Fix installation problems for Example 1

01-generate-mock-baselines/.gitignore

@@ -0,0 +1,2 @@
+data
+.snakemake

01-generate-mock-baselines/README.md

@@ -2,15 +2,34 @@
 
 This example generates a mock sky brightness image and a realistic set of baselines $(u,v)$, which are later used by other examples in this repository, and as a fixture within the MPoL test suite. It uses the IM Lup DSHARP dataset for realistic baselines, weight values, and source flux. 
 
+# Prerequisite
 
-# Description of Contents
+You should have already downloaded and extracted the IM Lup DSHARP dataset as described in [Example 00](../00-download-and-extract-datasets/README.md). Then, you will need to copy the `IM_Lup_baselines_and_weights.npz` into this directory in a new `data` folder. For example, from within this 01 folder, run
+
+```shell
+$ mkdir data
+$ cp ../00-download-and-extract-datasets/data/IM_Lup_baselines_and_weights.npz data/
+```
+
+# Installation
+
+You can install necessary Python packages into your environment by
+```shell
+$ pip install -r requirements.txt
+```
 
+and then you can run the code by
+
+```shell 
+snakemake -c1 all
+```
+
+# Description of Contents
 
-Note that this script does not sample mock visibility values $\mathcal{V}(u,v)$. That is done on the fly using `mpol.fourier.generate_fake_data` in scripts like `sgd/src/load_data.py`, so that sky image size, flux, and measurement noise level can be adjusted as needed.
+Note that this script does not actually sample mock visibility values $\mathcal{V}(u,v)$. That is done on the fly using `mpol.fourier.generate_fake_data` in scripts like `sgd/src/load_data.py`, so that sky image size, flux, and measurement noise level can be adjusted as needed on demand.
 
 * `create_butterfly.py` downloads a nice looking image from the `ceyda/smithsonian_butterflies` collection, uses PIL to greyscale and crop it, adjusts the flux value to match DSHARP IM Lup, then saves it as a numpy array.
-* `export_baselines.py` uses MPoL-dev/visread and casatools to extract real baselines from the IM Lup measurement set, and saves them as a numpy array. To save space, we take <5% of the visibilities.
 * `package_data.py` combines the two numpy arrays into a single archive, saved as `float32` to save space.
 
-Will create this mock image (sourced from `ceyda/smithsonian_butterflies`) stuffed into `data/mock_data.npz`
+The end result will be this mock image (sourced from `ceyda/smithsonian_butterflies`) stuffed into `data/mock_data.npz`
 ![Mock Image](img_plot.png)

01-generate-mock-baselines/Snakefile

@@ -1,28 +1,7 @@
 rule all:
     input:
-        "data/ms.ms",
         "data/mock_data.npz"
 
-rule download_ms:
-    output: temp("data/IMLup_continuum.ms.tgz")
-    shell: "wget https://almascience.eso.org/almadata/lp/DSHARP/MSfiles/IMLup_continuum.ms.tgz --directory-prefix=data/"
-
-rule untar_and_rename:
-    input: "data/IMLup_continuum.ms.tgz"
-    output: directory("data/ms.ms")
-    shell: 
-        "tar -xf {input} -C data/ --no-same-owner && "
-        "mv data/IMLup_continuum.ms {output}"
-
-# only uu and vv that have *all channels unflagged*
-# only 1 channel is taken from those available: [1, 8, 16]
-# 0.05 taken randomly from those
-# saved using float32
-rule export_baselines:
-    input: "data/ms.ms"
-    output: npz="data/baselines_and_weights.npz", plot="data/baseline_plot.png"
-    shell: "python export_baselines.py {input} {output.npz} {output.plot} --select_fraction 0.05"
-
 # saved using float32
 rule export_img:
     output: img="data/img.npy", plot="data/img_plot.png"
@@ -31,7 +10,7 @@ rule export_img:
 # now package these together into a single .npz 
 rule package:
     input:
-        baselines="data/baselines_and_weights.npz",
+        baselines="data/IM_Lup_baselines_and_weights.npz",
         img="data/img.npy"
     output:
         "data/mock_data.npz"

01-generate-mock-baselines/create_butterfly.py

@@ -2,7 +2,6 @@
 from PIL import Image, ImageOps, ImageMath
 import numpy as np
 import matplotlib.pyplot as plt
-import matplotlib.colors as mco
 import argparse
 
 dataset = load_dataset("ceyda/smithsonian_butterflies")
@@ -36,7 +35,7 @@ def process_image(
 
     # apodize
     im_apod = apodization_function(xsize, ysize, centerfrac, scale)
-    im_res = ImageMath.eval("a * b", a=im_invert, b=im_apod)
+    im_res = ImageMath.lambda_eval(lambda d: d["a"] * d["b"], a=im_invert, b=im_apod)
 
     # pad to square
     max_dim = np.maximum(xsize, ysize)
@@ -50,7 +49,7 @@ def process_image(
     a = np.array(im_small)
 
     # resizing operation can create some negative pixels, so
-    # best to just set these to mimimum, which should be 0
+    # best to just set these to the minimum, which should be 0
     a[a < 0] = 0.0
 
     b = a.astype("float64")

01-generate-mock-baselines/requirements.txt

@@ -1,6 +1,5 @@
-casatools
-casadata
 numpy
 snakemake
 datasets
+matplotlib
 Pillow