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Thinh Nguyen
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rename - add redirect url - add instruction
1 parent 8181f1b commit 593bcf7

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Welcome to DataJoint Workshop!"
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"# Welcome to DataJoint Tutorial!"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Congratulations! If you are reading this, then you have successfully connected to the workshop JupyterHub and opened up your very first workshop notebook!"
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"Congratulations! If you are reading this, then you have successfully connected to the workshop JupyterHub and opened up your very first workshop notebook!\n",
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"\n",
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"Before we start, remember that all of the edits you make in these tutorial notebooks are ***not persistent*** - they will be reset to the original content everytime you restart the server. However, you can easily download the notebooks that you're interested in keeping the changes."
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"dj.set_password()"
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"# To change your password, run: dj.set_password()"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"dj.config['database.password'] = 'ENTER YOUR NEW PASSWORD HERE'\n",
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"# if you did change your password, update the config\n",
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"# dj.config['database.password'] = 'ENTER YOUR NEW PASSWORD HERE'\n",
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"\n",
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"# and save it to dj_local_config.json\n",
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"dj.config.save_global()"
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Summary"
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"# Continue with the tutorial"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"If all of the above worked, then you are now ready to continue on with the tutorial!\n",
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"The next step in the tutorial is to:\n",
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"1. learn the basic of the DataJoint framework with [01-DataJoint 101](./01-DataJoint%20101%20-%20Interactive.ipynb)\n",
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"2. data ingestion in a pipeline, with practical examples in 2 domains:\n",
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" + [02-electrophysiology](./electrophysiology/02-Imported%20Tables%20-%20Interactive.ipynb)\n",
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" + [02-calcium imaging](./calcium_imaging/02-Imported%20Tables%20-%20Interactive.ipynb)\n",
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"3. more advanced topics on built-in automatic computation of the pipeline with DataJoint, also with practical examples from:\n",
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" + [03-electrophysiology](./electrophysiology/03-Computed%20Table%2C%20Lookup%20Table%2C%20and%20Part%20Table%20-%20Completed.ipynb)\n",
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" + [03-calcium imaging](./calcium_imaging/03-Computed%20Table%2C%20Lookup%20Table%2C%20and%20Part%20Table%20-%20Completed.ipynb)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"If all of the above worked, then you are now ready to continue on with the workshop! If you encounter any issues, be sure to let the instructor know and troubleshoot before moving on with the rest of the workshop."
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"As you will find out that these tutorial notebooks are meant to be interactive. There will be empty cells meant for you to write the code yourself, as mini exercies. To access the notebooks with complete solutions, visit this [folder](./completed)."
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]
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}
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calcium_imaging/02-Imported Tables - Interactive.ipynb

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"source": [
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"At this point, our pipeline contains the core elements with data populated, ready for further downstream analysis.\n",
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"\n",
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"In the next session, we are going to introduce the concept of `Computed` table, and `Lookup` table, as well as learning to set up a automated computation routine."
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"In the next [session](./03-Computed%20Table%2C%20Lookup%20Table%2C%20and%20Part%20Table%20-%20Interactive.ipynb), we are going to introduce the concept of `Computed` table, and `Lookup` table, as well as learning to set up a automated computation routine."
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]
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}
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],
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calcium_imaging/02-Imported Tables - Completed.ipynb renamed to completed/02-calcium_imaging - Imported Tables - Completed.ipynb

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],
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"source": [
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"from skimage import io\n",
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"im = io.imread('data/example_scan_01.tif')\n",
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"im = io.imread('../calcium_imaging/data/example_scan_01.tif')\n",
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"print(im.shape)"
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]
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},

electrophysiology/02-Imported Tables - Completed.ipynb renamed to completed/02-electrophysiology - Imported Tables - Completed.ipynb

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}
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],
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"source": [
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"filename = 'data/data_{mouse_id}_{session_date}.npy'.format(**key)\n",
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"filename = '../electrophysiology/data/data_{mouse_id}_{session_date}.npy'.format(**key)\n",
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"filename"
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]
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},
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" \"\"\"\n",
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" def make(self, key):\n",
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" # use key dictionary to determine the data file path\n",
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" data_file = \"data/data_{mouse_id}_{session_date}.npy\".format(**key)\n",
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" data_file = \"../electrophysiology/data/data_{mouse_id}_{session_date}.npy\".format(**key)\n",
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"\n",
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" # load the data\n",
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" data = np.load(data_file)\n",

calcium_imaging/03-Computed Table, Lookup Table, and Part Table - Completed.ipynb renamed to completed/03-calcium_imaging - Computed Table, Lookup Table, and Part Table - Completed.ipynb

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electrophysiology/03-Computed Tables - Completed.ipynb renamed to completed/03-electrophysiology - Computed Table, Lookup Table, and Part Table - Completed.ipynb

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electrophysiology/02-Imported Tables - Interactive.ipynb

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"source": [
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"At this point, our pipeline contains the core elements with data populated, ready for further downstream analysis.\n",
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"\n",
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"In the next session, we are going to introduce the concept of `Computed` table, and `Lookup` table, as well as learning to set up a automated computation routine."
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"In the next [session](./03-Computed%20Table%2C%20Lookup%20Table%2C%20and%20Part%20Table%20-%20Interactive.ipynb), we are going to introduce the concept of `Computed` table, and `Lookup` table, as well as learning to set up a automated computation routine."
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]
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}
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],

electrophysiology/03-Computed Tables - Interactive.ipynb renamed to electrophysiology/03-Computed Table, Lookup Table, and Part Table - Interactive.ipynb

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