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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "0400cc7f-2670-4055-a4a4-bf2e48724eb9",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from mesmerize_core import *\n",
"from mesmerize_viz import *\n",
"import numpy as np\n",
"from copy import deepcopy\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "79593a5e-e018-48ea-a0c9-7be1816d619b",
"metadata": {
"tags": []
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"source": [
"pd.options.display.max_colwidth = 120"
]
},
{
"cell_type": "markdown",
"id": "007f8e68-71d8-4cea-987a-9c7b945dfdd7",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"# Paths\n",
"\n",
"`set_parent_raw_data_path()` - sets the **top level raw data directory**\n",
"\n",
"For example, if you have your experimental data in the following dir:\n",
"\n",
"`/data/my_name/exp_top_level/....`\n",
"\n",
"Set `/data/my_name` as the \"parent raw data path\". This allows you to move `exp_top_level/...` between computers.\n",
"\n",
"On windows:\n",
"\n",
"`D:/my_name/exp_top_level/...`\n",
"\n",
"Set `D:/my_name` as the parent raw data path, and you can then move `exp_top_level/...` between computers.\n",
"\n",
"**Note: Even on windows just use `/`, you do not have to worry about the issue of `\\\\` and `\\` on windows if you use `pathlib` :D**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9611e9a9-1ebd-4a46-8667-83706f937806",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"# for this demo set this dir as the path to your `caiman_data` dir\n",
"set_parent_raw_data_path(\"/home/kushal/caiman_data/\")"
]
},
{
"cell_type": "markdown",
"id": "ffe64010-ff54-42e0-9781-fe94c00caffa",
"metadata": {},
"source": [
"### Batch path, this is where caiman outputs will be organized\n",
"\n",
"This can be anywhere, it does not need to be under the parent raw data path."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a66c0c1d-47f9-4b68-8840-e035b8a6a160",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"batch_path = get_parent_raw_data_path().joinpath(\"mesmerize-batch5/batch.pickle\")"
]
},
{
"cell_type": "markdown",
"id": "7e70fe53-3207-4f78-acf4-1d5f5a3c8809",
"metadata": {
"tags": []
},
"source": [
"**Note: We recommend using [pathlib](https://docs.python.org/3/library/pathlib.html) instead of manually managing paths as strings. `pathlib` is just a part of the Python standard library, it makes it much easier to deal with paths and saves a lot of time in the long-run! It also makes your paths compatible across operating systems.**"
]
},
{
"cell_type": "markdown",
"id": "e78ae2f3-ccab-4728-a34f-7068d8569cac",
"metadata": {},
"source": [
"### Create a new batch\n",
"\n",
"This creates a new pandas `DataFrame` with the columns that are necessary for mesmerize. In mesmerize we call this the **batch DataFrame**. You can add additional columns relevant to your experiment, but do not modify columns used by mesmerize.\n",
"\n",
"Note that when you create a DataFrame you will need to use `load_batch()` to load it later. You cannot use `create_batch()` to overwrite an existing batch DataFrame"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a555bf21-514f-4e1e-8413-5bfde7714d69",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"# create a new batch\n",
"# df = create_batch(batch_path)\n",
"# to load existing batches use `load_batch()`\n",
"df = load_batch(batch_path)"
]
},
{
"cell_type": "markdown",
"id": "e5383552-2dd6-4d93-a7e0-4ff1978da139",
"metadata": {},
"source": [
"### View the dataframe\n",
"\n",
"It is empty with the appropriate columns for mesmerize"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "381f777d-5f52-46fe-96b6-7e058d3aafff",
"metadata": {},
"outputs": [],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"id": "48a744f5-5158-45db-b0ca-5603fe749696",
"metadata": {},
"source": [
"# Motion Correction\n",
"\n",
"### Get raw movie\n",
"\n",
"An input movie must be anywhere within `raw data path` or `batch path`. We will use the Sue 2p example."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eb782ed2-6320-49c5-ac33-a7ec50adaabd",
"metadata": {},
"outputs": [],
"source": [
"# We'll use the Sue movie from caiman\n",
"# download it if you don't have it\n",
"from caiman.utils.utils import download_demo\n",
"download_demo()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ff288722-22b2-4b6f-9845-e504800d211d",
"metadata": {},
"outputs": [],
"source": [
"movie_path = get_parent_raw_data_path().joinpath(\"example_movies/Sue_2x_3000_40_-46.tif\")"
]
},
{
"cell_type": "markdown",
"id": "37b436ac-512d-438c-8d5c-1668930e65e6",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"### Motion correction parameters\n",
"\n",
"Parameters for all algos have the following structure:\n",
"\n",
"```python\n",
"{\"main\": {... params directly passed to caiman}}\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6e549707-d7d7-4703-986f-4e8795cfb3b6",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
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"source": [
"# We will start with one version of parameters\n",
"mcorr_params1 =\\\n",
"{\n",
" 'main': # this key is necessary for specifying that these are the \"main\" params for the algorithm\n",
" {\n",
" 'max_shifts': [24, 24],\n",
" 'strides': [48, 48],\n",
" 'overlaps': [24, 24],\n",
" 'max_deviation_rigid': 3,\n",
" 'border_nan': 'copy',\n",
" 'pw_rigid': True,\n",
" 'gSig_filt': None\n",
" },\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "605944e3-5555-4262-8031-42a4bec24c71",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"### Add a \"batch item\", this is the combination of:\n",
"* algorithm to run, `algo`\n",
"* input movie to run the algorithm on, `input_movie_path`\n",
"* parameters for the specified algorithm, `params`\n",
"* a name for you to keep track of things, usually the same as the movie filename, `item_name`"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6d9cee81-d6c7-4e2d-b67e-7cba84d6ec27",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"# add an item to the batch\n",
"df.caiman.add_item(\n",
" algo='mcorr',\n",
" input_movie_path=movie_path,\n",
" params=mcorr_params1,\n",
" item_name=movie_path.stem, # filename of the movie, but can be anything\n",
")\n",
"\n",
"df"
]
},
{
"cell_type": "markdown",
"id": "53be38ee-b991-48ff-90ad-e91bdae39444",
"metadata": {},
"source": [
"### Run item :D "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "beb610a1-850c-4746-9487-16b5e07d81f0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# iloc is used to index rows in the dataframe\n",
"df.iloc[0].caiman.run()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ebe6b769-dfdd-4488-96dd-0e686fb59575",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"df = df.caiman.reload_from_disk()\n",
"df"
]
},
{
"cell_type": "markdown",
"id": "202958b1-0fee-45ac-b3da-f3664b57d4d2",
"metadata": {},
"source": [
"# Visualize :D "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "efc19e8d-4af4-4130-9bd0-b9c9f77a1ada",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"mcorr_widget = df.mcorr.viz(data=[\"input\", \"mcorr\"])\n",
"mcorr_widget.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fff4cb5d-a857-445d-8f60-7b36675d20a1",
"metadata": {},
"outputs": [],
"source": []
}
],
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