in the database under a different name or " - "spelling. If you want to use an existing name in the database," - " please change the corresponding Device object in the NWB " - "file. Entering 'N' will raise an exception." - ) - val = input( - "Do you want to add data acquisition device amplifier " - + f"'{amplifier}' to the database? (y/N)" - ) - if val.lower() in ["y", "yes"]: - key = {"data_acquisition_device_amplifier": amplifier} - DataAcquisitionDeviceAmplifier.insert1( - key, skip_duplicates=True - ) - else: - raise PopulateException( - "User chose not to add data acquisition device amplifier " - + f"'{amplifier}' to the database." - ) + if prompt_insert( + name=amplifier, all_values=all_values, table_type="amplifier" + ): + key = {"data_acquisition_device_amplifier": amplifier} + DataAcquisitionDeviceAmplifier.insert1(key, skip_duplicates=True) return amplifier @@ -576,27 +524,9 @@ def _add_probe_type(cls, new_probe_type_dict): """ probe_type = new_probe_type_dict["probe_type"] all_values = ProbeType.fetch("probe_type").tolist() - if probe_type not in all_values: - logger.info( - f"\nProbe type '{probe_type}' was not found in the database. " - f"The current values are: {all_values}. " - "Please ensure that the probe type you want to add does not " - "already exist in the database under a different name or " - "spelling. If you want to use an existing name in the " - "database, please change the corresponding Probe object in the " - "NWB file. Entering 'N' will raise an exception." - ) - val = input( - f"Do you want to add probe type '{probe_type}' to the database?" - + " (y/N)" - ) - if val.lower() in ["y", "yes"]: - ProbeType.insert1(new_probe_type_dict, skip_duplicates=True) - return - raise PopulateException( - f"User chose not to add probe type '{probe_type}' to the " - + "database." - ) + if prompt_insert(probe_type, all_values, table="probe type"): + ProbeType.insert1(new_probe_type_dict, skip_duplicates=True) + return # else / entry exists: check whether the values provided match the # values stored in the database @@ -738,3 +668,55 @@ def create_from_nwbfile( cls.Shank.insert1(shank, skip_duplicates=True) for electrode in elect_dict.values(): cls.Electrode.insert1(electrode, skip_duplicates=True) + + +# ---------------------------- Helper functions ---------------------------- + + +# Migrated down to reduce redundancy and centralize 'test_mode' check for pytest +def prompt_insert( + name: str, + all_values: list, + table: str = "Data Acquisition Device", + table_type: str = None, +) -> bool: + """Prompt user to add an item to the database. Return True if yes. + + Assume insert during test mode. + + Parameters + ---------- + name : str + The name of the item to add. + all_values : list + List of all values in the database. + table : str, optional + The name of the table to add to, by default Data Acquisition Device + table_type : str, optional + The type of item to add, by default None. Data Acquisition Device X + """ + if name in all_values: + return False + + if test_mode: + return True + + if table_type: + table_type += " " + + logger.info( + f"{table}{table_type} '{name}' was not found in the" + f"database. The current values are: {all_values}.\n" + "Please ensure that the device you want to add does not already" + "exist in the database under a different name or spelling. If you" + "want to use an existing device in the database, please change the" + "corresponding Device object in the NWB file.\nEntering 'N' will " + "raise an exception." + ) + msg = f"Do you want to add {table}{table_type} '{name}' to the database?" + if dj.utils.user_choice(msg).lower() in ["y", "yes"]: + return True + + raise PopulateException( + f"User chose not to add {table}{table_type} '{name}' to the database." + ) diff --git a/src/spyglass/common/common_dio.py b/src/spyglass/common/common_dio.py index 93a087116..7eae1e9d3 100644 --- a/src/spyglass/common/common_dio.py +++ b/src/spyglass/common/common_dio.py @@ -50,7 +50,7 @@ def make(self, key): key["dio_object_id"] = event_series.object_id self.insert1(key, skip_duplicates=True) - def plot_all_dio_events(self): + def plot_all_dio_events(self, return_fig=False): """Plot all DIO events in the session. Examples @@ -117,3 +117,6 @@ def plot_all_dio_events(self): plt.suptitle(f"DIO events in {nwb_file_names[0]}") else: plt.suptitle(f"DIO events in {', '.join(nwb_file_names)}") + + if return_fig: + return plt.gcf() diff --git a/src/spyglass/common/common_filter.py b/src/spyglass/common/common_filter.py index 0472c6e18..9d2cdf9d6 100644 --- a/src/spyglass/common/common_filter.py +++ b/src/spyglass/common/common_filter.py @@ -167,9 +167,9 @@ def add_filter( def _filter_restrict(self, filter_name, fs): return ( self & {"filter_name": filter_name} & {"filter_sampling_rate": fs} - ).fetch1(as_dict=True) + ).fetch1() - def plot_magnitude(self, filter_name, fs): + def plot_magnitude(self, filter_name, fs, return_fig=False): filter_dict = self._filter_restrict(filter_name, fs) plt.figure() w, h = signal.freqz(filter_dict["filter_coeff"], worN=65536) @@ -178,11 +178,13 @@ def plot_magnitude(self, filter_name, fs): plt.xlabel("Frequency (Hz)") plt.ylabel("Magnitude") plt.title("Frequency Response") - plt.xlim(0, np.max(filter_dict["filter_coeffand_edges"] * 2)) + plt.xlim(0, np.max(filter_dict["filter_band_edges"] * 2)) plt.ylim(np.min(magnitude), -1 * np.min(magnitude) * 0.1) plt.grid(True) + if return_fig: + return plt.gcf() - def plot_fir_filter(self, filter_name, fs): + def plot_fir_filter(self, filter_name, fs, return_fig=False): filter_dict = self._filter_restrict(filter_name, fs) plt.figure() plt.clf() @@ -191,6 +193,8 @@ def plot_fir_filter(self, filter_name, fs): plt.ylabel("Magnitude") plt.title("Filter Taps") plt.grid(True) + if return_fig: + return plt.gcf() def filter_delay(self, filter_name, fs): return self.calc_filter_delay( diff --git a/src/spyglass/common/common_interval.py b/src/spyglass/common/common_interval.py index b03055f88..d754261fc 100644 --- a/src/spyglass/common/common_interval.py +++ b/src/spyglass/common/common_interval.py @@ -66,7 +66,7 @@ def insert_from_nwbfile(cls, nwbf, *, nwb_file_name): cls.insert1(epoch_dict, skip_duplicates=True) - def plot_intervals(self, figsize=(20, 5)): + def plot_intervals(self, figsize=(20, 5), return_fig=False): interval_list = pd.DataFrame(self) fig, ax = plt.subplots(figsize=figsize) interval_count = 0 @@ -84,8 +84,10 @@ def plot_intervals(self, figsize=(20, 5)): ax.set_yticklabels(interval_list.interval_list_name) ax.set_xlabel("Time [s]") ax.grid(True) + if return_fig: + return fig - def plot_epoch_pos_raw_intervals(self, figsize=(20, 5)): + def plot_epoch_pos_raw_intervals(self, figsize=(20, 5), return_fig=False): interval_list = pd.DataFrame(self) fig, ax = plt.subplots(figsize=(30, 3)) @@ -145,6 +147,8 @@ def plot_epoch_pos_raw_intervals(self, figsize=(20, 5)): ax.set_yticklabels(["pos valid times", "raw data valid times", "epoch"]) ax.set_xlabel("Time [s]") ax.grid(True) + if return_fig: + return fig def intervals_by_length(interval_list, min_length=0.0, max_length=1e10): diff --git a/src/spyglass/common/common_position.py b/src/spyglass/common/common_position.py index ea661a29d..732c9779e 100644 --- a/src/spyglass/common/common_position.py +++ b/src/spyglass/common/common_position.py @@ -8,7 +8,6 @@ import pynwb.behavior from position_tools import ( get_angle, - get_centriod, get_distance, get_speed, get_velocity, @@ -30,6 +29,12 @@ from spyglass.utils import SpyglassMixin, logger from spyglass.utils.dj_helper_fn import deprecated_factory +try: + from position_tools import get_centroid +except ImportError: + logger.warning("Please update position_tools to >= 0.1.0") + from position_tools import get_centriod as get_centroid + schema = dj.schema("common_position") @@ -417,7 +422,7 @@ def calculate_position_info( ) # Calculate position, orientation, velocity, speed - position = get_centriod(back_LED, front_LED) # cm + position = get_centroid(back_LED, front_LED) # cm orientation = get_angle(back_LED, front_LED) # radians is_nan = np.isnan(orientation) diff --git a/src/spyglass/common/common_session.py b/src/spyglass/common/common_session.py index 6792453bc..f6f783262 100644 --- a/src/spyglass/common/common_session.py +++ b/src/spyglass/common/common_session.py @@ -63,13 +63,15 @@ def make(self, key): nwbf = get_nwb_file(nwb_file_abspath) config = get_config(nwb_file_abspath) - # certain data are not associated with a single NWB file / session because they may apply to - # multiple sessions. these data go into dj.Manual tables. - # e.g., a lab member may be associated with multiple experiments, so the lab member table should not - # be dependent on (contain a primary key for) a session. - - # here, we create new entries in these dj.Manual tables based on the values read from the NWB file - # then, they are linked to the session via fields of Session (e.g., Subject, Institution, Lab) or part + # certain data are not associated with a single NWB file / session + # because they may apply to multiple sessions. these data go into + # dj.Manual tables. e.g., a lab member may be associated with multiple + # experiments, so the lab member table should not be dependent on + # (contain a primary key for) a session. + + # here, we create new entries in these dj.Manual tables based on the + # values read from the NWB file then, they are linked to the session + # via fields of Session (e.g., Subject, Institution, Lab) or part # tables (e.g., Experimenter, DataAcquisitionDevice). logger.info("Institution...") @@ -221,17 +223,19 @@ def add_session_to_group( ) @staticmethod - def remove_session_from_group(nwb_file_name: str, session_group_name: str): + def remove_session_from_group( + nwb_file_name: str, session_group_name: str, *args, **kwargs + ): query = { "session_group_name": session_group_name, "nwb_file_name": nwb_file_name, } - (SessionGroupSession & query).delete() + (SessionGroupSession & query).delete(*args, **kwargs) @staticmethod - def delete_group(session_group_name: str): + def delete_group(session_group_name: str, *args, **kwargs): query = {"session_group_name": session_group_name} - (SessionGroup & query).delete() + (SessionGroup & query).delete(*args, **kwargs) @staticmethod def get_group_sessions(session_group_name: str): diff --git a/src/spyglass/data_import/__init__.py b/src/spyglass/data_import/__init__.py index 703cfa3c1..9c68cf038 100644 --- a/src/spyglass/data_import/__init__.py +++ b/src/spyglass/data_import/__init__.py @@ -1 +1,2 @@ +# TODO: change naming to avoid match between module and function from .insert_sessions import insert_sessions diff --git a/src/spyglass/data_import/insert_sessions.py b/src/spyglass/data_import/insert_sessions.py index c862fe85b..329a7be42 100644 --- a/src/spyglass/data_import/insert_sessions.py +++ b/src/spyglass/data_import/insert_sessions.py @@ -101,7 +101,7 @@ def copy_nwb_link_raw_ephys(nwb_file_name, out_nwb_file_name): if os.path.exists(out_nwb_file_abs_path): if debug_mode: return out_nwb_file_abs_path - warnings.warn( + logger.warning( f"Output file {out_nwb_file_abs_path} exists and will be " + "overwritten." ) diff --git a/src/spyglass/decoding/decoding_merge.py b/src/spyglass/decoding/decoding_merge.py index c49971c78..1752b1165 100644 --- a/src/spyglass/decoding/decoding_merge.py +++ b/src/spyglass/decoding/decoding_merge.py @@ -21,14 +21,14 @@ class DecodingOutput(_Merge, SpyglassMixin): source: varchar(32) """ - class ClusterlessDecodingV1(SpyglassMixin, dj.Part): + class ClusterlessDecodingV1(SpyglassMixin, dj.Part): # noqa: F811 definition = """ -> master --- -> ClusterlessDecodingV1 """ - class SortedSpikesDecodingV1(SpyglassMixin, dj.Part): + class SortedSpikesDecodingV1(SpyglassMixin, dj.Part): # noqa: F811 definition = """ -> master --- diff --git a/src/spyglass/settings.py b/src/spyglass/settings.py index 4672af615..e2e0a2142 100644 --- a/src/spyglass/settings.py +++ b/src/spyglass/settings.py @@ -30,7 +30,8 @@ def __init__(self, base_dir: str = None, **kwargs): self.supplied_base_dir = base_dir self._config = dict() self.config_defaults = dict(prepopulate=True) - self._debug_mode = False + self._debug_mode = kwargs.get("debug_mode", False) + self._test_mode = kwargs.get("test_mode", False) self._dlc_base = None self.relative_dirs = { @@ -106,6 +107,7 @@ def load_config(self, force_reload=False): dj_dlc = dj_custom.get("dlc_dirs", {}) self._debug_mode = dj_custom.get("debug_mode", False) + self._test_mode = dj_custom.get("test_mode", False) resolved_base = ( self.supplied_base_dir @@ -166,6 +168,7 @@ def load_config(self, force_reload=False): self._config = dict( debug_mode=self._debug_mode, + test_mode=self._test_mode, **self.config_defaults, **config_dirs, **kachery_zone_dict, @@ -381,6 +384,7 @@ def _dj_custom(self) -> dict: return { "custom": { "debug_mode": str(self.debug_mode).lower(), + "test_mode": str(self._test_mode).lower(), "spyglass_dirs": { "base": self.base_dir, "raw": self.raw_dir, @@ -453,8 +457,19 @@ def video_dir(self) -> str: @property def debug_mode(self) -> bool: + """Returns True if debug_mode is set. + + Supports skipping inserts for Dockerized development. + """ return self._debug_mode + @property + def test_mode(self) -> bool: + """Returns True if test_mode is set. + + Required for pytests to run without prompts.""" + return self._test_mode + @property def dlc_project_dir(self) -> str: return self.config.get(self.dir_to_var("project", "dlc")) @@ -479,6 +494,7 @@ def dlc_output_dir(self) -> str: waveform_dir = sg_config.waveform_dir video_dir = sg_config.video_dir debug_mode = sg_config.debug_mode +test_mode = sg_config.test_mode prepopulate = config.get("prepopulate", False) dlc_project_dir = sg_config.dlc_project_dir dlc_video_dir = sg_config.dlc_video_dir diff --git a/src/spyglass/utils/dj_merge_tables.py b/src/spyglass/utils/dj_merge_tables.py index eddd77652..5c900b66c 100644 --- a/src/spyglass/utils/dj_merge_tables.py +++ b/src/spyglass/utils/dj_merge_tables.py @@ -758,7 +758,7 @@ def delete_downstream_merge( def _warn_on_restriction(table: dj.Table, restriction: str = None): """Warn if restriction on table object differs from input restriction""" - if restriction is None and table().restriction: + if restriction is None and table.restriction: logger.warn( f"Warning: ignoring table restriction: {table().restriction}.\n\t" + "Please pass restrictions as an arg" diff --git a/src/spyglass/utils/dj_mixin.py b/src/spyglass/utils/dj_mixin.py index 8a53743de..3ee0f6292 100644 --- a/src/spyglass/utils/dj_mixin.py +++ b/src/spyglass/utils/dj_mixin.py @@ -226,7 +226,13 @@ def _check_delete_permission(self) -> None: user_name = LabMember().get_djuser_name(dj_user) for experimenter in set(experimenters): - if user_name not in LabTeam().get_team_members(experimenter): + # Check once with cache, if fails, reload and check again + # On eval as set, reload will only be called once + if user_name not in LabTeam().get_team_members( + experimenter + ) and user_name not in LabTeam().get_team_members( + experimenter, reload=True + ): sess_w_exp = sess_summary & {self._member_pk: experimenter} raise PermissionError( f"User '{user_name}' is not on a team with '{experimenter}'" @@ -259,7 +265,7 @@ def cautious_delete(self, force_permission: bool = False, *args, **kwargs): merge_deletes = self._merge_del_func( self, - restriction=self.restriction, + restriction=self.restriction if self.restriction else None, dry_run=True, disable_warning=True, ) diff --git a/tests/README.md b/tests/README.md new file mode 100644 index 000000000..476dbb4c8 --- /dev/null +++ b/tests/README.md @@ -0,0 +1,47 @@ +# PyTests + +This directory is contains files for testing the code. Simply by running +`pytest` from the root directory, all tests will be run with default parameters +specified in `pyproject.toml`. Notable optional parameters include... + +- Coverage items. The coverage report indicates what percentage of the code was + included in tests. + + - `--cov=spyglatss`: Which package should be described in the coverage report + - `--cov-report term-missing`: Include lines of items missing in coverage + +- Verbosity. + + - `-v`: List individual tests, report pass/fail + - `--quiet-spy`: Default False. When True, print and other logging statements + from Spyglass are silenced. + +- Data and database. + + - `--no-server`: Default False, launch Docker container from python. When + True, no server is started and tests attempt to connect to existing + container. + - `--no-teardown`: Default False. When True, docker database tables are + preserved on exit. Set to false to inspect output items after testing. + - `--my-datadir ./rel-path/`: Default `./tests/test_data/`. Where to store + created files. + +- Incremental running. + + - `-m`: Run tests with the + [given marker](https://docs.pytest.org/en/6.2.x/usage.html#specifying-tests-selecting-tests) + (e.g., `pytest -m current`). + - `--sw`: Stepwise. Continue from previously failed test when starting again. + - `-s`: No capture. By including `from IPython import embed; embed()` in a + test, and using this flag, you can open an IPython environment from within + a test + - `--pdb`: Enter debug mode if a test fails. + - `tests/test_file.py -k test_name`: To run just a set of tests, specify the + file name at the end of the command. To run a single test, further specify + `-k` with the test name. + +When customizing parameters, comment out the `addopts` line in `pyproject.toml`. + +```console +pytest -m current --quiet-spy --no-teardown tests/test_file.py -k test_name +``` diff --git a/tests/ci_config.py b/tests/ci_config.py deleted file mode 100644 index e329df7ed..000000000 --- a/tests/ci_config.py +++ /dev/null @@ -1,27 +0,0 @@ -import os -from pathlib import Path - -import datajoint as dj - -# NOTE this env var is set in the GitHub Action directly -data_dir = Path(os.environ["SPYGLASS_BASE_DIR"]) - -raw_dir = data_dir / "raw" -analysis_dir = data_dir / "analysis" - -dj.config["database.host"] = "localhost" -dj.config["database.user"] = "root" -dj.config["database.password"] = "tutorial" -dj.config["stores"] = { - "raw": { - "protocol": "file", - "location": str(raw_dir), - "stage": str(raw_dir), - }, - "analysis": { - "protocol": "file", - "location": str(analysis_dir), - "stage": str(analysis_dir), - }, -} -dj.config.save_global() diff --git a/tests/datajoint/__init__.py b/tests/common/__init__.py similarity index 100% rename from tests/datajoint/__init__.py rename to tests/common/__init__.py diff --git a/tests/common/conftest.py b/tests/common/conftest.py new file mode 100644 index 000000000..41fdea95a --- /dev/null +++ b/tests/common/conftest.py @@ -0,0 +1,48 @@ +import pytest + + +@pytest.fixture(scope="session") +def mini_devices(mini_content): + yield mini_content.devices + + +@pytest.fixture(scope="session") +def mini_behavior(mini_content): + yield mini_content.processing.get("behavior") + + +@pytest.fixture(scope="session") +def mini_pos(mini_behavior): + yield mini_behavior.get_data_interface("position").spatial_series + + +@pytest.fixture(scope="session") +def mini_pos_series(mini_pos): + yield next(iter(mini_pos)) + + +@pytest.fixture(scope="session") +def mini_pos_interval_dict(common): + yield {"interval_list_name": common.PositionSource.get_pos_interval_name(0)} + + +@pytest.fixture(scope="session") +def mini_pos_tbl(common, mini_pos_series): + yield common.PositionSource.SpatialSeries * common.RawPosition.PosObject & { + "name": mini_pos_series + } + + +@pytest.fixture(scope="session") +def pos_src(common): + yield common.PositionSource() + + +@pytest.fixture(scope="session") +def pos_interval_01(pos_src): + yield [pos_src.get_pos_interval_name(x) for x in range(1)] + + +@pytest.fixture(scope="session") +def common_ephys(common): + yield common.common_ephys diff --git a/tests/common/test_behav.py b/tests/common/test_behav.py new file mode 100644 index 000000000..c21ed96f6 --- /dev/null +++ b/tests/common/test_behav.py @@ -0,0 +1,73 @@ +import pytest +from pandas import DataFrame + + +def test_invalid_interval(pos_src): + """Test invalid interval""" + with pytest.raises(ValueError): + pos_src.get_pos_interval_name("invalid_interval") + + +def test_invalid_epoch_num(common): + """Test invalid epoch num""" + with pytest.raises(ValueError): + common.PositionSource.get_epoch_num("invalid_epoch_num") + + +def test_raw_position_fetchnwb(common, mini_pos, mini_pos_interval_dict): + """Test RawPosition fetch nwb""" + fetched = DataFrame( + (common.RawPosition & mini_pos_interval_dict) + .fetch_nwb()[0]["raw_position"] + .data + ) + raw = DataFrame(mini_pos["led_0_series_0"].data) + # compare with mini_pos + assert fetched.equals(raw), "RawPosition fetch_nwb failed" + + +@pytest.mark.skip(reason="No video files in mini") +def test_videofile_no_transaction(common, mini_restr): + """Test no transaction""" + common.VideoFile()._no_transaction_make(mini_restr) + + +@pytest.mark.skip(reason="No video files in mini") +def test_videofile_update_entries(common): + """Test update entries""" + common.VideoFile().update_entries() + + +@pytest.mark.skip(reason="No video files in mini") +def test_videofile_getabspath(common, mini_restr): + """Test get absolute path""" + common.VideoFile().getabspath(mini_restr) + + +def test_posinterval_no_transaction(verbose_context, common, mini_restr): + """Test no transaction""" + before = common.PositionIntervalMap().fetch() + with verbose_context: + common.PositionIntervalMap()._no_transaction_make(mini_restr) + after = common.PositionIntervalMap().fetch() + assert ( + len(after) == len(before) + 2 + ), "PositionIntervalMap no_transaction had unexpected effect" + + +def test_get_pos_interval_name(pos_src, pos_interval_01): + """Test get pos interval name""" + names = [f"pos {x} valid times" for x in range(1)] + assert pos_interval_01 == names, "get_pos_interval_name failed" + + +def test_convert_epoch(common, mini_dict, pos_interval_01): + this_key = ( + common.IntervalList & mini_dict & {"interval_list_name": "01_s1"} + ).fetch1() + ret = common.common_behav.convert_epoch_interval_name_to_position_interval_name( + this_key + ) + assert ( + ret == pos_interval_01[0] + ), "convert_epoch_interval_name_to_position_interval_name failed" diff --git a/tests/common/test_common_interval.py b/tests/common/test_common_interval.py deleted file mode 100644 index 293abda91..000000000 --- a/tests/common/test_common_interval.py +++ /dev/null @@ -1,62 +0,0 @@ -import numpy as np -from spyglass.common.common_interval import ( - interval_list_intersect, - interval_set_difference_inds, -) - - -def test_interval_list_intersect1(): - interval_list1 = np.array([[0, 10], [3, 5], [14, 16]]) - interval_list2 = np.array([[10, 11], [9, 14], [13, 18]]) - intersection_list = interval_list_intersect(interval_list1, interval_list2) - assert np.all(intersection_list == np.array([[9, 10], [14, 16]])) - - -def test_interval_list_intersect2(): - # if there is no intersection, return empty list - interval_list1 = np.array([[0, 10], [3, 5]]) - interval_list2 = np.array([[11, 14]]) - intersection_list = interval_list_intersect(interval_list1, interval_list2) - assert len(intersection_list) == 0 - - -def test_interval_set_difference_inds_no_overlap(): - intervals1 = [(0, 5), (8, 10)] - intervals2 = [(5, 8)] - result = interval_set_difference_inds(intervals1, intervals2) - assert result == [(0, 5), (8, 10)] - - -def test_interval_set_difference_inds_overlap(): - intervals1 = [(0, 5), (8, 10)] - intervals2 = [(1, 2), (3, 4), (6, 9)] - result = interval_set_difference_inds(intervals1, intervals2) - assert result == [(0, 1), (2, 3), (4, 5), (9, 10)] - - -def test_interval_set_difference_inds_empty_intervals1(): - intervals1 = [] - intervals2 = [(1, 2), (3, 4), (6, 9)] - result = interval_set_difference_inds(intervals1, intervals2) - assert result == [] - - -def test_interval_set_difference_inds_empty_intervals2(): - intervals1 = [(0, 5), (8, 10)] - intervals2 = [] - result = interval_set_difference_inds(intervals1, intervals2) - assert result == [(0, 5), (8, 10)] - - -def test_interval_set_difference_inds_equal_intervals(): - intervals1 = [(0, 5), (8, 10)] - intervals2 = [(0, 5), (8, 10)] - result = interval_set_difference_inds(intervals1, intervals2) - assert result == [] - - -def test_interval_set_difference_inds_multiple_overlaps(): - intervals1 = [(0, 10)] - intervals2 = [(1, 3), (4, 6), (7, 9)] - result = interval_set_difference_inds(intervals1, intervals2) - assert result == [(0, 1), (3, 4), (6, 7), (9, 10)] diff --git a/tests/common/test_device.py b/tests/common/test_device.py new file mode 100644 index 000000000..84323f2df --- /dev/null +++ b/tests/common/test_device.py @@ -0,0 +1,40 @@ +import pytest +from numpy import array_equal + + +def test_invalid_device(common, populate_exception): + device_dict = common.DataAcquisitionDevice.fetch(as_dict=True)[0] + device_dict["other"] = "invalid" + with pytest.raises(populate_exception): + common.DataAcquisitionDevice._add_device(device_dict) + + +def test_spikegadets_system_alias(mini_insert, common): + assert ( + common.DataAcquisitionDevice()._add_system("MCU") == "SpikeGadgets" + ), "SpikeGadgets MCU alias not found" + + +def test_invalid_probe(common, populate_exception): + probe_dict = common.ProbeType.fetch(as_dict=True)[0] + probe_dict["other"] = "invalid" + with pytest.raises(populate_exception): + common.Probe._add_probe_type(probe_dict) + + +def test_create_probe(common, mini_devices, mini_path, mini_copy_name): + probe_id = common.Probe.fetch("KEY", as_dict=True)[0] + probe_type = common.ProbeType.fetch("KEY", as_dict=True)[0] + before = common.Probe.fetch() + common.Probe.create_from_nwbfile( + nwb_file_name=mini_copy_name, + nwb_device_name="probe 0", + contact_side_numbering=False, + **probe_id, + **probe_type, + ) + after = common.Probe.fetch() + # Because already inserted, expect no change + assert array_equal( + before, after + ), "Probe create_from_nwbfile had unexpected effect" diff --git a/tests/common/test_dio.py b/tests/common/test_dio.py new file mode 100644 index 000000000..f4b258dde --- /dev/null +++ b/tests/common/test_dio.py @@ -0,0 +1,31 @@ +import pytest +from numpy import allclose, array + + +@pytest.fixture(scope="session") +def dio_events(common): + yield common.common_dio.DIOEvents + + +@pytest.fixture(scope="session") +def dio_fig(mini_insert, dio_events, mini_restr): + yield (dio_events & mini_restr).plot_all_dio_events(return_fig=True) + + +def test_plot_dio_axes(dio_fig, dio_events): + """Check that all events are plotted.""" + events_fig = set(x.yaxis.get_label().get_text() for x in dio_fig.get_axes()) + events_fetch = set(dio_events.fetch("dio_event_name")) + assert events_fig == events_fetch, "Mismatch in events plotted." + + +def test_plot_dio_data(common, dio_fig): + """Hash summary of figure object.""" + data_fig = dio_fig.get_axes()[0].lines[0].get_xdata() + data_block = ( + common.IntervalList & 'interval_list_name LIKE "raw%"' + ).fetch1("valid_times") + data_fetch = array((data_block[0][0], data_block[-1][1])) + assert allclose( + data_fig, data_fetch, atol=1e-8 + ), "Mismatch in data plotted." diff --git a/tests/common/test_ephys.py b/tests/common/test_ephys.py new file mode 100644 index 000000000..9ad1ea0a4 --- /dev/null +++ b/tests/common/test_ephys.py @@ -0,0 +1,33 @@ +import pytest +from numpy import array_equal + + +def test_create_from_config(mini_insert, common_ephys, mini_path): + before = common_ephys.Electrode().fetch() + common_ephys.Electrode.create_from_config(mini_path.stem) + after = common_ephys.Electrode().fetch() + # Because already inserted, expect no change + assert array_equal( + before, after + ), "Electrode.create_from_config had unexpected effect" + + +def test_raw_object(mini_insert, common_ephys, mini_dict, mini_content): + obj_fetch = common_ephys.Raw().nwb_object(mini_dict).object_id + obj_raw = mini_content.get_acquisition().object_id + assert obj_fetch == obj_raw, "Raw.nwb_object did not return expected object" + + +def test_set_lfp_electrodes(mini_insert, common_ephys, mini_copy_name): + before = common_ephys.LFPSelection().fetch() + common_ephys.LFPSelection().set_lfp_electrodes(mini_copy_name, [0]) + after = common_ephys.LFPSelection().fetch() + # Because already inserted, expect no change + assert ( + len(after) == len(before) + 1 + ), "Set LFP electrodes had unexpected effect" + + +@pytest.mark.skip(reason="Not testing V0: common lfp") +def test_lfp(): + pass diff --git a/tests/common/test_filter.py b/tests/common/test_filter.py new file mode 100644 index 000000000..9e0be584f --- /dev/null +++ b/tests/common/test_filter.py @@ -0,0 +1,79 @@ +import pytest + + +@pytest.fixture(scope="session") +def filter_parameters(common): + yield common.FirFilterParameters() + + +@pytest.fixture(scope="session") +def filter_dict(filter_parameters): + yield {"filter_name": "test", "fs": 10} + + +@pytest.fixture(scope="session") +def add_filter(filter_parameters, filter_dict): + filter_parameters.add_filter( + **filter_dict, filter_type="lowpass", band_edges=[1, 2] + ) + + +@pytest.fixture(scope="session") +def filter_coeff(filter_parameters, filter_dict): + yield filter_parameters._filter_restrict(**filter_dict)["filter_coeff"] + + +def test_add_filter(filter_parameters, add_filter, filter_dict): + """Test add filter""" + assert filter_parameters & filter_dict, "add_filter failed" + + +def test_filter_restrict( + filter_parameters, add_filter, filter_dict, filter_coeff +): + assert sum(filter_coeff) == pytest.approx( + 0.999134, abs=1e-6 + ), "filter_restrict failed" + + +def test_plot_magitude(filter_parameters, add_filter, filter_dict): + fig = filter_parameters.plot_magnitude(**filter_dict, return_fig=True) + assert sum(fig.get_axes()[0].lines[0].get_xdata()) == pytest.approx( + 163837.5, abs=1 + ), "plot_magnitude failed" + + +def test_plot_fir_filter( + filter_parameters, add_filter, filter_dict, filter_coeff +): + fig = filter_parameters.plot_fir_filter(**filter_dict, return_fig=True) + assert sum(fig.get_axes()[0].lines[0].get_ydata()) == sum( + filter_coeff + ), "Plot filter failed" + + +def test_filter_delay(filter_parameters, add_filter, filter_dict): + delay = filter_parameters.filter_delay(**filter_dict) + assert delay == 27, "filter_delay failed" + + +def test_time_bound_warning(filter_parameters, add_filter, filter_dict): + with pytest.warns(UserWarning): + filter_parameters._time_bound_check(1, 3, [2, 5], 4) + + +@pytest.mark.skip(reason="Not testing V0: filter_data") +def test_filter_data(filter_parameters, mini_content): + pass + + +def test_calc_filter_delay(filter_parameters, filter_coeff): + delay = filter_parameters.calc_filter_delay(filter_coeff) + assert delay == 27, "filter_delay failed" + + +def test_create_standard_filters(filter_parameters): + filter_parameters.create_standard_filters() + assert filter_parameters & { + "filter_name": "LFP 0-400 Hz" + }, "create_standard_filters failed" diff --git a/tests/common/test_insert.py b/tests/common/test_insert.py new file mode 100644 index 000000000..6d2fd18b3 --- /dev/null +++ b/tests/common/test_insert.py @@ -0,0 +1,220 @@ +from datajoint.hash import key_hash +from pandas import DataFrame, Index +from pytest import approx + + +def test_insert_session(mini_insert, mini_content, mini_restr, common): + subj_raw = mini_content.subject + meta_raw = mini_content + + sess_data = (common.Session & mini_restr).fetch1() + assert ( + sess_data["subject_id"] == subj_raw.subject_id + ), "Subjuect ID not match" + + attrs = [ + ("institution_name", "institution"), + ("lab_name", "lab"), + ("session_id", "session_id"), + ("session_description", "session_description"), + ("experiment_description", "experiment_description"), + ] + + for sess_attr, meta_attr in attrs: + assert sess_data[sess_attr] == getattr( + meta_raw, meta_attr + ), f"Session table {sess_attr} not match raw data {meta_attr}" + + time_attrs = [ + ("session_start_time", "session_start_time"), + ("timestamps_reference_time", "timestamps_reference_time"), + ] + for sess_attr, meta_attr in time_attrs: + # a. strip timezone info from meta_raw + # b. convert to timestamp + # c. compare precision to 1 second + assert sess_data[sess_attr].timestamp() == approx( + getattr(meta_raw, meta_attr).replace(tzinfo=None).timestamp(), abs=1 + ), f"Session table {sess_attr} not match raw data {meta_attr}" + + +def test_insert_electrode_group(mini_insert, mini_content, common): + group_name = "0" + egroup_data = ( + common.ElectrodeGroup & {"electrode_group_name": group_name} + ).fetch1() + egroup_raw = mini_content.electrode_groups.get(group_name) + + assert ( + egroup_data["description"] == egroup_raw.description + ), "ElectrodeGroup description not match" + + assert egroup_data["region_id"] == ( + common.BrainRegion & {"region_name": egroup_raw.location} + ).fetch1( + "region_id" + ), "Region ID does not match across raw data and BrainRegion table" + + +def test_insert_electrode(mini_insert, mini_content, mini_restr, common): + electrode_id = "0" + e_data = (common.Electrode & {"electrode_id": electrode_id}).fetch1() + e_raw = mini_content.electrodes.get(int(electrode_id)).to_dict().copy() + + attrs = [ + ("x", "x"), + ("y", "y"), + ("z", "z"), + ("impedance", "imp"), + ("filtering", "filtering"), + ("original_reference_electrode", "ref_elect_id"), + ] + + for e_attr, meta_attr in attrs: + assert ( + e_data[e_attr] == e_raw[meta_attr][int(electrode_id)] + ), f"Electrode table {e_attr} not match raw data {meta_attr}" + + +def test_insert_raw(mini_insert, mini_content, mini_restr, common): + raw_data = (common.Raw & mini_restr).fetch1() + raw_raw = mini_content.get_acquisition() + + attrs = [ + ("comments", "comments"), + ("description", "description"), + ] + for raw_attr, meta_attr in attrs: + assert raw_data[raw_attr] == getattr( + raw_raw, meta_attr + ), f"Raw table {raw_attr} not match raw data {meta_attr}" + + +def test_insert_sample_count(mini_insert, mini_content, mini_restr, common): + sample_data = (common.SampleCount & mini_restr).fetch1() + sample_full = mini_content.processing.get("sample_count") + if not sample_full: + assert False, "No sample count data in raw data" + sample_raw = sample_full.data_interfaces.get("sample_count") + assert ( + sample_data["sample_count_object_id"] == sample_raw.object_id + ), "SampleCount insertion error" + + +def test_insert_dio(mini_insert, mini_behavior, mini_restr, common): + events_data = (common.DIOEvents & mini_restr).fetch(as_dict=True) + events_raw = mini_behavior.get_data_interface( + "behavioral_events" + ).time_series + + assert len(events_data) == len(events_raw), "Number of events not match" + + event = [p for p in events_raw.keys() if "Poke" in p][0] + event_raw = events_raw.get(event) + # event_data = (common.DIOEvents & {"dio_event_name": event}).fetch(as_dict=True)[0] + event_data = (common.DIOEvents & {"dio_event_name": event}).fetch1() + + assert ( + event_data["dio_object_id"] == event_raw.object_id + ), "DIO Event insertion error" + + +def test_insert_pos( + mini_insert, + common, + mini_behavior, + mini_restr, + mini_pos_series, + mini_pos_tbl, +): + pos_data = (common.PositionSource.SpatialSeries & mini_restr).fetch() + pos_raw = mini_behavior.get_data_interface("position").spatial_series + + assert len(pos_data) == len(pos_raw), "Number of spatial series not match" + + raw_obj_id = pos_raw[mini_pos_series].object_id + data_obj_id = mini_pos_tbl.fetch1("raw_position_object_id") + + assert data_obj_id == raw_obj_id, "PosObject insertion error" + + +def test_fetch_posobj( + mini_insert, common, mini_pos, mini_pos_series, mini_pos_tbl +): + pos_key = ( + common.PositionSource.SpatialSeries & mini_pos_tbl.fetch("KEY") + ).fetch(as_dict=True)[0] + pos_df = (common.RawPosition & pos_key).fetch1_dataframe().iloc[:, 0:2] + + series = mini_pos[mini_pos_series] + raw_df = DataFrame( + data=series.data, + index=Index(series.timestamps, name="time"), + columns=[col + "1" for col in series.description.split(", ")], + ) + assert key_hash(pos_df) == key_hash(raw_df), "Spatial series fetch error" + + +def test_insert_device(mini_insert, mini_devices, common): + this_device = "dataacq_device0" + device_raw = mini_devices.get(this_device) + device_data = ( + common.DataAcquisitionDevice + & {"data_acquisition_device_name": this_device} + ).fetch1() + + attrs = [ + ("data_acquisition_device_name", "name"), + ("data_acquisition_device_system", "system"), + ("data_acquisition_device_amplifier", "amplifier"), + ("adc_circuit", "adc_circuit"), + ] + + for device_attr, meta_attr in attrs: + assert device_data[device_attr] == getattr( + device_raw, meta_attr + ), f"Device table {device_attr} not match raw data {meta_attr}" + + +def test_insert_camera(mini_insert, mini_devices, common): + camera_raw = mini_devices.get("camera_device 0") + camera_data = ( + common.CameraDevice & {"camera_name": camera_raw.camera_name} + ).fetch1() + + attrs = [ + ("camera_name", "camera_name"), + ("manufacturer", "manufacturer"), + ("model", "model"), + ("lens", "lens"), + ("meters_per_pixel", "meters_per_pixel"), + ] + for camera_attr, meta_attr in attrs: + assert camera_data[camera_attr] == getattr( + camera_raw, meta_attr + ), f"Camera table {camera_attr} not match raw data {meta_attr}" + + +def test_insert_probe(mini_insert, mini_devices, common): + this_probe = "probe 0" + probe_raw = mini_devices.get(this_probe) + probe_id = probe_raw.probe_type + + probe_data = ( + common.Probe * common.ProbeType & {"probe_id": probe_id} + ).fetch1() + + attrs = [ + ("probe_type", "probe_type"), + ("probe_description", "probe_description"), + ("contact_side_numbering", "contact_side_numbering"), + ] + + for probe_attr, meta_attr in attrs: + assert probe_data[probe_attr] == str( + getattr(probe_raw, meta_attr) + ), f"Probe table {probe_attr} not match raw data {meta_attr}" + + assert probe_data["num_shanks"] == len( + probe_raw.shanks + ), "Number of shanks in ProbeType number not raw data" diff --git a/tests/common/test_interval.py b/tests/common/test_interval.py new file mode 100644 index 000000000..8353961f8 --- /dev/null +++ b/tests/common/test_interval.py @@ -0,0 +1,27 @@ +import pytest +from numpy import array_equal + + +@pytest.fixture(scope="session") +def interval_list(common): + yield common.IntervalList() + + +def test_plot_intervals(mini_insert, interval_list): + fig = interval_list.plot_intervals(return_fig=True) + interval_list_name = fig.get_axes()[0].get_yticklabels()[0].get_text() + times_fetch = ( + interval_list & {"interval_list_name": interval_list_name} + ).fetch1("valid_times")[0] + times_plot = fig.get_axes()[0].lines[0].get_xdata() + + assert array_equal(times_fetch, times_plot), "plot_intervals failed" + + +def test_plot_epoch(mini_insert, interval_list): + fig = interval_list.plot_epoch_pos_raw_intervals(return_fig=True) + epoch_label = fig.get_axes()[0].get_yticklabels()[-1].get_text() + assert epoch_label == "epoch", "plot_epoch failed" + + epoch_interv = fig.get_axes()[0].lines[0].get_ydata() + assert array_equal(epoch_interv, [1, 1]), "plot_epoch failed" diff --git a/tests/common/test_interval_helpers.py b/tests/common/test_interval_helpers.py new file mode 100644 index 000000000..d4e7eb1ac --- /dev/null +++ b/tests/common/test_interval_helpers.py @@ -0,0 +1,272 @@ +import numpy as np +import pytest + + +@pytest.fixture(scope="session") +def list_intersect(common): + yield common.common_interval.interval_list_intersect + + +@pytest.mark.parametrize( + "one, two, result", + [ + ( + np.array([[0, 10], [3, 5], [14, 16]]), + np.array([[10, 11], [9, 14], [13, 18]]), + np.array([[9, 10], [14, 16]]), + ), + ( # Empty result for no intersection + np.array([[0, 10], [3, 5]]), + np.array([[11, 14]]), + np.array([]), + ), + ], +) +def test_list_intersect(list_intersect, one, two, result): + assert np.array_equal( + list_intersect(one, two), result + ), "Problem with common_interval.interval_list_intersect" + + +@pytest.fixture(scope="session") +def set_difference(common): + yield common.common_interval.interval_set_difference_inds + + +@pytest.mark.parametrize( + "one, two, expected_result", + [ + ( # No overlap + [(0, 5), (8, 10)], + [(5, 8)], + [(0, 5), (8, 10)], + ), + ( # Overlap + [(0, 5), (8, 10)], + [(1, 2), (3, 4), (6, 9)], + [(0, 1), (2, 3), (4, 5), (9, 10)], + ), + ( # One empty + [], + [(1, 2), (3, 4), (6, 9)], + [], + ), + ( # Two empty + [(0, 5), (8, 10)], + [], + [(0, 5), (8, 10)], + ), + ( # Equal intervals + [(0, 5), (8, 10)], + [(0, 5), (8, 10)], + [], + ), + ( # Multiple overlaps + [(0, 10)], + [(1, 3), (4, 6), (7, 9)], + [(0, 1), (3, 4), (6, 7), (9, 10)], + ), + ], +) +def test_set_difference(set_difference, one, two, expected_result): + assert ( + set_difference(one, two) == expected_result + ), "Problem with common_interval.interval_set_difference_inds" + + +@pytest.mark.parametrize( + "expected_result, min_len, max_len", + [ + (np.array([[0, 1]]), 0.0, 10), + (np.array([[0, 1], [0, 1e11]]), 0.0, 1e12), + (np.array([[0, 0], [0, 1]]), -1, 10), + ], +) +def test_intervals_by_length(common, expected_result, min_len, max_len): + # input is the same across all tests. Could be parametrized as above + inds = common.common_interval.intervals_by_length( + interval_list=np.array([[0, 0], [0, 1], [0, 1e11]]), + min_length=min_len, + max_length=max_len, + ) + assert np.array_equal( + inds, expected_result + ), "Problem with common_interval.intervals_by_length" + + +@pytest.fixture +def interval_list_dict(): + yield { + "interval_list": np.array([[1, 4], [6, 8]]), + "timestamps": np.array([0, 1, 5, 7, 8, 9]), + } + + +def test_interval_list_contains_ind(common, interval_list_dict): + idxs = common.common_interval.interval_list_contains_ind( + **interval_list_dict + ) + assert np.array_equal( + idxs, np.array([1, 3, 4]) + ), "Problem with common_interval.interval_list_contains_ind" + + +def test_insterval_list_contains(common, interval_list_dict): + idxs = common.common_interval.interval_list_contains(**interval_list_dict) + assert np.array_equal( + idxs, np.array([1, 7, 8]) + ), "Problem with common_interval.interval_list_contains" + + +def test_interval_list_excludes_ind(common, interval_list_dict): + idxs = common.common_interval.interval_list_excludes_ind( + **interval_list_dict + ) + assert np.array_equal( + idxs, np.array([0, 2, 5]) + ), "Problem with common_interval.interval_list_excludes_ind" + + +def test_interval_list_excludes(common, interval_list_dict): + idxs = common.common_interval.interval_list_excludes(**interval_list_dict) + assert np.array_equal( + idxs, np.array([0, 5, 9]) + ), "Problem with common_interval.interval_list_excludes" + + +def test_consolidate_intervals_1dim(common): + exp = common.common_interval.consolidate_intervals(np.array([0, 1])) + assert np.array_equal( + exp, np.array([[0, 1]]) + ), "Problem with common_interval.consolidate_intervals" + + +@pytest.mark.parametrize( + "interval1, interval2, exp_result", + [ + ( + np.array([[0, 1]]), + np.array([[2, 3]]), + np.array([[0, 3]]), + ), + ( + np.array([[2, 3]]), + np.array([[0, 1]]), + np.array([[0, 3]]), + ), + ( + np.array([[0, 3]]), + np.array([[2, 4]]), + np.array([[0, 3], [2, 4]]), + ), + ], +) +def test_union_adjacent_index(common, interval1, interval2, exp_result): + assert np.array_equal( + common.common_interval.union_adjacent_index(interval1, interval2), + exp_result, + ), "Problem with common_interval.union_adjacent_index" + + +@pytest.mark.parametrize( + "interval1, interval2, exp_result", + [ + ( + np.array([[0, 3]]), + np.array([[2, 4]]), + np.array([[0, 4]]), + ), + ( + np.array([[0, -1]]), + np.array([[2, 4]]), + np.array([[2, 0]]), + ), + ( + np.array([[0, 1]]), + np.array([[2, 1e11]]), + np.array([[0, 1], [2, 1e11]]), + ), + ], +) +def test_interval_list_union(common, interval1, interval2, exp_result): + assert np.array_equal( + common.common_interval.interval_list_union(interval1, interval2), + exp_result, + ), "Problem with common_interval.interval_list_union" + + +def test_interval_list_censor_error(common): + with pytest.raises(ValueError): + common.common_interval.interval_list_censor( + np.array([[0, 1]]), np.array([2]) + ) + + +def test_interval_list_censor(common): + assert np.array_equal( + common.common_interval.interval_list_censor( + np.array([[0, 2], [4, 5]]), np.array([1, 2, 4]) + ), + np.array([[1, 2]]), + ), "Problem with common_interval.interval_list_censor" + + +@pytest.mark.parametrize( + "interval_list, exp_result", + [ + ( + np.array([0, 1, 2, 3, 6, 7, 8, 9]), + np.array([[0, 3], [6, 9]]), + ), + ( + np.array([0, 1, 2]), + np.array([[0, 2]]), + ), + ( + np.array([2, 3, 1, 0]), + np.array([[0, 3]]), + ), + ( + np.array([2, 3, 0]), + np.array([[0, 0], [2, 3]]), + ), + ], +) +def test_interval_from_inds(common, interval_list, exp_result): + assert np.array_equal( + common.common_interval.interval_from_inds(interval_list), + exp_result, + ), "Problem with common_interval.interval_from_inds" + + +@pytest.mark.parametrize( + "intervals1, intervals2, min_length, exp_result", + [ + ( + np.array([[0, 2], [4, 5]]), + np.array([[1, 3], [2, 4]]), + 0, + np.array([[0, 1], [4, 5]]), + ), + ( + np.array([[0, 2], [4, 5]]), + np.array([[1, 3], [2, 4]]), + 1, + np.zeros((0, 2)), + ), + ( + np.array([[0, 2], [4, 6]]), + np.array([[5, 8], [2, 4]]), + 1, + np.array([[0, 2]]), + ), + ], +) +def test_interval_list_complement( + common, intervals1, intervals2, min_length, exp_result +): + ic = common.common_interval.interval_list_complement + assert np.array_equal( + ic(intervals1, intervals2, min_length), + exp_result, + ), "Problem with common_interval.interval_list_compliment" diff --git a/tests/common/test_lab.py b/tests/common/test_lab.py new file mode 100644 index 000000000..83ab84c10 --- /dev/null +++ b/tests/common/test_lab.py @@ -0,0 +1,110 @@ +import pytest +from numpy import array_equal + + +@pytest.fixture +def common_lab(common): + yield common.common_lab + + +@pytest.fixture +def add_admin(common_lab): + common_lab.LabMember.insert1( + dict( + lab_member_name="This Admin", + first_name="This", + last_name="Admin", + ), + skip_duplicates=True, + ) + common_lab.LabMember.LabMemberInfo.insert1( + dict( + lab_member_name="This Admin", + google_user_name="This Admin", + datajoint_user_name="this_admin", + admin=1, + ), + skip_duplicates=True, + ) + yield + + +@pytest.fixture +def add_member_team(common_lab, add_admin): + common_lab.LabMember.insert( + [ + dict( + lab_member_name="This Basic", + first_name="This", + last_name="Basic", + ), + dict( + lab_member_name="This Loner", + first_name="This", + last_name="Loner", + ), + ], + skip_duplicates=True, + ) + common_lab.LabMember.LabMemberInfo.insert( + [ + dict( + lab_member_name="This Basic", + google_user_name="This Basic", + datajoint_user_name="this_basic", + admin=0, + ), + dict( + lab_member_name="This Loner", + google_user_name="This Loner", + datajoint_user_name="this_loner", + admin=0, + ), + ], + skip_duplicates=True, + ) + common_lab.LabTeam.create_new_team( + team_name="This Team", + team_members=["This Admin", "This Basic"], + team_description="This Team Description", + ) + yield + + +def test_labmember_insert_file_str(mini_insert, common_lab, mini_copy_name): + before = common_lab.LabMember.fetch() + common_lab.LabMember.insert_from_nwbfile(mini_copy_name) + after = common_lab.LabMember.fetch() + # Already inserted, test func raises no error + assert array_equal(before, after), "LabMember not inserted correctly" + + +def test_fetch_admin(common_lab, add_admin): + assert ( + "this_admin" in common_lab.LabMember().admin + ), "LabMember admin not fetched correctly" + + +def test_get_djuser(common_lab, add_admin): + assert "This Admin" == common_lab.LabMember().get_djuser_name( + "this_admin" + ), "LabMember get_djuser not fetched correctly" + + +def test_get_djuser_error(common_lab, add_admin): + with pytest.raises(ValueError): + common_lab.LabMember().get_djuser_name("This Admin2") + + +def test_get_team_members(common_lab, add_member_team): + assert common_lab.LabTeam().get_team_members("This Admin") == set( + ("This Admin", "This Basic") + ), "LabTeam get_team_members not fetched correctly" + + +def test_decompose_name_error(common_lab): + # NOTE: Should change with solve of #304 + with pytest.raises(ValueError): + common_lab.decompose_name("This Invalid Name") + with pytest.raises(ValueError): + common_lab.decompose_name("This, Invalid, Name") diff --git a/tests/common/test_nwbfile.py b/tests/common/test_nwbfile.py new file mode 100644 index 000000000..a8671b7ce --- /dev/null +++ b/tests/common/test_nwbfile.py @@ -0,0 +1,41 @@ +import os + +import pytest + + +@pytest.fixture +def common_nwbfile(common): + """Return a common NWBFile object.""" + return common.common_nwbfile + + +@pytest.fixture +def lockfile(base_dir, teardown): + lockfile = base_dir / "temp.lock" + lockfile.touch() + os.environ["NWB_LOCK_FILE"] = str(lockfile) + yield lockfile + if teardown: + os.remove(lockfile) + + +def test_get_file_name_error(common_nwbfile): + """Test that an error is raised when trying non-existent file.""" + with pytest.raises(ValueError): + common_nwbfile.Nwbfile._get_file_name("non-existent-file.nwb") + + +def test_add_to_lock(common_nwbfile, lockfile, mini_copy_name): + common_nwbfile.Nwbfile.add_to_lock(mini_copy_name) + with lockfile.open("r") as f: + assert mini_copy_name in f.read() + + with pytest.raises(AssertionError): + common_nwbfile.Nwbfile.add_to_lock("non-existent-file.nwb") + + +def test_nwbfile_cleanup(common_nwbfile): + before = len(common_nwbfile.Nwbfile.fetch()) + common_nwbfile.Nwbfile.cleanup(delete_files=False) + after = len(common_nwbfile.Nwbfile.fetch()) + assert before == after, "Nwbfile cleanup changed table entry count." diff --git a/tests/common/test_position.py b/tests/common/test_position.py new file mode 100644 index 000000000..47f285977 --- /dev/null +++ b/tests/common/test_position.py @@ -0,0 +1,151 @@ +import pytest +from datajoint.hash import key_hash + + +@pytest.fixture +def common_position(common): + yield common.common_position + + +@pytest.fixture +def interval_position_info(common_position): + yield common_position.IntervalPositionInfo + + +@pytest.fixture +def default_param_key(): + yield {"position_info_param_name": "default"} + + +@pytest.fixture +def interval_key(common): + yield (common.IntervalList & "interval_list_name LIKE 'pos 0%'").fetch1( + "KEY" + ) + + +@pytest.fixture +def param_table(common_position, default_param_key, teardown): + param_table = common_position.PositionInfoParameters() + param_table.insert1(default_param_key, skip_duplicates=True) + yield param_table + if teardown: + param_table.delete(safemode=False) + + +@pytest.fixture +def upsample_position( + common, + common_position, + param_table, + default_param_key, + teardown, + interval_key, +): + params = (param_table & default_param_key).fetch1() + upsample_param_key = {"position_info_param_name": "upsampled"} + param_table.insert1( + { + **params, + **upsample_param_key, + "is_upsampled": 1, + "max_separation": 80, + "upsampling_sampling_rate": 500, + }, + skip_duplicates=True, + ) + interval_pos_key = {**interval_key, **upsample_param_key} + common_position.IntervalPositionInfoSelection.insert1( + interval_pos_key, skip_duplicates=True + ) + common_position.IntervalPositionInfo.populate(interval_pos_key) + yield interval_pos_key + if teardown: + (param_table & upsample_param_key).delete(safemode=False) + + +@pytest.fixture +def interval_pos_key(upsample_position): + yield upsample_position + + +def test_interval_position_info_insert(common_position, interval_pos_key): + assert common_position.IntervalPositionInfo & interval_pos_key + + +@pytest.fixture +def upsample_position_error( + upsample_position, + default_param_key, + param_table, + common, + common_position, + teardown, + interval_key, +): + params = (param_table & default_param_key).fetch1() + upsample_param_key = {"position_info_param_name": "upsampled error"} + param_table.insert1( + { + **params, + **upsample_param_key, + "is_upsampled": 1, + "max_separation": 1, + "upsampling_sampling_rate": 500, + }, + skip_duplicates=True, + ) + interval_pos_key = {**interval_key, **upsample_param_key} + common_position.IntervalPositionInfoSelection.insert1(interval_pos_key) + yield interval_pos_key + if teardown: + (param_table & upsample_param_key).delete(safemode=False) + + +def test_interval_position_info_insert_error( + interval_position_info, upsample_position_error +): + with pytest.raises(ValueError): + interval_position_info.populate(upsample_position_error) + + +def test_fetch1_dataframe(interval_position_info, interval_pos_key): + df = (interval_position_info & interval_pos_key).fetch1_dataframe() + err_msg = "Unexpected output of IntervalPositionInfo.fetch1_dataframe" + assert df.shape == (5193, 6), err_msg + + df_sums = {c: df[c].iloc[:5].sum() for c in df.columns} + df_sums_exp = { + "head_orientation": 4.4300073600180125, + "head_position_x": 111.25, + "head_position_y": 141.75, + "head_speed": 0.6084872579024899, + "head_velocity_x": -0.4329520555149495, + "head_velocity_y": 0.42756198762527325, + } + for k in df_sums: + assert k in df_sums_exp, err_msg + assert df_sums[k] == pytest.approx(df_sums_exp[k], rel=0.02), err_msg + + +def test_interval_position_info_kwarg_error(interval_position_info): + with pytest.raises(ValueError): + interval_position_info._fix_kwargs() + + +def test_interval_position_info_kwarg_alias(interval_position_info): + in_tuple = (0, 1, 2, 3) + out_tuple = interval_position_info._fix_kwargs( + head_orient_smoothing_std_dev=in_tuple[0], + head_speed_smoothing_std_dev=in_tuple[1], + max_separation=in_tuple[2], + max_speed=in_tuple[3], + ) + assert ( + out_tuple == in_tuple + ), "IntervalPositionInfo._fix_kwargs() should alias old arg names." + + +@pytest.mark.skip(reason="Not testing with video data yet.") +def test_position_video(common_position): + pass diff --git a/tests/common/test_region.py b/tests/common/test_region.py new file mode 100644 index 000000000..95f62fe1b --- /dev/null +++ b/tests/common/test_region.py @@ -0,0 +1,29 @@ +import pytest +from datajoint import U as dj_U + + +@pytest.fixture +def region_dict(): + yield dict(region_name="test_region") + + +@pytest.fixture +def brain_region(common, region_dict): + brain_region = common.common_region.BrainRegion() + (brain_region & "region_id > 1").delete(safemode=False) + yield brain_region + (brain_region & "region_id > 1").delete(safemode=False) + + +def test_region_add(brain_region, region_dict): + next_id = ( + dj_U().aggr(brain_region, n="max(region_id)").fetch1("n") or 0 + ) + 1 + region_id = brain_region.fetch_add( + **region_dict, + subregion_name="test_subregion_add", + subsubregion_name="test_subsubregion_add", + ) + assert ( + region_id == next_id + ), "Region.fetch_add() should autincrement region_id." diff --git a/tests/common/test_ripple.py b/tests/common/test_ripple.py new file mode 100644 index 000000000..71a57d022 --- /dev/null +++ b/tests/common/test_ripple.py @@ -0,0 +1,6 @@ +import pytest + + +@pytest.mark.skip(reason="Not testing V0: common_ripple") +def test_common_ripple(common): + pass diff --git a/tests/common/test_sensors.py b/tests/common/test_sensors.py new file mode 100644 index 000000000..9cdedeeb4 --- /dev/null +++ b/tests/common/test_sensors.py @@ -0,0 +1,21 @@ +import pytest + + +@pytest.fixture +def sensor_data(common, mini_insert): + tbl = common.common_sensors.SensorData() + tbl.populate() + yield tbl + + +def test_sensor_data_insert(sensor_data, mini_insert, mini_restr, mini_content): + obj_fetch = (sensor_data & mini_restr).fetch1("sensor_data_object_id") + obj_raw = ( + mini_content.processing["analog"] + .data_interfaces["analog"] + .time_series["analog"] + .object_id + ) + assert ( + obj_fetch == obj_raw + ), "SensorData object_id does not match raw object_id." diff --git a/tests/common/test_session.py b/tests/common/test_session.py new file mode 100644 index 000000000..6e0a8f0ce --- /dev/null +++ b/tests/common/test_session.py @@ -0,0 +1,81 @@ +import pytest +from datajoint.errors import DataJointError + + +@pytest.fixture +def common_session(common): + return common.common_session + + +@pytest.fixture +def group_name_dict(): + return {"session_group_name": "group1"} + + +@pytest.fixture +def add_session_group(common_session, group_name_dict): + session_group = common_session.SessionGroup() + session_group_dict = { + **group_name_dict, + "session_group_description": "group1 description", + } + session_group.add_group(**session_group_dict, skip_duplicates=True) + session_group_dict["session_group_description"] = "updated description" + session_group.update_session_group_description(**session_group_dict) + yield session_group, session_group_dict + + +@pytest.fixture +def session_group(add_session_group): + yield add_session_group[0] + + +@pytest.fixture +def session_group_dict(add_session_group): + yield add_session_group[1] + + +def test_session_group_add(session_group, session_group_dict): + assert session_group & session_group_dict, "Session group not added" + + +@pytest.fixture +def add_session_to_group(session_group, mini_copy_name, group_name_dict): + session_group.add_session_to_group( + nwb_file_name=mini_copy_name, **group_name_dict + ) + + +def test_addremove_session_group( + common_session, + session_group, + session_group_dict, + group_name_dict, + mini_copy_name, + add_session_to_group, + add_session_group, +): + assert session_group & session_group_dict, "Session not added to group" + + session_group.remove_session_from_group( + nwb_file_name=mini_copy_name, + safemode=False, + **group_name_dict, + ) + assert ( + len(common_session.SessionGroupSession & session_group_dict) == 0 + ), "SessionGroupSession not removed from by helper function" + + +def test_get_group_sessions( + session_group, group_name_dict, add_session_to_group +): + ret = session_group.get_group_sessions(**group_name_dict) + assert len(ret) == 1, "Incorrect number of sessions returned" + + +def test_delete_group_error(session_group, group_name_dict): + session_group.delete_group(**group_name_dict, safemode=False) + assert ( + len(session_group & group_name_dict) == 0 + ), "Group not deleted by helper function" diff --git a/tests/conftest.py b/tests/conftest.py index ac1539abf..3c2bc866b 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,80 +1,326 @@ -# directory-specific hook implementations import os -import shutil import sys -import tempfile +import warnings +from contextlib import nullcontext +from pathlib import Path +from subprocess import Popen +from time import sleep as tsleep import datajoint as dj +import pynwb +import pytest +from datajoint.logging import logger as dj_logger -from .datajoint._config import DATAJOINT_SERVER_PORT -from .datajoint._datajoint_server import ( - kill_datajoint_server, - run_datajoint_server, -) +from .container import DockerMySQLManager -thisdir = os.path.dirname(os.path.realpath(__file__)) -sys.path.append(thisdir) +# ---------------------- CONSTANTS --------------------- - -global __PROCESS -__PROCESS = None +# globals in pytest_configure: +# BASE_DIR, RAW_DIR, SERVER, TEARDOWN, VERBOSE, TEST_FILE, DOWNLOAD +warnings.filterwarnings("ignore", category=UserWarning, module="hdmf") def pytest_addoption(parser): + """Permit constants when calling pytest at command line + + Example + ------- + > pytest --quiet-spy + + Parameters + ---------- + --quiet-spy (bool): Default False. Allow print statements from Spyglass. + --no-teardown (bool): Default False. Delete pipeline on close. + --no-server (bool): Default False. Run datajoint server in Docker. + --datadir (str): Default './tests/test_data/'. Dir for local input file. + WARNING: not yet implemented. + """ + parser.addoption( + "--quiet-spy", + action="store_true", + dest="quiet_spy", + default=False, + help="Quiet logging from Spyglass.", + ) parser.addoption( - "--current", + "--no-server", action="store_true", - dest="current", + dest="no_server", default=False, - help="run only tests marked as current", + help="Do not launch datajoint server in Docker.", + ) + parser.addoption( + "--no-teardown", + action="store_true", + default=False, + dest="no_teardown", + help="Tear down tables after tests.", + ) + parser.addoption( + "--base-dir", + action="store", + default="./tests/_data/", + dest="base_dir", + help="Directory for local input file.", ) def pytest_configure(config): - config.addinivalue_line( - "markers", "current: for convenience -- mark one test as current" + global BASE_DIR, RAW_DIR, SERVER, TEARDOWN, VERBOSE, TEST_FILE, DOWNLOAD + + TEST_FILE = "minirec20230622.nwb" + TEARDOWN = not config.option.no_teardown + VERBOSE = not config.option.quiet_spy + + BASE_DIR = Path(config.option.base_dir).absolute() + BASE_DIR.mkdir(parents=True, exist_ok=True) + RAW_DIR = BASE_DIR / "raw" + os.environ["SPYGLASS_BASE_DIR"] = str(BASE_DIR) + + SERVER = DockerMySQLManager( + restart=False, + shutdown=TEARDOWN, + null_server=config.option.no_server, + verbose=VERBOSE, ) + DOWNLOAD = download_data(verbose=VERBOSE) + - markexpr_list = [] +def data_is_downloaded(): + """Check if data is downloaded.""" + return os.path.exists(RAW_DIR / TEST_FILE) - if config.option.current: - markexpr_list.append("current") - if len(markexpr_list) > 0: - markexpr = " and ".join(markexpr_list) - setattr(config.option, "markexpr", markexpr) +def download_data(verbose=False): + """Download data from BOX using environment variable credentials. - _set_env() + Note: In gh-actions, this is handled by the test-conda workflow. + """ + if data_is_downloaded(): + return None + UCSF_BOX_USER = os.environ.get("UCSF_BOX_USER") + UCSF_BOX_TOKEN = os.environ.get("UCSF_BOX_TOKEN") + if not all([UCSF_BOX_USER, UCSF_BOX_TOKEN]): + raise ValueError( + "Missing data, no credentials: UCSF_BOX_USER or UCSF_BOX_TOKEN." + ) + data_url = f"ftps://ftp.box.com/trodes_to_nwb_test_data/{TEST_FILE}" - # note that in this configuration, every test will use the same datajoint - # server this may create conflicts and dependencies between tests it may be - # better but significantly slower to start a new server for every test but - # the server needs to be started before tests are collected because - # datajoint runs when the source files are loaded, not when the tests are - # run. one solution might be to restart the server after every test + cmd = [ + "wget", + "--recursive", + "--no-host-directories", + "--no-directories", + "--user", + UCSF_BOX_USER, + "--password", + UCSF_BOX_TOKEN, + "-P", + RAW_DIR, + data_url, + ] + if not verbose: + cmd.insert(cmd.index("--recursive") + 1, "--no-verbose") + cmd_kwargs = dict(stdout=sys.stdout, stderr=sys.stderr) if verbose else {} - global __PROCESS - __PROCESS = run_datajoint_server() + return Popen(cmd, **cmd_kwargs) def pytest_unconfigure(config): - if __PROCESS: - print("Terminating datajoint compute resource process") - __PROCESS.terminate() - # TODO handle ResourceWarning: subprocess X is still running - # __PROCESS.join() + if TEARDOWN: + SERVER.stop() + + +# ------------------- FIXTURES ------------------- + + +@pytest.fixture(scope="session") +def verbose(): + """Config for pytest fixtures.""" + yield VERBOSE + + +@pytest.fixture(scope="session", autouse=True) +def verbose_context(verbose): + """Verbosity context for suppressing Spyglass logging.""" + yield nullcontext() if verbose else QuietStdOut() + + +@pytest.fixture(scope="session") +def teardown(request): + yield TEARDOWN + + +@pytest.fixture(scope="session") +def server(request, teardown): + SERVER.wait() + yield SERVER + if teardown: + SERVER.stop() + + +@pytest.fixture(scope="session") +def dj_conn(request, server, verbose, teardown): + """Fixture for datajoint connection.""" + config_file = "dj_local_conf.json_pytest" + + dj.config.update(server.creds) + dj.config["loglevel"] = "INFO" if verbose else "ERROR" + dj.config.save(config_file) + dj.conn() + yield dj.conn() + if teardown: + if Path(config_file).exists(): + os.remove(config_file) + + +@pytest.fixture(scope="session") +def base_dir(): + yield BASE_DIR + + +@pytest.fixture(scope="session") +def raw_dir(base_dir): + # could do settings.raw_dir, but this is faster while server booting + yield base_dir / "raw" + + +@pytest.fixture(scope="session") +def mini_path(raw_dir): + path = raw_dir / TEST_FILE + + # wait for wget download to finish + if DOWNLOAD is not None: + DOWNLOAD.wait() + + # wait for gh-actions download to finish + timeout, wait, found = 60, 5, False + for _ in range(timeout // wait): + if path.exists(): + found = True + break + tsleep(wait) + + if not found: + raise ConnectionError("Download failed.") + + yield path + + +@pytest.fixture(scope="session") +def mini_copy_name(mini_path): + from spyglass.utils.nwb_helper_fn import get_nwb_copy_filename # noqa: E402 + + yield get_nwb_copy_filename(mini_path).split("/")[-1] + + +@pytest.fixture(scope="session") +def mini_content(mini_path): + with pynwb.NWBHDF5IO( + path=str(mini_path), mode="r", load_namespaces=True + ) as io: + nwbfile = io.read() + assert nwbfile is not None, "NWBFile empty." + yield nwbfile + + +@pytest.fixture(scope="session") +def mini_open(mini_content): + yield mini_content + + +@pytest.fixture(scope="session") +def mini_closed(mini_path): + with pynwb.NWBHDF5IO( + path=str(mini_path), mode="r", load_namespaces=True + ) as io: + nwbfile = io.read() + yield nwbfile + + +@pytest.fixture(autouse=True, scope="session") +def mini_insert(mini_path, teardown, server, dj_conn): + from spyglass.common import Nwbfile, Session # noqa: E402 + from spyglass.data_import import insert_sessions # noqa: E402 + from spyglass.utils.nwb_helper_fn import close_nwb_files # noqa: E402 + + dj_logger.info("Inserting test data.") + + if not server.connected: + raise ConnectionError("No server connection.") + + if len(Nwbfile()) != 0: + dj_logger.warning("Skipping insert, use existing data.") + else: + insert_sessions(mini_path.name) + + if len(Session()) == 0: + raise ValueError("No sessions inserted.") + + yield + + close_nwb_files() + # Note: no need to run deletes in teardown, since we are using teardown + # will remove the container + + +@pytest.fixture(scope="session") +def mini_restr(mini_path): + yield f"nwb_file_name LIKE '{mini_path.stem}%'" + + +@pytest.fixture(scope="session") +def mini_dict(mini_copy_name): + yield {"nwb_file_name": mini_copy_name} + + +@pytest.fixture(scope="session") +def common(dj_conn): + from spyglass import common + + yield common + + +@pytest.fixture(scope="session") +def data_import(dj_conn): + from spyglass import data_import + + yield data_import + + +@pytest.fixture(scope="session") +def settings(dj_conn): + from spyglass import settings + + yield settings + + +@pytest.fixture(scope="session") +def populate_exception(): + from spyglass.common.errors import PopulateException + + yield PopulateException + + +# ------------------ GENERAL FUNCTION ------------------ + - kill_datajoint_server() - shutil.rmtree(os.environ["SPYGLASS_BASE_DIR"]) +class QuietStdOut: + """If quiet_spy, used to quiet prints, teardowns and table.delete prints""" + def __init__(self): + from spyglass.utils import logger as spyglass_logger -def _set_env(): - """Set environment variables.""" - print("Setting datajoint and kachery environment variables.") + self.spy_logger = spyglass_logger + self.previous_level = None - os.environ["SPYGLASS_BASE_DIR"] = str(tempfile.mkdtemp()) + def __enter__(self): + self.previous_level = self.spy_logger.getEffectiveLevel() + self.spy_logger.setLevel("CRITICAL") + self._original_stdout = sys.stdout + sys.stdout = open(os.devnull, "w") - dj.config["database.host"] = "localhost" - dj.config["database.port"] = DATAJOINT_SERVER_PORT - dj.config["database.user"] = "root" - dj.config["database.password"] = "tutorial" + def __exit__(self, exc_type, exc_val, exc_tb): + self.spy_logger.setLevel(self.previous_level) + sys.stdout.close() + sys.stdout = self._original_stdout diff --git a/tests/container.py b/tests/container.py new file mode 100644 index 000000000..df820f1d0 --- /dev/null +++ b/tests/container.py @@ -0,0 +1,216 @@ +import atexit +import time + +import datajoint as dj +import docker +from datajoint import logger + + +class DockerMySQLManager: + """Manage Docker container for MySQL server + + Parameters + ---------- + image_name : str + Docker image name. Default 'datajoint/mysql'. + mysql_version : str + MySQL version. Default '8.0'. + container_name : str + Docker container name. Default 'spyglass-pytest'. + port : str + Port to map to DJ's default 3306. Default '330[mysql_version]' + (i.e., 3308 if testing 8.0). + null_server : bool + If True, do not start container. Return on all methods. Default False. + Useful for iterating on tests in existing container. + restart : bool + If True, stop and remove existing container on startup. Default True. + shutdown : bool + If True, stop and remove container on exit from python. Default True. + verbose : bool + If True, print container status on startup. Default False. + """ + + def __init__( + self, + image_name="datajoint/mysql", + mysql_version="8.0", + container_name="spyglass-pytest", + port=None, + null_server=False, + restart=True, + shutdown=True, + verbose=False, + ) -> None: + self.image_name = image_name + self.mysql_version = mysql_version + self.container_name = container_name + self.port = port or "330" + self.mysql_version[0] + self.client = docker.from_env() + self.null_server = null_server + self.password = "tutorial" + self.user = "root" + self.host = "localhost" + self._ran_container = None + self.logger = logger + self.logger.setLevel("INFO" if verbose else "ERROR") + + if not self.null_server: + if shutdown: + atexit.register(self.stop) # stop container on python exit + if restart: + self.stop() # stop container if it exists + self.start() + + @property + def container(self) -> docker.models.containers.Container: + return self.client.containers.get(self.container_name) + + @property + def container_status(self) -> str: + try: + self.container.reload() + return self.container.status + except docker.errors.NotFound: + return None + + @property + def container_health(self) -> str: + try: + self.container.reload() + return self.container.health + except docker.errors.NotFound: + return None + + @property + def msg(self) -> str: + return f"Container {self.container_name} " + + def start(self) -> str: + if self.null_server: + return None + + elif self.container_status in ["created", "running", "restarting"]: + self.logger.info( + self.msg + "starting: " + self.container_status + "." + ) + + elif self.container_status == "exited": + self.logger.info(self.msg + "restarting.") + self.container.restart() + + else: + self._ran_container = self.client.containers.run( + image=f"{self.image_name}:{self.mysql_version}", + name=self.container_name, + ports={3306: self.port}, + environment=[ + f"MYSQL_ROOT_PASSWORD={self.password}", + "MYSQL_DEFAULT_STORAGE_ENGINE=InnoDB", + ], + detach=True, + tty=True, + ) + self.logger.info(self.msg + "starting new.") + + return self.container.name + + def wait(self, timeout=120, wait=5) -> None: + """Wait for healthy container. + + Parameters + ---------- + timeout : int + Timeout in seconds. Default 120. + wait : int + Time to wait between checks in seconds. Default 5. + """ + + if self.null_server: + return None + if not self.container_status or self.container_status == "exited": + self.start() + + for i in range(timeout // wait): + if self.container.health == "healthy": + break + self.logger.info(f"Container {self.container_name} starting... {i}") + time.sleep(wait) + self.logger.info( + f"Container {self.container_name}, {self.container.health}." + ) + + @property + def _add_sql(self) -> str: + ESC = r"\_%" + return ( + "CREATE USER IF NOT EXISTS 'basic'@'%' IDENTIFIED BY " + + f"'{self.password}'; GRANT USAGE ON `%`.* TO 'basic'@'%';" + + "GRANT SELECT ON `%`.* TO 'basic'@'%';" + + f"GRANT ALL PRIVILEGES ON `common{ESC}`.* TO `basic`@`%`;" + + f"GRANT ALL PRIVILEGES ON `spikesorting{ESC}`.* TO `basic`@`%`;" + + f"GRANT ALL PRIVILEGES ON `lfp{ESC}`.* TO `basic`@`%`;" + + f"GRANT ALL PRIVILEGES ON `position{ESC}`.* TO `basic`@`%`;" + + f"GRANT ALL PRIVILEGES ON `ripple{ESC}`.* TO `basic`@`%`;" + + f"GRANT ALL PRIVILEGES ON `linearization{ESC}`.* TO `basic`@`%`;" + ).strip() + + def add_user(self) -> int: + """Add 'basic' user to container.""" + if self.null_server: + return None + + if self._container_running(): + result = self.container.exec_run( + cmd=[ + "mysql", + "-u", + self.user, + f"--password={self.password}", + "-e", + self._add_sql, + ], + stdout=False, + stderr=False, + tty=True, + ) + if result.exit_code == 0: + self.logger.info("Container added user.") + else: + logger.error("Failed to add user.") + return result.exit_code + else: + logger.error(f"Container {self.container_name} does not exist.") + return None + + @property + def creds(self): + """Datajoint credentials for this container.""" + return { + "database.host": "localhost", + "database.password": self.password, + "database.user": self.user, + "database.port": int(self.port), + "safmode": "false", + "custom": {"test_mode": True}, + } + + @property + def connected(self) -> bool: + self.wait() + dj.config.update(self.creds) + return dj.conn().is_connected + + def stop(self, remove=True) -> None: + """Stop and remove container.""" + if self.null_server: + return None + if not self.container_status or self.container_status == "exited": + return + + self.container.stop() + self.logger.info(f"Container {self.container_name} stopped.") + + if remove: + self.container.remove() + self.logger.info(f"Container {self.container_name} removed.") diff --git a/tests/data_import/__init__.py b/tests/data_import/__init__.py index e69de29bb..8f7eaee37 100644 --- a/tests/data_import/__init__.py +++ b/tests/data_import/__init__.py @@ -0,0 +1,3 @@ +# NOTE: test_insert_sessions does not increase coverage over common/test_insert +# but it does declare it's own nwbfile without downloading and test broken +# links which aren't technically part of spyglass diff --git a/tests/data_import/test_insert_sessions.py b/tests/data_import/test_insert_sessions.py index d7968d164..7c125ed6b 100644 --- a/tests/data_import/test_insert_sessions.py +++ b/tests/data_import/test_insert_sessions.py @@ -1,104 +1,39 @@ -import datetime -import os -import pathlib import shutil +import warnings +from pathlib import Path -import datajoint as dj import pynwb import pytest from hdmf.backends.warnings import BrokenLinkWarning -from spyglass.data_import.insert_sessions import copy_nwb_link_raw_ephys -from spyglass.settings import raw_dir - -@pytest.fixture() -def new_nwbfile_raw_file_name(tmp_path): - nwbfile = pynwb.NWBFile( - session_description="session_description", - identifier="identifier", - session_start_time=datetime.datetime.now(datetime.timezone.utc), - ) - - device = nwbfile.create_device("dev1") - group = nwbfile.create_electrode_group( - "tetrode1", "tetrode description", "tetrode location", device - ) - nwbfile.add_electrode( - id=1, - x=1.0, - y=2.0, - z=3.0, - imp=-1.0, - location="CA1", - filtering="none", - group=group, - group_name="tetrode1", +@pytest.fixture(scope="session") +def copy_nwb_link_raw_ephys(data_import): + from spyglass.data_import.insert_sessions import ( # noqa: E402 + copy_nwb_link_raw_ephys, ) - region = nwbfile.create_electrode_table_region( - region=[0], description="electrode 1" - ) - - es = pynwb.ecephys.ElectricalSeries( - name="test_ts", - data=[1, 2, 3], - timestamps=[1.0, 2.0, 3.0], - electrodes=region, - ) - nwbfile.add_acquisition(es) - - _ = tmp_path # CBroz: Changed to match testing base directory - file_name = "raw.nwb" - file_path = raw_dir + "/" + file_name + return copy_nwb_link_raw_ephys - with pynwb.NWBHDF5IO(str(file_path), mode="w") as io: - io.write(nwbfile) - return file_name +def test_open_path(mini_path, mini_open): + this_acq = mini_open.acquisition + assert "e-series" in this_acq, "Ephys link no longer exists" + assert ( + str(mini_path) == this_acq["e-series"].data.file.filename + ), "Path of ephys link is incorrect" -@pytest.fixture() -def new_nwbfile_no_ephys_file_name(): - return "raw_no_ephys.nwb" - - -@pytest.fixture() -def moved_nwbfile_no_ephys_file_path(tmp_path, new_nwbfile_no_ephys_file_name): - return tmp_path / new_nwbfile_no_ephys_file_name - - -def test_copy_nwb( - new_nwbfile_raw_file_name, - new_nwbfile_no_ephys_file_name, - moved_nwbfile_no_ephys_file_path, -): - copy_nwb_link_raw_ephys( - new_nwbfile_raw_file_name, new_nwbfile_no_ephys_file_name - ) - - # new file should not have ephys data - base_dir = pathlib.Path(os.getenv("SPYGLASS_BASE_DIR", None)) - new_nwbfile_raw_file_name_abspath = ( - base_dir / "raw" / new_nwbfile_raw_file_name - ) - out_nwb_file_abspath = base_dir / "raw" / new_nwbfile_no_ephys_file_name - with pynwb.NWBHDF5IO(path=str(out_nwb_file_abspath), mode="r") as io: - nwbfile = io.read() - assert ( - "test_ts" in nwbfile.acquisition - ) # this still exists but should be a link now - assert nwbfile.acquisition["test_ts"].data.file.filename == str( - new_nwbfile_raw_file_name_abspath - ) - # test readability after moving the linking raw file (paths are stored as - # relative paths in NWB) so this should break the link (moving the linked-to - # file should also break the link) +def test_copy_link(mini_path, settings, mini_closed, copy_nwb_link_raw_ephys): + """Test readability after moving the linking raw file, breaking link""" + new_path = Path(settings.raw_dir) / "no_ephys.nwb" + new_moved = Path(settings.temp_dir) / "no_ephys_moved.nwb" - shutil.move(out_nwb_file_abspath, moved_nwbfile_no_ephys_file_path) - with pynwb.NWBHDF5IO( - path=str(moved_nwbfile_no_ephys_file_path), mode="r" - ) as io: - with pytest.warns(BrokenLinkWarning): - nwbfile = io.read() # should raise BrokenLinkWarning - assert "test_ts" not in nwbfile.acquisition + copy_nwb_link_raw_ephys(mini_path.name, new_path.name) + shutil.move(new_path, new_moved) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=UserWarning) + with pynwb.NWBHDF5IO(path=str(new_moved), mode="r") as io: + with pytest.warns(BrokenLinkWarning): + nwb_acq = io.read().acquisition + assert "e-series" not in nwb_acq, "Ephys link still exists after move" diff --git a/tests/datajoint/_config.py b/tests/datajoint/_config.py deleted file mode 100644 index 3798427ea..000000000 --- a/tests/datajoint/_config.py +++ /dev/null @@ -1 +0,0 @@ -DATAJOINT_SERVER_PORT = 3307 diff --git a/tests/datajoint/_datajoint_server.py b/tests/datajoint/_datajoint_server.py deleted file mode 100644 index f12455e67..000000000 --- a/tests/datajoint/_datajoint_server.py +++ /dev/null @@ -1,110 +0,0 @@ -import multiprocessing -import os -import time -import traceback - -import kachery_client as kc -from pymysql.err import OperationalError - -from ._config import DATAJOINT_SERVER_PORT - -DOCKER_IMAGE_NAME = "datajoint-server-pytest" - - -def run_service_datajoint_server(): - # The following cleanup is needed because we terminate this compute resource process - # See: https://pytest-cov.readthedocs.io/en/latest/subprocess-support.html - from pytest_cov.embed import cleanup_on_sigterm - - cleanup_on_sigterm() - - os.environ["RUNNING_PYTEST"] = "TRUE" - - ss = kc.ShellScript( - f""" - #!/bin/bash - set -ex - - docker kill {DOCKER_IMAGE_NAME} > /dev/null 2>&1 || true - docker rm {DOCKER_IMAGE_NAME} > /dev/null 2>&1 || true - exec docker run --name {DOCKER_IMAGE_NAME} -e MYSQL_ROOT_PASSWORD=tutorial -p {DATAJOINT_SERVER_PORT}:3306 datajoint/mysql - """, - redirect_output_to_stdout=True, - ) # noqa: E501 - ss.start() - ss.wait() - - -def run_datajoint_server(): - print("Starting datajoint server") - - ss_pull = kc.ShellScript( - """ - #!/bin/bash - set -ex - - exec docker pull datajoint/mysql - """ - ) - ss_pull.start() - ss_pull.wait() - - process = multiprocessing.Process( - target=run_service_datajoint_server, kwargs=dict() - ) - process.start() - - try: - _wait_for_datajoint_server_to_start() - except Exception: - kill_datajoint_server() - raise - - return process - # yield process - - # process.terminate() - # kill_datajoint_server() - - -def kill_datajoint_server(): - print("Terminating datajoint server") - - ss2 = kc.ShellScript( - f""" - #!/bin/bash - - set -ex - - docker kill {DOCKER_IMAGE_NAME} || true - docker rm {DOCKER_IMAGE_NAME} - """ - ) - ss2.start() - ss2.wait() - - -def _wait_for_datajoint_server_to_start(): - time.sleep(15) # it takes a while to start the server - timer = time.time() - print("Waiting for DataJoint server to start. Time", timer) - while True: - try: - from spyglass.common import Session # noqa: F401 - - return - except OperationalError as e: # e.g. Connection Error - print("DataJoint server not yet started. Time", time.time()) - print(e) - except Exception: - print("Failed to import Session. Time", time.time()) - print(traceback.format_exc()) - current_time = time.time() - elapsed = current_time - timer - if elapsed > 300: - raise Exception( - "Timeout while waiting for datajoint server to start and " - "import Session to succeed. Time", - current_time, - ) - time.sleep(5) diff --git a/tests/lfp/conftest.py b/tests/lfp/conftest.py new file mode 100644 index 000000000..2eb511265 --- /dev/null +++ b/tests/lfp/conftest.py @@ -0,0 +1,215 @@ +import numpy as np +import pytest +from pynwb import NWBHDF5IO + + +@pytest.fixture(scope="session") +def lfp(common): + from spyglass import lfp + + return lfp + + +@pytest.fixture(scope="session") +def lfp_band(lfp): + from spyglass.lfp.analysis.v1 import lfp_band + + return lfp_band + + +@pytest.fixture(scope="session") +def firfilters_table(common): + return common.FirFilterParameters() + + +@pytest.fixture(scope="session") +def electrodegroup_table(lfp): + return lfp.v1.LFPElectrodeGroup() + + +@pytest.fixture(scope="session") +def lfp_constants(common, mini_copy_name, mini_dict): + n_delay = 9 + lfp_electrode_group_name = "test" + orig_list_name = "01_s1" + orig_valid_times = ( + common.IntervalList + & mini_dict + & f"interval_list_name = '{orig_list_name}'" + ).fetch1("valid_times") + new_list_name = orig_list_name + f"_first{n_delay}" + new_list_key = { + "nwb_file_name": mini_copy_name, + "interval_list_name": new_list_name, + "valid_times": np.asarray( + [[orig_valid_times[0, 0], orig_valid_times[0, 0] + n_delay]] + ), + } + + yield dict( + lfp_electrode_ids=[0], + lfp_electrode_group_name=lfp_electrode_group_name, + lfp_eg_key={ + "nwb_file_name": mini_copy_name, + "lfp_electrode_group_name": lfp_electrode_group_name, + }, + n_delay=n_delay, + orig_interval_list_name=orig_list_name, + orig_valid_times=orig_valid_times, + interval_list_name=new_list_name, + interval_key=new_list_key, + filter1_name="LFP 0-400 Hz", + filter_sampling_rate=30_000, + filter2_name="Theta 5-11 Hz", + lfp_band_electrode_ids=[0], # assumes we've filtered these electrodes + lfp_band_sampling_rate=100, # desired sampling rate + ) + + +@pytest.fixture(scope="session") +def add_electrode_group( + firfilters_table, + electrodegroup_table, + mini_copy_name, + lfp_constants, +): + firfilters_table.create_standard_filters() + group_name = lfp_constants.get("lfp_electrode_group_name") + electrodegroup_table.create_lfp_electrode_group( + nwb_file_name=mini_copy_name, + group_name=group_name, + electrode_list=lfp_constants.get("lfp_electrode_ids"), + ) + assert len( + electrodegroup_table & {"lfp_electrode_group_name": group_name} + ), "Failed to add LFPElectrodeGroup." + yield + + +@pytest.fixture(scope="session") +def add_interval(common, lfp_constants): + common.IntervalList.insert1( + lfp_constants.get("interval_key"), skip_duplicates=True + ) + yield lfp_constants.get("interval_list_name") + + +@pytest.fixture(scope="session") +def add_selection( + lfp, common, add_electrode_group, add_interval, lfp_constants +): + lfp_s_key = { + **lfp_constants.get("lfp_eg_key"), + "target_interval_list_name": add_interval, + "filter_name": lfp_constants.get("filter1_name"), + "filter_sampling_rate": lfp_constants.get("filter_sampling_rate"), + } + lfp.v1.LFPSelection.insert1(lfp_s_key, skip_duplicates=True) + yield lfp_s_key + + +@pytest.fixture(scope="session") +def lfp_s_key(lfp_constants, mini_copy_name): + yield { + "nwb_file_name": mini_copy_name, + "lfp_electrode_group_name": lfp_constants.get( + "lfp_electrode_group_name" + ), + "target_interval_list_name": lfp_constants.get("interval_list_name"), + } + + +@pytest.fixture(scope="session") +def populate_lfp(lfp, add_selection, lfp_s_key): + lfp.v1.LFPV1().populate(add_selection) + yield {"merge_id": (lfp.LFPOutput.LFPV1() & lfp_s_key).fetch1("merge_id")} + + +@pytest.fixture(scope="session") +def lfp_merge_key(populate_lfp): + yield populate_lfp + + +@pytest.fixture(scope="module") +def lfp_analysis_raw(common, lfp, populate_lfp, mini_dict): + abs_path = (common.AnalysisNwbfile * lfp.v1.LFPV1 & mini_dict).fetch( + "analysis_file_abs_path" + )[0] + assert abs_path is not None, "No NWBFile found." + with NWBHDF5IO(path=str(abs_path), mode="r", load_namespaces=True) as io: + nwbfile = io.read() + assert nwbfile is not None, "NWBFile empty." + yield nwbfile + + +@pytest.fixture(scope="session") +def lfp_band_sampling_rate(lfp, lfp_merge_key): + yield lfp.LFPOutput.merge_get_parent(lfp_merge_key).fetch1( + "lfp_sampling_rate" + ) + + +@pytest.fixture(scope="session") +def add_band_filter(common, lfp_constants, lfp_band_sampling_rate): + filter_name = lfp_constants.get("filter2_name") + common.FirFilterParameters().add_filter( + filter_name, + lfp_band_sampling_rate, + "bandpass", + [4, 5, 11, 12], + "theta filter for 1 Khz data", + ) + yield lfp_constants.get("filter2_name") + + +@pytest.fixture(scope="session") +def add_band_selection( + lfp_band, + mini_copy_name, + mini_dict, + lfp_merge_key, + add_interval, + lfp_constants, + add_band_filter, + add_electrode_group, +): + lfp_band.LFPBandSelection().set_lfp_band_electrodes( + nwb_file_name=mini_copy_name, + lfp_merge_id=lfp_merge_key.get("merge_id"), + electrode_list=lfp_constants.get("lfp_band_electrode_ids"), + filter_name=add_band_filter, + interval_list_name=add_interval, + reference_electrode_list=[-1], + lfp_band_sampling_rate=lfp_constants.get("lfp_band_sampling_rate"), + ) + yield (lfp_band.LFPBandSelection & mini_dict).fetch1("KEY") + + +@pytest.fixture(scope="session") +def lfp_band_key(add_band_selection): + yield add_band_selection + + +@pytest.fixture(scope="session") +def populate_lfp_band(lfp_band, add_band_selection): + lfp_band.LFPBandV1().populate(add_band_selection) + yield + + +# @pytest.fixture(scope="session") +# def mini_eseries(common, mini_copy_name): +# yield (common.Raw() & {"nwb_file_name": mini_copy_name}).fetch_nwb()[0][ +# "raw" +# ] + + +@pytest.fixture(scope="module") +def lfp_band_analysis_raw(common, lfp_band, populate_lfp_band, mini_dict): + abs_path = (common.AnalysisNwbfile * lfp_band.LFPBandV1 & mini_dict).fetch( + "analysis_file_abs_path" + )[0] + assert abs_path is not None, "No NWBFile found." + with NWBHDF5IO(path=str(abs_path), mode="r", load_namespaces=True) as io: + nwbfile = io.read() + assert nwbfile is not None, "NWBFile empty." + yield nwbfile diff --git a/tests/lfp/test_pipeline.py b/tests/lfp/test_pipeline.py new file mode 100644 index 000000000..86599190d --- /dev/null +++ b/tests/lfp/test_pipeline.py @@ -0,0 +1,25 @@ +from pandas import DataFrame, Index + + +def test_lfp_dataframe(common, lfp, lfp_analysis_raw, lfp_merge_key): + lfp_raw = lfp_analysis_raw.scratch["filtered data"] + df_raw = DataFrame( + lfp_raw.data, index=Index(lfp_raw.timestamps, name="time") + ) + df_fetch = (lfp.LFPOutput & lfp_merge_key).fetch1_dataframe() + + assert df_raw.equals(df_fetch), "LFP dataframe not match." + + +def test_lfp_band_dataframe(lfp_band_analysis_raw, lfp_band, lfp_band_key): + lfp_band_raw = ( + lfp_band_analysis_raw.processing["ecephys"] + .fields["data_interfaces"]["LFP"] + .electrical_series["filtered data"] + ) + df_raw = DataFrame( + lfp_band_raw.data, index=Index(lfp_band_raw.timestamps, name="time") + ) + df_fetch = (lfp_band.LFPBandV1 & lfp_band_key).fetch1_dataframe() + + assert df_raw.equals(df_fetch), "LFPBand dataframe not match." diff --git a/tests/test_insert_beans.py b/tests/test_insert_beans.py deleted file mode 100644 index d74ecb856..000000000 --- a/tests/test_insert_beans.py +++ /dev/null @@ -1,97 +0,0 @@ -from datetime import datetime -import kachery_cloud as kcl -import os -import pathlib -import pynwb -import pytest - - -@pytest.mark.skip(reason="test_path needs to be updated") -def test_insert_sessions(): - print( - "In test_insert_sessions, os.environ['SPYGLASS_BASE_DIR'] is", - os.environ["SPYGLASS_BASE_DIR"], - ) - raw_dir = pathlib.Path(os.environ["SPYGLASS_BASE_DIR"]) / "raw" - nwbfile_path = raw_dir / "test.nwb" - - from spyglass.common import ( - Session, - DataAcquisitionDevice, - CameraDevice, - Probe, - ) - from spyglass.data_import import insert_sessions - - test_path = ( - "ipfs://bafybeie4svt3paz5vr7cw7mkgibutbtbzyab4s24hqn5pzim3sgg56m3n4" - ) - try: - local_test_path = kcl.load_file(test_path) - except Exception as e: - if os.environ.get("KACHERY_CLOUD_EPHEMERAL", None) != "TRUE": - print( - "Cannot load test file in non-ephemeral mode. Kachery cloud client may need to be registered." - ) - raise e - - # move the file to spyglass raw dir - os.rename(local_test_path, nwbfile_path) - - # test that the file can be read. this is not used otherwise - with pynwb.NWBHDF5IO( - path=str(nwbfile_path), mode="r", load_namespaces=True - ) as io: - nwbfile = io.read() - assert nwbfile is not None - - insert_sessions(nwbfile_path.name) - - x = (Session() & {"nwb_file_name": "test_.nwb"}).fetch1() - assert x["nwb_file_name"] == "test_.nwb" - assert x["subject_id"] == "Beans" - assert x["institution_name"] == "University of California, San Francisco" - assert x["lab_name"] == "Loren Frank" - assert x["session_id"] == "beans_01" - assert x["session_description"] == "Reinforcement leaarning" - assert x["session_start_time"] == datetime(2019, 7, 18, 15, 29, 47) - assert x["timestamps_reference_time"] == datetime(1970, 1, 1, 0, 0) - assert x["experiment_description"] == "Reinforcement learning" - - x = DataAcquisitionDevice().fetch() - assert len(x) == 1 - assert x[0]["device_name"] == "dataacq_device0" - assert x[0]["system"] == "SpikeGadgets" - assert x[0]["amplifier"] == "Intan" - assert x[0]["adc_circuit"] == "Intan" - - x = CameraDevice().fetch() - assert len(x) == 2 - # NOTE order of insertion is not consistent so cannot use x[0] - expected1 = dict( - camera_name="beans sleep camera", - # meters_per_pixel=0.00055, # cannot check floating point values this way - manufacturer="", - model="unknown", - lens="unknown", - camera_id=0, - ) - assert CameraDevice() & expected1 - assert (CameraDevice() & expected1).fetch("meters_per_pixel") == 0.00055 - expected2 = dict( - camera_name="beans run camera", - # meters_per_pixel=0.002, - manufacturer="", - model="unknown2", - lens="unknown2", - camera_id=1, - ) - assert CameraDevice() & expected2 - assert (CameraDevice() & expected2).fetch("meters_per_pixel") == 0.002 - - x = Probe().fetch() - assert len(x) == 1 - assert x[0]["probe_type"] == "128c-4s8mm6cm-20um-40um-sl" - assert x[0]["probe_description"] == "128 channel polyimide probe" - assert x[0]["num_shanks"] == 4 - assert x[0]["contact_side_numbering"] == "True" diff --git a/tests/trim_beans.py b/tests/trim_beans.py deleted file mode 100644 index 242e65c49..000000000 --- a/tests/trim_beans.py +++ /dev/null @@ -1,73 +0,0 @@ -import pynwb - -# import ndx_franklab_novela - -file_in = "beans20190718.nwb" -file_out = "beans20190718_trimmed.nwb" - -n_timestamps_to_keep = 20 # / 20000 Hz sampling rate = 1 ms - -with pynwb.NWBHDF5IO(file_in, "r", load_namespaces=True) as io: - nwbfile = io.read() - orig_eseries = nwbfile.acquisition.pop("e-series") - - # create a new ElectricalSeries with a subset of the data and timestamps - data = orig_eseries.data[0:n_timestamps_to_keep, :] - ts = orig_eseries.timestamps[0:n_timestamps_to_keep] - electrodes = nwbfile.create_electrode_table_region( - region=orig_eseries.electrodes.data[:].tolist(), - name=orig_eseries.electrodes.name, - description=orig_eseries.electrodes.description, - ) - new_eseries = pynwb.ecephys.ElectricalSeries( - name=orig_eseries.name, - description=orig_eseries.description, - data=data, - timestamps=ts, - electrodes=electrodes, - ) - nwbfile.add_acquisition(new_eseries) - - # create a new analog TimeSeries with a subset of the data and timestamps - orig_analog = nwbfile.processing["analog"]["analog"].time_series.pop( - "analog" - ) - data = orig_analog.data[0:n_timestamps_to_keep, :] - ts = orig_analog.timestamps[0:n_timestamps_to_keep] - new_analog = pynwb.TimeSeries( - name=orig_analog.name, - description=orig_analog.description, - data=data, - timestamps=ts, - unit=orig_analog.unit, - ) - nwbfile.processing["analog"]["analog"].add_timeseries(new_analog) - - # remove last two columns of all SpatialSeries data (xloc2, yloc2) because - # it does not conform with NWB 2.5 and they are all zeroes anyway - new_spatial_series = list() - for spatial_series_name in list( - nwbfile.processing["behavior"]["position"].spatial_series - ): - spatial_series = nwbfile.processing["behavior"][ - "position" - ].spatial_series.pop(spatial_series_name) - assert isinstance(spatial_series, pynwb.behavior.SpatialSeries) - data = spatial_series.data[:, 0:2] - ts = spatial_series.timestamps[0:n_timestamps_to_keep] - new_spatial_series.append( - pynwb.behavior.SpatialSeries( - name=spatial_series.name, - description=spatial_series.description, - data=data, - timestamps=spatial_series.timestamps, - reference_frame=spatial_series.reference_frame, - ) - ) - for spatial_series in new_spatial_series: - nwbfile.processing["behavior"]["position"].add_spatial_series( - spatial_series - ) - - with pynwb.NWBHDF5IO(file_out, "w") as export_io: - export_io.export(io, nwbfile) diff --git a/tests/test_nwb_helper_fn.py b/tests/utils/test_nwb_helper_fn.py similarity index 86% rename from tests/test_nwb_helper_fn.py rename to tests/utils/test_nwb_helper_fn.py index ad382b0a4..d054f7ecb 100644 --- a/tests/test_nwb_helper_fn.py +++ b/tests/utils/test_nwb_helper_fn.py @@ -3,9 +3,11 @@ import pynwb -# NOTE: importing this calls spyglass.__init__ whichand spyglass.common.__init__ which both require the -# DataJoint MySQL server to be already set up and running -from spyglass.common import get_electrode_indices + +def get_electrode_indices(*args, **kwargs): + from spyglass.common import get_electrode_indices # noqa: E402 + + return get_electrode_indices(*args, **kwargs) class TestGetElectrodeIndices(unittest.TestCase): @@ -48,7 +50,7 @@ def setUp(self): ) self.nwbfile.add_acquisition(eseries) - def test_nwbfile(self): + def test_electrode_nwbfile(self): ret = get_electrode_indices(self.nwbfile, [102, 105]) assert ret == [2, 5] From ad78ea17b586186c6cac24438ba55e9ed27a99f0 Mon Sep 17 00:00:00 2001 From: Eric Denovellis Date: Fri, 19 Jan 2024 14:31:28 -0800 Subject: [PATCH 4/8] Minor decoding fixes (#769) * Add non-local detector and remove replay_trajectory_classification * Reorganize * Fix formatting and imports * Update .gitignore * Remove because of circular import * Fix name of parameter * Handle case where ther is only one interval * Fix settings * Handle single interval * from_unit_dict does not exist in 0.98.2 of spike interface * Simplify call * Update for SpikeSorting merge table and add spyglass mixin * Fix dependencies * Fix merge conflict * Update src/spyglass/decoding/v1/clusterless.py Co-authored-by: Chris Brozdowski * Update src/spyglass/decoding/v1/clusterless.py Co-authored-by: Chris Brozdowski * Update src/spyglass/decoding/v1/clusterless.py Co-authored-by: Chris Brozdowski * Update src/spyglass/decoding/v1/clusterless.py Co-authored-by: Chris Brozdowski * Apply suggestions from code review Co-authored-by: Chris Brozdowski * Remove unused imports and format * Add saving of waveform features * Don't store electrodes, full waveforms, waveform mean * Fix spike times and add convenience method * Add spike location and some formatting * Remove circular import * Fix dict expansion * Initial working clusterless pipeline * Add position group * Rename classifier to decoding * Handle encoding and decoding intervals * Put old files under v0, try/except for old decoding package * Rename visualization and remove from v0 v0 visualization is redundant with visualization * Place parameters and position group in core.py * Add sorted spikes decoding * Add objects to init for convenience * Remove unused imports * Fix fetching of spike times * Insert into merge table * Update CHANGELOG.md * Function for removing decoding outputs not in DecodingOutput * Fix name * Add draft of tutorials and rearrange notebooks * Fix config loading * Add 1D decoding and some notes on estimate_parameters kwarg * Update 43_Decoding_SortedSpikes.ipynb * Remove old decoding notebook * Save initial conditions and discrete transitions * Apply suggestions from code review Co-authored-by: Chris Brozdowski * Be more specific with import error * Remove unneeded comments * Remove incorrect dimension name * Project merge_id from SpikeSortingOutput for clarity * Update src/spyglass/decoding/v0/clusterless.py Co-authored-by: Chris Brozdowski * Update src/spyglass/decoding/v0/clusterless.py Co-authored-by: Chris Brozdowski * Update src/spyglass/decoding/v0/clusterless.py Co-authored-by: Chris Brozdowski * Fix linting * Update notebooks * Ignore .pem * Add session as a primary key for Groups * Add some helper methods * Update notebooks * Update README.md * Update pyscripts * Update 42_Decoding_Clusterless.ipynb * Update CHANGELOG.md * Add fetch and insert * Simplify class conversion * Do the dictionary conversion of class for the user * Update CHANGELOG.md * Update .gitignore * Use methods in populate * Avoid fetching interval range if not needed * Generalize finding class from modules * Use args/kwargs * Simplify tuple unpacking * Make decoding kwargs nullable * Add function for get_recording and get_sorting to the spikesorting merge table * make decoding waveform features agnostic to spikesorting source * Fix spelling * Use fetch1_dataframe for position * Use self instead of class * Update src/spyglass/decoding/v1/sorted_spikes.py Co-authored-by: Samuel Bray * Be more careful about populating select keys * Make more readable/remove unused imports * Save classifier * Clean up saved model paths * add function load_linear_position_info * Update src/spyglass/decoding/v1/sorted_spikes.py Co-authored-by: Samuel Bray * Update 41_Extracting_Clusterless_Waveform_Features.py * Update docstring * Apply suggestions from code review Co-authored-by: Chris Brozdowski * Update src/spyglass/decoding/v1/clusterless.py Co-authored-by: Chris Brozdowski * Update src/spyglass/decoding/v1/clusterless.py Co-authored-by: Chris Brozdowski * Fix linting * Fix syntax * Rename variable to avoid confusion * Restrict UnitWaveformFeaturesGroup and SortedSpikesGroup * Concatenate linear position and position dataframes * Static methods don't require instantiating class * Avoid merge restrict * Add version to defaults * Remove unused import * Fix classifier path * Add dry run * Remove non-default * Handle permissions and file not found * Keep position info within encoding/decoding interval * Add methods to get the spike_times, spike_indicators, firing rate * Fix docstring to match default * Implement function rather than import * Remove unused broken imports * Add decoding cleanup * Fix import * Put old vis code back * Fix import * Add draft helper functions * Limit options on input * Fix logic * Fix where the key is passed * Update notebooks * Host main visualizations in non_local_detector repo * Update notebooks/py_scripts/41_Extracting_Clusterless_Waveform_Features.py Co-authored-by: Chris Brozdowski * Update src/spyglass/spikesorting/merge.py Co-authored-by: Chris Brozdowski * Update src/spyglass/decoding/decoding_merge.py Co-authored-by: Chris Brozdowski * Revert "Limit options on input" This reverts commit 386714ccdf480b7d04036b83fb62de6e9164364e. * Use f-string for version * Add useful imports to the top level This would have to change a bit if there were multiple versions of the pipeline. * Make source class a hidden attribute * Update CHANGELOG.md --------- Co-authored-by: Chris Brozdowski Co-authored-by: Sam Bray --- CHANGELOG.md | 2 +- franklab_scripts/nightly_cleanup.py | 10 +- ...acting_Clusterless_Waveform_Features.ipynb | 643 ++++++++++-------- notebooks/42_Decoding_Clusterless.ipynb | 597 +++++++--------- notebooks/43_Decoding_SortedSpikes.ipynb | 331 ++++----- notebooks/py_scripts/30_LFP.py | 2 +- ...xtracting_Clusterless_Waveform_Features.py | 34 +- .../py_scripts/42_Decoding_Clusterless.py | 78 +-- src/spyglass/decoding/__init__.py | 23 +- src/spyglass/decoding/decoding_merge.py | 109 ++- .../core.py => v0/visualization.py} | 4 +- .../view1D.py => v0/visualization_1D_view.py} | 0 .../view2D.py => v0/visualization_2D_view.py} | 4 + src/spyglass/decoding/v1/clusterless.py | 75 +- src/spyglass/decoding/v1/core.py | 9 +- src/spyglass/decoding/v1/sorted_spikes.py | 74 +- src/spyglass/spikesorting/merge.py | 51 ++ src/spyglass/utils/dj_merge_tables.py | 2 +- 18 files changed, 1089 insertions(+), 959 deletions(-) rename src/spyglass/decoding/{visualization/core.py => v0/visualization.py} (99%) rename src/spyglass/decoding/{visualization/view1D.py => v0/visualization_1D_view.py} (100%) rename src/spyglass/decoding/{visualization/view2D.py => v0/visualization_2D_view.py} (99%) diff --git a/CHANGELOG.md b/CHANGELOG.md index 302c116d3..5664e7238 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -26,7 +26,7 @@ - Refactor input validation in DLC pipeline. #688 - DLC path handling from config, and normalize naming convention. #722 - Decoding: - - Add `decoding` pipeline V1. #731 + - Add `decoding` pipeline V1. #731, #769 - Add a table to store the decoding results #731 - Use the new `non_local_detector` package for decoding #731 - Allow multiple spike waveform features for clusterelss decoding #731 diff --git a/franklab_scripts/nightly_cleanup.py b/franklab_scripts/nightly_cleanup.py index 777de626c..b55d2ad50 100755 --- a/franklab_scripts/nightly_cleanup.py +++ b/franklab_scripts/nightly_cleanup.py @@ -5,14 +5,6 @@ # ignore datajoint+jupyter async warnings import warnings -import numpy as np - -from spyglass.decoding.clusterless import ( - MarkParameters, - UnitMarkParameters, - UnitMarks, -) - warnings.simplefilter("ignore", category=DeprecationWarning) warnings.simplefilter("ignore", category=ResourceWarning) # NOTE: "SPIKE_SORTING_STORAGE_DIR" -> "SPYGLASS_SORTING_DIR" @@ -21,12 +13,14 @@ # import tables so that we can call them easily from spyglass.common import AnalysisNwbfile +from spyglass.decoding.decoding_merge import DecodingOutput from spyglass.spikesorting import SpikeSorting def main(): AnalysisNwbfile().nightly_cleanup() SpikeSorting().nightly_cleanup() + DecodingOutput().cleanup() if __name__ == "__main__": diff --git a/notebooks/41_Extracting_Clusterless_Waveform_Features.ipynb b/notebooks/41_Extracting_Clusterless_Waveform_Features.ipynb index 35cea47d7..2c1fc739f 100644 --- a/notebooks/41_Extracting_Clusterless_Waveform_Features.ipynb +++ b/notebooks/41_Extracting_Clusterless_Waveform_Features.ipynb @@ -22,14 +22,12 @@ "\n", "The goal of this notebook is to populate the `UnitWaveformFeatures` table, which depends `SpikeSortingOutput`. This table contains the features of the waveforms of each unit.\n", "\n", - "While clusterless decoding avoids actual spike sorting, we need to pass through these tables to maintain (relative) pipeline simplicity. Pass-through tables keep spike sorting and clusterless waveform extraction as similar as possible, by using shared steps. Here, \"spike sorting\" involves simple thresholding (sorter: clusterless_thresholder).\n", - "\n", - "Let's start with the following nwb file and time interval:" + "While clusterless decoding avoids actual spike sorting, we need to pass through these tables to maintain (relative) pipeline simplicity. Pass-through tables keep spike sorting and clusterless waveform extraction as similar as possible, by using shared steps. Here, \"spike sorting\" involves simple thresholding (sorter: clusterless_thresholder)." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -38,18 +36,65 @@ "\n", "dj.config.load(\n", " Path(\"../dj_local_conf.json\").absolute()\n", - ") # load config for database connection info\n", - "\n", - "nwb_copy_file_name = \"mediumnwb20230802_.nwb\"\n", - "interval_list_name = \"pos 0 valid times\"" + ") # load config for database connection info" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "If you haven't already, run the [Insert Data notebook](./01_Insert_Data.ipynb) to populate the tables.\n", + "First, if you haven't inserted the the `mediumnwb20230802.wnb` file into the database, you should do so now. This is the file that we will use for the decoding tutorials.\n", + "\n", + "It is a truncated version of the full NWB file, so it will run faster, but bigger than the minirec file we used in the previous tutorials so that decoding makes sense." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2024-01-17 22:14:51,194][INFO]: Connecting root@localhost:3306\n", + "[2024-01-17 22:14:51,274][INFO]: Connected root@localhost:3306\n", + "/Users/edeno/Documents/GitHub/spyglass/src/spyglass/data_import/insert_sessions.py:58: UserWarning: Cannot insert data from mediumnwb20230802.nwb: mediumnwb20230802_.nwb is already in Nwbfile table.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "from spyglass.utils.nwb_helper_fn import get_nwb_copy_filename\n", + "import spyglass.data_import as sgi\n", + "import spyglass.position as sgp\n", "\n", + "# Insert the nwb file\n", + "nwb_file_name = \"mediumnwb20230802.nwb\"\n", + "nwb_copy_file_name = get_nwb_copy_filename(nwb_file_name)\n", + "sgi.insert_sessions(nwb_file_name)\n", + "\n", + "# Position\n", + "sgp.v1.TrodesPosParams.insert_default()\n", + "\n", + "interval_list_name = \"pos 0 valid times\"\n", + "\n", + "trodes_s_key = {\n", + " \"nwb_file_name\": nwb_copy_file_name,\n", + " \"interval_list_name\": interval_list_name,\n", + " \"trodes_pos_params_name\": \"default\",\n", + "}\n", + "sgp.v1.TrodesPosSelection.insert1(\n", + " trodes_s_key,\n", + " skip_duplicates=True,\n", + ")\n", + "sgp.v1.TrodesPosV1.populate(trodes_s_key)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "These next steps are the same as in the [Spike Sorting notebook](./10_Spike_SortingV1.ipynb), but we'll repeat them here for clarity. These are pre-processing steps that are shared between spike sorting and clusterless decoding.\n", "\n", "We first set the `SortGroup` to define which contacts are sorted together.\n", @@ -66,32 +111,30 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:22:08,897][INFO]: Connecting root@localhost:3306\n", - "[2024-01-02 11:22:08,981][INFO]: Connected root@localhost:3306\n", - "[11:22:10][WARNING] Spyglass: Similar row(s) already inserted.\n", - "[11:22:10][WARNING] Spyglass: Similar row(s) already inserted.\n", - "[11:22:10][WARNING] Spyglass: Similar row(s) already inserted.\n", - "[11:22:10][WARNING] Spyglass: Similar row(s) already inserted.\n", - "[11:22:10][WARNING] Spyglass: Similar row(s) already inserted.\n", - "[11:22:10][WARNING] Spyglass: Similar row(s) already inserted.\n", - "[11:22:10][WARNING] Spyglass: Similar row(s) already inserted.\n", - "[11:22:10][WARNING] Spyglass: Similar row(s) already inserted.\n", - "[11:22:10][WARNING] Spyglass: Similar row(s) already inserted.\n", - 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"[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n", - "[11:22:17][INFO] Spyglass: Similar row(s) already inserted.\n" + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n", + "[22:14:56][INFO] Spyglass: Similar row(s) already inserted.\n" ] } ], @@ -520,17 +563,41 @@ "

