# Run PCA on compression simulations


Notebooks contains steps for plotting combined Cytosim and Readdy compression
velocity simulations and applying Principal Component Analysis (PCA) on
individual fibers. By default, fibers coordinates are aligned in the yz-plane to
the positive y axis, keeping x axis coordinates unchanged, before running PCA.

- [Define simulation conditions](#define-simulation-conditions)
- [Load merged data](#load-merged-data)
- [Save aligned fibers](#save-aligned-fibers)
- [Plot aligned fibers](#plot-aligned-fibers)
- [Run PCA](#run-pca)
- [Save PCA results](#save-pca-results)
- [Save PCA trajectories](#save-pca-trajectories)
- [Save PCA transforms](#save-pca-transforms)
- [Plot PCA feature scatter](#plot-pca-feature-scatter)
- [Plot PCA inverse transform](#plot-pca-inverse-transform)

```python
if __name__ != "__main__":
    raise ImportError("This module is a notebook and is not meant to be imported")
```

```python
import pandas as pd
from io_collection.save.save_pickle import save_pickle

from subcell_pipeline.analysis.dimensionality_reduction.fiber_data import (
    get_merged_data,
    plot_fibers_by_key_and_seed,
    save_aligned_fibers,
)
from subcell_pipeline.analysis.dimensionality_reduction.pca_dim_reduction import (
    plot_pca_feature_scatter,
    plot_pca_inverse_transform,
    run_pca,
    save_pca_results,
    save_pca_trajectories,
    save_pca_transforms,
)
```

## Define simulation conditions

Defines the `ACTIN_COMPRESSION_VELOCITY` simulation series, which compresses a
single 500 nm actin fiber at four different velocities (4.7, 15, 47, and 150
μm/s) with five replicates each (random seeds 1, 2, 3, 4, and 5).

```python
# Name of the simulation series
series_name: str = "ACTIN_COMPRESSION_VELOCITY"

# S3 bucket Cytosim for input and output files
cytosim_bucket: str = "s3://cytosim-working-bucket"

# S3 bucket ReaDDy for input and output files
readdy_bucket: str = "s3://readdy-working-bucket"

# Random seeds for simulations
random_seeds: list[int] = [1, 2, 3, 4, 5]

# List of condition file keys for each velocity
condition_keys: list[str] = ["0047", "0150", "0470", "1500"]

# Location to save analysis results (S3 bucket or local path)
save_location: str = "s3://subcell-working-bucket"
```

## Load merged data

Load merged simulation data from Cytosim and ReaDDy. Data is aligned in the
yz-plane to the positive y axis, keeping x axis coordinates unchanged. Set
`align=False` to load un-aligned data instead.

```python
readdy_data = get_merged_data(readdy_bucket, series_name, condition_keys, random_seeds)
readdy_data["simulator"] = "readdy"
```

```python
cytosim_data = get_merged_data(
    cytosim_bucket, series_name, condition_keys, random_seeds
)
cytosim_data["simulator"] = "cytosim"
```

```python
data = pd.concat([cytosim_data, readdy_data])
data["repeat"] = data["seed"] - 1
data["velocity"] = data["key"].astype("int") / 10
```

## Save aligned fibers

```python
time_map = {
    ("cytosim", "0047"): 0.031685,
    ("cytosim", "0150"): 0.01,
    ("cytosim", "0470"): 0.00316,
    ("cytosim", "1500"): 0.001,
    ("readdy", "0047"): 100,
    ("readdy", "0150"): 100,
    ("readdy", "0470"): 100,
    ("readdy", "1500"): 100,
}

save_aligned_fibers(
    data,
    time_map,
    save_location,
    "dimensionality_reduction/actin_compression_aligned_fibers.json",
)
```

## Plot aligned fibers

```python
plot_fibers_by_key_and_seed(
    data, save_location, "dimensionality_reduction/actin_compression_aligned_fibers.png"
)
```

## Run PCA

```python
pca_results, pca = run_pca(data)
```

## Save PCA object

```python
save_pickle(save_location, "dimensionality_reduction/actin_compression_pca.pkl", pca)
```

## Save PCA results

The PCA results are saved with resampled rows, which shuffles the order of the
entries. Pre-shuffled data is useful for scatter plots showing each individual

```python
save_pca_results(
    pca_results,
    save_location,
    "dimensionality_reduction/actin_compression_pca_results.csv",
    resample=True,
)
```

## Save PCA trajectories

```python
save_pca_trajectories(
    pca_results,
    save_location,
    "dimensionality_reduction/actin_compression_pca_trajectories.json",
)
```

## Save PCA transforms

```python
points: list[list[float]] = [
    [-900, -600, -300, 0, 300, 600],
    [-600, -400, -200, 0, 200],
]

save_pca_transforms(
    pca,
    points,
    save_location,
    "dimensionality_reduction/actin_compression_pca_transforms.json",
)
```

## Plot PCA feature scatter

```python
features = {
    "SIMULATOR": {"READDY": "red", "CYTOSIM": "blue"},
    "TIME": "magma_r",
    "VELOCITY": (
        {
            4.7: 0,
            15: 1,
            47: 2,
            150: 3,
        },
        "magma_r",
    ),
    "REPEAT": "viridis",
}

plot_pca_feature_scatter(
    pca_results,
    features,
    pca,
    save_location,
    "dimensionality_reduction/actin_compression_pca_feature_scatter.png",
)
```

## Plot PCA inverse transform

```python
plot_pca_inverse_transform(
    pca,
    pca_results,
    save_location,
    "dimensionality_reduction/actin_compression_pca_inverse_transform.png",
)
```