diff --git a/.gitignore b/.gitignore index 956145d..91748e3 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,6 @@ src/ibc_api.egg-info src/ibc_api/__pycache__ +src/ibc_api/data +src/ibc-api ibc_data .ipynb_checkpoints \ No newline at end of file diff --git a/README.md b/README.md index bb8be8f..8dda350 100644 --- a/README.md +++ b/README.md @@ -20,16 +20,16 @@ import ibc_api.utils as ibc ibc.authenticate() # Fetch info on all available files -# Load as a pandas dataframe and save as ibc_data/available_statistic_map.csv -db = ibc.get_info(data_type="statistic_map") +# Load as a pandas dataframe and save as ibc_data/available_{data_type}.csv +db = ibc.get_info(data_type="volume_maps") # Keep statistic maps for sub-08, for task-Discount filtered_db = ibc.filter_data(db, subject_list=["sub-08"], task_list=["Discount"]) # Download all statistic maps for sub-08, task-Discount # Saved under ibc_data/resulting_smooth_maps/sub-08/task-Discount -# Also create ibc_data/downloaded_statistic_map.csv -# This contains downloaded file paths and time of download +# Also creates ibc_data/downloaded_volume_maps.csv +# which contains downloaded file paths and time of download downloaded_db = ibc.download_data(filtered_db, organise_by='task') ``` # Note diff --git a/examples/example.py b/examples/example.py index 511ac91..87080ad 100644 --- a/examples/example.py +++ b/examples/example.py @@ -4,14 +4,14 @@ ibc.authenticate() # Fetch info on all available files -# Load as a pandas dataframe and save as ibc_data/available_statistic_map.csv -db = ibc.get_info(data_type="statistic_map") +# Load as a pandas dataframe and save as ibc_data/available_{data_type}.csv +db = ibc.get_info(data_type="volume_maps") # Keep statistic maps for sub-08, for task-Discount filtered_db = ibc.filter_data(db, subject_list=["sub-08"], task_list=["Discount"]) # Download all statistic maps for sub-08, task-Discount # Saved under ibc_data/resulting_smooth_maps/sub-08/task-Discount -# Also creates ibc_data/downloaded_statistic_map.csv +# Also creates ibc_data/downloaded_volume_maps.csv # which contains downloaded file paths and time of download downloaded_db = ibc.download_data(filtered_db, organise_by='task') \ No newline at end of file diff --git a/examples/get_data.ipynb b/examples/get_data.ipynb index cfc8993..159e192 100644 --- a/examples/get_data.ipynb +++ b/examples/get_data.ipynb @@ -1,7 +1,6 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -12,7 +11,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -28,10 +26,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[siibra:INFO] Version: 0.4a47\n", + "[siibra:INFO] Version: 0.4a61\n", "[siibra:WARNING] This is a development release. Use at your own risk.\n", - "[siibra:INFO] Please file bugs and issues at https://github.com/FZJ-INM1-BDA/siibra-python.\n", - "[siibra:INFO] Clearing siibra cache at /home/himanshu/.cache/siibra.retrieval\n" + "[siibra:INFO] Please file bugs and issues at https://github.com/FZJ-INM1-BDA/siibra-python.\n" ] } ], @@ -40,7 +37,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -58,7 +54,7 @@ "output_type": "stream", "text": [ "***\n", - "To continue, please go to https://iam.ebrains.eu/auth/realms/hbp/device?user_code=SEBC-RHFZ\n", + "To continue, please go to https://iam.ebrains.eu/auth/realms/hbp/device?user_code=USBN-YMBC\n", "***\n", "ebrains token successfuly set.\n" ] @@ -69,15 +65,14 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "To see what is available for a given data type on IBC, we need fetch the file that contains that information.\n", "The following loads a CSV file with all that info as a pandas dataframe and\n", - "saves it as ``ibc_data/available_statistic_map.csv``.\n", + "saves it as ``ibc_data/available_{data_type}.csv``.\n", "\n", - "Let's do that for IBC statistic maps.\n", + "Let's do that for IBC volumetric contrast maps.\n", "\n" ] }, @@ -90,16 +85,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "[siibra:INFO] 33194 objects found for dataset 07ab1665-73b0-40c5-800e-557bc319109d returned.\n" + "[siibra:INFO] 139625 objects found for dataset ad04f919-7dcc-48d9-864a-d7b62af3d49d returned.\n" ] } ], "source": [ - "db = ibc.get_info(data_type=\"statistic_map\")" + "db = ibc.get_info(data_type=\"volume_maps\")" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -152,13 +146,13 @@ " \n", " 0\n", " fMRI-BOLD\n", - " statistic_map\n", + " volume_maps\n", " Z\n", " IBC\n", " ArchiStandard\n", " S\n", " 1\n", - " visual_sentence_comprehension, response_select...\n", + " visual_sentence_comprehension,response_selecti...\n", " trm_5873cd1c9d4c4\n", " http://www.cognitiveatlas.org/task/id/trm_5873...\n", " left hand button presses upon video instructions\n", @@ -168,65 +162,65 @@ " \n", " 1\n", " fMRI-BOLD\n", - " statistic_map\n", + " volume_maps\n", " Z\n", " IBC\n", " ArchiStandard\n", " S\n", " 1\n", - " visual_sentence_comprehension, response_select...