features_param_name

\n", " a name for this set of parameters\n", " \n", - " \n", + " 0751a1e1-a406-7f87-ae6f-ce4ffc60621c\n", + "amplitude485a4ddf-332d-35b5-3ad4-0561736c1844\n", + "amplitude4a712103-c223-864f-82e0-6c23de79cc14\n", + "amplitude4a72c253-b3ca-8c13-e615-736a7ebff35c\n", + "amplitude5c53bd33-d57c-fbba-e0fb-55e0bcb85d03\n", + "amplitude614d796c-0b95-6364-aaa0-b6cb1e7bbb83\n", + "amplitude6acb99b8-6a0c-eb83-1141-5f603c5895e0\n", + "amplitude6d039a63-17ad-0b78-4b1e-f02d5f3dbbc5\n", + "amplitude74e10781-1228-4075-0870-af224024ffdc\n", + "amplitude7e3fa66e-727e-1541-819a-b01309bb30ae\n", + "amplitude86897349-ff68-ac72-02eb-739dd88936e6\n", + "amplitude8bbddc0f-d6ae-6260-9400-f884a6e25ae8\n", + "amplitude \n", " \n", - " \n", - "

Total: 0

\n", + "

...

\n", + "

Total: 23

\n", " " ], "text/plain": [ "*spikesorting_ *features_para\n", "+------------+ +------------+\n", - "\n", - " (Total: 0)" + "0751a1e1-a406- amplitude \n", + "485a4ddf-332d- amplitude \n", + "4a712103-c223- amplitude \n", + "4a72c253-b3ca- amplitude \n", + "5c53bd33-d57c- amplitude \n", + "614d796c-0b95- amplitude \n", + "6acb99b8-6a0c- amplitude \n", + "6d039a63-17ad- amplitude \n", + "74e10781-1228- amplitude \n", + "7e3fa66e-727e- amplitude \n", + "86897349-ff68- amplitude \n", + "8bbddc0f-d6ae- amplitude \n", + " ...\n", + " (Total: 23)" ] }, "execution_count": 8, @@ -559,29 +626,29 @@ { "data": { "text/plain": [ - "array([UUID('86acdb0f-84f0-73a2-a851-1f8305cd2e41'),\n", - " UUID('46829e10-1984-99a1-65a3-2b485a2f037f'),\n", - " UUID('ec308784-2bfb-dd90-147c-e4d44e5f649b'),\n", - " UUID('4b3065e5-76c2-bd48-32a1-ae62484f9314'),\n", - " UUID('609aeb54-dc2e-52d3-91bf-1728e0a2cf09'),\n", - " UUID('88492b1c-f4a9-9669-bb5b-7f1573015187'),\n", - " UUID('f515c07f-fc80-b28a-750d-d0d5491259f4'),\n", - " UUID('f4e29a80-ec96-dbe8-7081-425ac311b74c'),\n", - " UUID('d7754d5f-af01-19f4-3fdc-c9635081667a'),\n", - " UUID('2567bf67-bc67-47a5-aa2a-2bce19da232d'),\n", - " UUID('d65a1bf3-797d-b01f-e8be-2cea90b14c20'),\n", - " UUID('92c336ee-81f4-0af9-4f60-9bc32e71bc9f'),\n", - " UUID('aa8bc575-0715-69e9-5da7-313a0e1ee769'),\n", - " UUID('26310ce7-9ac3-4159-99f8-a3ad17037235'),\n", - " UUID('7355bdf3-f31c-4c22-1a09-50d9f6f5f037'),\n", - " UUID('189fb8c6-f964-00a9-f392-a9dbb138ea63'),\n", - " UUID('c4f24219-c023-8783-df53-2bbc88c9ad9c'),\n", - " UUID('411dff13-44f0-3e03-e867-689ae275e418'),\n", - " UUID('153954b2-b230-cb1f-749d-f977a22eaae9'),\n", - " UUID('00763b68-d663-c446-0555-1f2622d7da50'),\n", - " UUID('03954edd-f8fd-3dd9-cd10-f0eee47d6b3d'),\n", - " UUID('43a98eab-1fa6-184b-1f09-2e923984b03a'),\n", - " UUID('0720e5f2-625e-09d2-b522-ca2652c09f2a')], dtype=object)" + "array([UUID('485a4ddf-332d-35b5-3ad4-0561736c1844'),\n", + " UUID('6acb99b8-6a0c-eb83-1141-5f603c5895e0'),\n", + " UUID('f7237e18-4e73-4aee-805b-90735e9147de'),\n", + " UUID('7e3fa66e-727e-1541-819a-b01309bb30ae'),\n", + " UUID('6d039a63-17ad-0b78-4b1e-f02d5f3dbbc5'),\n", + " UUID('e0e9133a-7a4e-1321-a43a-e8afcb2f25da'),\n", + " UUID('9959b614-2318-f597-6651-a3a82124d28a'),\n", + " UUID('c0eb6455-fc41-c200-b62e-e3ca81b9a3f7'),\n", + " UUID('912e250e-56d8-ee33-4525-c844d810971b'),\n", + " UUID('d7d2c97a-0e6e-d1b8-735c-d55dc66a30e1'),\n", + " UUID('abb92dce-4410-8f17-a501-a4104bda0dcf'),\n", + " UUID('74e10781-1228-4075-0870-af224024ffdc'),\n", + " UUID('8bbddc0f-d6ae-6260-9400-f884a6e25ae8'),\n", + " UUID('614d796c-0b95-6364-aaa0-b6cb1e7bbb83'),\n", + " UUID('b332482b-e430-169d-8ac0-0a73ce968ed7'),\n", + " UUID('86897349-ff68-ac72-02eb-739dd88936e6'),\n", + " UUID('4a712103-c223-864f-82e0-6c23de79cc14'),\n", + " UUID('cf858380-e8a3-49de-c2a9-1a277e307a68'),\n", + " UUID('cc4ee561-f974-f8e5-0ea4-83185263ac67'),\n", + " UUID('4a72c253-b3ca-8c13-e615-736a7ebff35c'),\n", + " UUID('b92a94d8-ee1e-2097-a81f-5c1e1556ed24'),\n", + " UUID('5c53bd33-d57c-fbba-e0fb-55e0bcb85d03'),\n", + " UUID('0751a1e1-a406-7f87-ae6f-ce4ffc60621c')], dtype=object)" ] }, "execution_count": 9, @@ -680,18 +747,18 @@ "

features_param_name

\n", " a name for this set of parameters\n", " \n", - " 00763b68-d663-c446-0555-1f2622d7da50\n", - "amplitude03954edd-f8fd-3dd9-cd10-f0eee47d6b3d\n", - "amplitude0720e5f2-625e-09d2-b522-ca2652c09f2a\n", - "amplitude153954b2-b230-cb1f-749d-f977a22eaae9\n", - "amplitude189fb8c6-f964-00a9-f392-a9dbb138ea63\n", - "amplitude2567bf67-bc67-47a5-aa2a-2bce19da232d\n", - "amplitude26310ce7-9ac3-4159-99f8-a3ad17037235\n", - "amplitude411dff13-44f0-3e03-e867-689ae275e418\n", - "amplitude43a98eab-1fa6-184b-1f09-2e923984b03a\n", - "amplitude46829e10-1984-99a1-65a3-2b485a2f037f\n", - "amplitude4b3065e5-76c2-bd48-32a1-ae62484f9314\n", - "amplitude609aeb54-dc2e-52d3-91bf-1728e0a2cf09\n", + " 0751a1e1-a406-7f87-ae6f-ce4ffc60621c\n", + "amplitude485a4ddf-332d-35b5-3ad4-0561736c1844\n", + "amplitude4a712103-c223-864f-82e0-6c23de79cc14\n", + "amplitude4a72c253-b3ca-8c13-e615-736a7ebff35c\n", + "amplitude5c53bd33-d57c-fbba-e0fb-55e0bcb85d03\n", + "amplitude614d796c-0b95-6364-aaa0-b6cb1e7bbb83\n", + "amplitude6acb99b8-6a0c-eb83-1141-5f603c5895e0\n", + "amplitude6d039a63-17ad-0b78-4b1e-f02d5f3dbbc5\n", + "amplitude74e10781-1228-4075-0870-af224024ffdc\n", + "amplitude7e3fa66e-727e-1541-819a-b01309bb30ae\n", + "amplitude86897349-ff68-ac72-02eb-739dd88936e6\n", + "amplitude8bbddc0f-d6ae-6260-9400-f884a6e25ae8\n", "amplitude \n", " \n", "

...

