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PSFGAN-GaMorNet

This repository contains source code for the PSFGAN-GaMorNet framework (discussed in "Using Machine Learning to Determine Morphologies of $z<1$ AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey").

CONTENTS

PSFGAN-GaMorNet
    ├── CONTENTS
    ├── Dependencies
    ├── Installation
    └── A comprehensive guide to using PSFGAN-GaMorNet
        ├── Introduction
        ├── Initial training with simulated galaxies
            ├── Data splitting
            ├── Simulated AGN creation
            ├── Training PSFGAN
            ├── Applying trained PSFGAN
            ├── Generating morphological labels
            └── Training and applying GaMorNet
        ├── Transfer learning with real galaxies
            ├── Data splitting
            ├── Realistic simulated AGN creation
            ├── Training PSFGAN
            ├── Applying trained PSFGAN
            ├── Generating morphological labels
            └── Fine-tuning and applying GaMorNet
        ├── Applying trained PSFGAN and GaMorNet on real AGN
            ├── Data splitting
            ├── Real AGN image normalization
            ├── Applying trained PSFGAN
            └── Applying trained GaMorNet
        └── Notes on our trained PSFGAN and GaMorNet models

Dependencies

Linux or OSX

Python 2.7 for PSFGAN

Python 3.6 for GaMorNet

Python modules (including but not limited to): NumPy, SciPy, Astropy, Pandas, TensorFlow, etc.

Installation

Clone this repository and change the present working directory to PSFGAN-GaMorNet/.

Alternatively, you can download our models from our Google Drive (including example datasets used by the guide and our trained models of PSFGAN and GaMorNet).

A comprehensive guide to using PSFGAN-GaMorNet

In this section, we will give an exhaustive guide about how to use our PSFGAN-GaMorNet framework as well as other related modules.

Introduction

This guide will allow readers to:

  1. Train their own version of PSFGAN-GaMorNet with simulated and/or real data (jump to 'Initial training with simulated galaxies' and 'Transfer learning with real galaxies')
  2. Apply our trained version of PSFGAN-GaMorNet on real AGN in the HSC Wide Survey (jump to 'Applying on real AGN')
  3. Use our code as a reference to build your customized computing framework based on PSFGAN and GaMorNet

Note: The PSFGAN-GaMorNet framework has multiple components, and they are expected to be executed in a fixed order. The output of the N-th component is by default the input of the (N+1)-th component. However, if you already have data that is equivalent to the output of the N-th component, you may skip using the N-th and all previous components and jump to the (N+1)-th component directly.

Initial training with simulated galaxies (self-contained)

In this section, we will illustrate details in training single-band PSFGAN and GaMorNet (from scratch), all using simulated galaxies (simulated AGN).

Before start, please make sure you have the following directory structure:

PSFGAN-GaMorNet/
├── PSFGAN 
    ├── add_label.py
    ├── config.py
    ├── data.py
    ├── data_split.py
    ├── galfit.py
    ├── model.py
    ├── normalizing.py
    ├── photometry.py
    ├── roouhsc.py
    ├── test.py
    ├── train.py
    ├── utils.py
    └── gal_sim_0_0.25
        └── g-band
            ├── catalog_star.csv
            ├── fits_star
            └── raw_data
                ├── images
                └── sim_para_all.csv
└── GaMorNet
    ├── main.py
    └── {other files and folders}

The PSFGAN-GaMorNet assumes raw data images are stored (in .fits format) in an images folder. There should also be a separate catalog file (in .csv format) that contains necessary information of each image. (Please refer to these files for detailed information)

We will use $150,000$ simulated galaxies (which were created w.r.t. 0<z<0.25 real galaxies in HSC Wide Survey) as example.

Data splitting

The first step is to split raw data images into five subsets:

  • fits_train (training set for PSFGAN)
  • fits_eval (validation set for PSFGAN)
  • gmn_train (training set for GaMorNet)
  • gmn_eval (validation set for GaMorNet)
  • fits_test (common test set)

To do so, we will need to use data_split.py. Set the following parameters to correct values before proceed:

  • core_path: path in which PSFGAN is stored (see above)
  • galaxy_main: core_path + 'gal_sim_0_0.25/'
  • filter_strings: ['g'] (filter(s) of raw data images)
  • desired_shape: [239, 239] (desired shape of output images in pixels)
  • --gmn_train, --gmn_eval, --psf_train, --psf_eval, and --test: set their default values to numbers of images you want each subset to have (they should sum up to $150,000$, the number of images in raw_data of gal_sim_0_0.25)
  • --shuffle: 1 (1 to shuffle images before splitting, 0 otherwise)
  • --source: 'gal_sim_0_0.25' (name of the source of the raw data --- this should be the same of the corresponding folder name)
  • --split: equal (equal will ensure roughly the same ratio of disks to bulges to indeterminates across subsets --- look for the corresponding part in the source code for unequal split)

Once these parameters are properly set, ran python PSFGAN-GaMorNet/PSFGAN/data_split.py. Corresponding folders and their associated catalogs will be created.

Simulated AGN creation

The next step is to create artificial AGN point sources, add them with simulated galaxies, and normalize all of these images using the chosen stretch function.

The PSFGAN-GaMorNet assumes star images (used to create AGN PS) are stored (in .fits format) in a fits_star folder. There should also be a separate catalog file, catalog_star.csv, that contains necessary information of each star. (Please refer to these files for detailed information)

Certain parameters need to be properly set before we proceed:

In config.py:

  • redshift: 'gal_sim_0_0.25' (name of the source of our data)
  • filters_: ['g'] (filter(s) of data images)
  • stretch_type and scale_factor: 'asinh' and 50 (we suggest to use these values to start --- feel free to change as you wish)
  • if redshift == 'gal_sim_0_0.25': then pixel_max_value: 25000 (the largest pixel value allowed (pre-normliazation)) Once this value is chosen it should be fixed for the entire dataset gal_sim_0_0.25. This value (25000) is adequate for gal_sim_0_0.25. If you are using your own data, please make sure no pixel value (pre-normliazation) is larger than pixel_max_value, and pixel_max_value is not far larger than the maximum pixel value (pre-normliazation).
  • max_contrast_ratio and min_contrast_ratio: 3.981 and 0.1 (what they do are self-evident)
  • uniform_logspace: True (contrast ratios will be uniformly distributed in logspace if this is True, or they will be uniformly distributed in linearspace if this is False)
  • num_star_per_psf: 50 (how many stars you want to use to create each artificial AGN PS)

In roouhsc.py:

  • --source: 'gal_sim_0_0.25' (name of the source of the data --- this should be the same of the corresponding folder name)
  • --crop: 0 (set this to be zero so images are not cropped during normalization)
  • --save_psf: 0 (whether to save created artificial AGN point sources. 0: No; 1: Yes)
  • --save_raw_input: 1 (whether to save created simulated AGN (simulated galaxies + added AGN point sources). 0: No; 1: Yes. We suggest to save them.)

Once all parameters are set, ran the following to create simulated AGN (and normalize all images using the chosen stretch function) for all five subsets:

python PSFGAN-GaMorNet/PSFGAN/roouhsc_agn.py --mode 0
python PSFGAN-GaMorNet/PSFGAN/roouhsc_agn.py --mode 1
python PSFGAN-GaMorNet/PSFGAN/roouhsc_agn.py --mode 2
python PSFGAN-GaMorNet/PSFGAN/roouhsc_agn.py --mode 3
python PSFGAN-GaMorNet/PSFGAN/roouhsc_agn.py --mode 4

Corresponding folders and associated catalogs will be created.