\n", + " visual_sentence_comprehension,response_selecti...\n", " trm_5873cd1c9d4c4\n", " http://www.cognitiveatlas.org/task/id/trm_5873...\n", " left hand button presses upon video instructions\n", - " sub-01_ses-07_task-ArchiStandard_dir-ap_space-...\n", + " sub-01_ses-00_task-ArchiStandard_dir-ffx_space...\n", " sub-01\n", " \n", " \n", " 2\n", " fMRI-BOLD\n", - " statistic_map\n", + " volume_maps\n", " Z\n", " IBC\n", " ArchiStandard\n", " S\n", " 1\n", - " visual_sentence_comprehension, response_select...\n", + " visual_sentence_comprehension,response_selecti...\n", " trm_5873cd1c9d4c4\n", " http://www.cognitiveatlas.org/task/id/trm_5873...\n", - " right hand button presses upon video instructions\n", - " sub-01_ses-00_task-ArchiStandard_dir-ap_space-...\n", + " left hand button presses upon video instructions\n", + " sub-01_ses-00_task-ArchiStandard_dir-pa_space-...\n", " sub-01\n", " \n", " \n", " 3\n", " fMRI-BOLD\n", - " statistic_map\n", + " volume_maps\n", " Z\n", " IBC\n", " ArchiStandard\n", " S\n", " 1\n", - " visual_sentence_comprehension, response_select...\n", + " visual_sentence_comprehension,response_selecti...\n", " trm_5873cd1c9d4c4\n", " http://www.cognitiveatlas.org/task/id/trm_5873...\n", - " right hand button presses upon video instructions\n", + " left hand button presses upon video instructions\n", " sub-01_ses-07_task-ArchiStandard_dir-ap_space-...\n", " sub-01\n", " \n", " \n", " 4\n", " fMRI-BOLD\n", - " statistic_map\n", + " volume_maps\n", " Z\n", " IBC\n", " ArchiStandard\n", " S\n", " 1\n", - " auditory_sentence_comprehension, response_sele...\n", + " visual_sentence_comprehension,response_selecti...\n", " trm_5873cd1c9d4c4\n", " http://www.cognitiveatlas.org/task/id/trm_5873...\n", - " left hand button presses upon audio instructions\n", - " sub-01_ses-00_task-ArchiStandard_dir-ap_space-...\n", + " left hand button presses upon video instructions\n", + " sub-01_ses-07_task-ArchiStandard_dir-ffx_space...\n", " sub-01\n", " \n", " \n", @@ -246,116 +240,116 @@ " ...\n", " \n", " \n", - " 18393\n", + " 26568\n", " fMRI-BOLD\n", - " statistic_map\n", + " volume_maps\n", " Z\n", " IBC\n", - " MathLanguage\n", + " RewProc\n", " S\n", " 1\n", - " theory-of-mind\n", - " trm_55217a9f473f0\n", - " http://www.cognitiveatlas.org/task/id/trm_5521...\n", - " NaN\n", - " sub-15_ses-30_task-MathLanguage_dir-ffx_space-...\n", + " reward_valuation,reward_processing\n", + " trm_550b5c1a7f4db\n", + " http://www.cognitiveatlas.org/task/id/trm_550b...\n", + " gained vs lost 20 or 10 units of reward\n", + " sub-15_ses-39_task-RewProc_dir-unknown_space-M...\n", " sub-15\n", " \n", " \n", - " 18394\n", + " 26569\n", " fMRI-BOLD\n", - " statistic_map\n", + " volume_maps\n", " Z\n", " IBC\n", - " MathLanguage\n", + " RewProc\n", " S\n", " 1\n", - " NaN\n", - " trm_55217a9f473f0\n", - " http://www.cognitiveatlas.org/task/id/trm_5521...\n", - " NaN\n", - " sub-15_ses-30_task-MathLanguage_dir-ffx_space-...\n", + " reward_valuation,reward_processing\n", + " trm_550b5c1a7f4db\n", + " http://www.cognitiveatlas.org/task/id/trm_550b...\n", + " gained vs lost 20 or 10 units of reward\n", + " sub-15_ses-39_task-RewProc_dir-unknown_space-M...\n", " sub-15\n", " \n", " \n", - " 18395\n", + " 26570\n", " fMRI-BOLD\n", - " statistic_map\n", + " volume_maps\n", " Z\n", " IBC\n", - " MathLanguage\n", + " RewProc\n", " S\n", " 1\n", - " theory-of-mind\n", - " trm_55217a9f473f0\n", - " http://www.cognitiveatlas.org/task/id/trm_5521...\n", - " NaN\n", - " sub-15_ses-30_task-MathLanguage_dir-ffx_space-...\n", + " risk_aversion,risk_processing,loss_aversion\n", + " trm_550b5c1a7f4db\n", + " http://www.cognitiveatlas.org/task/id/trm_550b...\n", + " lost vs gained 20 or 10 units of reward\n", + " sub-15_ses-39_task-RewProc_dir-ffx_space-MNI15...\n", " sub-15\n", " \n", " \n", - " 18396\n", + " 26571\n", " fMRI-BOLD\n", - " statistic_map\n", + " volume_maps\n", " Z\n", " IBC\n", - " MathLanguage\n", + " RewProc\n", " S\n", " 1\n", - " NaN\n", - " trm_55217a9f473f0\n", - " http://www.cognitiveatlas.org/task/id/trm_5521...\n", - " NaN\n", - " sub-15_ses-30_task-MathLanguage_dir-ffx_space-...\n", + " risk_aversion,risk_processing,loss_aversion\n", + " trm_550b5c1a7f4db\n", + " http://www.cognitiveatlas.org/task/id/trm_550b...\n", + " lost vs gained 20 or 10 units of reward\n", + " sub-15_ses-39_task-RewProc_dir-unknown_space-M...\n", " sub-15\n", " \n", " \n", - " 18397\n", + " 26572\n", " fMRI-BOLD\n", - " statistic_map\n", + " volume_maps\n", " Z\n", " IBC\n", - " MathLanguage\n", + " RewProc\n", " S\n", " 1\n", - " NaN\n", - " trm_55217a9f473f0\n", - " http://www.cognitiveatlas.org/task/id/trm_5521...\n", - " NaN\n", - " sub-15_ses-30_task-MathLanguage_dir-ffx_space-...\n", + " risk_aversion,risk_processing,loss_aversion\n", + " trm_550b5c1a7f4db\n", + " http://www.cognitiveatlas.org/task/id/trm_550b...\n", + " lost vs gained 20 or 10 units of reward\n", + " sub-15_ses-39_task-RewProc_dir-unknown_space-M...\n", " sub-15\n", " \n", " \n", "\n", - "

