### Import libraries and define selected genes

Source: https://simtissue.readthedocs.io/en/latest/tutorial/tissue_simulation.html

Imports necessary libraries and defines a list of genes to simulate. This is the initial setup for the tissue simulation tutorial.

```python
from pathlib import Path
import tifffile
import numpy as np
from simtissue.arbitrary_shape import  generate_ellipse
import time
import random
from tqdm import tqdm
from scipy import ndimage as ndi
from skimage.segmentation import watershed
from matplotlib import pyplot as plt
from scipy import sparse
import pandas
import scanpy

from simtissue.control_expression import filter_simulation, sim_spots_from_ref_anndata
from simtissue.tissue import simulate_single_cell_mask


### LIST OF GENE TO SIMULATE
selected_gene = ['Atp6v0d2', 'Abcg1',# AM
             'Rtkn2',  'Igfbp2', #AT1
             'Sftpc','Cxcl15', #AT2,
            'Cd79a', #B_cells
             'Ms4a2', 'Fcer1a', #Basophils
             'Ccdc153', #Ciliated
             'Scgb3a2', 'Scgb1a1',#Club
             'Cst3',#DC
             'Cdh5', 'Clec14a',  #EC
             'Inmt', 'Pcolce2', # Fibroblasts
             'C1qc', 'C1qa', 'C1qb', # 'C3ar1', #IM
             'Upk3b',# Mesotheliocytes
             'Ifitm6','Plac8',# Monocytes
            'Ms4a4b', 'Ccl5', 'Hcst', # NK_T_cells
             'Gzma', 'Ncr1',# NK_cells
             'S100a9',# Neutrophils
             'Mmrn1',#Platelets
           'Acta2','Myh11', # SMC
             'Cd3g', 'Cd3d' #T_cells
             ]

ref_anndata = scanpy.read('/media/tom/T7/simulation/test_set_simulation/test_anndata.h5ad')
```

--------------------------------

### Sample transcriptomic profile with sim_spots_from_ref_anndata()

Source: https://simtissue.readthedocs.io/en/latest/tutorial/tissue_simulation.html

Loops over cell mask files, calls sim_spots_from_ref_anndata() to generate anndata and dataframe, then saves them to specified paths. Uses scaling_factor=3 and remove_neighbors=True with max_dist=0.3.

```python
path_to_save_anndata = "/media/tom/T7/simulation/test_set_simulation/anndata/"
path_to_save_df = "/media/tom/T7/simulation/test_set_simulation/dataframe/"
list_anndata = []

for path_ic in Path(path_save_ind_cyto).glob("*.npy"):

    cell_mask = np.load(path_ic)

    anndata, df_spots = sim_spots_from_ref_anndata(

        ref_anndata = ref_anndata,
        cell_mask = cell_mask,
        image_name = 'test',
        annotation_column = "cell_ID",
        selected_gene = selected_gene,
        remove_neighbors = True,
        max_dist = 0.3,
        dict_scale = {"x":0.103, 'y':0.103, "z":0.300},
        scaling_factor=3
        )

    list_anndata.append(anndata)

    anndata.write_h5ad(str(Path(path_to_save_anndata) /(path_ic.stem + '.h5ad')))
    df_spots.to_csv(Path(path_to_save_df) /(path_ic.stem + '.npy'))
```

--------------------------------

### Import libraries for checkerboard simulation

Source: https://simtissue.readthedocs.io/en/latest/tutorial/checkerboard_shape.html

Imports required for the tutorial: numpy, matplotlib, scipy.ndimage, scikit-image, sklearn's sample_without_replacement, and scipy.sparse.csr_array.

```python
import numpy as np
from matplotlib import pyplot as plt
from scipy import ndimage as ndi
import skimage
from sklearn.utils.random import sample_without_replacement
from scipy.sparse import csr_array

```

--------------------------------

### Import simtissue modules

Source: https://simtissue.readthedocs.io/en/latest/tutorial/checkerboard_shape.html

