refactor: replace os.path with pathlib in workspace scripts

projects/cosmology/example_1_intro.py

@@ -79,7 +79,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from scipy import signal
-from os import path
+from pathlib import Path
 
 """
 __Plot__
@@ -124,12 +124,12 @@ def plot_grid(grid, title=None):
 In the strong lens image and noise map below, you can see this has already been performed, with the edge regions
 blank.
 """
-dataset_path = path.join("projects", "cosmology", "dataset")
+dataset_path = Path("projects") / "cosmology" / "dataset"
 
-data = np.load(file=path.join(dataset_path, "data.npy"))
+data = np.load(file=Path(dataset_path) / "data.npy")
 plot_array(array=data, title="Image of Strong Lens SDSSJ2303+1422")
 
-noise_map = np.load(file=path.join(dataset_path, "noise_map.npy"))
+noise_map = np.load(file=Path(dataset_path) / "noise_map.npy")
 plot_array(array=noise_map, title="Noise Map of Strong Lens SDSSJ2303+1422")
 
 """
@@ -150,7 +150,7 @@ def plot_grid(grid, title=None):
 model data. This is an example of how an `Analysis` class may be extended to include additional steps in the model
 fitting procedure.
 """
-psf = np.load(file=path.join(dataset_path, "psf.npy"))
+psf = np.load(file=Path(dataset_path) / "psf.npy")
 plot_array(array=psf, title="Point Spread Function of Strong Lens SDSSJ2303+1422")
 
 
@@ -166,7 +166,7 @@ def plot_grid(grid, title=None):
 This grid only contains (y,x) coordinates within the cricular mask that was applied to the data, as we only need to
 perform ray-tracing within this region.
 """
-grid = np.load(file=path.join(dataset_path, "grid.npy"))
+grid = np.load(file=Path(dataset_path) / "grid.npy")
 
 plot_grid(
     grid=grid,

projects/cosmology/example_2_multi_level_model.py

@@ -17,11 +17,10 @@
 """
 
 import os
-from os import path
 from autoconf import conf
 
 cwd = os.getcwd()
-config_path = path.join(cwd, "projects", "cosmology", "config")
+config_path = Path(cwd) / "projects" / "cosmology" / "config"
 conf.instance.push(new_path=config_path)
 
 # %matplotlib inline
@@ -34,6 +33,7 @@
 import src as cosmo
 import matplotlib.pyplot as plt
 import numpy as np
+from pathlib import Path
 
 """
 __Plot__
@@ -62,18 +62,18 @@ def plot_grid(grid, title=None):
 
 Now lets load and plot Hubble Space Telescope imaging data of the strong gravitational lens SDSSJ2303+1422.
 """
-dataset_path = path.join("projects", "cosmology", "dataset")
+dataset_path = Path("projects") / "cosmology" / "dataset"
 
-data = np.load(file=path.join(dataset_path, "data.npy"))
+data = np.load(file=Path(dataset_path) / "data.npy")
 plot_array(array=data, title="Image of Strong Lens SDSSJ2303+1422")
 
-noise_map = np.load(file=path.join(dataset_path, "noise_map.npy"))
+noise_map = np.load(file=Path(dataset_path) / "noise_map.npy")
 plot_array(array=noise_map, title="Noise Map of Strong Lens SDSSJ2303+1422")
 
-psf = np.load(file=path.join(dataset_path, "psf.npy"))
+psf = np.load(file=Path(dataset_path) / "psf.npy")
 plot_array(array=psf, title="Point Spread Function of Strong Lens SDSSJ2303+1422")
 
-grid = np.load(file=path.join(dataset_path, "grid.npy"))
+grid = np.load(file=Path(dataset_path) / "grid.npy")
 
 plot_grid(
     grid=grid,
@@ -257,7 +257,7 @@ def plot_grid(grid, title=None):
 """
 
 search = af.DynestyStatic(
-    path_prefix=path.join("projects", "cosmology"),
+    path_prefix=Path("projects") / "cosmology",
     name="multi_level",
     nlive=50,
     iterations_per_full_update=2500,

scripts/howtofit/chapter_1_introduction/tutorial_8_astronomy_example.py

@@ -61,9 +61,8 @@
 - **Chapter Wrap Up**: Summarize the completion of Chapter 1 and its applications to real astronomy.
 """
 
-# from autoconf import setup_notebook; setup_notebook()
+# from autoconf import setup_notebook; setup_notebook()
 