18398 rows × 13 columns

\n", + "

26573 rows × 13 columns

\n", "" ], "text/plain": [ - " modality image_type map_type study task analysis_level \\\n", - "0 fMRI-BOLD statistic_map Z IBC ArchiStandard S \n", - "1 fMRI-BOLD statistic_map Z IBC ArchiStandard S \n", - "2 fMRI-BOLD statistic_map Z IBC ArchiStandard S \n", - "3 fMRI-BOLD statistic_map Z IBC ArchiStandard S \n", - "4 fMRI-BOLD statistic_map Z IBC ArchiStandard S \n", - "... ... ... ... ... ... ... \n", - "18393 fMRI-BOLD statistic_map Z IBC MathLanguage S \n", - "18394 fMRI-BOLD statistic_map Z IBC MathLanguage S \n", - "18395 fMRI-BOLD statistic_map Z IBC MathLanguage S \n", - "18396 fMRI-BOLD statistic_map Z IBC MathLanguage S \n", - "18397 fMRI-BOLD statistic_map Z IBC MathLanguage S \n", + " modality image_type map_type study task analysis_level \\\n", + "0 fMRI-BOLD volume_maps Z IBC ArchiStandard S \n", + "1 fMRI-BOLD volume_maps Z IBC ArchiStandard S \n", + "2 fMRI-BOLD volume_maps Z IBC ArchiStandard S \n", + "3 fMRI-BOLD volume_maps Z IBC ArchiStandard S \n", + "4 fMRI-BOLD volume_maps Z IBC ArchiStandard S \n", + "... ... ... ... ... ... ... \n", + "26568 fMRI-BOLD volume_maps Z IBC RewProc S \n", + "26569 fMRI-BOLD volume_maps Z IBC RewProc S \n", + "26570 fMRI-BOLD volume_maps Z IBC RewProc S \n", + "26571 fMRI-BOLD volume_maps Z IBC RewProc S \n", + "26572 fMRI-BOLD volume_maps Z IBC RewProc S \n", "\n", " number_of_subjects tags \\\n", - "0 1 visual_sentence_comprehension, response_select... \n", - "1 1 visual_sentence_comprehension, response_select... \n", - "2 1 visual_sentence_comprehension, response_select... \n", - "3 1 visual_sentence_comprehension, response_select... \n", - "4 1 auditory_sentence_comprehension, response_sele... \n", + "0 1 visual_sentence_comprehension,response_selecti... \n", + "1 1 visual_sentence_comprehension,response_selecti... \n", + "2 1 visual_sentence_comprehension,response_selecti... \n", + "3 1 visual_sentence_comprehension,response_selecti... \n", + "4 1 visual_sentence_comprehension,response_selecti... \n", "... ... ... \n", - "18393 1 theory-of-mind \n", - "18394 1 NaN \n", - "18395 1 theory-of-mind \n", - "18396 1 NaN \n", - "18397 1 NaN \n", + "26568 1 reward_valuation,reward_processing \n", + "26569 1 reward_valuation,reward_processing \n", + "26570 1 risk_aversion,risk_processing,loss_aversion \n", + "26571 1 risk_aversion,risk_processing,loss_aversion \n", + "26572 1 risk_aversion,risk_processing,loss_aversion \n", "\n", " cognitive_paradigm_cogatlas \\\n", "0 trm_5873cd1c9d4c4 \n", @@ -364,11 +358,11 @@ "3 trm_5873cd1c9d4c4 \n", "4 trm_5873cd1c9d4c4 \n", "... ... \n", - "18393 trm_55217a9f473f0 \n", - "18394 trm_55217a9f473f0 \n", - "18395 trm_55217a9f473f0 \n", - "18396 trm_55217a9f473f0 \n", - "18397 trm_55217a9f473f0 \n", + "26568 trm_550b5c1a7f4db \n", + "26569 trm_550b5c1a7f4db \n", + "26570 trm_550b5c1a7f4db \n", + "26571 trm_550b5c1a7f4db \n", + "26572 trm_550b5c1a7f4db \n", "\n", " cognitive_paradigm_description_url \\\n", "0 http://www.cognitiveatlas.org/task/id/trm_5873... \n", @@ -377,39 +371,39 @@ "3 http://www.cognitiveatlas.org/task/id/trm_5873... \n", "4 http://www.cognitiveatlas.org/task/id/trm_5873... \n", "... ... \n", - "18393 http://www.cognitiveatlas.org/task/id/trm_5521... \n", - "18394 http://www.cognitiveatlas.org/task/id/trm_5521... \n", - "18395 http://www.cognitiveatlas.org/task/id/trm_5521... \n", - "18396 http://www.cognitiveatlas.org/task/id/trm_5521... \n", - "18397 http://www.cognitiveatlas.org/task/id/trm_5521... \n", + "26568 http://www.cognitiveatlas.org/task/id/trm_550b... \n", + "26569 http://www.cognitiveatlas.org/task/id/trm_550b... \n", + "26570 http://www.cognitiveatlas.org/task/id/trm_550b... \n", + "26571 http://www.cognitiveatlas.org/task/id/trm_550b... \n", + "26572 http://www.cognitiveatlas.org/task/id/trm_550b... \n", "\n", - " contrast_definition \\\n", - "0 left hand button presses upon video instructions \n", - "1 left hand button presses upon video instructions \n", - "2 right hand button presses upon video instructions \n", - "3 right hand button presses upon video instructions \n", - "4 left hand button presses upon audio instructions \n", - "... ... \n", - "18393 NaN \n", - "18394 NaN \n", - "18395 NaN \n", - "18396 NaN \n", - "18397 NaN \n", + " contrast_definition \\\n", + "0 left hand button presses upon video instructions \n", + "1 left hand button presses upon video instructions \n", + "2 left hand button presses upon video instructions \n", + "3 left hand button presses upon video instructions \n", + "4 left hand button presses upon video instructions \n", + "... ... \n", + "26568 gained vs lost 20 or 10 units of reward \n", + "26569 gained vs lost 20 or 10 units of