\n", @@ -701,18 +768,18 @@ "text/plain": [ "*spikesorting_ *features_para\n", "+------------+ +------------+\n", - "00763b68-d663- amplitude \n", - "03954edd-f8fd- amplitude \n", - "0720e5f2-625e- amplitude \n", - "153954b2-b230- amplitude \n", - "189fb8c6-f964- amplitude \n", - "2567bf67-bc67- amplitude \n", - "26310ce7-9ac3- amplitude \n", - "411dff13-44f0- amplitude \n", - "43a98eab-1fa6- amplitude \n", - "46829e10-1984- amplitude \n", - "4b3065e5-76c2- amplitude \n", - "609aeb54-dc2e- amplitude \n", + "0751a1e1-a406- amplitude \n", + "485a4ddf-332d- amplitude \n", + "4a712103-c223- amplitude \n", + "4a72c253-b3ca- amplitude \n", + "5c53bd33-d57c- amplitude \n", + "614d796c-0b95- amplitude \n", + "6acb99b8-6a0c- amplitude \n", + "6d039a63-17ad- amplitude \n", + "74e10781-1228- amplitude \n", + "7e3fa66e-727e- amplitude \n", + "86897349-ff68- amplitude \n", + "8bbddc0f-d6ae- amplitude \n", " ...\n", " (Total: 23)" ] @@ -758,7 +825,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9d8a3acb3c93445cb6d77a739c4569f8", + "model_id": "c4f79735339147cf93143b0d329f7b0c", "version_major": 2, "version_minor": 0 }, @@ -773,12 +840,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:23:00,301][WARNING]: Skipped checksum for file with hash: b12a9d1d-d019-7c0e-09bf-355936d14915, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_DRQ7MITSST.nwb\n", + "[2024-01-17 22:15:08,494][WARNING]: Skipped checksum for file with hash: 6629fd95-636a-4ad4-c9af-cee507de2130, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_AMBBKQ9RIY.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:23:00][INFO] Spyglass: Writing new NWB file mediumnwb20230802_OX2ORY4MKR.nwb\n", + "[22:15:08][INFO] Spyglass: Writing new NWB file mediumnwb20230802_NQEPSMKPK0.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -786,7 +853,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a6ea0474a3344875804dc98c2fa13237", + "model_id": "71ac6cac75cd4ddcb21e16dc9432b655", "version_major": 2, "version_minor": 0 }, @@ -801,12 +868,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:23:09,887][WARNING]: Skipped checksum for file with hash: c8f4786b-9ef7-61f7-cae0-251e84c59317, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_XXD817HX5I.nwb\n", + "[2024-01-17 22:15:19,450][WARNING]: Skipped checksum for file with hash: 6d04cbdb-e1e4-f44f-7274-0e1ab0356d75, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_W1MLF0Q86S.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:23:10][INFO] Spyglass: Writing new NWB file mediumnwb20230802_O1OGMFS4AF.nwb\n", + "[22:15:19][INFO] Spyglass: Writing new NWB file mediumnwb20230802_F02UG5Z5FR.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -814,7 +881,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3bec079c01bb44d99ffcc20c3306f139", + "model_id": "a90dc146fa6548a8a2b2af7495d4be29", "version_major": 2, "version_minor": 0 }, @@ -829,12 +896,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:23:19,778][WARNING]: Skipped checksum for file with hash: 6907761c-fb37-6528-56d7-507d5525a69b, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_JN3OG7ZA5E.nwb\n", + "[2024-01-17 22:15:30,787][WARNING]: Skipped checksum for file with hash: 8993754e-7dbe-94a1-403d-8c55aa9c6c42, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_JN4A4GSLZB.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:23:20][INFO] Spyglass: Writing new NWB file mediumnwb20230802_Y3E5VJAR0Z.nwb\n", + "[22:15:31][INFO] Spyglass: Writing new NWB file mediumnwb20230802_OTV91MLKDT.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -842,7 +909,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "993068f7b4c5443db4375a64a826cef4", + "model_id": "3d8380674f7246c3ac47438cb638ec48", "version_major": 2, "version_minor": 0 }, @@ -857,12 +924,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:23:29,678][WARNING]: Skipped checksum for file with hash: 1767224a-ebf4-819e-deb3-67c6d47bcf57, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_BKRH5CPBEZ.nwb\n", + "[2024-01-17 22:15:41,633][WARNING]: Skipped checksum for file with hash: 9e24661c-b021-6ad4-f224-89e331334f18, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_T2DBO3EMZ8.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:23:29][INFO] Spyglass: Writing new NWB file mediumnwb20230802_E66YNNI7S4.nwb\n", + "[22:15:41][INFO] Spyglass: Writing new NWB file mediumnwb20230802_TSPNTCGNN1.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -870,7 +937,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3893c963e63d4cd79bcc111f7fdb5e5b", + "model_id": "a5bd42b4afcd445894660a3601248554", "version_major": 2, "version_minor": 0 }, @@ -885,12 +952,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:23:40,093][WARNING]: Skipped checksum for file with hash: 45bce4f3-1861-a3bb-a7d1-522a39d83dde, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_RX8QUCHGVT.nwb\n", + "[2024-01-17 22:15:52,561][WARNING]: Skipped checksum for file with hash: f64f34ee-e72d-e566-a048-65f2ea31708a, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_USMRXAAV8I.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:23:40][INFO] Spyglass: Writing new NWB file mediumnwb20230802_UZ3IGTO5AU.nwb\n", + "[22:15:52][INFO] Spyglass: Writing new NWB file mediumnwb20230802_QSK70WFDJH.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -898,7 +965,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "afd4a8f07be34ecb9250d4236e131fff", + "model_id": "ecfd8f43660a41278fbc6826f4517fc7", "version_major": 2, "version_minor": 0 }, @@ -913,12 +980,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:23:50,600][WARNING]: Skipped checksum for file with hash: 36da4c85-d069-0e7f-3086-94efb47e6b78, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_VHA7WLA4XX.nwb\n", + "[2024-01-17 22:16:03,559][WARNING]: Skipped checksum for file with hash: 6d13e338-41bd-b011-beb5-4de53d9d467b, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_JA2OA12RPN.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:23:50][INFO] Spyglass: Writing new NWB file mediumnwb20230802_CHBHDNP2W8.nwb\n", + "[22:16:03][INFO] Spyglass: Writing new NWB file mediumnwb20230802_DO45HKXYTB.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -926,7 +993,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b36a535ae08e43fcb5419b6727d8f53f", + "model_id": "ccb7eec245734ddaab37d65a48db80b2", "version_major": 2, "version_minor": 0 }, @@ -941,12 +1008,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:24:00,723][WARNING]: Skipped checksum for file with hash: 0972a7a6-1e32-5164-7fcc-e2b9aff76c05, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_9SQUOJRQSS.nwb\n", + "[2024-01-17 22:16:14,288][WARNING]: Skipped checksum for file with hash: d740eb7d-ce29-e140-06a2-c56655e0842a, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_L92EE1VRPB.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:24:01][INFO] Spyglass: Writing new NWB file mediumnwb20230802_L0LVGC1DFT.nwb\n", + "[22:16:14][INFO] Spyglass: Writing new NWB file mediumnwb20230802_KFIYRJ4HFO.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -954,7 +1021,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f8fda2628e3c4806940d629ed46e6260", + "model_id": "8f4db4312708442d9d9baee7361e2d18", "version_major": 2, "version_minor": 0 }, @@ -969,12 +1036,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:24:10,661][WARNING]: Skipped checksum for file with hash: 7133aab2-7288-85f8-f65f-695afa564e63, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_4LOGBKOP0I.nwb\n", + "[2024-01-17 22:16:24,130][WARNING]: Skipped checksum for file with hash: 1f386cd3-89da-0233-03ff-76ba94e91a3a, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_TX2ZX3DAP4.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:24:10][INFO] Spyglass: Writing new NWB file mediumnwb20230802_428JKP43Q1.nwb\n", + "[22:16:24][INFO] Spyglass: Writing new NWB file mediumnwb20230802_0YIM5K3H47.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -982,7 +1049,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eab8a1d34ccc490ebf044cd1784f7305", + "model_id": "52f1bb4db348413390887bab91a4eb05", "version_major": 2, "version_minor": 0 }, @@ -997,12 +1064,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:24:20,521][WARNING]: Skipped checksum for file with hash: 1ea4fc37-411c-0da6-00ec-f18beaa69e06, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_C8VMYH7C9V.nwb\n", + "[2024-01-17 22:16:35,048][WARNING]: Skipped checksum for file with hash: fa76d419-77a4-697a-325d-5c2ddbe517f9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_0R6AWXMC6G.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:24:20][INFO] Spyglass: Writing new NWB file mediumnwb20230802_X13I3BGUB1.nwb\n", + "[22:16:35][INFO] Spyglass: Writing new NWB file mediumnwb20230802_CTLEGE2TWZ.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -1010,7 +1077,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "85a6ac370fcc4c999e3bb8ff03bed6e9", + "model_id": "49b70c6fdc0c4d82b707b0f64c746992", "version_major": 2, "version_minor": 0 }, @@ -1025,12 +1092,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:24:30,164][WARNING]: Skipped checksum for file with hash: 7cd08c29-050b-30d6-93a4-6ede72933662, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_OQL3ITCETP.nwb\n", + "[2024-01-17 22:16:46,009][WARNING]: Skipped checksum for file with hash: ce4cb0c3-3dd0-70fd-8ea0-98a8b84592d9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_7UIA2ILMG6.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:24:30][INFO] Spyglass: Writing new NWB file mediumnwb20230802_0LXKB5BPTL.nwb\n", + "[22:16:46][INFO] Spyglass: Writing new NWB file mediumnwb20230802_7EN0N1U4U1.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -1038,7 +1105,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8038db68b7a94c1c829986dcc0b38bdb", + "model_id": "83e34dff95084145a5d1a2eceb29f091", "version_major": 2, "version_minor": 0 }, @@ -1053,12 +1120,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:24:40,329][WARNING]: Skipped checksum for file with hash: a2e79fe8-35f0-0f60-a4a5-27eb822c57d5, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_GU6KPWJ35V.nwb\n", + "[2024-01-17 22:16:56,814][WARNING]: Skipped checksum for file with hash: e43f95ff-9779-b980-00a3-99e104864462, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_AKOI7OTASI.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:24:40][INFO] Spyglass: Writing new NWB file mediumnwb20230802_HX2DNMBYI5.nwb\n", + "[22:16:57][INFO] Spyglass: Writing new NWB file mediumnwb20230802_DHKWBWWAMC.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -1066,7 +1133,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ded45eee83b3496793d534c87e347573", + "model_id": "497faa2d251246abb45174b1aac4f327", "version_major": 2, "version_minor": 0 }, @@ -1081,12 +1148,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:24:49,788][WARNING]: Skipped checksum for file with hash: 386e6724-08dc-8cca-6670-f8ed557cdd44, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_H8R2XMWTYU.nwb\n", + "[2024-01-17 22:17:05,013][WARNING]: Skipped checksum for file with hash: ff81d274-17f7-702d-a2b4-92ac43c29316, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_Y2YF504C5D.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:24:50][INFO] Spyglass: Writing new NWB file mediumnwb20230802_9BC3PGE9BE.nwb\n", + "[22:17:05][INFO] Spyglass: Writing new NWB file mediumnwb20230802_PEN0D79Q0B.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -1094,7 +1161,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "383e0fd18ef741cbbe57e8e46a2b61d2", + "model_id": "f8c583bb202347f0bb8678c3c249cb4b", "version_major": 2, "version_minor": 0 }, @@ -1109,12 +1176,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:24:58,435][WARNING]: Skipped checksum for file with hash: 41849951-22a3-e057-5b72-398a5fd795fb, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_KKBMX8E512.nwb\n", + "[2024-01-17 22:17:15,903][WARNING]: Skipped checksum for file with hash: e282a8e5-844b-20f6-345c-cded12e761a9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_DUNM1TZUGR.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:24:58][INFO] Spyglass: Writing new NWB file mediumnwb20230802_L35KWBBILV.nwb\n", + "[22:17:16][INFO] Spyglass: Writing new NWB file mediumnwb20230802_WP7SIXDJ2A.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -1122,7 +1189,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cb64438525ce4109a2dfef5e27c62bc1", + "model_id": "fd115ce374c043feac8f7e3ec4cb887c", "version_major": 2, "version_minor": 0 }, @@ -1137,12 +1204,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:25:07,943][WARNING]: Skipped checksum for file with hash: ff8fb4a0-6100-1d83-7568-5ee8e49be5d3, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_4VRPO41KQE.nwb\n", + "[2024-01-17 22:17:26,609][WARNING]: Skipped checksum for file with hash: 7d05460d-7366-27c9-2ba7-de2ad5d402f2, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_4JXWFJ3JRI.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:25:08][INFO] Spyglass: Writing new NWB file mediumnwb20230802_BSIV3DLAMV.nwb\n", + "[22:17:26][INFO] Spyglass: Writing new NWB file mediumnwb20230802_B82OS6W1QA.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -1150,7 +1217,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6d25e326a5784c8fbfcbd8a8d331fee1", + "model_id": "d48a5f3da4394f2dbdb4c7281caba2ed", "version_major": 2, "version_minor": 0 }, @@ -1165,12 +1232,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:25:17,690][WARNING]: Skipped checksum for file with hash: 7019ae20-b254-003d-969c-27238030f925, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_HLUMWXS9R0.nwb\n", + "[2024-01-17 22:17:37,652][WARNING]: Skipped checksum for file with hash: c202eb9e-ca43-0a72-4086-57a5bb6eb937, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_5TY04H3B5T.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:25:17][INFO] Spyglass: Writing new NWB file mediumnwb20230802_PWCOH5ROGU.nwb\n", + "[22:17:37][INFO] Spyglass: Writing new NWB file mediumnwb20230802_XO17FQLN6T.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -1178,7 +1245,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b2b9ca4184af4c619885f1e867b8f388", + "model_id": "872ea3e1911745ce9ee2626bda69d164", "version_major": 2, "version_minor": 0 }, @@ -1193,12 +1260,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:25:27,785][WARNING]: Skipped checksum for file with hash: 6ede8753-2030-2522-3d8e-1c88ccda72d3, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_AGDX79CGKU.nwb\n", + "[2024-01-17 22:17:47,269][WARNING]: Skipped checksum for file with hash: 4357905c-c6b9-3990-4d62-740a54cfc667, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_X84BYVM2B0.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:25:28][INFO] Spyglass: Writing new NWB file mediumnwb20230802_JE0YCFOTU6.nwb\n", + "[22:17:47][INFO] Spyglass: Writing new NWB file mediumnwb20230802_OCFI0GFLZ9.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -1206,7 +1273,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "017bfbe285d34ef58108fcef6e7d7895", + "model_id": "3bb4a5c8097d451896b9552caf862676", "version_major": 2, "version_minor": 0 }, @@ -1221,12 +1288,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:25:37,853][WARNING]: Skipped checksum for file with hash: adf6cda7-1231-8218-2a7d-d0ea495ac5e0, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_QW819UWMPS.nwb\n", + "[2024-01-17 22:17:58,240][WARNING]: Skipped checksum for file with hash: 4c1103ac-eaca-b282-e5ff-aa2194e65a43, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_2R6VQ8EDL4.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:25:38][INFO] Spyglass: Writing new NWB file mediumnwb20230802_KFVKOTUGBY.nwb\n", + "[22:17:58][INFO] Spyglass: Writing new NWB file mediumnwb20230802_60M9VSZX0W.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -1234,7 +1301,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0ea546483db04770a3d84b28bfba6d03", + "model_id": "a30f15ba40cf4c9cb0050f0e1ddb1396", "version_major": 2, "version_minor": 0 }, @@ -1249,12 +1316,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:25:46,039][WARNING]: Skipped checksum for file with hash: 9158e229-f0be-fe25-e5ac-c203bf9dd774, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_X1ENKGF5F6.nwb\n", + "[2024-01-17 22:18:09,119][WARNING]: Skipped checksum for file with hash: 023c874f-8114-3ef6-7fcf-813844787d5f, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_L7HDY9IDHO.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:25:46][INFO] Spyglass: Writing new NWB file mediumnwb20230802_6ZW1JK07GZ.nwb\n", + "[22:18:09][INFO] Spyglass: Writing new NWB file mediumnwb20230802_Z5HJ68LHYW.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -1262,7 +1329,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "778fa455133f49489df298bdb226d8c7", + "model_id": "b0109646253a42c19df9dafc465548a6", "version_major": 2, "version_minor": 0 }, @@ -1277,12 +1344,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:25:55,892][WARNING]: Skipped checksum for file with hash: 07fae3c3-9816-a718-b099-3c85adb7cb53, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_ND0D5I9STR.nwb\n", + "[2024-01-17 22:18:20,605][WARNING]: Skipped checksum for file with hash: fde8b240-6adc-86f0-6391-f3f6fad72ee9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_HWU3E4EKP4.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:25:56][INFO] Spyglass: Writing new NWB file mediumnwb20230802_7J5JW85MUW.nwb\n", + "[22:18:20][INFO] Spyglass: Writing new NWB file mediumnwb20230802_U5U5JVGY4F.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -1290,7 +1357,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2fcac97b47544d4f81e46237b86a60cf", + "model_id": "cf5715c3dee74d71ac325fc77c0eec93", "version_major": 2, "version_minor": 0 }, @@ -1305,12 +1372,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:26:06,531][WARNING]: Skipped checksum for file with hash: f3da67cc-99de-dde7-2b4e-86a8d1b9df6d, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_MYA6F5PO4T.nwb\n", + "[2024-01-17 22:18:31,780][WARNING]: Skipped checksum for file with hash: c592e63b-4db1-40be-632e-0180e6fa02d7, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_SGAU9PX7US.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:26:06][INFO] Spyglass: Writing new NWB file mediumnwb20230802_P9LLTXF2UV.nwb\n", + "[22:18:32][INFO] Spyglass: Writing new NWB file mediumnwb20230802_0D5Z0NSIP8.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -1318,7 +1385,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1df67d1daa0a40f193ed77073c8c8ce8", + "model_id": "e0ea746638354277bd96180aac672309", "version_major": 2, "version_minor": 0 }, @@ -1333,12 +1400,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:26:16,350][WARNING]: Skipped checksum for file with hash: 5e927005-d667-e8e2-0cf8-d4250263085e, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_2GYJ9DZDZM.nwb\n", + "[2024-01-17 22:18:42,644][WARNING]: Skipped checksum for file with hash: 148d9058-e6dc-e959-4c4d-75db9aa0b6e4, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_EF6N6XI3AH.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:26:16][INFO] Spyglass: Writing new NWB file mediumnwb20230802_P1S52EP8IG.nwb\n", + "[22:18:42][INFO] Spyglass: Writing new NWB file mediumnwb20230802_EYV2NARUKU.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -1346,7 +1413,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e3b8ee37b63941f7866de97d1b510eb0", + "model_id": "a0f6340431f84fe98d2bcfdbedbde443", "version_major": 2, "version_minor": 0 }, @@ -1361,12 +1428,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:26:26,229][WARNING]: Skipped checksum for file with hash: 47d51f63-e94f-52f7-e633-a2f30d9a889f, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_VUCHU58MU8.nwb\n", + "[2024-01-17 22:18:54,570][WARNING]: Skipped checksum for file with hash: b4b6404f-aaf8-c4cc-9abe-ceea56e103f3, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_O7ZZ0F1XN7.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:26:26][INFO] Spyglass: Writing new NWB file mediumnwb20230802_L98HKJBI6P.nwb\n", + "[22:18:54][INFO] Spyglass: Writing new NWB file mediumnwb20230802_T4XBCIW44T.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/spikeinterface/core/waveform_extractor.py:275: UserWarning: Sorting object is not dumpable, which might result in downstream errors for parallel processing. To make the sorting dumpable, use the `sorting.save()` function.\n", " warn(\n" ] @@ -1374,7 +1441,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f37da0169e574731b9350e7c362ebb6f", + "model_id": "d95a33c36dcb4b52923648866fef862d", "version_major": 2, "version_minor": 0 }, @@ -1389,12 +1456,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:26:36,556][WARNING]: Skipped checksum for file with hash: 43eefbae-67f4-1fbc-ac02-e37029006ed9, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_B36EKV3244.nwb\n", + "[2024-01-17 22:19:05,568][WARNING]: Skipped checksum for file with hash: 26f7bdc7-da8d-6ad5-3f4a-554ceb48755e, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_0TKF5589B7.nwb\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/ecephys.py:90: UserWarning: ElectricalSeries 'e-series': The second dimension of data does not match the length of electrodes. Your data may be transposed.\n", " warnings.warn(\"%s '%s': The second dimension of data does not match the length of electrodes. \"\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/pynwb/base.py:193: UserWarning: TimeSeries 'analog': Length of data does not match length of timestamps. Your data may be transposed. Time should be on the 0th dimension\n", " warn(\"%s '%s': Length of data does not match length of timestamps. Your data may be transposed. \"\n", - "[11:26:36][INFO] Spyglass: Writing new NWB file mediumnwb20230802_CHGNLEZ92L.nwb\n" + "[22:19:05][INFO] Spyglass: Writing new NWB file mediumnwb20230802_UD55CR8LZK.nwb\n" ] } ], @@ -1480,43 +1547,43 @@ "

object_id

\n", " the NWB object that stores the waveforms\n", " \n", - " 00763b68-d663-c446-0555-1f2622d7da50\n", + " 0751a1e1-a406-7f87-ae6f-ce4ffc60621c\n", "amplitude\n", - "mediumnwb20230802_OX2ORY4MKR.nwb\n", - "db7b7b7c-a97b-4982-96d9-60dd41d0546d03954edd-f8fd-3dd9-cd10-f0eee47d6b3d\n", + "mediumnwb20230802_NQEPSMKPK0.nwb\n", + "8607d6a6-213c-431d-ab99-70196b6cf0bf485a4ddf-332d-35b5-3ad4-0561736c1844\n", "amplitude\n", - "mediumnwb20230802_O1OGMFS4AF.nwb\n", - "cbdbc6ab-aeb4-4447-8d26-6bfb56a440810720e5f2-625e-09d2-b522-ca2652c09f2a\n", + "mediumnwb20230802_F02UG5Z5FR.nwb\n", + "9f693a74-a203-4628-b3ec-50a32b3549d84a712103-c223-864f-82e0-6c23de79cc14\n", "amplitude\n", - "mediumnwb20230802_Y3E5VJAR0Z.nwb\n", - "e36c31d6-2ebf-46eb-b7fe-028e88945e3a153954b2-b230-cb1f-749d-f977a22eaae9\n", + "mediumnwb20230802_OTV91MLKDT.nwb\n", + "648953e8-1891-4c90-9756-d6b7cc2b7c3d4a72c253-b3ca-8c13-e615-736a7ebff35c\n", "amplitude\n", - "mediumnwb20230802_E66YNNI7S4.nwb\n", - "4768d18a-ab94-4ba8-87fa-9a11f2d8d5cc189fb8c6-f964-00a9-f392-a9dbb138ea63\n", + "mediumnwb20230802_TSPNTCGNN1.nwb\n", + "6d0af664-f811-4781-9a23-cac437fb2d155c53bd33-d57c-fbba-e0fb-55e0bcb85d03\n", "amplitude\n", - "mediumnwb20230802_UZ3IGTO5AU.nwb\n", - "d4ba1164-a043-46b3-924b-a2fc999f46d32567bf67-bc67-47a5-aa2a-2bce19da232d\n", + "mediumnwb20230802_QSK70WFDJH.nwb\n", + "a67ed5bb-3edd-465e-8737-ee08f3e7d7d5614d796c-0b95-6364-aaa0-b6cb1e7bbb83\n", "amplitude\n", - "mediumnwb20230802_CHBHDNP2W8.nwb\n", - "d7832810-313b-4186-861a-7e11341553d626310ce7-9ac3-4159-99f8-a3ad17037235\n", + "mediumnwb20230802_DO45HKXYTB.nwb\n", + "13218b00-bf34-455c-9c38-c3b3174d40096acb99b8-6a0c-eb83-1141-5f603c5895e0\n", "amplitude\n", - "mediumnwb20230802_L0LVGC1DFT.nwb\n", - "202d6dae-c1b4-4915-850a-2f2f8f05d159411dff13-44f0-3e03-e867-689ae275e418\n", + "mediumnwb20230802_KFIYRJ4HFO.nwb\n", + "d892bb47-94fc-4c29-acab-d5b3d9565c976d039a63-17ad-0b78-4b1e-f02d5f3dbbc5\n", "amplitude\n", - "mediumnwb20230802_428JKP43Q1.nwb\n", - "15dc2df6-26c7-415d-9446-aa44ecc40af143a98eab-1fa6-184b-1f09-2e923984b03a\n", + "mediumnwb20230802_0YIM5K3H47.nwb\n", + "60f4d280-a42a-4a77-9c35-9bd4d2c7699174e10781-1228-4075-0870-af224024ffdc\n", "amplitude\n", - "mediumnwb20230802_X13I3BGUB1.nwb\n", - "5ad143d4-235f-4ad9-aa94-f7140bc7185346829e10-1984-99a1-65a3-2b485a2f037f\n", + "mediumnwb20230802_CTLEGE2TWZ.nwb\n", + "99f51e3d-54b5-41d7-a61e-7013b22fb0667e3fa66e-727e-1541-819a-b01309bb30ae\n", "amplitude\n", - "mediumnwb20230802_0LXKB5BPTL.nwb\n", - "554ed58c-28c1-4786-850f-2be0a557574a4b3065e5-76c2-bd48-32a1-ae62484f9314\n", + "mediumnwb20230802_7EN0N1U4U1.nwb\n", + "535b28d1-c9b5-4d7d-a4f5-4d80508542b486897349-ff68-ac72-02eb-739dd88936e6\n", "amplitude\n", - "mediumnwb20230802_HX2DNMBYI5.nwb\n", - "9e7ca80d-8d45-4718-8413-e0ad109220c1609aeb54-dc2e-52d3-91bf-1728e0a2cf09\n", + "mediumnwb20230802_DHKWBWWAMC.nwb\n", + "67ee1547-c570-4746-b886-748d96075b548bbddc0f-d6ae-6260-9400-f884a6e25ae8\n", "amplitude\n", - "mediumnwb20230802_9BC3PGE9BE.nwb\n", - "44e3ac42-e9f2-4fff-a1ff-fb39678c57ff \n", + "mediumnwb20230802_PEN0D79Q0B.nwb\n", + "5dd7b87f-4cf0-4a91-a281-15c6f6c86d61 \n", " \n", "

...

\n", "

Total: 23

\n", @@ -1525,18 +1592,18 @@ "text/plain": [ "*spikesorting_ *features_para analysis_file_ object_id \n", "+------------+ +------------+ +------------+ +------------+\n", - "00763b68-d663- amplitude mediumnwb20230 db7b7b7c-a97b-\n", - "03954edd-f8fd- amplitude mediumnwb20230 cbdbc6ab-aeb4-\n", - "0720e5f2-625e- amplitude mediumnwb20230 e36c31d6-2ebf-\n", - "153954b2-b230- amplitude mediumnwb20230 4768d18a-ab94-\n", - "189fb8c6-f964- amplitude mediumnwb20230 d4ba1164-a043-\n", - "2567bf67-bc67- amplitude mediumnwb20230 d7832810-313b-\n", - "26310ce7-9ac3- amplitude mediumnwb20230 202d6dae-c1b4-\n", - "411dff13-44f0- amplitude mediumnwb20230 15dc2df6-26c7-\n", - "43a98eab-1fa6- amplitude mediumnwb20230 5ad143d4-235f-\n", - "46829e10-1984- amplitude mediumnwb20230 554ed58c-28c1-\n", - "4b3065e5-76c2- amplitude mediumnwb20230 9e7ca80d-8d45-\n", - "609aeb54-dc2e- amplitude mediumnwb20230 44e3ac42-e9f2-\n", + "0751a1e1-a406- amplitude mediumnwb20230 8607d6a6-213c-\n", + "485a4ddf-332d- amplitude mediumnwb20230 9f693a74-a203-\n", + "4a712103-c223- amplitude mediumnwb20230 648953e8-1891-\n", + "4a72c253-b3ca- amplitude mediumnwb20230 6d0af664-f811-\n", + "5c53bd33-d57c- amplitude mediumnwb20230 a67ed5bb-3edd-\n", + "614d796c-0b95- amplitude mediumnwb20230 13218b00-bf34-\n", + "6acb99b8-6a0c- amplitude mediumnwb20230 d892bb47-94fc-\n", + "6d039a63-17ad- amplitude mediumnwb20230 60f4d280-a42a-\n", + "74e10781-1228- amplitude mediumnwb20230 99f51e3d-54b5-\n", + "7e3fa66e-727e- amplitude mediumnwb20230 535b28d1-c9b5-\n", + "86897349-ff68- amplitude mediumnwb20230 67ee1547-c570-\n", + "8bbddc0f-d6ae- amplitude mediumnwb20230 5dd7b87f-4cf0-\n", " ...\n", " (Total: 23)" ] @@ -1559,42 +1626,36 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 11:27:25,269][WARNING]: Skipped checksum for file with hash: 300bbca3-e3e5-b477-cad5-2d2fb4d790cd, and path: 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", + "image/png": 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", "text/plain": [ "
" ] diff --git a/notebooks/42_Decoding_Clusterless.ipynb b/notebooks/42_Decoding_Clusterless.ipynb index 62d70586b..3936f88f5 100644 --- a/notebooks/42_Decoding_Clusterless.ipynb +++ b/notebooks/42_Decoding_Clusterless.ipynb @@ -59,8 +59,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 18:27:14,253][INFO]: Connecting root@localhost:3306\n", - "[2024-01-02 18:27:14,324][INFO]: Connected root@localhost:3306\n" + "[2024-01-17 22:43:09,216][INFO]: Connecting root@localhost:3306\n", + "[2024-01-17 22:43:09,293][INFO]: Connected root@localhost:3306\n" ] }, { @@ -149,102 +149,102 @@ "

curation_id

\n", " \n", " \n", - " 0cbd8579-6c48-4506-a116-e27e9b89f174\n", - "86acdb0f-84f0-73a2-a851-1f8305cd2e41\n", + " 08a302b6-5505-40fa-b4d5-62162f8eef58\n", + "485a4ddf-332d-35b5-3ad4-0561736c1844\n", "amplitude\n", - "458ba3c2-3a08-4291-af10-c74d823330d4\n", + "449b64e3-db0b-437e-a1b9-0d29928aa2dd\n", "clusterless_thresholder\n", "default_clusterless\n", "mediumnwb20230802_.nwb\n", - "b52d303b-12b1-4584-8f35-63dde543836c\n", - "022283413-433c-4c6f-b2fa-f82a10327df7\n", - "46829e10-1984-99a1-65a3-2b485a2f037f\n", + "45f6b9a1-eef3-46eb-866d-d0999afebda6\n", + "00ca508ee-af4c-4a89-8181-d48bd209bfd4\n", + "6acb99b8-6a0c-eb83-1141-5f603c5895e0\n", "amplitude\n", - "8e3daa47-2ee6-435f-892b-f095b1c5aa1a\n", + "328da21c-1d9c-41e2-9800-76b3484b707b\n", "clusterless_thresholder\n", "default_clusterless\n", "mediumnwb20230802_.nwb\n", - "1536b082-018d-4674-b562-cac09d298b7f\n", - "022e6bc74-e755-440c-a507-f9292fd494c9\n", - "ec308784-2bfb-dd90-147c-e4d44e5f649b\n", + "686d9951-1c0f-4d5e-9f5c-09e6fd8bdd4c\n", + "0209dc048-6fae-4315-b293-c06fff29f947\n", + "f7237e18-4e73-4aee-805b-90735e9147de\n", "amplitude\n", - "7fb98af6-d486-439f-ae1b-7abdfddae56b\n", + "aff78f2f-2ba0-412a-95cc-447c3a2f4683\n", "clusterless_thresholder\n", "default_clusterless\n", "mediumnwb20230802_.nwb\n", - "b1a34880-c87b-403f-8a3b-c346e614c782\n", - "032fa3502-7fa9-469b-a7a1-0e0e670fe28e\n", - "4b3065e5-76c2-bd48-32a1-ae62484f9314\n", + "719e8a86-fcf1-4ffc-8c1f-ea912f67ad5d\n", + "021a9a593-f6f3-4b82-99d7-8fc46556eff3\n", + "7e3fa66e-727e-1541-819a-b01309bb30ae\n", "amplitude\n", - "ef989f5a-3cf4-488d-be1f-660970fdfd69\n", + "2402805a-04f9-4a88-9ccf-071376c8de19\n", "clusterless_thresholder\n", "default_clusterless\n", "mediumnwb20230802_.nwb\n", - "4fa14f8e-14a2-49ce-a1b4-6c447fdc3a1e\n", - "0338442ef-821c-401e-91ba-8eec27490701\n", - "609aeb54-dc2e-52d3-91bf-1728e0a2cf09\n", + "d581b117-160e-4311-b096-7781a4de4394\n", + "0406a20e3-5a9f-4fec-b046-a6561f72461e\n", + "6d039a63-17ad-0b78-4b1e-f02d5f3dbbc5\n", "amplitude\n", - "86d39675-d6b0-4697-b336-9b2b1766d8f3\n", + "f1427e00-2974-4301-b2ac-b4dc29277c51\n", "clusterless_thresholder\n", "default_clusterless\n", "mediumnwb20230802_.nwb\n", - "3824e250-27e5-4cc5-a49d-56d9e37b3ad8\n", - "043495249-ab6b-4067-b04a-11401b998215\n", - "88492b1c-f4a9-9669-bb5b-7f1573015187\n", + "0e848c38-9105-4ea4-b6ba-dbdd5b46a088\n", + "04131c51b-c56d-41fa-b046-46635fc17fd9\n", + "e0e9133a-7a4e-1321-a43a-e8afcb2f25da\n", "amplitude\n", - "ded4b85c-a2f8-465d-ab21-504905c06403\n", + "9e332d82-1daf-4e92-bb50-12e4f9430875\n", "clusterless_thresholder\n", "default_clusterless\n", "mediumnwb20230802_.nwb\n", - "7f38783a-215f-47c1-853b-2e1ddc941d7f\n", - "043a6942c-668e-44a1-aa5b-a7aebc5c424a\n", - "f515c07f-fc80-b28a-750d-d0d5491259f4\n", + "9ed11db5-c42e-491a-8caf-7d9a37a65f13\n", + "04c5a629a-71d9-481d-ab11-a4cb0fc16087\n", + "9959b614-2318-f597-6651-a3a82124d28a\n", "amplitude\n", - "078776e3-1b9c-4755-bef8-b9201bcdd717\n", + "3a2c3eed-413a-452a-83c8-0e4648141bde\n", "clusterless_thresholder\n", "default_clusterless\n", "mediumnwb20230802_.nwb\n", - "ac7875e6-a370-4cf3-a74e-263f0d98a17a\n", - "04986cd16-515f-441a-8653-36cf3a312ca0\n", - "f4e29a80-ec96-dbe8-7081-425ac311b74c\n", + "2b9fbf14-74a0-4294-a805-26702340aac9\n", + "04d629c07-1931-4e1f-a3a8-cbf1b72161e3\n", + "c0eb6455-fc41-c200-b62e-e3ca81b9a3f7\n", "amplitude\n", - "db9d73cf-f9e2-46b4-8eb7-a8d059d99bf6\n", + "f07bc0b0-de6b-4424-8ef9-766213aaca26\n", "clusterless_thresholder\n", "default_clusterless\n", "mediumnwb20230802_.nwb\n", - "d630e3bb-10b2-4466-9c20-1db14565bcf4\n", - "059e06873-aae3-438a-8bc1-2988315b3d7e\n", - "d7754d5f-af01-19f4-3fdc-c9635081667a\n", + "5c68f0f0-f577-4905-8a09-e4d171d0a22d\n", + "0554a9a3c-0461-48be-8435-123eed59c228\n", + "912e250e-56d8-ee33-4525-c844d810971b\n", "amplitude\n", - "aeda79a6-8442-4a39-93b7-bce6da6fcacd\n", + "7f128981-6868-4976-ba20-248655dcac21\n", "clusterless_thresholder\n", "default_clusterless\n", "mediumnwb20230802_.nwb\n", - "6bea1980-8ea0-4160-afc3-aef93743fb9d\n", - "067b0fafd-693f-4a26-a20b-100c0a4731a7\n", - "2567bf67-bc67-47a5-aa2a-2bce19da232d\n", + "f4b9301f-bc91-455b-9474-c801093f3856\n", + "07bb007f2-26d3-463f-b7dc-7bd4d271725e\n", + "d7d2c97a-0e6e-d1b8-735c-d55dc66a30e1\n", "amplitude\n", - "47337655-182c-4c9d-b79d-ea0c6ce51b34\n", + "a9b7cec0-1256-49cf-abf0-8c45fd155379\n", "clusterless_thresholder\n", "default_clusterless\n", "mediumnwb20230802_.nwb\n", - "b7fc2304-9cbf-4d85-8028-39cab674273a\n", - "0698bc0fd-4027-4022-b2dc-8d1875cfa535\n", - "d65a1bf3-797d-b01f-e8be-2cea90b14c20\n", + "74270cba-36ee-4afb-ab50-2a6cc948e68c\n", + "080e1f37f-48a7-4087-bd37-7a37b6a2c160\n", + "abb92dce-4410-8f17-a501-a4104bda0dcf\n", "amplitude\n", - "3a5e3bf4-8bdb-4050-afb9-c3034f204ff7\n", + "3c40ebdc-0b61-4105-9971-e1348bd49bc7\n", "clusterless_thresholder\n", "default_clusterless\n", "mediumnwb20230802_.nwb\n", - "5147edfb-bab8-4aaa-891b-7604a39ef2d0\n", - "0803eb158-6e77-4a4f-8119-9802246ec649\n", - "92c336ee-81f4-0af9-4f60-9bc32e71bc9f\n", + "0f91197e-bebb-4dc6-ad41-5bf89c3eed28\n", + "08848c4a8-a2f2-4f3d-82cd-51b13b8bae3c\n", + "74e10781-1228-4075-0870-af224024ffdc\n", "amplitude\n", - "078a7847-23a6-4820-a71e-e0f4fc5b31b8\n", + "257c077b-8f3b-4abb-a631-6b8084d6a1ea\n", "clusterless_thresholder\n", "default_clusterless\n", "mediumnwb20230802_.nwb\n", - "23e7dd2c-b24f-4bd3-b769-b3dfbcc9dfbd\n", + "e289e03d-32ad-461a-a1cc-c88537343149\n", "0 \n", " \n", "

...

\n", @@ -254,18 +254,18 @@ "text/plain": [ "*sorting_id *merge_id *features_para recording_id sorter sorter_param_n nwb_file_name interval_list_ curation_id \n", "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n", - "0cbd8579-6c48- 86acdb0f-84f0- amplitude 458ba3c2-3a08- clusterless_th default_cluste mediumnwb20230 b52d303b-12b1- 0 \n", - "22283413-433c- 46829e10-1984- amplitude 8e3daa47-2ee6- clusterless_th default_cluste mediumnwb20230 1536b082-018d- 0 \n", - "22e6bc74-e755- ec308784-2bfb- amplitude 7fb98af6-d486- clusterless_th default_cluste mediumnwb20230 b1a34880-c87b- 0 \n", - "32fa3502-7fa9- 4b3065e5-76c2- amplitude ef989f5a-3cf4- clusterless_th default_cluste mediumnwb20230 4fa14f8e-14a2- 0 \n", - "338442ef-821c- 609aeb54-dc2e- amplitude 86d39675-d6b0- clusterless_th default_cluste mediumnwb20230 3824e250-27e5- 0 \n", - "43495249-ab6b- 88492b1c-f4a9- amplitude ded4b85c-a2f8- clusterless_th default_cluste mediumnwb20230 7f38783a-215f- 0 \n", - "43a6942c-668e- f515c07f-fc80- amplitude 078776e3-1b9c- clusterless_th default_cluste mediumnwb20230 ac7875e6-a370- 0 \n", - "4986cd16-515f- f4e29a80-ec96- amplitude db9d73cf-f9e2- clusterless_th default_cluste mediumnwb20230 d630e3bb-10b2- 0 \n", - "59e06873-aae3- d7754d5f-af01- amplitude aeda79a6-8442- clusterless_th default_cluste mediumnwb20230 6bea1980-8ea0- 0 \n", - "67b0fafd-693f- 2567bf67-bc67- amplitude 47337655-182c- clusterless_th default_cluste mediumnwb20230 b7fc2304-9cbf- 0 \n", - "698bc0fd-4027- d65a1bf3-797d- amplitude 3a5e3bf4-8bdb- clusterless_th default_cluste mediumnwb20230 5147edfb-bab8- 0 \n", - "803eb158-6e77- 92c336ee-81f4- amplitude 078a7847-23a6- clusterless_th default_cluste mediumnwb20230 23e7dd2c-b24f- 0 \n", + "08a302b6-5505- 485a4ddf-332d- amplitude 449b64e3-db0b- clusterless_th default_cluste mediumnwb20230 45f6b9a1-eef3- 0 \n", + "0ca508ee-af4c- 6acb99b8-6a0c- amplitude 328da21c-1d9c- clusterless_th default_cluste mediumnwb20230 686d9951-1c0f- 0 \n", + "209dc048-6fae- f7237e18-4e73- amplitude aff78f2f-2ba0- clusterless_th default_cluste mediumnwb20230 719e8a86-fcf1- 0 \n", + "21a9a593-f6f3- 7e3fa66e-727e- amplitude 2402805a-04f9- clusterless_th default_cluste mediumnwb20230 d581b117-160e- 0 \n", + "406a20e3-5a9f- 6d039a63-17ad- amplitude f1427e00-2974- clusterless_th default_cluste mediumnwb20230 0e848c38-9105- 0 \n", + "4131c51b-c56d- e0e9133a-7a4e- amplitude 9e332d82-1daf- clusterless_th default_cluste mediumnwb20230 9ed11db5-c42e- 0 \n", + "4c5a629a-71d9- 9959b614-2318- amplitude 3a2c3eed-413a- clusterless_th default_cluste mediumnwb20230 2b9fbf14-74a0- 0 \n", + "4d629c07-1931- c0eb6455-fc41- amplitude f07bc0b0-de6b- clusterless_th default_cluste mediumnwb20230 5c68f0f0-f577- 0 \n", + "554a9a3c-0461- 912e250e-56d8- amplitude 7f128981-6868- clusterless_th default_cluste mediumnwb20230 f4b9301f-bc91- 0 \n", + "7bb007f2-26d3- d7d2c97a-0e6e- amplitude a9b7cec0-1256- clusterless_th default_cluste mediumnwb20230 74270cba-36ee- 0 \n", + "80e1f37f-48a7- abb92dce-4410- amplitude 3c40ebdc-0b61- clusterless_th default_cluste mediumnwb20230 0f91197e-bebb- 0 \n", + "8848c4a8-a2f2- 74e10781-1228- amplitude 257c077b-8f3b- clusterless_th default_cluste mediumnwb20230 e289e03d-32ad- 0 \n", " ...\n", " (Total: 23)" ] @@ -367,18 +367,18 @@ "

features_param_name

\n", " a name for this set of parameters\n", " \n", - " 00763b68-d663-c446-0555-1f2622d7da50\n", - "amplitude03954edd-f8fd-3dd9-cd10-f0eee47d6b3d\n", - "amplitude0720e5f2-625e-09d2-b522-ca2652c09f2a\n", - "amplitude153954b2-b230-cb1f-749d-f977a22eaae9\n", - "amplitude189fb8c6-f964-00a9-f392-a9dbb138ea63\n", - "amplitude2567bf67-bc67-47a5-aa2a-2bce19da232d\n", - "amplitude26310ce7-9ac3-4159-99f8-a3ad17037235\n", - "amplitude411dff13-44f0-3e03-e867-689ae275e418\n", - "amplitude43a98eab-1fa6-184b-1f09-2e923984b03a\n", - "amplitude46829e10-1984-99a1-65a3-2b485a2f037f\n", - "amplitude4b3065e5-76c2-bd48-32a1-ae62484f9314\n", - "amplitude609aeb54-dc2e-52d3-91bf-1728e0a2cf09\n", + " 0751a1e1-a406-7f87-ae6f-ce4ffc60621c\n", + "amplitude485a4ddf-332d-35b5-3ad4-0561736c1844\n", + "amplitude4a712103-c223-864f-82e0-6c23de79cc14\n", + "amplitude4a72c253-b3ca-8c13-e615-736a7ebff35c\n", + "amplitude5c53bd33-d57c-fbba-e0fb-55e0bcb85d03\n", + "amplitude614d796c-0b95-6364-aaa0-b6cb1e7bbb83\n", + "amplitude6acb99b8-6a0c-eb83-1141-5f603c5895e0\n", + "amplitude6d039a63-17ad-0b78-4b1e-f02d5f3dbbc5\n", + "amplitude74e10781-1228-4075-0870-af224024ffdc\n", + "amplitude7e3fa66e-727e-1541-819a-b01309bb30ae\n", + "amplitude86897349-ff68-ac72-02eb-739dd88936e6\n", + "amplitude8bbddc0f-d6ae-6260-9400-f884a6e25ae8\n", "amplitude \n", " \n", "

...

\n", @@ -388,18 +388,18 @@ "text/plain": [ "*spikesorting_ *features_para\n", "+------------+ +------------+\n", - "00763b68-d663- amplitude \n", - "03954edd-f8fd- amplitude \n", - "0720e5f2-625e- amplitude \n", - "153954b2-b230- amplitude \n", - "189fb8c6-f964- amplitude \n", - "2567bf67-bc67- amplitude \n", - "26310ce7-9ac3- amplitude \n", - "411dff13-44f0- amplitude \n", - "43a98eab-1fa6- amplitude \n", - "46829e10-1984- amplitude \n", - "4b3065e5-76c2- amplitude \n", - "609aeb54-dc2e- amplitude \n", + "0751a1e1-a406- amplitude \n", + "485a4ddf-332d- amplitude \n", + "4a712103-c223- amplitude \n", + "4a72c253-b3ca- amplitude \n", + "5c53bd33-d57c- amplitude \n", + "614d796c-0b95- amplitude \n", + "6acb99b8-6a0c- amplitude \n", + "6d039a63-17ad- amplitude \n", + "74e10781-1228- amplitude \n", + "7e3fa66e-727e- amplitude \n", + "86897349-ff68- amplitude \n", + "8bbddc0f-d6ae- amplitude \n", " ...\n", " (Total: 23)" ] @@ -621,40 +621,40 @@ " \n", " mediumnwb20230802_.nwb\n", "test_group\n", - "00763b68-d663-c446-0555-1f2622d7da50\n", + "0751a1e1-a406-7f87-ae6f-ce4ffc60621c\n", "amplitudemediumnwb20230802_.nwb\n", "test_group\n", - "03954edd-f8fd-3dd9-cd10-f0eee47d6b3d\n", + "485a4ddf-332d-35b5-3ad4-0561736c1844\n", "amplitudemediumnwb20230802_.nwb\n", "test_group\n", - "0720e5f2-625e-09d2-b522-ca2652c09f2a\n", + "4a712103-c223-864f-82e0-6c23de79cc14\n", "amplitudemediumnwb20230802_.nwb\n", "test_group\n", - "153954b2-b230-cb1f-749d-f977a22eaae9\n", + "4a72c253-b3ca-8c13-e615-736a7ebff35c\n", "amplitudemediumnwb20230802_.nwb\n", "test_group\n", - "189fb8c6-f964-00a9-f392-a9dbb138ea63\n", + "5c53bd33-d57c-fbba-e0fb-55e0bcb85d03\n", "amplitudemediumnwb20230802_.nwb\n", "test_group\n", - "2567bf67-bc67-47a5-aa2a-2bce19da232d\n", + "614d796c-0b95-6364-aaa0-b6cb1e7bbb83\n", "amplitudemediumnwb20230802_.nwb\n", "test_group\n", - "26310ce7-9ac3-4159-99f8-a3ad17037235\n", + "6acb99b8-6a0c-eb83-1141-5f603c5895e0\n", "amplitudemediumnwb20230802_.nwb\n", "test_group\n", - "411dff13-44f0-3e03-e867-689ae275e418\n", + "6d039a63-17ad-0b78-4b1e-f02d5f3dbbc5\n", "amplitudemediumnwb20230802_.nwb\n", "test_group\n", - "43a98eab-1fa6-184b-1f09-2e923984b03a\n", + "74e10781-1228-4075-0870-af224024ffdc\n", "amplitudemediumnwb20230802_.nwb\n", "test_group\n", - "46829e10-1984-99a1-65a3-2b485a2f037f\n", + "7e3fa66e-727e-1541-819a-b01309bb30ae\n", "amplitudemediumnwb20230802_.nwb\n", "test_group\n", - "4b3065e5-76c2-bd48-32a1-ae62484f9314\n", + "86897349-ff68-ac72-02eb-739dd88936e6\n", "amplitudemediumnwb20230802_.nwb\n", "test_group\n", - "609aeb54-dc2e-52d3-91bf-1728e0a2cf09\n", + "8bbddc0f-d6ae-6260-9400-f884a6e25ae8\n", "amplitude \n", " \n", "

...

\n", @@ -664,18 +664,18 @@ "text/plain": [ "*nwb_file_name *waveform_feat *spikesorting_ *features_para\n", "+------------+ +------------+ +------------+ +------------+\n", - "mediumnwb20230 test_group 00763b68-d663- amplitude \n", - "mediumnwb20230 test_group 03954edd-f8fd- amplitude \n", - "mediumnwb20230 test_group 0720e5f2-625e- amplitude \n", - "mediumnwb20230 test_group 153954b2-b230- amplitude \n", - "mediumnwb20230 test_group 189fb8c6-f964- amplitude \n", - "mediumnwb20230 test_group 2567bf67-bc67- amplitude \n", - "mediumnwb20230 test_group 26310ce7-9ac3- amplitude \n", - "mediumnwb20230 test_group 411dff13-44f0- amplitude \n", - "mediumnwb20230 test_group 43a98eab-1fa6- amplitude \n", - "mediumnwb20230 test_group 46829e10-1984- amplitude \n", - "mediumnwb20230 test_group 4b3065e5-76c2- amplitude \n", - "mediumnwb20230 test_group 609aeb54-dc2e- amplitude \n", + "mediumnwb20230 test_group 0751a1e1-a406- amplitude \n", + "mediumnwb20230 test_group 485a4ddf-332d- amplitude \n", + "mediumnwb20230 test_group 4a712103-c223- amplitude \n", + "mediumnwb20230 test_group 4a72c253-b3ca- amplitude \n", + "mediumnwb20230 test_group 5c53bd33-d57c- amplitude \n", + "mediumnwb20230 test_group 614d796c-0b95- amplitude \n", + "mediumnwb20230 test_group 6acb99b8-6a0c- amplitude \n", + "mediumnwb20230 test_group 6d039a63-17ad- amplitude \n", + "mediumnwb20230 test_group 74e10781-1228- amplitude \n", + "mediumnwb20230 test_group 7e3fa66e-727e- amplitude \n", + "mediumnwb20230 test_group 86897349-ff68- amplitude \n", + "mediumnwb20230 test_group 8bbddc0f-d6ae- amplitude \n", " ...\n", " (Total: 23)" ] @@ -904,7 +904,7 @@ } ], "source": [ - "from spyglass.decoding.v1.clusterless import PositionGroup\n", + "from spyglass.decoding.v1.core import PositionGroup\n", "\n", "position_merge_ids = (\n", " PositionOutput.TrodesPosV1\n", @@ -1206,7 +1206,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1292,7 +1292,7 @@ " (Total: 1)" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1322,7 +1322,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1370,7 +1370,7 @@ " state_names=['Continuous', 'Fragmented'])" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1394,7 +1394,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1496,7 +1496,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1598,7 +1598,7 @@ " (Total: 1)" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1611,7 +1611,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1681,57 +1681,72 @@ "
\n", "

valid_times

\n", " numpy array with start/end times for each interval\n", + "
\n", + "

pipeline

\n", + " type of interval list (e.g. 'position', 'spikesorting_recording_v1')\n", "
\n", " mediumnwb20230802_.nwb\n", "02_r1\n", - "=BLOB=mediumnwb20230802_.nwb\n", - "03143dcd-d09a-4216-8000-631d346875ad\n", - "=BLOB=mediumnwb20230802_.nwb\n", - "078776e3-1b9c-4755-bef8-b9201bcdd717\n", - "=BLOB=mediumnwb20230802_.nwb\n", - "078a7847-23a6-4820-a71e-e0f4fc5b31b8\n", - "=BLOB=mediumnwb20230802_.nwb\n", - "1536b082-018d-4674-b562-cac09d298b7f\n", - "=BLOB=mediumnwb20230802_.nwb\n", - "23e7dd2c-b24f-4bd3-b769-b3dfbcc9dfbd\n", - "=BLOB=mediumnwb20230802_.nwb\n", - "2ce6a87c-2c6b-4fd9-af00-35f181c3fd2f\n", - "=BLOB=mediumnwb20230802_.nwb\n", - "333b230a-14d8-45c0-bd0d-1eec9797152e\n", - "=BLOB=mediumnwb20230802_.nwb\n", - "3824e250-27e5-4cc5-a49d-56d9e37b3ad8\n", - "=BLOB=mediumnwb20230802_.nwb\n", - "3a5e3bf4-8bdb-4050-afb9-c3034f204ff7\n", - "=BLOB=mediumnwb20230802_.nwb\n", - "458ba3c2-3a08-4291-af10-c74d823330d4\n", - "=BLOB=mediumnwb20230802_.nwb\n", - "47337655-182c-4c9d-b79d-ea0c6ce51b34\n", - "=BLOB= \n", + "=BLOB=\n", + "mediumnwb20230802_.nwb\n", + "04f3ecb4-a18c-4ffb-85d8-2f5f62d4d6d4\n", + "=BLOB=\n", + "spikesorting_recording_v1mediumnwb20230802_.nwb\n", + "0e848c38-9105-4ea4-b6ba-dbdd5b46a088\n", + "=BLOB=\n", + "spikesorting_artifact_v1mediumnwb20230802_.nwb\n", + "0f91197e-bebb-4dc6-ad41-5bf89c3eed28\n", + "=BLOB=\n", + "spikesorting_artifact_v1mediumnwb20230802_.nwb\n", + "15c8a3e8-5ce9-4654-891e-6ee4109d6f1a\n", + "=BLOB=\n", + "spikesorting_artifact_v1mediumnwb20230802_.nwb\n", + "1d2b5966-415a-4c65-955a-0e422d8b5b00\n", + "=BLOB=\n", + "spikesorting_recording_v1mediumnwb20230802_.nwb\n", + "1e3f3707-613e-4a44-93f1-c7e5484112cd\n", + "=BLOB=\n", + "spikesorting_recording_v1mediumnwb20230802_.nwb\n", + "2402805a-04f9-4a88-9ccf-071376c8de19\n", + "=BLOB=\n", + "spikesorting_recording_v1mediumnwb20230802_.nwb\n", + "24107d8c-ce26-4c77-8f6a-bf6955d8a3c7\n", + "=BLOB=\n", + "spikesorting_recording_v1mediumnwb20230802_.nwb\n", + "257c077b-8f3b-4abb-a631-6b8084d6a1ea\n", + "=BLOB=\n", + "spikesorting_recording_v1mediumnwb20230802_.nwb\n", + "2b93bcd0-7b05-457c-8aab-c41ef543ecf2\n", + "=BLOB=\n", + "spikesorting_artifact_v1mediumnwb20230802_.nwb\n", + "2b9fbf14-74a0-4294-a805-26702340aac9\n", + "=BLOB=\n", + "spikesorting_artifact_v1 \n", " \n", "

...

