Documentation upgrades

perwin · Mar 13, 2023 · fdb57ce · fdb57ce
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diff --git a/README.md b/README.md
@@ -4,8 +4,6 @@ PyImfit is a Python wrapper for [Imfit](https://github.com/perwin/imfit), a C++-
 2D models to scientific images. It is specialized for fitting astronomical images of galaxies, but can in 
 principle be used to fit any 2D Numpy array of data. 
 
-**WARNING: This is currently a work in progress; use at your own risk!**
-
 [Changelog](./CHANGELOG.md)
 
 [![Build Status](https://travis-ci.org/perwin/pyimfit.svg?branch=master)](https://travis-ci.org/perwin/pyimfit)

diff --git a/docs/installation.md b/docs/installation.md
@@ -7,7 +7,7 @@ Python 3, then you don't need to explicitly specify `pip3`.
 
 ### Standard Installation: macOS
 
-A precompiled binary version ("wheel") of PyImfit for macOS can be installed from PyPI via `pip`:
+A precompiled Intel (x86-64) binary version ("wheel") of PyImfit for macOS can be installed from PyPI via `pip`:
 
     $ pip3 install pyimfit
 
@@ -20,6 +20,17 @@ by `pip` if they are not already present):
 Astropy is also useful for reading in FITS files as numpy arrays (and is required by the
 unit tests).
 
+#### Installation on Apple Silicon (e.g., M1, M2) Macs
+
+At present, the precompiled version of PyImfit is an x86-64 ("Intel") binary. This will not
+work with arm64 (Apple Silicon, including M1 and M2 chips) versions of Python. However, you *can* 
+run PyImfit on an arm64-based Mac by running an x86-64 version of Python. How you do this depends 
+on how you prefer to install and use Python (e.g., python.org installers vs conda vs Homebrew); there
+are various suggestions available online for how to do this, depending on how you prefer to install Python.
+
+(The plan is to have a future release of PyImfit that natively supports arm64 as well as x86-64.)
+
+
 
 ### Standard Installation: Linux
 

diff --git a/docs/sample_usage.md b/docs/sample_usage.md
@@ -26,8 +26,9 @@ and then fit the model to the data:
 
     # load the image data and image characteristics and do a standard fit
     # (using default chi^2 statistics and Levenberg-Marquardt solver)
-    imfit_fitter.fit(image_data, gain=4.725, read_noise=4.3, original_sky=130.14)
-
+    fit_result = imfit_fitter.fit(image_data, gain=4.725, read_noise=4.3, original_sky=130.14)
+    print(fit_result)
+
     # check the fit and print the resulting best-fit parameter values
     if imfit_fitter.fitConverged is True:
         print("Fit converged: chi^2 = {0}, reduced chi^2 = {1}".format(imfit_fitter.fitStatistic,
@@ -51,7 +52,7 @@ and then fit the model to the data:
     imfit_fitter2.loadData(image_data, mask=mask_image_data, gain=4.725, read_noise=4.3, original_sky=130.14)
 
     # do the fit, using Nelder-Mead simplex (instead of default Levenberg-Marquardt) as the solver
-    imfit_fitter2.doFit(solver="NM")
+    fit_result2 = imfit_fitter2.doFit(solver="NM")
 
 
 You can also programmatically construct a model within Python (rather than having

diff --git a/new_notes_and_todo_for_python-wrapper_code.txt b/new_notes_and_todo_for_python-wrapper_code.txt
@@ -2,7 +2,8 @@ PLAN:
 
 ** GENERAL/FUTURE TO-DO
 
-[ ] Update Linux libimfit.a to include count_cpu_cores.o
+[ ] Add indications of n_iterations to FitResult output in case of NM or DE fits
+    -- currently, FitResult.nIter = -1 when they succeed
 
 
 [X] Add ftol keyword to fitting.py --