reward \n", + "26570 lost vs gained 20 or 10 units of reward \n", + "26571 lost vs gained 20 or 10 units of reward \n", + "26572 lost vs gained 20 or 10 units of reward \n", "\n", " path subject \n", "0 sub-01_ses-00_task-ArchiStandard_dir-ap_space-... sub-01 \n", - "1 sub-01_ses-07_task-ArchiStandard_dir-ap_space-... sub-01 \n", - "2 sub-01_ses-00_task-ArchiStandard_dir-ap_space-... sub-01 \n", + "1 sub-01_ses-00_task-ArchiStandard_dir-ffx_space... sub-01 \n", + "2 sub-01_ses-00_task-ArchiStandard_dir-pa_space-... sub-01 \n", "3 sub-01_ses-07_task-ArchiStandard_dir-ap_space-... sub-01 \n", - "4 sub-01_ses-00_task-ArchiStandard_dir-ap_space-... sub-01 \n", + "4 sub-01_ses-07_task-ArchiStandard_dir-ffx_space... sub-01 \n", "... ... ... \n", - "18393 sub-15_ses-30_task-MathLanguage_dir-ffx_space-... sub-15 \n", - "18394 sub-15_ses-30_task-MathLanguage_dir-ffx_space-... sub-15 \n", - "18395 sub-15_ses-30_task-MathLanguage_dir-ffx_space-... sub-15 \n", - "18396 sub-15_ses-30_task-MathLanguage_dir-ffx_space-... sub-15 \n", - "18397 sub-15_ses-30_task-MathLanguage_dir-ffx_space-... sub-15 \n", + "26568 sub-15_ses-39_task-RewProc_dir-unknown_space-M... sub-15 \n", + "26569 sub-15_ses-39_task-RewProc_dir-unknown_space-M... sub-15 \n", + "26570 sub-15_ses-39_task-RewProc_dir-ffx_space-MNI15... sub-15 \n", + "26571 sub-15_ses-39_task-RewProc_dir-unknown_space-M... sub-15 \n", + "26572 sub-15_ses-39_task-RewProc_dir-unknown_space-M... sub-15 \n", "\n", - "[18398 rows x 13 columns]" + "[26573 rows x 13 columns]" ] }, "execution_count": 4, @@ -422,11 +416,10 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "There are over 18000 statistic maps available for download.\n", + "There are over 26000 statistic maps available for download.\n", "But since it's a pandas dataframe, we can filter it to get just what we want.\n", "Let's see how many statistic maps are available for each task.\n", "\n" @@ -440,50 +433,63 @@ { "data": { "text/plain": [ - "Audio 2793\n", + "Audio 2926\n", + "MathLanguage 2880\n", "ArchiStandard 1794\n", "RSVPLanguage 1729\n", - "Audi 1488\n", - "MTTWE 988\n", "MTTNS 912\n", + "MTTWE 912\n", + "Audi 900\n", + "SpatialNavigation 864\n", "ArchiSocial 702\n", "Self 660\n", "Visu 576\n", - "ArchiSpatial 546\n", - "ArchiEmotional 546\n", + "BiologicalMotion1 550\n", + "BiologicalMotion2 550\n", + "VSTMC 550\n", "HcpWm 546\n", + "ArchiEmotional 546\n", + "ArchiSpatial 546\n", + "RewProc 459\n", + "FaceBody 450\n", "HcpMotor 429\n", "MVEB 396\n", - "MathLanguage 390\n", "DotPatterns 363\n", + "NARPS 360\n", "WardAndAllport 330\n", + "Scene 330\n", + "TwoByTwo 330\n", + "Attention 330\n", + "EmoReco 330\n", "MCSE 324\n", "Moto 324\n", - "MVIS 216\n", + "SelectiveStopSignal 264\n", + "StopNogo 231\n", "Lec1 216\n", + "MVIS 216\n", + "EmoMem 198\n", "VSTM 180\n", + "FingerTapping 165\n", + "HcpLanguage 156\n", "HcpGambling 156\n", "HcpEmotion 156\n", - "HcpLanguage 156\n", "HcpSocial 117\n", "HcpRelational 117\n", "PreferenceFaces 111\n", - "Enumeration 108\n", - "Lec2 108\n", - "PreferenceFood 108\n", - "PainMovie 108\n", - "EmotionalPain 108\n", "TheoryOfMind 108\n", + "EmotionalPain 108\n", + "PreferenceFood 108\n", + "Lec2 108\n", "PreferenceHouses 108\n", + "Enumeration 108\n", + "PainMovie 108\n", "PreferencePaintings 105\n", + "Stroop 99\n", + "Catell 99\n", + "StopSignal 99\n", "ColumbiaCards 96\n", "Bang 72\n", - "Attention 30\n", - "TwoByTwo 30\n", - "SelectiveStopSignal 24\n", - "StopSignal 9\n", - "Stroop 9\n", - "Discount 6\n", + "Discount 66\n", "Name: task, dtype: int64" ] }, @@ -497,7 +503,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -506,12 +511,10 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "For this example, let's just download the 6 maps from Discount task. You can filter the maps for a task like this.\n", - "\n" + "For this example, let's just download the maps from Discount task, only for sub-08. You can filter the maps for tasks and subjects like this.\n" ] }, { @@ -557,15 +560,15 @@ " \n", " \n", " \n", - " 3183\n", + " 12500\n", " fMRI-BOLD\n", - " statistic_map\n", + " volume_maps\n", " Z\n", " IBC\n", " Discount\n", " S\n", " 1\n", - " response_conflict, selective_control\n", + " response_conflict,selective_control\n", " trm_566748c929afc\n", " http://www.cognitiveatlas.org/task/id/trm_5667...\n", " effect of delay on reward\n", @@ -573,31 +576,31 @@ " sub-08\n", " \n", " \n", - " 3184\n", + " 12501\n", " fMRI-BOLD\n", - " statistic_map\n", + " volume_maps\n", " Z\n", " IBC\n", " Discount\n", " S\n", " 1\n", - " incentive salience, selective_control\n", + " response_conflict,selective_control\n", " trm_566748c929afc\n", " http://www.cognitiveatlas.org/task/id/trm_5667...\n", - " effect of reward gain\n", - " sub-08_ses-27_task-Discount_dir-ap_space-MNI15...\n", + " effect of delay on reward\n", + " sub-08_ses-27_task-Discount_dir-ffx_space-MNI1...