pkg publish

.dockerignore

@@ -34,3 +34,4 @@ config
 other
 env_linux_3.11
 env_linux_3.10
+env_linux_3.9

.env.dev

@@ -2,5 +2,5 @@ USER=mh
 SSH=false
 PYPIU=mhariri
 PYPIP=password
-
+PYPI_TOKEN=token
 dataset=/mnt/rstor/CSE_BME_AXM788/home/mxh1029/

Makefile

@@ -35,7 +35,7 @@ PROTOCOL := HTTP
 endif
 URL := $(PROTOCOL)://$(HOST):$(PORT)
 
-.PHONY: env test all dev clean dev pyserve gen-commands $(SRC) $(DIST) $(BUILD)
+.PHONY: env test all dev clean dev pyserve gen-commands $(SRC) $(DIST) $(BUILD) py.make
 
 .DEFAULT_GOAL := test
 
@@ -111,10 +111,12 @@ pkg-check:
 		$(PY) -m pip install --upgrade twine
 		twine check dist/*
 
-pkg-publish-test:
-		twine upload --config-file .pypirc.test -r testpypi dist/*  --verbose 
 
-pkg-publish:
+pkg-publish-test: .pypirc
+		twine upload --config-file .pypirc -r testpypi dist/*  --verbose 
+
+
+pkg-publish: .pypirc
 		twine upload --config-file .pypirc dist/* --verbose  
 
 pkg-flit-init:
@@ -171,9 +173,9 @@ type-prod:
 script-upgrade:
 		./scripts/upgrade_dependencies.sh
 
-gen-commands:
-		$(foreach file,$(PY_FILES),$(shell echo "\n$(subst /,-,$(subst $(SRC)/,,$(basename $(file)))):\n\t\t$(PY) $(file)" >> py.make))
 
+temp-rm:
+		rm py.make
 
-gen-commands:
+gen-commands: temp-rm
 		$(foreach file,$(PY_FILES),$(shell echo "\n$(subst /,-,$(subst $(SRC)/,,$(basename $(file)))):\n\t\t$(PY) $(file)" >> py.make))

README.md

@@ -1,9 +1,35 @@
 # MedViz
 
+## Usage
+
+Install the package using pip
+
+```bash
+    pip install medviz
+```
+Import the package and use the layered_plot function
+
+```python
+    import medviz
+
+    medviz.layered_plot(image_path="dataset/1-1.nii", mask_paths=["dataset/small_bowel.nii", "dataset/1-1-label.nii"], mask_colors=["red", "yellow"], title="Layered Plot")
+```
+
+## Development
+
+### Setup
+
+- make env
+- source env_platform_ver/bin/activate
+- make
+- make check
+- make pireq
+### Required packages
 
-## Required packages
 - Numpy
 - Matplotlib
+- Nibabel
+
 ### Slider- To do
 
 - [x] Max
@@ -12,11 +38,4 @@
 
 
 
-## Development
-
-make env
-source env_platform_ver/bin/activate
-make
-make check
-make pireq
 

README.rst

@@ -1,5 +1,3 @@
 MedViz
-===============
 
-Documentation_ -- GitHub_ 
 

examples/image_mask.py

@@ -0,0 +1,5 @@
+import medviz
+
+medviz.layered_plot(image_path="dataset/1-1.nii", mask_paths=["dataset/small_bowel.nii", "dataset/1-1-label.nii"], mask_colors=["red", "yellow"], title="Layered Plot")
+
+

