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{"receipt_id":"agent1-7","seq":7,"branch_id":"agent1","kind":"command","cmd":"ls -la /PyAutoFit/autofit/non_linear/grid/","path":null,"exit_code":0,"stdout_tail":"total 8\ndrwxrwxrwx 1 root root  118 Apr 30 15:12 .\ndrwxrwxrwx 1 root root  210 Apr 30 15:12 ..\n-rw-r--r-- 1 root root    0 Feb 21  2026 __init__.py\n-rw-r--r-- 1 root root 2371 Feb 21  2026 grid_list.py\ndrwxrwxrwx 1 root root   86 Apr 30 15:12 grid_search\ndrwxrwxrwx 1 root root   52 Apr 30 15:12 sensitivity\n-rw-r--r-- 1 root root 1735 Feb 21  2026 simple_grid.py\n","stderr_tail":"","stdout_sha256":"0658e53242657da12b89fbf2fe45141f4fda19a0ead451f28ec4c5d0c843bec0","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-7.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:43:42.127630Z","duration_s":0.003625,"timed_out":false,"image_before":"e8e7a1c2-157e-4c8b-9221-12832ae6677c","image_after":"862fca3e-51d1-4384-9460-3b104c6f6ec4","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-8","seq":8,"branch_id":"agent1","kind":"command","cmd":"ls -la /PyAutoFit/autofit/non_linear/grid/sensitivity/","path":null,"exit_code":0,"stdout_tail":"total 24\ndrwxrwxrwx 1 root root    52 Apr 30 15:12 .\ndrwxrwxrwx 1 root root   118 Apr 30 15:12 ..\n-rw-r--r-- 1 root root 11209 Feb 21  2026 __init__.py\n-rw-r--r-- 1 root root  4403 Feb 21  2026 job.py\n-rw-r--r-- 1 root root  3397 Feb 21  2026 result.py\n","stderr_tail":"","stdout_sha256":"98725f4369464f5fe1b02d28e2d5786b57eabe000e64764c21b8dd31ad34130e","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-8.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:43:44.451251Z","duration_s":0.003577,"timed_out":false,"image_before":"862fca3e-51d1-4384-9460-3b104c6f6ec4","image_after":"1486e3aa-8f90-4ebe-9092-e9a382478b0f","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-9","seq":9,"branch_id":"agent1","kind":"command","cmd":"find /PyAutoFit -name \"*.py\" | xargs grep -l \"class Sensitivity\" 2>/dev/null","path":null,"exit_code":0,"stdout_tail":"/PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py\n/PyAutoFit/autofit/non_linear/grid/sensitivity/result.py\n","stderr_tail":"","stdout_sha256":"185a65e242d31c787949195c49e440435185ab3d7c134892184e75a3a3e73081","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-9.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:43:46.719885Z","duration_s":0.023459999999999998,"timed_out":false,"image_before":"1486e3aa-8f90-4ebe-9092-e9a382478b0f","image_after":"4de9194c-79c0-4796-be55-1866c1961548","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-10","seq":10,"branch_id":"agent1","kind":"command","cmd":"head -100 /PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py","path":null,"exit_code":0,"stdout_tail":"import csv\r\nimport logging\r\nimport os\r\nfrom copy import copy\r\nfrom pathlib import Path\r\nfrom typing import List, Generator, Callable, ClassVar, Optional, Union, Tuple\r\n\r\nfrom autoconf.dictable import to_dict\r\nfrom autofit.mapper.model import ModelInstance\r\nfrom autofit.mapper.prior_model.abstract import AbstractPriorModel\r\nfrom autofit.non_linear.grid.grid_search import make_lists, Sequential\r\nfrom autofit.non_linear.grid.sensitivity.job import Job\r\nfrom autofit.non_linear.grid.sensitivity.job import JobResult\r\nfrom autofit.non_linear.grid.sensitivity.result import SensitivityResult\r\nfrom autofit.non_linear.parallel import Process\r\nfrom autofit.text.text_util import padding\r\n\r\nclass Sensitivity:\r\n    def __init__(\r\n        self,\r\n        base_model: AbstractPriorModel,\r\n        perturb_model: AbstractPriorModel,\r\n        simulation_instance,\r\n        paths,\r\n        simulate_cls: Callable,\r\n        base_fit_cls: Callable,\r\n        perturb_fit_cls: Callable,\r\n        job_cls: ClassVar = Job,\r\n        perturb_model_prior_func : Optional[Callable] = None,\r\n        number_of_steps: Union[Tuple[int, ...], int] = 4,\r\n        number_of_cores: int = 2,\r\n        limit_scale: int = 1,\r\n    ):\r\n        \"\"\"\r\n        Perform sensitivity mapping to evaluate whether a perturbation\r\n        can be detected if it occurs in different parts of an image.\r\n\r\n        For a range from 0 to 1 with step_size, for each dimension of the\r\n        perturb_model, a perturbation is created and used in conjunction\r\n        with the instance to create an image.\r\n\r\n        For each of these images, a fit is run with just the model and with both\r\n        the model and perturb_model to compare how much better the image\r\n        can be fit if the perturbation is included.