\n", " sub-08\n", " \n", " \n", - " 9809\n", + " 12502\n", " fMRI-BOLD\n", - " statistic_map\n", + " volume_maps\n", " Z\n", " IBC\n", " Discount\n", " S\n", " 1\n", - " response_conflict, selective_control\n", + " response_conflict,selective_control\n", " trm_566748c929afc\n", " http://www.cognitiveatlas.org/task/id/trm_5667...\n", " effect of delay on reward\n", @@ -605,51 +608,51 @@ " sub-08\n", " \n", " \n", - " 9810\n", + " 12503\n", " fMRI-BOLD\n", - " statistic_map\n", + " volume_maps\n", " Z\n", " IBC\n", " Discount\n", " S\n", " 1\n", - " incentive salience, selective_control\n", + " incentive_salience,selective_control\n", " trm_566748c929afc\n", " http://www.cognitiveatlas.org/task/id/trm_5667...\n", " effect of reward gain\n", - " sub-08_ses-27_task-Discount_dir-pa_space-MNI15...\n", + " sub-08_ses-27_task-Discount_dir-ap_space-MNI15...\n", " sub-08\n", " \n", " \n", - " 15823\n", + " 12504\n", " fMRI-BOLD\n", - " statistic_map\n", + " volume_maps\n", " Z\n", " IBC\n", " Discount\n", " S\n", " 1\n", - " response_conflict, selective_control\n", + " incentive_salience,selective_control\n", " trm_566748c929afc\n", " http://www.cognitiveatlas.org/task/id/trm_5667...\n", - " effect of delay on reward\n", + " effect of reward gain\n", " sub-08_ses-27_task-Discount_dir-ffx_space-MNI1...\n", " sub-08\n", " \n", " \n", - " 15824\n", + " 12505\n", " fMRI-BOLD\n", - " statistic_map\n", + " volume_maps\n", " Z\n", " IBC\n", " Discount\n", " S\n", " 1\n", - " incentive salience, selective_control\n", + " incentive_salience,selective_control\n", " trm_566748c929afc\n", " http://www.cognitiveatlas.org/task/id/trm_5667...\n", " effect of reward gain\n", - " sub-08_ses-27_task-Discount_dir-ffx_space-MNI1...\n", + " sub-08_ses-27_task-Discount_dir-pa_space-MNI15...\n", " sub-08\n", " \n", " \n", @@ -657,53 +660,53 @@ "" ], "text/plain": [ - " modality image_type map_type study task analysis_level \\\n", - "3183 fMRI-BOLD statistic_map Z IBC Discount S \n", - "3184 fMRI-BOLD statistic_map Z IBC Discount S \n", - "9809 fMRI-BOLD statistic_map Z IBC Discount S \n", - "9810 fMRI-BOLD statistic_map Z IBC Discount S \n", - "15823 fMRI-BOLD statistic_map Z IBC Discount S \n", - "15824 fMRI-BOLD statistic_map Z IBC Discount S \n", + " modality image_type map_type study task analysis_level \\\n", + "12500 fMRI-BOLD volume_maps Z IBC Discount S \n", + "12501 fMRI-BOLD volume_maps Z IBC Discount S \n", + "12502 fMRI-BOLD volume_maps Z IBC Discount S \n", + "12503 fMRI-BOLD volume_maps Z IBC Discount S \n", + "12504 fMRI-BOLD volume_maps Z IBC Discount S \n", + "12505 fMRI-BOLD volume_maps Z IBC Discount S \n", "\n", - " number_of_subjects tags \\\n", - "3183 1 response_conflict, selective_control \n", - "3184 1 incentive salience, selective_control \n", - "9809 1 response_conflict, selective_control \n", - "9810 1 incentive salience, selective_control \n", - "15823 1 response_conflict, selective_control \n", - "15824 1 incentive salience, selective_control \n", + " number_of_subjects tags \\\n", + "12500 1 response_conflict,selective_control \n", + "12501 1 response_conflict,selective_control \n", + "12502 1 response_conflict,selective_control \n", + "12503 1 incentive_salience,selective_control \n", + "12504 1 incentive_salience,selective_control \n", + "12505 1 incentive_salience,selective_control \n", "\n", " cognitive_paradigm_cogatlas \\\n", - "3183 trm_566748c929afc \n", - "3184 trm_566748c929afc \n", - "9809 trm_566748c929afc \n", - "9810 trm_566748c929afc \n", - "15823 trm_566748c929afc \n", - "15824 trm_566748c929afc \n", + "12500 trm_566748c929afc \n", + "12501 trm_566748c929afc \n", + "12502 trm_566748c929afc \n", + "12503 trm_566748c929afc \n", + "12504 trm_566748c929afc \n", + "12505 trm_566748c929afc \n", "\n", " cognitive_paradigm_description_url \\\n", - "3183 http://www.cognitiveatlas.org/task/id/trm_5667... \n", - "3184 http://www.cognitiveatlas.org/task/id/trm_5667... \n", - "9809 http://www.cognitiveatlas.org/task/id/trm_5667... \n", - "9810 http://www.cognitiveatlas.org/task/id/trm_5667... \n", - "15823 http://www.cognitiveatlas.org/task/id/trm_5667... \n", - "15824 http://www.cognitiveatlas.org/task/id/trm_5667... \n", + "12500 http://www.cognitiveatlas.org/task/id/trm_5667... \n", + "12501 http://www.cognitiveatlas.org/task/id/trm_5667... \n", + "12502 http://www.cognitiveatlas.org/task/id/trm_5667... \n", + "12503 http://www.cognitiveatlas.org/task/id/trm_5667... \n", + "12504 http://www.cognitiveatlas.org/task/id/trm_5667... \n", + "12505 http://www.cognitiveatlas.org/task/id/trm_5667... \n", "\n", " contrast_definition \\\n", - "3183 effect of delay on reward \n", - "3184 effect of reward gain \n", - "9809 effect of delay on reward \n", - "9810 effect of reward gain \n", - "15823 effect of delay on reward \n", - "15824 effect of reward gain \n", + "12500 effect of delay on reward \n", + "12501 effect