Normalized simulated galaxies and simulated AGN are stored (in .npy format) in corresponding folders under PSFGAN-GaMorNet/PSFGAN/gal_sim_0_0.25/g-band/asinh_50/npy_input/. So as their associated catalogs.

Training PSFGAN

Now we start training a version of PSFGAN from scratch using (normalized) simulated galaxies/AGN.

Note: please use a Python 2.7 environment for PSFGAN related tasks.

Relevant parameters and settings:

In config.py:

  • learning_rate: 0.00005 (we suggest to use 0.00005 for this particular dataset, yet please feel free to explore other values)
  • attention_parameter: 0.05 (it governs the relative importance of the central focused region in loss calculation --- see below)
  • model_path: '' (during training, the model path must be an empty string)
  • start_epoch and max_epoch: 0 and 20 (that is, to train for 20 epochs)
  • img_size: 239
  • train_size: 239

Other parameters should be kept the same as in the previous step.

In model.py:

  • self.image_00: tf.slice(self.image, [0, 108, 108, 0], [1, 22, 22, conf.img_channel])
  • self.cond_00: tf.slice(self.cond, [0, 108, 108, 0], [1, 22, 22, conf.img_channel])
  • self.g_img_00: tf.slice(self.gen_img, [0, 108, 108, 0], [1, 22, 22, conf.img_channel])

This sets a square region between pixel [108, 108] and pixel [130, 130]. When calculating the loss, pixels within this region will be treated 1/attention_parameter times more important than generic pixels on the entire image. In the paper, we refer to this region as "attention window".

Besides, in discriminator(self, img, cond, reuse) and generator(self, cond), you may want to modify their structures. The default structure is suitable for gal_sim_0_0.25.

Once they are properly set, ran python PSFGAN-GaMorNet/PSFGAN/train.py to train a new version of PSFGAN from scratch using corresponding training data of simulated galaxies/AGN created in the previous step.

The trained model will be saved in PSFGAN-GaMorNet/PSFGAN/gal_sim_0_0.25/g-band/asinh_50/lintrain_classic_PSFGAN_0.05/lr_5e-05/model/ .

Application results of trained model (individual epochs) on corresponding validation data will be saved under PSFGAN-GaMorNet/PSFGAN/gal_sim_0_0.25/g-band/asinh_50/lintrain_classic_PSFGAN_0.05/lr_5e-05/PSFGAN_output/.

Remember to change .../PSFGAN_output/ to a different name since application results on test data will also be saved in the same location.

Applying trained PSFGAN

Now it's time to apply the trained PSFGAN on the training/validation sets for GaMorNet and the common test set. We need to remove the added AGN point sources for these subsets with the aid of PSFGAN.

Set the following parameters before proceed: In config.py:

  • model_path: PSFGAN-GaMorNet/PSFGAN/gal_sim_0_0.25/g-band/asinh_50/lintrain_classic_PSFGAN_0.05/lr_5e-05/model/model.ckpt (location of the trained model)
  • test_epoch: 20 (by default, this should equal to max_epoch --- assuming we want to apply the trained model at the maximum epoch)

Then, please make sure path PSFGAN-GaMorNet/PSFGAN/gal_sim_0_0.25/g-band/asinh_50/lintrain_classic_PSFGAN_0.05/lr_5e-05/PSFGAN_output/ is empty.

Ran python PSFGAN-GaMorNet/PSFGAN/test.py --mode gmn_train to apply the trained model on the training set for GaMorNet. Change .../PSFGAN_output/ to another name (e.g. .../PSFGAN_output_gmn_train/) to make sure path PSFGAN-GaMorNet/PSFGAN/gal_sim_0_0.25/g-band/asinh_50/lintrain_classic_PSFGAN_0.05/lr_5e-05/PSFGAN_output/ is kept empty.

Repeat this step with mode gmn_train (test) to apply the trained model on the validation set for GaMorNet (common test set). Change the folder name after each time of application.

Generating morphological labels

Before training GaMorNet, one should create morphologcial labels for galaxies in the training/validation sets for GaMorNet and the common test set, using relevant parameters in corresponding catalogs.

To do so, simply ran the following:

python PSFGAN-GaMorNet/PSFGAN/add_label.py --input_path 'PSFGAN-GaMorNet/PSFGAN/gal_sim_0_0.25/g-band/asinh_50/npy_input/catalog_gmn_train_npy_input.csv' --source 'gal_sim_0_0.25' 
python PSFGAN-GaMorNet/PSFGAN/add_label.py --input_path 'PSFGAN-GaMorNet/PSFGAN/gal_sim_0_0.25/g-band/asinh_50/npy_input/catalog_gmn_eval_npy_input.csv' --source 'gal_sim_0_0.25' 
python PSFGAN-GaMorNet/PSFGAN/add_label.py --input_path 'PSFGAN-GaMorNet/PSFGAN/gal_sim_0_0.25/g-band/asinh_50/npy_input/catalog_test_npy_input.csv' --source 'gal_sim_0_0.25'

These commends will create new catalogs based on original catalogs. Each new catalog will contain three additional columns: is_disk, is_indeterminate, and is_bulge.

Please refer to the paper for exact rules we used in morphologcial label creation.

Training and applying GaMorNet

Now we start training a version of GaMorNet from scratch using (normalized) recovered (simulated) host galaxies in gmn_train.

Notes:

  1. Please use a Python 3.6 environment for GaMorNet related tasks.
  2. Install appropriate versions of CUDA and cuDNN if you are using a GPU.
  3. In our experiment, we used Python 3.6.12 with cuDNN 7.6.2.24 and CUDA 10.0.130.
  4. See GaMorNet Tutorial Pages about GPU for more information.

To start, please find the main.py file under PSFGAN-GaMorNet/GaMorNet/. Since we can directly load/unload GaMorNet modules, we have integrated all necessary GaMorNet related operations in this file.

In main.py:

If you are using a GPU, ran the following before loading GaMorNet modules:

### Preparation
# First, we will check whether we have access to a GPU (required by the GPU version GaMorNet)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
if tf.test.gpu_device_name() != '/device:GPU:0':
  print('WARNING: GPU device not found.')
else:
  print('SUCCESS: Found GPU: {}'.format(tf.test.gpu_device_name()))

print(tf.__version__)
print(gamornet.__version__)

Then, we should set a few parameters:

  • filter_string: 'g' (note: GaMorNet is single-band by design)
  • image_shape: [239, 239] (should be the same image shape as in previous steps)
  • img_center: [119, 119] (location of the user-defined central pixel)

Training and validation set paths:

  • train_set: 'PSFGAN-GaMorNet/PSFGAN/gal_sim_0_0.25/%s-band/asinh_50/lintrain_classic_PSFGAN_0.05/lr_5e-05/PSFGAN_output_gmn_train/epoch_20/fits_output/' % filter_string (location of the training set for GaMorNet, recovered galaxies --- assuming you have changed the corresponding folder name as mentioned in previous steps)
  • train_catalog: pandas.read_csv(glob.glob('PSFGAN-GaMorNet/PSFGAN/gal_sim_0_0.25/%s-band/asinh_50/npy_input/catalog_gmn_train_npy_input_labeled.csv' % filter_string)[0]) (location of the corresponding catalog of the training set for GaMorNet --- please make sure to use the morphologically labelled version!)
  • eval_set: 'PSFGAN-GaMorNet/PSFGAN/gal_sim_0_0.25/%s-band/asinh_50/lintrain_classic_PSFGAN_0.05/lr_5e-05/PSFGAN_output_gmn_eval/epoch_20/fits_output/' % filter_string (location of the validation set for GaMorNet, recovered galaxies --- assuming you have changed the corresponding folder name as mentioned in previous steps)
  • eval_catalog: pandas.read_csv(glob.glob('PSFGAN-GaMorNet/PSFGAN/gal_sim_0_0.25/%s-band/asinh_50/npy_input/catalog_gmn_eval_npy_input_labeled.csv' % filter_string)[0]) (location of the corresponding catalog of the validation set for GaMorNet --- please make sure to use the morphologically labelled version!)

