LSSTDESC · ismael-mendoza · Jan 14, 2022 · Jan 5, 2022 · Jan 5, 2022 · Jan 14, 2022
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
@@ -47,7 +47,7 @@ jobs:
       - name: Install poetry dependencies
         run: |
           poetry run pip install Cython
-          poetry install --extras "galsim-hub scarlet"
+          poetry install --extras "scarlet"
           poetry run pip install git+https://github.com/pmelchior/scarlet
 
       - name: Run Tests
@@ -56,7 +56,7 @@ jobs:
 
       - name: Run notebooks
         run: |
-          poetry run pytest --nbval notebooks --sanitize-with tests/sanitize.cfg --ignore "notebooks/01c-galsim_hub_tutorial.ipynb" --ignore "notebooks/01b-scarlet-measure.ipynb"
+          poetry run pytest --nbval notebooks --sanitize-with tests/sanitize.cfg --ignore "notebooks/01b-scarlet-measure.ipynb"
 
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v1

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -34,7 +34,7 @@ poetry shell
 pre-commit install
 ```
 
-If you have any problems with the installation, they are probably due to `galsim`. It is recommended that you follow the instructions for [installing galsim](https://galsim-developers.github.io/GalSim/_build/html/install.html) first (inside the `btk37` conda environment), and then try again. Another potential issue might be `tensorflow` and/or `galsim_hub`, if you suspect that might be the problem please create an issue in the BTK github. Note that these two dependencies only work on `python 3.7`.
+If you have any problems with the installation, they are probably due to `galsim`. It is recommended that you follow the instructions for [installing galsim](https://galsim-developers.github.io/GalSim/_build/html/install.html) first (inside the `btk37` conda environment), and then try again.
 
 ## Pull Requests
 

diff --git a/btk/draw_blends.py b/btk/draw_blends.py
@@ -9,7 +9,6 @@
 import galsim
 import numpy as np
 from astropy.table import Column
-from astropy.table import Table
 
 from btk import DEFAULT_SEED
 from btk.create_blend_generator import BlendGenerator
@@ -330,10 +329,10 @@ def render_mini_batch(self, blend_list, psf, wcs, survey, seedseq_minibatch, ext
             survey (dict): Dictionary containing survey information.
             seedseq_minibatch (numpy.random.SeedSequence): Numpy object for generating
                 random seeds (for the noise generation).
-            extra_data: This field can be used if some data need to be generated
+            extra_data: This field can be used if some data needs to be generated
                 before getting to the step where single galaxies are drawn. It should
-                have a "shape" of (batch_size,n_blend,...) where n_blend is the number
-                of objects in a blend. See GalsimHubGenerator for an example of usage.
+                have a "shape" of (batch_size, n_blend,...) where n_blend is the number
+                of objects in a blend.
 
         Returns:
             `numpy.ndarray` of blend images and isolated galaxy images, along with
@@ -542,107 +541,3 @@ def render_single(self, entry, filt, psf, survey, extra_data):
         gal_conv = gal_conv.shift(entry["ra"], entry["dec"])
 
         return gal_conv.drawImage(nx=pix_stamp_size, ny=pix_stamp_size, scale=survey.pixel_scale)
-
-
-class GalsimHubGenerator(DrawBlendsGenerator):
-    """Implementation of DrawBlendsGenerator for drawing galaxies simulated with galsim_hub.
-
-    Galsim Hub (https://github.com/McWilliamsCenter/galsim_hub) is a framework
-    for generating real-looking galaxies using deep learning models.
-    """
-
-    compatible_catalogs = ("CosmosCatalog",)
-
-    def check_compatibility(self, survey):
-        """Checks the compatibility between the catalog and a given survey.
