rhayes777 / pyautoconf Goto Github PK

[figure]
colors=['k', 'g']

def remove_spaces_and_commas_from_colors(colors):

    colors = [color.strip(",") for color in colors]
    colors = [color.strip(" ") for color in colors]
    return list(filter(None, colors))

        json_file = path.join(
            "{}".format(path.dirname(path.realpath(__file__))), "files", "tracer.json"
        )

        g0 = al.Galaxy(
            redshift=0.5, mass_profile=al.mp.SphIsothermal(einstein_radius=1.0)
        )
        g1 = al.Galaxy(redshift=1.0)

        tracer = al.Tracer.from_galaxies(galaxies=[g0, g1])

>       tracer.output_to_json(file_path=json_file)

test_autolens/lens/test_ray_tracing.py:2505:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../PyAutoConf/autoconf/dictable.py:168: in output_to_json
    json.dump(self.dict(), f, indent=4)
autolens/lens/ray_tracing.py:134: in dict
    tracer_dict = super().dict()
../PyAutoConf/autoconf/dictable.py:82: in dict
    return as_dict(self)
../PyAutoConf/autoconf/dictable.py:67: in as_dict
    **{
../PyAutoConf/autoconf/dictable.py:68: in <dictcomp>
    key: as_dict(value)
../PyAutoConf/autoconf/dictable.py:67: in as_dict
    **{
../PyAutoConf/autoconf/dictable.py:68: in <dictcomp>
    key: as_dict(value)
../PyAutoConf/autoconf/dictable.py:42: in as_dict
    return nd_array_as_dict(obj)
../PyAutoConf/autoconf/dictable.py:17: in nd_array_as_dict
    "array": obj.tolist(),
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = <Quantity 67.74 km / (Mpc s)>

    def tolist(self):
>       raise NotImplementedError("cannot make a list of Quantities.  Get "
                                  "list of values with q.value.tolist()")
E       NotImplementedError: cannot make a list of Quantities.  Get list of values with q.value.tolist()

/home/jammy/venvs/PyAuto/lib/python3.8/site-packages/astropy/units/quantity.py:1323: NotImplementedError

[figure]
figsize=(7, 7)

      if isinstance(self.kwargs["figsize"], str):
          self.kwargs["figsize"] = tuple(
              map(int, self.kwargs["figsize"][1:-1].split(","))
          )

you should not open the config files in append mode, this asks for writing permission, which the pipeline (fortunately) doesn't have.
In fact, just completely removing the access mode parameter will default to "r" (https://docs.python.org/3/library/functions.html#open), which will work fine.

Furthermore, in output_to_json(), you also should remove the "+" in with 'open(file_path, "w+") as f:', because as is, if the file already exists, the new json data will be appended at the end of the file and the file won't be readable anymore by standard json. So it's better to use "w" alone for the access mode, so that a possibly already existing file is overwritten and the created one is usable.

    @classmethod
    def from_json(cls, file_path: str) -> "Dictable":
        """
        Load the dictable object to a .json file, whereby all attributes are converted from the .json file's dictionary
        representation to create the instance of the object

        A json file of the instance can be created from the .json file via the `output_to_json` method.

        Parameters
        ----------
        file_path
            The path to the .json file that the dictionary representation of the object is loaded from.
        """
        with open(file_path, "r+") as f:
            cls_dict = json.load(f)

        return cls.from_dict(cls_dict=cls_dict)

    def output_to_json(self, file_path: str):
        """
        Output the dictable object to a .json file, whereby all attributes are converted to a dictionary representation
        first.

        An instane of the object can be created from the .json file via the `from_json` method.

        Parameters
        ----------
        file_path
            The path to the .json file that the dictionary representation of the object is written too.
        """
        with open(file_path, "w+") as f:
            json.dump(self.dict(), f, indent=4)

clocker = ac.Clocker2D(
    iterations=5,
    parallel_express=5,
    serial_express=5
)

clocker.output_to_json(file_path="cti_clocker.json")

{
    "type": "VIS_CTI_Autocti.VIS_CTI_Clocker.two_d.Clocker2D",
    "iterations": 5,
    "parallel_roe": {
        "type": "SHE_ArCTICPy.roe.ROE",
        "dwell_times": {
            "type": "numpy.ndarray"
        },
        "empty_traps_between_columns": true,
        "empty_traps_for_first_transfers": false,
        "force_release_away_from_readout": true,
        "use_integer_express_matrix": false
    },
    "parallel_express": 5,
    "parallel_offset": 0,
    "parallel_window_start": 0,
    "parallel_window_stop": -1,
    "parallel_poisson_traps": false,
    "parallel_fast_pixels": null,
    "serial_roe": {
        "type": "SHE_ArCTICPy.roe.ROE",
        "dwell_times": {
            "type": "numpy.ndarray"
        },
        "empty_traps_between_columns": true,
        "empty_traps_for_first_transfers": false,
        "force_release_away_from_readout": true,
        "use_integer_express_matrix": false
    },
    "serial_express": 5,
    "serial_offset": 0,
    "serial_window_start": 0,
    "serial_window_stop": -1,
    "serial_fast_pixels": null,
    "verbosity": 0,
    "poisson_seed": -1,
    "euclid_orientation_hack": false
}

        "type": "SHE_ArCTICPy.roe.ROE",
        "dwell_times": {
            "type": "numpy.ndarray"
        },

[superscript]
ModelComponent0=M0
ModelComponent1=M1
Gaussian=g
Exponential=e

  superscript:
    exponential: e
    gaussian: g
    modelcomponent0: M0
    modelcomponent1: M1