And the common test set paths:

  • pre_psf: 'PSFGAN-GaMorNet/PSFGAN/gal_sim_0_0.25/%s-band/fits_test/' % filter_string (location of the common test set, original galaxies)
  • post_psf: 'PSFGAN-GaMorNet/PSFGAN/gal_sim_0_0.25/%s-band/asinh_50/lintrain_classic_PSFGAN_0.05/lr_5e-05/PSFGAN_output_test/epoch_20/fits_output/' % filter_string (location of the common test set, recovered galaxies --- assuming you have changed the corresponding folder name as mentioned in previous steps)
  • cond_input: 'PSFGAN-GaMorNet/PSFGAN/gal_sim_0_0.25/%s-band/fits_test_condinput/' % filter_string (location of the common test set, original galaxies + AGN point sources --- assuming you have saved the simulated AGN as suggested in previous steps)
  • test_catalog: pandas.read_csv(glob.glob('PSFGAN-GaMorNet/PSFGAN/gal_sim_0_0.25/%s-band/asinh_50/npy_input/catalog_test_npy_input_labeled.csv' % filter_string)[0]) (location of the corresponding catalog of the common test set --- please make sure to use the morphologically labelled version!)

Once these parameters and paths are properly set, load all functions from:

### Defination
# Define a function to convert row_num into object_id
def row_to_id(catalog, row_num):
    return catalog.at[row_num, 'object_id']

to

# Define a function to save prediction labels (save to test_catalog)
def save_labels(pre_prediction_labels, post_prediction_labels, cond_prediction_labels, radius=0.0, type='',
                catalog=test_catalog, catalog_folder=''):
    length_catalog = len(catalog)

    catalog['pre_disk'] = [0.0]*length_catalog
    catalog['pre_indeterminate'] = [0.0]*length_catalog
    catalog['pre_bulge'] = [0.0]*length_catalog
    catalog['post_disk'] = [0.0] * length_catalog
    catalog['post_indeterminate'] = [0.0] * length_catalog
    catalog['post_bulge'] = [0.0] * length_catalog
    catalog['cond_disk'] = [0.0]*length_catalog
    catalog['cond_indeterminate'] = [0.0]*length_catalog
    catalog['cond_bulge'] = [0.0]*length_catalog

    row_num_list = list(range(length_catalog))
    for row_num in row_num_list:
        catalog.at[row_num, 'pre_disk'] = pre_prediction_labels[row_num, 0]
        catalog.at[row_num, 'pre_indeterminate'] = pre_prediction_labels[row_num, 1]
        catalog.at[row_num, 'pre_bulge'] = pre_prediction_labels[row_num, 2]
        catalog.at[row_num, 'post_disk'] = post_prediction_labels[row_num, 0]
        catalog.at[row_num, 'post_indeterminate'] = post_prediction_labels[row_num, 1]
        catalog.at[row_num, 'post_bulge'] = post_prediction_labels[row_num, 2]
        catalog.at[row_num, 'cond_disk'] = cond_prediction_labels[row_num, 0]
        catalog.at[row_num, 'cond_indeterminate'] = cond_prediction_labels[row_num, 1]
        catalog.at[row_num, 'cond_bulge'] = cond_prediction_labels[row_num, 2]

    # Save the catalog
    if type == '':
        catalog.to_csv(catalog_folder + 'catalog_test_entirety_' + str(length_catalog) + '.csv', index=False)
    else:
        catalog.to_csv(catalog_folder + 'catalog_test_entirety_' + str(length_catalog) + '_' + type + str(int(radius)) + '.csv', index=False)

Then, we are ready to invoke GaMorNet.

Load data: (please change row number limits accordingly if you are using a different split than the one described in the paper.)

radius = 0.0
type = ''

# Load data
# For sim_gal_0_0.25, sim_gal_0.25_0.5, sim_gal_0.5_0.75, sim_gal_0.5_1.0
training_imgs_0, training_labels_0 = load_train_data(row_num_limits=[0, 45000], radius=radius, type=type)
training_imgs_1, training_labels_1 = load_train_data(row_num_limits=[45000, 90000], radius=radius, type=type)
training_imgs = np.concatenate((training_imgs_0, training_imgs_1), axis=0)
training_labels = np.concatenate((training_labels_0, training_labels_1), axis=0)
validation_imgs, validation_labels = load_eval_data(row_num_limits=[0, 10000], radius=radius, type=type)

Train the model:

train_model_folder = 'PSFGAN-GaMorNet/GaMorNet/saves/{your favorite folder name}' 
if not os.path.exists(train_model_folder):
    os.makedirs(train_model_folder)
gamornet_train_keras(training_imgs=training_imgs, training_labels=training_labels, validation_imgs=validation_imgs, validation_labels=validation_labels,
                     input_shape=(239, 239, 1),
                     files_save_path=train_model_folder,
                     epochs=100, checkpoint_freq=0,
                     batch_size=128, lr=0.00005, momentum=0.9, decay=0.0, nesterov=False, loss='categorical_crossentropy',
                     load_model=False, model_load_path='./', save_model=True, verbose=2)

Note: for arguments in 'gamornet_train_keras', please refer to GaMorNet API Documentation for more information.

Apply the model: (please change row number limits accordingly if you are using a different split than the one described in the paper.)

# Load pre and post PSFGAN results along with conditional inputs
pre_imgs = load_pre_psf(row_num_limits=[0, 40000], radius=radius, type=type)
post_imgs = load_post_psf(row_num_limits=[0, 40000], radius=radius, type=type)
cond_imgs = load_cond_inputs(row_num_limits=[0, 40000], radius=radius, type=type)
rsdl_imgs = post_imgs - pre_imgs
# Use the model to make prediction
test_model ='PSFGAN-GaMorNet/GaMorNet/saves/{your favorite folder name}/trained_model.hdf5'
pre_prediction_labels = gamornet_predict_keras(img_array=pre_imgs,
                                                 model_load_path=test_model,
                                                 input_shape=(image_shape[0], image_shape[1], 1),
                                                 batch_size=64, individual_arrays=False)
post_prediction_labels = gamornet_predict_keras(img_array=post_imgs,
                                                  model_load_path=test_model,
                                                  input_shape=(image_shape[0], image_shape[1], 1),
                                                  batch_size=64, individual_arrays=False)
cond_prediction_labels = gamornet_predict_keras(img_array=cond_imgs,
                                                  model_load_path=test_model,
                                                  input_shape=(image_shape[0], image_shape[1], 1),
                                                  batch_size=64, individual_arrays=False)

At last, save their outputs:

## Save the prediction labels together with the test catalog (creating a new catalog)
# Remember to set the catalog folder.
save_labels(pre_prediction_labels=pre_prediction_labels, post_prediction_labels=post_prediction_labels,
            cond_prediction_labels=cond_prediction_labels, radius=radius, type=type,
            catalog_folder={where you want to create a catalog containing model outputs})

Transfer learning with real galaxies (please read 'Initial training with simulated galaxies' first)

In this section, we will illustrate details in training multi-band PSFGAN and fine-tuning previously trained GaMorNet, all using real galaxies (realistic simulated AGN).