-
-        Args:
-            survey (btk.survey.Survey): Survey to check
-        """
-        if type(self.catalog).__name__ not in self.compatible_catalogs:
-            raise ValueError(
-                f"The catalog provided is of the wrong type. The types of "
-                f"catalogs available for the {type(self).__name__} are {self.compatible_catalogs}"
-            )
-
-    def __init__(
-        self,
-        catalog,
-        sampling_function,
-        surveys: list,
-        batch_size=8,
-        stamp_size=24,
-        cpus=1,
-        verbose=False,
-        add_noise=True,
-        shifts=None,
-        indexes=None,
-        channels_last=False,
-        galsim_hub_model="hub:Lanusse2020",
-        param_names=["flux_radius", "mag_auto", "zphot"],
-        save_path=None,
-        seed=DEFAULT_SEED,
-    ):  # noqa: D417
-        """Initializes the GalsimHubGenerator class.
-
-        Args:
-            galsim_hub_model (str): Source of the model to use. Can be
-                    either a distant model or a local one, see the
-                    galsim_hub repo for more information.
-            param_names (list): list of the parameters with which
-                    the generation is parametrized; this is unique to
-                    each model.
-        """
-        super().__init__(
-            catalog,
-            sampling_function,
-            surveys,
-            batch_size=batch_size,
-            stamp_size=stamp_size,
-            cpus=cpus,
-            verbose=verbose,
-            add_noise=add_noise,
-            shifts=shifts,
-            indexes=indexes,
-            channels_last=channels_last,
-            save_path=save_path,
-            seed=seed,
-        )
-        import galsim_hub
-
-        self.galsim_hub_model = galsim_hub.GenerativeGalaxyModel(galsim_hub_model)
-        self.param_names = param_names
-
-    def render_mini_batch(self, blend_list, psf, wcs, survey, seed):
-        """Returns isolated and blended images for blend catalogs in blend_list.
-
-        Here we generate the images for all galaxies in the batch at the same
-        time, since galsim_hub is optimized for batch generation.
-        """
-        galsim_hub_params = Table()
-        for p in self.param_names:
-            column = Column(np.concatenate([blend[p] for blend in blend_list]), p)
-            galsim_hub_params.add_column(column)
-
-        base_images = self.galsim_hub_model.sample(galsim_hub_params)
-        base_images_l = []
-        index = 0
-        for blend in blend_list:
-            base_images_l.append(base_images[index : index + len(blend)])
-            index += len(blend)
-
-        return super().render_mini_batch(blend_list, psf, wcs, survey, seed, base_images_l)
-
-    def render_single(self, entry, filt, psf, survey, extra_data):
-        """Returns the Galsim Image of an isolated galaxy."""
-        base_image = extra_data
-        base_image = galsim.Convolve(base_image, psf)
-        gal_flux = get_flux(entry["ref_mag"], filt, survey)
-        base_image = base_image.withFlux(gal_flux)
-        base_image = base_image.shift(entry["ra"], entry["dec"])
-
-        pix_stamp_size = int(self.stamp_size / survey.pixel_scale)
-        galaxy_image = base_image.drawImage(
-            nx=pix_stamp_size, ny=pix_stamp_size, scale=survey.pixel_scale, dtype=np.float64
-        )
-        return galaxy_image
diff --git a/btk/sampling_functions.py b/btk/sampling_functions.py
@@ -129,70 +129,6 @@ def __call__(self, table, shifts=None, indexes=None):
         return blend_table
 
 
-class DefaultSamplingGalsimHub(SamplingFunction):
-    """Default sampling function used for producing blend tables, esp. for galsim_hub."""
-
-    def __init__(self, max_number=2, stamp_size=24.0, maxshift=None, seed=DEFAULT_SEED):
-        """Initialize default sampling function for galsim_hub.
-
-        Args:
-            max_number (int): Defined in parent class
-            stamp_size (float): Size of the desired stamp.
-            maxshift (float): Magnitude of maximum value of shift. If None then it
-                             is set as one-tenth the stamp size. (in arcseconds)
-            seed (int): Seed to initialize randomness for reproducibility.
-        """
-        super().__init__(max_number, seed)
-        self.stamp_size = stamp_size
-        self.maxshift = maxshift if maxshift else self.stamp_size / 10.0
-
-    @property
-    def compatible_catalogs(self):
-        """Defined in parent class."""
-        return "CosmosCatalog"
-
-    def __call__(self, table, shifts=None, indexes=None):
-        """Method returning a blend sample from table.