-from os import path
 import numpy as np
 import matplotlib.pyplot as plt
 from scipy import signal
@@ -109,15 +108,15 @@ def plot_grid(grid, title=None):
 The noise-map has a few strange off-centre features which are an artefact of the telescope. Don't worry about these
 features.
 """
-dataset_path = path.join("dataset", "howtofit", "chapter_1", "astro", "simple")
+dataset_path = Path("dataset") / "howtofit" / "chapter_1" / "astro" / "simple"
 
 """
 __Dataset Auto-Simulation__
 
 If the dataset does not already exist on your system, it will be created by running the corresponding
 simulator script. This ensures that all example scripts can be run without manually simulating data first.
 """
-if not path.exists(dataset_path):
+if not Path(dataset_path).exists():
     import subprocess
     import sys
 
@@ -126,10 +125,10 @@ def plot_grid(grid, title=None):
         check=True,
     )
 
-data = np.load(file=path.join(dataset_path, "data.npy"))
+data = np.load(file=Path(dataset_path) / "data.npy")
 plot_array(array=data, title="Image of Galaxy")
 
-noise_map = np.load(file=path.join(dataset_path, "noise_map.npy"))
+noise_map = np.load(file=Path(dataset_path) / "noise_map.npy")
 plot_array(array=noise_map, title="Noise Map of Galaxy")
 
 """
@@ -143,7 +142,7 @@ def plot_grid(grid, title=None):
 We load and plot the mask below to show you how it is applied to the data, and we will use it in 
 the `log_likelihood_function` below to ensure these regions are not fitted.
 """
-mask = np.load(file=path.join(dataset_path, "mask.npy"))
+mask = np.load(file=Path(dataset_path) / "mask.npy")
 plot_array(array=mask, title="Mask of Galaxy")
 
 """
@@ -160,7 +159,7 @@ def plot_grid(grid, title=None):
 When fitting the data and in the `log_likelihood_function` below, the PSF is used to create the model data. This 
 demonstrates how an `Analysis` class can be extended to include additional steps in the model fitting process.
 """
-psf = np.load(file=path.join(dataset_path, "psf.npy"))
+psf = np.load(file=Path(dataset_path) / "psf.npy")
 plot_array(array=psf, title="Point Spread Function of Galaxy ?")
 
 """
@@ -174,7 +173,7 @@ def plot_grid(grid, title=None):
 This grid includes only (y,x) coordinates within the circular mask applied to the data, as we only need to perform 
 calculations within this masked region.
 """
-grid = np.load(file=path.join(dataset_path, "grid.npy"))
+grid = np.load(file=Path(dataset_path) / "grid.npy")
 
 plot_grid(
     grid=grid,
@@ -779,6 +778,7 @@ def model_data_from_instance(self, instance):
 
 This illustrates why we perform model-fitting, we can take complex data and infer simple, interpretable properties
 from it which provide insight into the physical processes generating the data. This is the core goal of the scientific
+from pathlib import Path
 method and the use of models to explain observations.
 """
 print(result.info)

searches/pyswarms/example.py

@@ -12,18 +12,18 @@
 autofit's base classes and can be used as drop-in replacements.
 """
 import numpy as np
-from os import path
 
 from autoconf import conf
 
 # Register the config directory shipped with this repo so that
 # PySwarmsGlobal/Local can find their YAML defaults.
 workspace_path = path.dirname(path.dirname(path.dirname(path.abspath(__file__))))
-conf.instance.push(new_path=path.join(workspace_path, "config"))
+conf.instance.push(new_path=Path(workspace_path) / "config")
 
 from searches.pyswarms.globe import PySwarmsGlobal
 
 import autofit as af
+from pathlib import Path
 
 # --- Define a simple 1D Gaussian model ---
 

searches/ultranest/example.py

@@ -17,18 +17,18 @@
 classes and can be used as a drop-in replacement.
 """
 import numpy as np
-from os import path
 
 from autoconf import conf
 
 # Register the config directory shipped with this repo so that
 # UltraNest can find its YAML defaults.
 workspace_path = path.dirname(path.dirname(path.dirname(path.abspath(__file__))))
-conf.instance.push(new_path=path.join(workspace_path, "config"))
+conf.instance.push(new_path=Path(workspace_path) / "config")
 
 from searches.ultranest.search import UltraNest
 
 import autofit as af
+from pathlib import Path
 
 # --- Define a simple 1D Gaussian model ---
 

searches/ultranest/search.py

@@ -238,7 +238,7 @@ def prior_transform(cube):
         log_dir = self.paths.search_internal_path
 
         try:
-            checkpoint_exists = os.path.exists(log_dir / "chains")
+            checkpoint_exists = Path(log_dir / "chains").exists()
         except TypeError:
             checkpoint_exists = False