Before proceed, please familarize yourself about the "Initial training with simulated galaxies" section. Since there is a huge overlap, we will not go over every detail. Instead, we will highlight their difference (whenever there is one).

Please make sure you have the following directory structure:

PSFGAN-GaMorNet/
├── PSFGAN 
    ├── add_label.py
    ├── config.py
    ├── data.py
    ├── data_split.py
    ├── galfit.py
    ├── model.py
    ├── normalizing.py
    ├── photometry.py
    ├── roouhsc.py
    ├── test.py
    ├── train.py
    ├── utils.py
    └── simard
        ├──  g-band
            ├── catalog_star.csv
            ├── fits_star
            └── raw_data
                ├── images
                └── Simard_Match_HSCWideAll_SelectedRows.csv
        ├── r-band
            └── {same as in g-band}
        ├── i-band
            └── {same as in g-band}
        ├── z-band
            └── {same as in g-band}
        └── y-band
            └── {same as in g-band}
└── GaMorNet
    ├── main.py
    └── {other files and folders}

Note that each real galaxy has images in five HSC Wide filters: g, r, i, z, and y. Please refer to image files and catalogs for detailed information.

We will use $50,873$ real galaxies (mostly with $z&lt;0.25$) selected from Simard et al. (2011) (which are also imaged in HSC Wide Survey) as example.

Data splitting

Similar as in the "Initial training with simulated galaxies" section. Use data_split.py to split data. Set parameters accordingly. Ran python PSFGAN-GaMorNet/PSFGAN/data_split.py.

Notes:

  • filter_strings: this should now be ['g', 'r', 'i', 'z', 'y']
  • desired_shape: this is still [239, 239]

Realistic simulated AGN creation

The next step is to create artificial AGN point sources, add them with real galaxies, and normalize all of these images using the chosen stretch function. This is similar as in the "Initial training with simulated galaxies" section, except now we have five filters.

Star images and catalogs should have the same format as in the "Initial training with simulated galaxies" section.

Notes on a few parameters:

In config.py:

  • filters_: this should now be ['g', 'r', 'i', 'z', 'y']
  • if redshift == 'simard': then pixel_max_value: 45000 should be adequate for our dataset (Once this value is chosen it should be fixed for the entire dataset simard)

Please be aware that in each filter, we can have an individual AGN PS to host galaxy flux contrast ratio. In reality, the five contrast ratios are not independent. Nonetheless, for illustration purposes, we use five independently sampled contrast ratios in our guide. Please modify the corresponding code block accordingly, should you want to use real AGN SEDs (for example) to create realistically mutually-dependent contrast ratios in the five HSC Wide filters.

  • max_contrast_ratio and min_contrast_ratio: 3.981 and 0.1
  • uniform_logspace: True
  • num_star_per_psf: 50

Set other parameters accordingly.

In roouhsc.py:

  • --save_raw_input: 1 (We suggest to save them.) Others are similar as in the "Initial training with simulated galaxies" section. Set them accordingly.

Once all parameters are set, ran the following to create realistic simulated AGN (and normalize all images using the chosen stretch function):

python PSFGAN-GaMorNet/PSFGAN/roouhsc_agn.py --mode 0
python PSFGAN-GaMorNet/PSFGAN/roouhsc_agn.py --mode 1
python PSFGAN-GaMorNet/PSFGAN/roouhsc_agn.py --mode 2
python PSFGAN-GaMorNet/PSFGAN/roouhsc_agn.py --mode 3
python PSFGAN-GaMorNet/PSFGAN/roouhsc_agn.py --mode 4

Training PSFGAN

Now we start training a version of multi-band PSFGAN from scratch using (normalized) realistic simulated galaxies/AGN.

Note: please use a Python 2.7 environment for PSFGAN related tasks.

Notes on parameters:

In config.py:

  • learning_rate: 0.00009
  • attention_parameter: 0.05
  • model_path: '' (during training, the model path must be an empty string)
  • start_epoch and max_epoch: 0 and 20 (that is, to train for 20 epochs)
  • img_size: 239
  • train_size: 239

Other parameters should be kept the same as in the previous step.

In model.py:

  • self.image_00: tf.slice(self.image, [0, 108, 108, 0], [1, 22, 22, conf.img_channel])
  • self.cond_00: tf.slice(self.cond, [0, 108, 108, 0], [1, 22, 22, conf.img_channel])
  • self.g_img_00: tf.slice(self.gen_img, [0, 108, 108, 0], [1, 22, 22, conf.img_channel])

Besides, in discriminator(self, img, cond, reuse) and generator(self, cond), you may want to modify their structures. The default structure is suitable for an image shape of [239, 239].

Once they are properly set, ran python PSFGAN-GaMorNet/PSFGAN/train.py to train a new version of PSFGAN from scratch using corresponding training data of realistic simulated galaxies/AGN created in the previous step.

Five identical copies of the trained multi-band model will be saved in similar locations in each of the five filter-subfolders.

Remember to change .../PSFGAN_output/ to a different name IN EACH FILTER since application results on test data will also be saved in the same locations.

Applying trained PSFGAN

Now it's time to apply the trained PSFGAN on the training/validation sets for GaMorNet and the common test set. We need to remove the added AGN point sources for these subsets with the aid of PSFGAN.

Set the following parameters before proceed:

In config.py:

  • model_path: PSFGAN-GaMorNet/PSFGAN/simard/g-band/asinh_50/lintrain_classic_PSFGAN_0.05/lr_9e-05/model/model.ckpt (location of the trained model --- you can also change g-band to other filter-subfolders since models saved in different filter-subfolders are identical)
  • test_epoch: 20 (by default, this should equal to max_epoch --- assuming we want to apply the trained model at the maximum epoch)

Then, please make sure path PSFGAN-GaMorNet/PSFGAN/simard/{filter}-band/asinh_50/lintrain_classic_PSFGAN_0.05/lr_9e-05/PSFGAN_output/ is empty for each of the five filters.

You can now use command python PSFGAN-GaMorNet/PSFGAN/test.py --mode gmn_train and two other commands for --mode gmn_eval and --mode test to apply the trained model to remove added AGN point sources for GaMorNet and for the common test set. Change the according path names as in the "Initial training with simulated galaxies" section. The only difference is that one needs to change folder names and keep original paths empty in all FIVE filter-subfolders.

Generating morphological labels

Ran the following to create morphological labels for each filter-subfolder:

python PSFGAN-GaMorNet/PSFGAN/add_label.py --input_path 'PSFGAN-GaMorNet/PSFGAN/simard/g-band/asinh_50/npy_input/catalog_gmn_train_npy_input.csv' --source 'simard' --use_label 'n'
python PSFGAN-GaMorNet/PSFGAN/add_label.py --input_path 'PSFGAN-GaMorNet/PSFGAN/simard/g-band/asinh_50/npy_input/catalog_gmn_eval_npy_input.csv' --source 'simard' --use_label 'n'
python PSFGAN-GaMorNet/PSFGAN/add_label.py --input_path 'PSFGAN-GaMorNet/PSFGAN/simard/g-band/asinh_50/npy_input/catalog_test_npy_input.csv' --source 'simard' --use_label 'n'