Imports simtissue modules and functions used in the tutorial: checkerboard_mask, generate_ellipse, add_sphere_nuclei, plot_contour, create_fix_profile, simulate_arbritrary_expression, and get_dict_coord_map.

```python
import sys


import simtissue
from simtissue.arbitrary_shape import checkerboard_mask, generate_ellipse, add_sphere_nuclei

from simtissue.plot import plot_contour
from simtissue.control_expression import create_fix_profile, simulate_arbritrary_expression
from simtissue.utils import get_dict_coord_map

```

--------------------------------

### Load simulation with pickle

Source: https://simtissue.readthedocs.io/en/latest/tutorial/checkerboard_shape.html

Loads the previously saved simulation from the pickle file and displays the AnnData object. The output shows the same structure as before.

```python
file_pi2 = open(filename, 'rb')
anndata = pickle.load(file_pi2)
anndata

```

--------------------------------

### Save simulation with pickle

Source: https://simtissue.readthedocs.io/en/latest/tutorial/checkerboard_shape.html

Saves the simulated AnnData object to a pickle file named 'simulated_image.h5ad' using pickle.dump with protocol -1.

```python
import pickle
from pathlib import Path
filename = "simulated_image.h5ad"
with open(filename, 'wb') as handle:
    pickle.dump(anndata, handle, -1)

```

--------------------------------

### Generate cytoplasm masks with simulate_single_cell_mask()

Source: https://simtissue.readthedocs.io/en/latest/tutorial/tissue_simulation.html

Loop over nuclei mask TIFF files, crop them to the cytoplasm, then run `simulate_single_cell_mask()` to produce individual cytoplasm masks. Uses `limiter`tiffile` and `numpy`, and saves results as `.npy` files.

```python
path_folder_mask_nuclei = "/media/tom/T7/simulation/test_set_simulation/mask_nuclei"
path_folder_cytoplasm = "/media/tom/T7/simulation/test_set_simulation/cytoplasm_mask"
path_save_ind_cyto = "/media/tom/T7/simulation/test_set_simulation/individual_cytoplasm"


for path_mask_nuclei in Path(path_folder_mask_nuclei).glob('*.tiff'):
    print(path_mask_nuclei)
    ##load nuclei
    center_nuclei = tifffile.imread(path_mask_nuclei)[20:35, 200:1000, :1200]
    ## load cytoplasm
    mask_cyto = np.load(Path(path_folder_cytoplasm) /
                                (path_mask_nuclei.name + ".npy"))[20:35, 200:1000, :1200]
    ##
    ind_cyto = simulate_single_cell_mask(
                            mask_nuclei,
                            cyto = mask_cyto,
                            scale=np.array([3, 1.03, 1.03]),
                            intervals_speed=[[0.5, 0.8], [1.3, 4]],
                            median_kernel=7,
                            random_seed = None
                                      )
    np.save(Path(path_save_ind_cyto) / path_mask_nuclei.stem, ind_cyto)

```

--------------------------------

### simtissue.control_expression.sim_spots_from_ref_anndata

Source: https://simtissue.readthedocs.io/en/latest/api/control_expression.html

Simulates the expression of a list of genes in a cell mask by sampling RNA profiles from a reference anndata object. RNA molecules are then sampled from a uniform spatial distribution in each cell.

```APIDOC
## sim_spots_from_ref_anndata

### Description
This function simulate the expression of a list of genes in a mask of cytoplasm. It sample each RNA profile from a reference anndata object. The RNA molecule are then sampled from a uniform spatial distribution in each cell.

### Parameters
- **ref_anndata** (anndata object) - Required - andata object with the reference expression profile to sample.
- **cell_mask** (np.ndarray) - Required - Cell mask.
- **selected_gene** (list) - Required - List of genes to simulate. Example: ['gene1', 'gene2'] from the reference anndata.
- **image_name** (str) - Optional - Name of the image to add in annData. Default: ''.
- **annotation_column** (str) - Optional - Column name of the cell type in the reference anndata. Default: 'cell_ID'.
- **remove_neighbors** (bool) - Optional - If True remove the RNA molecules that are too close. Default: True.
- **max_dist** (float) - Optional - Max distance between spots to merge if remove_neighbors is True. Default: 0.3.
- **dict_scale** (dict) - Optional - Scale of the image in z,y,x in µm. Default: {'x': 0.103, 'y': 0.103, 'z': 0.3}.
- **random_indice** (list) - Optional - If not None, list of cell index to sample from the reference anndata. Default: None.
- **scaling_factor** (int) - Optional - Scaling factor. Default: 3.

### Returns
- an **anndata object** (not explicitly stated) with simulated expression.
```