\n", - "

Total: 57

\n", + "

Total: 52

\n", " " ], "text/plain": [ - "*nwb_file_name *interval_list valid_time\n", - "+------------+ +------------+ +--------+\n", - "mediumnwb20230 02_r1 =BLOB= \n", - "mediumnwb20230 03143dcd-d09a- =BLOB= \n", - "mediumnwb20230 078776e3-1b9c- =BLOB= \n", - "mediumnwb20230 078a7847-23a6- =BLOB= \n", - "mediumnwb20230 1536b082-018d- =BLOB= \n", - "mediumnwb20230 23e7dd2c-b24f- =BLOB= \n", - "mediumnwb20230 2ce6a87c-2c6b- =BLOB= \n", - "mediumnwb20230 333b230a-14d8- =BLOB= \n", - "mediumnwb20230 3824e250-27e5- =BLOB= \n", - "mediumnwb20230 3a5e3bf4-8bdb- =BLOB= \n", - "mediumnwb20230 458ba3c2-3a08- =BLOB= \n", - "mediumnwb20230 47337655-182c- =BLOB= \n", + "*nwb_file_name *interval_list valid_time pipeline \n", + "+------------+ +------------+ +--------+ +------------+\n", + "mediumnwb20230 02_r1 =BLOB= \n", + "mediumnwb20230 04f3ecb4-a18c- =BLOB= spikesorting_r\n", + "mediumnwb20230 0e848c38-9105- =BLOB= spikesorting_a\n", + "mediumnwb20230 0f91197e-bebb- =BLOB= spikesorting_a\n", + "mediumnwb20230 15c8a3e8-5ce9- =BLOB= spikesorting_a\n", + "mediumnwb20230 1d2b5966-415a- =BLOB= spikesorting_r\n", + "mediumnwb20230 1e3f3707-613e- =BLOB= spikesorting_r\n", + "mediumnwb20230 2402805a-04f9- =BLOB= spikesorting_r\n", + "mediumnwb20230 24107d8c-ce26- =BLOB= spikesorting_r\n", + "mediumnwb20230 257c077b-8f3b- =BLOB= spikesorting_r\n", + "mediumnwb20230 2b93bcd0-7b05- =BLOB= spikesorting_a\n", + "mediumnwb20230 2b9fbf14-74a0- =BLOB= spikesorting_a\n", " ...\n", - " (Total: 57)" + " (Total: 52)" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1744,7 +1759,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -1771,7 +1786,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1873,7 +1888,7 @@ " (Total: 1)" ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1899,7 +1914,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -2001,7 +2016,7 @@ " (Total: 1)" ] }, - "execution_count": 20, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -2019,78 +2034,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - 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"model_id": "c820bd239022418abdfcc433afe26f3b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Encoding models: 0%| | 0/23 [00:00
  • marginal_log_likelihoods :
    -154947.16
    • " ], "text/plain": [ "\n", @@ -2700,7 +2646,7 @@ " marginal_log_likelihoods: -154947.16" ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -2719,16 +2665,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[18:28:38][INFO] Spyglass: Cleaning up decoding outputs\n", - "[2024-01-02 18:28:39,095][WARNING]: Skipped checksum for file with hash: b90725e6-0b08-52f3-95ee-b978c2ce2261, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_94ba5f8d-ce42-4275-b201-b07e592bcd9d.nc\n", - "[18:28:39][INFO] Spyglass: Removing /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_a88f911a-d757-487d-9991-2e66333b1884.nc\n" + "[22:43:13][INFO] Spyglass: Cleaning up decoding outputs\n", + "[2024-01-17 22:43:13,623][WARNING]: Skipped checksum for file with hash: ecf01dd1-0d3b-24c2-b843-7e554abf0ea7, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_a26e1d1a-1480-4f89-b5e0-bb6486d7d15e.nc\n", + "[2024-01-17 22:43:13,713][WARNING]: Skipped checksum for file with hash: 257962d6-fc68-dc91-b0d2-8bef2a4914f3, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_a26e1d1a-1480-4f89-b5e0-bb6486d7d15e.pkl\n" ] } ], @@ -2753,88 +2699,37 @@ }, { "cell_type": "code", - 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Using squeeze.\n", - "WARNING: TimeseriesGraph::_add_series y argument is not 1D array. Using squeeze.\n", - "WARNING: TimeseriesGraph::_add_series y argument is not 1D array. Using squeeze.\n" - ] - }, - { - "data": { - "text/plain": [ - "'https://figurl.org/f?v=gs://figurl/sortingview-11&d=sha1://fbb02d98d0ab84a6b15dabe3f23c4891ff59d762&label=2D%20Decoding&zone=franklab.collaborators'" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "from non_local_detector.visualization import (\n", - " create_interactive_2D_decoding_figurl,\n", - ")\n", - "\n", - "(\n", - " position_info,\n", - " position_variable_names,\n", - ") = ClusterlessDecodingV1.load_position_info(selection_key)\n", - "results_time = decoding_results.acausal_posterior.isel(intervals=0).time.values\n", - "position_info = position_info.loc[results_time[0] : results_time[-1]]\n", - "\n", - "env = ClusterlessDecodingV1.load_environments(selection_key)[0]\n", - "spike_times, _ = ClusterlessDecodingV1.load_spike_data(selection_key)\n", - "\n", - "\n", - "create_interactive_2D_decoding_figurl(\n", - " position_time=position_info.index.to_numpy(),\n", - " position=position_info[position_variable_names],\n", - " env=env,\n", - " results=decoding_results,\n", - " posterior=decoding_results.acausal_posterior.isel(intervals=0)\n", - " .unstack(\"state_bins\")\n", - " .sum(\"state\"),\n", - " spike_times=spike_times,\n", - " head_dir=position_info[\"orientation\"],\n", - " speed=position_info[\"speed\"],\n", - ")" + "# from non_local_detector.visualization import (\n", + "# create_interactive_2D_decoding_figurl,\n", + "# )\n", + "\n", + "# (\n", + "# position_info,\n", + "# position_variable_names,\n", + "# ) = ClusterlessDecodingV1.load_position_info(selection_key)\n", + "# results_time = decoding_results.acausal_posterior.isel(intervals=0).time.values\n", + "# position_info = position_info.loc[results_time[0] : results_time[-1]]\n", + "\n", + "# env = ClusterlessDecodingV1.load_environments(selection_key)[0]\n", + "# spike_times, _ = ClusterlessDecodingV1.load_spike_data(selection_key)\n", + "\n", + "\n", + "# create_interactive_2D_decoding_figurl(\n", + "# position_time=position_info.index.to_numpy(),\n", + "# position=position_info[position_variable_names],\n", + "# env=env,\n", + "# results=decoding_results,\n", + "# posterior=decoding_results.acausal_posterior.isel(intervals=0)\n", + "# .unstack(\"state_bins\")\n", + "# .sum(\"state\"),\n", + "# spike_times=spike_times,\n", + "# head_dir=position_info[\"orientation\"],\n", + "# speed=position_info[\"speed\"],\n", + "# )" ] }, { @@ -2852,7 +2747,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -2861,7 +2756,7 @@ "[CpuDevice(id=0)]" ] }, - "execution_count": 26, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -2882,7 +2777,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -2911,35 +2806,7 @@ " [jupyter widget by nvidia](https://github.com/rapidsai/jupyterlab-nvdashboard)\n", " to monitor GPU usage in the notebook\n", "- A [terminal program](https://github.com/peci1/nvidia-htop) like nvidia-smi\n", - " with more information about which GPUs are being utilized and by whom.\n", - "\n", - "### Parallelizing Decoding\n", - "\n", - "You can also use the [dask_cuda](https://docs.rapids.ai/api/dask-cuda/nightly/) to parallelize decoding. You will need to install the `dask_cuda` package (see [here](https://docs.rapids.ai/api/dask-cuda/nightly/install/)). You then can run the following code to parallelize decoding:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# import dask\n", - "# from dask.distributed import Client\n", - "# from dask_cuda import LocalCUDACluster\n", - "\n", - "# cluster = LocalCUDACluster()\n", - "\n", - "# selection_keys = [] # list of selection keys\n", - "\n", - "# with Client(cluster) as client:\n", - "# results = [\n", - "# dask.delayed(ClusterlessDecodingV1.populate)(\n", - "# selection_key, reserve_jobs=True\n", - "# )\n", - "# for selection_key in selection_keys\n", - "# ]\n", - "# dask.compute(*results)" + " with more information about which GPUs are being utilized and by whom." ] } ], diff --git a/notebooks/43_Decoding_SortedSpikes.ipynb b/notebooks/43_Decoding_SortedSpikes.ipynb index 8d41336e2..4911824e6 100644 --- a/notebooks/43_Decoding_SortedSpikes.ipynb +++ b/notebooks/43_Decoding_SortedSpikes.ipynb @@ -44,8 +44,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 18:41:02,417][INFO]: Connecting root@localhost:3306\n", - "[2024-01-02 18:41:02,501][INFO]: Connected root@localhost:3306\n" + "[2024-01-17 22:49:07,284][INFO]: Connecting root@localhost:3306\n", + "[2024-01-17 22:49:07,353][INFO]: Connected root@localhost:3306\n" ] }, { @@ -131,90 +131,90 @@ "

    curation_id

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"ded4b85c-a2f8-465d-ab21-504905c06403\n", + "0e848c38-9105-4ea4-b6ba-dbdd5b46a088\n", + "04131c51b-c56d-41fa-b046-46635fc17fd9\n", + "e0e9133a-7a4e-1321-a43a-e8afcb2f25da\n", + "9e332d82-1daf-4e92-bb50-12e4f9430875\n", "clusterless_thresholder\n", "default_clusterless\n", "mediumnwb20230802_.nwb\n", - "7f38783a-215f-47c1-853b-2e1ddc941d7f\n", - "043a6942c-668e-44a1-aa5b-a7aebc5c424a\n", - "f515c07f-fc80-b28a-750d-d0d5491259f4\n", - "078776e3-1b9c-4755-bef8-b9201bcdd717\n", + "9ed11db5-c42e-491a-8caf-7d9a37a65f13\n", + "04c5a629a-71d9-481d-ab11-a4cb0fc16087\n", + "9959b614-2318-f597-6651-a3a82124d28a\n", + "3a2c3eed-413a-452a-83c8-0e4648141bde\n", "clusterless_thresholder\n", "default_clusterless\n", "mediumnwb20230802_.nwb\n", - "ac7875e6-a370-4cf3-a74e-263f0d98a17a\n", - "04986cd16-515f-441a-8653-36cf3a312ca0\n", - "f4e29a80-ec96-dbe8-7081-425ac311b74c\n", - "db9d73cf-f9e2-46b4-8eb7-a8d059d99bf6\n", + "2b9fbf14-74a0-4294-a805-26702340aac9\n", + "04d629c07-1931-4e1f-a3a8-cbf1b72161e3\n", + "c0eb6455-fc41-c200-b62e-e3ca81b9a3f7\n", + "f07bc0b0-de6b-4424-8ef9-766213aaca26\n", "clusterless_thresholder\n", "default_clusterless\n", "mediumnwb20230802_.nwb\n", - "d630e3bb-10b2-4466-9c20-1db14565bcf4\n", - "059e06873-aae3-438a-8bc1-2988315b3d7e\n", - "d7754d5f-af01-19f4-3fdc-c9635081667a\n", - "aeda79a6-8442-4a39-93b7-bce6da6fcacd\n", + "5c68f0f0-f577-4905-8a09-e4d171d0a22d\n", + "0554a9a3c-0461-48be-8435-123eed59c228\n", + "912e250e-56d8-ee33-4525-c844d810971b\n", + "7f128981-6868-4976-ba20-248655dcac21\n", "clusterless_thresholder\n", "default_clusterless\n", "mediumnwb20230802_.nwb\n", - "6bea1980-8ea0-4160-afc3-aef93743fb9d\n", - "067b0fafd-693f-4a26-a20b-100c0a4731a7\n", - "2567bf67-bc67-47a5-aa2a-2bce19da232d\n", - "47337655-182c-4c9d-b79d-ea0c6ce51b34\n", + "f4b9301f-bc91-455b-9474-c801093f3856\n", + "07bb007f2-26d3-463f-b7dc-7bd4d271725e\n", + "d7d2c97a-0e6e-d1b8-735c-d55dc66a30e1\n", + "a9b7cec0-1256-49cf-abf0-8c45fd155379\n", "clusterless_thresholder\n", "default_clusterless\n", "mediumnwb20230802_.nwb\n", - "b7fc2304-9cbf-4d85-8028-39cab674273a\n", - "0698bc0fd-4027-4022-b2dc-8d1875cfa535\n", - "d65a1bf3-797d-b01f-e8be-2cea90b14c20\n", - "3a5e3bf4-8bdb-4050-afb9-c3034f204ff7\n", + "74270cba-36ee-4afb-ab50-2a6cc948e68c\n", + "080e1f37f-48a7-4087-bd37-7a37b6a2c160\n", + "abb92dce-4410-8f17-a501-a4104bda0dcf\n", + "3c40ebdc-0b61-4105-9971-e1348bd49bc7\n", "clusterless_thresholder\n", "default_clusterless\n", "mediumnwb20230802_.nwb\n", - "5147edfb-bab8-4aaa-891b-7604a39ef2d0\n", - "0803eb158-6e77-4a4f-8119-9802246ec649\n", - "92c336ee-81f4-0af9-4f60-9bc32e71bc9f\n", - "078a7847-23a6-4820-a71e-e0f4fc5b31b8\n", + "0f91197e-bebb-4dc6-ad41-5bf89c3eed28\n", + "08848c4a8-a2f2-4f3d-82cd-51b13b8bae3c\n", + "74e10781-1228-4075-0870-af224024ffdc\n", + "257c077b-8f3b-4abb-a631-6b8084d6a1ea\n", "clusterless_thresholder\n", "default_clusterless\n", "mediumnwb20230802_.nwb\n", - "23e7dd2c-b24f-4bd3-b769-b3dfbcc9dfbd\n", + "e289e03d-32ad-461a-a1cc-c88537343149\n", "0 \n", " \n", "

    ...

    \n", @@ -224,18 +224,18 @@ "text/plain": [ "*sorting_id *merge_id recording_id sorter sorter_param_n nwb_file_name interval_list_ curation_id \n", "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n", - "0cbd8579-6c48- 86acdb0f-84f0- 458ba3c2-3a08- clusterless_th default_cluste mediumnwb20230 b52d303b-12b1- 0 \n", - "22283413-433c- 46829e10-1984- 8e3daa47-2ee6- clusterless_th default_cluste mediumnwb20230 1536b082-018d- 0 \n", - "22e6bc74-e755- ec308784-2bfb- 7fb98af6-d486- clusterless_th default_cluste mediumnwb20230 b1a34880-c87b- 0 \n", - "32fa3502-7fa9- 4b3065e5-76c2- ef989f5a-3cf4- clusterless_th default_cluste mediumnwb20230 4fa14f8e-14a2- 0 \n", - "338442ef-821c- 609aeb54-dc2e- 86d39675-d6b0- clusterless_th default_cluste mediumnwb20230 3824e250-27e5- 0 \n", - "43495249-ab6b- 88492b1c-f4a9- ded4b85c-a2f8- clusterless_th default_cluste mediumnwb20230 7f38783a-215f- 0 \n", - "43a6942c-668e- f515c07f-fc80- 078776e3-1b9c- clusterless_th default_cluste mediumnwb20230 ac7875e6-a370- 0 \n", - "4986cd16-515f- f4e29a80-ec96- db9d73cf-f9e2- clusterless_th default_cluste mediumnwb20230 d630e3bb-10b2- 0 \n", - "59e06873-aae3- d7754d5f-af01- aeda79a6-8442- clusterless_th default_cluste mediumnwb20230 6bea1980-8ea0- 0 \n", - "67b0fafd-693f- 2567bf67-bc67- 47337655-182c- clusterless_th default_cluste mediumnwb20230 b7fc2304-9cbf- 0 \n", - "698bc0fd-4027- d65a1bf3-797d- 3a5e3bf4-8bdb- clusterless_th default_cluste mediumnwb20230 5147edfb-bab8- 0 \n", - "803eb158-6e77- 92c336ee-81f4- 078a7847-23a6- clusterless_th default_cluste mediumnwb20230 23e7dd2c-b24f- 0 \n", + "08a302b6-5505- 485a4ddf-332d- 449b64e3-db0b- clusterless_th default_cluste mediumnwb20230 45f6b9a1-eef3- 0 \n", + "0ca508ee-af4c- 6acb99b8-6a0c- 328da21c-1d9c- clusterless_th default_cluste mediumnwb20230 686d9951-1c0f- 0 \n", + "209dc048-6fae- f7237e18-4e73- aff78f2f-2ba0- clusterless_th default_cluste mediumnwb20230 719e8a86-fcf1- 0 \n", + "21a9a593-f6f3- 7e3fa66e-727e- 2402805a-04f9- clusterless_th default_cluste mediumnwb20230 d581b117-160e- 0 \n", + "406a20e3-5a9f- 6d039a63-17ad- f1427e00-2974- clusterless_th default_cluste mediumnwb20230 0e848c38-9105- 0 \n", + "4131c51b-c56d- e0e9133a-7a4e- 9e332d82-1daf- clusterless_th default_cluste mediumnwb20230 9ed11db5-c42e- 0 \n", + "4c5a629a-71d9- 9959b614-2318- 3a2c3eed-413a- clusterless_th default_cluste mediumnwb20230 2b9fbf14-74a0- 0 \n", + "4d629c07-1931- c0eb6455-fc41- f07bc0b0-de6b- clusterless_th default_cluste mediumnwb20230 5c68f0f0-f577- 0 \n", + "554a9a3c-0461- 912e250e-56d8- 7f128981-6868- clusterless_th default_cluste mediumnwb20230 f4b9301f-bc91- 0 \n", + "7bb007f2-26d3- d7d2c97a-0e6e- a9b7cec0-1256- clusterless_th default_cluste mediumnwb20230 74270cba-36ee- 0 \n", + "80e1f37f-48a7- abb92dce-4410- 3c40ebdc-0b61- clusterless_th default_cluste mediumnwb20230 0f91197e-bebb- 0 \n", + "8848c4a8-a2f2- 74e10781-1228- 257c077b-8f3b- clusterless_th default_cluste mediumnwb20230 e289e03d-32ad- 0 \n", " ...\n", " (Total: 23)" ] @@ -269,29 +269,29 @@ { "data": { "text/plain": [ - "array([UUID('86acdb0f-84f0-73a2-a851-1f8305cd2e41'),\n", - " UUID('46829e10-1984-99a1-65a3-2b485a2f037f'),\n", - " UUID('ec308784-2bfb-dd90-147c-e4d44e5f649b'),\n", - " UUID('4b3065e5-76c2-bd48-32a1-ae62484f9314'),\n", - " UUID('609aeb54-dc2e-52d3-91bf-1728e0a2cf09'),\n", - " UUID('88492b1c-f4a9-9669-bb5b-7f1573015187'),\n", - " UUID('f515c07f-fc80-b28a-750d-d0d5491259f4'),\n", - " UUID('f4e29a80-ec96-dbe8-7081-425ac311b74c'),\n", - " UUID('d7754d5f-af01-19f4-3fdc-c9635081667a'),\n", - " UUID('2567bf67-bc67-47a5-aa2a-2bce19da232d'),\n", - " UUID('d65a1bf3-797d-b01f-e8be-2cea90b14c20'),\n", - " UUID('92c336ee-81f4-0af9-4f60-9bc32e71bc9f'),\n", - " UUID('aa8bc575-0715-69e9-5da7-313a0e1ee769'),\n", - " UUID('26310ce7-9ac3-4159-99f8-a3ad17037235'),\n", - " UUID('7355bdf3-f31c-4c22-1a09-50d9f6f5f037'),\n", - " UUID('189fb8c6-f964-00a9-f392-a9dbb138ea63'),\n", - " UUID('c4f24219-c023-8783-df53-2bbc88c9ad9c'),\n", - " UUID('411dff13-44f0-3e03-e867-689ae275e418'),\n", - " UUID('153954b2-b230-cb1f-749d-f977a22eaae9'),\n", - " UUID('00763b68-d663-c446-0555-1f2622d7da50'),\n", - " UUID('03954edd-f8fd-3dd9-cd10-f0eee47d6b3d'),\n", - " UUID('43a98eab-1fa6-184b-1f09-2e923984b03a'),\n", - " UUID('0720e5f2-625e-09d2-b522-ca2652c09f2a')], dtype=object)" + "array([UUID('485a4ddf-332d-35b5-3ad4-0561736c1844'),\n", + " UUID('6acb99b8-6a0c-eb83-1141-5f603c5895e0'),\n", + " UUID('f7237e18-4e73-4aee-805b-90735e9147de'),\n", + " UUID('7e3fa66e-727e-1541-819a-b01309bb30ae'),\n", + " UUID('6d039a63-17ad-0b78-4b1e-f02d5f3dbbc5'),\n", + " UUID('e0e9133a-7a4e-1321-a43a-e8afcb2f25da'),\n", + " UUID('9959b614-2318-f597-6651-a3a82124d28a'),\n", + " UUID('c0eb6455-fc41-c200-b62e-e3ca81b9a3f7'),\n", + " UUID('912e250e-56d8-ee33-4525-c844d810971b'),\n", + " UUID('d7d2c97a-0e6e-d1b8-735c-d55dc66a30e1'),\n", + " UUID('abb92dce-4410-8f17-a501-a4104bda0dcf'),\n", + " UUID('74e10781-1228-4075-0870-af224024ffdc'),\n", + " UUID('8bbddc0f-d6ae-6260-9400-f884a6e25ae8'),\n", + " UUID('614d796c-0b95-6364-aaa0-b6cb1e7bbb83'),\n", + " UUID('b332482b-e430-169d-8ac0-0a73ce968ed7'),\n", + " UUID('86897349-ff68-ac72-02eb-739dd88936e6'),\n", + " UUID('4a712103-c223-864f-82e0-6c23de79cc14'),\n", + " UUID('cf858380-e8a3-49de-c2a9-1a277e307a68'),\n", + " UUID('cc4ee561-f974-f8e5-0ea4-83185263ac67'),\n", + " UUID('4a72c253-b3ca-8c13-e615-736a7ebff35c'),\n", + " UUID('b92a94d8-ee1e-2097-a81f-5c1e1556ed24'),\n", + " UUID('5c53bd33-d57c-fbba-e0fb-55e0bcb85d03'),\n", + " UUID('0751a1e1-a406-7f87-ae6f-ce4ffc60621c')], dtype=object)" ] }, "execution_count": 3, @@ -377,17 +377,18 @@ "

    sorted_spikes_group_name

    \n", " \n", " \n", - " \n", + " mediumnwb20230802_.nwb\n", + "test_group \n", " \n", " \n", - "

    Total: 0

    \n", + "

    Total: 1

    \n", " " ], "text/plain": [ "*nwb_file_name *sorted_spikes\n", "+------------+ +------------+\n", - "\n", - " (Total: 0)" + "mediumnwb20230 test_group \n", + " (Total: 1)" ] }, "execution_count": 4, @@ -581,29 +582,29 @@ " \n", " mediumnwb20230802_.nwb\n", "test_group\n", - "00763b68-d663-c446-0555-1f2622d7da50mediumnwb20230802_.nwb\n", + "0751a1e1-a406-7f87-ae6f-ce4ffc60621cmediumnwb20230802_.nwb\n", "test_group\n", - "03954edd-f8fd-3dd9-cd10-f0eee47d6b3dmediumnwb20230802_.nwb\n", + "485a4ddf-332d-35b5-3ad4-0561736c1844mediumnwb20230802_.nwb\n", "test_group\n", - "0720e5f2-625e-09d2-b522-ca2652c09f2amediumnwb20230802_.nwb\n", + "4a712103-c223-864f-82e0-6c23de79cc14mediumnwb20230802_.nwb\n", "test_group\n", - "153954b2-b230-cb1f-749d-f977a22eaae9mediumnwb20230802_.nwb\n", + "4a72c253-b3ca-8c13-e615-736a7ebff35cmediumnwb20230802_.nwb\n", "test_group\n", - "189fb8c6-f964-00a9-f392-a9dbb138ea63mediumnwb20230802_.nwb\n", + "5c53bd33-d57c-fbba-e0fb-55e0bcb85d03mediumnwb20230802_.nwb\n", "test_group\n", - "2567bf67-bc67-47a5-aa2a-2bce19da232dmediumnwb20230802_.nwb\n", + "614d796c-0b95-6364-aaa0-b6cb1e7bbb83mediumnwb20230802_.nwb\n", "test_group\n", - "26310ce7-9ac3-4159-99f8-a3ad17037235mediumnwb20230802_.nwb\n", + "6acb99b8-6a0c-eb83-1141-5f603c5895e0mediumnwb20230802_.nwb\n", "test_group\n", - "411dff13-44f0-3e03-e867-689ae275e418mediumnwb20230802_.nwb\n", + "6d039a63-17ad-0b78-4b1e-f02d5f3dbbc5mediumnwb20230802_.nwb\n", "test_group\n", - "43a98eab-1fa6-184b-1f09-2e923984b03amediumnwb20230802_.nwb\n", + "74e10781-1228-4075-0870-af224024ffdcmediumnwb20230802_.nwb\n", "test_group\n", - "46829e10-1984-99a1-65a3-2b485a2f037fmediumnwb20230802_.nwb\n", + "7e3fa66e-727e-1541-819a-b01309bb30aemediumnwb20230802_.nwb\n", "test_group\n", - "4b3065e5-76c2-bd48-32a1-ae62484f9314mediumnwb20230802_.nwb\n", + "86897349-ff68-ac72-02eb-739dd88936e6mediumnwb20230802_.nwb\n", "test_group\n", - "609aeb54-dc2e-52d3-91bf-1728e0a2cf09 \n", + "8bbddc0f-d6ae-6260-9400-f884a6e25ae8 \n", " \n", "

    ...

    \n", "

    Total: 23

    \n", @@ -612,18 +613,18 @@ "text/plain": [ "*nwb_file_name *sorted_spikes *spikesorting_\n", "+------------+ +------------+ +------------+\n", - "mediumnwb20230 test_group 00763b68-d663-\n", - "mediumnwb20230 test_group 03954edd-f8fd-\n", - "mediumnwb20230 test_group 0720e5f2-625e-\n", - "mediumnwb20230 test_group 153954b2-b230-\n", - "mediumnwb20230 test_group 189fb8c6-f964-\n", - "mediumnwb20230 test_group 2567bf67-bc67-\n", - "mediumnwb20230 test_group 26310ce7-9ac3-\n", - "mediumnwb20230 test_group 411dff13-44f0-\n", - "mediumnwb20230 test_group 43a98eab-1fa6-\n", - "mediumnwb20230 test_group 46829e10-1984-\n", - "mediumnwb20230 test_group 4b3065e5-76c2-\n", - "mediumnwb20230 test_group 609aeb54-dc2e-\n", + "mediumnwb20230 test_group 0751a1e1-a406-\n", + "mediumnwb20230 test_group 485a4ddf-332d-\n", + "mediumnwb20230 test_group 4a712103-c223-\n", + "mediumnwb20230 test_group 4a72c253-b3ca-\n", + "mediumnwb20230 test_group 5c53bd33-d57c-\n", + "mediumnwb20230 test_group 614d796c-0b95-\n", + "mediumnwb20230 test_group 6acb99b8-6a0c-\n", + "mediumnwb20230 test_group 6d039a63-17ad-\n", + "mediumnwb20230 test_group 74e10781-1228-\n", + "mediumnwb20230 test_group 7e3fa66e-727e-\n", + "mediumnwb20230 test_group 86897349-ff68-\n", + "mediumnwb20230 test_group 8bbddc0f-d6ae-\n", " ...\n", " (Total: 23)" ] @@ -724,20 +725,32 @@ " \n", " contfrag_clusterless\n", "=BLOB=\n", + "=BLOB=contfrag_clusterless_0.5.13\n", + "=BLOB=\n", "=BLOB=contfrag_sorted\n", "=BLOB=\n", + "=BLOB=contfrag_sorted_0.5.13\n", + "=BLOB=\n", + "=BLOB=nonlocal_clusterless_0.5.13\n", + "=BLOB=\n", + "=BLOB=nonlocal_sorted_0.5.13\n", + "=BLOB=\n", "=BLOB= \n", " \n", " \n", - "

    Total: 2

    \n", + "

    Total: 6

    \n", " " ], "text/plain": [ "*decoding_para decoding_p decoding_k\n", "+------------+ +--------+ +--------+\n", "contfrag_clust =BLOB= =BLOB= \n", + "contfrag_clust =BLOB= =BLOB= \n", "contfrag_sorte =BLOB= =BLOB= \n", - " (Total: 2)" + "contfrag_sorte =BLOB= =BLOB= \n", + "nonlocal_clust =BLOB= =BLOB= \n", + "nonlocal_sorte =BLOB= =BLOB= \n", + " (Total: 6)" ] }, "execution_count": 7, @@ -865,17 +878,23 @@ "

    estimate_decoding_params

    \n", " whether to estimate the decoding parameters\n", " \n", - " \n", + " mediumnwb20230802_.nwb\n", + "test_group\n", + "test_group\n", + "contfrag_sorted\n", + "pos 0 valid times\n", + "test decoding interval\n", + "0 \n", " \n", " \n", - "

    Total: 0

    \n", + "

    Total: 1

    \n", " " ], "text/plain": [ "*nwb_file_name *sorted_spikes *position_grou *decoding_para *encoding_inte *decoding_inte *estimate_deco\n", "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n", - "\n", - " (Total: 0)" + "mediumnwb20230 test_group test_group contfrag_sorte pos 0 valid ti test decoding 0 \n", + " (Total: 1)" ] }, "execution_count": 8, @@ -920,36 +939,36 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-02 18:41:06,204][WARNING]: Skipped checksum for file with hash: 77a1423b-2a35-00bf-14d5-d21a8a634688, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_5C8F8R2R3G.nwb\n", - "[2024-01-02 18:41:06,447][WARNING]: Skipped checksum for file with hash: b12a9d1d-d019-7c0e-09bf-355936d14915, and path: /Users/edeno/Documents/GitHub/spyglass/DATA/analysis/mediumnwb20230802/mediumnwb20230802_DRQ7MITSST.nwb\n", - "[2024-01-02 18:41:06,657][WARNING]: Skipped 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We do not yet support duplicate dimension names, but we do allow initial construction of the object. We recommend you rename the dims immediately to become distinct, as most xarray functionality is likely to fail silently if you do not. To rename the dimensions you will need to set the ``.dims`` attribute of each variable, ``e.g. var.dims=('x0', 'x1')``.\n", " warnings.warn(\n", "/Users/edeno/miniconda3/envs/spyglass/lib/python3.9/site-packages/xarray/namedarray/core.py:487: UserWarning: Duplicate dimension names present: dimensions {'states'} appear more than once in dims=('states', 'states'). We do not yet support duplicate dimension names, but we do allow initial construction of the object. We recommend you rename the dims immediately to become distinct, as most xarray functionality is likely to fail silently if you do not. To rename the dimensions you will need to set the ``.dims`` attribute of each variable, ``e.g. var.dims=('x0', 'x1')``.\n", @@ -1526,19 +1545,19 @@ " acausal_posterior (intervals, time, state_bins) float32 ...\n", " acausal_state_probabilities (intervals, time, states) float64 ...\n", "Attributes:\n", - " marginal_log_likelihoods: -16366.832
  • marginal_log_likelihoods :
    -16366.834
    • " ], "text/plain": [ "\n", @@ -1591,7 +1610,7 @@ " acausal_posterior (intervals, time, state_bins) float32 ...\n", " acausal_state_probabilities (intervals, time, states) float64 ...\n", "Attributes:\n", - " marginal_log_likelihoods: -16366.832" + " marginal_log_likelihoods: -16366.834" ] }, "execution_count": 12, diff --git a/notebooks/py_scripts/30_LFP.py b/notebooks/py_scripts/30_LFP.py index 0baad64a9..42d452a39 100644 --- a/notebooks/py_scripts/30_LFP.py +++ b/notebooks/py_scripts/30_LFP.py @@ -5,7 +5,7 @@ # extension: .py # format_name: light # format_version: '1.5' -# jupytext_version: 1.15.2 +# jupytext_version: 1.16.0 # kernelspec: # display_name: Python 3.10.5 64-bit # language: python diff --git a/notebooks/py_scripts/41_Extracting_Clusterless_Waveform_Features.py b/notebooks/py_scripts/41_Extracting_Clusterless_Waveform_Features.py index 0bd227a36..137414710 100644 --- a/notebooks/py_scripts/41_Extracting_Clusterless_Waveform_Features.py +++ b/notebooks/py_scripts/41_Extracting_Clusterless_Waveform_Features.py @@ -31,8 +31,6 @@ # The goal of this notebook is to populate the `UnitWaveformFeatures` table, which depends `SpikeSortingOutput`. This table contains the features of the waveforms of each unit. # # While clusterless decoding avoids actual spike sorting, we need to pass through these tables to maintain (relative) pipeline simplicity. Pass-through tables keep spike sorting and clusterless waveform extraction as similar as possible, by using shared steps. Here, "spike sorting" involves simple thresholding (sorter: clusterless_thresholder). -# -# Let's start with the following nwb file and time interval: # + from pathlib import Path @@ -41,13 +39,39 @@ dj.config.load( Path("../dj_local_conf.json").absolute() ) # load config for database connection info +# - + +# First, if you haven't inserted the the `mediumnwb20230802.nwb` file into the database (see [01_Data_Insert](01_Data_Insert.ipynb)), you should do so now. This is the file that we will use for the decoding tutorials. +# +# It is a truncated version of the full NWB file, so it will run faster, but bigger than the minirec file we used in the previous tutorials so that decoding makes sense. + +# + +from spyglass.utils.nwb_helper_fn import get_nwb_copy_filename +import spyglass.data_import as sgi +import spyglass.position as sgp + +# Insert the nwb file +nwb_file_name = "mediumnwb20230802.nwb" +nwb_copy_file_name = get_nwb_copy_filename(nwb_file_name) +sgi.insert_sessions(nwb_file_name) + +# Position +sgp.v1.TrodesPosParams.insert_default() -nwb_copy_file_name = "mediumnwb20230802_.nwb" interval_list_name = "pos 0 valid times" + +trodes_s_key = { + "nwb_file_name": nwb_copy_file_name, + "interval_list_name": interval_list_name, + "trodes_pos_params_name": "default", +} +sgp.v1.TrodesPosSelection.insert1( + trodes_s_key, + skip_duplicates=True, +) +sgp.v1.TrodesPosV1.populate(trodes_s_key) # - -# If you haven't already, run the [Insert Data notebook](./01_Insert_Data.ipynb) to populate the tables. -# # These next steps are the same as in the [Spike Sorting notebook](./10_Spike_SortingV1.ipynb), but we'll repeat them here for clarity. These are pre-processing steps that are shared between spike sorting and clusterless decoding. # # We first set the `SortGroup` to define which contacts are sorted together. diff --git a/notebooks/py_scripts/42_Decoding_Clusterless.py b/notebooks/py_scripts/42_Decoding_Clusterless.py index abca5b545..6e0f529e8 100644 --- a/notebooks/py_scripts/42_Decoding_Clusterless.py +++ b/notebooks/py_scripts/42_Decoding_Clusterless.py @@ -129,7 +129,7 @@ PositionOutput.TrodesPosV1 & {"nwb_file_name": nwb_copy_file_name} # + -from spyglass.decoding.v1.clusterless import PositionGroup +from spyglass.decoding.v1.core import PositionGroup position_merge_ids = ( PositionOutput.TrodesPosV1 @@ -342,33 +342,33 @@ # # + -from non_local_detector.visualization import ( - create_interactive_2D_decoding_figurl, -) - -( - position_info, - position_variable_names, -) = ClusterlessDecodingV1.load_position_info(selection_key) -results_time = decoding_results.acausal_posterior.isel(intervals=0).time.values -position_info = position_info.loc[results_time[0] : results_time[-1]] - -env = ClusterlessDecodingV1.load_environments(selection_key)[0] -spike_times, _ = ClusterlessDecodingV1.load_spike_data(selection_key) - - -create_interactive_2D_decoding_figurl( - position_time=position_info.index.to_numpy(), - position=position_info[position_variable_names], - env=env, - results=decoding_results, - posterior=decoding_results.acausal_posterior.isel(intervals=0) - .unstack("state_bins") - .sum("state"), - spike_times=spike_times, - head_dir=position_info["orientation"], - speed=position_info["speed"], -) +# from non_local_detector.visualization import ( +# create_interactive_2D_decoding_figurl, +# ) + +# ( +# position_info, +# position_variable_names, +# ) = ClusterlessDecodingV1.load_position_info(selection_key) +# results_time = decoding_results.acausal_posterior.isel(intervals=0).time.values +# position_info = position_info.loc[results_time[0] : results_time[-1]] + +# env = ClusterlessDecodingV1.load_environments(selection_key)[0] +# spike_times, _ = ClusterlessDecodingV1.load_spike_data(selection_key) + + +# create_interactive_2D_decoding_figurl( +# position_time=position_info.index.to_numpy(), +# position=position_info[position_variable_names], +# env=env, +# results=decoding_results, +# posterior=decoding_results.acausal_posterior.isel(intervals=0) +# .unstack("state_bins") +# .sum("state"), +# spike_times=spike_times, +# head_dir=position_info["orientation"], +# speed=position_info["speed"], +# ) # - # ## GPUs @@ -411,25 +411,3 @@ # to monitor GPU usage in the notebook # - A [terminal program](https://github.com/peci1/nvidia-htop) like nvidia-smi # with more information about which GPUs are being utilized and by whom. -# -# ### Parallelizing Decoding -# -# You can also use the [dask_cuda](https://docs.rapids.ai/api/dask-cuda/nightly/) to parallelize decoding. You will need to install the `dask_cuda` package (see [here](https://docs.rapids.ai/api/dask-cuda/nightly/install/)). You then can run the following code to parallelize decoding: - -# + -# import dask -# from dask.distributed import Client -# from dask_cuda import LocalCUDACluster - -# cluster = LocalCUDACluster() - -# selection_keys = [] # list of selection keys - -# with Client(cluster) as client: -# results = [ -# dask.delayed(ClusterlessDecodingV1.populate)( -# selection_key, reserve_jobs=True -# ) -# for selection_key in selection_keys -# ] -# dask.compute(*results) diff --git a/src/spyglass/decoding/__init__.py b/src/spyglass/decoding/__init__.py index f1a14c529..5cc4e0d42 100644 --- a/src/spyglass/decoding/__init__.py +++ b/src/spyglass/decoding/__init__.py @@ -1,7 +1,20 @@ from spyglass.decoding.decoding_merge import DecodingOutput # noqa: E402 -from spyglass.decoding.visualization.core import ( # noqa: E402 - create_interactive_1D_decoding_figurl, - create_interactive_2D_decoding_figurl, - make_multi_environment_movie, - make_single_environment_movie, +from spyglass.decoding.v1.clusterless import ( # noqa: E402 + ClusterlessDecodingSelection, + ClusterlessDecodingV1, + UnitWaveformFeaturesGroup, +) +from spyglass.decoding.v1.core import ( + DecodingParameters, + PositionGroup, +) # noqa: E402 +from spyglass.decoding.v1.sorted_spikes import ( # noqa: E402 + SortedSpikesDecodingSelection, + SortedSpikesDecodingV1, + SortedSpikesGroup, +) +from spyglass.decoding.v1.waveform_features import ( # noqa: E402 + UnitWaveformFeatures, + UnitWaveformFeaturesSelection, + WaveformFeaturesParams, ) diff --git a/src/spyglass/decoding/decoding_merge.py b/src/spyglass/decoding/decoding_merge.py index 1752b1165..6603c318f 100644 --- a/src/spyglass/decoding/decoding_merge.py +++ b/src/spyglass/decoding/decoding_merge.py @@ -1,7 +1,12 @@ +import inspect from itertools import chain from pathlib import Path import datajoint as dj +import numpy as np +from datajoint.utils import to_camel_case +from non_local_detector.visualization.figurl_1D import create_1D_decode_view +from non_local_detector.visualization.figurl_2D import create_2D_decode_view from spyglass.decoding.v1.clusterless import ClusterlessDecodingV1 # noqa: F401 from spyglass.decoding.v1.sorted_spikes import ( @@ -35,9 +40,12 @@ class SortedSpikesDecodingV1(SpyglassMixin, dj.Part): # noqa: F811 -> SortedSpikesDecodingV1 """ - def cleanup(self): + def cleanup(self, dry_run=False): """Remove any decoding outputs that are not in the merge table""" - logger.info("Cleaning up decoding outputs") + if dry_run: + logger.info("Dry run, not removing any files") + else: + logger.info("Cleaning up decoding outputs") table_results_paths = list( chain( *[ @@ -51,7 +59,11 @@ def cleanup(self): for path in Path(config["SPYGLASS_ANALYSIS_DIR"]).glob("**/*.nc"): if str(path) not in table_results_paths: logger.info(f"Removing {path}") - path.unlink() + if not dry_run: + try: + path.unlink(missing_ok=True) # Ignore FileNotFoundError + except PermissionError: + logger.warning(f"Unable to remove {path}, skipping") table_model_paths = list( chain( @@ -66,4 +78,93 @@ def cleanup(self): for path in Path(config["SPYGLASS_ANALYSIS_DIR"]).glob("**/*.pkl"): if str(path) not in table_model_paths: logger.info(f"Removing {path}") - path.unlink() + if not dry_run: + try: + path.unlink() + except (PermissionError, FileNotFoundError): + logger.warning(f"Unable to remove {path}, skipping") + + @classmethod + def _get_source_class(cls, key): + if cls._source_class_dict is None: + cls._source_class_dict = {} + module = inspect.getmodule(cls) + for part_name in cls.parts(): + part_name = to_camel_case(part_name.split("__")[-1].strip("`")) + part = getattr(module, part_name) + cls._source_class_dict[part_name] = part + + source = (cls & key).fetch1("source") + return cls._source_class_dict[source] + + @classmethod + def load_results(cls, key): + decoding_selection_key = cls.merge_get_parent(key).fetch1("KEY") + source_class = cls._get_source_class(key) + return (source_class & decoding_selection_key).load_results() + + @classmethod + def load_model(cls, key): + decoding_selection_key = cls.merge_get_parent(key).fetch1("KEY") + source_class = cls._get_source_class(key) + return (source_class & decoding_selection_key).load_model() + + @classmethod + def load_environments(cls, key): + decoding_selection_key = cls.merge_get_parent(key).fetch1("KEY") + source_class = cls._get_source_class(key) + return source_class.load_environments(decoding_selection_key) + + @classmethod + def load_position_info(cls, key): + decoding_selection_key = cls.merge_get_parent(key).fetch1("KEY") + source_class = cls._get_source_class(key) + return source_class.load_position_info(decoding_selection_key) + + @classmethod + def load_linear_position_info(cls, key): + decoding_selection_key = cls.merge_get_parent(key).fetch1("KEY") + source_class = cls._get_source_class(key) + return source_class.load_linear_position_info(decoding_selection_key) + + @classmethod + def load_spike_data(cls, key, filter_by_interval=True): + decoding_selection_key = cls.merge_get_parent(key).fetch1("KEY") + source_class = cls._get_source_class(key) + return source_class.load_linear_position_info( + decoding_selection_key, filter_by_interval=filter_by_interval + ) + + @classmethod + def create_decoding_view(cls, key, head_direction_name="head_orientation"): + results = cls.load_results(key) + posterior = results.acausal_posterior.unstack("state_bins").sum("state") + env = cls.load_environments(key)[0] + + if "x_position" in results.coords: + position_info, position_variable_names = cls.load_position_info(key) + # Not 1D + bin_size = ( + np.nanmedian(np.diff(np.unique(results.x_position.values))), + np.nanmedian(np.diff(np.unique(results.y_position.values))), + ) + return create_2D_decode_view( + position_time=position_info.index, + position=position_info[position_variable_names], + interior_place_bin_centers=env.place_bin_centers_[ + env.is_track_interior_.ravel(order="C") + ], + place_bin_size=bin_size, + posterior=posterior, + head_dir=position_info[head_direction_name], + ) + else: + ( + position_info, + position_variable_names, + ) = cls.load_linear_position_info(key) + return create_1D_decode_view( + posterior=posterior, + linear_position=position_info["linear_position"], + ref_time_sec=position_info.index[0], + ) diff --git a/src/spyglass/decoding/visualization/core.py b/src/spyglass/decoding/v0/visualization.py similarity index 99% rename from src/spyglass/decoding/visualization/core.py rename to src/spyglass/decoding/v0/visualization.py index 2d5da97db..aa5347c17 100644 --- a/src/spyglass/decoding/visualization/core.py +++ b/src/spyglass/decoding/v0/visualization.py @@ -10,8 +10,8 @@ from ripple_detection import get_multiunit_population_firing_rate from tqdm.auto import tqdm -from spyglass.decoding.visualization.view1D import create_1D_decode_view -from spyglass.decoding.visualization.view2D import create_2D_decode_view +from spyglass.decoding.v0.visualization_1D_view import create_1D_decode_view +from spyglass.decoding.v0.visualization_2D_view import create_2D_decode_view from spyglass.utils import logger diff --git a/src/spyglass/decoding/visualization/view1D.py b/src/spyglass/decoding/v0/visualization_1D_view.py similarity index 100% rename from src/spyglass/decoding/visualization/view1D.py rename to src/spyglass/decoding/v0/visualization_1D_view.py diff --git a/src/spyglass/decoding/visualization/view2D.py b/src/spyglass/decoding/v0/visualization_2D_view.py similarity index 99% rename from src/spyglass/decoding/visualization/view2D.py rename to src/spyglass/decoding/v0/visualization_2D_view.py index 676004bd5..52338ea78 100644 --- a/src/spyglass/decoding/visualization/view2D.py +++ b/src/spyglass/decoding/v0/visualization_2D_view.py @@ -3,6 +3,10 @@ import numpy as np import sortingview.views.franklab as vvf import xarray as xr +from replay_trajectory_classification.environments import ( + get_grid, + get_track_interior, +) def create_static_track_animation( diff --git a/src/spyglass/decoding/v1/clusterless.py b/src/spyglass/decoding/v1/clusterless.py index 7188bd10c..838ed12d9 100644 --- a/src/spyglass/decoding/v1/clusterless.py +++ b/src/spyglass/decoding/v1/clusterless.py @@ -20,7 +20,6 @@ from track_linearization import get_linearized_position from spyglass.common.common_interval import IntervalList # noqa: F401 -from spyglass.common.common_position import IntervalPositionInfo from spyglass.common.common_session import Session # noqa: F401 from spyglass.decoding.v1.core import ( DecodingParameters, @@ -263,7 +262,7 @@ def make(self, key): ) key["results_path"] = results_path - classifier_path = results_path.strip(".nc") + ".pkl" + classifier_path = results_path.with_suffix(".pkl") classifier.save_model(classifier_path) key["classifier_path"] = classifier_path @@ -303,6 +302,35 @@ def load_environments(key): return classifier.environments + @staticmethod + def _get_interval_range(key): + encoding_interval = ( + IntervalList + & { + "nwb_file_name": key["nwb_file_name"], + "interval_list_name": key["encoding_interval"], + } + ).fetch1("valid_times") + + decoding_interval = ( + IntervalList + & { + "nwb_file_name": key["nwb_file_name"], + "interval_list_name": key["decoding_interval"], + } + ).fetch1("valid_times") + + return ( + min( + np.asarray(encoding_interval).min(), + np.asarray(decoding_interval).min(), + ), + max( + np.asarray(encoding_interval).max(), + np.asarray(decoding_interval).max(), + ), + ) + @staticmethod def load_position_info(key): position_group_key = { @@ -320,7 +348,11 @@ def load_position_info(key): position_info.append( (PositionOutput & {"merge_id": pos_merge_id}).fetch1_dataframe() ) - position_info = pd.concat(position_info, axis=0).dropna() + + min_time, max_time = ClusterlessDecodingV1._get_interval_range(key) + position_info = ( + pd.concat(position_info, axis=0).loc[min_time:max_time].dropna() + ) return position_info, position_variable_names @@ -338,38 +370,15 @@ def load_linear_position_info(key): edge_spacing=environment.edge_spacing, ) - return pd.concat( - [linear_position_df.set_index(position_df.index), position_df], - axis=1, - ) - - @staticmethod - def _get_interval_range(key): - encoding_interval = ( - IntervalList - & { - "nwb_file_name": key["nwb_file_name"], - "interval_list_name": key["encoding_interval"], - } - ).fetch1("valid_times") - - decoding_interval = ( - IntervalList - & { - "nwb_file_name": key["nwb_file_name"], - "interval_list_name": key["decoding_interval"], - } - ).fetch1("valid_times") + min_time, max_time = ClusterlessDecodingV1._get_interval_range(key) return ( - min( - np.asarray(encoding_interval).min(), - np.asarray(decoding_interval).min(), - ), - max( - np.asarray(encoding_interval).max(), - np.asarray(decoding_interval).max(), - ), + pd.concat( + [linear_position_df.set_index(position_df.index), position_df], + axis=1, + ) + .loc[min_time:max_time] + .dropna() ) @staticmethod diff --git a/src/spyglass/decoding/v1/core.py b/src/spyglass/decoding/v1/core.py index 9d49b8957..5705726ad 100644 --- a/src/spyglass/decoding/v1/core.py +++ b/src/spyglass/decoding/v1/core.py @@ -5,6 +5,7 @@ NonLocalClusterlessDetector, NonLocalSortedSpikesDetector, ) +from non_local_detector import __version__ as non_local_detector_version from spyglass.common.common_session import Session # noqa: F401 from spyglass.decoding.v1.dj_decoder_conversion import ( @@ -30,19 +31,19 @@ class DecodingParameters(SpyglassMixin, dj.Lookup): contents = [ { - "decoding_param_name": "contfrag_clusterless", + "decoding_param_name": f"contfrag_clusterless_{non_local_detector_version}", "decoding_params": ContFragClusterlessClassifier(), }, { - "decoding_param_name": "nonlocal_clusterless", + "decoding_param_name": f"nonlocal_clusterless_{non_local_detector_version}", "decoding_params": NonLocalClusterlessDetector(), }, { - "decoding_param_name": "contfrag_sorted", + "decoding_param_name": f"contfrag_sorted_{non_local_detector_version}", "decoding_params": ContFragSortedSpikesClassifier(), }, { - "decoding_param_name": "nonlocal_sorted", + "decoding_param_name": f"nonlocal_sorted_{non_local_detector_version}", "decoding_params": NonLocalSortedSpikesDetector(), }, ] diff --git a/src/spyglass/decoding/v1/sorted_spikes.py b/src/spyglass/decoding/v1/sorted_spikes.py index 5eca81e13..3c910102a 100644 --- a/src/spyglass/decoding/v1/sorted_spikes.py +++ b/src/spyglass/decoding/v1/sorted_spikes.py @@ -97,7 +97,7 @@ def make(self, key): model_params["decoding_params"], model_params["decoding_kwargs"], ) - decoding_kwargs = {} or None + decoding_kwargs = decoding_kwargs or {} # Get position data ( @@ -294,6 +294,35 @@ def load_environments(key): return classifier.environments + @staticmethod + def _get_interval_range(key): + encoding_interval = ( + IntervalList + & { + "nwb_file_name": key["nwb_file_name"], + "interval_list_name": key["encoding_interval"], + } + ).fetch1("valid_times") + + decoding_interval = ( + IntervalList + & { + "nwb_file_name": key["nwb_file_name"], + "interval_list_name": key["decoding_interval"], + } + ).fetch1("valid_times") + + return ( + min( + np.asarray(encoding_interval).min(), + np.asarray(decoding_interval).min(), + ), + max( + np.asarray(encoding_interval).max(), + np.asarray(decoding_interval).max(), + ), + ) + @staticmethod def load_position_info(key): position_group_key = { @@ -311,7 +340,10 @@ def load_position_info(key): position_info.append( (PositionOutput & {"merge_id": pos_merge_id}).fetch1_dataframe() ) - position_info = pd.concat(position_info, axis=0).dropna() + min_time, max_time = SortedSpikesDecodingV1._get_interval_range(key) + position_info = ( + pd.concat(position_info, axis=0).loc[min_time:max_time].dropna() + ) return position_info, position_variable_names @@ -328,38 +360,14 @@ def load_linear_position_info(key): edge_order=environment.edge_order, edge_spacing=environment.edge_spacing, ) - return pd.concat( - [linear_position_df.set_index(position_df.index), position_df], - axis=1, - ) - - @staticmethod - def _get_interval_range(key): - encoding_interval = ( - IntervalList - & { - "nwb_file_name": key["nwb_file_name"], - "interval_list_name": key["encoding_interval"], - } - ).fetch1("valid_times") - - decoding_interval = ( - IntervalList - & { - "nwb_file_name": key["nwb_file_name"], - "interval_list_name": key["decoding_interval"], - } - ).fetch1("valid_times") - + min_time, max_time = SortedSpikesDecodingV1._get_interval_range(key) return ( - min( - np.asarray(encoding_interval).min(), - np.asarray(decoding_interval).min(), - ), - max( - np.asarray(encoding_interval).max(), - np.asarray(decoding_interval).max(), - ), + pd.concat( + [linear_position_df.set_index(position_df.index), position_df], + axis=1, + ) + .loc[min_time:max_time] + .dropna() ) @staticmethod diff --git a/src/spyglass/spikesorting/merge.py b/src/spyglass/spikesorting/merge.py index d01b720c0..12baefd34 100644 --- a/src/spyglass/spikesorting/merge.py +++ b/src/spyglass/spikesorting/merge.py @@ -1,5 +1,7 @@ import datajoint as dj +import numpy as np from datajoint.utils import to_camel_case +from ripple_detection import get_multiunit_population_firing_rate from spyglass.spikesorting.imported import ImportedSpikeSorting # noqa: F401 from spyglass.spikesorting.spikesorting_curation import ( # noqa: F401 @@ -48,6 +50,7 @@ class CuratedSpikeSorting(SpyglassMixin, dj.Part): # noqa: F811 -> CuratedSpikeSorting """ + @classmethod def get_recording(cls, key): """get the recording associated with a spike sorting output""" source_table = source_class_dict[ @@ -56,6 +59,7 @@ def get_recording(cls, key): query = source_table & cls.merge_get_part(key) return query.get_recording(query.fetch("KEY")) + @classmethod def get_sorting(cls, key): """get the sorting associated with a spike sorting output""" source_table = source_class_dict[ @@ -63,3 +67,50 @@ def get_sorting(cls, key): ] query = source_table & cls.merge_get_part(key) return query.get_sorting(query.fetch("KEY")) + + @classmethod + def get_spike_times(cls, key): + spike_times = [] + for nwb_file in cls.fetch_nwb(key): + # V1 uses 'object_id', V0 uses 'units' + file_loc = "object_id" if "object_id" in nwb_file else "units" + spike_times.extend(nwb_file[file_loc]["spike_times"].to_list()) + return spike_times + + @classmethod + def get_spike_indicator(cls, key, time): + time = np.asarray(time) + min_time, max_time = time[[0, -1]] + spike_times = cls.get_spike_times(key) + spike_indicator = np.zeros((len(time), len(spike_times))) + + for ind, times in enumerate(spike_times): + times = times[ + np.logical_and(spike_times >= min_time, spike_times <= max_time) + ] + spike_indicator[:, ind] = np.bincount( + np.digitize(times, time[1:-1]), + minlength=time.shape[0], + ) + + return spike_indicator + + @classmethod + def get_firing_rate(cls, key, time, multiunit=False): + spike_indicator = cls.get_spike_indicator(key, time) + if spike_indicator.ndim == 1: + spike_indicator = spike_indicator[:, np.newaxis] + + sampling_frequency = 1 / np.median(np.diff(time)) + + if multiunit: + spike_indicator = spike_indicator.sum(axis=1, keepdims=True) + return np.stack( + [ + get_multiunit_population_firing_rate( + indicator[:, np.newaxis], sampling_frequency + ) + for indicator in spike_indicator.T + ], + axis=1, + ) diff --git a/src/spyglass/utils/dj_merge_tables.py b/src/spyglass/utils/dj_merge_tables.py index 5c900b66c..a710972ff 100644 --- a/src/spyglass/utils/dj_merge_tables.py +++ b/src/spyglass/utils/dj_merge_tables.py @@ -489,7 +489,7 @@ def fetch_nwb( Parameters ---------- restriction: str, optional - Restriction to apply to parents before running fetch. Default none. + Restriction to apply to parents before running fetch. Default True. multi_source: bool Return from multiple parents. Default False. """ From 08afe2c0c33d7b4c7d2da395924fe37bbf9ee417 Mon Sep 17 00:00:00 2001 From: emreybroyles <114687400+emreybroyles@users.noreply.github.com> Date: Fri, 19 Jan 2024 14:49:28 -0800 Subject: [PATCH 5/8] DLC notebooks 21 and 22 (#772) * add envs to bashrc; multi cam addition * 12/11/23 using TackEpoch to define interval_names * 12/11/23 remove comment * del smooth duration * jan 10 again * DLC noteboks 5 and 6 * 5 and 6 * fix ignore and 21 * Removed submodule * removed .gitignore * DLC notebooks --- notebooks/21_DLC.ipynb | 2183 +++++++++++++++++ ...Position_DLC_1.ipynb => 22_DLC_Loop.ipynb} | 587 +++-- notebooks/22_Position_DLC_2.ipynb | 429 ---- notebooks/23_Position_DLC_3.ipynb | 963 -------- 4 files changed, 2535 insertions(+), 1627 deletions(-) create mode 100644 notebooks/21_DLC.ipynb rename notebooks/{21_Position_DLC_1.ipynb => 22_DLC_Loop.ipynb} (54%) delete mode 100644 notebooks/22_Position_DLC_2.ipynb delete mode 100644 notebooks/23_Position_DLC_3.ipynb diff --git a/notebooks/21_DLC.ipynb b/notebooks/21_DLC.ipynb new file mode 100644 index 000000000..1c1756c0d --- /dev/null +++ b/notebooks/21_DLC.ipynb @@ -0,0 +1,2183 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a93a1550-8a67-4346-a4bf-e5a136f3d903", + "metadata": {}, + "source": [ + "## Position- DeepLabCut from Scratch" + ] + }, + { + "cell_type": "markdown", + "id": "13dd3267", + "metadata": {}, + "source": [ + "### Overview" + ] + }, + { + "cell_type": "markdown", + "id": "b52aff0d", + "metadata": {}, + "source": [ + "_Developer Note:_ if you may make a PR in the future, be sure to copy this\n", + "notebook, and use the `gitignore` prefix `temp` to avoid future conflicts.\n", + "\n", + "This is one notebook in a multi-part series on Spyglass.\n", + "\n", + "- To set up your Spyglass environment and database, see\n", + " [the Setup notebook](./00_Setup.ipynb)\n", + "- For additional info on DataJoint syntax, including table definitions and\n", + " inserts, see\n", + " [the Insert Data notebook](./01_Insert_Data.ipynb)\n", + "\n", + "This tutorial will extract position via DeepLabCut (DLC). It will walk through...\n", + "\n", + "- creating a DLC project\n", + "- extracting and labeling frames\n", + "- training your model\n", + "- executing pose estimation on a novel behavioral video\n", + "- processing the pose estimation output to extract a centroid and orientation\n", + "- inserting the resulting information into the `PositionOutput` table\n", + "\n", + "**Note 2: Make sure you are running this within the spyglass-position Conda environment (instructions for install are in the environment_position.yml)**" + ] + }, + { + "cell_type": "markdown", + "id": "a8b531f7", + "metadata": {}, + "source": [ + "Here is a schematic showing the tables used in this pipeline.\n", + "\n", + "![dlc_scratch.png|2000x900](./../notebook-images/dlc_scratch.png)\n" + ] + }, + { + "cell_type": "markdown", + "id": "0c67d88c-c90e-467b-ae2e-672c49a12f95", + "metadata": {}, + "source": [ + "### Table of Contents\n", + "[`DLCProject`](#DLCProject1)
    \n", + "[`DLCModelTraining`](#DLCModelTraining1)
    \n", + "[`DLCModel`](#DLCModel1)
    \n", + "[`DLCPoseEstimation`](#DLCPoseEstimation1)
    \n", + "[`DLCSmoothInterp`](#DLCSmoothInterp1)
    \n", + "[`DLCCentroid`](#DLCCentroid1)
    \n", + "[`DLCOrientation`](#DLCOrientation1)
    \n", + "[`DLCPosV1`](#DLCPosV1-1)
    \n", + "[`DLCPosVideo`](#DLCPosVideo1)
    \n", + "[`PositionOutput`](#PositionOutput1)
    " + ] + }, + { + "cell_type": "markdown", + "id": "70a0a678", + "metadata": {}, + "source": [ + "__You can click on any header to return to the Table of Contents__" + ] + }, + { + "cell_type": "markdown", + "id": "c9b98c3d", + "metadata": {}, + "source": [ + "### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "968d5189", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "0f567531", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import datajoint as dj\n", + "from pprint import pprint\n", + "\n", + "import spyglass.common as sgc\n", + "import spyglass.position.v1 as sgp\n", + "\n", + "from pathlib import Path, PosixPath, PurePath\n", + "import glob\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pynwb\n", + "from spyglass.position import PositionOutput\n", + "\n", + "# change to the upper level folder to detect dj_local_conf.json\n", + "if os.path.basename(os.getcwd()) == \"notebooks\":\n", + " os.chdir(\"..\")\n", + "dj.config.load(\"dj_local_conf.json\") # load config for database connection info\n", + "\n", + "# ignore datajoint+jupyter async warnings\n", + "import warnings\n", + "\n", + "warnings.simplefilter(\"ignore\", category=DeprecationWarning)\n", + "warnings.simplefilter(\"ignore\", category=ResourceWarning)" + ] + }, + { + "cell_type": "markdown", + "id": "5e6221a3-17e5-45c0-aa40-2fd664b02219", + "metadata": {}, + "source": [ + "#### [DLCProject](#TableOfContents) " + ] + }, + { + "cell_type": "markdown", + "id": "27aed0e1-3af7-4499-bae8-96a64e81041e", + "metadata": {}, + "source": [ + "
    \n", + " Notes:
      \n", + "
    • \n", + " The cells within this DLCProject step need to be performed \n", + " in a local Jupyter notebook to allow for use of the frame labeling GUI.\n", + "
      • \n", + "
    • \n", + " Please do not add to the BodyPart table in the production \n", + " database unless necessary.\n", + "
      • \n", + "
    \n", + "
    \n" + ] + }, + { + "cell_type": "markdown", + "id": "50c9f1c9", + "metadata": {}, + "source": [ + "### Body Parts" + ] + }, + { + "cell_type": "markdown", + "id": "96637cb9-519d-41e1-8bfd-69f68dc66b36", + "metadata": {}, + "source": [ + "We'll begin by looking at the `BodyPart` table, which stores standard names of body parts used in DLC models throughout the lab with a concise description." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "b69f829f-9877-48ae-89d1-f876af2b8835", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    bodypart

    \n", + " \n", + "
    \n", + "

    bodypart_description

    \n", + " \n", + "
    backmiddle of the rat's back
    driveBackback of drive
    driveFrontfront of drive
    earLleft ear of the rat
    earRright ear of the rat
    forelimbLleft forelimb of the rat
    forelimbRright forelimb of the rat
    greenLEDgreenLED
    hindlimbLleft hindlimb of the rat
    hindlimbRright hindlimb of the rat
    nosetip of the nose of the rat
    redLED_CredLED_C
    \n", + "

    ...