python PSFGAN-GaMorNet/PSFGAN/add_label.py --input_path 'PSFGAN-GaMorNet/PSFGAN/simard/r-band/asinh_50/npy_input/catalog_gmn_train_npy_input.csv' --source 'simard' --use_label 'n'
python PSFGAN-GaMorNet/PSFGAN/add_label.py --input_path 'PSFGAN-GaMorNet/PSFGAN/simard/r-band/asinh_50/npy_input/catalog_gmn_eval_npy_input.csv' --source 'simard' --use_label 'n'
python PSFGAN-GaMorNet/PSFGAN/add_label.py --input_path 'PSFGAN-GaMorNet/PSFGAN/simard/r-band/asinh_50/npy_input/catalog_test_npy_input.csv' --source 'simard' --use_label 'n'

python PSFGAN-GaMorNet/PSFGAN/add_label.py --input_path 'PSFGAN-GaMorNet/PSFGAN/simard/i-band/asinh_50/npy_input/catalog_gmn_train_npy_input.csv' --source 'simard' --use_label 'n'
python PSFGAN-GaMorNet/PSFGAN/add_label.py --input_path 'PSFGAN-GaMorNet/PSFGAN/simard/i-band/asinh_50/npy_input/catalog_gmn_eval_npy_input.csv' --source 'simard' --use_label 'n'
python PSFGAN-GaMorNet/PSFGAN/add_label.py --input_path 'PSFGAN-GaMorNet/PSFGAN/simard/i-band/asinh_50/npy_input/catalog_test_npy_input.csv' --source 'simard' --use_label 'n'

python PSFGAN-GaMorNet/PSFGAN/add_label.py --input_path 'PSFGAN-GaMorNet/PSFGAN/simard/z-band/asinh_50/npy_input/catalog_gmn_train_npy_input.csv' --source 'simard' --use_label 'n'
python PSFGAN-GaMorNet/PSFGAN/add_label.py --input_path 'PSFGAN-GaMorNet/PSFGAN/simard/z-band/asinh_50/npy_input/catalog_gmn_eval_npy_input.csv' --source 'simard' --use_label 'n'
python PSFGAN-GaMorNet/PSFGAN/add_label.py --input_path 'PSFGAN-GaMorNet/PSFGAN/simard/z-band/asinh_50/npy_input/catalog_test_npy_input.csv' --source 'simard' --use_label 'n'

python PSFGAN-GaMorNet/PSFGAN/add_label.py --input_path 'PSFGAN-GaMorNet/PSFGAN/simard/y-band/asinh_50/npy_input/catalog_gmn_train_npy_input.csv' --source 'simard' --use_label 'n'
python PSFGAN-GaMorNet/PSFGAN/add_label.py --input_path 'PSFGAN-GaMorNet/PSFGAN/simard/y-band/asinh_50/npy_input/catalog_gmn_eval_npy_input.csv' --source 'simard' --use_label 'n'
python PSFGAN-GaMorNet/PSFGAN/add_label.py --input_path 'PSFGAN-GaMorNet/PSFGAN/simard/y-band/asinh_50/npy_input/catalog_test_npy_input.csv' --source 'simard' --use_label 'n'

Please refer to the paper for exact rules we used in morphologcial label creation.

Fine-tuning and applying GaMorNet

Now we start fine-tuning the version of GaMorNet previously trained from scratch in the "Initial training with simulated galaxies" section, using (normalized) recovered (real) host galaxies in gmn_train.

Notes:

  1. Please use a Python 3.6 environment for GaMorNet related tasks.
  2. Install appropriate versions of CUDA and cuDNN if you are using a GPU.
  3. In our experiment, we used Python 3.6.12 with cuDNN 7.6.2.24 and CUDA 10.0.130.
  4. See GaMorNet Tutorial Pages about GPU for more information.

To start, please find the main.py file under PSFGAN-GaMorNet/GaMorNet/.

In main.py:

If you are using a GPU, ran the following before loading GaMorNet modules:

### Preparation
# First, we will check whether we have access to a GPU (required by the GPU version GaMorNet)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
if tf.test.gpu_device_name() != '/device:GPU:0':
  print('WARNING: GPU device not found.')
else:
  print('SUCCESS: Found GPU: {}'.format(tf.test.gpu_device_name()))

print(tf.__version__)
print(gamornet.__version__)

Please be aware that although we used a multi-band PSFGAN, GaMorNet is single-band by design. Please ran the following steps for each of the five filters (or for a subset of filters you're interested).

Set a few parameters:

  • filter_string: {filter} (e.g. 'g', 'r', etc.)
  • image_shape: [239, 239]
  • img_center: [119, 119]

Then, set training set, validation set, and common test set paths accordingly, in the chosen filter-subfolder.

Once these parameters and paths are properly set, load all functions needed.

Then, we are ready to invoke GaMorNet.

Load data: (please change row number limits accordingly if you are using a different split than the one described in the paper.)

radius = 0.0
type = ''

# Load data
# For simard
training_imgs, training_labels = load_train_data(row_num_limits=[0, 32400], radius=radius, type=type)
validation_imgs, validation_labels = load_eval_data(row_num_limits=[0, 3600], radius=radius, type=type)

Fine-tune the previously learned model:

previous_model_folder = '{location of the previously trained model folder}'
tl_model_folder = '{destination folder --- fine-tuned model will be saved there}'
if not os.path.exists(tl_model_folder):
    os.makedirs(tl_model_folder)
gamornet_tl_keras(training_imgs=training_imgs, training_labels=training_labels, validation_imgs=validation_imgs, validation_labels=validation_labels,
                  input_shape=(239, 239, 1),
                  load_layers_bools=[True, True, True, True, True, True, True, True],
                  trainable_bools=[False, False, False, True, True, True, True, True],
                  model_load_path=previous_model_folder+'trained_model.hdf5',
                  files_save_path=tl_model_folder,
                  epochs=200, checkpoint_freq=0,
                  batch_size=128, lr=5e-05, momentum=0.9, decay=0.0, nesterov=False, loss='categorical_crossentropy',
                  save_model=True, verbose=2)

Note: for arguments in 'gamornet_train_keras', please refer to GaMorNet API Documentation for more information.

Apply the model: (please change row number limits accordingly if you are using a different split than the one described in the paper.)

# Load pre and post PSFGAN results along with conditional inputs
pre_imgs = load_pre_psf(row_num_limits=[0, 9000], radius=radius, type=type)
post_imgs = load_post_psf(row_num_limits=[0, 9000], radius=radius, type=type)
cond_imgs = load_cond_inputs(row_num_limits=[0, 9000], radius=radius, type=type)
rsdl_imgs = post_imgs - pre_imgs
# Use the model to make prediction
test_model ='{location of the fine-tuned model folder}/trained_model.hdf5'
pre_prediction_labels = gamornet_predict_keras(img_array=pre_imgs,
                                                 model_load_path=test_model,
                                                 input_shape=(image_shape[0], image_shape[1], 1),
                                                 batch_size=64, individual_arrays=False)
post_prediction_labels = gamornet_predict_keras(img_array=post_imgs,
                                                  model_load_path=test_model,
                                                  input_shape=(image_shape[0], image_shape[1], 1),
                                                  batch_size=64, individual_arrays=False)
cond_prediction_labels = gamornet_predict_keras(img_array=cond_imgs,
                                                  model_load_path=test_model,
                                                  input_shape=(image_shape[0], image_shape[1], 1),
                                                  batch_size=64, individual_arrays=False)

At last, save their outputs:

## Save the prediction labels together with the test catalog (creating a new catalog)
# Remember to set the catalog folder.
save_labels(pre_prediction_labels=pre_prediction_labels, post_prediction_labels=post_prediction_labels,
            cond_prediction_labels=cond_prediction_labels, radius=radius, type=type,
            catalog_folder={where you want to create a catalog containing model outputs})

Applying trained PSFGAN and GaMorNet on real AGN (self-contained)

In this section, we will illustrate details of how to apply trained (multi-band) PSFGAN and GaMorNet on real AGN (from HSC Wide) in order to morphologically classify their host galaxies.