--------------------------------

### simtissue.control_expression.create_fix_profile

Source: https://simtissue.readthedocs.io/en/latest/api/control_expression.html

Associates each cell index to a fixed RNA profile based on a dictionary of profiles and a cell type distribution. It takes two dictionaries and returns an RNA, while also returning an expression vector for each cell.

```APIDOC
## create_fix_profile

### Description
Associates each cell index to a fixed RNA profile.

### Parameters
- **dict_profile** (dict) - Required - Dictionary defining the RNA profile of each cell type. Example: {'typeA': [120, 0], 'typeB': [0, 10]}
- **cell_type_distribution** (dict) - Required - Dictionary of the cell type distribution. Example: {'typeA': list(range(0, 110, 2)), 'typeB': list(range(1, 110, 2))}

### Returns
- **dict_cell_type_label_rna** - {cell_type : {cell_id : expression vector}}
- **dico_cell_index** - {cell_id : {type: cell_type, rnaseq : expression vector}}
```

--------------------------------

### Plot final simulation with expression spots

Source: https://simtissue.readthedocs.io/en/latest/tutorial/checkerboard_shape.html

Plots the final simulation contour with nuclei and expression spots. Uses dico_color to map gene A to red and gene B to blue, with spot_size=3. The output includes the same warning as before.

```python
plot_contour(mask_cyto = mask_cyto,
                 mask_nuclei = mask_nuclei,
                 figsize = (15,15),
                anndata = anndata,
                dico_color = {"A" : "r", "B": "b"},
                spot_size = 3)

```

--------------------------------

### generate_ellipse(masks: ndarray, cell_id: int, radius_zyx: list = None, rad_min: float = 0.5, rad_max: float = 2)

Source: https://simtissue.readthedocs.io/en/latest/api/arbitrary_shape.html

Generates nuclei as an ellipse in the chosen cell. Returns the ellipse mask and ellipse parameters.

```APIDOC
## generate_ellipse(masks: ndarray, cell_id: int, radius_zyx: list = None, rad_min: float = 0.5, rad_max: float = 2)

### Description
Generate nuclei as ellipse in the chosen cell.

### Parameters
- **masks** (np.ndarray) - Required - Cell mask.
- **cell_id** (int) - Required - Index of the chosen cell.
- **radius_zyx** (list) - Optional - List [Rz, Ry, Rx] of the fixed parameters of the ellipse. Leave it None to choose these parameters randomly between rad_min and rad_max.
- **rad_min** (float) - Optional - Minimum value of the radius if radius_zyx is None. Default is 0.5.
- **rad_max** (float) - Optional - Maximum value of the radius if radius_zyx is None. Default is 2.

### Returns
- **ellipse mask** - The generated ellipse mask.
- **ellipse parameter** - The parameters of the ellipse.
```

--------------------------------

### add_sphere_nuclei(mask_cyto: ndarray, cube_square_size: int = 100, nuclei_radius: int = 25, list_nuclei: list = [])

Source: https://simtissue.readthedocs.io/en/latest/api/arbitrary_shape.html

Adds nuclei as spheres in the chosen cell. Returns the mask of the nuclei.

```APIDOC
## add_sphere_nuclei(mask_cyto: ndarray, cube_square_size: int = 100, nuclei_radius: int = 25, list_nuclei: list = [])

### Description
Add nuclei as sphere in the chosen cell.

### Parameters
- **mask_cyto** (np.ndarray) - Required - Cell mask.
- **cube_square_size** (int) - Optional - Size of the cube in pixels. Default is 100.
- **nuclei_radius** (float) - Optional - Radius of the nuclei in pixels as a percentage of cube_square_size. Default is 25.
- **list_nuclei** (list) - Optional - List of nuclei to add. Default is empty list.

### Returns
- **mask of the nuclei** - The generated nuclei mask.
```

