installation/min-python-310

.github/workflows/ci.yml

@@ -32,9 +32,6 @@ jobs:
           # - macos-latest
           # - windows-latest
         include:
-          - name: python 3.9
-            toxenv: py39
-            python-version: 3.9
 
           - name: python 3.10
             toxenv: py310
@@ -44,6 +41,10 @@ jobs:
             toxenv: py311
             python-version: 3.11
 
+          - name: python 3.12
+            toxenv: py312
+            python-version: 3.12
+
     steps:
       - name: Checkout code
         uses: actions/checkout@v4

CHANGES.rst

@@ -1,3 +1,14 @@
+1.1.1 (unreleased)
+==================
+
+installation
+------------
+- Set minimum python version to 3.10, include py312 in test suite, replace deprecated pkg_resources with importlib.metadata [#73]
+- Set minimum tensorflow version to 2.16.1 [#73]
+- Pre-trained neural networks updated for compatibility with Keras 3 [#73]
+- Dockerfile images now use debian bookworm [#73]
+
+
 1.1.0 (2024-04-09)
 ==================
 

MANIFEST.IN

@@ -8,6 +8,3 @@ include *.md
 
 # Include the license file
 include LICENSE
-
-# Include setup.py
-include setup.py

README.md

@@ -1,8 +1,11 @@
 # spacekit
 
-[![Powered by Astropy](http://img.shields.io/badge/powered%20by-AstroPy-orange.svg?style=flat)](http://www.astropy.org)
 ![GitHub license](https://img.shields.io/github/license/spacetelescope/spacekit?color=black)
 [![CodeFactor](https://www.codefactor.io/repository/github/spacetelescope/spacekit/badge)](https://www.codefactor.io/repository/github/spacetelescope/spacekit)
+[![Build Status](https://github.com/spacetelescope/spacekit/workflows/ci/badge.svg?branch=main)](https://github.com/spacetelescope/spacekit/actions)
+[![Powered by STScI Badge](https://img.shields.io/badge/powered%20by-STScI-blue.svg?colorA=707170&colorB=3e8ddd&style=flat)](http://www.stsci.edu)
+[![Powered by Astropy](http://img.shields.io/badge/powered%20by-AstroPy-orange.svg?style=flat)](http://www.astropy.org)
+[![DOI](https://zenodo.org/badge/11169350.svg)](https://zenodo.org/badge/latestdoi/11169350)
 
 Astronomical Data Science and Machine Learning Toolkit
 

docker/images/dashboard_image/Dockerfile

@@ -1,4 +1,4 @@
-FROM debian:bullseye-slim
+FROM debian:bookworm-slim
 ENV DEV_HOME=/home/developer
 
 RUN apt update && \

pyproject.toml

@@ -1,6 +1,6 @@
 [build-system]
 requires = [
-    "setuptools>=42",
+    "setuptools>=61.2",
     "wheel"
 ]
 build-backend = "setuptools.build_meta"

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = spacekit
-version = 1.1.0
+version = 1.1.1rc1
 author = Ru Keïn
 author_email = [email protected]
 license = MIT
@@ -16,27 +16,28 @@ project_urls =
     Documentation = https://spacekit.readthedocs.io
     Tracker = https://github.com/spacetelescope/spacekit/issues
 classifiers=
-    Programming Language :: Python :: 3
+    Intended Audience :: Science/Research
+    Topic :: Scientific/Engineering :: Astronomy
     License :: OSI Approved :: MIT License
+    Programming Language :: Python :: 3
     Operating System :: OS Independent
-    Development Status :: 3 - Alpha
-    Programming Language :: Python :: 3.9
     Programming Language :: Python :: 3.10
     Programming Language :: Python :: 3.11
+    Programming Language :: Python :: 3.12
 
 [options]
 zip_safe = False
 packages = find:
 include_package_data = True
-python_requires = >=3.7
+python_requires = >=3.10
 setup_requires = 
-    setuptools >=42
+    setuptools >=61.2
 
 install_requires =
-    tensorflow<2.16
+    tensorflow>=2.16.1
     astropy
     boto3
-    numpy>=1.19
+    numpy>=1.22
     pandas
     scikit-learn>=1.3.2
 

spacekit/__init__.py

@@ -1,8 +1,8 @@
-from pkg_resources import get_distribution, DistributionNotFound
+import importlib.metadata
 
 try:
-    __version__ = get_distribution(__name__).version
-except DistributionNotFound:
+    __version__ = importlib.metadata.version(__name__)
+except importlib.metadata.PackageNotFoundError:
     pass  # package is not installed
 