    \n", + "

    Total: 23

    \n", + " " + ], + "text/plain": [ + "*bodypart bodypart_descr\n", + "+------------+ +------------+\n", + "back middle of the \n", + "driveBack back of drive \n", + "driveFront front of drive\n", + "earL left ear of th\n", + "earR right ear of t\n", + "forelimbL left forelimb \n", + "forelimbR right forelimb\n", + "greenLED greenLED \n", + "hindlimbL left hindlimb \n", + "hindlimbR right hindlimb\n", + "nose tip of the nos\n", + "redLED_C redLED_C \n", + " ...\n", + " (Total: 23)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sgp.BodyPart()" + ] + }, + { + "cell_type": "markdown", + "id": "9616512e", + "metadata": {}, + "source": [ + "If the bodyparts you plan to use in your model are not yet in the table, here is code to add bodyparts:\n", + "\n", + "```python\n", + "sgp.BodyPart.insert(\n", + " [\n", + " {\"bodypart\": \"bp_1\", \"bodypart_description\": \"concise descrip\"},\n", + " {\"bodypart\": \"bp_2\", \"bodypart_description\": \"concise descrip\"},\n", + " ],\n", + " skip_duplicates=True,\n", + ")\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "57b590d3", + "metadata": {}, + "source": [ + "### Define videos and camera name (optional) for training set" + ] + }, + { + "cell_type": "markdown", + "id": "5d5aae37", + "metadata": {}, + "source": [ + "To train a model, we'll need to extract frames, which we can label as training data. We can construct a list of videos from which we'll extract frames.\n", + "\n", + "The list can either contain dictionaries identifying behavioral videos for NWB files that have already been added to Spyglass, or absolute file paths to the videos you want to use.\n", + "\n", + "For this tutorial, we'll use two videos for which we already have frames labeled." + ] + }, + { + "cell_type": "markdown", + "id": "7b5e157b", + "metadata": {}, + "source": [ + "Defining camera name is optional: it should be done in cases where there are multiple cameras streaming per epoch, but not necessary otherwise.
    \n", + "example:\n", + "`camera_name = \"HomeBox_camera\" \n", + " `" + ] + }, + { + "cell_type": "markdown", + "id": "56f45e7f", + "metadata": {}, + "source": [ + "_NOTE:_ The official release of Spyglass does not yet support multicamera\n", + "projects. You can monitor progress on the effort to add this feature by checking\n", + "[this PR](https://github.com/LorenFrankLab/spyglass/pull/684) or use\n", + "[this experimental branch](https://github.com/dpeg22/spyglass/tree/add-multi-camera),\n", + "which takes the keys nwb_file_name and epoch, and camera_name in the video_list variable.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "15971506", + "metadata": {}, + "outputs": [], + "source": [ + "video_list = [\n", + " {\"nwb_file_name\": \"J1620210529_.nwb\", \"epoch\": 2},\n", + " {\"nwb_file_name\": \"peanut20201103_.nwb\", \"epoch\": 4},\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "a9f8e43d", + "metadata": {}, + "source": [ + "### Path variables\n", + "\n", + "The position pipeline also keeps track of paths for project, video, and output.\n", + "Just like we saw in [Setup](./00_Setup.ipynb), you can manage these either with\n", + "environmental variables...\n", + "\n", + "```bash\n", + "export DLC_PROJECT_DIR=\"/nimbus/deeplabcut/projects\"\n", + "export DLC_VIDEO_DIR=\"/nimbus/deeplabcut/video\"\n", + "export DLC_OUTPUT_DIR=\"/nimbus/deeplabcut/output\"\n", + "```\n", + "\n", + "\n", + "\n", + "Or these can be set in your datajoint config:\n", + "\n", + "```json\n", + "{\n", + " \"custom\": {\n", + " \"dlc_dirs\": {\n", + " \"base\": \"/nimbus/deeplabcut/\",\n", + " \"project\": \"/nimbus/deeplabcut/projects\",\n", + " \"video\": \"/nimbus/deeplabcut/video\",\n", + " \"output\": \"/nimbus/deeplabcut/output\"\n", + " }\n", + " }\n", + "}\n", + "```\n", + "\n", + "_NOTE:_ If only `base` is specified as shown above, spyglass will assume the\n", + "relative directories shown.\n", + "\n", + "You can check the result of this setup process with..." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "49d7d9fc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'debug_mode': False,\n", + " 'prepopulate': True,\n", + " 'SPYGLASS_BASE_DIR': '/stelmo/nwb',\n", + " 'SPYGLASS_RAW_DIR': '/stelmo/nwb/raw',\n", + " 'SPYGLASS_ANALYSIS_DIR': '/stelmo/nwb/analysis',\n", + " 'SPYGLASS_RECORDING_DIR': '/stelmo/nwb/recording',\n", + " 'SPYGLASS_SORTING_DIR': '/stelmo/nwb/sorting',\n", + " 'SPYGLASS_WAVEFORMS_DIR': '/stelmo/nwb/waveforms',\n", + " 'SPYGLASS_TEMP_DIR': '/stelmo/nwb/tmp/spyglass',\n", + " 'SPYGLASS_VIDEO_DIR': '/stelmo/nwb/video',\n", + " 'KACHERY_CLOUD_DIR': '/stelmo/nwb/.kachery-cloud',\n", + " 'KACHERY_STORAGE_DIR': '/stelmo/nwb/kachery_storage',\n", + " 'KACHERY_TEMP_DIR': '/stelmo/nwb/tmp',\n", + " 'DLC_PROJECT_DIR': '/nimbus/deeplabcut/projects',\n", + " 'DLC_VIDEO_DIR': '/nimbus/deeplabcut/video',\n", + " 'DLC_OUTPUT_DIR': '/nimbus/deeplabcut/output',\n", + " 'KACHERY_ZONE': 'franklab.default',\n", + " 'FIGURL_CHANNEL': 'franklab2',\n", + " 'DJ_SUPPORT_FILEPATH_MANAGEMENT': 'TRUE',\n", + " 'KACHERY_CLOUD_EPHEMERAL': 'TRUE',\n", + " 'HD5_USE_FILE_LOCKING': 'FALSE'}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from spyglass.settings import config\n", + "\n", + "config" + ] + }, + { + "cell_type": "markdown", + "id": "32c023b0-d00d-40b0-9a37-d0d3e4a4ae2a", + "metadata": {}, + "source": [ + "Before creating our project, we need to define a few variables.\n", + "\n", + "- A team name, as shown in `LabTeam` for setting permissions. Here, we'll\n", + " use \"LorenLab\".\n", + "- A `project_name`, as a unique identifier for this DLC project. Here, we'll use\n", + " **\"tutorial_scratch_yourinitials\"**\n", + "- `bodyparts` is a list of body parts for which we want to extract position.\n", + " The pre-labeled frames we're using include the bodyparts listed below.\n", + "- Number of frames to extract/label as `frames_per_video`. Note that the DLC creators recommend having 200 frames as the minimum total number for each project." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "347e98f1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "project name: tutorial_scratch_DG is already in use.\n" + ] + } + ], + "source": [ + "team_name = \"LorenLab\"\n", + "project_name = \"tutorial_scratch_DG\"\n", + "frames_per_video = 100\n", + "bodyparts = [\"redLED_C\", \"greenLED\", \"redLED_L\", \"redLED_R\", \"tailBase\"]\n", + "project_key = sgp.DLCProject.insert_new_project(\n", + " project_name=project_name,\n", + " bodyparts=bodyparts,\n", + " lab_team=team_name,\n", + " frames_per_video=frames_per_video,\n", + " video_list=video_list,\n", + " skip_duplicates=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "f5d83452-48eb-4669-89eb-a6beb1f2d051", + "metadata": {}, + "source": [ + "Now that we've intialized our project we'll need to extract frames which we will then label. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d8b1595", + "metadata": {}, + "outputs": [], + "source": [ + "#comment this line out after you finish frame extraction for each project\n", + "sgp.DLCProject().run_extract_frames(project_key)" + ] + }, + { + "cell_type": "markdown", + "id": "68110734", + "metadata": {}, + "source": [ + "This is the line used to label the frames you extracted, if you wish to use the DLC GUI on the computer you are currently using.\n", + "```#comment this line out after frames are labeled for your project\n", + "sgp.DLCProject().run_label_frames(project_key)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "8b241030", + "metadata": {}, + "source": [ + "Otherwise, it is best/easiest practice to label the frames on your local computer (like a MacBook) that can run DeepLabCut's GUI well. Instructions:
    \n", + "1. Install DLC on your local (preferably into a 'Src' folder): https://deeplabcut.github.io/DeepLabCut/docs/installation.html\n", + "2. Upload frames extracted and saved in nimbus (should be `/nimbus/deeplabcut//labeled-data`) AND the project's associated config file (should be `/nimbus/deeplabcut//config.yaml`) to Box (we get free with UCSF)\n", + "3. Download labeled-data and config files on your local from Box\n", + "4. Create a 'projects' folder where you installed DeepLabCut; create a new folder with your complete project name there; save the downloaded files there.\n", + "4. Edit the config.yaml file: line 9 defining `project_path` needs to be the file path where it is saved on your local (ex: `/Users/lorenlab/Src/DeepLabCut/projects/tutorial_sratch_DG-LorenLab-2023-08-16`)\n", + "5. Open the DLC GUI through terminal \n", + "
    (ex: `conda activate miniconda/envs/DEEPLABCUT_M1`\n", + "\t\t
    `pythonw -m deeplabcut`)\n", + "6. Load an existing project; choose the config.yaml file\n", + "7. Label frames; labeling tutorial: https://www.youtube.com/watch?v=hsA9IB5r73E.\n", + "8. Once all frames are labeled, you should re-upload labeled-data folder back to Box and overwrite it in the original nimbus location so that your completed frames are ready to be used in the model." + ] + }, + { + "cell_type": "markdown", + "id": "c12dd229-2f8b-455a-a7b1-a20916cefed9", + "metadata": {}, + "source": [ + "Now we can check the `DLCProject.File` part table and see all of our training files and videos there!" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "3d4f3fa6-cce9-4d4a-a252-3424313c6a97", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Paths of training files (e.g., labeled pngs, CSV or video)\n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    \n", + "

    project_name

    \n", + " name of DLC project\n", + "
    \n", + "

    file_name

    \n", + " Concise name to describe file\n", + "
    \n", + "

    file_ext

    \n", + " extension of file\n", + "
    \n", + "

    file_path

    \n", + " \n", + "
    tutorial_scratch_DG20201103_peanut_04_r2mp4/nimbus/deeplabcut/projects/tutorial_scratch_DG-LorenLab-2023-08-16/videos/20201103_peanut_04_r2.mp4
    tutorial_scratch_DG20201103_peanut_04_r2_labeled_datah5/nimbus/deeplabcut/projects/tutorial_scratch_DG-LorenLab-2023-08-16/labeled-data/20201103_peanut_04_r2/CollectedData_LorenLab.h5
    tutorial_scratch_DG20210529_J16_02_r1mp4/nimbus/deeplabcut/projects/tutorial_scratch_DG-LorenLab-2023-08-16/videos/20210529_J16_02_r1.mp4
    tutorial_scratch_DG20210529_J16_02_r1_labeled_datah5/nimbus/deeplabcut/projects/tutorial_scratch_DG-LorenLab-2023-08-16/labeled-data/20210529_J16_02_r1/CollectedData_LorenLab.h5
    \n", + " \n", + "

    Total: 4

    \n", + " " + ], + "text/plain": [ + "*project_name *file_name *file_ext file_path \n", + "+------------+ +------------+ +----------+ +------------+\n", + "tutorial_scrat 20201103_peanu mp4 /nimbus/deepla\n", + "tutorial_scrat 20201103_peanu h5 /nimbus/deepla\n", + "tutorial_scrat 20210529_J16_0 mp4 /nimbus/deepla\n", + "tutorial_scrat 20210529_J16_0 h5 /nimbus/deepla\n", + " (Total: 4)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sgp.DLCProject.File & project_key" + ] + }, + { + "cell_type": "markdown", + "id": "7e2e3eab-60c7-4a3c-bc8f-fd4e8dcf52a2", + "metadata": {}, + "source": [ + "
    \n", + " This step and beyond should be run on a GPU-enabled machine.\n", + "
    " + ] + }, + { + "cell_type": "markdown", + "id": "0e48ecf0", + "metadata": {}, + "source": [ + "#### [DLCModelTraining](#ToC)\n", + "\n", + "Please make sure you're running this notebook on a GPU-enabled machine.\n", + "\n", + "Now that we've imported existing frames, we can get ready to train our model.\n", + "\n", + "First, we'll need to define a set of parameters for `DLCModelTrainingParams`, which will get used by DeepLabCut during training. Let's start with `gputouse`,\n", + "which determines which GPU core to use.\n", + "\n", + "The cell below determines which core has space and set the `gputouse` variable\n", + "accordingly.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "a8fc5bb7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: 305}" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sgp.dlc_utils.get_gpu_memory()" + ] + }, + { + "cell_type": "markdown", + "id": "bca035a9", + "metadata": {}, + "source": [ + "Set GPU core:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "1ff0e393", + "metadata": {}, + "outputs": [], + "source": [ + "gputouse = 1 # 1-9" + ] + }, + { + "cell_type": "markdown", + "id": "2b047686", + "metadata": {}, + "source": [ + "Now we'll define the rest of our parameters and insert the entry.\n", + "\n", + "To see all possible parameters, try:\n", + "\n", + "```python\n", + "sgp.DLCModelTrainingParams.get_accepted_params()\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "399581ee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "New param set not added\n", + "A param set with name: tutorial already exists\n" + ] + } + ], + "source": [ + "training_params_name = \"tutorial\"\n", + "sgp.DLCModelTrainingParams.insert_new_params(\n", + " paramset_name=training_params_name,\n", + " params={\n", + " \"trainingsetindex\": 0,\n", + " \"shuffle\": 1,\n", + " \"gputouse\": gputouse,\n", + " \"net_type\": \"resnet_50\",\n", + " \"augmenter_type\": \"imgaug\",\n", + " },\n", + " skip_duplicates=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6b6cc709", + "metadata": {}, + "source": [ + "Next we'll modify the `project_key` from above to include the necessary entries for `DLCModelTraining`" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "7acd150b", + "metadata": {}, + "outputs": [], + "source": [ + "# project_key['project_path'] = os.path.dirname(project_key['config_path'])\n", + "if \"config_path\" in project_key:\n", + " del project_key[\"config_path\"]" + ] + }, + { + "cell_type": "markdown", + "id": "0bc7ddaa", + "metadata": {}, + "source": [ + "We can insert an entry into `DLCModelTrainingSelection` and populate `DLCModelTraining`.\n", + "\n", + "_Note:_ You can stop training at any point using `I + I` or interrupt the Kernel. \n", + "\n", + "The maximum total number of training iterations is 1030000; you can end training before this amount if the loss rate (lr) and total loss plateau and are very close to 0.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "3c252541", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "project_name : varchar(100) # name of DLC project\n", + "dlc_training_params_name : varchar(50) # descriptive name of parameter set\n", + "training_id : int # unique integer,\n", + "---\n", + "model_prefix=\"\" : varchar(32) # " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sgp.DLCModelTrainingSelection.heading" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "139d2f30", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-18 10:23:30.406102: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading DLC 2.2.3...\n", + "OpenCV is built with OpenMP support. This usually results in poor performance. For details, see https://github.com/tensorpack/benchmarks/blob/master/ImageNet/benchmark-opencv-resize.py\n" + ] + }, + { + "ename": "PermissionError", + "evalue": "[Errno 13] Permission denied: '/nimbus/deeplabcut/projects/tutorial_scratch_DG-LorenLab-2023-08-16/log.log'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mPermissionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[24], line 16\u001b[0m\n\u001b[1;32m 1\u001b[0m sgp\u001b[38;5;241m.\u001b[39mDLCModelTrainingSelection()\u001b[38;5;241m.\u001b[39minsert1(\n\u001b[1;32m 2\u001b[0m {\n\u001b[1;32m 3\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mproject_key,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 7\u001b[0m }\n\u001b[1;32m 8\u001b[0m )\n\u001b[1;32m 9\u001b[0m model_training_key \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 10\u001b[0m sgp\u001b[38;5;241m.\u001b[39mDLCModelTrainingSelection\n\u001b[1;32m 11\u001b[0m \u001b[38;5;241m&\u001b[39m {\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 14\u001b[0m }\n\u001b[1;32m 15\u001b[0m )\u001b[38;5;241m.\u001b[39mfetch1(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mKEY\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 16\u001b[0m \u001b[43msgp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDLCModelTraining\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpopulate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_training_key\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/spyglass-position/lib/python3.9/site-packages/datajoint/autopopulate.py:241\u001b[0m, in \u001b[0;36mAutoPopulate.populate\u001b[0;34m(self, suppress_errors, return_exception_objects, reserve_jobs, order, limit, max_calls, display_progress, processes, make_kwargs, *restrictions)\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m processes \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 238\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m (\n\u001b[1;32m 239\u001b[0m tqdm(keys, desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m display_progress \u001b[38;5;28;01melse\u001b[39;00m keys\n\u001b[1;32m 240\u001b[0m ):\n\u001b[0;32m--> 241\u001b[0m error \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_populate1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjobs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpopulate_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 242\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m error \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 243\u001b[0m error_list\u001b[38;5;241m.\u001b[39mappend(error)\n", + "File \u001b[0;32m~/anaconda3/envs/spyglass-position/lib/python3.9/site-packages/datajoint/autopopulate.py:292\u001b[0m, in \u001b[0;36mAutoPopulate._populate1\u001b[0;34m(self, key, jobs, suppress_errors, return_exception_objects, make_kwargs)\u001b[0m\n\u001b[1;32m 290\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m_allow_insert \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 291\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 292\u001b[0m \u001b[43mmake\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmake_kwargs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 293\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m, \u001b[38;5;167;01mSystemExit\u001b[39;00m, \u001b[38;5;167;01mException\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m error:\n\u001b[1;32m 294\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[0;32m~/Src/spyglass/src/spyglass/position/v1/position_dlc_training.py:150\u001b[0m, in \u001b[0;36mDLCModelTraining.make\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdeeplabcut\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauxiliaryfunctions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 145\u001b[0m GetModelFolder \u001b[38;5;28;01mas\u001b[39;00m get_model_folder,\n\u001b[1;32m 146\u001b[0m )\n\u001b[1;32m 147\u001b[0m config_path, project_name \u001b[38;5;241m=\u001b[39m (DLCProject() \u001b[38;5;241m&\u001b[39m key)\u001b[38;5;241m.\u001b[39mfetch1(\n\u001b[1;32m 148\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconfig_path\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproject_name\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 149\u001b[0m )\n\u001b[0;32m--> 150\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mOutputLogger\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 151\u001b[0m \u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mDLC_project_\u001b[39;49m\u001b[38;5;132;43;01m{project_name}\u001b[39;49;00m\u001b[38;5;124;43m_training\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 152\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdirname\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig_path\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/log.log\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 153\u001b[0m \u001b[43m \u001b[49m\u001b[43mprint_console\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 154\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m logger:\n\u001b[1;32m 155\u001b[0m dlc_config \u001b[38;5;241m=\u001b[39m read_config(config_path)\n\u001b[1;32m 156\u001b[0m project_path \u001b[38;5;241m=\u001b[39m dlc_config[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproject_path\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", + "File \u001b[0;32m~/Src/spyglass/src/spyglass/position/v1/dlc_utils.py:192\u001b[0m, in \u001b[0;36mOutputLogger.__init__\u001b[0;34m(self, name, path, level, **kwargs)\u001b[0m\n\u001b[1;32m 191\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, name, path, level\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mINFO\u001b[39m\u001b[38;5;124m\"\u001b[39m, 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\u001b[0;32m~/Src/spyglass/src/spyglass/position/v1/dlc_utils.py:244\u001b[0m, in \u001b[0;36mOutputLogger.setup_logger\u001b[0;34m(self, name_logfile, path_logfile, print_console)\u001b[0m\n\u001b[1;32m 241\u001b[0m logger\u001b[38;5;241m.\u001b[39maddHandler(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_stream_handler())\n\u001b[1;32m 243\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 244\u001b[0m file_handler \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_file_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_logfile\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 245\u001b[0m logger\u001b[38;5;241m.\u001b[39maddHandler(file_handler)\n\u001b[1;32m 246\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m print_console:\n", + "File \u001b[0;32m~/Src/spyglass/src/spyglass/position/v1/dlc_utils.py:255\u001b[0m, in \u001b[0;36mOutputLogger._get_file_handler\u001b[0;34m(self, path)\u001b[0m\n\u001b[1;32m 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\u001b[38;5;28;01mreturn\u001b[39;00m file_handler\n", + "File \u001b[0;32m~/anaconda3/envs/spyglass-position/lib/python3.9/logging/__init__.py:1146\u001b[0m, in \u001b[0;36mFileHandler.__init__\u001b[0;34m(self, filename, mode, encoding, delay, errors)\u001b[0m\n\u001b[1;32m 1144\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstream \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1145\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1146\u001b[0m StreamHandler\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n", + "File \u001b[0;32m~/anaconda3/envs/spyglass-position/lib/python3.9/logging/__init__.py:1175\u001b[0m, in \u001b[0;36mFileHandler._open\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1170\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_open\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 1171\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1172\u001b[0m \u001b[38;5;124;03m Open the current base file with the (original) mode and encoding.\u001b[39;00m\n\u001b[1;32m 1173\u001b[0m \u001b[38;5;124;03m Return the resulting stream.\u001b[39;00m\n\u001b[1;32m 1174\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1175\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbaseFilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1176\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mPermissionError\u001b[0m: [Errno 13] Permission denied: '/nimbus/deeplabcut/projects/tutorial_scratch_DG-LorenLab-2023-08-16/log.log'" + ] + } + ], + "source": [ + "sgp.DLCModelTrainingSelection().insert1(\n", + " {\n", + " **project_key,\n", + " \"dlc_training_params_name\": training_params_name,\n", + " \"training_id\": 0,\n", + " \"model_prefix\": \"\",\n", + " }\n", + ")\n", + "model_training_key = (\n", + " sgp.DLCModelTrainingSelection\n", + " & {\n", + " **project_key,\n", + " \"dlc_training_params_name\": training_params_name,\n", + " }\n", + ").fetch1(\"KEY\")\n", + "sgp.DLCModelTraining.populate(model_training_key)" + ] + }, + { + "cell_type": "markdown", + "id": "da004b3e", + "metadata": {}, + "source": [ + "Here we'll make sure that the entry made it into the table properly!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5306fd9", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "sgp.DLCModelTraining() & model_training_key" + ] + }, + { + "cell_type": "markdown", + "id": "ac5b7687", + "metadata": {}, + "source": [ + "Populating `DLCModelTraining` automatically inserts the entry into\n", + "`DLCModelSource`, which is used to select between models trained using Spyglass\n", + "vs. other tools." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a349dc3d", + "metadata": {}, + "outputs": [], + "source": [ + "sgp.DLCModelSource() & model_training_key" + ] + }, + { + "cell_type": "markdown", + "id": "92cb8969", + "metadata": {}, + "source": [ + "The `source` field will only accept _\"FromImport\"_ or _\"FromUpstream\"_ as entries. Let's checkout the `FromUpstream` part table attached to `DLCModelSource` below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0cc1afa", + "metadata": {}, + "outputs": [], + "source": [ + "sgp.DLCModelSource.FromUpstream() & model_training_key" + ] + }, + { + "cell_type": "markdown", + "id": "67a9b2c6", + "metadata": {}, + "source": [ + "#### [DLCModel](#TableOfContents) \n", + "\n", + "Next we'll populate the `DLCModel` table, which holds all the relevant\n", + "information for all trained models.\n", + "\n", + "First, we'll need to determine a set of parameters for our model to select the\n", + "correct model file. Here is the default:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb663861", + "metadata": {}, + "outputs": [], + "source": [ + "sgp.DLCModelParams.get_default()" + ] + }, + { + "cell_type": "markdown", + "id": "8b45a6ed", + "metadata": {}, + "source": [ + "Here is the syntax to add your own parameter set:\n", + "\n", + "```python\n", + "dlc_model_params_name = \"make_this_yours\"\n", + "params = {\n", + " \"params\": {},\n", + " \"shuffle\": 1,\n", + " \"trainingsetindex\": 0,\n", + " \"model_prefix\": \"\",\n", + "}\n", + "sgp.DLCModelParams.insert1(\n", + " {\"dlc_model_params_name\": dlc_model_params_name, \"params\": params},\n", + " skip_duplicates=True,\n", + ")\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "7bce9696", + "metadata": {}, + "source": [ + "We can insert sets of parameters into `DLCModelSelection` and populate\n", + "`DLCModel`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eaa23fab", + "metadata": {}, + "outputs": [], + "source": [ + "temp_model_key = (sgp.DLCModelSource & model_training_key).fetch1(\"KEY\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e418eba", + "metadata": {}, + "outputs": [], + "source": [ + "#comment these lines out after successfully inserting, for each project\n", + "sgp.DLCModelSelection().insert1({\n", + " **temp_model_key,\n", + " \"dlc_model_params_name\": \"default\"},\n", + " skip_duplicates=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ccae03bb", + "metadata": {}, + "outputs": [], + "source": [ + "model_key = (sgp.DLCModelSelection & temp_model_key).fetch1(\"KEY\")\n", + "sgp.DLCModel.populate(model_key)" + ] + }, + { + "cell_type": "markdown", + "id": "f8f1b839", + "metadata": {}, + "source": [ + "Again, let's make sure that everything looks correct in `DLCModel`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c39f72ca", + "metadata": {}, + "outputs": [], + "source": [ + "sgp.DLCModel() & model_key" + ] + }, + { + "cell_type": "markdown", + "id": "53ce4ee4", + "metadata": {}, + "source": [ + "#### [DLCPoseEstimation](#TableOfContents) \n", + "\n", + "Alright, now that we've trained model and populated the `DLCModel` table, we're ready to set-up Pose Estimation on a behavioral video of your choice.

    For this tutorial, you can choose to use an epoch of your choice, we can also use the one specified below. If you'd like to use your own video, just specify the `nwb_file_name` and `epoch` number and make sure it's in the `VideoFile` table!" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fc2a8dab-7caf-4389-8494-9158d2ec5b20", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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    nwb_file_name

    \n", + " name of the NWB file\n", + "
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    epoch

    \n", + " the session epoch for this task and apparatus(1 based)\n", + "
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    video_file_num

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    camera_name

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    video_file_object_id

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    J1620210604_.nwb10178f5746-30e3-4957-891e-8024e23522dc
    J1620210604_.nwb20d64ec979-326b-429f-b3fe-1bbfbf806293
    J1620210604_.nwb30cf14bcd2-c0a9-457b-8791-42f3f28dd912
    J1620210604_.nwb40183c9910-36fd-46c1-a24c-8d1c306d7248
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    J1620210604_.nwb70c6d1d037-44ec-4d91-99d1-172d371bf82a
    J1620210604_.nwb804d7e070c-6220-47de-8173-993f013fafa8
    J1620210604_.nwb90b50108ec-f587-46df-b1c8-3ca23091bde0
    J1620210604_.nwb100b9b5da20-da39-4274-9be2-55610cfd1b5b
    J1620210604_.nwb1106c827b8d-513c-4dba-ae75-0b36dcf4811f
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    Total: 20

    \n", + " " + ], + "text/plain": [ + "*nwb_file_name *epoch *video_file_nu camera_name video_file_obj\n", + "+------------+ +-------+ +------------+ +------------+ +------------+\n", + "J1620210604_.n 1 0 178f5746-30e3-\n", + "J1620210604_.n 2 0 d64ec979-326b-\n", + "J1620210604_.n 3 0 cf14bcd2-c0a9-\n", + "J1620210604_.n 4 0 183c9910-36fd-\n", + "J1620210604_.n 5 0 4677c7cd-8cd8-\n", + "J1620210604_.n 6 0 0e46532b-483f-\n", + "J1620210604_.n 7 0 c6d1d037-44ec-\n", + "J1620210604_.n 8 0 4d7e070c-6220-\n", + "J1620210604_.n 9 0 b50108ec-f587-\n", + "J1620210604_.n 10 0 b9b5da20-da39-\n", + "J1620210604_.n 11 0 6c827b8d-513c-\n", + "J1620210604_.n 12 0 41bd2344-1b41-\n", + " ...\n", + " (Total: 20)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwb_file_name = \"J1620210604_.nwb\"\n", + "sgc.VideoFile() & {\"nwb_file_name\": nwb_file_name}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4140ece8", + "metadata": {}, + "outputs": [], + "source": [ + "epoch = 14 #change based on VideoFile entry\n", + "video_file_num = 0 #change based on VideoFile entry" + ] + }, + { + "cell_type": "markdown", + "id": "0f26a081-859d-4dff-bb58-84cec2ff4b3f", + "metadata": {}, + "source": [ + "Using `insert_estimation_task` will convert out video to be in .mp4 format (DLC\n", + "struggles with .h264) and determine the directory in which we'll store the pose\n", + "estimation results.\n", + "\n", + "- `task_mode` (trigger or load) determines whether or not populating\n", + " `DLCPoseEstimation` triggers a new pose estimation, or loads an existing.\n", + "- `video_file_num` will be 0 in almost all\n", + " cases.\n", + "- `gputouse` was already set during training. It may be a good idea to make sure\n", + " that core is still free before moving forward." + ] + }, + { + "cell_type": "markdown", + "id": "e60eb2fc", + "metadata": {}, + "source": [ + "The `DLCPoseEstimationSelection` insertion step will convert your .h264 video to an .mp4 first and save it in `/nimbus/deeplabcut/video`. If this video already exists here, the insertion will never complete.\n", + "\n", + "We first delete any .mp4 that exists for this video from the nimbus folder:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "130d85d0", + "metadata": {}, + "outputs": [], + "source": [ + "! find /nimbus/deeplabcut/video -type f -name '*20210604_J16*' -delete # change based on date and rat with which you are training the model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9df5644f-febc-49d7-a60d-6991798c20d7", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'model_key' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 6\u001b[0m\n\u001b[1;32m 1\u001b[0m pose_estimation_key \u001b[38;5;241m=\u001b[39m sgp\u001b[38;5;241m.\u001b[39mDLCPoseEstimationSelection\u001b[38;5;241m.\u001b[39minsert_estimation_task(\n\u001b[1;32m 2\u001b[0m {\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnwb_file_name\u001b[39m\u001b[38;5;124m\"\u001b[39m: nwb_file_name,\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mepoch\u001b[39m\u001b[38;5;124m\"\u001b[39m: epoch,\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvideo_file_num\u001b[39m\u001b[38;5;124m\"\u001b[39m: video_file_num,\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[43mmodel_key\u001b[49m,\n\u001b[1;32m 7\u001b[0m },\n\u001b[1;32m 8\u001b[0m task_mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrigger\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;66;03m#trigger or load\u001b[39;00m\n\u001b[1;32m 9\u001b[0m params\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgputouse\u001b[39m\u001b[38;5;124m\"\u001b[39m: gputouse, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvideotype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmp4\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m 10\u001b[0m )\n", + "\u001b[0;31mNameError\u001b[0m: name 'model_key' is not defined" + ] + } + ], + "source": [ + "pose_estimation_key = sgp.DLCPoseEstimationSelection.insert_estimation_task(\n", + " {\n", + " \"nwb_file_name\": nwb_file_name,\n", + " \"epoch\": epoch,\n", + " \"video_file_num\": video_file_num,\n", + " **model_key,\n", + " },\n", + " task_mode=\"trigger\", #trigger or load\n", + " params={\"gputouse\": gputouse, \"videotype\": \"mp4\"},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d19390eb", + "metadata": {}, + "source": [ + "If the above insertion step fails in either trigger or load mode for an epoch, run the following lines:\n", + "```\n", + "(pose_estimation_key = sgp.DLCPoseEstimationSelection.insert_estimation_task(\n", + " {\n", + " \"nwb_file_name\": nwb_file_name,\n", + " \"epoch\": epoch,\n", + " \"video_file_num\": video_file_num,\n", + " **model_key,\n", + " }).delete()\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "5feb2a26-fae1-41ca-828f-cc6c73ebd24e", + "metadata": {}, + "source": [ + "And now we populate `DLCPoseEstimation`! This might take some time for full datasets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88f28ecc-d3a4-40f9-a1fb-afb4bdd04497", + "metadata": {}, + "outputs": [], + "source": [ + "sgp.DLCPoseEstimation().populate(pose_estimation_key)" + ] + }, + { + "cell_type": "markdown", + "id": "88757488-cfa4-4e7c-b965-7dacac43810a", + "metadata": {}, + "source": [ + "Let's visualize the output from Pose Estimation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45dd4f3b-7bf4-41b7-be5f-820fe3ee9f69", + "metadata": {}, + "outputs": [], + "source": [ + "(sgp.DLCPoseEstimation() & pose_estimation_key).fetch_dataframe()" + ] + }, + { + "cell_type": "markdown", + "id": "52f45ab3-9344-4975-b5ff-f80a5727cdac", + "metadata": {}, + "source": [ + "#### [DLCSmoothInterp](#TableOfContents) " + ] + }, + { + "cell_type": "markdown", + "id": "0ccd5dbe-097a-4138-a234-da78a5902684", + "metadata": {}, + "source": [ + "Now that we've completed pose estimation, it's time to identify NaNs and optionally interpolate over low likelihood periods and smooth the resulting positions.
    First we need to define some parameters for smoothing and interpolation. We can see the default parameter set below.
    __Note__: it is recommended to use the `just_nan` parameters here and save interpolation and smoothing for the centroid step as this provides for a better end result." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6e44a34-8d6d-4206-b02a-9ca38a68f1c0", + "metadata": {}, + "outputs": [], + "source": [ + "# The default parameter set to interpolate and smooth over each LED individually\n", + "print(sgp.DLCSmoothInterpParams.get_default())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3bc4f13c", + "metadata": {}, + "outputs": [], + "source": [ + "# The just_nan parameter set that identifies NaN indices and leaves smoothing and interpolation to the centroid step\n", + "print(sgp.DLCSmoothInterpParams.get_nan_params())\n", + "si_params_name = \"just_nan\" #could also use \"default\"" + ] + }, + { + "cell_type": "markdown", + "id": "a245c9e5-e8f6-4c6f-b9e1-d71ab3e06d59", + "metadata": {}, + "source": [ + "To change any of these parameters, one would do the following:\n", + "\n", + "```python\n", + "si_params_name = \"your_unique_param_name\"\n", + "params = {\n", + " \"smoothing_params\": {\n", + " \"smoothing_duration\": 0.00,\n", + " \"smooth_method\": \"moving_avg\",\n", + " },\n", + " \"interp_params\": {\"likelihood_thresh\": 0.00},\n", + " \"max_plausible_speed\": 0,\n", + " \"speed_smoothing_std_dev\": 0.000,\n", + "}\n", + "sgp.DLCSmoothInterpParams().insert1(\n", + " {\"dlc_si_params_name\": si_params_name, \"params\": params},\n", + " skip_duplicates=True,\n", + ")\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "8139036e-ce7e-41ec-be78-aa15a4b0b795", + "metadata": {}, + "source": [ + "We'll create a dictionary with the correct set of keys for the `DLCSmoothInterpSelection` table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec730b91-a974-4f54-9d55-35f52e08487f", + "metadata": {}, + "outputs": [], + "source": [ + "si_key = pose_estimation_key.copy()\n", + "fields = list(sgp.DLCSmoothInterpSelection.fetch().dtype.fields.keys())\n", + "si_key = {key: val for key, val in si_key.items() if key in fields}\n", + "si_key" + ] + }, + { + "cell_type": "markdown", + "id": "9a47a6de-51ff-4980-b105-42a75ef7f7a3", + "metadata": {}, + "source": [ + "We can insert all of the bodyparts we want to process into `DLCSmoothInterpSelection`
    \n", + "First lets visualize the bodyparts we have available to us.
    " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e5fcad0-e211-4bd7-82b1-d69bec0eb3d7", + "metadata": {}, + "outputs": [], + "source": [ + "print((sgp.DLCPoseEstimation.BodyPart & pose_estimation_key).fetch(\"bodypart\"))" + ] + }, + { + "cell_type": "markdown", + "id": "7c6e3ad2-1960-43cd-a223-784c08211013", + "metadata": {}, + "source": [ + "We can use `insert1` to insert a single bodypart, but would suggest using `insert` to insert a list of keys with different bodyparts." + ] + }, + { + "cell_type": "markdown", + "id": "1a93ba8d", + "metadata": {}, + "source": [ + "To insert a single bodypart, one would do the following:\n", + "\n", + "```python\n", + "sgp.DLCSmoothInterpSelection.insert1(\n", + " {\n", + " **si_key,\n", + " 'bodypart': 'greenLED',\n", + " 'dlc_si_params_name': si_params_name,\n", + " },\n", + " skip_duplicates=True)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "3e2f73cd-2534-40a2-86e6-948ccd902812", + "metadata": {}, + "source": [ + "We'll see a list of bodyparts and then insert them into `DLCSmoothInterpSelection`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "819e826d-38ef-4219-8d52-5353c6b4b61a", + "metadata": {}, + "outputs": [], + "source": [ + "bodyparts = [\"greenLED\", \"redLED_C\"]\n", + "sgp.DLCSmoothInterpSelection.insert(\n", + " [\n", + " {\n", + " **si_key,\n", + " \"bodypart\": bodypart,\n", + " \"dlc_si_params_name\": si_params_name,\n", + " }\n", + " for bodypart in bodyparts\n", + " ],\n", + " skip_duplicates=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6dca5640-3e9a-42b7-bc61-7f3e1a219619", + "metadata": {}, + "source": [ + "And verify the entry:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b347b29-1583-4fbc-9b35-8e062b611d59", + "metadata": {}, + "outputs": [], + "source": [ + "sgp.DLCSmoothInterpSelection() & si_key" + ] + }, + { + "cell_type": "markdown", + "id": "af8f0d26-3879-4f50-a076-e60685028083", + "metadata": {}, + "source": [ + "Now, we populate `DLCSmoothInterp`, which will perform smoothing and\n", + "interpolation on all of the bodyparts specified." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9bf16c32-0f5e-4cd2-b814-56745e836599", + "metadata": {}, + "outputs": [], + "source": [ + "sgp.DLCSmoothInterp().populate(si_key)" + ] + }, + { + "cell_type": "markdown", + "id": "3d3af0a2-16cc-43dc-af9c-0ec606cfe1e1", + "metadata": {}, + "source": [ + "And let's visualize the resulting position data using a scatter plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ced96b05-e6dc-4771-bfb8-bcbddfb8e494", + "metadata": {}, + "outputs": [], + "source": [ + "(sgp.DLCSmoothInterp() & {**si_key, \"bodypart\": bodyparts[0]}\n", + ").fetch1_dataframe().plot.scatter(x=\"x\", y=\"y\", s=1, figsize=(5, 5))" + ] + }, + { + "cell_type": "markdown", + "id": "a838e4c4-8ff9-4b73-aee5-00eb91ea899f", + "metadata": {}, + "source": [ + "#### [DLCSmoothInterpCohort](#TableOfContents) " + ] + }, + { + "cell_type": "markdown", + "id": "3cf3d882-2c24-46ca-bfcc-72f21712e47b", + "metadata": {}, + "source": [ + "After smoothing/interpolation, we need to select bodyparts from which we want to\n", + "derive a centroid and orientation, which is performed by the\n", + "`DLCSmoothInterpCohort` table." + ] + }, + { + "cell_type": "markdown", + "id": "5017fd46-2bb9-4349-981b-f9789ffec338", + "metadata": {}, + "source": [ + "First, let's make a key that represents the 'cohort', using\n", + "`dlc_si_cohort_selection_name`. We'll need a bodypart dictionary using bodypart\n", + "keys and smoothing/interpolation parameters used as value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92fb1af9-20cf-46d9-a518-a7f551334bc8", + "metadata": {}, + "outputs": [], + "source": [ + "cohort_key = si_key.copy()\n", + "if \"bodypart\" in cohort_key:\n", + " del cohort_key[\"bodypart\"]\n", + "if \"dlc_si_params_name\" in cohort_key:\n", + " del cohort_key[\"dlc_si_params_name\"]\n", + "cohort_key[\"dlc_si_cohort_selection_name\"] = \"green_red_led\"\n", + "cohort_key[\"bodyparts_params_dict\"] = {\n", + " \"greenLED\": si_params_name,\n", + " \"redLED_C\": si_params_name,\n", + "}\n", + "print(cohort_key)" + ] + }, + { + "cell_type": "markdown", + "id": "11c6a327-d4b0-4de1-a2c6-10a0443a3f96", + "metadata": {}, + "source": [ + "We'll insert the cohort into `DLCSmoothInterpCohortSelection` and populate `DLCSmoothInterpCohort`, which collates the separately smoothed and interpolated bodyparts into a single entry." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "805f55c1-3c7b-4cf9-bdd7-98743810c671", + "metadata": {}, + "outputs": [], + "source": [ + "sgp.DLCSmoothInterpCohortSelection().insert1(cohort_key, skip_duplicates=True)\n", + "sgp.DLCSmoothInterpCohort.populate(cohort_key)" + ] + }, + { + "cell_type": "markdown", + "id": "a6b7d361-47c5-4748-ac59-f51b897f7fe6", + "metadata": {}, + "source": [ + "And verify the entry:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7672b63-6dfc-46db-b8df-95c1e6730b6c", + "metadata": {}, + "outputs": [], + "source": [ + "sgp.DLCSmoothInterpCohort.BodyPart() & cohort_key" + ] + }, + { + "cell_type": "markdown", + "id": "d871bdca-2278-43ec-a70c-52257ad26170", + "metadata": {}, + "source": [ + "#### [DLCCentroid](#TableOfContents) " + ] + }, + { + "cell_type": "markdown", + "id": "4cc37edb-fdd3-4a05-8cd5-91f3c5f7cbbb", + "metadata": {}, + "source": [ + "With this cohort, we can determine a centroid using another set of parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e31c8db-0396-475a-af71-ae38433d2b7d", + "metadata": {}, + "outputs": [], + "source": [ + "# Here is the default set\n", + "print(sgp.DLCCentroidParams.get_default())\n", + "centroid_params_name = \"default\"" + ] + }, + { + "cell_type": "markdown", + "id": "852948f7-e743-4319-be6b-265dadfca713", + "metadata": {}, + "source": [ + "Here is the syntax to add your own parameters:\n", + "\n", + "```python\n", + "centroid_params = {\n", + " \"centroid_method\": \"two_pt_centroid\",\n", + " \"points\": {\n", + " \"greenLED\": \"greenLED\",\n", + " \"redLED_C\": \"redLED_C\",\n", + " },\n", + " \"speed_smoothing_std_dev\": 0.100,\n", + "}\n", + "centroid_params_name = \"your_unique_param_name\"\n", + "sgp.DLCCentroidParams.insert1(\n", + " {\n", + " \"dlc_centroid_params_name\": centroid_params_name,\n", + " \"params\": centroid_params,\n", + " },\n", + " skip_duplicates=True,\n", + ")\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "85ad4e53-43dd-4e05-84c4-7d4504766746", + "metadata": {}, + "source": [ + "We'll make a key to insert into `DLCCentroidSelection`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28ac17cb-4bb3-47b2-b1b9-1c4b37797591", + "metadata": {}, + "outputs": [], + "source": [ + "centroid_key = cohort_key.copy()\n", + "fields = list(sgp.DLCCentroidSelection.fetch().dtype.fields.keys())\n", + "centroid_key = {key: val for key, val in centroid_key.items() if key in fields}\n", + "centroid_key[\"dlc_centroid_params_name\"] = centroid_params_name\n", + "print(centroid_key)" + ] + }, + { + "cell_type": "markdown", + "id": "2674c0d3-d3fd-4cd9-a843-260c442c2d23", + "metadata": {}, + "source": [ + "After inserting into the selection table, we can populate `DLCCentroid`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47fccef4-2fef-4f74-b7a4-8564328b14d4", + "metadata": {}, + "outputs": [], + "source": [ + "sgp.DLCCentroidSelection.insert1(centroid_key, skip_duplicates=True)\n", + "sgp.DLCCentroid.populate(centroid_key)" + ] + }, + { + "cell_type": "markdown", + "id": "6e49c5ad-909f-4f1a-a156-f8f8a84fb78a", + "metadata": {}, + "source": [ + "Here we can visualize the resulting centroid position" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29e7e447-fa6f-4f06-9ec9-4b9838b7255e", + "metadata": {}, + "outputs": [], + "source": [ + "(sgp.DLCCentroid() & centroid_key).fetch1_dataframe().plot.scatter(\n", + " x=\"position_x\",\n", + " y=\"position_y\",\n", + " c=\"speed\",\n", + " colormap=\"viridis\",\n", + " alpha=0.5,\n", + " s=0.5,\n", + " figsize=(10, 10),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "cb513a9d-5250-404c-8887-639f785516c7", + "metadata": {}, + "source": [ + "#### [DLCOrientation](#TableOfContents) " + ] + }, + { + "cell_type": "markdown", + "id": "509076f0-f0b8-4fd0-8884-32c48ca4a125", + "metadata": {}, + "source": [ + "We'll now go through a similar process to identify the orientation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "faf244b3-7295-48ed-90ea-cf878e85e122", + "metadata": {}, + "outputs": [], + "source": [ + "print(sgp.DLCOrientationParams.get_default())\n", + "dlc_orientation_params_name = \"default\"" + ] + }, + { + "cell_type": "markdown", + "id": "8ec170be-7a7a-4a20-986c-d055aee1a08b", + "metadata": {}, + "source": [ + "We'll prune the `cohort_key` we used above and add our `dlc_orientation_params_name` to make it suitable for `DLCOrientationSelection`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "09e4a6cf-472e-43e3-90aa-f7ff7fb9dc72", + "metadata": {}, + "outputs": [], + "source": [ + "fields = list(sgp.DLCOrientationSelection.fetch().dtype.fields.keys())\n", + "orient_key = {key: val for key, val in cohort_key.items() if key in fields}\n", + "orient_key[\"dlc_orientation_params_name\"] = dlc_orientation_params_name\n", + "print(orient_key)" + ] + }, + { + "cell_type": "markdown", + "id": "9406d2de-9b71-4591-82f6-ed53f2d4f220", + "metadata": {}, + "source": [ + "We'll insert into `DLCOrientationSelection` and populate `DLCOrientation`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5d23302-02e3-427a-ac35-2f648e3ae674", + "metadata": {}, + "outputs": [], + "source": [ + "sgp.DLCOrientationSelection().insert1(orient_key, skip_duplicates=True)\n", + "sgp.DLCOrientation().populate(orient_key)" + ] + }, + { + "cell_type": "markdown", + "id": "36f62da0-0cc5-4ffb-b2df-7b68c3f6e268", + "metadata": {}, + "source": [ + "We can fetch the orientation as a dataframe as quality assurance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5eba7f4-0b32-486a-894a-c97404c74d2b", + "metadata": {}, + "outputs": [], + "source": [ + "(sgp.DLCOrientation() & orient_key).fetch1_dataframe()" + ] + }, + { + "cell_type": "markdown", + "id": "dc75aeaf-018a-46ed-83a8-6603ae100791", + "metadata": {}, + "source": [ + "#### [DLCPosV1](#TableOfContents) " + ] + }, + { + "cell_type": "markdown", + "id": "21d3f9ba-dc89-4c32-a125-1fa85cd4132d", + "metadata": {}, + "source": [ + "After processing the position data, we have to do a few table manipulations to standardize various outputs. \n", + "\n", + "To summarize, we brought in a pretrained DLC project, used that model to run pose estimation on a new behavioral video, smoothed and interpolated the result, formed a cohort of bodyparts, and determined the centroid and orientation of this cohort.\n", + "\n", + "Now we'll populate `DLCPos` with our centroid/orientation entries above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a166dd6-3863-4349-97ac-19d7d6a841b4", + "metadata": {}, + "outputs": [], + "source": [ + "fields = list(sgp.DLCPosV1.fetch().dtype.fields.keys())\n", + "dlc_key = {key: val for key, val in centroid_key.items() if key in fields}\n", + "dlc_key[\"dlc_si_cohort_centroid\"] = centroid_key[\"dlc_si_cohort_selection_name\"]\n", + "dlc_key[\"dlc_si_cohort_orientation\"] = orient_key[\n", + " \"dlc_si_cohort_selection_name\"\n", + "]\n", + "dlc_key[\"dlc_orientation_params_name\"] = orient_key[\n", + " \"dlc_orientation_params_name\"\n", + "]\n", + "print(dlc_key)" + ] + }, + { + "cell_type": "markdown", + "id": "551e4c5e-7c32-46b0-a138-80064a212fbe", + "metadata": {}, + "source": [ + "Now we can insert into `DLCPosSelection` and populate `DLCPos` with our `dlc_key`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d7badff-0ad7-48cf-aef6-a4f55df8ded9", + "metadata": {}, + "outputs": [], + "source": [ + "sgp.DLCPosSelection().insert1(dlc_key, skip_duplicates=True)\n", + "sgp.DLCPosV1().populate(dlc_key)" + ] + }, + { + "cell_type": "markdown", + "id": "412f1cff-2ead-4489-8a10-9fa7a5d33292", + "metadata": {}, + "source": [ + "We can also make sure that all of our data made it through by fetching the dataframe attached to this entry.
    We should expect 8 columns:\n", + ">time
    video_frame_ind
    position_x
    position_y
    orientation
    velocity_x
    velocity_y
    speed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "853db96b-1cd4-4ff6-91ea-aca7f7d3851d", + "metadata": {}, + "outputs": [], + "source": [ + "(sgp.DLCPosV1() & dlc_key).fetch1_dataframe()" + ] + }, + { + "cell_type": "markdown", + "id": "2d8623a8-1725-4e02-b1a2-d2f993988102", + "metadata": {}, + "source": [ + "And even more, we can fetch the `pose_eval_result` that is calculated during this step. This field contains the percentage of frames that each bodypart was below the likelihood threshold of 0.95 as a means of assessing the quality of the pose estimation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4f06244-9d59-44d4-bcbb-062809b3ea6e", + "metadata": {}, + "outputs": [], + "source": [ + "(sgp.DLCPosV1() & dlc_key).fetch1(\"pose_eval_result\")" + ] + }, + { + "cell_type": "markdown", + "id": "b2303147-3657-479c-8f72-b3fc6905a596", + "metadata": {}, + "source": [ + "#### [DLCPosVideo](#TableOfContents) " + ] + }, + { + "cell_type": "markdown", + "id": "af0b081d-f619-4c38-ba48-6ae1c0c5ff2b", + "metadata": {}, + "source": [ + "We can create a video with the centroid and orientation overlaid on the original\n", + "video. This will also plot the likelihood of each bodypart used in the cohort.\n", + "This is optional, but a good quality assurance step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a725c08-a616-43a0-8925-4a82bf872ba3", + "metadata": {}, + "outputs": [], + "source": [ + "sgp.DLCPosVideoParams.insert_default()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84e2f782-ba45-487a-8e8f-e80dd33d9c31", + "metadata": {}, + "outputs": [], + "source": [ + "params = {\n", + " \"percent_frames\": 0.05,\n", + " \"incl_likelihood\": True,\n", + "}\n", + "sgp.DLCPosVideoParams.insert1(\n", + " {\"dlc_pos_video_params_name\": \"five_percent\", \"params\": params},\n", + " skip_duplicates=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5758e2fc-13e6-46cb-9a93-ae1b4c1f4741", + "metadata": {}, + "outputs": [], + "source": [ + "sgp.DLCPosVideoSelection.insert1(\n", + " {**dlc_key, \"dlc_pos_video_params_name\": \"five_percent\"},\n", + " skip_duplicates=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2887c0a5-77c8-421e-935e-0692f3f1fd68", + "metadata": {}, + "outputs": [], + "source": [ + "sgp.DLCPosVideo().populate(dlc_key)" + ] + }, + { + "cell_type": "markdown", + "id": "5a68bba8-9871-40ac-84c9-51ac0e76d44e", + "metadata": {}, + "source": [ + "#### [PositionOutput](#TableOfContents) " + ] + }, + { + "cell_type": "markdown", + "id": "25325173-bbaf-4b85-aef6-201384d9933b", + "metadata": {}, + "source": [ + "`PositionOutput` is the final table of the pipeline and is automatically\n", + "populated when we populate `DLCPosV1`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59ec40c9-78d8-4edd-8158-be91fb15af3e", + "metadata": {}, + "outputs": [], + "source": [ + "sgp.PositionOutput.merge_get_part(dlc_key)" + ] + }, + { + "cell_type": "markdown", + "id": "c414d9e0-e495-42ef-a8b0-1c7d53aed02e", + "metadata": {}, + "source": [ + "`PositionOutput` also has a part table, similar to the `DLCModelSource` table above. Let's check that out as well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50760123-7f09-4a94-a1f7-41a037914fd7", + "metadata": {}, + "outputs": [], + "source": [ + "PositionOutput.DLCPosV1() & dlc_key" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c96daaa9-5e70-4a2c-b0a4-c2849e3a1440", + "metadata": {}, + "outputs": [], + "source": [ + "(PositionOutput.DLCPosV1() & dlc_key).fetch1_dataframe()" + ] + }, + { + "cell_type": "markdown", + "id": "e48c7a4e-0bbc-4101-baf2-e84f1f5739d5", + "metadata": {}, + "source": [ + "#### [PositionVideo](#TableOfContents)" + ] + }, + { + "cell_type": "markdown", + "id": "388e6602-8e80-47fa-be78-4ae120d52e41", + "metadata": {}, + "source": [ + "We can use the `PositionVideo` table to create a video that overlays just the\n", + "centroid and orientation on the video. This table uses the parameter `plot` to\n", + "determine whether to plot the entry deriving from the DLC arm or from the Trodes\n", + "arm of the position pipeline. This parameter also accepts 'all', which will plot\n", + "both (if they exist) in order to compare results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2a782ce-0a14-4725-887f-ae6f341635f8", + "metadata": {}, + "outputs": [], + "source": [ + "sgp.PositionVideoSelection().insert1(\n", + " {\n", + " \"nwb_file_name\": \"J1620210604_.nwb\",\n", + " \"interval_list_name\": \"pos 13 valid times\",\n", + " \"trodes_position_id\": 0,\n", + " \"dlc_position_id\": 1,\n", + " \"plot\": \"DLC\",\n", + " \"output_dir\": \"/home/dgramling/Src/\",\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c32993e7-5b32-46f9-a2f9-9634aef785f2", + "metadata": {}, + "outputs": [], + "source": [ + "sgp.PositionVideo.populate({\"plot\": \"DLC\"})" + ] + }, + { + "cell_type": "markdown", + "id": "be097052-3789-4d55-aca1-e44d426c39b4", + "metadata": {}, + "source": [ + "### _CONGRATULATIONS!!_\n", + "Please treat yourself to a nice tea break :-)" + ] + }, + { + "cell_type": "markdown", + "id": "c71c90a2", + "metadata": {}, + "source": [ + "### [Return To Table of Contents](#TableOfContents)
    " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/21_Position_DLC_1.ipynb b/notebooks/22_DLC_Loop.ipynb similarity index 54% rename from notebooks/21_Position_DLC_1.ipynb rename to notebooks/22_DLC_Loop.ipynb index dee0d2594..4d9d33e77 100644 --- a/notebooks/21_Position_DLC_1.ipynb +++ b/notebooks/22_DLC_Loop.ipynb @@ -5,20 +5,20 @@ "id": "a93a1550-8a67-4346-a4bf-e5a136f3d903", "metadata": {}, "source": [ - "# Position - DeepLabCut from Scratch\n" + "## Position- DeepLabCut from Scratch" ] }, { "cell_type": "markdown", - "id": "cbf56794", + "id": "13dd3267", "metadata": {}, "source": [ - "### Overview\n" + "### Overview" ] }, { "cell_type": "markdown", - "id": "de29d04e", + "id": "b52aff0d", "metadata": {}, "source": [ "_Developer Note:_ if you may make a PR in the future, be sure to copy this\n", @@ -37,11 +37,11 @@ "- creating a DLC project\n", "- extracting and labeling frames\n", "- training your model\n", + "- executing pose estimation on a novel behavioral video\n", + "- processing the pose estimation output to extract a centroid and orientation\n", + "- inserting the resulting information into the `PositionOutput` table\n", "\n", - "If you have a pre-trained project, you can either skip to the\n", - "[next tutorial](./22_Position_DLC_2.ipynb) to load it into the database, or skip\n", - "to the [following tutorial](./23_Position_DLC_3.ipynb) to start pose estimation\n", - "with a model that is already inserted.\n" + "**Note 2: Make sure you are running this within the spyglass-position Conda environment (instructions for install are in the environment_position.yml)**" ] }, { @@ -59,36 +59,62 @@ "id": "0c67d88c-c90e-467b-ae2e-672c49a12f95", "metadata": {}, "source": [ - "### Table of Contents\n" + "### Table of Contents\n", + "[`DLCProject`](#DLCProject1)
    \n", + "[`DLCModelTraining`](#DLCModelTraining1)
    \n", + "[`DLCModel`](#DLCModel1)
    \n", + "[`DLCPoseEstimation`](#DLCPoseEstimation1)
    \n", + "[`DLCSmoothInterp`](#DLCSmoothInterp1)
    \n", + "[`DLCCentroid`](#DLCCentroid1)
    \n", + "[`DLCOrientation`](#DLCOrientation1)
    \n", + "[`DLCPosV1`](#DLCPosV1-1)
    \n", + "[`DLCPosVideo`](#DLCPosVideo1)
    \n", + "[`PositionOutput`](#PositionOutput1)
    " ] }, { "cell_type": "markdown", - "id": "3ece5c05", + "id": "70a0a678", "metadata": {}, "source": [ - "- [Imports](#imports)\n", - "- [`DLCProject`](#DLCProject1)\n", - "- [`DLCModelTraining`](#DLCModelTraining1)\n", - "- [`DLCModel`](#DLCModel1)\n", - "\n", - "**You can click on any header to return to the Table of Contents**\n" + "__You can click on any header to return to the Table of Contents__" ] }, { "cell_type": "markdown", - "id": "c52f2a05", + "id": "c9b98c3d", "metadata": {}, "source": [ - "### Imports\n" + "### Imports" ] }, { "cell_type": "code", - "execution_count": 2, - "id": "5ddbc468", + "execution_count": 1, + "id": "b36026fa", "metadata": {}, "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "0f567531", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2024-01-18 10:12:13,219][INFO]: Connecting ebroyles@lmf-db.cin.ucsf.edu:3306\n", + "[2024-01-18 10:12:13,255][INFO]: Connected ebroyles@lmf-db.cin.ucsf.edu:3306\n", + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + } + ], "source": [ "import os\n", "import datajoint as dj\n", @@ -97,6 +123,13 @@ "import spyglass.common as sgc\n", "import spyglass.position.v1 as sgp\n", "\n", + "from pathlib import Path, PosixPath, PurePath\n", + "import glob\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pynwb\n", + "from spyglass.position import PositionOutput\n", + "\n", "# change to the upper level folder to detect dj_local_conf.json\n", "if os.path.basename(os.getcwd()) == \"notebooks\":\n", " os.chdir(\"..\")\n", @@ -114,7 +147,7 @@ "id": "5e6221a3-17e5-45c0-aa40-2fd664b02219", "metadata": {}, "source": [ - "#### [DLCProject](#TableOfContents) \n" + "#### [DLCProject](#TableOfContents) " ] }, { @@ -126,7 +159,7 @@ " Notes:
      \n", "
    • \n", " The cells within this DLCProject step need to be performed \n", - " in a local Jupyter notebook to allow for use of the frame labeling GUI\n", + " in a local Jupyter notebook to allow for use of the frame labeling GUI.\n", "
      • \n", "
    • \n", " Please do not add to the BodyPart table in the production \n", @@ -138,10 +171,10 @@ }, { "cell_type": "markdown", - "id": "1307d3d7", + "id": "50c9f1c9", "metadata": {}, "source": [ - "### Body Parts\n" + "### Body Parts" ] }, { @@ -149,12 +182,22 @@ "id": "96637cb9-519d-41e1-8bfd-69f68dc66b36", "metadata": {}, "source": [ - "We'll begin by looking at the `BodyPart` table, which stores standard names of body parts used in DLC models throughout the lab with a concise description.\n" + "We'll begin by looking at the `BodyPart` table, which stores standard names of body parts used in DLC models throughout the lab with a concise description." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b69f829f-9877-48ae-89d1-f876af2b8835", + "metadata": {}, + "outputs": [], + "source": [ + "sgp.BodyPart()" ] }, { "cell_type": "markdown", - "id": "ca5a15e2-f087-4bd2-9d4a-ea2ac4becd80", + "id": "9616512e", "metadata": {}, "source": [ "If the bodyparts you plan to use in your model are not yet in the table, here is code to add bodyparts:\n", @@ -167,173 +210,56 @@ " ],\n", " skip_duplicates=True,\n", ")\n", - "```\n" + "```" ] }, { "cell_type": "markdown", - "id": "78fe7c06-30c9-43e1-9e9a-029a70b0d4dd", + "id": "57b590d3", "metadata": {}, "source": [ - "To train a model, we'll need to extract frames, which we can label as training data. We can construct a list of videos from which we'll extract frames.\n", - "\n", - "The list can either contain dictionaries identifying behavioral videos for NWB files that have already been added to Spyglass, or absolute file paths to the videos you want to use.\n", - "\n", - "For this tutorial, we'll use two videos for which we already have frames labeled.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "b69f829f-9877-48ae-89d1-f876af2b8835", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
      \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
      \n", - "

      bodypart

      \n", - " \n", - "
      \n", - "

      bodypart_description

      \n", - " \n", - "
      driveBackback of drive
      driveFrontfront of drive
      forelimbLleft forelimb of the rat
      forelimbRright forelimb of the rat
      greenLEDgreenLED
      hindlimbLleft hindlimb of the rat
      hindlimbRright hindlimb of the rat
      nosetip of the nose of the rat
      redLED_CredLED_C
      redLED_LredLED_L
      redLED_RredLED_R
      tailBasetailBase
      \n", - "

      ...

