I've been reproducing the experiments in Ovejero and recently have been trying to run the Paltas notebooks, and managed to test MCMC with the Population Analysis notebook. However, I cannot create my own config file or train the model with the provided example configs. In both cases, it ends in an error with kwargs_synthesis
The paltas package is installed at:
/anaconda3/envs/paltas/lib/python3.11/site-packages/paltas/
(I've tried without Anaconda with the same results)
The repository is cloned at:
/home/parlange/paltas/
Where do you initialize your jupyter lab kernel so that root_path is correct and you can access the /paltas/notebooks/ folder?
If I initialize at /home/parlange/paltas/ the root_path is:
/home/parlange/anaconda3/envs/paltas/lib/python3.11/site-packages/paltas/Configs/config_simple.py
(*removed /Examples/ because the package does not have that folder)
As requested, downloaded and extracted the COSMOS dataset into the cloned repository [COSMOS_23.5_training_sample.tar.gz].
Terminal commands. Tried both:
python generate.py /home/parlange/paltas/paltas/Configs/Examples/config_all.py example --n 100
python generate.py /home/parlange/anaconda3/envs/paltas/lib/python3.11/site-packages/paltas/Configs/config_all.py example --n 100
Output:
python generate.py /home/parlange/anaconda3/envs/paltas/lib/python3.11/site-packages/paltas/Configs/config_all.py example --n 100
/home/parlange/anaconda3/envs/paltas/lib/python3.11/site-packages/numba/core/decorators.py:262: NumbaDeprecationWarning: numba.generated_jit is deprecated. Please see the documentation at: https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-generated-jit for more information and advice on a suitable replacement.
warnings.warn(msg, NumbaDeprecationWarning)
Save folder path: example
/home/parlange/anaconda3/envs/paltas/lib/python3.11/site-packages/paltas/Sampling/sampler.py:110: UserWarning: Parameter in cross dict specified elsewhere! Will be overwritten
warnings.warn('Parameter in cross dict specified elsewhere!'
0%| | 0/100 [00:00<?, ?it/s]/home/parlange/anaconda3/envs/paltas/lib/python3.11/site-packages/paltas/Configs/config_handler.py:528: RuntimeWarning: kwargs_numerics supersampling_factor modified for drizzle
warnings.warn('kwargs_numerics supersampling_factor modified '
/home/parlange/anaconda3/envs/paltas/lib/python3.11/site-packages/paltas/Configs/config_handler.py:535: RuntimeWarning: kwargs_numerics point_source_supersampling_factor modified for drizzle
warnings.warn('kwargs_numerics point_source_supersampling_factor'
Traceback (most recent call last):
File "/home/parlange/anaconda3/envs/paltas/lib/python3.11/site-packages/paltas/generate.py", line 137, in <module>
main()
File "/home/parlange/anaconda3/envs/paltas/lib/python3.11/site-packages/paltas/generate.py", line 81, in main
image, metadata = config_handler.draw_image(new_sample=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/parlange/anaconda3/envs/paltas/lib/python3.11/site-packages/paltas/Configs/config_handler.py", line 646, in draw_image
image,metadata = self._draw_image_drizzle()
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/parlange/anaconda3/envs/paltas/lib/python3.11/site-packages/paltas/Configs/config_handler.py", line 544, in _draw_image_drizzle
image_ss, metadata = self._draw_image_standard(add_noise=False,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/parlange/anaconda3/envs/paltas/lib/python3.11/site-packages/paltas/Configs/config_handler.py", line 414, in _draw_image_standard
kwargs_model, kwargs_params = self.get_lenstronomy_models_kwargs(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/parlange/anaconda3/envs/paltas/lib/python3.11/site-packages/paltas/Configs/config_handler.py", line 190, in get_lenstronomy_models_kwargs
self.main_deflector_class.draw_main_deflector())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/parlange/anaconda3/envs/paltas/lib/python3.11/site-packages/paltas/MainDeflector/simple_deflectors.py", line 133, in draw_main_deflector
p_names = ProfileListBase._import_class(model,None,None).param_names
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: ProfileListBase._import_class() missing 1 required positional argument: 'kwargs_synthesis'
0%| | 0/100 [00:02<?, ?it/s]
Thank you for releasing your work on strong lensing.
Hi @swagnercarena
I've been reproducing the experiments in Ovejero and recently have been trying to run the Paltas notebooks, and managed to test MCMC with the Population Analysis notebook. However, I cannot create my own config file or train the model with the provided example configs. In both cases, it ends in an error with kwargs_synthesis
The paltas package is installed at: /anaconda3/envs/paltas/lib/python3.11/site-packages/paltas/
(I've tried without Anaconda with the same results)
The repository is cloned at: /home/parlange/paltas/
Where do you initialize your jupyter lab kernel so that root_path is correct and you can access the /paltas/notebooks/ folder?
If I initialize at /home/parlange/paltas/ the root_path is:
config_handler joins
config_handler = ConfigHandler(os.path.join(root_path,'/paltas/Configs/config_simple.py'))Result:
/home/parlange/anaconda3/envs/paltas/lib/python3.11/site-packages/paltas/Configs/config_simple.py(*removed /Examples/ because the package does not have that folder)As requested, downloaded and extracted the COSMOS dataset into the cloned repository [COSMOS_23.5_training_sample.tar.gz].
Terminal commands. Tried both:
python generate.py /home/parlange/paltas/paltas/Configs/Examples/config_all.py example --n 100python generate.py /home/parlange/anaconda3/envs/paltas/lib/python3.11/site-packages/paltas/Configs/config_all.py example --n 100Output:
Thank you for releasing your work on strong lensing.