Bump pillow from 9.2.0 to 9.3.0 in /src/pipeline

.gitignore

@@ -1,3 +1,136 @@
+.vscode/
+*.log
+parameters.yaml
+cvat.yaml
+parallel_settings.yaml
+# pycharm stuff
+.idea/
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+*.pyc
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
 build/
-egg-info/
-parallel_settings.yaml
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+# images
+*.jpg
+*.tif
+
+tests/
+
+**/nohup.out*

archive/cell_density/.gitignore

@@ -0,0 +1 @@
+show_cell_density_manager.ipynb

archive/cell_density/CellDensityManager.py

@@ -0,0 +1,147 @@
+import sys
+sys.path.append('/scratch/programming/pipeline/pipeline')
+from Controllers.SqlController import SqlController
+import plotly.graph_objects as go
+import numpy as np
+import os
+from atlas.Atlas import Atlas
+from atlas.Assembler import Assembler,get_v7_volume_and_origin,get_assembled_atlas_v7
+from scipy.ndimage import zoom
+import matplotlib.pyplot as plt
+
+class CellDensityManager(SqlController):
+    def __init__(self,animal):
+        super().__init__(animal)
+        self.assembler = get_assembled_atlas_v7()
+        self.downsampled_atlas = zoom(self.assembler.combined_volume, (0.1, 0.1, 0.1))
+        self.resolution = self.get_resolution(self.animal.prep_id).reshape(-1,1)
+        self.transformation = self.get_transformation(source = self.animal.prep_id,destination = 'Atlas', transformation_type = 'Similarity')
+
+    def get_atlas_volume(self):
+        controller = SqlController(self.animal.prep_id)
+        atlas = Atlas(atlas = 'atlasV7')
+        atlas.get_com_array()
+        assembler = Assembler(check=False,side = '_R')
+        assembler.volumes,assembler.origins = get_v7_volume_and_origin()
+        assembler.sqlController = atlas.sqlController
+        assembler.structures = list(assembler.volumes.keys())
+        segment_to_id = controller.get_segment_to_id_where_segment_are_brain_regions()
+        for i in segment_to_id:
+            segment_to_id[i]=1
+        assembler.assemble_all_structure_volume(segment_to_id)
+        return assembler.combined_volume
+
+    def plot_3d_volume(self,volume,X,Y,Z,*args,**kwargs):
+        return go.Volume(
+            x=X.flatten(),
+            y=Y.flatten(),
+            z=Z.flatten(),
+            value=volume.flatten(),
+            *args,**kwargs,
+            )
+
+    def hist3d(self,data):
+        origin = np.min(data,axis = 1).astype(int)
+        end = np.max(data,axis = 1).astype(int)+1
+        data = data - np.min(data,axis = 1).reshape(-1,1)
+        shape = np.max(data,axis = 1).astype(int)
+        density = np.zeros(shape)
+        npoints = data.shape[1]
+        for i in range(npoints):
+            point = data[:,i].astype(int)-1
+            density[point[0],point[1],point[2]] += 1
+        return density,origin,end     
+
+    def transform_data_to_atlas_space(self,data):
+        data = data*self.resolution
+        tfdata = self.transformation.forward_transform_points(data.T).T/np.array([10,10,20]).reshape(-1,1)
+        tfdata = tfdata*0.1*np.array([1,1,2]).reshape(-1,1)
+        return tfdata
+
+    def get_atlas_plot(self,):
+        shape = self.downsampled_atlas.shape
+        X, Y, Z = eval(f'np.mgrid[  0:{shape[0]}:{shape[0]}j, \
+                                        0:{shape[1]}:{shape[1]}j, \
+                                        0:{shape[2]*2}:{shape[2]}j]')
+        atlas = go.Volume(
+                x=X.flatten(),
+                y=Y.flatten(),
+                z=Z.flatten(),
+                value=self.downsampled_atlas.flatten(),
+                isomin=1,
+                isomax=40,
+                opacity=0.5, 
+                surface_count=2, )
+        return atlas
+
+    def get_density_plot(self,tfdata):
+        density,origin,end = self.hist3d(tfdata)
+        shape = density.shape
+        X, Y, Z = eval(f'np.mgrid[  {origin[0]}:{end[0]}:{shape[0]}j, \
+                                        {origin[1]}:{end[1]}:{shape[1]}j, \
+                                        {origin[2]}:{end[2]}:{shape[2]}j]')
+        cell_density = go.Volume(
+                x=X.flatten(),
+                y=Y.flatten(),
+                z=Z.flatten(),
+                value=density.flatten(),
+                isomin=1,
+                isomax=20,
+                opacity=0.5, 
+                surface_count=20, )
+        return cell_density
+
+    def plot_atlas_and_cell(self,data):
+        tfdata = self.transform_data_to_atlas_space(data)
+        atlas = self.get_atlas_plot()
+        cells = go.Scatter3d(x=tfdata[0], y=tfdata[1], z=tfdata[2],marker=dict(size=5,opacity=0.2),
+                                           mode='markers')
+        fig = go.Figure(data=[atlas,cells])
+        fig['layout']['scene']['aspectmode'] = "data"
+        fig.update_layout(scene_aspectmode="data", scene_camera_eye=dict (x=1, y=1, z=1))
+        fig.show()
+
+    def plot_atlas_and_cell_density(self,data):
+        tfdata = self.transform_data_to_atlas_space(data)
+        atlas = self.get_atlas_plot()
+        cell_density = self.get_density_plot(tfdata)
+        fig = go.Figure(data=[atlas,cell_density])
+        fig['layout']['scene']['aspectmode'] = "data"
+        fig.update_layout(scene_aspectmode="data", scene_camera_eye=dict (x=1, y=1, z=1))
+        fig.show()
+
+    def get_id_to_structure(self,segment_to_id):
+        id_to_structure = {}
+        ids = list(segment_to_id.values())
+        structures = list(segment_to_id.keys())
+        for i,id in enumerate(np.unique(ids)):
+            index = np.where(ids==id)[0][0]
+            structure = structures[index]
+            if '_' in structure:
+                structure = structure.split('_')[0]
+            id_to_structure[id] = structure
+        return id_to_structure
+
+    def get_cell_count_per_structure(self,data):
+        tfdata = self.transform_data_to_atlas_space(data)
+        npoints = tfdata.shape[1]
+        category = np.ones(npoints)*-1
+        for i in range(npoints):
+            pointi = tfdata[:,i].astype(int)-1
+            if np.all(pointi>0) and np.all(pointi<self.downsampled_atlas.shape):
+                category[i] = self.downsampled_atlas[pointi[0],pointi[1],pointi[2]]
+        segment_to_id = self.get_segment_to_id_where_segment_are_brain_regions()
+        id_to_structure = self.get_id_to_structure(segment_to_id)
+        ids,structures = list(id_to_structure.keys()),list(id_to_structure.values())
+        cell_count_per_structure={}
+        for i,id in enumerate(ids):
+            cell_count_per_structure[structures[i]] =  sum(category==id)
+        return ids, cell_count_per_structure
+
+    def plot_cell_distribution(self,data):
+        ids, cell_count_per_structure = self.get_cell_count_per_structure(data)
+        structures,counts = list(cell_count_per_structure.keys()),list(cell_count_per_structure.values())
+        plt.figure(figsize = [15,8])
+        plt.title('Sure Detection Distribution for DK41')
+        plt.bar(ids,counts, width = 0.4);
+        plt.xticks(ids, structures, rotation=45);