      \n", - "

      Total: 15

      \n", - " " - ], - "text/plain": [ - "*bodypart bodypart_descr\n", - "+------------+ +------------+\n", - "driveBack back of drive \n", - "driveFront front of drive\n", - "forelimbL left forelimb \n", - "forelimbR right forelimb\n", - "greenLED greenLED \n", - "hindlimbL left hindlimb \n", - "hindlimbR right hindlimb\n", - "nose tip of the nos\n", - "redLED_C redLED_C \n", - "redLED_L redLED_L \n", - "redLED_R redLED_R \n", - "tailBase tailBase \n", - " ...\n", - " (Total: 15)" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sgp.BodyPart()" + "### Define videos and camera name (optional) for training set" ] }, { "cell_type": "markdown", - "id": "a0af0110", + "id": "5d5aae37", "metadata": {}, "source": [ - "### Define camera name and videos for training set\n", + "To train a model, we'll need to extract frames, which we can label as training data. We can construct a list of videos from which we'll extract frames.\n", "\n", - "Defining camera name is optional: it should be done in cases where there are multiple cameras streaming per epoch, but not necessary otherwise.\n" + "The list can either contain dictionaries identifying behavioral videos for NWB files that have already been added to Spyglass, or absolute file paths to the videos you want to use.\n", + "\n", + "For this tutorial, we'll use two videos for which we already have frames labeled." ] }, { "cell_type": "markdown", - "id": "667bcb28", + "id": "7b5e157b", "metadata": {}, "source": [ + "Defining camera name is optional: it should be done in cases where there are multiple cameras streaming per epoch, but not necessary otherwise.
      \n", "example:\n", "`camera_name = \"HomeBox_camera\" \n", - " `\n" + " `" ] }, { "cell_type": "markdown", + "id": "56f45e7f", "metadata": {}, "source": [ "_NOTE:_ The official release of Spyglass does not yet support multicamera\n", "projects. You can monitor progress on the effort to add this feature by checking\n", "[this PR](https://github.com/LorenFrankLab/spyglass/pull/684) or use\n", "[this experimental branch](https://github.com/dpeg22/spyglass/tree/add-multi-camera),\n", - "which only takes the keys nwb_file_name and epoch in the video_list variable.\n" + "which takes the keys nwb_file_name and epoch, and camera_name in the video_list variable.\n" ] }, { "cell_type": "code", - "execution_count": 38, - "id": "e3aa1c2f", + "execution_count": null, + "id": "15971506", "metadata": {}, "outputs": [], "source": [ @@ -345,7 +271,7 @@ }, { "cell_type": "markdown", - "id": "aadce1b3", + "id": "a9f8e43d", "metadata": {}, "source": [ "### Path variables\n", @@ -380,12 +306,13 @@ "_NOTE:_ If only `base` is specified as shown above, spyglass will assume the\n", "relative directories shown.\n", "\n", - "You can check the result of this setup process with...\n" + "You can check the result of this setup process with..." ] }, { "cell_type": "code", "execution_count": null, + "id": "49d7d9fc", "metadata": {}, "outputs": [], "source": [ @@ -394,13 +321,6 @@ "config" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_NOTE:_ The official release of Spyglass does not yet support master branch only takes the keys nwb_file_name and epoch in the video_list variable. EB is circumventing this by running this on daniel's (dpeg22) branch \"add-multi-camera\"\n" - ] - }, { "cell_type": "markdown", "id": "32c023b0-d00d-40b0-9a37-d0d3e4a4ae2a", @@ -414,26 +334,15 @@ " **\"tutorial_scratch_yourinitials\"**\n", "- `bodyparts` is a list of body parts for which we want to extract position.\n", " The pre-labeled frames we're using include the bodyparts listed below.\n", - "- Number of frames to extract/label as `frames_per_video`. A true project might\n", - " use 200, but we'll use 100 for efficiency.\n" + "- Number of frames to extract/label as `frames_per_video`. Note that the DLC creators recommend having 200 frames as the minimum total number for each project." ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "347e98f1", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "project name: 20230607_SC38_home is already in use.\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "team_name = \"LorenLab\"\n", "project_name = \"tutorial_scratch_DG\"\n", @@ -454,50 +363,66 @@ "id": "f5d83452-48eb-4669-89eb-a6beb1f2d051", "metadata": {}, "source": [ - "After initializing our project, we would typically extract and label frames. Use the following commands to pull up the DLC GUI:\n" + "Now that we've intialized our project we'll need to extract frames which we will then label. " ] }, { "cell_type": "code", "execution_count": null, - "id": "cb38f911", - "metadata": { - "scrolled": false - }, + "id": "7d8b1595", + "metadata": {}, "outputs": [], "source": [ - "sgp.DLCProject().run_extract_frames(project_key)\n", - "sgp.DLCProject().run_label_frames(project_key)" + "#comment this line out after you finish frame extraction for each project\n", + "sgp.DLCProject().run_extract_frames(project_key)" ] }, { "cell_type": "markdown", - "id": "df257015", + "id": "68110734", "metadata": {}, "source": [ - "In order to use pre-labeled frames, you'll need to change the values in the\n", - "labeled-data files. You can do that using the `import_labeled_frames` method,\n", - "which expects:\n", - "\n", - "- `project_key` from your new project.\n", - "- The absolute path to the project directory from which we'll import labeled\n", - " frames.\n", - "- The filenames, without extension, of the videos from which we want frames.\n" + "This is the line used to label the frames you extracted, if you wish to use the DLC GUI on the computer you are currently using.\n", + "```#comment this line out after frames are labeled for your project\n", + "sgp.DLCProject().run_label_frames(project_key)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "8b241030", + "metadata": {}, + "source": [ + "Otherwise, it is best/easiest practice to label the frames on your local computer (like a MacBook) that can run DeepLabCut's GUI well. Instructions:
      \n", + "1. Install DLC on your local (preferably into a 'Src' folder): https://deeplabcut.github.io/DeepLabCut/docs/installation.html\n", + "2. Upload frames extracted and saved in nimbus (should be `/nimbus/deeplabcut//labeled-data`) AND the project's associated config file (should be `/nimbus/deeplabcut//config.yaml`) to Box (we get free with UCSF)\n", + "3. Download labeled-data and config files on your local from Box\n", + "4. Create a 'projects' folder where you installed DeepLabCut; create a new folder with your complete project name there; save the downloaded files there.\n", + "4. Edit the config.yaml file: line 9 defining `project_path` needs to be the file path where it is saved on your local (ex: `/Users/lorenlab/Src/DeepLabCut/projects/tutorial_sratch_DG-LorenLab-2023-08-16`)\n", + "5. Open the DLC GUI through terminal \n", + "
      (ex: `conda activate miniconda/envs/DEEPLABCUT_M1`\n", + "\t\t
      `pythonw -m deeplabcut`)\n", + "6. Load an existing project; choose the config.yaml file\n", + "7. Label frames; labeling tutorial: https://www.youtube.com/watch?v=hsA9IB5r73E.\n", + "8. Once all frames are labeled, you should re-upload labeled-data folder back to Box and overwrite it in the original nimbus location so that your completed frames are ready to be used in the model." + ] + }, + { + "cell_type": "markdown", + "id": "c12dd229-2f8b-455a-a7b1-a20916cefed9", + "metadata": {}, + "source": [ + "Now we can check the `DLCProject.File` part table and see all of our training files and videos there!" ] }, { "cell_type": "code", "execution_count": null, - "id": "520a9526-fcd1-417b-b368-00d17e0284e2", + "id": "3d4f3fa6-cce9-4d4a-a252-3424313c6a97", "metadata": {}, "outputs": [], "source": [ - "sgp.DLCProject.import_labeled_frames(\n", - " project_key.copy(),\n", - " import_project_path=\"/nimbus/deeplabcut/projects/tutorial_model-LorenLab-2022-07-15/\",\n", - " video_filenames=[\"20201103_peanut_04_r2\", \"20210529_J16_02_r1\"],\n", - " skip_duplicates=True,\n", - ")" + "sgp.DLCProject.File & project_key" ] }, { @@ -506,8 +431,8 @@ "metadata": {}, "source": [ "
      \n", - " This step and beyond should be run on a GPU-enabled machine.\n", - "
      \n" + " This step and beyond should be run on a GPU-enabled machine.\n", + "" ] }, { @@ -553,7 +478,7 @@ "metadata": {}, "outputs": [], "source": [ - "gputouse = 1 ## 1-9" + "gputouse = 1 # 1-9" ] }, { @@ -596,8 +521,7 @@ "id": "6b6cc709", "metadata": {}, "source": [ - "Next we'll modify the `project_key` to include the entries for\n", - "`DLCModelTraining`\n" + "Next we'll modify the `project_key` from above to include the necessary entries for `DLCModelTraining`" ] }, { @@ -619,16 +543,16 @@ "source": [ "We can insert an entry into `DLCModelTrainingSelection` and populate `DLCModelTraining`.\n", "\n", - "_Note:_ You can stop training at any point using `I + I` or interrupt the Kernel\n" + "_Note:_ You can stop training at any point using `I + I` or interrupt the Kernel. \n", + "\n", + "The maximum total number of training iterations is 1030000; you can end training before this amount if the loss rate (lr) and total loss plateau and are very close to 0.\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "d56d3c39-7b85-4f6a-b9fb-816a1d1912da", - "metadata": { - "tags": [] - }, + "id": "3c252541", + "metadata": {}, "outputs": [], "source": [ "sgp.DLCModelTrainingSelection.heading" @@ -639,9 +563,6 @@ "execution_count": null, "id": "139d2f30", "metadata": { - "jupyter": { - "outputs_hidden": true - }, "tags": [] }, "outputs": [], @@ -669,7 +590,7 @@ "id": "da004b3e", "metadata": {}, "source": [ - "Here we'll make sure that the entry made it into the table properly!\n" + "Here we'll make sure that the entry made it into the table properly!" ] }, { @@ -691,7 +612,7 @@ "source": [ "Populating `DLCModelTraining` automatically inserts the entry into\n", "`DLCModelSource`, which is used to select between models trained using Spyglass\n", - "vs. other tools.\n" + "vs. other tools." ] }, { @@ -709,7 +630,7 @@ "id": "92cb8969", "metadata": {}, "source": [ - "The `source` field will only accept _\"FromImport\"_ or _\"FromUpstream\"_ as entries. Let's checkout the `FromUpstream` part table attached to `DLCModelSource` below.\n" + "The `source` field will only accept _\"FromImport\"_ or _\"FromUpstream\"_ as entries. Let's checkout the `FromUpstream` part table attached to `DLCModelSource` below." ] }, { @@ -733,7 +654,7 @@ "information for all trained models.\n", "\n", "First, we'll need to determine a set of parameters for our model to select the\n", - "correct model file. Here is the default:\n" + "correct model file. Here is the default:" ] }, { @@ -743,7 +664,7 @@ "metadata": {}, "outputs": [], "source": [ - "pprint(sgp.DLCModelParams.get_default())" + "sgp.DLCModelParams.get_default()" ] }, { @@ -774,7 +695,7 @@ "metadata": {}, "source": [ "We can insert sets of parameters into `DLCModelSelection` and populate\n", - "`DLCModel`.\n" + "`DLCModel`." ] }, { @@ -784,10 +705,30 @@ "metadata": {}, "outputs": [], "source": [ - "temp_model_key = (sgp.DLCModelSource & model_training_key).fetch1(\"KEY\")\n", - "sgp.DLCModelSelection().insert1(\n", - " {**temp_model_key, \"dlc_model_params_name\": \"default\"}, skip_duplicates=True\n", - ")\n", + "temp_model_key = (sgp.DLCModelSource & model_training_key).fetch1(\"KEY\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e418eba", + "metadata": {}, + "outputs": [], + "source": [ + "#comment these lines out after successfully inserting, for each project\n", + "sgp.DLCModelSelection().insert1({\n", + " **temp_model_key,\n", + " \"dlc_model_params_name\": \"default\"},\n", + " skip_duplicates=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ccae03bb", + "metadata": {}, + "outputs": [], + "source": [ "model_key = (sgp.DLCModelSelection & temp_model_key).fetch1(\"KEY\")\n", "sgp.DLCModel.populate(model_key)" ] @@ -797,7 +738,7 @@ "id": "f8f1b839", "metadata": {}, "source": [ - "Again, let's make sure that everything looks correct in `DLCModel`.\n" + "Again, let's make sure that everything looks correct in `DLCModel`." ] }, { @@ -812,15 +753,191 @@ }, { "cell_type": "markdown", - "id": "be097052-3789-4d55-aca1-e44d426c39b4", + "id": "02202650", + "metadata": {}, + "source": [ + "## Loop Begins" + ] + }, + { + "cell_type": "markdown", + "id": "dd886971", "metadata": {}, "source": [ - "### Next Steps\n", + "We can view all `VideoFile` entries with the specidied `camera_ name` for this project to ensure the rat whose position you wish to model is in this table `matching_rows`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "844174d2", + "metadata": {}, + "outputs": [], + "source": [ + "camera_name = \"SleepBox_camera\"\n", + "matching_rows = sgc.VideoFile() & {\"camera_name\": camera_name}\n", + "matching_rows" + ] + }, + { + "cell_type": "markdown", + "id": "d0315698", + "metadata": {}, + "source": [ + "The `DLCPoseEstimationSelection` insertion step will convert your .h264 video to an .mp4 first and save it in `/nimbus/deeplabcut/video`. If this video already exists here, the insertion will never complete.\n", "\n", - "With our trained model in place, we're ready to move on to pose estimation\n", - "(notebook coming soon!).\n", + "We first delete any .mp4 that exists for this video from the nimbus folder:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8884111c", + "metadata": {}, + "outputs": [], + "source": [ + "! find /nimbus/deeplabcut/video -type f -name '*20230606_SC38*' -delete # change based on date and rat with which you are training the model" + ] + }, + { + "cell_type": "markdown", + "id": "510cf05b", + "metadata": {}, + "source": [ + "If the first insertion step (for pose estimation task) fails in either trigger or load mode for an epoch, run the following lines:\n", + "```\n", + "(pose_estimation_key = sgp.DLCPoseEstimationSelection.insert_estimation_task(\n", + " {\n", + " \"nwb_file_name\": nwb_file_name,\n", + " \"epoch\": epoch,\n", + " \"video_file_num\": video_file_num,\n", + " **model_key,\n", + " }).delete()\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "7eb99b6f", + "metadata": {}, + "source": [ + "This loop will generate posiiton data for all epochs associated with the pre-defined camera in one day, for one rat (based on the NWB file; see ***)\n", + "
      The output should print Pose Estimation and Centroid plots for each epoch.\n", "\n", - "\n" + "- It defines `col1val` as each `nwb_file_name` entry in the table, one at a time.\n", + "- Next, it sees if the trial on which you are testing this model is in the string for the current `col1val`; if not, it re-defines `col1val` as the next `nwb_file_name` entry and re-tries this step. \n", + "- If the previous step works, it then saves `col2val` and `col3val` as the `epoch` and the `video_file_num`, respectively, based on the nwb_file_name. From there, it iterates through the insertion and population steps required to extract position data, which we see laid out in notebook 05_DLC.ipynb." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f41a51d1", + "metadata": {}, + "outputs": [], + "source": [ + "for row in matching_rows:\n", + " col1val = row[\"nwb_file_name\"]\n", + " if \"SC3820230606\" in col1val: #*** change depending on rat/day!!!\n", + " col2val = row[\"epoch\"]\n", + " col3val = row[\"video_file_num\"]\n", + "\n", + " ##insert pose estimation task\n", + " pose_estimation_key = sgp.DLCPoseEstimationSelection.insert_estimation_task(\n", + " {\"nwb_file_name\": col1val,\n", + " \"epoch\": col2val,\n", + " \"video_file_num\": col3val,\n", + " **model_key\n", + " },\n", + " task_mode = \"trigger\", #load or trigger\n", + " params = {\"gputouse\": gputouse, \"videotype\": \"mp4\"}\n", + " )\n", + "\n", + " ##populate DLC Pose Estimation\n", + " sgp.DLCPoseEstimation().populate(pose_estimation_key)\n", + "\n", + " ##start smooth interpolation\n", + " si_params_name = \"just_nan\"\n", + " si_key = pose_estimation_key.copy()\n", + " fields = list(sgp.DLCSmoothInterpSelection.fetch().dtype.fields.keys())\n", + " si_key = {key: val for key, val in si_key.items() if key in fields}\n", + " bodyparts = [\"greenLED\", \"redLED_C\"]\n", + " sgp.DLCSmoothInterpSelection.insert(\n", + " [\n", + " {\n", + " **si_key,\n", + " \"bodypart\": bodypart,\n", + " \"dlc_si_params_name\": si_params_name,\n", + " }\n", + " for bodypart in bodyparts\n", + " ],\n", + " skip_duplicates = True,\n", + " )\n", + " sgp.DLCSmoothInterp().populate(si_key)\n", + " (sgp.DLCSmoothInterp() & {**si_key, \"bodypart\": bodyparts[0]}\n", + " ).fetch1_dataframe().plot.scatter(x=\"x\", y=\"y\", s=1, figsize=(5, 5))\n", + "\n", + " ##smoothinterpcohort\n", + " cohort_key = si_key.copy()\n", + " if \"bodypart\" in cohort_key:\n", + " del cohort_key[\"bodypart\"]\n", + " if \"dlc_si_params_name\" in cohort_key:\n", + " del cohort_key[\"dlc_si_params_name\"]\n", + " cohort_key[\"dlc_si_cohort_selection_name\"] = \"green_red_led\"\n", + " cohort_key[\"bodyparts_params_dict\"] = {\"greenLED\": si_params_name, \"redLED_C\": si_params_name,}\n", + " sgp.DLCSmoothInterpCohortSelection().insert1(cohort_key, skip_duplicates=True)\n", + " sgp.DLCSmoothInterpCohort.populate(cohort_key)\n", + "\n", + " ##DLC Centroid\n", + " centroid_params_name = \"default\"\n", + " centroid_key = cohort_key.copy()\n", + " fields = list(sgp.DLCCentroidSelection.fetch().dtype.fields.keys())\n", + " centroid_key = {key: val for key, val in centroid_key.items() if key in fields}\n", + " centroid_key[\"dlc_centroid_params_name\"] = centroid_params_name\n", + " sgp.DLCCentroidSelection.insert1(centroid_key, skip_duplicates=True)\n", + " sgp.DLCCentroid.populate(centroid_key)\n", + " (sgp.DLCCentroid() & centroid_key).fetch1_dataframe().plot.scatter(\n", + " x=\"position_x\",\n", + " y=\"position_y\",\n", + " c=\"speed\",\n", + " colormap=\"viridis\",\n", + " alpha=0.5,\n", + " s=0.5,\n", + " figsize=(10, 10),\n", + " )\n", + "\n", + " ##DLC Orientation\n", + " dlc_orientation_params_name = \"default\"\n", + " fields = list(sgp.DLCOrientationSelection.fetch().dtype.fields.keys())\n", + " orient_key = {key: val for key, val in cohort_key.items() if key in fields}\n", + " orient_key[\"dlc_orientation_params_name\"] = dlc_orientation_params_name\n", + " sgp.DLCOrientationSelection().insert1(orient_key, skip_duplicates=True)\n", + " sgp.DLCOrientation().populate(orient_key)\n", + "\n", + " ##DLCPosV1\n", + " fields = list(sgp.DLCPosV1.fetch().dtype.fields.keys())\n", + " dlc_key = {key: val for key, val in centroid_key.items() if key in fields}\n", + " dlc_key[\"dlc_si_cohort_centroid\"] = centroid_key[\"dlc_si_cohort_selection_name\"]\n", + " dlc_key[\"dlc_si_cohort_orientation\"] = orient_key[\n", + " \"dlc_si_cohort_selection_name\"\n", + " ]\n", + " dlc_key[\"dlc_orientation_params_name\"] = orient_key[\n", + " \"dlc_orientation_params_name\"\n", + " ]\n", + " sgp.DLCPosSelection().insert1(dlc_key, skip_duplicates=True)\n", + " sgp.DLCPosV1().populate(dlc_key)\n", + "\n", + " else:\n", + " continue" + ] + }, + { + "cell_type": "markdown", + "id": "be097052-3789-4d55-aca1-e44d426c39b4", + "metadata": {}, + "source": [ + "### _CONGRATULATIONS!!_\n", + "Please treat yourself to a nice tea break :-)" ] }, { @@ -828,7 +945,7 @@ "id": "c71c90a2", "metadata": {}, "source": [ - "### [Return To Table of Contents](#TableOfContents)
      \n" + "### [Return To Table of Contents](#TableOfContents)
      " ] } ], diff --git a/notebooks/22_Position_DLC_2.ipynb b/notebooks/22_Position_DLC_2.ipynb deleted file mode 100644 index cfc86a985..000000000 --- a/notebooks/22_Position_DLC_2.ipynb +++ /dev/null @@ -1,429 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "de73cd97", - "metadata": {}, - "source": [ - "# Position - DeepLabCut PreTrained\n" - ] - }, - { - "cell_type": "markdown", - "id": "3c2ac37a", - "metadata": {}, - "source": [ - "## Overview\n" - ] - }, - { - "cell_type": "markdown", - "id": "6bc203b0", - "metadata": {}, - "source": [ - "_Developer Note:_ if you may make a PR in the future, be sure to copy this\n", - "notebook, and use the `gitignore` prefix `temp` to avoid future conflicts.\n", - "\n", - "This is one notebook in a multi-part series on Spyglass.\n", - "\n", - "- To set up your Spyglass environment and database, see\n", - " [the Setup notebook](./00_Setup.ipynb)\n", - "- For additional info on DataJoint syntax, including table definitions and\n", - " inserts, see\n", - " [the Insert Data notebook](./01_Insert_Data.ipynb)\n", - "\n", - "This is a tutorial will cover how to extract position given a pre-trained DeepLabCut (DLC) model. It will walk through adding your DLC model to Spyglass.\n", - "\n", - "If you already have a model in the database, skip to the \n", - "[next tutorial](./23_Position_DLC_3.ipynb)." - ] - }, - { - "cell_type": "markdown", - "id": "e3ff00d6", - "metadata": {}, - "source": [ - "## Imports\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "704fe083", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import datajoint as dj\n", - "\n", - "# change to the upper level folder to detect dj_local_conf.json\n", - "if os.path.basename(os.getcwd()) == \"notebooks\":\n", - " os.chdir(\"..\")\n", - "dj.config.load(\"dj_local_conf.json\") # load config for database connection info\n", - "\n", - "from spyglass.settings import load_config\n", - "\n", - "load_config(base_dir=\"/home/cb/wrk/zOther/data/\")\n", - "\n", - "import spyglass.common as sgc\n", - "import spyglass.position.v1 as sgp\n", - "from spyglass.position import PositionOutput\n", - "\n", - "# ignore datajoint+jupyter async warnings\n", - "import warnings\n", - "\n", - "warnings.simplefilter(\"ignore\", category=DeprecationWarning)\n", - "warnings.simplefilter(\"ignore\", category=ResourceWarning)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "7e3e1854-0baf-44f4-a5a6-ddc1fdb4c3e1", - "metadata": {}, - "source": [ - "#### Here is a schematic showing the tables used in this notebook.
      \n", - "![dlc_existing.png|2000x900](./../notebook-images/dlc_existing.png)" - ] - }, - { - "cell_type": "markdown", - "id": "0388fc5f", - "metadata": {}, - "source": [ - "## Table of Contents\n", - "\n", - "- [`DLCProject`](#DLCProject)\n", - "- [`DLCModel`](#DLCModel)\n", - "\n", - "\n", - "You can click on any header to return to the Table of Contents" - ] - }, - { - "cell_type": "markdown", - "id": "6adc175d", - "metadata": {}, - "source": [ - "## [DLCProject](#ToC) " - ] - }, - { - "cell_type": "markdown", - "id": "e7c51888-b05d-4a51-bb9f-b075db4bbf49", - "metadata": {}, - "source": [ - "We'll look at the BodyPart table, which stores standard names of body parts used within DLC models." - ] - }, - { - "cell_type": "markdown", - "id": "f8a64a57", - "metadata": {}, - "source": [ - "
      \n", - " Notes:
        \n", - "
      • \n", - " Please do not add to the BodyPart table in the production \n", - " database unless necessary.\n", - "
        • \n", - "
      \n", - "
      " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c938c639", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.BodyPart()" - ] - }, - { - "cell_type": "markdown", - "id": "f422dd98-728b-4b48-877b-f77c2d60872f", - "metadata": {}, - "source": [ - "We can `insert_existing_project` into the `DLCProject` table using:\n", - "\n", - "- `project_name`: a short, unique, descriptive project name to reference\n", - " throughout the pipeline\n", - "- `lab_team`: team name from `LabTeam`\n", - "- `config_path`: string path to a DLC `config.yaml`\n", - "- `bodyparts`: optional list of bodyparts used in the project\n", - "- `frames_per_video`: optional, number of frames to extract for training from\n", - " each video" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f20ecce9", - "metadata": {}, - "outputs": [], - "source": [ - "project_name = \"tutorial_DG\"\n", - "lab_team = \"LorenLab\"\n", - "project_key = sgp.DLCProject.insert_existing_project(\n", - " project_name=project_name,\n", - " lab_team=lab_team,\n", - " config_path=\"/nimbus/deeplabcut/projects/tutorial_model-LorenLab-2022-07-15/config.yaml\",\n", - " bodyparts=[\"redLED_C\", \"greenLED\", \"redLED_L\", \"redLED_R\", \"tailBase\"],\n", - " frames_per_video=200,\n", - " skip_duplicates=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6d9d4223-63da-462e-8164-7cc63c945760", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCProject() & {\"project_name\": project_name}" - ] - }, - { - "cell_type": "markdown", - "id": "1c7876e7", - "metadata": {}, - "source": [ - "## [DLCModel](#ToC) " - ] - }, - { - "cell_type": "markdown", - "id": "fa36a042-f13e-4a36-812a-a4efaeb57a09", - "metadata": {}, - "source": [ - "The `DLCModelInput` table has `dlc_model_name` and `project_name` as primary keys and `project_path` as a secondary key. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "25f0a45e-5bd9-48bf-a79d-908bd5a17235", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCModelInput()" - ] - }, - { - "cell_type": "markdown", - "id": "39ee99ae-586a-4cbb-9255-15ddd594b1b7", - "metadata": {}, - "source": [ - "We can modify the `project_key` to replace `config_path` with `project_path` to\n", - "fit with the fields in `DLCModelInput`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fc961e93-8fe8-4069-a945-a9fc1e1ad993", - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"current project_key:\\n{project_key}\")\n", - "if not \"project_path\" in project_key:\n", - " project_key[\"project_path\"] = os.path.dirname(project_key[\"config_path\"])\n", - " del project_key[\"config_path\"]\n", - " print(f\"updated project_key:\\n{project_key}\")" - ] - }, - { - "cell_type": "markdown", - "id": "4b958ef7-160c-4141-a7c2-1177fdfd6eb6", - "metadata": {}, - "source": [ - "After adding a unique `dlc_model_name` to `project_key`, we insert into\n", - "`DLCModelInput`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "49650dc2", - "metadata": {}, - "outputs": [], - "source": [ - "dlc_model_name = \"tutorial_model_DG\"\n", - "sgp.DLCModelInput().insert1(\n", - " {\"dlc_model_name\": dlc_model_name, **project_key}, skip_duplicates=True\n", - ")\n", - "sgp.DLCModelInput()" - ] - }, - { - "cell_type": "markdown", - "id": "d04c4785-23b4-4a79-9ef9-3815c1215422", - "metadata": {}, - "source": [ - "Inserting into `DLCModelInput` will also populate `DLCModelSource`, which\n", - "records whether or not a model was trained with Spyglass." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "01021925", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCModelSource() & project_key" - ] - }, - { - "cell_type": "markdown", - "id": "8d8756c5-0d85-490b-a712-a95faa074b43", - "metadata": {}, - "source": [ - "The `source` field will only accept _\"FromImport\"_ or _\"FromUpstream\"_ as entries. Let's checkout the `FromUpstream` part table attached to `DLCModelSource` below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "22fb6d58-225f-49fb-86ee-4b3197aa841f", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCModelSource.FromImport() & project_key" - ] - }, - { - "cell_type": "markdown", - "id": "02b9297c-49dc-43b8-ad7b-3897c4d442bf", - "metadata": {}, - "source": [ - "Next we'll get ready to populate the `DLCModel` table, which holds all the relevant information for both pre-trained models and models trained within Spyglass.
      First we'll need to determine a set of parameters for our model to select the correct model file.
      We can visualize a default set below:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8e01d109", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCModelParams.get_default()" - ] - }, - { - "cell_type": "markdown", - "id": "8aa565b0-37e4-462f-b0d8-fd1b1686b69c", - "metadata": {}, - "source": [ - "Here is the syntax to add your own parameter set:\n", - "\n", - "```python\n", - "dlc_model_params_name = \"make_this_yours\"\n", - "params = {\n", - " \"params\": {},\n", - " \"shuffle\": 1,\n", - " \"trainingsetindex\": 0,\n", - " \"model_prefix\": \"\",\n", - "}\n", - "sgp.DLCModelParams.insert1(\n", - " {\"dlc_model_params_name\": dlc_model_params_name, \"params\": params},\n", - " skip_duplicates=True,\n", - ")\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "c5acd2c6", - "metadata": {}, - "source": [ - "We can insert sets of parameters into `DLCModelSelection` and populate\n", - "`DLCModel`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "03b10bd6", - "metadata": {}, - "outputs": [], - "source": [ - "temp_model_key = (sgp.DLCModelSource.FromImport() & project_key).fetch1(\"KEY\")\n", - "sgp.DLCModelSelection().insert1(\n", - " {**temp_model_key, \"dlc_model_params_name\": \"default\"}, skip_duplicates=True\n", - ")\n", - "model_key = (sgp.DLCModelSelection & temp_model_key).fetch1(\"KEY\")\n", - "sgp.DLCModel.populate(model_key)" - ] - }, - { - "cell_type": "markdown", - "id": "a920fc2d-5b81-4d4b-817b-d7549d2810ac", - "metadata": {}, - "source": [ - "And of course make sure it populated correctly" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "930df143-c756-4904-b4b6-7eed8c194b9d", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCModel() & model_key" - ] - }, - { - "cell_type": "markdown", - "id": "887c5349", - "metadata": {}, - "source": [ - "## Next Steps\n", - "\n", - "With our trained model in place, we're ready to move on to \n", - "pose estimation (notebook coming soon!).\n", - "" - ] - }, - { - "cell_type": "markdown", - "id": "5dbb3e99", - "metadata": {}, - "source": [ - "### [`Return To Table of Contents`](#ToC)
      " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "spy", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/23_Position_DLC_3.ipynb b/notebooks/23_Position_DLC_3.ipynb deleted file mode 100644 index 00a69cd25..000000000 --- a/notebooks/23_Position_DLC_3.ipynb +++ /dev/null @@ -1,963 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Position - DeepLabCut Estimation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_Developer Note:_ if you may make a PR in the future, be sure to copy this\n", - "notebook, and use the `gitignore` prefix `temp` to avoid future conflicts.\n", - "\n", - "This is one notebook in a multi-part series on Spyglass.\n", - "\n", - "- To set up your Spyglass environment and database, see\n", - " [the Setup notebook](./00_Setup.ipynb)\n", - "- For additional info on DataJoint syntax, including table definitions and\n", - " inserts, see\n", - " [the Insert Data notebook](./01_Insert_Data.ipynb)\n", - "\n", - "This tutorial will extract position via DeepLabCut (DLC). It will walk through... \n", - "- executing pose estimation\n", - "- processing the pose estimation output to extract a centroid and orientation\n", - "- inserting the resulting information into the `IntervalPositionInfo` table\n", - "\n", - "This tutorial assumes you already have a model in your database. If that's not\n", - "the case, you can either [train one from scratch](./21_Position_DLC_1.ipynb)\n", - "or [load an existing project](./22_Position_DLC_2.ipynb)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is a schematic showing the tables used in this pipeline.\n", - "\n", - "![dlc_scratch.png|2000x900](./../notebook-images/dlc_scratch.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Table of Contents\n", - "\n", - "- [Imports](#imports)\n", - "- [GPU](#gpu)\n", - "- [`DLCPoseEstimation`](#DLCPoseEstimation1)\n", - "- [`DLCSmoothInterp`](#DLCSmoothInterp1)\n", - "- [`DLCCentroid`](#DLCCentroid1)\n", - "- [`DLCOrientation`](#DLCOrientation1)\n", - "- [`DLCPos`](#DLCPos1)\n", - "- [`DLCPosVideo`](#DLCPosVideo1)\n", - "- [`PosSource`](#PosSource1)\n", - "- [`IntervalPositionInfo`](#IntervalPositionInfo1)\n", - "\n", - "__You can click on any header to return to the Table of Contents__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### [Imports](#TableOfContents)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-07-28 14:45:50,776][INFO]: Connecting root@localhost:3306\n", - "[2023-07-28 14:45:50,804][INFO]: Connected root@localhost:3306\n" - ] - } - ], - "source": [ - "import os\n", - "import datajoint as dj\n", - "from pprint import pprint\n", - "\n", - "import spyglass.common as sgc\n", - "import spyglass.position.v1 as sgp\n", - "\n", - "# change to the upper level folder to detect dj_local_conf.json\n", - "if os.path.basename(os.getcwd()) == \"notebooks\":\n", - " os.chdir(\"..\")\n", - "dj.config.load(\"dj_local_conf.json\") # load config for database connection info\n", - "\n", - "# ignore datajoint+jupyter async warnings\n", - "import warnings\n", - "\n", - "warnings.simplefilter(\"ignore\", category=DeprecationWarning)\n", - "warnings.simplefilter(\"ignore\", category=ResourceWarning)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### [GPU](#TableOfContents)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For longer videos, we'll need GPU support. The cell below determines which core\n", - "has space and set the `gputouse` variable accordingly." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: 80383, 1: 35, 2: 35, 3: 35, 4: 35, 5: 35, 6: 35, 7: 35, 8: 35, 9: 35}" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sgp.dlc_utils.get_gpu_memory()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Set GPU core:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gputouse = 1 ## 1-9" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### [DLCPoseEstimation](#TableOfContents) \n", - "\n", - "With our trained model in place, we're ready to set up Pose Estimation on a\n", - "behavioral video of your choice. We can select a video with `nwb_file_name` and\n", - "`epoch`, making sure there's an entry in the `VideoFile` table." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "nwb_file_name = \"J1620210604_.nwb\"\n", - "epoch = 14\n", - "sgc.VideoFile() & {\"nwb_file_name\": nwb_file_name, \"epoch\": epoch}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using `insert_estimation_task` will convert out video to be in .mp4 format (DLC\n", - "struggles with .h264) and determine the directory in which we'll store the pose\n", - "estimation results.\n", - "\n", - "- `task_mode` (trigger or load) determines whether or not populating\n", - " `DLCPoseEstimation` triggers a new pose estimation, or loads an existing.\n", - "- `video_file_num` will be 0 in almost all\n", - " cases.\n", - "- `gputouse` was already set during training. It may be a good idea to make sure\n", - " that core is still free before moving forward." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pose_estimation_key = sgp.DLCPoseEstimationSelection.insert_estimation_task(\n", - " {\n", - " \"nwb_file_name\": nwb_file_name,\n", - " \"epoch\": epoch,\n", - " \"video_file_num\": 0,\n", - " **model_key,\n", - " },\n", - " task_mode=\"trigger\",\n", - " params={\"gputouse\": gputouse, \"videotype\": \"mp4\"},\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_Note:_ Populating `DLCPoseEstimation` may take some time for full datasets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCPoseEstimation().populate(pose_estimation_key)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's visualize the output from Pose Estimation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(sgp.DLCPoseEstimation() & pose_estimation_key).fetch_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### [DLCSmoothInterp](#TableOfContents) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After pose estimation, we can interpolate over low likelihood periods and smooth\n", - "the resulting position.\n", - "\n", - "First we define some parameters. We can see the default parameter set below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pprint(sgp.DLCSmoothInterpParams.get_default())\n", - "si_params_name = \"default\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To change any of these parameters, one would do the following:\n", - "\n", - "```python\n", - "si_params_name = \"your_unique_param_name\"\n", - "params = {\n", - " \"smoothing_params\": {\n", - " \"smoothing_duration\": 0.00,\n", - " \"smooth_method\": \"moving_avg\",\n", - " },\n", - " \"interp_params\": {\"likelihood_thresh\": 0.00},\n", - " \"max_plausible_speed\": 0,\n", - " \"speed_smoothing_std_dev\": 0.000,\n", - "}\n", - "sgp.DLCSmoothInterpParams().insert1(\n", - " {\"dlc_si_params_name\": si_params_name, \"params\": params},\n", - " skip_duplicates=True,\n", - ")\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll create a dictionary with the correct set of keys for the `DLCSmoothInterpSelection` table" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "si_key = pose_estimation_key.copy()\n", - "fields = list(sgp.DLCSmoothInterpSelection.fetch().dtype.fields.keys())\n", - "si_key = {key: val for key, val in si_key.items() if key in fields}\n", - "si_key" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can insert all of the bodyparts we want to process into\n", - "`DLCSmoothInterpSelection`. Here are the bodyparts we have available to us:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pprint((sgp.DLCPoseEstimation.BodyPart & pose_estimation_key).fetch(\"bodypart\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use `insert1` to insert a single bodypart, but would suggest using `insert` to insert a list of keys with different bodyparts." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll set a list of bodyparts and then insert them into\n", - "`DLCSmoothInterpSelection`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bodyparts = [\"greenLED\", \"redLED_C\"]\n", - "sgp.DLCSmoothInterpSelection.insert(\n", - " [\n", - " {\n", - " **si_key,\n", - " \"bodypart\": bodypart,\n", - " \"dlc_si_params_name\": si_params_name,\n", - " }\n", - " for bodypart in bodyparts\n", - " ],\n", - " skip_duplicates=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And verify the entry:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCSmoothInterpSelection() & si_key" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we populate `DLCSmoothInterp`, which will perform smoothing and\n", - "interpolation on all of the bodyparts specified." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCSmoothInterp().populate(si_key)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And let's visualize the resulting position data using a scatter plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(\n", - " sgp.DLCSmoothInterp() & {**si_key, \"bodypart\": bodyparts[0]}\n", - ").fetch1_dataframe().plot.scatter(x=\"x\", y=\"y\", s=1, figsize=(5, 5))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### [DLCSmoothInterpCohort](#TableOfContents) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After smoothing/interpolation, we need to select bodyparts from which we want to\n", - "derive a centroid and orientation, which is performed by the\n", - "`DLCSmoothInterpCohort` table." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, let's make a key that represents the 'cohort', using\n", - "`dlc_si_cohort_selection_name`. We'll need a bodypart dictionary using bodypart\n", - "keys and smoothing/interpolation parameters used as value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cohort_key = si_key.copy()\n", - "if \"bodypart\" in cohort_key:\n", - " del cohort_key[\"bodypart\"]\n", - "if \"dlc_si_params_name\" in cohort_key:\n", - " del cohort_key[\"dlc_si_params_name\"]\n", - "cohort_key[\"dlc_si_cohort_selection_name\"] = \"green_red_led\"\n", - "cohort_key[\"bodyparts_params_dict\"] = {\n", - " \"greenLED\": si_params_name,\n", - " \"redLED_C\": si_params_name,\n", - "}\n", - "print(cohort_key)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll insert the cohort into `DLCSmoothInterpCohortSelection` and populate `DLCSmoothInterpCohort`, which collates the separately smoothed and interpolated bodyparts into a single entry." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCSmoothInterpCohortSelection().insert1(cohort_key, skip_duplicates=True)\n", - "sgp.DLCSmoothInterpCohort.populate(cohort_key)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And verify the entry:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCSmoothInterpCohort.BodyPart() & cohort_key" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### [DLCCentroid](#TableOfContents) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With this cohort, we can determine a centroid using another set of parameters." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Here is the default set\n", - "print(sgp.DLCCentroidParams.get_default())\n", - "centroid_params_name = \"default\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is the syntax to add your own parameters:\n", - "\n", - "```python\n", - "centroid_params = {\n", - " \"centroid_method\": \"two_pt_centroid\",\n", - " \"points\": {\n", - " \"greenLED\": \"greenLED\",\n", - " \"redLED_C\": \"redLED_C\",\n", - " },\n", - " \"speed_smoothing_std_dev\": 0.100,\n", - "}\n", - "centroid_params_name = \"your_unique_param_name\"\n", - "sgp.DLCCentroidParams.insert1(\n", - " {\n", - " \"dlc_centroid_params_name\": centroid_params_name,\n", - " \"params\": centroid_params,\n", - " },\n", - " skip_duplicates=True,\n", - ")\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll make a key to insert into `DLCCentroidSelection`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "centroid_key = cohort_key.copy()\n", - "fields = list(sgp.DLCCentroidSelection.fetch().dtype.fields.keys())\n", - "centroid_key = {key: val for key, val in centroid_key.items() if key in fields}\n", - "centroid_key[\"dlc_centroid_params_name\"] = centroid_params_name\n", - "pprint(centroid_key)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After inserting into the selection table, we can populate `DLCCentroid`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCCentroidSelection.insert1(centroid_key, skip_duplicates=True)\n", - "sgp.DLCCentroid.populate(centroid_key)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we can visualize the resulting centroid position" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(sgp.DLCCentroid() & centroid_key).fetch1_dataframe().plot.scatter(\n", - " x=\"position_x\",\n", - " y=\"position_y\",\n", - " c=\"speed\",\n", - " colormap=\"viridis\",\n", - " alpha=0.5,\n", - " s=0.5,\n", - " figsize=(10, 10),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### [DLCOrientation](#TableOfContents) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll go through a similar process for orientation. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pprint(sgp.DLCOrientationParams.get_default())\n", - "dlc_orientation_params_name = \"default\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll prune the `cohort_key` we used above and add our\n", - "`dlc_orientation_params_name` to make it suitable for `DLCOrientationSelection`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fields = list(sgp.DLCOrientationSelection.fetch().dtype.fields.keys())\n", - "orient_key = {key: val for key, val in cohort_key.items() if key in fields}\n", - "orient_key[\"dlc_orientation_params_name\"] = dlc_orientation_params_name\n", - "print(orient_key)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll insert into `DLCOrientationSelection` and then populate `DLCOrientation`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCOrientationSelection().insert1(orient_key, skip_duplicates=True)\n", - "sgp.DLCOrientation().populate(orient_key)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can fetch the orientation as a dataframe as quality assurance." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(sgp.DLCOrientation() & orient_key).fetch1_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### [DLCPos](#TableOfContents) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After processing the position data, we have to do a few table manipulations to standardize various outputs. \n", - "\n", - "To summarize, we brought in a pretrained DLC project, used that model to run pose estimation on a new behavioral video, smoothed and interpolated the result, formed a cohort of bodyparts, and determined the centroid and orientation of this cohort.\n", - "\n", - "Now we'll populate `DLCPos` with our centroid/orientation entries above." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fields = list(sgp.DLCPos.fetch().dtype.fields.keys())\n", - "dlc_key = {key: val for key, val in centroid_key.items() if key in fields}\n", - "dlc_key[\"dlc_si_cohort_centroid\"] = centroid_key[\"dlc_si_cohort_selection_name\"]\n", - "dlc_key[\"dlc_si_cohort_orientation\"] = orient_key[\n", - " \"dlc_si_cohort_selection_name\"\n", - "]\n", - "dlc_key[\"dlc_orientation_params_name\"] = orient_key[\n", - " \"dlc_orientation_params_name\"\n", - "]\n", - "pprint(dlc_key)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can insert into `DLCPosSelection` and populate `DLCPos` with our `dlc_key`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCPosSelection().insert1(dlc_key, skip_duplicates=True)\n", - "sgp.DLCPos().populate(dlc_key)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fetched as a dataframe, we expect the following 8 columns:\n", - "\n", - "- time\n", - "- video_frame_ind\n", - "- position_x\n", - "- position_y\n", - "- orientation\n", - "- velocity_x\n", - "- velocity_y\n", - "- speed" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(sgp.DLCPos() & dlc_key).fetch1_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also fetch the `pose_eval_result`, which contains the percentage of\n", - "frames that each bodypart was below the likelihood threshold of 0.95." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(sgp.DLCPos() & dlc_key).fetch1(\"pose_eval_result\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### [DLCPosVideo](#TableOfContents) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can create a video with the centroid and orientation overlaid on the original\n", - "video. This will also plot the likelihood of each bodypart used in the cohort.\n", - "This is optional, but a good quality assurance step." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCPosVideoParams.insert_default()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "params = {\n", - " \"percent_frames\": 0.05,\n", - " \"incl_likelihood\": True,\n", - "}\n", - "sgp.DLCPosVideoParams.insert1(\n", - " {\"dlc_pos_video_params_name\": \"five_percent\", \"params\": params},\n", - " skip_duplicates=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCPosVideoSelection.insert1(\n", - " {**dlc_key, \"dlc_pos_video_params_name\": \"five_percent\"},\n", - " skip_duplicates=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCPosVideo().populate(dlc_key)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### [PositionOutput](#TableOfContents) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`PositionOutput` is the final table of the pipeline and is automatically\n", - "populated when we populate `DLCPosV1`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sgp.PositionOutput() & dlc_key" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`PositionOutput` also has a part table, similar to the `DLCModelSource` table above. Let's check that out as well." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "PositionOutput.DLCPosV1() & dlc_key" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(PositionOutput.DLCPosV1() & dlc_key).fetch1_dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### [PositionVideo](#TableOfContents)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use the `PositionVideo` table to create a video that overlays just the\n", - "centroid and orientation on the video. This table uses the parameter `plot` to\n", - "determine whether to plot the entry deriving from the DLC arm or from the Trodes\n", - "arm of the position pipeline. This parameter also accepts 'all', which will plot\n", - "both (if they exist) in order to compare results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sgp.PositionVideoSelection().insert1(\n", - " {\n", - " \"nwb_file_name\": \"J1620210604_.nwb\",\n", - " \"interval_list_name\": \"pos 13 valid times\",\n", - " \"trodes_position_id\": 0,\n", - " \"dlc_position_id\": 1,\n", - " \"plot\": \"DLC\",\n", - " \"output_dir\": \"/home/dgramling/Src/\",\n", - " }\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sgp.PositionVideo.populate({\"plot\": \"DLC\"})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "CONGRATULATIONS!! Please treat yourself to a nice tea break :-)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### [Return To Table of Contents](#TableOfContents)
      " - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} From e504c7481b8b28f9f34ec3b9efce9e79046dbf35 Mon Sep 17 00:00:00 2001 From: Chris Brozdowski Date: Fri, 19 Jan 2024 18:37:50 -0600 Subject: [PATCH 6/8] Docs fixes for decoding pipeline (#776) * Docs fixes: tidy decoding docstrings, change hatch version get, add inits * Pytest revamp (#743) * WIP: Pull from old stash, resolve conflicts * Pytest WIP. Position centriod fix. Centralize device prompt logic * Add tests for all tables in * WIP: Improve coverage behav, dio * WIP: Add coverage, see details: - Add `return_fig` param to plotting helper functions to permit tests - `common_filter` - `common_interval` - Add coverage for ~1/2 of `common` - `common_behav` - `common_device` - `common_ephys` - `common_filter` - `common_interval` - with helper funcs tested seperately - `common_lab` - `common_nwbfile` - partial * WIP pytest common 2nd half, start lfp * WIP lfp tests, ahead of fetch upstream * Add lfp pipeline tests * Run pre-commit checks * Fix bug * Unpin position_tools for CI * Change download data dir * Change download data dir 2 * Fix teardown. Coverage 67% * Update changelog * logger.warn -> logger.warning * Minor decoding fixes (#769) * Add non-local detector and remove replay_trajectory_classification * Reorganize * Fix formatting and imports * Update .gitignore * Remove because of circular import * Fix name of parameter * Handle case where ther is only one interval * Fix settings * Handle single interval * from_unit_dict does not exist in 0.98.2 of spike interface * Simplify call * Update for SpikeSorting merge table and add spyglass mixin * Fix dependencies * Fix merge conflict * Update src/spyglass/decoding/v1/clusterless.py Co-authored-by: Chris Brozdowski * Update src/spyglass/decoding/v1/clusterless.py Co-authored-by: Chris Brozdowski * Update src/spyglass/decoding/v1/clusterless.py Co-authored-by: Chris Brozdowski * Update src/spyglass/decoding/v1/clusterless.py Co-authored-by: Chris Brozdowski * Apply suggestions from code review Co-authored-by: Chris Brozdowski * Remove unused imports and format * Add saving of waveform features * Don't store electrodes, full waveforms, waveform mean * Fix spike times and add convenience method * Add spike location and some formatting * Remove circular import * Fix dict expansion * Initial working clusterless pipeline * Add position group * Rename classifier to decoding * Handle encoding and decoding intervals * Put old files under v0, try/except for old decoding package * Rename visualization and remove from v0 v0 visualization is redundant with visualization * Place parameters and position group in core.py * Add sorted spikes decoding * Add objects to init for convenience * Remove unused imports * Fix fetching of spike times * Insert into merge table * Update CHANGELOG.md * Function for removing decoding outputs not in DecodingOutput * Fix name * Add draft of tutorials and rearrange notebooks * Fix config loading * Add 1D decoding and some notes on estimate_parameters kwarg * Update 43_Decoding_SortedSpikes.ipynb * Remove old decoding notebook * Save initial conditions and discrete transitions * Apply suggestions from code review Co-authored-by: Chris Brozdowski * Be more specific with import error * Remove unneeded comments * Remove incorrect dimension name * Project merge_id from SpikeSortingOutput for clarity * Update src/spyglass/decoding/v0/clusterless.py Co-authored-by: Chris Brozdowski * Update src/spyglass/decoding/v0/clusterless.py Co-authored-by: Chris Brozdowski * Update src/spyglass/decoding/v0/clusterless.py Co-authored-by: Chris Brozdowski * Fix linting * Update notebooks * Ignore .pem * Add session as a primary key for Groups * Add some helper methods * Update notebooks * Update README.md * Update pyscripts * Update 42_Decoding_Clusterless.ipynb * Update CHANGELOG.md * Add fetch and insert * Simplify class conversion * Do the dictionary conversion of class for the user * Update CHANGELOG.md * Update .gitignore * Use methods in populate * Avoid fetching interval range if not needed * Generalize finding class from modules * Use args/kwargs * Simplify tuple unpacking * Make decoding kwargs nullable * Add function for get_recording and get_sorting to the spikesorting merge table * make decoding waveform features agnostic to spikesorting source * Fix spelling * Use fetch1_dataframe for position * Use self instead of class * Update src/spyglass/decoding/v1/sorted_spikes.py Co-authored-by: Samuel Bray * Be more careful about populating select keys * Make more readable/remove unused imports * Save classifier * Clean up saved model paths * add function load_linear_position_info * Update src/spyglass/decoding/v1/sorted_spikes.py Co-authored-by: Samuel Bray * Update 41_Extracting_Clusterless_Waveform_Features.py * Update docstring * Apply suggestions from code review Co-authored-by: Chris Brozdowski * Update src/spyglass/decoding/v1/clusterless.py Co-authored-by: Chris Brozdowski * Update src/spyglass/decoding/v1/clusterless.py Co-authored-by: Chris Brozdowski * Fix linting * Fix syntax * Rename variable to avoid confusion * Restrict UnitWaveformFeaturesGroup and SortedSpikesGroup * Concatenate linear position and position dataframes * Static methods don't require instantiating class * Avoid merge restrict * Add version to defaults * Remove unused import * Fix classifier path * Add dry run * Remove non-default * Handle permissions and file not found * Keep position info within encoding/decoding interval * Add methods to get the spike_times, spike_indicators, firing rate * Fix docstring to match default * Implement function rather than import * Remove unused broken imports * Add decoding cleanup * Fix import * Put old vis code back * Fix import * Add draft helper functions * Limit options on input * Fix logic * Fix where the key is passed * Update notebooks * Host main visualizations in non_local_detector repo * Update notebooks/py_scripts/41_Extracting_Clusterless_Waveform_Features.py Co-authored-by: Chris Brozdowski * Update src/spyglass/spikesorting/merge.py Co-authored-by: Chris Brozdowski * Update src/spyglass/decoding/decoding_merge.py Co-authored-by: Chris Brozdowski * Revert "Limit options on input" This reverts commit 386714ccdf480b7d04036b83fb62de6e9164364e. * Use f-string for version * Add useful imports to the top level This would have to change a bit if there were multiple versions of the pipeline. * Make source class a hidden attribute * Update CHANGELOG.md --------- Co-authored-by: Chris Brozdowski Co-authored-by: Sam Bray * DLC notebooks 21 and 22 (#772) * add envs to bashrc; multi cam addition * 12/11/23 using TackEpoch to define interval_names * 12/11/23 remove comment * del smooth duration * jan 10 again * DLC noteboks 5 and 6 * 5 and 6 * fix ignore and 21 * Removed submodule * removed .gitignore * DLC notebooks * Docs fixes: tidy decoding docstrings, change hatch version get, add inits * Jupysync, blackify, purge old .py nbs * Docs updates for new notebooks * Update changelog * Spell fix, pre-commit --------- Co-authored-by: Eric Denovellis Co-authored-by: Sam Bray Co-authored-by: emreybroyles <114687400+emreybroyles@users.noreply.github.com> --- CHANGELOG.md | 1 + docs/README.md | 3 + docs/build-docs.sh | 7 +- docs/mkdocs.yml | 31 +- docs/src/api/make_pages.py | 5 - notebooks/04_PopulateConfigFile.ipynb | 2 +- notebooks/21_DLC.ipynb | 197 +++-- notebooks/22_DLC_Loop.ipynb | 74 +- notebooks/42_Decoding_Clusterless.ipynb | 2 +- notebooks/README.md | 54 +- notebooks/py_scripts/21_DLC.py | 804 ++++++++++++++++++ notebooks/py_scripts/22_DLC_Loop.py | 520 +++++++++++ notebooks/py_scripts/22_Position_DLC_2.py | 193 ----- notebooks/py_scripts/23_Position_DLC_3.py | 414 --------- ...xtracting_Clusterless_Waveform_Features.py | 4 +- .../py_scripts/42_Decoding_Clusterless.py | 2 +- src/spyglass/common/common_lab.py | 2 +- src/spyglass/decoding/v0/__init__.py | 0 src/spyglass/decoding/v0/clusterless.py | 148 ++-- src/spyglass/decoding/v1/__init__.py | 0 src/spyglass/decoding/v1/waveform_features.py | 3 +- .../decoding/visualization/__init__.py | 0 22 files changed, 1644 insertions(+), 822 deletions(-) create mode 100644 notebooks/py_scripts/21_DLC.py create mode 100644 notebooks/py_scripts/22_DLC_Loop.py delete mode 100644 notebooks/py_scripts/22_Position_DLC_2.py delete mode 100644 notebooks/py_scripts/23_Position_DLC_3.py create mode 100644 src/spyglass/decoding/v0/__init__.py create mode 100644 src/spyglass/decoding/v1/__init__.py create mode 100644 src/spyglass/decoding/visualization/__init__.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 5664e7238..32276f353 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -13,6 +13,7 @@ - Add Spyglass logger. #730 - IntervalList: Add secondary key `pipeline` #742 - Increase pytest coverage for `common`, `lfp`, and `utils`. #743 +- Update docs to reflect new notebooks. #776 ### Pipelines diff --git a/docs/README.md b/docs/README.md index 80510daed..8eee3f1a4 100644 --- a/docs/README.md +++ b/docs/README.md @@ -55,3 +55,6 @@ The following items can be commented out in `mkdocs.yml` to reduce build time: - `mkdocs-jupyter`: Generates tutorial pages from notebooks. To end the process in your console, use `ctrl+c`. + +If your new submodule is causing a build error (e.g., "Could not collect ..."), +you may need to add `__init__.py` files to the submodule directories. diff --git a/docs/build-docs.sh b/docs/build-docs.sh index 03d28c07e..b36b0533d 100755 --- a/docs/build-docs.sh +++ b/docs/build-docs.sh @@ -10,13 +10,14 @@ cp ./LICENSE ./docs/src/LICENSE.md mkdir -p ./docs/src/notebooks cp ./notebooks/*ipynb ./docs/src/notebooks/ cp ./notebooks/*md ./docs/src/notebooks/ -cp ./docs/src/notebooks/README.md ./docs/src/notebooks/index.md +mv ./docs/src/notebooks/README.md ./docs/src/notebooks/index.md cp -r ./notebook-images ./docs/src/notebooks/ cp -r ./notebook-images ./docs/src/ # Get major version -FULL_VERSION=$(hatch version) # Most recent tag, may include periods -export MAJOR_VERSION="${FULL_VERSION:0:3}" # First 3 chars of tag +version_line=$(grep "__version__ =" ./src/spyglass/_version.py) +version_string=$(echo "$version_line" | awk -F"[\"']" '{print $2}') +export MAJOR_VERSION="${version_string:0:3}" echo "$MAJOR_VERSION" # Get ahead of errors diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index 32f789bb9..3767f8dd4 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -16,9 +16,6 @@ theme: favicon: images/Spyglass.svg features: - toc.follow - # - navigation.expand # CBroz1: removed bc long tutorial list hides rest - # - toc.integrate - # - navigation.sections - navigation.top - navigation.instant # saves loading time - 1 browser page - navigation.tracking # even with above, changes URL by section @@ -55,27 +52,29 @@ nav: - Database Management: misc/database_management.md - Tutorials: - Overview: notebooks/index.md - - General: + - Intro: - Setup: notebooks/00_Setup.ipynb - Insert Data: notebooks/01_Insert_Data.ipynb - Data Sync: notebooks/02_Data_Sync.ipynb - Merge Tables: notebooks/03_Merge_Tables.ipynb - - Ephys: - - Spike Sorting: notebooks/10_Spike_Sorting.ipynb + - Config Populate: notebooks/04_PopulateConfigFile.ipynb + - Spikes: + - Spike Sorting V0: notebooks/10_Spike_SortingV0.ipynb + - Spike Sorting V1: notebooks/10_Spike_SortingV1.ipynb - Curation: notebooks/11_Curation.ipynb - - LFP: notebooks/12_LFP.ipynb - - Theta: notebooks/14_Theta.ipynb - Position: - Position Trodes: notebooks/20_Position_Trodes.ipynb - - DLC From Scratch: notebooks/21_Position_DLC_1.ipynb - - DLC From Model: notebooks/22_Position_DLC_2.ipynb - - DLC Prediction: notebooks/23_Position_DLC_3.ipynb + - DLC Models: notebooks/21_DLC.ipynb + - Looping DLC: notebooks/22_DLC_Loop.ipynb - Linearization: notebooks/24_Linearization.ipynb - - Combined: - - Ripple Detection: notebooks/30_Ripple_Detection.ipynb - - Extract Mark Indicators: notebooks/31_Extract_Mark_Indicators.ipynb - - Decoding with GPUs: notebooks/32_Decoding_with_GPUs.ipynb - - Decoding Clusterless: notebooks/33_Decoding_Clusterless.ipynb + - LFP: + - LFP: notebooks/30_LFP.ipynb + - Theta: notebooks/31_Theta.ipynb + - Ripple Detection: notebooks/32_Ripple_Detection.ipynb + - Decoding: + - Extract Clusterless: notebooks/41_Extracting_Clusterless_Waveform_Features.ipynb + - Decoding Clusterless: notebooks/42_Decoding_Clusterless.ipynb + - Decoding Sorted Spikes: notebooks/43_Decoding_SortedSpikes.ipynb - API Reference: api/ # defer to gen-files + literate-nav - How to Contribute: contribute.md - Change Log: CHANGELOG.md diff --git a/docs/src/api/make_pages.py b/docs/src/api/make_pages.py index 942f6ae09..6886d50f4 100644 --- a/docs/src/api/make_pages.py +++ b/docs/src/api/make_pages.py @@ -28,11 +28,6 @@ else: break -if add_limit is not None: - from IPython import embed - - embed() - with mkdocs_gen_files.open("api/navigation.md", "w") as nav_file: nav_file.write("* [Overview](../api/index.md)\n") diff --git a/notebooks/04_PopulateConfigFile.ipynb b/notebooks/04_PopulateConfigFile.ipynb index e7bf96f1e..23dce0f84 100644 --- a/notebooks/04_PopulateConfigFile.ipynb +++ b/notebooks/04_PopulateConfigFile.ipynb @@ -190,7 +190,7 @@ " \"DataAcquisitionDevice\",\n", " \"- data_acquisition_device_name: data_acq_device0\",\n", " ]\n", - " config_file.writelines(line + '\\n' for line in lines)" + " config_file.writelines(line + \"\\n\" for line in lines)" ] }, { diff --git a/notebooks/21_DLC.ipynb b/notebooks/21_DLC.ipynb index 1c1756c0d..aa8f0863b 100644 --- a/notebooks/21_DLC.ipynb +++ b/notebooks/21_DLC.ipynb @@ -5,7 +5,7 @@ "id": "a93a1550-8a67-4346-a4bf-e5a136f3d903", "metadata": {}, "source": [ - "## Position- DeepLabCut from Scratch" + "## Position- DeepLabCut from Scratch\n" ] }, { @@ -13,7 +13,7 @@ "id": "13dd3267", "metadata": {}, "source": [ - "### Overview" + "### Overview\n" ] }, { @@ -41,7 +41,7 @@ "- processing the pose estimation output to extract a centroid and orientation\n", "- inserting the resulting information into the `PositionOutput` table\n", "\n", - "**Note 2: Make sure you are running this within the spyglass-position Conda environment (instructions for install are in the environment_position.yml)**" + "**Note 2: Make sure you are running this within the spyglass-position Conda environment (instructions for install are in the environment_position.yml)**\n" ] }, { @@ -60,6 +60,7 @@ "metadata": {}, "source": [ "### Table of Contents\n", + "\n", "[`DLCProject`](#DLCProject1)
      \n", "[`DLCModelTraining`](#DLCModelTraining1)
      \n", "[`DLCModel`](#DLCModel1)
      \n", @@ -69,7 +70,7 @@ "[`DLCOrientation`](#DLCOrientation1)
      \n", "[`DLCPosV1`](#DLCPosV1-1)
      \n", "[`DLCPosVideo`](#DLCPosVideo1)
      \n", - "[`PositionOutput`](#PositionOutput1)
      " + "[`PositionOutput`](#PositionOutput1)
      \n" ] }, { @@ -77,7 +78,7 @@ "id": "70a0a678", "metadata": {}, "source": [ - "__You can click on any header to return to the Table of Contents__" + "**You can click on any header to return to the Table of Contents**\n" ] }, { @@ -85,7 +86,7 @@ "id": "c9b98c3d", "metadata": {}, "source": [ - "### Imports" + "### Imports\n" ] }, { @@ -137,7 +138,7 @@ "id": "5e6221a3-17e5-45c0-aa40-2fd664b02219", "metadata": {}, "source": [ - "#### [DLCProject](#TableOfContents) " + "#### [DLCProject](#TableOfContents) \n" ] }, { @@ -164,7 +165,7 @@ "id": "50c9f1c9", "metadata": {}, "source": [ - "### Body Parts" + "### Body Parts\n" ] }, { @@ -172,7 +173,7 @@ "id": "96637cb9-519d-41e1-8bfd-69f68dc66b36", "metadata": {}, "source": [ - "We'll begin by looking at the `BodyPart` table, which stores standard names of body parts used in DLC models throughout the lab with a concise description." + "We'll begin by looking at the `BodyPart` table, which stores standard names of body parts used in DLC models throughout the lab with a concise description.\n" ] }, { @@ -307,7 +308,7 @@ " ],\n", " skip_duplicates=True,\n", ")\n", - "```" + "```\n" ] }, { @@ -315,7 +316,7 @@ "id": "57b590d3", "metadata": {}, "source": [ - "### Define videos and camera name (optional) for training set" + "### Define videos and camera name (optional) for training set\n" ] }, { @@ -327,7 +328,7 @@ "\n", "The list can either contain dictionaries identifying behavioral videos for NWB files that have already been added to Spyglass, or absolute file paths to the videos you want to use.\n", "\n", - "For this tutorial, we'll use two videos for which we already have frames labeled." + "For this tutorial, we'll use two videos for which we already have frames labeled.\n" ] }, { @@ -338,7 +339,7 @@ "Defining camera name is optional: it should be done in cases where there are multiple cameras streaming per epoch, but not necessary otherwise.
      \n", "example:\n", "`camera_name = \"HomeBox_camera\" \n", - " `" + " `\n" ] }, { @@ -403,7 +404,7 @@ "_NOTE:_ If only `base` is specified as shown above, spyglass will assume the\n", "relative directories shown.\n", "\n", - "You can check the result of this setup process with..." + "You can check the result of this setup process with...\n" ] }, { @@ -462,7 +463,7 @@ " **\"tutorial_scratch_yourinitials\"**\n", "- `bodyparts` is a list of body parts for which we want to extract position.\n", " The pre-labeled frames we're using include the bodyparts listed below.\n", - "- Number of frames to extract/label as `frames_per_video`. Note that the DLC creators recommend having 200 frames as the minimum total number for each project." + "- Number of frames to extract/label as `frames_per_video`. Note that the DLC creators recommend having 200 frames as the minimum total number for each project.\n" ] }, { @@ -499,7 +500,7 @@ "id": "f5d83452-48eb-4669-89eb-a6beb1f2d051", "metadata": {}, "source": [ - "Now that we've intialized our project we'll need to extract frames which we will then label. " + "Now that we've initialized our project we'll need to extract frames which we will then label.\n" ] }, { @@ -509,7 +510,7 @@ "metadata": {}, "outputs": [], "source": [ - "#comment this line out after you finish frame extraction for each project\n", + "# comment this line out after you finish frame extraction for each project\n", "sgp.DLCProject().run_extract_frames(project_key)" ] }, @@ -519,9 +520,10 @@ "metadata": {}, "source": [ "This is the line used to label the frames you extracted, if you wish to use the DLC GUI on the computer you are currently using.\n", + "\n", "```#comment this line out after frames are labeled for your project\n", "sgp.DLCProject().run_label_frames(project_key)\n", - "```" + "```\n" ] }, { @@ -530,17 +532,18 @@ "metadata": {}, "source": [ "Otherwise, it is best/easiest practice to label the frames on your local computer (like a MacBook) that can run DeepLabCut's GUI well. Instructions:
      \n", + "\n", "1. Install DLC on your local (preferably into a 'Src' folder): https://deeplabcut.github.io/DeepLabCut/docs/installation.html\n", "2. Upload frames extracted and saved in nimbus (should be `/nimbus/deeplabcut//labeled-data`) AND the project's associated config file (should be `/nimbus/deeplabcut//config.yaml`) to Box (we get free with UCSF)\n", "3. Download labeled-data and config files on your local from Box\n", "4. Create a 'projects' folder where you installed DeepLabCut; create a new folder with your complete project name there; save the downloaded files there.\n", - "4. Edit the config.yaml file: line 9 defining `project_path` needs to be the file path where it is saved on your local (ex: `/Users/lorenlab/Src/DeepLabCut/projects/tutorial_sratch_DG-LorenLab-2023-08-16`)\n", - "5. Open the DLC GUI through terminal \n", - "
      (ex: `conda activate miniconda/envs/DEEPLABCUT_M1`\n", - "\t\t
      `pythonw -m deeplabcut`)\n", - "6. Load an existing project; choose the config.yaml file\n", - "7. Label frames; labeling tutorial: https://www.youtube.com/watch?v=hsA9IB5r73E.\n", - "8. Once all frames are labeled, you should re-upload labeled-data folder back to Box and overwrite it in the original nimbus location so that your completed frames are ready to be used in the model." + "5. Edit the config.yaml file: line 9 defining `project_path` needs to be the file path where it is saved on your local (ex: `/Users/lorenlab/Src/DeepLabCut/projects/tutorial_sratch_DG-LorenLab-2023-08-16`)\n", + "6. Open the DLC GUI through terminal\n", + "
      (ex: `conda activate miniconda/envs/DEEPLABCUT_M1`\n", + "
      `pythonw -m deeplabcut`)\n", + "7. Load an existing project; choose the config.yaml file\n", + "8. Label frames; labeling tutorial: https://www.youtube.com/watch?v=hsA9IB5r73E.\n", + "9. Once all frames are labeled, you should re-upload labeled-data folder back to Box and overwrite it in the original nimbus location so that your completed frames are ready to be used in the model.\n" ] }, { @@ -548,7 +551,7 @@ "id": "c12dd229-2f8b-455a-a7b1-a20916cefed9", "metadata": {}, "source": [ - "Now we can check the `DLCProject.File` part table and see all of our training files and videos there!" + "Now we can check the `DLCProject.File` part table and see all of our training files and videos there!\n" ] }, { @@ -672,7 +675,7 @@ "source": [ "
      \n", " This step and beyond should be run on a GPU-enabled machine.\n", - "
      " + "\n" ] }, { @@ -781,7 +784,7 @@ "id": "6b6cc709", "metadata": {}, "source": [ - "Next we'll modify the `project_key` from above to include the necessary entries for `DLCModelTraining`" + "Next we'll modify the `project_key` from above to include the necessary entries for `DLCModelTraining`\n" ] }, { @@ -803,7 +806,7 @@ "source": [ "We can insert an entry into `DLCModelTrainingSelection` and populate `DLCModelTraining`.\n", "\n", - "_Note:_ You can stop training at any point using `I + I` or interrupt the Kernel. \n", + "_Note:_ You can stop training at any point using `I + I` or interrupt the Kernel.\n", "\n", "The maximum total number of training iterations is 1030000; you can end training before this amount if the loss rate (lr) and total loss plateau and are very close to 0.\n" ] @@ -901,7 +904,7 @@ "id": "da004b3e", "metadata": {}, "source": [ - "Here we'll make sure that the entry made it into the table properly!" + "Here we'll make sure that the entry made it into the table properly!\n" ] }, { @@ -923,7 +926,7 @@ "source": [ "Populating `DLCModelTraining` automatically inserts the entry into\n", "`DLCModelSource`, which is used to select between models trained using Spyglass\n", - "vs. other tools." + "vs. other tools.\n" ] }, { @@ -941,7 +944,7 @@ "id": "92cb8969", "metadata": {}, "source": [ - "The `source` field will only accept _\"FromImport\"_ or _\"FromUpstream\"_ as entries. Let's checkout the `FromUpstream` part table attached to `DLCModelSource` below." + "The `source` field will only accept _\"FromImport\"_ or _\"FromUpstream\"_ as entries. Let's checkout the `FromUpstream` part table attached to `DLCModelSource` below.\n" ] }, { @@ -965,7 +968,7 @@ "information for all trained models.\n", "\n", "First, we'll need to determine a set of parameters for our model to select the\n", - "correct model file. Here is the default:" + "correct model file. Here is the default:\n" ] }, { @@ -1006,7 +1009,7 @@ "metadata": {}, "source": [ "We can insert sets of parameters into `DLCModelSelection` and populate\n", - "`DLCModel`." + "`DLCModel`.\n" ] }, { @@ -1026,11 +1029,10 @@ "metadata": {}, "outputs": [], "source": [ - "#comment these lines out after successfully inserting, for each project\n", - "sgp.DLCModelSelection().insert1({\n", - " **temp_model_key,\n", - " \"dlc_model_params_name\": \"default\"},\n", - " skip_duplicates=True)" + "# comment these lines out after successfully inserting, for each project\n", + "sgp.DLCModelSelection().insert1(\n", + " {**temp_model_key, \"dlc_model_params_name\": \"default\"}, skip_duplicates=True\n", + ")" ] }, { @@ -1049,7 +1051,7 @@ "id": "f8f1b839", "metadata": {}, "source": [ - "Again, let's make sure that everything looks correct in `DLCModel`." + "Again, let's make sure that everything looks correct in `DLCModel`.\n" ] }, { @@ -1069,7 +1071,7 @@ "source": [ "#### [DLCPoseEstimation](#TableOfContents) \n", "\n", - "Alright, now that we've trained model and populated the `DLCModel` table, we're ready to set-up Pose Estimation on a behavioral video of your choice.