--------------------------------

### Simulate arbitrary cell expression with simulate_arbritrary_expression

Source: https://simtissue.readthedocs.io/en/latest/tutorial/checkerboard_shape.html

Simulates arbitrary cell expression using simulate_arbritrary_expression. Defines RNA profiles for two cell types (typeA: [50,0], typeB: [0,200]), assigns even indices to typeA and odd to typeB, and creates an AnnData object with 110 observations and 2 variables.

```python
### generate_anndata

## RNA profile of each cell type
genes_list_to_simulate = ['A', 'B']
dict_profile = {'typeA': [50, 0],
                  'typeB': [0, 200]
             }

## cell type of each cell index
cell_index_typeA = [i for i in range(1, 111) if i % 2 == 0]
cell_index_typeB = [i for i in range(1, 111) if i % 2 == 1]



cell_type_distribution={'typeA': cell_index_typeA,
                        'typeB': cell_index_typeB,}

image_name = "image0"

## get the anndata with simulate_arbritrary_expression()
anndata = simulate_arbritrary_expression(dict_profile,
                cell_type_distribution,
                mask_cyto = mask_cyto,
                genes_list_to_simulate = ['A', 'B'],
                image_name = "image0"
        )
anndata

```

--------------------------------

### Plot final simulation with matplotlib

Source: https://simtissue.readthedocs.io/en/latest/tutorial/tissue_simulation.html

Reads the saved dataframe, loads a nuclei mask TIFF, and plots the spots for each selected gene over the nuclei image. Uses gist_gray colormap and scatter with s=1.

```python
from matplotlib import colors
import pandas as pd
df_spots = pd.read_csv(Path(path_to_save_df) /(path_ic.stem + '.npy'))
mask_nuclei = tifffile.imread(Path(path_folder_mask_nuclei) / (path_ic.stem + '.tiff'))[20:35, 200:1000, :1200]
mip_nuclei = np.amax(mask_nuclei, 0)

dico_spots_pos = {}
for gene in selected_gene:
    y = df_spots[df_spots["gene"] == gene]["y"]
    x = df_spots[df_spots["gene"] == gene]["x"]

    dico_spots_pos[gene] = list(zip( y, x))


fig, ax = plt.subplots(figsize = (15,15))
ax.imshow(mip_nuclei > 0, cmap='gist_gray', alpha = 0.8)
ax.imshow(mip_nuclei, cmap='gist_gray', alpha = 0.8)
for gene in dico_spots_pos.keys():
    if len(dico_spots_pos[gene]) == 0:
        continue
    ax.scatter(np.array(dico_spots_pos[gene])[:,1], np.array(dico_spots_pos[gene])[:,0], cmap="Pastel1",  #  gene_color_dico[gene],   #'#%02X%02X%02X' % (r(),r(),r()),
               s=1)
plt.show()
```

--------------------------------

### elbow_grid_cube()

Source: https://simtissue.readthedocs.io/en/latest/api/arbitrary_shape.html

Generates a grid of elbow shapes with hard-coded dimensions. Returns mask_cyto and mask_nuclei.

```APIDOC
## elbow_grid_cube()

### Description
Function to generate grid of elbow shape. The size of the cell mask is hard coded to 1800x1600 and the size square cell is 100 pixel, the size of nuclei is 25 pixel.

### Parameters
None.

### Returns
- **mask_cyto** - The cell mask.
- **mask_nuclei** - The nuclei mask.
```