of delay on reward \n", + "12502 effect of delay on reward \n", + "12503 effect of reward gain \n", + "12504 effect of reward gain \n", + "12505 effect of reward gain \n", "\n", " path subject \n", - "3183 sub-08_ses-27_task-Discount_dir-ap_space-MNI15... sub-08 \n", - "3184 sub-08_ses-27_task-Discount_dir-ap_space-MNI15... sub-08 \n", - "9809 sub-08_ses-27_task-Discount_dir-pa_space-MNI15... sub-08 \n", - "9810 sub-08_ses-27_task-Discount_dir-pa_space-MNI15... sub-08 \n", - "15823 sub-08_ses-27_task-Discount_dir-ffx_space-MNI1... sub-08 \n", - "15824 sub-08_ses-27_task-Discount_dir-ffx_space-MNI1... sub-08 " + "12500 sub-08_ses-27_task-Discount_dir-ap_space-MNI15... sub-08 \n", + "12501 sub-08_ses-27_task-Discount_dir-ffx_space-MNI1... sub-08 \n", + "12502 sub-08_ses-27_task-Discount_dir-pa_space-MNI15... sub-08 \n", + "12503 sub-08_ses-27_task-Discount_dir-ap_space-MNI15... sub-08 \n", + "12504 sub-08_ses-27_task-Discount_dir-ffx_space-MNI1... sub-08 \n", + "12505 sub-08_ses-27_task-Discount_dir-pa_space-MNI15... sub-08 " ] }, "execution_count": 6, @@ -712,12 +715,11 @@ } ], "source": [ - "filtered_db = ibc.filter_data(db, task_list=[\"Discount\"])\n", + "filtered_db = ibc.filter_data(db, task_list=[\"Discount\"], subject_list=[\"sub-08\"])\n", "filtered_db" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -725,7 +727,7 @@ "\n", "The following will save the requested maps under\n", "``ibc_data/resulting_smooth_maps/sub-08/task-Discount`` \n", - "(or whatever subject you chose). And will also create a local CSV file ``ibc_data/downloaded_statistic_map.csv`` to track the downloaded files. This will contain local file paths and the time they were downloaded at, and is updated everytime you download new files.\n" + "(or whatever subject you chose). And will also create a local CSV file ``ibc_data/downloaded_volume_maps.csv`` to track the downloaded files. This will contain local file paths and the time they were downloaded at, and is updated everytime you download new files.\n" ] }, { @@ -737,22 +739,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "[siibra:INFO] 33194 objects found for dataset 07ab1665-73b0-40c5-800e-557bc319109d returned.\n", - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:05<00:00, 1.15it/s]" + "[siibra:INFO] 139625 objects found for dataset ad04f919-7dcc-48d9-864a-d7b62af3d49d returned.\n", + "100%|███████████████████████████████████████████████████████| 6/6 [00:01<00:00, 5.65it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Downloaded requested files from IBC statistic_map dataset. See ibc_data/downloaded_statistic_map.csv for details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" + "Downloaded requested files from IBC volume_maps dataset. See ibc_data/downloaded_volume_maps.csv for details.\n" ] }, { @@ -784,32 +779,32 @@ " \n", " 0\n", " ibc_data/resulting_smooth_maps/sub-08/task-Dis...\n", - " 2023-05-12 11:36:49.203279\n", + " 2023-07-25 18:23:01.270473\n", " \n", " \n", " 1\n", " ibc_data/resulting_smooth_maps/sub-08/task-Dis...\n", - " 2023-05-12 11:36:50.066253\n", + " 2023-07-25 18:23:01.439383\n", " \n", " \n", " 2\n", " ibc_data/resulting_smooth_maps/sub-08/task-Dis...\n", - " 2023-05-12 11:36:50.994438\n", + " 2023-07-25 18:23:01.611974\n", " \n", " \n", " 3\n", " ibc_data/resulting_smooth_maps/sub-08/task-Dis...\n", - " 2023-05-12 11:36:51.869514\n", + " 2023-07-25 18:23:01.801682\n", " \n", " \n", " 4\n", " ibc_data/resulting_smooth_maps/sub-08/task-Dis...\n", - " 2023-05-12 11:36:52.702647\n", + " 2023-07-25 18:23:01.982841\n", " \n", " \n", " 5\n", " ibc_data/resulting_smooth_maps/sub-08/task-Dis...\n", - " 2023-05-12 11:36:53.589603\n", + " 2023-07-25 18:23:02.156350\n", " \n", " \n", "\n", @@ -825,12 +820,12 @@ "5 ibc_data/resulting_smooth_maps/sub-08/task-Dis... \n", "\n", " downloaded_on \n", - "0 2023-05-12 11:36:49.203279 \n", - "1 2023-05-12 11:36:50.066253 \n", - "2 2023-05-12 11:36:50.994438 \n", - "3 2023-05-12 11:36:51.869514 \n", - "4 2023-05-12 11:36:52.702647 \n", - "5 2023-05-12 11:36:53.589603 " + "0 2023-07-25 18:23:01.270473 \n", + "1 2023-07-25 18:23:01.439383 \n", + "2 2023-07-25 18:23:01.611974 \n", + "3 2023-07-25 18:23:01.801682 \n", + "4 2023-07-25 18:23:01.982841 \n", + "5 2023-07-25 18:23:02.156350 " ] }, "execution_count": 7, @@ -844,7 +839,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -856,10 +850,18 @@ "execution_count": 8, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/himanshu/Desktop/ibc_analysis/ibcpy/lib/python3.8/site-packages/nilearn/plotting/img_plotting.py:300: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0\n", + " anat_img = load_mni152_template()\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -868,9 +870,9 @@ }, { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -883,6 +885,13 @@ "map_path = downloaded_db[\"local_path\"][0]\n", "plot_stat_map(map_path)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/src/ibc_api/metadata.py