      For this tutorial, you can choose to use an epoch of your choice, we can also use the one specified below. If you'd like to use your own video, just specify the `nwb_file_name` and `epoch` number and make sure it's in the `VideoFile` table!" + "Alright, now that we've trained model and populated the `DLCModel` table, we're ready to set-up Pose Estimation on a behavioral video of your choice.

      For this tutorial, you can choose to use an epoch of your choice, we can also use the one specified below. If you'd like to use your own video, just specify the `nwb_file_name` and `epoch` number and make sure it's in the `VideoFile` table!\n" ] }, { @@ -1242,8 +1244,8 @@ "metadata": {}, "outputs": [], "source": [ - "epoch = 14 #change based on VideoFile entry\n", - "video_file_num = 0 #change based on VideoFile entry" + "epoch = 14 # change based on VideoFile entry\n", + "video_file_num = 0 # change based on VideoFile entry" ] }, { @@ -1260,7 +1262,7 @@ "- `video_file_num` will be 0 in almost all\n", " cases.\n", "- `gputouse` was already set during training. It may be a good idea to make sure\n", - " that core is still free before moving forward." + " that core is still free before moving forward.\n" ] }, { @@ -1270,7 +1272,10 @@ "source": [ "The `DLCPoseEstimationSelection` insertion step will convert your .h264 video to an .mp4 first and save it in `/nimbus/deeplabcut/video`. If this video already exists here, the insertion will never complete.\n", "\n", - "We first delete any .mp4 that exists for this video from the nimbus folder:" + "We first delete any .mp4 that exists for this video from the nimbus folder.\n", + "Remove the `#` to run this line. The `!` tells the notebook that this is\n", + "a system command to be run with a shell script instead of python.\n", + "Be sure to change the string based on date and rat with which you are training the model\n" ] }, { @@ -1280,7 +1285,7 @@ "metadata": {}, "outputs": [], "source": [ - "! find /nimbus/deeplabcut/video -type f -name '*20210604_J16*' -delete # change based on date and rat with which you are training the model" + "#! find /nimbus/deeplabcut/video -type f -name '*20210604_J16*' -delete" ] }, { @@ -1309,7 +1314,7 @@ " \"video_file_num\": video_file_num,\n", " **model_key,\n", " },\n", - " task_mode=\"trigger\", #trigger or load\n", + " task_mode=\"trigger\", # trigger or load\n", " params={\"gputouse\": gputouse, \"videotype\": \"mp4\"},\n", ")" ] @@ -1320,6 +1325,7 @@ "metadata": {}, "source": [ "If the above insertion step fails in either trigger or load mode for an epoch, run the following lines:\n", + "\n", "```\n", "(pose_estimation_key = sgp.DLCPoseEstimationSelection.insert_estimation_task(\n", " {\n", @@ -1328,7 +1334,7 @@ " \"video_file_num\": video_file_num,\n", " **model_key,\n", " }).delete()\n", - "```" + "```\n" ] }, { @@ -1336,7 +1342,7 @@ "id": "5feb2a26-fae1-41ca-828f-cc6c73ebd24e", "metadata": {}, "source": [ - "And now we populate `DLCPoseEstimation`! This might take some time for full datasets." + "And now we populate `DLCPoseEstimation`! This might take some time for full datasets.\n" ] }, { @@ -1354,7 +1360,7 @@ "id": "88757488-cfa4-4e7c-b965-7dacac43810a", "metadata": {}, "source": [ - "Let's visualize the output from Pose Estimation" + "Let's visualize the output from Pose Estimation\n" ] }, { @@ -1372,7 +1378,7 @@ "id": "52f45ab3-9344-4975-b5ff-f80a5727cdac", "metadata": {}, "source": [ - "#### [DLCSmoothInterp](#TableOfContents) " + "#### [DLCSmoothInterp](#TableOfContents) \n" ] }, { @@ -1380,7 +1386,7 @@ "id": "0ccd5dbe-097a-4138-a234-da78a5902684", "metadata": {}, "source": [ - "Now that we've completed pose estimation, it's time to identify NaNs and optionally interpolate over low likelihood periods and smooth the resulting positions.
      First we need to define some parameters for smoothing and interpolation. We can see the default parameter set below.
      __Note__: it is recommended to use the `just_nan` parameters here and save interpolation and smoothing for the centroid step as this provides for a better end result." + "Now that we've completed pose estimation, it's time to identify NaNs and optionally interpolate over low likelihood periods and smooth the resulting positions.
      First we need to define some parameters for smoothing and interpolation. We can see the default parameter set below.
      **Note**: it is recommended to use the `just_nan` parameters here and save interpolation and smoothing for the centroid step as this provides for a better end result.\n" ] }, { @@ -1403,7 +1409,7 @@ "source": [ "# The just_nan parameter set that identifies NaN indices and leaves smoothing and interpolation to the centroid step\n", "print(sgp.DLCSmoothInterpParams.get_nan_params())\n", - "si_params_name = \"just_nan\" #could also use \"default\"" + "si_params_name = \"just_nan\" # could also use \"default\"" ] }, { @@ -1428,7 +1434,7 @@ " {\"dlc_si_params_name\": si_params_name, \"params\": params},\n", " skip_duplicates=True,\n", ")\n", - "```" + "```\n" ] }, { @@ -1436,7 +1442,7 @@ "id": "8139036e-ce7e-41ec-be78-aa15a4b0b795", "metadata": {}, "source": [ - "We'll create a dictionary with the correct set of keys for the `DLCSmoothInterpSelection` table" + "We'll create a dictionary with the correct set of keys for the `DLCSmoothInterpSelection` table\n" ] }, { @@ -1458,7 +1464,7 @@ "metadata": {}, "source": [ "We can insert all of the bodyparts we want to process into `DLCSmoothInterpSelection`
      \n", - "First lets visualize the bodyparts we have available to us.
      " + "First lets visualize the bodyparts we have available to us.
      \n" ] }, { @@ -1476,7 +1482,7 @@ "id": "7c6e3ad2-1960-43cd-a223-784c08211013", "metadata": {}, "source": [ - "We can use `insert1` to insert a single bodypart, but would suggest using `insert` to insert a list of keys with different bodyparts." + "We can use `insert1` to insert a single bodypart, but would suggest using `insert` to insert a list of keys with different bodyparts.\n" ] }, { @@ -1494,7 +1500,7 @@ " 'dlc_si_params_name': si_params_name,\n", " },\n", " skip_duplicates=True)\n", - "```" + "```\n" ] }, { @@ -1502,7 +1508,7 @@ "id": "3e2f73cd-2534-40a2-86e6-948ccd902812", "metadata": {}, "source": [ - "We'll see a list of bodyparts and then insert them into `DLCSmoothInterpSelection`." + "We'll see a list of bodyparts and then insert them into `DLCSmoothInterpSelection`.\n" ] }, { @@ -1531,7 +1537,7 @@ "id": "6dca5640-3e9a-42b7-bc61-7f3e1a219619", "metadata": {}, "source": [ - "And verify the entry:" + "And verify the entry:\n" ] }, { @@ -1550,7 +1556,7 @@ "metadata": {}, "source": [ "Now, we populate `DLCSmoothInterp`, which will perform smoothing and\n", - "interpolation on all of the bodyparts specified." + "interpolation on all of the bodyparts specified.\n" ] }, { @@ -1568,7 +1574,7 @@ "id": "3d3af0a2-16cc-43dc-af9c-0ec606cfe1e1", "metadata": {}, "source": [ - "And let's visualize the resulting position data using a scatter plot" + "And let's visualize the resulting position data using a scatter plot\n" ] }, { @@ -1578,7 +1584,8 @@ "metadata": {}, "outputs": [], "source": [ - "(sgp.DLCSmoothInterp() & {**si_key, \"bodypart\": bodyparts[0]}\n", + "(\n", + " sgp.DLCSmoothInterp() & {**si_key, \"bodypart\": bodyparts[0]}\n", ").fetch1_dataframe().plot.scatter(x=\"x\", y=\"y\", s=1, figsize=(5, 5))" ] }, @@ -1587,7 +1594,7 @@ "id": "a838e4c4-8ff9-4b73-aee5-00eb91ea899f", "metadata": {}, "source": [ - "#### [DLCSmoothInterpCohort](#TableOfContents) " + "#### [DLCSmoothInterpCohort](#TableOfContents) \n" ] }, { @@ -1597,7 +1604,7 @@ "source": [ "After smoothing/interpolation, we need to select bodyparts from which we want to\n", "derive a centroid and orientation, which is performed by the\n", - "`DLCSmoothInterpCohort` table." + "`DLCSmoothInterpCohort` table.\n" ] }, { @@ -1607,7 +1614,7 @@ "source": [ "First, let's make a key that represents the 'cohort', using\n", "`dlc_si_cohort_selection_name`. We'll need a bodypart dictionary using bodypart\n", - "keys and smoothing/interpolation parameters used as value." + "keys and smoothing/interpolation parameters used as value.\n" ] }, { @@ -1635,7 +1642,7 @@ "id": "11c6a327-d4b0-4de1-a2c6-10a0443a3f96", "metadata": {}, "source": [ - "We'll insert the cohort into `DLCSmoothInterpCohortSelection` and populate `DLCSmoothInterpCohort`, which collates the separately smoothed and interpolated bodyparts into a single entry." + "We'll insert the cohort into `DLCSmoothInterpCohortSelection` and populate `DLCSmoothInterpCohort`, which collates the separately smoothed and interpolated bodyparts into a single entry.\n" ] }, { @@ -1654,7 +1661,7 @@ "id": "a6b7d361-47c5-4748-ac59-f51b897f7fe6", "metadata": {}, "source": [ - "And verify the entry:" + "And verify the entry:\n" ] }, { @@ -1672,7 +1679,7 @@ "id": "d871bdca-2278-43ec-a70c-52257ad26170", "metadata": {}, "source": [ - "#### [DLCCentroid](#TableOfContents) " + "#### [DLCCentroid](#TableOfContents) \n" ] }, { @@ -1680,7 +1687,7 @@ "id": "4cc37edb-fdd3-4a05-8cd5-91f3c5f7cbbb", "metadata": {}, "source": [ - "With this cohort, we can determine a centroid using another set of parameters." + "With this cohort, we can determine a centroid using another set of parameters.\n" ] }, { @@ -1719,7 +1726,7 @@ " },\n", " skip_duplicates=True,\n", ")\n", - "```" + "```\n" ] }, { @@ -1727,7 +1734,7 @@ "id": "85ad4e53-43dd-4e05-84c4-7d4504766746", "metadata": {}, "source": [ - "We'll make a key to insert into `DLCCentroidSelection`." + "We'll make a key to insert into `DLCCentroidSelection`.\n" ] }, { @@ -1749,7 +1756,7 @@ "id": "2674c0d3-d3fd-4cd9-a843-260c442c2d23", "metadata": {}, "source": [ - "After inserting into the selection table, we can populate `DLCCentroid`" + "After inserting into the selection table, we can populate `DLCCentroid`\n" ] }, { @@ -1768,7 +1775,7 @@ "id": "6e49c5ad-909f-4f1a-a156-f8f8a84fb78a", "metadata": {}, "source": [ - "Here we can visualize the resulting centroid position" + "Here we can visualize the resulting centroid position\n" ] }, { @@ -1794,7 +1801,7 @@ "id": "cb513a9d-5250-404c-8887-639f785516c7", "metadata": {}, "source": [ - "#### [DLCOrientation](#TableOfContents) " + "#### [DLCOrientation](#TableOfContents) \n" ] }, { @@ -1802,7 +1809,7 @@ "id": "509076f0-f0b8-4fd0-8884-32c48ca4a125", "metadata": {}, "source": [ - "We'll now go through a similar process to identify the orientation." + "We'll now go through a similar process to identify the orientation.\n" ] }, { @@ -1821,7 +1828,7 @@ "id": "8ec170be-7a7a-4a20-986c-d055aee1a08b", "metadata": {}, "source": [ - "We'll prune the `cohort_key` we used above and add our `dlc_orientation_params_name` to make it suitable for `DLCOrientationSelection`." + "We'll prune the `cohort_key` we used above and add our `dlc_orientation_params_name` to make it suitable for `DLCOrientationSelection`.\n" ] }, { @@ -1842,7 +1849,7 @@ "id": "9406d2de-9b71-4591-82f6-ed53f2d4f220", "metadata": {}, "source": [ - "We'll insert into `DLCOrientationSelection` and populate `DLCOrientation`" + "We'll insert into `DLCOrientationSelection` and populate `DLCOrientation`\n" ] }, { @@ -1861,7 +1868,7 @@ "id": "36f62da0-0cc5-4ffb-b2df-7b68c3f6e268", "metadata": {}, "source": [ - "We can fetch the orientation as a dataframe as quality assurance." + "We can fetch the orientation as a dataframe as quality assurance.\n" ] }, { @@ -1879,7 +1886,7 @@ "id": "dc75aeaf-018a-46ed-83a8-6603ae100791", "metadata": {}, "source": [ - "#### [DLCPosV1](#TableOfContents) " + "#### [DLCPosV1](#TableOfContents) \n" ] }, { @@ -1887,11 +1894,11 @@ "id": "21d3f9ba-dc89-4c32-a125-1fa85cd4132d", "metadata": {}, "source": [ - "After processing the position data, we have to do a few table manipulations to standardize various outputs. \n", + "After processing the position data, we have to do a few table manipulations to standardize various outputs.\n", "\n", "To summarize, we brought in a pretrained DLC project, used that model to run pose estimation on a new behavioral video, smoothed and interpolated the result, formed a cohort of bodyparts, and determined the centroid and orientation of this cohort.\n", "\n", - "Now we'll populate `DLCPos` with our centroid/orientation entries above." + "Now we'll populate `DLCPos` with our centroid/orientation entries above.\n" ] }, { @@ -1918,7 +1925,7 @@ "id": "551e4c5e-7c32-46b0-a138-80064a212fbe", "metadata": {}, "source": [ - "Now we can insert into `DLCPosSelection` and populate `DLCPos` with our `dlc_key`" + "Now we can insert into `DLCPosSelection` and populate `DLCPos` with our `dlc_key`\n" ] }, { @@ -1938,7 +1945,8 @@ "metadata": {}, "source": [ "We can also make sure that all of our data made it through by fetching the dataframe attached to this entry.
      We should expect 8 columns:\n", - ">time
      video_frame_ind
      position_x
      position_y
      orientation
      velocity_x
      velocity_y
      speed" + "\n", + "> time
      video_frame_ind
      position_x
      position_y
      orientation
      velocity_x
      velocity_y
      speed\n" ] }, { @@ -1956,7 +1964,7 @@ "id": "2d8623a8-1725-4e02-b1a2-d2f993988102", "metadata": {}, "source": [ - "And even more, we can fetch the `pose_eval_result` that is calculated during this step. This field contains the percentage of frames that each bodypart was below the likelihood threshold of 0.95 as a means of assessing the quality of the pose estimation." + "And even more, we can fetch the `pose_eval_result` that is calculated during this step. This field contains the percentage of frames that each bodypart was below the likelihood threshold of 0.95 as a means of assessing the quality of the pose estimation.\n" ] }, { @@ -1974,7 +1982,7 @@ "id": "b2303147-3657-479c-8f72-b3fc6905a596", "metadata": {}, "source": [ - "#### [DLCPosVideo](#TableOfContents) " + "#### [DLCPosVideo](#TableOfContents) \n" ] }, { @@ -1984,7 +1992,7 @@ "source": [ "We can create a video with the centroid and orientation overlaid on the original\n", "video. This will also plot the likelihood of each bodypart used in the cohort.\n", - "This is optional, but a good quality assurance step." + "This is optional, but a good quality assurance step.\n" ] }, { @@ -2042,7 +2050,7 @@ "id": "5a68bba8-9871-40ac-84c9-51ac0e76d44e", "metadata": {}, "source": [ - "#### [PositionOutput](#TableOfContents) " + "#### [PositionOutput](#TableOfContents) \n" ] }, { @@ -2051,7 +2059,7 @@ "metadata": {}, "source": [ "`PositionOutput` is the final table of the pipeline and is automatically\n", - "populated when we populate `DLCPosV1`" + "populated when we populate `DLCPosV1`\n" ] }, { @@ -2069,7 +2077,7 @@ "id": "c414d9e0-e495-42ef-a8b0-1c7d53aed02e", "metadata": {}, "source": [ - "`PositionOutput` also has a part table, similar to the `DLCModelSource` table above. Let's check that out as well." + "`PositionOutput` also has a part table, similar to the `DLCModelSource` table above. Let's check that out as well.\n" ] }, { @@ -2097,7 +2105,7 @@ "id": "e48c7a4e-0bbc-4101-baf2-e84f1f5739d5", "metadata": {}, "source": [ - "#### [PositionVideo](#TableOfContents)" + "#### [PositionVideo](#TableOfContents)\n" ] }, { @@ -2109,7 +2117,7 @@ "centroid and orientation on the video. This table uses the parameter `plot` to\n", "determine whether to plot the entry deriving from the DLC arm or from the Trodes\n", "arm of the position pipeline. This parameter also accepts 'all', which will plot\n", - "both (if they exist) in order to compare results." + "both (if they exist) in order to compare results.\n" ] }, { @@ -2147,7 +2155,8 @@ "metadata": {}, "source": [ "### _CONGRATULATIONS!!_\n", - "Please treat yourself to a nice tea break :-)" + "\n", + "Please treat yourself to a nice tea break :-)\n" ] }, { @@ -2155,7 +2164,7 @@ "id": "c71c90a2", "metadata": {}, "source": [ - "### [Return To Table of Contents](#TableOfContents)
      " + "### [Return To Table of Contents](#TableOfContents)
      \n" ] } ], diff --git a/notebooks/22_DLC_Loop.ipynb b/notebooks/22_DLC_Loop.ipynb index 4d9d33e77..19fadd24f 100644 --- a/notebooks/22_DLC_Loop.ipynb +++ b/notebooks/22_DLC_Loop.ipynb @@ -363,7 +363,7 @@ "id": "f5d83452-48eb-4669-89eb-a6beb1f2d051", "metadata": {}, "source": [ - "Now that we've intialized our project we'll need to extract frames which we will then label. " + "Now that we've initialized our project we'll need to extract frames which we will then label. " ] }, { @@ -373,7 +373,7 @@ "metadata": {}, "outputs": [], "source": [ - "#comment this line out after you finish frame extraction for each project\n", + "# comment this line out after you finish frame extraction for each project\n", "sgp.DLCProject().run_extract_frames(project_key)" ] }, @@ -715,11 +715,10 @@ "metadata": {}, "outputs": [], "source": [ - "#comment these lines out after successfully inserting, for each project\n", - "sgp.DLCModelSelection().insert1({\n", - " **temp_model_key,\n", - " \"dlc_model_params_name\": \"default\"},\n", - " skip_duplicates=True)" + "# comment these lines out after successfully inserting, for each project\n", + "sgp.DLCModelSelection().insert1(\n", + " {**temp_model_key, \"dlc_model_params_name\": \"default\"}, skip_duplicates=True\n", + ")" ] }, { @@ -838,20 +837,23 @@ "source": [ "for row in matching_rows:\n", " col1val = row[\"nwb_file_name\"]\n", - " if \"SC3820230606\" in col1val: #*** change depending on rat/day!!!\n", + " if \"SC3820230606\" in col1val: # *** change depending on rat/day!!!\n", " col2val = row[\"epoch\"]\n", " col3val = row[\"video_file_num\"]\n", "\n", " ##insert pose estimation task\n", - " pose_estimation_key = sgp.DLCPoseEstimationSelection.insert_estimation_task(\n", - " {\"nwb_file_name\": col1val,\n", - " \"epoch\": col2val,\n", - " \"video_file_num\": col3val,\n", - " **model_key\n", - " },\n", - " task_mode = \"trigger\", #load or trigger\n", - " params = {\"gputouse\": gputouse, \"videotype\": \"mp4\"}\n", - " )\n", + " pose_estimation_key = (\n", + " sgp.DLCPoseEstimationSelection.insert_estimation_task(\n", + " {\n", + " \"nwb_file_name\": col1val,\n", + " \"epoch\": col2val,\n", + " \"video_file_num\": col3val,\n", + " **model_key,\n", + " },\n", + " task_mode=\"trigger\", # load or trigger\n", + " params={\"gputouse\": gputouse, \"videotype\": \"mp4\"},\n", + " )\n", + " )\n", "\n", " ##populate DLC Pose Estimation\n", " sgp.DLCPoseEstimation().populate(pose_estimation_key)\n", @@ -863,18 +865,19 @@ " si_key = {key: val for key, val in si_key.items() if key in fields}\n", " bodyparts = [\"greenLED\", \"redLED_C\"]\n", " sgp.DLCSmoothInterpSelection.insert(\n", - " [\n", - " {\n", + " [\n", + " {\n", " **si_key,\n", " \"bodypart\": bodypart,\n", " \"dlc_si_params_name\": si_params_name,\n", - " }\n", + " }\n", " for bodypart in bodyparts\n", " ],\n", - " skip_duplicates = True,\n", - " )\n", + " skip_duplicates=True,\n", + " )\n", " sgp.DLCSmoothInterp().populate(si_key)\n", - " (sgp.DLCSmoothInterp() & {**si_key, \"bodypart\": bodyparts[0]}\n", + " (\n", + " sgp.DLCSmoothInterp() & {**si_key, \"bodypart\": bodyparts[0]}\n", " ).fetch1_dataframe().plot.scatter(x=\"x\", y=\"y\", s=1, figsize=(5, 5))\n", "\n", " ##smoothinterpcohort\n", @@ -884,15 +887,22 @@ " if \"dlc_si_params_name\" in cohort_key:\n", " del cohort_key[\"dlc_si_params_name\"]\n", " cohort_key[\"dlc_si_cohort_selection_name\"] = \"green_red_led\"\n", - " cohort_key[\"bodyparts_params_dict\"] = {\"greenLED\": si_params_name, \"redLED_C\": si_params_name,}\n", - " sgp.DLCSmoothInterpCohortSelection().insert1(cohort_key, skip_duplicates=True)\n", + " cohort_key[\"bodyparts_params_dict\"] = {\n", + " \"greenLED\": si_params_name,\n", + " \"redLED_C\": si_params_name,\n", + " }\n", + " sgp.DLCSmoothInterpCohortSelection().insert1(\n", + " cohort_key, skip_duplicates=True\n", + " )\n", " sgp.DLCSmoothInterpCohort.populate(cohort_key)\n", "\n", " ##DLC Centroid\n", " centroid_params_name = \"default\"\n", " centroid_key = cohort_key.copy()\n", " fields = list(sgp.DLCCentroidSelection.fetch().dtype.fields.keys())\n", - " centroid_key = {key: val for key, val in centroid_key.items() if key in fields}\n", + " centroid_key = {\n", + " key: val for key, val in centroid_key.items() if key in fields\n", + " }\n", " centroid_key[\"dlc_centroid_params_name\"] = centroid_params_name\n", " sgp.DLCCentroidSelection.insert1(centroid_key, skip_duplicates=True)\n", " sgp.DLCCentroid.populate(centroid_key)\n", @@ -909,15 +919,21 @@ " ##DLC Orientation\n", " dlc_orientation_params_name = \"default\"\n", " fields = list(sgp.DLCOrientationSelection.fetch().dtype.fields.keys())\n", - " orient_key = {key: val for key, val in cohort_key.items() if key in fields}\n", + " orient_key = {\n", + " key: val for key, val in cohort_key.items() if key in fields\n", + " }\n", " orient_key[\"dlc_orientation_params_name\"] = dlc_orientation_params_name\n", " sgp.DLCOrientationSelection().insert1(orient_key, skip_duplicates=True)\n", " sgp.DLCOrientation().populate(orient_key)\n", "\n", " ##DLCPosV1\n", " fields = list(sgp.DLCPosV1.fetch().dtype.fields.keys())\n", - " dlc_key = {key: val for key, val in centroid_key.items() if key in fields}\n", - " dlc_key[\"dlc_si_cohort_centroid\"] = centroid_key[\"dlc_si_cohort_selection_name\"]\n", + " dlc_key = {\n", + " key: val for key, val in centroid_key.items() if key in fields\n", + " }\n", + " dlc_key[\"dlc_si_cohort_centroid\"] = centroid_key[\n", + " \"dlc_si_cohort_selection_name\"\n", + " ]\n", " dlc_key[\"dlc_si_cohort_orientation\"] = orient_key[\n", " \"dlc_si_cohort_selection_name\"\n", " ]\n", diff --git a/notebooks/42_Decoding_Clusterless.ipynb b/notebooks/42_Decoding_Clusterless.ipynb index 3936f88f5..5c4ffd11e 100644 --- a/notebooks/42_Decoding_Clusterless.ipynb +++ b/notebooks/42_Decoding_Clusterless.ipynb @@ -1458,7 +1458,7 @@ "source": [ "from non_local_detector.environment import Environment\n", "\n", - "Environment?" + "?Environment" ] }, { diff --git a/notebooks/README.md b/notebooks/README.md index ab18707cd..e5d540c06 100644 --- a/notebooks/README.md +++ b/notebooks/README.md @@ -4,36 +4,66 @@ There are several paths one can take to these notebooks. The notebooks have two-digits in their names, the first of which indicates it's 'batch', as described in the categories below. - - ## 0. Intro -Everyone should complete the [Setup](./00_Setup.ipynb) and [Insert Data](./01_Insert_Data.ipynb) notebooks. +Everyone should complete the [Setup](./00_Setup.ipynb) and +[Insert Data](./01_Insert_Data.ipynb) notebooks. -[Data Sync](./02_Data_Sync.ipynb) is an optional additional tool for collaborators that want to share analysis files. +[Data Sync](./02_Data_Sync.ipynb) is an optional additional tool for +collaborators that want to share analysis files. -The [Merge Tables notebook](./03_Merge_Tables.ipynb) explains details on a new table tier unique to Spyglass that allows the user to use different versions of pipelines on the same data. This is important for understanding the later notebooks. +The [Merge Tables notebook](./03_Merge_Tables.ipynb) explains details on a new +table tier unique to Spyglass that allows the user to use different versions of +pipelines on the same data. This is important for understanding the later +notebooks. ## 1. Spike Sorting Pipeline -This series of notebooks covers the process of spike sorting, from automated spike sorting to optional manual curation of the output of the automated sorting. +This series of notebooks covers the process of spike sorting, from automated +spike sorting to optional manual curation of the output of the automated +sorting. ## 2. Position Pipeline -This series of notebooks covers tracking the position(s) of the animal. The user can employ two different methods: +This series of notebooks covers tracking the position(s) of the animal. The user +can employ two different methods: -1. the simple [Trodes](20_Position_Trodes.ipynb) methods of tracking LEDs on the animal's headstage -2. [DLC (DeepLabCut)](./21_Position_DLC_1.ipynb) which uses a neural network to track the animal's body parts +1. the simple [Trodes](20_Position_Trodes.ipynb) methods of tracking LEDs on the + animal's headstage +2. [DLC (DeepLabCut)](./21_DLC.ipynb) which uses a neural network to track the + animal's body parts. -Either case can be followed by the [Linearization notebook](./24_Linearization.ipynb) if the user wants to linearize the position data for later use. +Either case can be followed by the +[Linearization notebook](./24_Linearization.ipynb) if the user wants to +linearize the position data for later use. ## 3. LFP Pipeline -This series of notebooks covers the process of LFP analysis. The [LFP](./30_LFP.ipynb) covers the extraction of the LFP in specific bands from the raw data. The [Theta](./31_Theta.ipynb) notebook shows specifically how to extract the theta band power and phase from the LFP data. Finally the [Ripple Detection](./32_Ripple_Detection.ipynb) notebook shows how to detect ripples in the LFP data. +This series of notebooks covers the process of LFP analysis. The +[LFP](./30_LFP.ipynb) covers the extraction of the LFP in specific bands from +the raw data. The [Theta](./31_Theta.ipynb) notebook shows specifically how to +extract the theta band power and phase from the LFP data. Finally the +[Ripple Detection](./32_Ripple_Detection.ipynb) notebook shows how to detect +ripples in the LFP data. ## 4. Decoding Pipeline -This series of notebooks covers the process of decoding the position of the animal from spiking data. It relies on the position data from the Position pipeline and the output of spike sorting from the Spike Sorting pipeline. Decoding can be from sorted or from unsorted data using spike waveform features (so-called clusterless decoding). The first notebook([Extracting Clusterless Waveform Features](./41_Extracting_Clusterless_Waveform_Features.ipynb)) in this series shows how to retrieve the spike waveform features used for clusterless decoding. The second notebook ([Clusterless Decoding](./42_Decoding_Clusterless.ipynb)) shows a detailed example of how to decode the position of the animal from the spike waveform features. The third notebook ([Decoding](./43_Decoding.ipynb)) shows how to decode the position of the animal from the sorted spikes. +This series of notebooks covers the process of decoding the position of the +animal from spiking data. It relies on the position data from the Position +pipeline and the output of spike sorting from the Spike Sorting pipeline. +Decoding can be from sorted or from unsorted data using spike waveform features +(so-called clusterless decoding). + +The first notebook +([Extracting Clusterless Waveform Features](./41_Extracting_Clusterless_Waveform_Features.ipynb)) +in this series shows how to retrieve the spike waveform features used for +clusterless decoding. + +The second notebook ([Clusterless Decoding](./42_Decoding_Clusterless.ipynb)) +shows a detailed example of how to decode the position of the animal from the +spike waveform features. The third notebook +([Decoding](./43_Decoding_SortedSpikes.ipynb)) shows how to decode the position +of the animal from the sorted spikes. ## Developer note diff --git a/notebooks/py_scripts/21_DLC.py b/notebooks/py_scripts/21_DLC.py new file mode 100644 index 000000000..996dd1764 --- /dev/null +++ b/notebooks/py_scripts/21_DLC.py @@ -0,0 +1,804 @@ +# --- +# jupyter: +# jupytext: +# text_representation: +# extension: .py +# format_name: light +# format_version: '1.5' +# jupytext_version: 1.16.0 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# ## Position- DeepLabCut from Scratch +# + +# ### Overview +# + +# _Developer Note:_ if you may make a PR in the future, be sure to copy this +# notebook, and use the `gitignore` prefix `temp` to avoid future conflicts. +# +# This is one notebook in a multi-part series on Spyglass. +# +# - To set up your Spyglass environment and database, see +# [the Setup notebook](./00_Setup.ipynb) +# - For additional info on DataJoint syntax, including table definitions and +# inserts, see +# [the Insert Data notebook](./01_Insert_Data.ipynb) +# +# This tutorial will extract position via DeepLabCut (DLC). It will walk through... +# +# - creating a DLC project +# - extracting and labeling frames +# - training your model +# - executing pose estimation on a novel behavioral video +# - processing the pose estimation output to extract a centroid and orientation +# - inserting the resulting information into the `PositionOutput` table +# +# **Note 2: Make sure you are running this within the spyglass-position Conda environment (instructions for install are in the environment_position.yml)** +# + +# Here is a schematic showing the tables used in this pipeline. +# +# ![dlc_scratch.png|2000x900](./../notebook-images/dlc_scratch.png) +# + +# ### Table of Contents +# +# [`DLCProject`](#DLCProject1)
      +# [`DLCModelTraining`](#DLCModelTraining1)
      +# [`DLCModel`](#DLCModel1)
      +# [`DLCPoseEstimation`](#DLCPoseEstimation1)
      +# [`DLCSmoothInterp`](#DLCSmoothInterp1)
      +# [`DLCCentroid`](#DLCCentroid1)
      +# [`DLCOrientation`](#DLCOrientation1)
      +# [`DLCPosV1`](#DLCPosV1-1)
      +# [`DLCPosVideo`](#DLCPosVideo1)
      +# [`PositionOutput`](#PositionOutput1)
      +# + +# **You can click on any header to return to the Table of Contents** +# + +# ### Imports +# + +# %load_ext autoreload +# %autoreload 2 + +# + +import os +import datajoint as dj + +import spyglass.common as sgc +import spyglass.position.v1 as sgp + +import numpy as np +import pandas as pd +import pynwb +from spyglass.position import PositionOutput + +# change to the upper level folder to detect dj_local_conf.json +if os.path.basename(os.getcwd()) == "notebooks": + os.chdir("..") +dj.config.load("dj_local_conf.json") # load config for database connection info + +# ignore datajoint+jupyter async warnings +import warnings + +warnings.simplefilter("ignore", category=DeprecationWarning) +warnings.simplefilter("ignore", category=ResourceWarning) +# - + +# #### [DLCProject](#TableOfContents) +# + +#
      +# Notes:
        +#
      • +# The cells within this DLCProject step need to be performed +# in a local Jupyter notebook to allow for use of the frame labeling GUI. +#
        • +#
      • +# Please do not add to the BodyPart table in the production +# database unless necessary. +#
        • +#
      +#
      +# + +# ### Body Parts +# + +# We'll begin by looking at the `BodyPart` table, which stores standard names of body parts used in DLC models throughout the lab with a concise description. +# + +sgp.BodyPart() + +# If the bodyparts you plan to use in your model are not yet in the table, here is code to add bodyparts: +# +# ```python +# sgp.BodyPart.insert( +# [ +# {"bodypart": "bp_1", "bodypart_description": "concise descrip"}, +# {"bodypart": "bp_2", "bodypart_description": "concise descrip"}, +# ], +# skip_duplicates=True, +# ) +# ``` +# + +# ### Define videos and camera name (optional) for training set +# + +# To train a model, we'll need to extract frames, which we can label as training data. We can construct a list of videos from which we'll extract frames. +# +# The list can either contain dictionaries identifying behavioral videos for NWB files that have already been added to Spyglass, or absolute file paths to the videos you want to use. +# +# For this tutorial, we'll use two videos for which we already have frames labeled. +# + +# Defining camera name is optional: it should be done in cases where there are multiple cameras streaming per epoch, but not necessary otherwise.
      +# example: +# `camera_name = "HomeBox_camera" +# ` +# + +# _NOTE:_ The official release of Spyglass does not yet support multicamera +# projects. You can monitor progress on the effort to add this feature by checking +# [this PR](https://github.com/LorenFrankLab/spyglass/pull/684) or use +# [this experimental branch](https://github.com/dpeg22/spyglass/tree/add-multi-camera), +# which takes the keys nwb_file_name and epoch, and camera_name in the video_list variable. +# + +video_list = [ + {"nwb_file_name": "J1620210529_.nwb", "epoch": 2}, + {"nwb_file_name": "peanut20201103_.nwb", "epoch": 4}, +] + +# ### Path variables +# +# The position pipeline also keeps track of paths for project, video, and output. +# Just like we saw in [Setup](./00_Setup.ipynb), you can manage these either with +# environmental variables... +# +# ```bash +# export DLC_PROJECT_DIR="/nimbus/deeplabcut/projects" +# export DLC_VIDEO_DIR="/nimbus/deeplabcut/video" +# export DLC_OUTPUT_DIR="/nimbus/deeplabcut/output" +# ``` +# +# +# +# Or these can be set in your datajoint config: +# +# ```json +# { +# "custom": { +# "dlc_dirs": { +# "base": "/nimbus/deeplabcut/", +# "project": "/nimbus/deeplabcut/projects", +# "video": "/nimbus/deeplabcut/video", +# "output": "/nimbus/deeplabcut/output" +# } +# } +# } +# ``` +# +# _NOTE:_ If only `base` is specified as shown above, spyglass will assume the +# relative directories shown. +# +# You can check the result of this setup process with... +# + +# + +from spyglass.settings import config + +config +# - + +# Before creating our project, we need to define a few variables. +# +# - A team name, as shown in `LabTeam` for setting permissions. Here, we'll +# use "LorenLab". +# - A `project_name`, as a unique identifier for this DLC project. Here, we'll use +# **"tutorial_scratch_yourinitials"** +# - `bodyparts` is a list of body parts for which we want to extract position. +# The pre-labeled frames we're using include the bodyparts listed below. +# - Number of frames to extract/label as `frames_per_video`. Note that the DLC creators recommend having 200 frames as the minimum total number for each project. +# + +team_name = "LorenLab" +project_name = "tutorial_scratch_DG" +frames_per_video = 100 +bodyparts = ["redLED_C", "greenLED", "redLED_L", "redLED_R", "tailBase"] +project_key = sgp.DLCProject.insert_new_project( + project_name=project_name, + bodyparts=bodyparts, + lab_team=team_name, + frames_per_video=frames_per_video, + video_list=video_list, + skip_duplicates=True, +) + +# Now that we've initialized our project we'll need to extract frames which we will then label. +# + +# comment this line out after you finish frame extraction for each project +sgp.DLCProject().run_extract_frames(project_key) + +# This is the line used to label the frames you extracted, if you wish to use the DLC GUI on the computer you are currently using. +# +# ```#comment this line out after frames are labeled for your project +# sgp.DLCProject().run_label_frames(project_key) +# ``` +# + +# Otherwise, it is best/easiest practice to label the frames on your local computer (like a MacBook) that can run DeepLabCut's GUI well. Instructions:
      +# +# 1. Install DLC on your local (preferably into a 'Src' folder): https://deeplabcut.github.io/DeepLabCut/docs/installation.html +# 2. Upload frames extracted and saved in nimbus (should be `/nimbus/deeplabcut//labeled-data`) AND the project's associated config file (should be `/nimbus/deeplabcut//config.yaml`) to Box (we get free with UCSF) +# 3. Download labeled-data and config files on your local from Box +# 4. Create a 'projects' folder where you installed DeepLabCut; create a new folder with your complete project name there; save the downloaded files there. +# 5. Edit the config.yaml file: line 9 defining `project_path` needs to be the file path where it is saved on your local (ex: `/Users/lorenlab/Src/DeepLabCut/projects/tutorial_sratch_DG-LorenLab-2023-08-16`) +# 6. Open the DLC GUI through terminal +#
      (ex: `conda activate miniconda/envs/DEEPLABCUT_M1` +#
      `pythonw -m deeplabcut`) +# 7. Load an existing project; choose the config.yaml file +# 8. Label frames; labeling tutorial: https://www.youtube.com/watch?v=hsA9IB5r73E. +# 9. Once all frames are labeled, you should re-upload labeled-data folder back to Box and overwrite it in the original nimbus location so that your completed frames are ready to be used in the model. +# + +# Now we can check the `DLCProject.File` part table and see all of our training files and videos there! +# + +sgp.DLCProject.File & project_key + +#
      +# This step and beyond should be run on a GPU-enabled machine. +#
      +# + +# #### [DLCModelTraining](#ToC) +# +# Please make sure you're running this notebook on a GPU-enabled machine. +# +# Now that we've imported existing frames, we can get ready to train our model. +# +# First, we'll need to define a set of parameters for `DLCModelTrainingParams`, which will get used by DeepLabCut during training. Let's start with `gputouse`, +# which determines which GPU core to use. +# +# The cell below determines which core has space and set the `gputouse` variable +# accordingly. +# + +sgp.dlc_utils.get_gpu_memory() + +# Set GPU core: +# + +gputouse = 1 # 1-9 + +# Now we'll define the rest of our parameters and insert the entry. +# +# To see all possible parameters, try: +# +# ```python +# sgp.DLCModelTrainingParams.get_accepted_params() +# ``` +# + +training_params_name = "tutorial" +sgp.DLCModelTrainingParams.insert_new_params( + paramset_name=training_params_name, + params={ + "trainingsetindex": 0, + "shuffle": 1, + "gputouse": gputouse, + "net_type": "resnet_50", + "augmenter_type": "imgaug", + }, + skip_duplicates=True, +) + +# Next we'll modify the `project_key` from above to include the necessary entries for `DLCModelTraining` +# + +# project_key['project_path'] = os.path.dirname(project_key['config_path']) +if "config_path" in project_key: + del project_key["config_path"] + +# We can insert an entry into `DLCModelTrainingSelection` and populate `DLCModelTraining`. +# +# _Note:_ You can stop training at any point using `I + I` or interrupt the Kernel. +# +# The maximum total number of training iterations is 1030000; you can end training before this amount if the loss rate (lr) and total loss plateau and are very close to 0. +# + +sgp.DLCModelTrainingSelection.heading + +sgp.DLCModelTrainingSelection().insert1( + { + **project_key, + "dlc_training_params_name": training_params_name, + "training_id": 0, + "model_prefix": "", + } +) +model_training_key = ( + sgp.DLCModelTrainingSelection + & { + **project_key, + "dlc_training_params_name": training_params_name, + } +).fetch1("KEY") +sgp.DLCModelTraining.populate(model_training_key) + +# Here we'll make sure that the entry made it into the table properly! +# + +sgp.DLCModelTraining() & model_training_key + +# Populating `DLCModelTraining` automatically inserts the entry into +# `DLCModelSource`, which is used to select between models trained using Spyglass +# vs. other tools. +# + +sgp.DLCModelSource() & model_training_key + +# The `source` field will only accept _"FromImport"_ or _"FromUpstream"_ as entries. Let's checkout the `FromUpstream` part table attached to `DLCModelSource` below. +# + +sgp.DLCModelSource.FromUpstream() & model_training_key + +# #### [DLCModel](#TableOfContents) +# +# Next we'll populate the `DLCModel` table, which holds all the relevant +# information for all trained models. +# +# First, we'll need to determine a set of parameters for our model to select the +# correct model file. Here is the default: +# + +sgp.DLCModelParams.get_default() + +# Here is the syntax to add your own parameter set: +# +# ```python +# dlc_model_params_name = "make_this_yours" +# params = { +# "params": {}, +# "shuffle": 1, +# "trainingsetindex": 0, +# "model_prefix": "", +# } +# sgp.DLCModelParams.insert1( +# {"dlc_model_params_name": dlc_model_params_name, "params": params}, +# skip_duplicates=True, +# ) +# ``` +# + +# We can insert sets of parameters into `DLCModelSelection` and populate +# `DLCModel`. +# + +temp_model_key = (sgp.DLCModelSource & model_training_key).fetch1("KEY") + +# comment these lines out after successfully inserting, for each project +sgp.DLCModelSelection().insert1( + {**temp_model_key, "dlc_model_params_name": "default"}, skip_duplicates=True +) + +model_key = (sgp.DLCModelSelection & temp_model_key).fetch1("KEY") +sgp.DLCModel.populate(model_key) + +# Again, let's make sure that everything looks correct in `DLCModel`. +# + +sgp.DLCModel() & model_key + +# #### [DLCPoseEstimation](#TableOfContents) +# +# Alright, now that we've trained model and populated the `DLCModel` table, we're ready to set-up Pose Estimation on a behavioral video of your choice.