--------------------------------

### simtissue.control_expression.simulate_arbritrary_expression

Source: https://simtissue.readthedocs.io/en/latest/api/control_expression.html

Simulates the expression of a list of genes in a cytoplasm mask. The RNA positions are sampled from a uniform spatial distribution in the cytoplasm.

```APIDOC
## simulate_arbritrary_expression

### Description
This function simulate the expression of a list of genes in a mask of cytoplasm. The rna position are sample from a uniform spatial distribution in the cytoplasm.

### Parameters
- **dict_profile** (dict) - Required - Dictionary of the expression profile of each cell type. Example: {'typeA': [120, 0], 'typeB': [0, 10]}
- **cell_type_distribution** (dict) - Required - Dictionary of the cell type distribution. Example: {'typeA': list(range(0, 110, 2)), 'typeB': list(range(1, 110, 2))}
- **mask_cyto** (np.ndarray) - Required - Cytoplasm mask.
- **genes_list_to_simulate** - Required - List of genes to simulate. Example: ['gene1', 'gene2']
- **image_name** (str) - Optional - Name of the image to add in annData. Default: 'img0'

### Returns
- **anndata object** - anndata object with the simulated expression profile and coordinates.
```

--------------------------------

### checkerboard_mask(shape=[54, 1000, 1100], cube_square_size=100)

Source: https://simtissue.readthedocs.io/en/latest/api/arbitrary_shape.html

Generates a checkerboard cell mask with specified dimensions and cube size. Returns the checkerboard mask.

```APIDOC
## checkerboard_mask(shape=[54, 1000, 1100], cube_square_size=100)

### Description
Generate checkerboard cell mask.

### Parameters
- **shape** (list[int]) - Optional - Dimension of the mask [z, y, x]. Default is [54, 1000, 1100].
- **cube_square_size** (int) - Optional - Size of the cube in pixels. Default is 100.

### Returns
- **checkerboard mask** - The generated checkerboard mask.
```

--------------------------------

### simtissue.control_expression.filter_simulation

Source: https://simtissue.readthedocs.io/en/latest/api/control_expression.html

Merges overlapping spots as a single spot. It takes a list of spots positions and merges them based on a maximum distance.

```APIDOC
## filter_simulation

### Description
This function Merge overlapping spots as a single spot.

### Parameters
- **spots_position** (list) - Required - List of spots position.
- **max_dist** (float) - Optional - Max distance between spots to merge in the scale of dict_scale. Default: 3.
- **dict_scale** (dict) - Optional - Scale dictionary. Default: {'x': 1, 'y': 1, 'z': 1}.

### Returns
- **list of spots position with merged spots** - list of the spots positions after merging.
```

--------------------------------

### simtissue.tissue.simulate_single_cell_mask()

Source: https://simtissue.readthedocs.io/en/latest/api/tissue.html

Generates a single cell mask from a nuclei segmentation mask. Individual cytoplasms are defined by growing cells from segmented nuclei, with each cell growing at a random speed to add irregularity in cell size.

```APIDOC
## simulate_single_cell_mask()

### Description
Generates a single cell mask from a nuclei segmentation mask. Individual cytoplasms are defined by growing cells from segmented nuclei, with each cell growing at a random speed to add irregularity in cell size.

### Function Signature
simtissue.tissue.simulate_single_cell_mask(mask_nuclei: ndarray, cyto: ndarray = None, scale: ndarray = array([3., 1.03, 1.03]), intervals_speed: list = [[0.5, 0.8], [1.3, 4]], median_kernel = 7, random_seed = None)

### Parameters
- **mask_nuclei** (np.ndarray) - Required - Nuclei segmentation mask.
- **cyto** (np.ndarray) - Optional - Cytoplasm segmentation mask to simulate area without cell.
- **scale** (np.ndarray) - Optional - Scale of the image in z, y, x. Default: array([3., 1.03, 1.03]).
- **intervals_speed** (list) - Optional - Speed range of the cell growth. Default: [[0.5, 0.8], [1.3, 4]].
- **median_kernel** (int) - Optional - Median kernel size to smooth the cell mask. Default: 7.
- **random_seed** (int) - Optional - Random seed for reproducibility.

### Returns
- **cell mask** - The generated cell mask.
```