b/src/ibc_api/metadata.py new file mode 100644 index 0000000..71e140f --- /dev/null +++ b/src/ibc_api/metadata.py @@ -0,0 +1,163 @@ +"""Functions to fetch metadata about the available IBC datasets.""" + +import json +import os + +REMOTE_ROOT = ( + "https://api.github.com/repos/individual-brain-charting/docs/contents" +) + +LOCAL_ROOT = os.path.join(os.path.dirname(__file__), "data") +os.makedirs(LOCAL_ROOT, exist_ok=True) + +SUBJECTS = [ + f"sub-{sub:02d}" for sub in [1, 2, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15] +] + + +def _load_json(data_file): + """Read a given json file + + Parameters + ---------- + data_file : str + path to json file to read + + Returns + ------- + dict + json file loaded as a dictionary + """ + with open(data_file, "r") as f: + data = json.load(f) + + return data + + +def select_dataset(data_type, metadata=None): + """Select metadata of the requested dataset + + Parameters + ---------- + data_type : str + what dataset to select, could be one of 'volume_maps', 'surface_maps', 'preprocessed', 'raw' + metadata : dict, optional + dictionary object containing version info, dataset ids etc, by default None + + Returns + ------- + dict + the metadata of latest version of the requested dataset + + Raises + ------ + KeyError + if the requested dataset is not found in the metadata + """ + if metadata is None: + metadata = fetch_metadata() + try: + dataset = metadata[data_type] + except KeyError: + raise KeyError( + f"Dataset type {data_type} not found in IBC collection." + ) + latest_version = _find_latest_version(dataset) + dataset = dataset[latest_version] + return dataset + + +def _find_latest_version(dataset): + """Find the latest version of the dataset + + Parameters + ---------- + dataset : list of dicts + value of one of the datasets in the metadata, probably with multiple versions in a list + + Returns + ------- + int + index of the latest version of the dataset + """ + latest_version_index = 0 + latest_version = 0 + for i, data in enumerate(dataset): + if data["version"] > latest_version: + latest_version = data["version"] + latest_version_index = i + + return latest_version_index + + +def fetch_remote_file( + file, + remote_root=REMOTE_ROOT, + local_root=LOCAL_ROOT, +): + """Fetch a file from the IBC docs repo + + Parameters + ---------- + file : str + name of the file to fetch + remote_root : str, optional + root link to wherever the file is stores, by default REMOTE_ROOT + local_root : str, optional + location to write the fetched file, by default LOCAL_ROOT + + Returns + ------- + str + full path of the fetched file + """ + # Link to the json file on the IBC docs repo + remote_file = f"{remote_root}/{file}" + # save the file locally + save_as = os.path.join(local_root, file) + # use curl with github api to download the file + os.system( + f"curl -s -S -L -H 'Accept: application/vnd.github.v4.raw' -H 'X-GitHub-Api-Version: 2022-11-28' {remote_file} -o '{save_as}'" + ) + + # Return the data + return save_as + + +def fetch_metadata(file="datasets.json"): + """Fetch the metadata file from the IBC docs repo + + Parameters + ---------- + file : str, optional + name of the file, by default "datasets.json" + + Returns + ------- + dict + json file loaded as a dictionary + """ + # Fetch the datasets.json file + data_file = fetch_remote_file(file) + + # Load the data as a dictionary + return _load_json(data_file) + + +def fetch_dataset_db(data_type, metadata=None): + """Fetch csv containing file-by-file information about the requested dataset. + + Parameters + ---------- + data_type : str + what dataset to select, could be one of 'volume_maps', 'surface_maps', 'preprocessed', 'raw' + metadata : dict, optional + dictionary object containing version info, dataset ids etc, by default None + Returns + ------- + str + full path of the fetched file csv file + """ + dataset = select_dataset(data_type, metadata) + + return fetch_remote_file(dataset["db_file"]) diff --git a/src/ibc_api/utils.py b/src/ibc_api/utils.py index 7b9df0e..5f9fe77 100644 --- a/src/ibc_api/utils.py +++ b/src/ibc_api/utils.py @@ -1,5 +1,5 @@ """API to fetch IBC data from EBRAINS via Human Data Gateway using siibra. -#TODO add other data sources: neurovault, openneuro""" +""" import siibra from siibra.retrieval.repositories import EbrainsHdgConnector @@ -8,26 +8,17 @@ import nibabel from siibra.retrieval.cache import CACHE import pandas as pd -from io import BytesIO from datetime import datetime +from . import metadata as md # clear cache -CACHE.clear() +CACHE.run_maintenance() # dataset ids on ebrains -DATASET_ID = { - "statistic_map": "07ab1665-73b0-40c5-800e-557bc319109d", - "preprocessed": "3ca4f5a1-647b-4829-8107-588a699763c1", - "raw": "8ddf749f-fb1d-4d16-acc3-fbde91b90e24", -} - -# path to csv file with information about all statistic maps on