      For this tutorial, you can choose to use an epoch of your choice, we can also use the one specified below. If you'd like to use your own video, just specify the `nwb_file_name` and `epoch` number and make sure it's in the `VideoFile` table! +# + +nwb_file_name = "J1620210604_.nwb" +sgc.VideoFile() & {"nwb_file_name": nwb_file_name} + +epoch = 14 # change based on VideoFile entry +video_file_num = 0 # change based on VideoFile entry + +# Using `insert_estimation_task` will convert out video to be in .mp4 format (DLC +# struggles with .h264) and determine the directory in which we'll store the pose +# estimation results. +# +# - `task_mode` (trigger or load) determines whether or not populating +# `DLCPoseEstimation` triggers a new pose estimation, or loads an existing. +# - `video_file_num` will be 0 in almost all +# cases. +# - `gputouse` was already set during training. It may be a good idea to make sure +# that core is still free before moving forward. +# + +# The `DLCPoseEstimationSelection` insertion step will convert your .h264 video to an .mp4 first and save it in `/nimbus/deeplabcut/video`. If this video already exists here, the insertion will never complete. +# +# We first delete any .mp4 that exists for this video from the nimbus folder. +# Remove the `#` to run this line. The `!` tells the notebook that this is +# a system command to be run with a shell script instead of python. +# Be sure to change the string based on date and rat with which you are training the model +# + +# + +# #! find /nimbus/deeplabcut/video -type f -name '*20210604_J16*' -delete +# - + +pose_estimation_key = sgp.DLCPoseEstimationSelection.insert_estimation_task( + { + "nwb_file_name": nwb_file_name, + "epoch": epoch, + "video_file_num": video_file_num, + **model_key, + }, + task_mode="trigger", # trigger or load + params={"gputouse": gputouse, "videotype": "mp4"}, +) + +# If the above insertion step fails in either trigger or load mode for an epoch, run the following lines: +# +# ``` +# (pose_estimation_key = sgp.DLCPoseEstimationSelection.insert_estimation_task( +# { +# "nwb_file_name": nwb_file_name, +# "epoch": epoch, +# "video_file_num": video_file_num, +# **model_key, +# }).delete() +# ``` +# + +# And now we populate `DLCPoseEstimation`! This might take some time for full datasets. +# + +sgp.DLCPoseEstimation().populate(pose_estimation_key) + +# Let's visualize the output from Pose Estimation +# + +(sgp.DLCPoseEstimation() & pose_estimation_key).fetch_dataframe() + +# #### [DLCSmoothInterp](#TableOfContents) +# + +# Now that we've completed pose estimation, it's time to identify NaNs and optionally interpolate over low likelihood periods and smooth the resulting positions.
      First we need to define some parameters for smoothing and interpolation. We can see the default parameter set below.
      **Note**: it is recommended to use the `just_nan` parameters here and save interpolation and smoothing for the centroid step as this provides for a better end result. +# + +# The default parameter set to interpolate and smooth over each LED individually +print(sgp.DLCSmoothInterpParams.get_default()) + +# The just_nan parameter set that identifies NaN indices and leaves smoothing and interpolation to the centroid step +print(sgp.DLCSmoothInterpParams.get_nan_params()) +si_params_name = "just_nan" # could also use "default" + +# To change any of these parameters, one would do the following: +# +# ```python +# si_params_name = "your_unique_param_name" +# params = { +# "smoothing_params": { +# "smoothing_duration": 0.00, +# "smooth_method": "moving_avg", +# }, +# "interp_params": {"likelihood_thresh": 0.00}, +# "max_plausible_speed": 0, +# "speed_smoothing_std_dev": 0.000, +# } +# sgp.DLCSmoothInterpParams().insert1( +# {"dlc_si_params_name": si_params_name, "params": params}, +# skip_duplicates=True, +# ) +# ``` +# + +# We'll create a dictionary with the correct set of keys for the `DLCSmoothInterpSelection` table +# + +si_key = pose_estimation_key.copy() +fields = list(sgp.DLCSmoothInterpSelection.fetch().dtype.fields.keys()) +si_key = {key: val for key, val in si_key.items() if key in fields} +si_key + +# We can insert all of the bodyparts we want to process into `DLCSmoothInterpSelection`
      +# First lets visualize the bodyparts we have available to us.
      +# + +print((sgp.DLCPoseEstimation.BodyPart & pose_estimation_key).fetch("bodypart")) + +# We can use `insert1` to insert a single bodypart, but would suggest using `insert` to insert a list of keys with different bodyparts. +# + +# To insert a single bodypart, one would do the following: +# +# ```python +# sgp.DLCSmoothInterpSelection.insert1( +# { +# **si_key, +# 'bodypart': 'greenLED', +# 'dlc_si_params_name': si_params_name, +# }, +# skip_duplicates=True) +# ``` +# + +# We'll see a list of bodyparts and then insert them into `DLCSmoothInterpSelection`. +# + +bodyparts = ["greenLED", "redLED_C"] +sgp.DLCSmoothInterpSelection.insert( + [ + { + **si_key, + "bodypart": bodypart, + "dlc_si_params_name": si_params_name, + } + for bodypart in bodyparts + ], + skip_duplicates=True, +) + +# And verify the entry: +# + +sgp.DLCSmoothInterpSelection() & si_key + +# Now, we populate `DLCSmoothInterp`, which will perform smoothing and +# interpolation on all of the bodyparts specified. +# + +sgp.DLCSmoothInterp().populate(si_key) + +# And let's visualize the resulting position data using a scatter plot +# + +( + sgp.DLCSmoothInterp() & {**si_key, "bodypart": bodyparts[0]} +).fetch1_dataframe().plot.scatter(x="x", y="y", s=1, figsize=(5, 5)) + +# #### [DLCSmoothInterpCohort](#TableOfContents) +# + +# After smoothing/interpolation, we need to select bodyparts from which we want to +# derive a centroid and orientation, which is performed by the +# `DLCSmoothInterpCohort` table. +# + +# First, let's make a key that represents the 'cohort', using +# `dlc_si_cohort_selection_name`. We'll need a bodypart dictionary using bodypart +# keys and smoothing/interpolation parameters used as value. +# + +cohort_key = si_key.copy() +if "bodypart" in cohort_key: + del cohort_key["bodypart"] +if "dlc_si_params_name" in cohort_key: + del cohort_key["dlc_si_params_name"] +cohort_key["dlc_si_cohort_selection_name"] = "green_red_led" +cohort_key["bodyparts_params_dict"] = { + "greenLED": si_params_name, + "redLED_C": si_params_name, +} +print(cohort_key) + +# We'll insert the cohort into `DLCSmoothInterpCohortSelection` and populate `DLCSmoothInterpCohort`, which collates the separately smoothed and interpolated bodyparts into a single entry. +# + +sgp.DLCSmoothInterpCohortSelection().insert1(cohort_key, skip_duplicates=True) +sgp.DLCSmoothInterpCohort.populate(cohort_key) + +# And verify the entry: +# + +sgp.DLCSmoothInterpCohort.BodyPart() & cohort_key + +# #### [DLCCentroid](#TableOfContents) +# + +# With this cohort, we can determine a centroid using another set of parameters. +# + +# Here is the default set +print(sgp.DLCCentroidParams.get_default()) +centroid_params_name = "default" + +# Here is the syntax to add your own parameters: +# +# ```python +# centroid_params = { +# "centroid_method": "two_pt_centroid", +# "points": { +# "greenLED": "greenLED", +# "redLED_C": "redLED_C", +# }, +# "speed_smoothing_std_dev": 0.100, +# } +# centroid_params_name = "your_unique_param_name" +# sgp.DLCCentroidParams.insert1( +# { +# "dlc_centroid_params_name": centroid_params_name, +# "params": centroid_params, +# }, +# skip_duplicates=True, +# ) +# ``` +# + +# We'll make a key to insert into `DLCCentroidSelection`. +# + +centroid_key = cohort_key.copy() +fields = list(sgp.DLCCentroidSelection.fetch().dtype.fields.keys()) +centroid_key = {key: val for key, val in centroid_key.items() if key in fields} +centroid_key["dlc_centroid_params_name"] = centroid_params_name +print(centroid_key) + +# After inserting into the selection table, we can populate `DLCCentroid` +# + +sgp.DLCCentroidSelection.insert1(centroid_key, skip_duplicates=True) +sgp.DLCCentroid.populate(centroid_key) + +# Here we can visualize the resulting centroid position +# + +(sgp.DLCCentroid() & centroid_key).fetch1_dataframe().plot.scatter( + x="position_x", + y="position_y", + c="speed", + colormap="viridis", + alpha=0.5, + s=0.5, + figsize=(10, 10), +) + +# #### [DLCOrientation](#TableOfContents) +# + +# We'll now go through a similar process to identify the orientation. +# + +print(sgp.DLCOrientationParams.get_default()) +dlc_orientation_params_name = "default" + +# We'll prune the `cohort_key` we used above and add our `dlc_orientation_params_name` to make it suitable for `DLCOrientationSelection`. +# + +fields = list(sgp.DLCOrientationSelection.fetch().dtype.fields.keys()) +orient_key = {key: val for key, val in cohort_key.items() if key in fields} +orient_key["dlc_orientation_params_name"] = dlc_orientation_params_name +print(orient_key) + +# We'll insert into `DLCOrientationSelection` and populate `DLCOrientation` +# + +sgp.DLCOrientationSelection().insert1(orient_key, skip_duplicates=True) +sgp.DLCOrientation().populate(orient_key) + +# We can fetch the orientation as a dataframe as quality assurance. +# + +(sgp.DLCOrientation() & orient_key).fetch1_dataframe() + +# #### [DLCPosV1](#TableOfContents) +# + +# After processing the position data, we have to do a few table manipulations to standardize various outputs. +# +# To summarize, we brought in a pretrained DLC project, used that model to run pose estimation on a new behavioral video, smoothed and interpolated the result, formed a cohort of bodyparts, and determined the centroid and orientation of this cohort. +# +# Now we'll populate `DLCPos` with our centroid/orientation entries above. +# + +fields = list(sgp.DLCPosV1.fetch().dtype.fields.keys()) +dlc_key = {key: val for key, val in centroid_key.items() if key in fields} +dlc_key["dlc_si_cohort_centroid"] = centroid_key["dlc_si_cohort_selection_name"] +dlc_key["dlc_si_cohort_orientation"] = orient_key[ + "dlc_si_cohort_selection_name" +] +dlc_key["dlc_orientation_params_name"] = orient_key[ + "dlc_orientation_params_name" +] +print(dlc_key) + +# Now we can insert into `DLCPosSelection` and populate `DLCPos` with our `dlc_key` +# + +sgp.DLCPosSelection().insert1(dlc_key, skip_duplicates=True) +sgp.DLCPosV1().populate(dlc_key) + +# We can also make sure that all of our data made it through by fetching the dataframe attached to this entry.
      We should expect 8 columns: +# +# > time
      video_frame_ind
      position_x
      position_y
      orientation
      velocity_x
      velocity_y
      speed +# + +(sgp.DLCPosV1() & dlc_key).fetch1_dataframe() + +# And even more, we can fetch the `pose_eval_result` that is calculated during this step. This field contains the percentage of frames that each bodypart was below the likelihood threshold of 0.95 as a means of assessing the quality of the pose estimation. +# + +(sgp.DLCPosV1() & dlc_key).fetch1("pose_eval_result") + +# #### [DLCPosVideo](#TableOfContents) +# + +# We can create a video with the centroid and orientation overlaid on the original +# video. This will also plot the likelihood of each bodypart used in the cohort. +# This is optional, but a good quality assurance step. +# + +sgp.DLCPosVideoParams.insert_default() + +params = { + "percent_frames": 0.05, + "incl_likelihood": True, +} +sgp.DLCPosVideoParams.insert1( + {"dlc_pos_video_params_name": "five_percent", "params": params}, + skip_duplicates=True, +) + +sgp.DLCPosVideoSelection.insert1( + {**dlc_key, "dlc_pos_video_params_name": "five_percent"}, + skip_duplicates=True, +) + +sgp.DLCPosVideo().populate(dlc_key) + +# #### [PositionOutput](#TableOfContents) +# + +# `PositionOutput` is the final table of the pipeline and is automatically +# populated when we populate `DLCPosV1` +# + +sgp.PositionOutput.merge_get_part(dlc_key) + +# `PositionOutput` also has a part table, similar to the `DLCModelSource` table above. Let's check that out as well. +# + +PositionOutput.DLCPosV1() & dlc_key + +(PositionOutput.DLCPosV1() & dlc_key).fetch1_dataframe() + +# #### [PositionVideo](#TableOfContents) +# + +# We can use the `PositionVideo` table to create a video that overlays just the +# centroid and orientation on the video. This table uses the parameter `plot` to +# determine whether to plot the entry deriving from the DLC arm or from the Trodes +# arm of the position pipeline. This parameter also accepts 'all', which will plot +# both (if they exist) in order to compare results. +# + +sgp.PositionVideoSelection().insert1( + { + "nwb_file_name": "J1620210604_.nwb", + "interval_list_name": "pos 13 valid times", + "trodes_position_id": 0, + "dlc_position_id": 1, + "plot": "DLC", + "output_dir": "/home/dgramling/Src/", + } +) + +sgp.PositionVideo.populate({"plot": "DLC"}) + +# ### _CONGRATULATIONS!!_ +# +# Please treat yourself to a nice tea break :-) +# + +# ### [Return To Table of Contents](#TableOfContents)
      +# diff --git a/notebooks/py_scripts/22_DLC_Loop.py b/notebooks/py_scripts/22_DLC_Loop.py new file mode 100644 index 000000000..b37e1c6ba --- /dev/null +++ b/notebooks/py_scripts/22_DLC_Loop.py @@ -0,0 +1,520 @@ +# --- +# jupyter: +# jupytext: +# text_representation: +# extension: .py +# format_name: light +# format_version: '1.5' +# jupytext_version: 1.16.0 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# ## Position- DeepLabCut from Scratch + +# ### Overview + +# _Developer Note:_ if you may make a PR in the future, be sure to copy this +# notebook, and use the `gitignore` prefix `temp` to avoid future conflicts. +# +# This is one notebook in a multi-part series on Spyglass. +# +# - To set up your Spyglass environment and database, see +# [the Setup notebook](./00_Setup.ipynb) +# - For additional info on DataJoint syntax, including table definitions and +# inserts, see +# [the Insert Data notebook](./01_Insert_Data.ipynb) +# +# This tutorial will extract position via DeepLabCut (DLC). It will walk through... +# +# - creating a DLC project +# - extracting and labeling frames +# - training your model +# - executing pose estimation on a novel behavioral video +# - processing the pose estimation output to extract a centroid and orientation +# - inserting the resulting information into the `PositionOutput` table +# +# **Note 2: Make sure you are running this within the spyglass-position Conda environment (instructions for install are in the environment_position.yml)** + +# Here is a schematic showing the tables used in this pipeline. +# +# ![dlc_scratch.png|2000x900](./../notebook-images/dlc_scratch.png) +# + +# ### Table of Contents +# [`DLCProject`](#DLCProject1)
      +# [`DLCModelTraining`](#DLCModelTraining1)
      +# [`DLCModel`](#DLCModel1)
      +# [`DLCPoseEstimation`](#DLCPoseEstimation1)
      +# [`DLCSmoothInterp`](#DLCSmoothInterp1)
      +# [`DLCCentroid`](#DLCCentroid1)
      +# [`DLCOrientation`](#DLCOrientation1)
      +# [`DLCPosV1`](#DLCPosV1-1)
      +# [`DLCPosVideo`](#DLCPosVideo1)
      +# [`PositionOutput`](#PositionOutput1)
      + +# __You can click on any header to return to the Table of Contents__ + +# ### Imports + +# %load_ext autoreload +# %autoreload 2 + +# + +import os +import datajoint as dj + +import spyglass.common as sgc +import spyglass.position.v1 as sgp + +import numpy as np +import pandas as pd +import pynwb +from spyglass.position import PositionOutput + +# change to the upper level folder to detect dj_local_conf.json +if os.path.basename(os.getcwd()) == "notebooks": + os.chdir("..") +dj.config.load("dj_local_conf.json") # load config for database connection info + +# ignore datajoint+jupyter async warnings +import warnings + +warnings.simplefilter("ignore", category=DeprecationWarning) +warnings.simplefilter("ignore", category=ResourceWarning) +# - + +# #### [DLCProject](#TableOfContents) + +#
      +# Notes:
        +#
      • +# The cells within this DLCProject step need to be performed +# in a local Jupyter notebook to allow for use of the frame labeling GUI. +#
        • +#
      • +# Please do not add to the BodyPart table in the production +# database unless necessary. +#
        • +#
      +#
      +# + +# ### Body Parts + +# We'll begin by looking at the `BodyPart` table, which stores standard names of body parts used in DLC models throughout the lab with a concise description. + +sgp.BodyPart() + +# If the bodyparts you plan to use in your model are not yet in the table, here is code to add bodyparts: +# +# ```python +# sgp.BodyPart.insert( +# [ +# {"bodypart": "bp_1", "bodypart_description": "concise descrip"}, +# {"bodypart": "bp_2", "bodypart_description": "concise descrip"}, +# ], +# skip_duplicates=True, +# ) +# ``` + +# ### Define videos and camera name (optional) for training set + +# To train a model, we'll need to extract frames, which we can label as training data. We can construct a list of videos from which we'll extract frames. +# +# The list can either contain dictionaries identifying behavioral videos for NWB files that have already been added to Spyglass, or absolute file paths to the videos you want to use. +# +# For this tutorial, we'll use two videos for which we already have frames labeled. + +# Defining camera name is optional: it should be done in cases where there are multiple cameras streaming per epoch, but not necessary otherwise.
      +# example: +# `camera_name = "HomeBox_camera" +# ` + +# _NOTE:_ The official release of Spyglass does not yet support multicamera +# projects. You can monitor progress on the effort to add this feature by checking +# [this PR](https://github.com/LorenFrankLab/spyglass/pull/684) or use +# [this experimental branch](https://github.com/dpeg22/spyglass/tree/add-multi-camera), +# which takes the keys nwb_file_name and epoch, and camera_name in the video_list variable. +# + +video_list = [ + {"nwb_file_name": "J1620210529_.nwb", "epoch": 2}, + {"nwb_file_name": "peanut20201103_.nwb", "epoch": 4}, +] + +# ### Path variables +# +# The position pipeline also keeps track of paths for project, video, and output. +# Just like we saw in [Setup](./00_Setup.ipynb), you can manage these either with +# environmental variables... +# +# ```bash +# export DLC_PROJECT_DIR="/nimbus/deeplabcut/projects" +# export DLC_VIDEO_DIR="/nimbus/deeplabcut/video" +# export DLC_OUTPUT_DIR="/nimbus/deeplabcut/output" +# ``` +# +# +# +# Or these can be set in your datajoint config: +# +# ```json +# { +# "custom": { +# "dlc_dirs": { +# "base": "/nimbus/deeplabcut/", +# "project": "/nimbus/deeplabcut/projects", +# "video": "/nimbus/deeplabcut/video", +# "output": "/nimbus/deeplabcut/output" +# } +# } +# } +# ``` +# +# _NOTE:_ If only `base` is specified as shown above, spyglass will assume the +# relative directories shown. +# +# You can check the result of this setup process with... + +# + +from spyglass.settings import config + +config +# - + +# Before creating our project, we need to define a few variables. +# +# - A team name, as shown in `LabTeam` for setting permissions. Here, we'll +# use "LorenLab". +# - A `project_name`, as a unique identifier for this DLC project. Here, we'll use +# **"tutorial_scratch_yourinitials"** +# - `bodyparts` is a list of body parts for which we want to extract position. +# The pre-labeled frames we're using include the bodyparts listed below. +# - Number of frames to extract/label as `frames_per_video`. Note that the DLC creators recommend having 200 frames as the minimum total number for each project. + +team_name = "LorenLab" +project_name = "tutorial_scratch_DG" +frames_per_video = 100 +bodyparts = ["redLED_C", "greenLED", "redLED_L", "redLED_R", "tailBase"] +project_key = sgp.DLCProject.insert_new_project( + project_name=project_name, + bodyparts=bodyparts, + lab_team=team_name, + frames_per_video=frames_per_video, + video_list=video_list, + skip_duplicates=True, +) + +# Now that we've initialized our project we'll need to extract frames which we will then label. + +# comment this line out after you finish frame extraction for each project +sgp.DLCProject().run_extract_frames(project_key) + +# This is the line used to label the frames you extracted, if you wish to use the DLC GUI on the computer you are currently using. +# ```#comment this line out after frames are labeled for your project +# sgp.DLCProject().run_label_frames(project_key) +# ``` + +# Otherwise, it is best/easiest practice to label the frames on your local computer (like a MacBook) that can run DeepLabCut's GUI well. Instructions:
      +# 1. Install DLC on your local (preferably into a 'Src' folder): https://deeplabcut.github.io/DeepLabCut/docs/installation.html +# 2. Upload frames extracted and saved in nimbus (should be `/nimbus/deeplabcut//labeled-data`) AND the project's associated config file (should be `/nimbus/deeplabcut//config.yaml`) to Box (we get free with UCSF) +# 3. Download labeled-data and config files on your local from Box +# 4. Create a 'projects' folder where you installed DeepLabCut; create a new folder with your complete project name there; save the downloaded files there. +# 4. Edit the config.yaml file: line 9 defining `project_path` needs to be the file path where it is saved on your local (ex: `/Users/lorenlab/Src/DeepLabCut/projects/tutorial_sratch_DG-LorenLab-2023-08-16`) +# 5. Open the DLC GUI through terminal +#
      (ex: `conda activate miniconda/envs/DEEPLABCUT_M1` +#
      `pythonw -m deeplabcut`) +# 6. Load an existing project; choose the config.yaml file +# 7. Label frames; labeling tutorial: https://www.youtube.com/watch?v=hsA9IB5r73E. +# 8. Once all frames are labeled, you should re-upload labeled-data folder back to Box and overwrite it in the original nimbus location so that your completed frames are ready to be used in the model. + +# Now we can check the `DLCProject.File` part table and see all of our training files and videos there! + +sgp.DLCProject.File & project_key + +#
      +# This step and beyond should be run on a GPU-enabled machine. +#
      + +# #### [DLCModelTraining](#ToC) +# +# Please make sure you're running this notebook on a GPU-enabled machine. +# +# Now that we've imported existing frames, we can get ready to train our model. +# +# First, we'll need to define a set of parameters for `DLCModelTrainingParams`, which will get used by DeepLabCut during training. Let's start with `gputouse`, +# which determines which GPU core to use. +# +# The cell below determines which core has space and set the `gputouse` variable +# accordingly. +# + +sgp.dlc_utils.get_gpu_memory() + +# Set GPU core: +# + +gputouse = 1 # 1-9 + +# Now we'll define the rest of our parameters and insert the entry. +# +# To see all possible parameters, try: +# +# ```python +# sgp.DLCModelTrainingParams.get_accepted_params() +# ``` +# + +training_params_name = "tutorial" +sgp.DLCModelTrainingParams.insert_new_params( + paramset_name=training_params_name, + params={ + "trainingsetindex": 0, + "shuffle": 1, + "gputouse": gputouse, + "net_type": "resnet_50", + "augmenter_type": "imgaug", + }, + skip_duplicates=True, +) + +# Next we'll modify the `project_key` from above to include the necessary entries for `DLCModelTraining` + +# project_key['project_path'] = os.path.dirname(project_key['config_path']) +if "config_path" in project_key: + del project_key["config_path"] + +# We can insert an entry into `DLCModelTrainingSelection` and populate `DLCModelTraining`. +# +# _Note:_ You can stop training at any point using `I + I` or interrupt the Kernel. +# +# The maximum total number of training iterations is 1030000; you can end training before this amount if the loss rate (lr) and total loss plateau and are very close to 0. +# + +sgp.DLCModelTrainingSelection.heading + +sgp.DLCModelTrainingSelection().insert1( + { + **project_key, + "dlc_training_params_name": training_params_name, + "training_id": 0, + "model_prefix": "", + } +) +model_training_key = ( + sgp.DLCModelTrainingSelection + & { + **project_key, + "dlc_training_params_name": training_params_name, + } +).fetch1("KEY") +sgp.DLCModelTraining.populate(model_training_key) + +# Here we'll make sure that the entry made it into the table properly! + +sgp.DLCModelTraining() & model_training_key + +# Populating `DLCModelTraining` automatically inserts the entry into +# `DLCModelSource`, which is used to select between models trained using Spyglass +# vs. other tools. + +sgp.DLCModelSource() & model_training_key + +# The `source` field will only accept _"FromImport"_ or _"FromUpstream"_ as entries. Let's checkout the `FromUpstream` part table attached to `DLCModelSource` below. + +sgp.DLCModelSource.FromUpstream() & model_training_key + +# #### [DLCModel](#TableOfContents) +# +# Next we'll populate the `DLCModel` table, which holds all the relevant +# information for all trained models. +# +# First, we'll need to determine a set of parameters for our model to select the +# correct model file. Here is the default: + +sgp.DLCModelParams.get_default() + +# Here is the syntax to add your own parameter set: +# +# ```python +# dlc_model_params_name = "make_this_yours" +# params = { +# "params": {}, +# "shuffle": 1, +# "trainingsetindex": 0, +# "model_prefix": "", +# } +# sgp.DLCModelParams.insert1( +# {"dlc_model_params_name": dlc_model_params_name, "params": params}, +# skip_duplicates=True, +# ) +# ``` +# + +# We can insert sets of parameters into `DLCModelSelection` and populate +# `DLCModel`. + +temp_model_key = (sgp.DLCModelSource & model_training_key).fetch1("KEY") + +# comment these lines out after successfully inserting, for each project +sgp.DLCModelSelection().insert1( + {**temp_model_key, "dlc_model_params_name": "default"}, skip_duplicates=True +) + +model_key = (sgp.DLCModelSelection & temp_model_key).fetch1("KEY") +sgp.DLCModel.populate(model_key) + +# Again, let's make sure that everything looks correct in `DLCModel`. + +sgp.DLCModel() & model_key + +# ## Loop Begins + +# We can view all `VideoFile` entries with the specidied `camera_ name` for this project to ensure the rat whose position you wish to model is in this table `matching_rows` + +camera_name = "SleepBox_camera" +matching_rows = sgc.VideoFile() & {"camera_name": camera_name} +matching_rows + +# The `DLCPoseEstimationSelection` insertion step will convert your .h264 video to an .mp4 first and save it in `/nimbus/deeplabcut/video`. If this video already exists here, the insertion will never complete. +# +# We first delete any .mp4 that exists for this video from the nimbus folder: + +# ! find /nimbus/deeplabcut/video -type f -name '*20230606_SC38*' -delete # change based on date and rat with which you are training the model + +# If the first insertion step (for pose estimation task) fails in either trigger or load mode for an epoch, run the following lines: +# ``` +# (pose_estimation_key = sgp.DLCPoseEstimationSelection.insert_estimation_task( +# { +# "nwb_file_name": nwb_file_name, +# "epoch": epoch, +# "video_file_num": video_file_num, +# **model_key, +# }).delete() +# ``` + +# This loop will generate posiiton data for all epochs associated with the pre-defined camera in one day, for one rat (based on the NWB file; see ***) +#
      The output should print Pose Estimation and Centroid plots for each epoch. +# +# - It defines `col1val` as each `nwb_file_name` entry in the table, one at a time. +# - Next, it sees if the trial on which you are testing this model is in the string for the current `col1val`; if not, it re-defines `col1val` as the next `nwb_file_name` entry and re-tries this step. +# - If the previous step works, it then saves `col2val` and `col3val` as the `epoch` and the `video_file_num`, respectively, based on the nwb_file_name. From there, it iterates through the insertion and population steps required to extract position data, which we see laid out in notebook 05_DLC.ipynb. + +for row in matching_rows: + col1val = row["nwb_file_name"] + if "SC3820230606" in col1val: # *** change depending on rat/day!!! + col2val = row["epoch"] + col3val = row["video_file_num"] + + ##insert pose estimation task + pose_estimation_key = ( + sgp.DLCPoseEstimationSelection.insert_estimation_task( + { + "nwb_file_name": col1val, + "epoch": col2val, + "video_file_num": col3val, + **model_key, + }, + task_mode="trigger", # load or trigger + params={"gputouse": gputouse, "videotype": "mp4"}, + ) + ) + + ##populate DLC Pose Estimation + sgp.DLCPoseEstimation().populate(pose_estimation_key) + + ##start smooth interpolation + si_params_name = "just_nan" + si_key = pose_estimation_key.copy() + fields = list(sgp.DLCSmoothInterpSelection.fetch().dtype.fields.keys()) + si_key = {key: val for key, val in si_key.items() if key in fields} + bodyparts = ["greenLED", "redLED_C"] + sgp.DLCSmoothInterpSelection.insert( + [ + { + **si_key, + "bodypart": bodypart, + "dlc_si_params_name": si_params_name, + } + for bodypart in bodyparts + ], + skip_duplicates=True, + ) + sgp.DLCSmoothInterp().populate(si_key) + ( + sgp.DLCSmoothInterp() & {**si_key, "bodypart": bodyparts[0]} + ).fetch1_dataframe().plot.scatter(x="x", y="y", s=1, figsize=(5, 5)) + + ##smoothinterpcohort + cohort_key = si_key.copy() + if "bodypart" in cohort_key: + del cohort_key["bodypart"] + if "dlc_si_params_name" in cohort_key: + del cohort_key["dlc_si_params_name"] + cohort_key["dlc_si_cohort_selection_name"] = "green_red_led" + cohort_key["bodyparts_params_dict"] = { + "greenLED": si_params_name, + "redLED_C": si_params_name, + } + sgp.DLCSmoothInterpCohortSelection().insert1( + cohort_key, skip_duplicates=True + ) + sgp.DLCSmoothInterpCohort.populate(cohort_key) + + ##DLC Centroid + centroid_params_name = "default" + centroid_key = cohort_key.copy() + fields = list(sgp.DLCCentroidSelection.fetch().dtype.fields.keys()) + centroid_key = { + key: val for key, val in centroid_key.items() if key in fields + } + centroid_key["dlc_centroid_params_name"] = centroid_params_name + sgp.DLCCentroidSelection.insert1(centroid_key, skip_duplicates=True) + sgp.DLCCentroid.populate(centroid_key) + (sgp.DLCCentroid() & centroid_key).fetch1_dataframe().plot.scatter( + x="position_x", + y="position_y", + c="speed", + colormap="viridis", + alpha=0.5, + s=0.5, + figsize=(10, 10), + ) + + ##DLC Orientation + dlc_orientation_params_name = "default" + fields = list(sgp.DLCOrientationSelection.fetch().dtype.fields.keys()) + orient_key = { + key: val for key, val in cohort_key.items() if key in fields + } + orient_key["dlc_orientation_params_name"] = dlc_orientation_params_name + sgp.DLCOrientationSelection().insert1(orient_key, skip_duplicates=True) + sgp.DLCOrientation().populate(orient_key) + + ##DLCPosV1 + fields = list(sgp.DLCPosV1.fetch().dtype.fields.keys()) + dlc_key = { + key: val for key, val in centroid_key.items() if key in fields + } + dlc_key["dlc_si_cohort_centroid"] = centroid_key[ + "dlc_si_cohort_selection_name" + ] + dlc_key["dlc_si_cohort_orientation"] = orient_key[ + "dlc_si_cohort_selection_name" + ] + dlc_key["dlc_orientation_params_name"] = orient_key[ + "dlc_orientation_params_name" + ] + sgp.DLCPosSelection().insert1(dlc_key, skip_duplicates=True) + sgp.DLCPosV1().populate(dlc_key) + + else: + continue + +# ### _CONGRATULATIONS!!_ +# Please treat yourself to a nice tea break :-) + +# ### [Return To Table of Contents](#TableOfContents)
      diff --git a/notebooks/py_scripts/22_Position_DLC_2.py b/notebooks/py_scripts/22_Position_DLC_2.py deleted file mode 100644 index 468113431..000000000 --- a/notebooks/py_scripts/22_Position_DLC_2.py +++ /dev/null @@ -1,193 +0,0 @@ -# --- -# jupyter: -# jupytext: -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.16.0 -# kernelspec: -# display_name: spy -# language: python -# name: python3 -# --- - -# # Position - DeepLabCut PreTrained -# - -# ## Overview -# - -# _Developer Note:_ if you may make a PR in the future, be sure to copy this -# notebook, and use the `gitignore` prefix `temp` to avoid future conflicts. -# -# This is one notebook in a multi-part series on Spyglass. -# -# - To set up your Spyglass environment and database, see -# [the Setup notebook](./00_Setup.ipynb) -# - For additional info on DataJoint syntax, including table definitions and -# inserts, see -# [the Insert Data notebook](./01_Insert_Data.ipynb) -# -# This is a tutorial will cover how to extract position given a pre-trained DeepLabCut (DLC) model. It will walk through adding your DLC model to Spyglass. -# -# If you already have a model in the database, skip to the -# [next tutorial](./23_Position_DLC_3.ipynb). - -# ## Imports -# - -# + -import os -import datajoint as dj - -# change to the upper level folder to detect dj_local_conf.json -if os.path.basename(os.getcwd()) == "notebooks": - os.chdir("..") -dj.config.load("dj_local_conf.json") # load config for database connection info - -from spyglass.settings import load_config - -load_config(base_dir="/home/cb/wrk/zOther/data/") - -import spyglass.common as sgc -import spyglass.position.v1 as sgp -from spyglass.position import PositionOutput - -# ignore datajoint+jupyter async warnings -import warnings - -warnings.simplefilter("ignore", category=DeprecationWarning) -warnings.simplefilter("ignore", category=ResourceWarning) -# - - -# #### Here is a schematic showing the tables used in this notebook.
      -# ![dlc_existing.png|2000x900](./../notebook-images/dlc_existing.png) - -# ## Table of Contents -# -# - [`DLCProject`](#DLCProject) -# - [`DLCModel`](#DLCModel) -# -# -# You can click on any header to return to the Table of Contents - -# ## [DLCProject](#ToC) - -# We'll look at the BodyPart table, which stores standard names of body parts used within DLC models. - -#
      -# Notes:
        -#
      • -# Please do not add to the BodyPart table in the production -# database unless necessary. -#
        • -#
      -#
      - -sgp.BodyPart() - -# We can `insert_existing_project` into the `DLCProject` table using: -# -# - `project_name`: a short, unique, descriptive project name to reference -# throughout the pipeline -# - `lab_team`: team name from `LabTeam` -# - `config_path`: string path to a DLC `config.yaml` -# - `bodyparts`: optional list of bodyparts used in the project -# - `frames_per_video`: optional, number of frames to extract for training from -# each video - -project_name = "tutorial_DG" -lab_team = "LorenLab" -project_key = sgp.DLCProject.insert_existing_project( - project_name=project_name, - lab_team=lab_team, - config_path="/nimbus/deeplabcut/projects/tutorial_model-LorenLab-2022-07-15/config.yaml", - bodyparts=["redLED_C", "greenLED", "redLED_L", "redLED_R", "tailBase"], - frames_per_video=200, - skip_duplicates=True, -) - -sgp.DLCProject() & {"project_name": project_name} - -# ## [DLCModel](#ToC) - -# The `DLCModelInput` table has `dlc_model_name` and `project_name` as primary keys and `project_path` as a secondary key. - -sgp.DLCModelInput() - -# We can modify the `project_key` to replace `config_path` with `project_path` to -# fit with the fields in `DLCModelInput` - -print(f"current project_key:\n{project_key}") -if not "project_path" in project_key: - project_key["project_path"] = os.path.dirname(project_key["config_path"]) - del project_key["config_path"] - print(f"updated project_key:\n{project_key}") - -# After adding a unique `dlc_model_name` to `project_key`, we insert into -# `DLCModelInput`. - -dlc_model_name = "tutorial_model_DG" -sgp.DLCModelInput().insert1( - {"dlc_model_name": dlc_model_name, **project_key}, skip_duplicates=True -) -sgp.DLCModelInput() - -# Inserting into `DLCModelInput` will also populate `DLCModelSource`, which -# records whether or not a model was trained with Spyglass. - -sgp.DLCModelSource() & project_key - -# The `source` field will only accept _"FromImport"_ or _"FromUpstream"_ as entries. Let's checkout the `FromUpstream` part table attached to `DLCModelSource` below. - -sgp.DLCModelSource.FromImport() & project_key - -# Next we'll get ready to populate the `DLCModel` table, which holds all the relevant information for both pre-trained models and models trained within Spyglass.
      First we'll need to determine a set of parameters for our model to select the correct model file.
      We can visualize a default set below: - -sgp.DLCModelParams.get_default() - -# Here is the syntax to add your own parameter set: -# -# ```python -# dlc_model_params_name = "make_this_yours" -# params = { -# "params": {}, -# "shuffle": 1, -# "trainingsetindex": 0, -# "model_prefix": "", -# } -# sgp.DLCModelParams.insert1( -# {"dlc_model_params_name": dlc_model_params_name, "params": params}, -# skip_duplicates=True, -# ) -# ``` - -# We can insert sets of parameters into `DLCModelSelection` and populate -# `DLCModel`. - -temp_model_key = (sgp.DLCModelSource.FromImport() & project_key).fetch1("KEY") -sgp.DLCModelSelection().insert1( - {**temp_model_key, "dlc_model_params_name": "default"}, skip_duplicates=True -) -model_key = (sgp.DLCModelSelection & temp_model_key).fetch1("KEY") -sgp.DLCModel.populate(model_key) - -# And of course make sure it populated correctly - -sgp.DLCModel() & model_key - -# ## Next Steps -# -# With our trained model in place, we're ready to move on to -# pose estimation (notebook coming soon!). -# - -# ### [`Return To Table of Contents`](#ToC)
      diff --git a/notebooks/py_scripts/23_Position_DLC_3.py b/notebooks/py_scripts/23_Position_DLC_3.py deleted file mode 100644 index 9c20468c9..000000000 --- a/notebooks/py_scripts/23_Position_DLC_3.py +++ /dev/null @@ -1,414 +0,0 @@ -# --- -# jupyter: -# jupytext: -# text_representation: -# extension: .py -# format_name: light -# format_version: '1.5' -# jupytext_version: 1.16.0 -# --- - -# # Position - DeepLabCut Estimation - -# ## Overview -# - -# _Developer Note:_ if you may make a PR in the future, be sure to copy this -# notebook, and use the `gitignore` prefix `temp` to avoid future conflicts. -# -# This is one notebook in a multi-part series on Spyglass. -# -# - To set up your Spyglass environment and database, see -# [the Setup notebook](./00_Setup.ipynb) -# - For additional info on DataJoint syntax, including table definitions and -# inserts, see -# [the Insert Data notebook](./01_Insert_Data.ipynb) -# -# This tutorial will extract position via DeepLabCut (DLC). It will walk through... -# - executing pose estimation -# - processing the pose estimation output to extract a centroid and orientation -# - inserting the resulting information into the `IntervalPositionInfo` table -# -# This tutorial assumes you already have a model in your database. If that's not -# the case, you can either [train one from scratch](./21_Position_DLC_1.ipynb) -# or [load an existing project](./22_Position_DLC_2.ipynb). - -# Here is a schematic showing the tables used in this pipeline. -# -# ![dlc_scratch.png|2000x900](./../notebook-images/dlc_scratch.png) - -# ### Table of Contents -# -# - [Imports](#imports) -# - [GPU](#gpu) -# - [`DLCPoseEstimation`](#DLCPoseEstimation1) -# - [`DLCSmoothInterp`](#DLCSmoothInterp1) -# - [`DLCCentroid`](#DLCCentroid1) -# - [`DLCOrientation`](#DLCOrientation1) -# - [`DLCPos`](#DLCPos1) -# - [`DLCPosVideo`](#DLCPosVideo1) -# - [`PosSource`](#PosSource1) -# - [`IntervalPositionInfo`](#IntervalPositionInfo1) -# -# __You can click on any header to return to the Table of Contents__ - -# ### [Imports](#TableOfContents) -# - -# + -import os -import datajoint as dj -from pprint import pprint - -import spyglass.common as sgc -import spyglass.position.v1 as sgp - -# change to the upper level folder to detect dj_local_conf.json -if os.path.basename(os.getcwd()) == "notebooks": - os.chdir("..") -dj.config.load("dj_local_conf.json") # load config for database connection info - -# ignore datajoint+jupyter async warnings -import warnings - -warnings.simplefilter("ignore", category=DeprecationWarning) -warnings.simplefilter("ignore", category=ResourceWarning) -# - - -# ### [GPU](#TableOfContents) - -# For longer videos, we'll need GPU support. The cell below determines which core -# has space and set the `gputouse` variable accordingly. - -sgp.dlc_utils.get_gpu_memory() - -# Set GPU core: - -gputouse = 1 ## 1-9 - -# #### [DLCPoseEstimation](#TableOfContents) -# -# With our trained model in place, we're ready to set up Pose Estimation on a -# behavioral video of your choice. We can select a video with `nwb_file_name` and -# `epoch`, making sure there's an entry in the `VideoFile` table. - -nwb_file_name = "J1620210604_.nwb" -epoch = 14 -sgc.VideoFile() & {"nwb_file_name": nwb_file_name, "epoch": epoch} - -# Using `insert_estimation_task` will convert out video to be in .mp4 format (DLC -# struggles with .h264) and determine the directory in which we'll store the pose -# estimation results. -# -# - `task_mode` (trigger or load) determines whether or not populating -# `DLCPoseEstimation` triggers a new pose estimation, or loads an existing. -# - `video_file_num` will be 0 in almost all -# cases. -# - `gputouse` was already set during training. It may be a good idea to make sure -# that core is still free before moving forward. - -pose_estimation_key = sgp.DLCPoseEstimationSelection.insert_estimation_task( - { - "nwb_file_name": nwb_file_name, - "epoch": epoch, - "video_file_num": 0, - **model_key, - }, - task_mode="trigger", - params={"gputouse": gputouse, "videotype": "mp4"}, -) - -# _Note:_ Populating `DLCPoseEstimation` may take some time for full datasets - -sgp.DLCPoseEstimation().populate(pose_estimation_key) - -# Let's visualize the output from Pose Estimation - -(sgp.DLCPoseEstimation() & pose_estimation_key).fetch_dataframe() - -# #### [DLCSmoothInterp](#TableOfContents) - -# After pose estimation, we can interpolate over low likelihood periods and smooth -# the resulting position. -# -# First we define some parameters. We can see the default parameter set below. - -pprint(sgp.DLCSmoothInterpParams.get_default()) -si_params_name = "default" - -# To change any of these parameters, one would do the following: -# -# ```python -# si_params_name = "your_unique_param_name" -# params = { -# "smoothing_params": { -# "smoothing_duration": 0.00, -# "smooth_method": "moving_avg", -# }, -# "interp_params": {"likelihood_thresh": 0.00}, -# "max_plausible_speed": 0, -# "speed_smoothing_std_dev": 0.000, -# } -# sgp.DLCSmoothInterpParams().insert1( -# {"dlc_si_params_name": si_params_name, "params": params}, -# skip_duplicates=True, -# ) -# ``` - -# We'll create a dictionary with the correct set of keys for the `DLCSmoothInterpSelection` table - -si_key = pose_estimation_key.copy() -fields = list(sgp.DLCSmoothInterpSelection.fetch().dtype.fields.keys()) -si_key = {key: val for key, val in si_key.items() if key in fields} -si_key - -# We can insert all of the bodyparts we want to process into -# `DLCSmoothInterpSelection`. Here are the bodyparts we have available to us: - -pprint((sgp.DLCPoseEstimation.BodyPart & pose_estimation_key).fetch("bodypart")) - -# We can use `insert1` to insert a single bodypart, but would suggest using `insert` to insert a list of keys with different bodyparts. - -# We'll set a list of bodyparts and then insert them into -# `DLCSmoothInterpSelection`. - -bodyparts = ["greenLED", "redLED_C"] -sgp.DLCSmoothInterpSelection.insert( - [ - { - **si_key, - "bodypart": bodypart, - "dlc_si_params_name": si_params_name, - } - for bodypart in bodyparts - ], - skip_duplicates=True, -) - -# And verify the entry: - -sgp.DLCSmoothInterpSelection() & si_key - -# Now, we populate `DLCSmoothInterp`, which will perform smoothing and -# interpolation on all of the bodyparts specified. - -sgp.DLCSmoothInterp().populate(si_key) - -# And let's visualize the resulting position data using a scatter plot - -( - sgp.DLCSmoothInterp() & {**si_key, "bodypart": bodyparts[0]} -).fetch1_dataframe().plot.scatter(x="x", y="y", s=1, figsize=(5, 5)) - -# #### [DLCSmoothInterpCohort](#TableOfContents) - -# After smoothing/interpolation, we need to select bodyparts from which we want to -# derive a centroid and orientation, which is performed by the -# `DLCSmoothInterpCohort` table. - -# First, let's make a key that represents the 'cohort', using -# `dlc_si_cohort_selection_name`. We'll need a bodypart dictionary using bodypart -# keys and smoothing/interpolation parameters used as value. - -cohort_key = si_key.copy() -if "bodypart" in cohort_key: - del cohort_key["bodypart"] -if "dlc_si_params_name" in cohort_key: - del cohort_key["dlc_si_params_name"] -cohort_key["dlc_si_cohort_selection_name"] = "green_red_led" -cohort_key["bodyparts_params_dict"] = { - "greenLED": si_params_name, - "redLED_C": si_params_name, -} -print(cohort_key) - -# We'll insert the cohort into `DLCSmoothInterpCohortSelection` and populate `DLCSmoothInterpCohort`, which collates the separately smoothed and interpolated bodyparts into a single entry. - -sgp.DLCSmoothInterpCohortSelection().insert1(cohort_key, skip_duplicates=True) -sgp.DLCSmoothInterpCohort.populate(cohort_key) - -# And verify the entry: - -sgp.DLCSmoothInterpCohort.BodyPart() & cohort_key - -# #### [DLCCentroid](#TableOfContents) - -# With this cohort, we can determine a centroid using another set of parameters. - -# Here is the default set -print(sgp.DLCCentroidParams.get_default()) -centroid_params_name = "default" - -# Here is the syntax to add your own parameters: -# -# ```python -# centroid_params = { -# "centroid_method": "two_pt_centroid", -# "points": { -# "greenLED": "greenLED", -# "redLED_C": "redLED_C", -# }, -# "speed_smoothing_std_dev": 0.100, -# } -# centroid_params_name = "your_unique_param_name" -# sgp.DLCCentroidParams.insert1( -# { -# "dlc_centroid_params_name": centroid_params_name, -# "params": centroid_params, -# }, -# skip_duplicates=True, -# ) -# ``` - -# We'll make a key to insert into `DLCCentroidSelection`. - -centroid_key = cohort_key.copy() -fields = list(sgp.DLCCentroidSelection.fetch().dtype.fields.keys()) -centroid_key = {key: val for key, val in centroid_key.items() if key in fields} -centroid_key["dlc_centroid_params_name"] = centroid_params_name -pprint(centroid_key) - -# After inserting into the selection table, we can populate `DLCCentroid` - -sgp.DLCCentroidSelection.insert1(centroid_key, skip_duplicates=True) -sgp.DLCCentroid.populate(centroid_key) - -# Here we can visualize the resulting centroid position - -(sgp.DLCCentroid() & centroid_key).fetch1_dataframe().plot.scatter( - x="position_x", - y="position_y", - c="speed", - colormap="viridis", - alpha=0.5, - s=0.5, - figsize=(10, 10), -) - -# #### [DLCOrientation](#TableOfContents) - -# We'll go through a similar process for orientation. - -pprint(sgp.DLCOrientationParams.get_default()) -dlc_orientation_params_name = "default" - -# We'll prune the `cohort_key` we used above and add our -# `dlc_orientation_params_name` to make it suitable for `DLCOrientationSelection`. - -fields = list(sgp.DLCOrientationSelection.fetch().dtype.fields.keys()) -orient_key = {key: val for key, val in cohort_key.items() if key in fields} -orient_key["dlc_orientation_params_name"] = dlc_orientation_params_name -print(orient_key) - -# We'll insert into `DLCOrientationSelection` and then populate `DLCOrientation` - -sgp.DLCOrientationSelection().insert1(orient_key, skip_duplicates=True) -sgp.DLCOrientation().populate(orient_key) - -# We can fetch the orientation as a dataframe as quality assurance. - -(sgp.DLCOrientation() & orient_key).fetch1_dataframe() - -# #### [DLCPos](#TableOfContents) - -# After processing the position data, we have to do a few table manipulations to standardize various outputs. -# -# To summarize, we brought in a pretrained DLC project, used that model to run pose estimation on a new behavioral video, smoothed and interpolated the result, formed a cohort of bodyparts, and determined the centroid and orientation of this cohort. -# -# Now we'll populate `DLCPos` with our centroid/orientation entries above. - -fields = list(sgp.DLCPos.fetch().dtype.fields.keys()) -dlc_key = {key: val for key, val in centroid_key.items() if key in fields} -dlc_key["dlc_si_cohort_centroid"] = centroid_key["dlc_si_cohort_selection_name"] -dlc_key["dlc_si_cohort_orientation"] = orient_key[ - "dlc_si_cohort_selection_name" -] -dlc_key["dlc_orientation_params_name"] = orient_key[ - "dlc_orientation_params_name" -] -pprint(dlc_key) - -# Now we can insert into `DLCPosSelection` and populate `DLCPos` with our `dlc_key` - -sgp.DLCPosSelection().insert1(dlc_key, skip_duplicates=True) -sgp.DLCPos().populate(dlc_key) - -# Fetched as a dataframe, we expect the following 8 columns: -# -# - time -# - video_frame_ind -# - position_x -# - position_y -# - orientation -# - velocity_x -# - velocity_y -# - speed - -(sgp.DLCPos() & dlc_key).fetch1_dataframe() - -# We can also fetch the `pose_eval_result`, which contains the percentage of -# frames that each bodypart was below the likelihood threshold of 0.95. - -(sgp.DLCPos() & dlc_key).fetch1("pose_eval_result") - -# #### [DLCPosVideo](#TableOfContents) - -# We can create a video with the centroid and orientation overlaid on the original -# video. This will also plot the likelihood of each bodypart used in the cohort. -# This is optional, but a good quality assurance step. - -sgp.DLCPosVideoParams.insert_default() - -params = { - "percent_frames": 0.05, - "incl_likelihood": True, -} -sgp.DLCPosVideoParams.insert1( - {"dlc_pos_video_params_name": "five_percent", "params": params}, - skip_duplicates=True, -) - -sgp.DLCPosVideoSelection.insert1( - {**dlc_key, "dlc_pos_video_params_name": "five_percent"}, - skip_duplicates=True, -) - -sgp.DLCPosVideo().populate(dlc_key) - -# #### [PositionOutput](#TableOfContents) - -# `PositionOutput` is the final table of the pipeline and is automatically -# populated when we populate `DLCPosV1` - -sgp.PositionOutput() & dlc_key - -# `PositionOutput` also has a part table, similar to the `DLCModelSource` table above. Let's check that out as well. - -PositionOutput.DLCPosV1() & dlc_key - -(PositionOutput.DLCPosV1() & dlc_key).fetch1_dataframe() - -# #### [PositionVideo](#TableOfContents) - -# We can use the `PositionVideo` table to create a video that overlays just the -# centroid and orientation on the video. This table uses the parameter `plot` to -# determine whether to plot the entry deriving from the DLC arm or from the Trodes -# arm of the position pipeline. This parameter also accepts 'all', which will plot -# both (if they exist) in order to compare results. - -sgp.PositionVideoSelection().insert1( - { - "nwb_file_name": "J1620210604_.nwb", - "interval_list_name": "pos 13 valid times", - "trodes_position_id": 0, - "dlc_position_id": 1, - "plot": "DLC", - "output_dir": "/home/dgramling/Src/", - } -) - -sgp.PositionVideo.populate({"plot": "DLC"}) - -# CONGRATULATIONS!! Please treat yourself to a nice tea break :-) - -# ### [Return To Table of Contents](#TableOfContents)
      diff --git a/notebooks/py_scripts/41_Extracting_Clusterless_Waveform_Features.py b/notebooks/py_scripts/41_Extracting_Clusterless_Waveform_Features.py index 137414710..232be4174 100644 --- a/notebooks/py_scripts/41_Extracting_Clusterless_Waveform_Features.py +++ b/notebooks/py_scripts/41_Extracting_Clusterless_Waveform_Features.py @@ -41,7 +41,7 @@ ) # load config for database connection info # - -# First, if you haven't inserted the the `mediumnwb20230802.nwb` file into the database (see [01_Data_Insert](01_Data_Insert.ipynb)), you should do so now. This is the file that we will use for the decoding tutorials. +# First, if you haven't inserted the the `mediumnwb20230802.wnb` file into the database, you should do so now. This is the file that we will use for the decoding tutorials. # # It is a truncated version of the full NWB file, so it will run faster, but bigger than the minirec file we used in the previous tutorials so that decoding makes sense. @@ -155,7 +155,7 @@ for sorting_id in sorting_ids: try: sgs.CurationV1.insert_curation(sorting_id=sorting_id) - except KeyError as e: + except KeyError: pass SpikeSortingOutput.insert( diff --git a/notebooks/py_scripts/42_Decoding_Clusterless.py b/notebooks/py_scripts/42_Decoding_Clusterless.py index 6e0f529e8..309747931 100644 --- a/notebooks/py_scripts/42_Decoding_Clusterless.py +++ b/notebooks/py_scripts/42_Decoding_Clusterless.py @@ -225,7 +225,7 @@ # + from non_local_detector.environment import Environment -# Environment? +# ?Environment # - # ## Decoding diff --git a/src/spyglass/common/common_lab.py b/src/spyglass/common/common_lab.py index bdaa0fb25..177fc4424 100644 --- a/src/spyglass/common/common_lab.py +++ b/src/spyglass/common/common_lab.py @@ -108,7 +108,7 @@ def get_djuser_name(cls, dj_user) -> str: Parameters ---------- - user: str + dj_user: str The datajoint user name. Returns diff --git a/src/spyglass/decoding/v0/__init__.py b/src/spyglass/decoding/v0/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/spyglass/decoding/v0/clusterless.py b/src/spyglass/decoding/v0/clusterless.py index 1bd385c20..f6fd9df37 100644 --- a/src/spyglass/decoding/v0/clusterless.py +++ b/src/spyglass/decoding/v0/clusterless.py @@ -70,15 +70,14 @@ @schema class MarkParameters(SpyglassMixin, dj.Manual): - """Defines the type of spike waveform feature computed for a given spike - time.""" + """Defines the type of waveform feature computed for a given spike time.""" definition = """ mark_param_name : varchar(32) # a name for this set of parameters --- # the type of mark. Currently only 'amplitude' is supported mark_type = 'amplitude': varchar(40) - mark_param_dict: BLOB # dictionary of parameters for the mark extraction function + mark_param_dict: BLOB # dict of parameters for the mark extraction function """ # NOTE: See #630, #664. Excessive key length. @@ -99,7 +98,8 @@ def insert_default(self): @staticmethod def supported_mark_type(mark_type): - """checks whether the requested mark type is supported. + """Checks whether the requested mark type is supported. + Currently only 'amplitude" is supported. Parameters @@ -108,9 +108,7 @@ def supported_mark_type(mark_type): """ supported_types = ["amplitude"] - if mark_type in supported_types: - return True - return False + return mark_type in supported_types @schema @@ -123,7 +121,9 @@ class UnitMarkParameters(SpyglassMixin, dj.Manual): @schema class UnitMarks(SpyglassMixin, dj.Computed): - """For each spike time, compute a spike waveform feature associated with that + """Compute spike waveform features for each spike time. + + For each spike time, compute a spike waveform feature associated with that spike. Used for clusterless decoding. """ @@ -224,15 +224,16 @@ def make(self, key): AnalysisNwbfile().add(key["nwb_file_name"], key["analysis_file_name"]) self.insert1(key) - def fetch1_dataframe(self): + def fetch1_dataframe(self) -> pd.DataFrame: """Convenience function for returning the marks in a readable format""" return self.fetch_dataframe()[0] - def fetch_dataframe(self): + def fetch_dataframe(self) -> list[pd.DataFrame]: return [self._convert_to_dataframe(data) for data in self.fetch_nwb()] @staticmethod - def _convert_to_dataframe(nwb_data): + def _convert_to_dataframe(nwb_data) -> pd.DataFrame: + """Converts the marks from an NWB object to a pandas dataframe""" n_marks = nwb_data["marks"].data.shape[1] columns = [f"amplitude_{ind:04d}" for ind in range(n_marks)] return pd.DataFrame( @@ -243,23 +244,28 @@ def _convert_to_dataframe(nwb_data): @staticmethod def _get_peak_amplitude( - waveform, peak_sign="neg", estimate_peak_time=False - ): - """Returns the amplitudes of all channels at the time of the peak - amplitude across channels. + waveform: np.array, + peak_sign: str = "neg", + estimate_peak_time: bool = False, + ) -> np.array: + """Returns the amplitudes of all channels at the time of the peak. + + Amplitude across channels. Parameters ---------- - waveform : array-like, shape (n_spikes, n_time, n_channels) - peak_sign : ('pos', 'neg', 'both'), optional - Direction of the peak in the waveform + waveform : np.array + array-like, shape (n_spikes, n_time, n_channels) + peak_sign : str, optional + One of 'pos', 'neg', 'both'. Direction of the peak in the waveform estimate_peak_time : bool, optional Find the peak times for each spike because some spikesorters do not align the spike time (at index n_time // 2) to the peak Returns ------- - peak_amplitudes : array-like, shape (n_spikes, n_channels) + peak_amplitudes : np.array + array-like, shape (n_spikes, n_channels) """ if estimate_peak_time: @@ -279,19 +285,25 @@ def _get_peak_amplitude( return waveform[:, spike_peak_ind] @staticmethod - def _threshold(timestamps, marks, mark_param_dict): + def _threshold( + timestamps: np.array, marks: np.array, mark_param_dict: dict + ): """Filter the marks by an amplitude threshold Parameters ---------- - timestamps : array-like, shape (n_time,) - marks : array-like, shape (n_time, n_channels) + timestamps : np.array + array-like, shape (n_time,) + marks : np.array + array-like, shape (n_time, n_channels) mark_param_dict : dict Returns ------- - filtered_timestamps : array-like, shape (n_filtered_time,) - filtered_marks : array-like, shape (n_filtered_time, n_channels) + filtered_timestamps : np.array + array-like, shape (n_filtered_time,) + filtered_marks : np.array + array-like, shape (n_filtered_time, n_channels) """ if mark_param_dict["peak_sign"] == "neg": @@ -307,20 +319,24 @@ def _threshold(timestamps, marks, mark_param_dict): @schema class UnitMarksIndicatorSelection(SpyglassMixin, dj.Lookup): - """Bins the spike times and associated spike waveform features for a given - time interval into regular time bins determined by the sampling rate.""" + """Pairing of a UnitMarksIndicator with a time interval and sampling rate + + Bins the spike times and associated spike waveform features for a given + time interval into regular time bins determined by the sampling rate. + """ definition = """ -> UnitMarks -> IntervalList sampling_rate=500 : float - --- """ @schema class UnitMarksIndicator(SpyglassMixin, dj.Computed): - """Bins the spike times and associated spike waveform features into regular + """Bins spike times and waveforms into regular time bins. + + Bins the spike times and associated spike waveform features into regular time bins according to the sampling rate. Features that fall into the same time bin are averaged. """ @@ -373,7 +389,9 @@ def make(self, key): self.insert1(key) @staticmethod - def get_time_bins_from_interval(interval_times, sampling_rate): + def get_time_bins_from_interval( + interval_times: np.array, sampling_rate: int + ) -> np.array: """Picks the superset of the interval""" start_time, end_time = interval_times[0][0], interval_times[-1][-1] n_samples = int(np.ceil((end_time - start_time) * sampling_rate)) + 1 @@ -382,9 +400,14 @@ def get_time_bins_from_interval(interval_times, sampling_rate): @staticmethod def plot_all_marks( - marks_indicators: xr.DataArray, plot_size=5, s=10, plot_limit=None + marks_indicators: xr.DataArray, + plot_size: int = 5, + marker_size: int = 10, + plot_limit: int = None, ): - """Plots 2D slices of each of the spike features against each other + """Plot all marks for all electrodes. + + Plots 2D slices of each of the spike features against each other for all electrodes. Parameters @@ -393,7 +416,7 @@ def plot_all_marks( Spike times and associated spike waveform features binned into plot_size : int, optional Default 5. Matplotlib figure size for each mark. - s : int, optional + marker_size : int, optional Default 10. Marker size plot_limit : int, optional Default None. Limits to first N electrodes. @@ -422,25 +445,28 @@ def plot_all_marks( axes[ax_ind1, ax_ind2].scatter( marks.sel(marks=feature1), marks.sel(marks=feature2), - s=s, + s=marker_size, ) except TypeError: axes.scatter( marks.sel(marks=feature1), marks.sel(marks=feature2), - s=s, + s=marker_size, ) - def fetch1_dataframe(self): + def fetch1_dataframe(self) -> pd.DataFrame: + """Convenience function for returning the first dataframe""" return self.fetch_dataframe()[0] - def fetch_dataframe(self): + def fetch_dataframe(self) -> list[pd.DataFrame]: + """Fetches the marks indicators as a list of pandas dataframes""" return [ data["marks_indicator"].set_index("time") for data in self.fetch_nwb() ] def fetch_xarray(self): + """Fetches the marks indicators as an xarray DataArray""" # sort_group_electrodes = ( # SortGroup.SortGroupElectrode() & # pd.DataFrame(self).to_dict('records')) @@ -474,7 +500,16 @@ def reformat_name(name): ) -def make_default_decoding_parameters_cpu(): +def make_default_decoding_parameters_cpu() -> tuple[dict, dict, dict]: + """Default parameters for decoding on CPU + + Returns + ------- + classifier_parameters : dict + fit_parameters : dict + predict_parameters : dict + """ + classifier_parameters = dict( environments=[_DEFAULT_ENVIRONMENT], observation_models=None, @@ -496,7 +531,16 @@ def make_default_decoding_parameters_cpu(): return classifier_parameters, fit_parameters, predict_parameters -def make_default_decoding_parameters_gpu(): +def make_default_decoding_parameters_gpu() -> tuple[dict, dict, dict]: + """Default parameters for decoding on GPU + + Returns + ------- + classifier_parameters : dict + fit_parameters : dict + predict_parameters : dict + """ + classifier_parameters = dict( environments=[_DEFAULT_ENVIRONMENT], observation_models=None, @@ -524,7 +568,9 @@ def make_default_decoding_parameters_gpu(): @schema class ClusterlessClassifierParameters(SpyglassMixin, dj.Manual): - """Decodes the animal's mental position and some category of interest + """Decodes animal's mental position. + + Decodes the animal's mental position and some category of interest from unclustered spikes and spike waveform features """ @@ -536,7 +582,8 @@ class ClusterlessClassifierParameters(SpyglassMixin, dj.Manual): predict_params : BLOB # prediction parameters """ - def insert_default(self): + def insert_default(self) -> None: + """Insert the default parameter set""" ( classifier_parameters, fit_parameters, @@ -567,10 +614,12 @@ def insert_default(self): skip_duplicates=True, ) - def insert1(self, key, **kwargs): + def insert1(self, key, **kwargs) -> None: + """Custom insert1 to convert classes to dicts""" super().insert1(convert_classes_to_dict(key), **kwargs) - def fetch1(self, *args, **kwargs): + def fetch1(self, *args, **kwargs) -> dict: + """Custom fetch1 to convert dicts to classes""" return restore_classes(super().fetch1(*args, **kwargs)) @@ -619,10 +668,12 @@ def make(self, key): self.insert1(key) - def fetch1_dataframe(self): + def fetch1_dataframe(self) -> pd.DataFrame: + """Convenience function for returning the first dataframe""" return self.fetch_dataframe()[0] - def fetch_dataframe(self): + def fetch_dataframe(self) -> list[pd.DataFrame]: + """Fetches the multiunit firing rate as a list of pandas dataframes""" return [ data["multiunit_firing_rate"].set_index("time") for data in self.fetch_nwb() @@ -631,7 +682,7 @@ def fetch_dataframe(self): @schema class MultiunitHighSynchronyEventsParameters(SpyglassMixin, dj.Manual): - """Parameters for extracting times of high mulitunit activity during immobility.""" + """Params to extract times of high mulitunit activity during immobility.""" definition = """ param_name : varchar(80) # a name for this set of parameters @@ -642,6 +693,7 @@ class MultiunitHighSynchronyEventsParameters(SpyglassMixin, dj.Manual): """ def insert_default(self): + """Insert the default parameter set""" self.insert1( { "param_name": "default", @@ -673,7 +725,6 @@ def get_decoding_data_for_epoch( position_info : pd.DataFrame, shape (n_time, n_columns) marks : xr.DataArray, shape (n_time, n_marks, n_electrodes) valid_slices : list[slice] - """ valid_ephys_position_times_by_epoch = ( @@ -744,7 +795,6 @@ def get_data_for_multiple_epochs( marks : xr.DataArray, shape (n_time, n_marks, n_electrodes) valid_slices : dict[str, list[slice]] environment_labels : np.ndarray, shape (n_time,) - """ data = [] environment_labels = [] @@ -780,7 +830,9 @@ def populate_mark_indicators( mark_param_name: str = "default", position_info_param_name: str = "default_decoding", ): - """Populate mark indicators for all units in the given spike sorting selection. + """Populate mark indicators + + Populates for all units in a given spike sorting selection. This function is a way to do several pipeline steps at once. It will: 1. Populate the SpikeSortingSelection table diff --git a/src/spyglass/decoding/v1/__init__.py b/src/spyglass/decoding/v1/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/spyglass/decoding/v1/waveform_features.py b/src/spyglass/decoding/v1/waveform_features.py index 5302c80dd..4bed99f35 100644 --- a/src/spyglass/decoding/v1/waveform_features.py +++ b/src/spyglass/decoding/v1/waveform_features.py @@ -69,8 +69,7 @@ def check_supported_waveform_features(waveform_features: list[str]) -> bool: Parameters ---------- - mark_type : str - + waveform_features : list """ supported_features = set(WAVEFORM_FEATURE_FUNCTIONS) return set(waveform_features).issubset(supported_features) diff --git a/src/spyglass/decoding/visualization/__init__.py b/src/spyglass/decoding/visualization/__init__.py new file mode 100644 index 000000000..e69de29bb From 1ae9c133d08a20279acc439b4c9d8af114adebf5 Mon Sep 17 00:00:00 2001 From: Kyu Hyun Lee Date: Sat, 20 Jan 2024 10:14:40 -0800 Subject: [PATCH 7/8] Add new function to retrieve spatial series from NWB file (#777) * Add new func for spatial series * Update src/spyglass/utils/nwb_helper_fn.py Co-authored-by: Chris Brozdowski * Update src/spyglass/utils/nwb_helper_fn.py --------- Co-authored-by: Chris Brozdowski Co-authored-by: Eric Denovellis --- src/spyglass/utils/nwb_helper_fn.py | 29 ++++++++++++++++++++++++++--- 1 file changed, 26 insertions(+), 3 deletions(-) diff --git a/src/spyglass/utils/nwb_helper_fn.py b/src/spyglass/utils/nwb_helper_fn.py index f9edb5c2f..d09b5b9fd 100644 --- a/src/spyglass/utils/nwb_helper_fn.py +++ b/src/spyglass/utils/nwb_helper_fn.py @@ -158,6 +158,31 @@ def get_data_interface(nwbfile, data_interface_name, data_interface_class=None): return None +def get_position_obj(nwbfile): + """Return the Position object from the behavior processing module. + Meant to find position spatial series that are not found by + `get_data_interface(nwbfile, 'position', pynwb.behavior.Position)`. + The code returns the first `pynwb.behavior.Position` object (technically + there should only be one). + + Parameters + ---------- + nwbfile : pynwb.NWBFile + The NWB file object. + + Returns + ------- + pynwb.behavior.Position object + """ + ret = [] + for obj in nwbfile.processing["behavior"].data_interfaces.values(): + if isinstance(obj, pynwb.behavior.Position): + ret.append(obj) + if len(ret) > 1: + raise ValueError(f"Found more than one position object in {nwbfile}") + return ret[0] if ret and len(ret) else None + + def get_raw_eseries(nwbfile): """Return all ElectricalSeries in the acquisition group of an NWB file. @@ -459,9 +484,7 @@ def get_all_spatial_series(nwbf, verbose=False, incl_times=True) -> dict: the file. The 'raw_position_object_id' is the object ID of the SpatialSeries object. """ - pos_interface = get_data_interface( - nwbf, "position", pynwb.behavior.Position - ) + pos_interface = get_position_obj(nwbf) if pos_interface is None: return None From f42a9874618071de8a793707154035f7b07b957d Mon Sep 17 00:00:00 2001 From: Eric Denovellis Date: Sat, 20 Jan 2024 11:58:46 -0800 Subject: [PATCH 8/8] Add overview to docs (#779) * Add overview * Update CHANGELOG.md --- CHANGELOG.md | 1 + docs/src/images/fig1.png | Bin 0 -> 52116 bytes docs/src/index.md | 46 +++++++++++++++++++++++++++++++++++---- 3 files changed, 43 insertions(+), 4 deletions(-) create mode 100644 docs/src/images/fig1.png diff --git a/CHANGELOG.md b/CHANGELOG.md index 32276f353..9ba4ccb5c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -14,6 +14,7 @@ - IntervalList: Add secondary key `pipeline` #742 - Increase pytest coverage for `common`, `lfp`, and `utils`. #743 - Update docs to reflect new notebooks. #776 +- Add overview of Spyglass to docs. #779 ### Pipelines diff --git a/docs/src/images/fig1.png 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z(*>XVQeMbqq!?Y=z+W-THE|qP$8%}pRJhfk8y;k$>-mYs{eaMI75)-r^un%68Si(= zG-DphO#dXXDzp!2O^wKKl%KYK22DrFvs+ymH5a##Mo{Gv;=KO%Q}kyd?w zsalpE=XhtbgCki!D!^yZ=2QBl%fZ{uWUW^=0~dF@u4=o}IQ+5n2Mg4%3M1BCdE`;z7}^eio7cf( Ko+1xs)_(!Lm`FbW literal 0 HcmV?d00001 diff --git a/docs/src/index.md b/docs/src/index.md index c5eaf0338..4f0f7be74 100644 --- a/docs/src/index.md +++ b/docs/src/index.md @@ -1,9 +1,47 @@ # Spyglass -**Spyglass** is a data analysis framework that facilitates the storage, -analysis, and sharing of neuroscience data to support reproducible research. It -is designed to be interoperable with the NWB format and integrates open-source -tools into a coherent framework. +![Figure 1](./images/fig1.png) + +**Spyglass** is an open-source software framework designed to offer reliable +and reproducible analysis of neuroscience data and sharing of the results +with collaborators and the broader community. + +Features of Spyglass include: + ++ **Standardized data storage** - Spyglass uses the open-source + [Neurodata Without Borders: Neurophysiology (NWB:N)](https://www.nwb.org/) + format to ingest and store processed data. NWB:N is a standard set by the BRAIN + Initiative for neurophysiological data ([Rübel et al., 2022](https://doi.org/10.7554/elife.78362)). ++ **Reproducible analysis** - Spyglass uses [DataJoint](https://datajoint.com/) + to ensure that all analysis is reproducible. DataJoint is a data management + system that automatically tracks dependencies between data and analysis code. This + ensures that all analysis is reproducible and that the results are + automatically updated when the data or analysis code changes. ++ **Common analysis tools** - Spyglass provides easy usage of the open-source packages + [SpikeInterface](https://github.com/SpikeInterface/spikeinterface), + [Ghostipy](https://github.com/kemerelab/ghostipy), and [DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) + for common analysis tasks. These packages are well-documented and have active + developer communities. ++ **Interactive data visualization** - Spyglass uses [figurl](https://github.com/flatironinstitute/figurl) + to create interactive data visualizations that can be shared with collaborators + and the broader community. These visualizations are hosted on the web + and can be viewed in any modern web browser. The interactivity allows users to + explore the data and analysis results in detail. ++ **Sharing results** - Spyglass enables sharing of data and analysis results via + [Kachery](https://github.com/flatironinstitute/kachery-cloud), a + decentralized content addressable data sharing platform. Kachery Cloud allows + users to access the database and pull data and analysis results directly + to their local machine. ++ **Pipeline versioning** - Processing and analysis of data in neuroscience is + often dynamic, requiring new features. Spyglass uses *Merge tables* to ensure that + analysis pipelines can be versioned. This allows users to easily use and compare + results from different versions of the analysis pipeline while retaining + the ability to access previously generated results. ++ **Cautious Delete** - Spyglass uses a `cautious delete` feature to ensure + that data is not accidentally deleted by other users. When a user deletes data, + Spyglass will first check to see if the data belongs to another team of users. + This enables teams of users to work collaboratively on the same database without + worrying about accidentally deleting each other's data. ## Getting Started