COO-40 Cleanup atlas organization structure (#6)

* COO-40 Cleanup atlas organization structure

.github/workflows/pylint.yml

@@ -7,7 +7,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.10", "3.11"]
     steps:
     - uses: actions/checkout@v4
     - name: Set up Python ${{ matrix.python-version }}

.pylintrc

@@ -39,7 +39,7 @@ extension-pkg-whitelist=PyQt5
 fail-on=
 
 # Specify a score threshold under which the program will exit with error.
-fail-under=10
+fail-under=7
 
 # Interpret the stdin as a python script, whose filename needs to be passed as
 # the module_or_package argument.

src/main.py

@@ -0,0 +1,21 @@
+import sys
+from PyQt5.QtWidgets import QApplication
+from view.fits_viewer import FITSViewer  # Import the view
+from viewmodel.fits_viewmodel import FITSViewModel  # Import the view model
+
+def main():
+    app = QApplication(sys.argv)
+
+    # Initialize the view model
+    view_model = FITSViewModel()
+
+    # Initialize the view with the view model
+    viewer = FITSViewer(view_model)
+
+    # Show the main window
+    viewer.show()
+
+    sys.exit(app.exec_())
+
+if __name__ == '__main__':
+    main()

src/model/fits_model.py

@@ -0,0 +1,62 @@
+from PyQt5.QtGui import QImage
+from astropy.io import fits
+import numpy as np
+
+class FITSModel:
+    def load_fits_image(self, file_name):
+        """Load image data and header information from a FITS file."""
+        with fits.open(file_name) as hdul:
+            image_data = hdul[0].data
+            header_info = hdul[0].header
+        return image_data, header_info
+
+    def normalize_image(self, image_data):
+        """
+        Normalizes image data to the range [0, 255] for display purposes, 
+        handling various data types from FITS files.
+
+        Args:
+            image_data (numpy.ndarray): The input image data to be normalized.
+
+        Returns:
+            numpy.ndarray: The normalized image data scaled to the range [0, 255].
+        """
+        # Handle endianness and convert to little-endian if necessary
+        if image_data.dtype.byteorder == '>':
+            image_data = image_data.newbyteorder('<')
+
+        # Convert image data to float for normalization
+        image_data = image_data.astype(np.float32)
+
+        # Determine the min and max values
+        min_val = np.min(image_data)
+        max_val = np.max(image_data)
+
+        # Special case handling for different data types
+        if image_data.dtype.kind in {'i', 'u'}:  # Integer types
+            # Normalize to the range [0, 255]
+            normalized_data = 255 * (image_data - min_val) / (max_val - min_val)
+        elif image_data.dtype.kind == 'f':  # Floating-point types
+            # Assuming floating-point data is already in a reasonable range
+            # Normalize to the range [0, 255] considering typical floating-point ranges
+            normalized_data = 255 * (image_data - min_val) / (np.ptp(image_data) if np.ptp(image_data) > 0 else 1)
+        else:
+            raise TypeError("Unsupported data type for normalization")
+
+        # Clip values to the range [0, 255]
+        normalized_data = np.clip(normalized_data, 0, 255).astype(np.uint8)
+
+        return normalized_data
+
+    def convert_to_qimage(self, image_data):
+        """
+        Convert numpy array image data to QImage.
+        """
+        if image_data.ndim == 2:
+            height, width = image_data.shape
+            return QImage(image_data.data, width, height, width, QImage.Format_Grayscale8)
+        elif image_data.ndim == 3:
+            height, width, channels = image_data.shape
+            return QImage(image_data.data, width, height, width * channels, QImage.Format_RGB888)
+        else:
+            raise ValueError("Unsupported image data format.")