-
-        This sampling function is almost identical to the default one, except that an
-        additionnal cut on the flux radius is applied. This is done to avoid having
-        galaxies too large being selected, as generating large galaxies causes artifacts
-        in galsim_hub images.
-
-        Args:
-            table (Astropy.table): Table containing entries corresponding to galaxies
-                                   from which to sample.
-            shifts (list): Contains arbitrary shifts to be applied instead of random ones.
-                           Should of the form [dx,dy] where dx and dy are the lists
-                           containing the x and y shifts.
-            indexes (list): Contains the indexes of the galaxies to use.
-
-        Returns:
-            Astropy.table with entries corresponding to one blend.
-        """
-        number_of_objects = self.rng.integers(1, self.max_number + 1)
-        (q,) = np.where((table["ref_mag"] <= 25.3) & (table["flux_radius"] <= 24.0))
-
-        if indexes is None:
-            blend_table = table[self.rng.choice(q, size=number_of_objects)]
-        else:
-            blend_table = table[indexes]
-        blend_table["ra"] = 0.0
-        blend_table["dec"] = 0.0
-        if shifts is None:
-            dx, dy = _get_random_center_shift(number_of_objects, self.maxshift, self.rng)
-        else:
-            dx, dy = shifts
-        blend_table["ra"] += dx
-        blend_table["dec"] += dy
-
-        if np.any(blend_table["ra"] > self.stamp_size / 2.0) or np.any(
-            blend_table["dec"] > self.stamp_size / 2.0
-        ):
-            warnings.warn("Object center lies outside the stamp")
-        return blend_table
-
-
 class BasicSampling(SamplingFunction):
     """Example of basic sampling function features.
 

diff --git a/conf/config.yaml b/conf/config.yaml
@@ -25,14 +25,6 @@ max_shift: Null
 add_noise: True
 channels_last: False
 
-# galsim hub
-galsim_hub:
-  model: "hub:Lanusse2020"
-  param_names:
-    - "flux_radius"
-    - "mag_auto"
-    - "zphot"
-
 # measure
 measure_kwargs: Null
 measure_functions:

diff --git a/conf/draw_blends/galsim_hub.yaml b/conf/draw_blends/galsim_hub.yaml
diff --git a/conf/hydra/help/btk_help.yaml b/conf/hydra/help/btk_help.yaml
@@ -36,7 +36,7 @@ template: |-
   Here are all the available options for the CLI.
 
   * sampling: Specify the sampling function to be used, options:
-      {default, galsim_hub, group_sampling, group_sampling_numbered}
+      {default, group_sampling, group_sampling_numbered}
 
   * catalog: Attribute group consisting of two sub-attributes.
       * catalog: Name of the BTK catalog class, options: {catsim, cosmos}
@@ -61,7 +61,7 @@ template: |-
       add your own config file to conf/surveys. See the CLI section of the tutorial for
       more details.
 
-    * draw_blends: Which draw_blend_generator to use, options are {catsim, cosmos, galsim_hub}
+    * draw_blends: Which draw_blend_generator to use, options are {catsim, cosmos}
 
     * save_path: Absolute path to a (preferably) empty directory where you would like to save
       results of running BTK.
@@ -80,13 +80,6 @@ template: |-
 
     * channels_last: Whether to use have images in channel last format (True) or not (False).
 
-    * galsim_hub: Attribute group consisting of options:
-        * model: Which galsim_hub model to use (default: 'hub:Lanusse2020')
-        * param_names: list of the parameters with which the generation is parametrized; this
-          is unique to each model (default: `['flux_radius', 'mag_auto', 'zphot']`).
-
-        NOTE: This flag has no effect unless `draw_blends=galsim_hub`
-
     * measure_kwargs: Dictionary or list of dictionaries containing the keyword arguments to be
         passed in to each measure_function.
 

diff --git a/conf/sampling/galsim_hub.yaml b/conf/sampling/galsim_hub.yaml
diff --git a/docs/source/catalog.rst b/docs/source/catalog.rst
@@ -16,11 +16,6 @@ The Cosmic Evolution Survey (COSMOS) is an astronomical survey based on multiple
 
 BTK includes an implementation of COSMOS real galaxy rendering, providing the possibility to get more realistic blends.