medviz/__init__.py

@@ -0,0 +1 @@
+from .layered_plot import *

medviz/image_masks_axial.py

@@ -0,0 +1,41 @@
+import numpy as np
+import nibabel as nib
+import matplotlib.pyplot as plt
+from matplotlib.widgets import Slider
+
+# Load the CT scan and mask images
+ct_img = nib.load("dataset/1-1.nii")
+mask_img1 = nib.load("dataset/small_bowel.nii")
+mask_img2 = nib.load("dataset/1-1-label.nii")
+
+# Get the image data and mask data
+ct_data = ct_img.get_fdata()
+mask_data1 = mask_img1.get_fdata()
+mask_data2 = mask_img2.get_fdata()
+
+# Rotate the images 90 degrees clockwise
+ct_data = np.rot90(ct_data, axes=(1, 0))
+mask_data1 = np.rot90(mask_data1, axes=(1, 0))
+mask_data2 = np.rot90(mask_data2, axes=(1, 0))
+
+# Create a figure with a slider
+fig, ax = plt.subplots()
+plt.subplots_adjust(bottom=0.25)
+ax.imshow(ct_data[:, :, ct_data.shape[2]//2], cmap='gray')
+ax.contour(mask_data1[:, :, mask_data1.shape[2]//2], colors='r', levels=[0.5])
+ax.contour(mask_data2[:, :, mask_data2.shape[2]//2], colors='b', levels=[0.5])
+ax.set_xlabel('Slice Number')
+slider_ax = plt.axes([0.2, 0.1, 0.6, 0.03])
+slider = Slider(slider_ax, 'Slice', 0, ct_data.shape[2]-1, valinit=ct_data.shape[2]//2, valstep=1)
+
+# Update the plot when the slider value changes
+def update(val):
+    slice_num = int(slider.val)
+    ax.clear()
+    ax.imshow(ct_data[:, :, slice_num], cmap='gray')
+    ax.contour(mask_data1[:, :, slice_num], colors='r', levels=[0.5])
+    ax.contour(mask_data2[:, :, slice_num], colors='y', levels=[0.5])
+    ax.set_xlabel('Slice Number')
+
+slider.on_changed(update)
+plt.show()

medviz/layered_plot.py

@@ -0,0 +1,47 @@
+import numpy as np
+import nibabel as nib
+import matplotlib.pyplot as plt
+from matplotlib.widgets import Slider
+
+
+
+
+
+def layered_plot(image_path, mask_paths, mask_colors=["red", "yellow"], title="Layered Plot"):
+    print("Loading images...")
+# Load the CT scan and mask imagesfsdf
+    ct_img = nib.load(image_path)
+    mask_img1 = nib.load(mask_paths[0])
+    mask_img2 = nib.load(mask_paths[1])
+
+    # Get the image data and mask data
+    ct_data = ct_img.get_fdata()
+    mask_data1 = mask_img1.get_fdata()
+    mask_data2 = mask_img2.get_fdata()
+
+    # Rotate the images 90 degrees clockwise
+    ct_data = np.rot90(ct_data, axes=(1, 0))
+    mask_data1 = np.rot90(mask_data1, axes=(1, 0))
+    mask_data2 = np.rot90(mask_data2, axes=(1, 0))
+
+    # Create a figure with a slider
+    fig, ax = plt.subplots()
+    plt.subplots_adjust(bottom=0.25)
+    ax.imshow(ct_data[:, :, ct_data.shape[2]//2], cmap='gray')
+    ax.contour(mask_data1[:, :, mask_data1.shape[2]//2], colors='r', levels=[0.5])
+    ax.contour(mask_data2[:, :, mask_data2.shape[2]//2], colors='b', levels=[0.5])
+    ax.set_xlabel('Slice Number')
+    slider_ax = plt.axes([0.2, 0.1, 0.6, 0.03])
+    slider = Slider(slider_ax, 'Slice', 0, ct_data.shape[2]-1, valinit=ct_data.shape[2]//2, valstep=1)
+
+    # Update the plot when the slider value changes
+    def update(val):
+        slice_num = int(slider.val)
+        ax.clear()
+        ax.imshow(ct_data[:, :, slice_num], cmap='gray')
+        ax.contour(mask_data1[:, :, slice_num], colors='r', levels=[0.5])
+        ax.contour(mask_data2[:, :, slice_num], colors='y', levels=[0.5])
+        ax.set_xlabel('Slice Number')
+
+    slider.on_changed(update)
+    plt.show()