 # def reload(mod):

spacekit/builder/architect.py

@@ -21,7 +21,6 @@
     MaxPool3D,
     GlobalAveragePooling3D,
 )
-
 from tensorflow.keras.metrics import RootMeanSquaredError as RMSE
 from spacekit.generator.augment import augment_data, augment_image
 from spacekit.analyzer.track import stopwatch
@@ -86,7 +85,7 @@ def load_saved_model(
         """Load saved keras model from local disk (located at the ``model_path`` attribute) or a pre-trained model from 
         spacekit.skopes.trained_networks (if ``model_path`` attribute is None). Example for ``compile_params``:
         ``dict(loss="binary_crossentropy",metrics=["accuracy"],\
-        optimizer=Adam(learning_rate=optimizers.schedules.ExponentialDecay(lr=1e-4, \
+        optimizer=Adam(learning_rate=optimizers.schedules.ExponentialDecay(1e-4, \
         decay_steps=100000, decay_rate=0.96, staircase=True)))``
 
         Parameters
@@ -328,7 +327,7 @@ def set_callbacks(self, patience=15):
         list
             [callbacks.ModelCheckpoint, callbacks.EarlyStopping]
         """
-        model_name = str(self.model.name_scope().rstrip("/"))
+        model_name = str(self.model.name)
         checkpoint_cb = callbacks.ModelCheckpoint(
             f"{model_name}_checkpoint.h5", save_best_only=True
         )
@@ -362,7 +361,7 @@ def save_model(self, weights=True, output_path=".", keras_archive=True, parent_d
             save model using new (preferred) keras archive format, by default True
         """
         if self.name is None:
-            self.name = str(self.model.name_scope().rstrip("/"))
+            self.name = str(self.model.name)
             datestamp = dt.datetime.now().isoformat().split("T")[0]
             model_name = f"{self.name}_{datestamp}"
         else:
@@ -440,7 +439,7 @@ def batch_fit(self):
         tf.keras.model.history
             Keras training history
         """
-        model_name = str(self.model.name_scope().rstrip("/").upper())
+        model_name = str(self.model.name).upper()
         self.log.info("FITTING MODEL...")
         validation_data = (
             (self.X_test, self.y_test) if self.X_test is not None else None
@@ -483,7 +482,7 @@ def fit(self, params=None):
         """
         if params is not None:
             self.fit_params(**params)
-        model_name = str(self.model.name_scope().rstrip("/").upper())
+        model_name = str(self.model.name).upper()
         self.log.info("FITTING MODEL...")
         validation_data = (
             (self.X_test, self.y_test) if self.X_test is not None else None
@@ -983,7 +982,7 @@ def batch(self):
             for i in range(self.batch_size):
                 xa[i] = augment_data(xa[i])
                 xb[i] = augment_image(xb[i])
-            yield [xa, xb], yb
+            yield (xa, xb), yb
 
 
 class BuilderCNN2D(Builder):

spacekit/datasets/meta.py

@@ -1,12 +1,12 @@
 calcloud = {
-    "uri": "https://zenodo.org/record/10895592/files",
+    "uri": "https://zenodo.org/record/11169350/files",
     "data": {
         "2022-02-14": {
             "fname": "hst_cal_std_2022-02-14.zip?download=1",
-            "hash": "e9375c65feb413a660cb737a9a8a3a73",
+            "hash": "3ca674b0d81e98e319f6e80d78ce8db8",
             "desc": "data, model and training results",
             "key": "2022-02-14-1644848448",
-            "size": "11.4MB",
+            "size": "9.7MB",
         },
         "2021-11-04": {
             "fname": "hst_cal_std_2021-11-04.zip?download=1",
@@ -32,29 +32,29 @@
     },
     "model": {
         "fname": "hst_cal.zip",
-        "hash": "ffe7d00504ad5d6ebc79488abe612474",
+        "hash": "acb07516b9385ce7182577ef7216ae75",
         "desc": "hst calcloud resource prediction models",
         "key": "hst_cal",
-        "size": "385.42kB",
+        "size": "385.47kB",
     },
 }
 