 
-Galsim_Hub
------------
-Galsim_Hub is a framework for deep learning image generation models, developped by François Lanusse, which is available `in this page <https://github.com/McWilliamsCenter/galsim_hub>`_ .
-
-BTK supports drawing galaxies generated using any galsim_hub compatible model; the default one generates COSMOS-like galaxies and is described in `this paper <https://arxiv.org/abs/2008.03833>`_, but the BTK implementation should be compatible with any model.
 
 .. Cosmo DC2
 .. ---------

diff --git a/docs/source/cli.rst b/docs/source/cli.rst
@@ -32,8 +32,6 @@ Here is a list of all the options of BTK you can customize directly from the CLI
 
     - default
 
-    - galsim_hub
-
     - group_sampling
 
     - group_sampling_numbered
@@ -81,8 +79,6 @@ Here is a list of all the options of BTK you can customize directly from the CLI
 
     - cosmos
 
-    - galsim_hub
-
 * ``save_path``: Absolute path to a (preferably) empty directory where you would like to save results of running BTK.
 
 * ``cpus``: Number of cpus you would like to use for multiprocessing.
@@ -99,12 +95,6 @@ Here is a list of all the options of BTK you can customize directly from the CLI
 
 * ``channels_last``: Whether to use have images in channel last format (True) or not (False).
 
-* ``galsim_hub``: Attribute group consisting of options:
-
-    - ``model``: Which galsim_hub model to use (default: 'hub:Lanusse2020')
-
-    - ``param_names``: list of the parameters with which the generation is parametrized; this is unique to each model (default: ``['flux_radious, 'mag_auto', 'zphot']``). **NOTE**: This flag is ignored unless ``draw_blends=galsim_hub``
-
 * ``measure_kwargs``: Dictionary or list of dictionaries containing the keyword arguments to be passed in to each measure_function.
 
 * ``measure_functions``: List of measure_functions to be ran, options:

diff --git a/docs/source/install.rst b/docs/source/install.rst
@@ -50,14 +50,10 @@ This should install all other missing dependencies if necessary. You can then im
 
 Optional packages
 ------------------------------
-BTK comes with two optional set of dependencies that can be installed: ``galsim_hub`` and ``scarlet``. You can install them alongside ``btk`` as follows:
+BTK comes with an optional set of dependencies that can be installed for ``scarlet``. You can install them alongside ``btk`` as follows:
 
 .. code-block::
 
-    # install galsim_hub and its dependencies.
-    # NOTE: it only works on python3.7
-    pip install blending_toolkit[galsim_hub]
-
     # install latest version of scarlet and its dependencies.
     pip install blending_toolkit[scarlet]
     pip install git+https://github.com/pmelchior/scarlet
diff --git a/docs/source/tutorials.rst b/docs/source/tutorials.rst
@@ -535,26 +535,6 @@ However, BTK still retrieves the ``flux_radius`` information from this catalog,
 
 4. (optional) One of the two catalogs can contain multiband magnitudes using the format just described.
 
-
-Galsim_Hub tutorial
---------------------
-
-BTK supports galaxy image generation with ``galsim_hub``; please refer to `this page <https://github.com/McWilliamsCenter/galsim_hub>`_ for more details on ``galsim_hub``. Please note that ``galsim_hub`` only works with ``python 3.7``.
-
-The steps for using the galsim_hub generation are very similar to those from the previous section. Before starting this tutorial, you must install ``galsim_hub``, which can be done using pip.
-
-::
-
-  pip install galsim_hub
-
-Alternatively, you can optionally install ``galsim_hub`` along with BTK:
-
-::
-
-  pip install btk[galsim-hub]
-
-You can find a notebook version of this tutorial in this `link <https://github.com/LSSTDESC/BlendingToolKit/blob/main/notebooks/01c-galsim_hub_tutorial.ipynb>`_.
-
 SCARLET implementation
 -----------------------