--------------------------------

### remove_nuclei(mask_nuclei: ndarray, percent_to_remove: float = 0.5, list_nuc_to_keep: list = None)

Source: https://simtissue.readthedocs.io/en/latest/api/arbitrary_shape.html

Removes nuclei from the existing nuclei mask to generate cells without nuclei, as in smFISH experiments. Returns the mask of nuclei with some removed.

```APIDOC
## remove_nuclei(mask_nuclei: ndarray, percent_to_remove: float = 0.5, list_nuc_to_keep: list = None)

### Description
Remove nuclei from the existing nuclei mask to generate cell without nuclei like in smFISH experiment.

### Parameters
- **mask_nuclei** (np.ndarray) - Required - Nuclei mask.
- **percent_to_remove** (float) - Optional - Percent of nuclei to remove. Default is 0.5.
- **list_nuc_to_keep** (list) - Optional - List of nuclei to keep. Default is None.

### Returns
- **mask of the nuclei without some nuclei** - The resulting nuclei mask.
```

--------------------------------

### Generate checkerboard mask and plot contour

Source: https://simtissue.readthedocs.io/en/latest/tutorial/checkerboard_shape.html

Generates a checkerboard mask of shape [1, 1000, 1100] with cube_square_size=100 and plots its contour. The output includes a warning 'list index out of range' and 'no contour for cell 110.0'.

```python
mask_cyto = checkerboard_mask(shape = [1, 1000, 1100],
                         cube_square_size = 100)
plot_contour(mask_cyto = mask_cyto, figsize = (5,5))

```

--------------------------------

### Remove percentage of nuclei with remove_nuclei

Source: https://simtissue.readthedocs.io/en/latest/tutorial/tissue_simulation.html

Loop over nuclei masks, load each, then call `remove_nuclei` to randomly remove a percentage of its nuclei. The result is saved the same way as the original. Note: `libreria` `plural` argument is `percent_to_remove` (0.1 meaning 10%).

```python
path_folder_mask_nuclei_remove = "/media/tom/T7/simulation/test_set_simulation/mask_nuclei"
Path(path_folder_mask_nuclei_remove, exist_ok=True)

from simtissue.arbitrary_shape import remove_nuclei
for path_mask_nuclei in Path(path_folder_mask_nuclei).glob('*.tiff'):
    print(path_mask_nuclei)
    print(path_mask_nuclei.name + ".npy")
    ##load nuclei
    mask_nuclei = tifffile.imread(path_mask_nuclei)[20:35, 200:1000, :1200]
    new_mask_nuclei = remove_nuclei(
        mask_nuclei,
        percent_to_remove = 0.1,
        list_nuc_to_keep = None)
    np.save(Path(path_folder_mask_nuclei_remove) / path_mask_nuclei.stem, ind_cyto)

```

--------------------------------

### Add nuclei to checkerboard mask

Source: https://simtissue.readthedocs.io/en/latest/tutorial/checkerboard_shape.html

Adds nuclei to the checkerboard mask. It leaves 20% of cells without nuclei by sampling 80% of unique cell indices, then calls add_sphere_nuclei with nuclei_radius=25 and plots the contour. The output includes the same warning as before.

```python
  
## leave 20% of the cells without cytoplasm
list_nuclei_unique = np.unique(mask_cyto)
index  = sample_without_replacement(len(list_nuclei_unique), int(0.80 * len(list_nuclei_unique)))
list_nuclei = list_nuclei_unique[index]
mask_nuclei = add_sphere_nuclei(mask_cyto = mask_cyto,
                     cube_square_size  = 100,
                     nuclei_radius = 25,
                     list_nuclei = list_nuclei)

plot_contour(mask_cyto = mask_cyto, mask_nuclei = mask_nuclei, figsize = (5,5))
  

```

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