 svm = {
-    "uri": "https://zenodo.org/record/10895592/files",
+    "uri": "https://zenodo.org/record/11169350/files",
     "data": {
         "2022-02-14": {
             "fname": "hst_drz_svm_2022-02-14.zip?download=1",
-            "hash": "3d6ade5510cc7aad4c2c45329c0751fe",
+            "hash": "2660e4457b19ce01b625d7a668d04e89",
             "desc": "latest model, data and training results",
             "key": "2022-02-14-1644850390",
-            "size": "18MB",
+            "size": "5MB",
         },
         "2022-01-30": {
             "fname": "hst_drz_svm_2022-01-30.zip?download=1",
-            "hash": "9ad9b95c20b82877f1ea7b0fa64b10a1",
+            "hash": "370fdbdb8d2b45b2ded998dbcc6927a5",
             "desc": "updated model, data and training results",
             "key": "2022-01-30-1643523529",
-            "size": "18MB",
+            "size": "5MB",
         },
         "2022-01-16": {
             "fname": "hst_drz_svm_2022-01-16.zip?download=1",
@@ -80,7 +80,7 @@
     },
     "model": {
         "fname": "svm_align.zip",
-        "hash": "af3b019cc3079cb29fd01bae58e9560d",
+        "hash": "c98492b1e729407c72a69d2c739232be",
         "desc": "hst svm alignment prediction models",
         "key": "svm_align",
         "size": "4.92MB",
@@ -89,7 +89,7 @@
 
 
 jwst_cal = {
-    "uri": "https://zenodo.org/record/10895592/files",
+    "uri": "https://zenodo.org/record/11169350/files",
     "data": {
         "2023-08-02": {
             "fname": "",
@@ -101,16 +101,16 @@
     },
     "model": {
         "fname": "jwst_cal.zip",
-        "hash": "f3b6dbcc3fc92a6f806f5b5464e68c02",
+        "hash": "c2c6764bcf1092e993d061be43075e3d",
         "desc": "JWST Calibration Processing Resource Prediction Models",
         "key": "jwst_cal",
-        "size": "1.5MB",
+        "size": "526.89kB",
     },
 }
 
 
 k2 = {
-    "uri": "https://zenodo.org/record/10895592/files",
+    "uri": "https://zenodo.org/record/11169350/files",
     "data": {
         "test": {
             "fname": "k2-exo-flux-ts-test.csv.zip?download=1",
@@ -142,19 +142,19 @@
     "calcloud": {
         "basepath": "spacekit.builder.trained_networks",
         "fname": "hst_cal.zip",
-        "hash": "f3422c98d93520898e10d068a1e434dd5caf40e632760687df377c1b2bccff57",
-        "size": "385.42kB",
+        "hash": "3dcfd4760154dee890f7943e237e679a43eef82d3c0838ff62ecf17e4c3702fb",
+        "size": "385.47kB",
     },
     "svm": {
         "basepath": "spacekit.builder.trained_networks",
         "fname": "svm_align.zip",
-        "hash": "577a87fab157fdadc657af0dcd67b55af2d89d855d5597bd96671b20ff135636",
+        "hash": "cda062ba768374802e4e8b919c0dcce79ce3b72e29004cc09601a67da454b008",
         "size": "4.92MB",
     },
     "jwst_cal": {
         "basepath": "spacekit.builder.trained_networks",
         "fname": "jwst_cal.zip",
-        "hash": "b61d0281fd149e2b0ffcded81ba54b24b479c5b7191072be2bfb59464d779187",
-        "size": "1.5MB",
+        "hash": "31142d47e5fc31650b49edb44dfa3db5f1d09380864b9560e2dded456723dfe6",
+        "size": "526.89kB",
     },
 }

tests/data_plugin.py

@@ -43,7 +43,7 @@ def pytest_configure(config):
             tmp_path_factory = TempPathFactory(
                 config.option.basetemp, trace=config.trace.get("tmpdir"), _ispytest=True
             )
-        data_uri = "https://zenodo.org/record/10895592/files/pytest_data.tgz?download=1"
+        data_uri = "https://zenodo.org/record/11169350/files/pytest_data.tgz?download=1"
         basepath = tmp_path_factory.getbasetemp()
         target_path = os.path.join(basepath, "pytest_data.tgz")
         with open(target_path, "wb") as f:

tox.ini

@@ -1,7 +1,7 @@
 [tox]
 isolated_build = True
 envlist =
-    py3{9,10,11}
+    py3{10,11,12}
     check-{style,security}
     pep517
     #test{,-oldestdeps,-devdeps}