Before start, please make sure you have the following directory structure:

PSFGAN-GaMorNet/
├── PSFGAN 
    ├── add_label.py
    ├── config.py
    ├── data.py
    ├── data_split_agn.py
    ├── galfit.py
    ├── model.py
    ├── normalizing.py
    ├── photometry.py
    ├── roouhsc_agn.py
    ├── test.py
    ├── train.py
    ├── utils.py
    └── {target dataset name}
        ├──  g-band
            └── raw_data
                ├── images
                └── {catalog in .csv format}
        ├── r-band
            └── {same as in g-band}
        ├── i-band
            └── {same as in g-band}
        ├── z-band
            └── {same as in g-band}
        └── y-band
            └── {same as in g-band}
└── GaMorNet
    ├── main.py
    └── {other files and folders}

Note: in this guide we assume your target dataset has images in five HSC Wide filters, but of course it can have less than five (if you are not using our trained models).

(In each filter) for the target dataset, its raw data images should be stored (in .fits format) in an image folder. There should also be a separate catalog file (in .csv format) that contains necessary information of each image. The file name of each .fits image as well as its corresponding row in the catalog can have various forms. Please change codes in data_split_agn.py, roouhsc_agn.py, etc. appropriately so images can be correctly processed.

Data splitting

Essentially, the first step we want to do is to put all raw images in the common test dataset folder(s) (fits_test). No actual data split is done. We will use data_split_agn.py to do so. This file is forked from data_split.py. We still call this step as 'data splitting' to keep their accordance.

In data_split_agn.py, set the following parameters to the correct values before proceed:

  • core_path: path in which PSFGAN is stored (see above)
  • galaxy_main: core_path + '{target dataset name}/'
  • filter_strings: ['g', 'r', 'i', 'z', 'y'] (filter(s) of raw data images of the target dataset (also subject to the trained models) --- if you are using our models, it has to be ['g', 'r', 'i', 'z', 'y'])
  • desired_shape: [239, 239] (desired shape of output images in pixels --- this is subject to the trained models you want to use --- it has to be [239, 239] if you are using our trained models)
  • --test: set its default value to the number of galaxies your target dataset has
  • --shuffle: 1 (1 to shuffle images before splitting, 0 otherwise)
  • --source: '{target dataset name}' (name of the target dataset --- this should be the same of the corresponding folder name)

You should also add appropriate codes at the ends of the following two blocks to process catalog(s) of the raw images:

        elif source == "liu":
            column_list = ['object_id', 'ra', 'dec', 'specz_redshift', 'specz_flag_homogeneous',
                          'z', 
                          filter_string + '_total_flux']       
                elif source == "liu":
                    test_catalogs[i] = test_catalogs[i].append({'object_id': obj_id,
                    'ra': current_row['ra'],
                    'dec': current_row['dec'],
                    'specz_redshift': current_row['specz_redshift'],
                    'specz_flag_homogeneous': current_row['specz_flag_homogeneous'],
                    'z': current_row['z'],
                    filter_strings[i] + '_total_flux': (current_row[filter_strings[i] + '_cmodel_flux'])*nJy_to_adu_per_AA_filters[i]}, ignore_index=True)

At last, change the following block appropriately to process raw images.

    for row_num in row_num_list:
        if (source == "nair") or (source == "gabor_0.3_0.5") or (source == "gabor_0.5_0.75") or (source == "povic_0_0.5") or (source == "povic_0.5_0.75") or (source == "stemo_0.2_0.5") or (source == "stemo_0.5_1.0") or (source == "liu"):
            obj_id = int(row_num)

        # Read the images
        images = []
        for i in range(num_filters):
            if (source == "nair") or (source == "gabor_0.3_0.5") or (source == "gabor_0.5_0.75") or (source == "povic_0_0.5") or (source == "povic_0.5_0.75") or (source == "stemo_0.2_0.5") or (source == "stemo_0.5_1.0") or (source == "liu"):
                fits_path = '%s/%s-cutout-*.fits' % (hsc_folders[i], obj_id)
            file = glob.glob(fits_path)[0]
            image = fits.getdata(file)
            images.append(image)

Once these parameters are properly set, ran python PSFGAN-GaMorNet/PSFGAN/data_split_agn.py. Corresponding folders and their associated catalogs will be created.

Real AGN image normalization

The next step is to normalize all images of real AGN in the common test set using the chosen stretch function. (In case you've read previous sections) there is no need to create artificial AGN point sources --- real AGN also have them. For the same reason, we don't need star images and their associated catalogs.

Certain parameters need to be properly set before we proceed:

In config.py:

  • redshift: '{target dataset name}'
  • filters_: ['g', 'r', 'i', 'z', 'y'] (filter(s) of data images of the target dataset (also subject to the trained models) --- if you are using our models, it has to be ['g', 'r', 'i', 'z', 'y'])
  • stretch_type and scale_factor: 'asinh' and 50 (they are subject to the trained models --- if you are using our trained models, please keep these values fixed as so)
  • if redshift == '{target dataset name}': then pixel_max_value (please add this block): (the largest pixel value allowed (pre-normliazation)) please refer to the 'Notes on our trained PSFGAN and GaMorNet models' section for details. This value is subject to the trained model you want to use and thus can not be adjusted.

In roouhsc_agn.py:

  • --source: '{target dataset name}' (name of the target dataset --- this should be the same of the corresponding folder name)
  • --crop: 0 (set this to be zero so images are not cropped during normalization)

Once all parameters are set, ran the following to normalize all images in the common test set using the chosen stretch function:

python PSFGAN-GaMorNet/PSFGAN/roouhsc_agn.py

Corresponding folders and associated catalogs will be created.

Applying trained PSFGAN

Note: please use a Python 2.7 environment for PSFGAN related tasks.

Since we are using trained models, there is no need to train any additional models (if you've read previous sections).

We now apply the multi-band PSFGAN from the trained models we want to use on real AGN in the common test set. It will remove AGN point sources and generate recovered host galaxies for GaMorNet to classify (in each filter).

Set the following parameters before proceed:

In config.py:

  • learning_rate: (just for creating corresponding folder names) please refer to the 'Notes on our trained PSFGAN and GaMorNet models' section for details.
  • attention_parameter: 0.05 (just for creating corresponding folder names)
  • model_path: '{location of the trained PSFGAN you want to use}'
  • beta1: 0.5
  • L1_lambda: 100
  • sum_lambda: 0
  • test_epoch: please refer to the 'Notes on our trained PSFGAN and GaMorNet models' section for details.
  • img_size: 239
  • train_size: 239

Other parameters should be kept the same as in the previous step.

In model.py:

  • self.image_00, self.cond_00, and self.g_img_00: please refer to the 'Notes on our trained PSFGAN and GaMorNet models' section for details. These values are subject to the trained model you want to use and should be fixed.

The default discriminator(self, img, cond, reuse) and generator(self, cond) structures fit our trained PSFGAN and GaMorNet models. If you are using our trained models, please leave them as so.

Then, ran python PSFGAN-GaMorNet/PSFGAN/test.py --mode test to apply the trained PSFGAN on the common test set. Outputs will be saved in PSFGAN-GaMorNet/PSFGAN/{target dataset name}/{filter}-band/{stretch_type}_{scale_factor}/lintrain_classic_PSFGAN_{attention_parameter}/lr_{learning_rate}/PSFGAN_output/epoch_{test_epoch}/fits_output/ for each {filter}.

