# 🐍 Step-by-Step Guide: Installing the Inscopix Python API

### ⚠️ Disclaimer
Before you begin, make sure you have **Anaconda** installed on your PC.
If not, you can follow the official installation guide — don’t panic, it’s easy!
*(These instructions are written for Windows systems.)*

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## 1. Locate the Inscopix Installation Path
*(You can perform this step using the Windows File Explorer.)*

If Inscopix is installed under the `./adm` user and you don’t have administrative access, no worries — we can work around that by copying the **Data Processing** folder.

Example: default installation path

C:\Program Files\Inscopix\Data Processing

Copy this folder and paste it into your own user directory so that you have permission to modify its contents.
It’s also recommended to **rename the folder** (remove spaces) to make it easier to use in scripts.

Example of my case, I am user uid-1908

C:\Users\uid-1908\

Once you paste the folder, you will have:

`C:\Users\uid-1908\DataProcessing`

## 2. Installing `isx` (Inscopix Python API)
You’ll need the **Anaconda Prompt** for this step and the ones that follow.

1. Open your **Anaconda Prompt**.
2. Navigate to the folder you just created in Step 1 using the `cd` command.

If you’re currently in the wrong directory, move up one level with `cd..`
For example, if you’re in another user’s folder:

 PS C:\Users\uid-1919> cd.. 
 PS C:\Users> 

Now navigate to your own user folder and then to **DataProcessing**:

 PS C:\Users> cd .\uid-1908
 PS C:\Users\uid-1908> cd .\DataProcessing
 PS C:\Users\uid-1908\DataProcessing> 

Once inside the `DataProcessing` folder, create a new **Mamba environment** using the following command:

PS C:\Users\uid-1908\DataProcessing> mamba create -n isxenv -f enviroment.yml

If everything installs correctly, activate the environment: `conda activate isxenv`

If you encounter issues during the environment setup (e.g., missing dependencies from `environment.yml`), try installing manually using `pip`:
First activate the enviroment you already created, and then install it again using `pip`

PS C:\Users\uid-1908\DataProcessing> conda activate isxenv
(isxenv)PS C:\Users\uid-1908\DataProcessing> pip install -e .

Do not miss the `.` at the end
## 3. Install JupyterLab to Run the Notebooks
The analysis scripts in this repository are designed to be run in **Jupyter Notebooks**.
You’ll also need to make sure you’re using **Python 3.6** for compatibility.

Install both with:

(isxenv)PS C:\Users\uid-1908\DataProcessing> mamba install jupyterlab python=3.6 

Once installation is complete, navigate to the folder where your datasets and cloned GitHub repository are stored.
*(They should be in the same directory for smooth operation.)*

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### 3.1 Clone the GitHub Repository
If you haven’t cloned the repository yet, you can do so with:

##### IN CONSTRUCTION ... (comming soon)
:construction:

### BONUS PART: How to catch the preprocessing pipeline ?

┌───────────────────────────────────────────────────────┐
│ Calcium Imaging Preprocessing Pipeline │
├───────────────────────────────────────────────────────┤
│ 1. Data Organization (TIFF stacks + metadata) │
│ 2. Motion Correction (Rigid/Non-rigid) │
│ 3. Neuropil Correction (Background subtraction) │
│ 4. Cell Segmentation (ROI extraction)
│ 5. Signal Extraction (dF/F calculation) │
│ 6. Spike Inference (Deconvolution) / depends of the mains │
│ 7. Denoising (Photobleaching, drift, noise removal) │
│ 8. Alignment with Behavioral Data (Optional) │
│ 9. Quality Control (Visual inspection) │
└───────────────────────────────────────────────────────┘

Jupyter lab misundertanding with CV2
cv2 cv2.color

### 💡 Tips
- Always activate your `isxenv` environment before running any scripts.
- If something doesn’t work, double-check that your paths and permissions are correct.
- Keep your `environment.yml` file handy in case you need to recreate the setup later.

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### 🧾 License
These instructions are provided for educational and research purposes.
Inscopix software and APIs remain the property of **Inscopix, Inc.**