EBRAINS -STAT_MAPS_DB = "resulting_smooth_maps/ibc_neurovault.csv" +METADATA = md.fetch_metadata() # all subjects in IBC dataset -SUBJECTS = [ - "sub-%02d" % i for i in [1, 2, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15] -] +SUBJECTS = md.SUBJECTS def authenticate(): @@ -37,24 +28,23 @@ def authenticate(): siibra.fetch_ebrains_token() -def _connect_ebrains(data_type="statistic_map"): +def _connect_ebrains(data_type="volume_maps", metadata=METADATA): """Connect to given IBC dataset on EBRAINS via Human Data Gateway. Parameters ---------- data_type : str, optional - dataset to fetch, by default "statistic_map", can be one of - ["statistic_map", "raw", "preprocessed"] + dataset to fetch, by default "statistic_map", can be one of + ["volume_maps", "surface_maps", "preprocessed", "raw] Returns ------- EbrainsHdgConnector connector to the dataset """ - try: - dataset_id = DATASET_ID[data_type] - except KeyError: - return ValueError(f"Unknown data type: {data_type}") + # get the dataset id + dataset = md.select_dataset(data_type, metadata) + dataset_id = dataset["id"] return EbrainsHdgConnector(dataset_id) @@ -84,14 +74,14 @@ def _create_root_dir(dir_path=None): return dir_path -def get_info(data_type="statistic_map", save_to=None): +def get_info(data_type="volume_maps", save_to=None, metadata=METADATA): """Fetch a csv file describing each file in a given IBC dataset on EBRAINS. Parameters ---------- data_type : str, optional - dataset to fetch, by default "statistic_map", TODO one of - ["statistic_map", "raw", "preprocessed"] + dataset to fetch, by default "volume_maps", one of + ["volume_maps", "surface_maps", "raw", "preprocessed"] save_as : str or None, optional filename to save this csv as, by default None, if None saves as "ibc_data/available_{data_type}.csv" @@ -103,18 +93,12 @@ def get_info(data_type="statistic_map", save_to=None): """ # connect to ebrains dataset connector = _connect_ebrains(data_type) - if data_type == "statistic_map": - # file with all information about the dataset - db_file = STAT_MAPS_DB - # get the file - db = connector.get( - db_file, - decode_func=lambda b: pd.read_csv(BytesIO(b), delimiter=","), - ) - db.drop(columns=["Unnamed: 0"], inplace=True) - # TODO add other data types: raw, preprocessed, etc. - else: - return ValueError(f"Unknown data type: {data_type}") + # file with all information about the dataset + db_file = md.fetch_dataset_db(data_type, metadata) + # load the file as dataframe + db = pd.read_csv(db_file) + db.drop(columns=["Unnamed: 0"], inplace=True, errors="ignore") + db["image_type"] = [data_type for _ in range(len(db))] # save the database file save_to = _create_root_dir(save_to) save_as = os.path.join(save_to, f"available_{data_type}.csv") @@ -150,7 +134,7 @@ def filter_data(db, subject_list=SUBJECTS, task_list=False): return filtered_db -def get_file_paths(db): +def get_file_paths(db, metadata=METADATA): """Get the file paths for each file in a (filtered) dataframe. Parameters @@ -173,12 +157,8 @@ def get_file_paths(db): _file_names = db["path"].tolist() # update file names to be relative to the dataset file_names = [] + root_dir = md.select_dataset(data_type, metadata)["root"] for file in _file_names: - if data_type == "statistic_map": - root_dir = "resulting_smooth_maps" - # TODO add other data types: raw, preprocessed, etc. - else: - return ValueError(f"Unknown data type: {data_type}") # get the subject and session sub_ses = file.split("_")[:2] # put the file path together @@ -286,22 +266,23 @@ def _download_file(src_file, dst_file, connector): if not os.path.exists(dst_file): # load the file from ebrains src_data = connector.get(src_file) - if type(src_data) is nibabel.nifti1.Nifti1Image: - src_data.to_filename(dst_file) - # TODO add other data like json, etc. + if type(src_data) == nibabel.nifti1.Nifti1Image: + nibabel.save(src_data, dst_file) + elif type(src_data) == nibabel.gifti.gifti.GiftiImage: + nibabel.save(src_data, dst_file, mode="compat") else: return ValueError( f"Don't know how to save file {src_file}" f" of type {type(src_data)}" ) - return dst_file, datetime.now() + return dst_file else: print(f"File {dst_file} already exists, skipping download.") - return [], [] + return [] -def download_data(db, save_to=None, organise_by="session"): +def download_data(db, save_to=None, organise_by="session", metadata=METADATA): """Download the files in a (filtered) dataframe. Parameters @@ -340,7 +321,8 @@ def download_data(db, save_to=None, organise_by="session"): ) # file path to save the data dst_file = os.path.join(dst_file_head, dst_file_base) - file_name, file_time = _download_file(src_file, dst_file, connector) + file_name = _download_file(src_file, dst_file, connector) + file_time = datetime.now() local_db = _update_local_db(local_db_file, file_name, file_time) # keep cache < 2GiB, delete oldest files first CACHE.run_maintenance()