Applying trained GaMorNet

We now apply each version of GaMorNet from the trained models we want to use on recovered host galaxies of real AGN in the common test set. Please be advised that although the multi-band PSFGAN deals with five filters simultaneously, each version of GaMorNet is single-band by design. So if there are five filters (if you are using our trained models, for example), one needs to apply five versions of GaMorNet --- each on images in the corresponding filter.

Notes:

  1. Please use a Python 3.6 environment for GaMorNet related tasks.
  2. Install appropriate versions of CUDA and cuDNN if you are using a GPU.
  3. In our experiment, we used Python 3.6.12 with cuDNN 7.6.2.24 and CUDA 10.0.130.
  4. See GaMorNet Tutorial Pages about GPU for more information.

To start, please find the main.py file under PSFGAN-GaMorNet/GaMorNet/.

In main.py:

If you are using a GPU, ran the following before loading GaMorNet modules:

### Preparation
# First, we will check whether we have access to a GPU (required by the GPU version GaMorNet)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
if tf.test.gpu_device_name() != '/device:GPU:0':
  print('WARNING: GPU device not found.')
else:
  print('SUCCESS: Found GPU: {}'.format(tf.test.gpu_device_name()))

print(tf.__version__)
print(gamornet.__version__)

Please be aware that although we used a multi-band PSFGAN, GaMorNet is single-band by design. Please ran the following steps for each filter that the trained models you want to use can support. If you are using our trained models, all five HSC Wide filters are available.

Set a few parameters:

  • filter_string: {filter} (e.g. 'g', 'r', etc.)
  • image_shape: [239, 239]
  • img_center: [119, 119]

Common test set paths:

  • pre_psf: 'PSFGAN-GaMorNet/PSFGAN/{target dataset name}/%s-band/fits_test/' % filter_string (location of the common test set, real AGN --- this is now just a place holder since we know know exactly what the host galaxies of real AGN would be)
  • post_psf: 'PSFGAN-GaMorNet/PSFGAN/{target dataset name}/%s-band/{stretch_type}_{scale_factor}/lintrain_classic_PSFGAN_{attention_parameter}/lr_{learning_rate}/PSFGAN_output/epoch_{test_epoch}/fits_output/' % filter_string (location of the common test set, recovered host galaxies)
  • cond_input: 'PSFGAN-GaMorNet/PSFGAN/{target dataset name}/%s-band/fits_test/' % filter_string (location of the common test set, real AGN)
  • test_catalog: pandas.read_csv(glob.glob('PSFGAN-GaMorNet/PSFGAN/{target dataset name}/%s-band/{stretch_type}_{scale_factor}/npy_input/catalog_test_npy_input.csv' % filter_string)[0]) (location of the corresponding catalog of the common test set)

Once these parameters and paths are properly set, load all functions from:

### Defination
# Define a function to convert row_num into object_id
def row_to_id(catalog, row_num):
    return catalog.at[row_num, 'object_id']

to

# Define a function to save prediction labels (save to test_catalog)
def save_labels(pre_prediction_labels, post_prediction_labels, cond_prediction_labels, radius=0.0, type='',
                catalog=test_catalog, catalog_folder=''):
    length_catalog = len(catalog)

    catalog['pre_disk'] = [0.0]*length_catalog
    catalog['pre_indeterminate'] = [0.0]*length_catalog
    catalog['pre_bulge'] = [0.0]*length_catalog
    catalog['post_disk'] = [0.0] * length_catalog
    catalog['post_indeterminate'] = [0.0] * length_catalog
    catalog['post_bulge'] = [0.0] * length_catalog
    catalog['cond_disk'] = [0.0]*length_catalog
    catalog['cond_indeterminate'] = [0.0]*length_catalog
    catalog['cond_bulge'] = [0.0]*length_catalog

    row_num_list = list(range(length_catalog))
    for row_num in row_num_list:
        catalog.at[row_num, 'pre_disk'] = pre_prediction_labels[row_num, 0]
        catalog.at[row_num, 'pre_indeterminate'] = pre_prediction_labels[row_num, 1]
        catalog.at[row_num, 'pre_bulge'] = pre_prediction_labels[row_num, 2]
        catalog.at[row_num, 'post_disk'] = post_prediction_labels[row_num, 0]
        catalog.at[row_num, 'post_indeterminate'] = post_prediction_labels[row_num, 1]
        catalog.at[row_num, 'post_bulge'] = post_prediction_labels[row_num, 2]
        catalog.at[row_num, 'cond_disk'] = cond_prediction_labels[row_num, 0]
        catalog.at[row_num, 'cond_indeterminate'] = cond_prediction_labels[row_num, 1]
        catalog.at[row_num, 'cond_bulge'] = cond_prediction_labels[row_num, 2]

    # Save the catalog
    if type == '':
        catalog.to_csv(catalog_folder + 'catalog_test_entirety_' + str(length_catalog) + '.csv', index=False)
    else:
        catalog.to_csv(catalog_folder + 'catalog_test_entirety_' + str(length_catalog) + '_' + type + str(int(radius)) + '.csv', index=False)

Then, we are ready to invoke GaMorNet.

Load data and apply the model: (please change row number limits accordingly depending on the number of galaxies in your common test set.)

radius = 0.0
type = ''

# Load pre and post PSFGAN results along with conditional inputs
pre_imgs = load_pre_psf(row_num_limits=[0, {number of galaxies in common test set}], radius=radius, type=type)
post_imgs = load_post_psf(row_num_limits=[0, {number of galaxies in common test set}], radius=radius, type=type)
cond_imgs = load_cond_inputs(row_num_limits=[0, {number of galaxies in common test set}], radius=radius, type=type)
rsdl_imgs = post_imgs - pre_imgs
# Use the model to make prediction
test_model ='{location of the trained GaMorNet model in this filter}'
pre_prediction_labels = gamornet_predict_keras(img_array=pre_imgs,
                                                 model_load_path=test_model,
                                                 input_shape=(image_shape[0], image_shape[1], 1),
                                                 batch_size=64, individual_arrays=False)
post_prediction_labels = gamornet_predict_keras(img_array=post_imgs,
                                                  model_load_path=test_model,
                                                  input_shape=(image_shape[0], image_shape[1], 1),
                                                  batch_size=64, individual_arrays=False)
cond_prediction_labels = gamornet_predict_keras(img_array=cond_imgs,
                                                  model_load_path=test_model,
                                                  input_shape=(image_shape[0], image_shape[1], 1),
                                                  batch_size=64, individual_arrays=False)

Note: for arguments in 'gamornet_train_keras', please refer to GaMorNet API Documentation for more information.

At last, save their outputs:

## Save the prediction labels together with the test catalog (creating a new catalog)
# Remember to set the catalog folder.
save_labels(pre_prediction_labels=pre_prediction_labels, post_prediction_labels=post_prediction_labels,
            cond_prediction_labels=cond_prediction_labels, radius=radius, type=type,
            catalog_folder={where you want to create a catalog containing model outputs})

In each filter, the morphological classification results on the recovered host galaxies are saved in column 'post_disk', 'post_indeterminate', and 'post_bulge' in the output catalog. (please refer to the paper for meanings of these values). By default, the output catalog is named 'catalog_test_entirety_{number of galaxies in common test set}.csv'.

