dm6718 · dillontryhorn · Jun 25, 2025 · Jun 25, 2025
diff --git a/.gitignore b/.gitignore
@@ -0,0 +1,48 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Distribution / packaging
+build/
+dist/
+*.egg-info/
+*.egg
+.eggs/
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Virtual environments
+venv/
+env/
+.venv/
+.conda/
+*.env
+
+# PyInstaller
+*.spec
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+coverage.xml
+*.cover
+*.py,cover
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# Pyre type checker
+.pyre/
+
+# VSCode and IDEs
+.vscode/
+.idea/
+
+# macOS and Windows crap
+.DS_Store
+Thumbs.db
diff --git a/README.md b/README.md
@@ -1,43 +1,54 @@
 # RITSAR
 Synthetic Aperture Radar (SAR) Image Processing Toolbox for Python
 
-Before installation, please make sure you have the following:
-- SciPy. Comes with many Python distributions such as Enthought Canopy, Python(x,y), and Anaconda.  Development was done using the Anaconda distribution which can be downloaded for free from https://store.continuum.io/cshop/anaconda/. 
-- OpenCV (optional). If using the omega-k algorithm, OpenCV is required. Instructions for installing OpenCV for Python can be found at  https://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_setup/py_table_of_contents_setup/py_table_of_contents_setup.html#py-table-of-content-setup.
-- Spectral (optional).  Needed to interface with .envi files.  Can be installed from the command line using
+I modified this repo's source code for use with python 3.12 using anaconda. The original repo is here https://github.com/dm6718/RITSAR
+
+I didn't modify the reference sheet as I tried to preserve the parameter data format as best I could.
 
-  $ pip install spectral
+## Pre-requisite Installation
 
-  alternatively, Spectral can be downloaded here: http://www.spectralpython.net/ 
-
-To get started, first make sure your SciPy and NumPy libraries are up-to-date.  With Anaconda, this can be done by typing the following into a terminal or command prompt:
+$ conda create -n ritsar python=3.12
 
-$ conda update conda
+$ conda activate ritsar
 
-$ conda update anaconda
+$ conda install ipython
 
-Once you've ensured the required libraries are up-to-date, download the zip file and extract it to a directory that from here on will be referred to as \<ritsar_dir\>.  Open up a command line or terminal and type:
+Optional (Verify ipython is using conda): 
 
-$ cd \<ritsar_dir\>
+	$ ipython -c "import sys; print(sys.executable)"
 
-$ python setup.py install
+$ pip install scipy opencv-python spectral numpy matplotlib
+
+- SciPy. Comes with many Python distributions such as Enthought Canopy, Python(x,y), and Anaconda.  Development was done using the Anaconda distribution which can be downloaded for free from https://store.continuum.io/cshop/anaconda/. 
+- OpenCV (optional). If using the omega-k algorithm, OpenCV is required. Instructions for installing OpenCV for Python can be found at  https://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_setup/py_table_of_contents_setup/py_table_of_contents_setup.html#py-table-of-content-setup.
+- Spectral (optional).  Needed to interface with .envi files.  Can be installed from the command line using
+
+## Clone this repo to your computer
+
+$ cd ./RITSAR
+
+$ pip install .
 
 then...
 
 $ cd ./examples
 
+You can execute these with python
+
+$ python FFBPmp_demo.py
+
+You can also use pylab if you like that kind of thing
+
 $ ipython --pylab
 
 From the ipython console, type:
 
 In [1]: %run FFBPmp_demo
 
-In [2]: import matplotlib.pylab as plt; plt.show()
-
-or run any other demo.  Alternatively, you can open up the demos in an IDE of your choice to experiment with the different options available.
+or run any other demo. And you can open up the demos in an IDE of your choice to experiment with the different options available.
 
 Current capabilities include modeling the phase history for a collection of point targets as well as processing phase histories using the polar format, omega-k, backprojection, digitally spotlighted backprojection, fast-factorized backprojection, and fast-factorized backprojection with multi-processing algorithms.  Autofocusing can also be performed using the Phase Gradient Algorithm.  The current version can interface with AFRL Gotcha and DIRSIG data as well as a data set provided by Sandia.
 
 Data included with this toolset includes a small subset of the AFRL Gotcha data provided by AFRL/SNA.  The full data set can be downloaded separately from https://www.sdms.afrl.af.mil/index.php?collection=gotcha after user registration.  Also included is a single dataset from Sandia National Labs.
 
-If anyone is interested in collaborating, I can be reached at [email protected]. Ideas on how to incorporate a GUI would be greatly appreciated.
+The original code owner wanted to develop a GUI, so reach out to them at [email protected] if interested (but that email was posted 9 years ago).
diff --git a/examples/AutoFocus_demo.py b/examples/AutoFocus_demo.py
@@ -65,3 +65,5 @@
 #Output image
 plt.figure()
 imgTools.imshow(img_af, dB_scale = [-45,0])
+
+plt.show()
diff --git a/examples/DSBP_demo.py b/examples/DSBP_demo.py
@@ -96,7 +96,7 @@
 
 plt.subplot(1,2,1)
 plt.title('Full Backprojection')
-imgTools.imshow(img_bp[177-N/2:177+N/2,202-N/2:202+N/2], dB_scale = [-25,0], extent = extent)
+imgTools.imshow(img_bp[177-N//2:177+N//2,202-N//2:202+N//2], dB_scale = [-25,0], extent = extent)
 plt.xlabel('meters'); plt.ylabel('meters')
 
 plt.subplot(1,2,2)

diff --git a/examples/dictionaries/SARplatform.py b/examples/dictionaries/SARplatform.py
@@ -62,7 +62,7 @@ def plat_dict(aux = []):
         R_c = pos[npulses/2]
     else:
         R_c = np.mean(
-                pos[npulses/2-1:npulses/2+1],
+                pos[npulses//2-1:npulses//2+1],
                 axis = 0)
 
     #Coherent integration angle

diff --git a/examples/dictionaries/SARplatform.pyc b/examples/dictionaries/SARplatform.pyc
diff --git a/examples/dictionaries/SARplatformUHF.py b/examples/dictionaries/SARplatformUHF.py
@@ -55,10 +55,10 @@ def plat_dict(aux = []):
 
     #Vector to scene center at synthetic aperture center
     if np.mod(npulses,2)>0:
-        R_c = pos[npulses/2]
+        R_c = pos[npulses//2]
     else:
         R_c = np.mean(
-                pos[npulses/2-1:npulses/2+1],
+                pos[npulses//2-1:npulses//2+1],
                 axis = 0)
 
     #Coherent integration angle

diff --git a/examples/dictionaries/SARplatformUHF.pyc b/examples/dictionaries/SARplatformUHF.pyc
diff --git a/pyproject.toml b/pyproject.toml
@@ -0,0 +1,3 @@
+[build-system]
+requires = ["setuptools", "wheel"]
+build-backend = "setuptools.build_meta"