Notes on our trained PSFGAN and GaMorNet models (please read with 'Applying trained PSFGAN and GaMorNet on real AGN')

As illustrated in the paper, in each of the three redshift bins, we trained a version of multi-band PSFGAN and five versions of GaMorNet

Low redshift bin (0<z<0.25)
    └── Multi-band PSFGAN
        ├── GaMorNet for g-band
        ├── GaMorNet for r-band
        ├── GaMorNet for i-band
        ├── GaMorNet for z-band
        └── GaMorNet for y-band
Mid redshift bin (0.25<z<0.5)
    └── Multi-band PSFGAN 
        ├── GaMorNet for g-band
        ├── GaMorNet for r-band
        ├── GaMorNet for i-band
        ├── GaMorNet for z-band
        └── GaMorNet for y-band
High redshift bin (0.5<z<1.0)
    └── Multi-band PSFGAN 
        ├── GaMorNet for g-band
        ├── GaMorNet for r-band
        ├── GaMorNet for i-band
        ├── GaMorNet for z-band
        └── GaMorNet for y-band

If you want to apply our model(s) on a dataset, please make sure that:

  • Sources in the dataset you're using are imaged in all five filters of the HSC Wide Survey
  • Each image has a size of 239 by 239 pixels (or very close --- data_split_agn.py can slightly change image size)
  • All sources are real AGN (active galaxies) and the AGN PS to host galaxy flux ratio in any filter is not too high (say not greater than 4 --- subtracting an extremely luminous AGN PS using PSFGAN may leave a much larger residual and can thus confuse GaMorNet for accurate morphology classification.
  • All sources can be put into one of the three optional redshift ranges: 0<z<0.25, 0.25<z<0.5, 0.5<z<1.0. If this condition is not met, you may still apply our models, but results may not be optimal.

Once you have decided which redshift bin to use, please apply the trained multi-band PSFGAN (in that redshift bin) to remove AGN point sources. Then, in each of the five filters (or in a subset of filters you are interested), apply the trained GaMorNet (in that redshift bin) to predict morphology of the PSFGAN-recovered host galaxy in that particular filter.

Since our models are trained as linked pairs, it is expected that our trained GaMorNet can only be used on recovered host galaxy images that are generated by our trained multi-band PSFGAN. One may not apply our trained GaMorNet on inactive galaxies (for instance). Also, PSFGAN and GaMorNet from different redshift bins can not be used together.

For details of how to apply our trained models, please refer to the 'Applying trained PSFGAN and GaMorNet on real AGN' section.

Here we will highlight parameters that should be set exactly as so when using our models:

In config.py:

  • learning_rate: 0.00009 if you are using our low redshift bin models; 0.00002 if you are using our mid redshift bin models; 0.000005 if you are using our high redshift bin models.
  • if redshift == '{target dataset name}': then pixel_max_value: 45000 if you are using our low redshift bin models; 10000 if you are using our mid redshift bin models; 1000 if you are using our high redshift bin models.
  • test_epoch: 20 if you are using our low redshift bin models; 40 if you are using our mid redshift bin models; 40 if you are using our high redshift bin models.

In model.py:

If you are using our low redshift bin models:

self.image_00 = tf.slice(self.image, [0, 108, 108, 0], [1, 22, 22, conf.img_channel])
self.cond_00 = tf.slice(self.cond, [0, 108, 108, 0], [1, 22, 22, conf.img_channel])
self.g_img_00 = tf.slice(self.gen_img, [0, 108, 108, 0], [1, 22, 22, conf.img_channel])

If you are using our mid redshift bin models:

self.image_00 = tf.slice(self.image, [0, 111, 111, 0], [1, 16, 16, conf.img_channel])
self.cond_00 = tf.slice(self.cond, [0, 111, 111, 0], [1, 16, 16, conf.img_channel])
self.g_img_00 = tf.slice(self.gen_img, [0, 111, 111, 0], [1, 16, 16, conf.img_channel])

If you are using our high redshift bin models:

self.image_00 = tf.slice(self.image, [0, 113, 113, 0], [1, 12, 12, conf.img_channel])
self.cond_00 = tf.slice(self.cond, [0, 113, 113, 0], [1, 12, 12, conf.img_channel])
self.g_img_00 = tf.slice(self.gen_img, [0, 113, 113, 0], [1, 12, 12, conf.img_channel])

Also, make sure that the following blocks are exactly as so:

    def discriminator(self, img, cond, reuse):
        dim = len(img.get_shape())
        with tf.variable_scope("disc", reuse=reuse):
            image = tf.concat([img, cond], dim - 1)
            feature = conf.conv_channel_base
            h0 = lrelu(conv2d(image, feature, name="h0"))
            h1 = lrelu(batch_norm(conv2d(h0, feature * 2, name="h1"), "h1"))
            h2 = lrelu(batch_norm(conv2d(h1, feature * 4, name="h2"), "h2"))
            h3 = lrelu(batch_norm(conv2d(h2, feature * 8, name="h3"), "h3"))
            h4 = linear(tf.reshape(h3, [1, -1]), 1, "linear")
        return h4
    def generator(self, cond):
        with tf.variable_scope("gen"):
            feature = conf.conv_channel_base
            e1 = conv2d(cond, feature, name="e1")
            e2 = batch_norm(conv2d(lrelu(e1), feature * 2, name="e2"), "e2")
            e3 = batch_norm(conv2d(lrelu(e2), feature * 4, name="e3"), "e3")
            e4 = batch_norm(conv2d(lrelu(e3), feature * 8, name="e4"), "e4")
            e5 = batch_norm(conv2d(lrelu(e4), feature * 8, name="e5"), "e5")
            e6 = batch_norm(conv2d(lrelu(e5), feature * 8, name="e6"), "e6")
            e7 = batch_norm(conv2d(lrelu(e6), feature * 8, name="e7"), "e7")
            #e8 = batch_norm(conv2d(lrelu(e7), feature * 8, name="e8"), "e8")

            size = conf.img_size
            num = [0] * 9
            for i in range(1, 9):
                num[9 - i] = size
                size = (size + 1) / 2

            #d1 = deconv2d(tf.nn.relu(e8), [1, num[1], num[1], feature * 8], name="d1")
            #d1 = tf.concat([tf.nn.dropout(batch_norm(d1, "d1"), 0.5), e7], 3)
            d2 = deconv2d(tf.nn.relu(e7), [1, num[2], num[2], feature * 8], name="d2")
            d2 = tf.concat([tf.nn.dropout(batch_norm(d2, "d2"), 0.5), e6], 3)
            d3 = deconv2d(tf.nn.relu(d2), [1, num[3], num[3], feature * 8], name="d3")
            d3 = tf.concat([tf.nn.dropout(batch_norm(d3, "d3"), 0.5), e5], 3)
            d4 = deconv2d(tf.nn.relu(d3), [1, num[4], num[4], feature * 8], name="d4")
            d4 = tf.concat([batch_norm(d4, "d4"), e4], 3)
            d5 = deconv2d(tf.nn.relu(d4), [1, num[5], num[5], feature * 4], name="d5")
            d5 = tf.concat([batch_norm(d5, "d5"), e3], 3)
            d6 = deconv2d(tf.nn.relu(d5), [1, num[6], num[6], feature * 2], name="d6")
            d6 = tf.concat([batch_norm(d6, "d6"), e2], 3)
            d7 = deconv2d(tf.nn.relu(d6), [1, num[7], num[7], feature], name="d7")
            d7 = tf.concat([batch_norm(d7, "d7"), e1], 3)
            d8 = deconv2d(tf.nn.relu(d7), [1, num[8], num[8], conf.img_channel], name="d8")

            return tf.nn.tanh(d8)

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Modifications for the Stripe 82X catalog

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