\r\n\r\n        Parameters\r\n        ----------\r\n        base_model\r\n            A model that fits the instance well\r\n        perturb_model\r\n            A model which provides a perturbations to be applied to the instance\r\n            before creating images\r\n        simulation_instance\r\n            An instance of a model to which perturbations are applied prior to\r\n            images being generated\r\n        simulate_cls\r\n            A class which simulates images from each perturb instance that sensitivity mapping is performed on.\r\n        base_fit_cls\r\n            The class which fits the base model to each simulated dataset of the sensitivity map.\r\n        perturb_fit_cls\r\n            The class which fits the perturb model to each simulated dataset of the sensitivity map.\r\n        number_of_steps\r\n            The number of steps for each dimension of the sensitivity grid. If input as a float the dimensions are\r\n            all that value. If input as a tuple of length the number of dimensions, each tuple value is the number of\r\n            steps in that dimension.\r\n        number_of_cores\r\n            How many cores does this computer have?\r\n        limit_scale\r\n            Scales the priors for each perturbation model.\r\n                A scale of 1 means priors have limits the same size as the grid square.\r\n                A scale of 2 means priors have limits larger than the grid square with\r\n                    width twice a grid square.\r\n                A scale of 0.5 means priors have limits smaller than the grid square\r\n                    with width half a grid square.\r\n        \"\"\"\r\n        self.logger = logging.getLogger(f\"Sensitivity ({paths.name})\")\r\n\r\n        self.logger.info(\"Creating\")\r\n\r\n        self.instance = simulation_instance\r\n        self.model = base_model\r\n        self.perturb_model = perturb_model\r\n\r\n        self.paths = paths\r\n\r\n        self.simulate_cls = simulate_cls\r\n        self.base_fit_cls = base_fit_cls\r\n        self.perturb_fit_cls = perturb_fit_cls\r\n\r\n        self.perturb_model_prior_func = perturb_model_prior_func\r\n\r\n        self.job_cls = job_cls\r\n\r\n        self.number_of_steps = number_of_steps\r\n        self.number_of_cores = number_of_cores\r\n\r\n        self.limit_scale = limit_scale\r\n\r\n    def run(self) -> SensitivityResult:\r\n        \"\"\"\r\n","stderr_tail":"","stdout_sha256":"7f3597f09c967455a383ed924fcca1e3dd8d40a31a6e8596061d102da97a9a90","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-10.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:43:50.136115Z","duration_s":0.002874,"timed_out":false,"image_before":"4de9194c-79c0-4796-be55-1866c1961548","image_after":"7f3385fe-a18c-4708-aa17-83d67a292b0f","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-11","seq":11,"branch_id":"agent1","kind":"command","cmd":"wc -l /PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py","path":null,"exit_code":0,"stdout_tail":"300 /PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py\n","stderr_tail":"","stdout_sha256":"28ef1a2077b5488cb2168fbc49a230fd004888d87b4914a20a4984c3ed61a959","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-11.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:43:52.095472Z","duration_s":0.002641,"timed_out":false,"image_before":"7f3385fe-a18c-4708-aa17-83d67a292b0f","image_after":"70aa53fe-d945-4acc-9127-48dc3b1caf5f","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-12","seq":12,"branch_id":"agent1","kind":"command","cmd":"sed -n '280,310p' /PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py","path":null,"exit_code":0,"stdout_tail":"                    perturb_instance=perturb_instance,\r\n                    perturb_model=perturb_model\r\n                )\r\n\r\n            simulate_instance = copy(self.instance)\r\n            simulate_instance.perturb = perturb_instance\r\n\r\n            paths = self.paths.for_sub_analysis(\r\n                label,\r\n            )\r\n\r\n            yield self.job_cls(\r\n                simulate_instance=simulate_instance,\r\n                model=self.model,\r\n                perturb_model=perturb_model,\r\n                base_instance=self.instance,\r\n                simulate_cls=self.simulate_cls,\r\n                base_fit_cls=self.base_fit_cls,\r\n                perturb_fit_cls=self.perturb_fit_cls,\r\n                paths=paths,\r\n                number=number,\r\n            )","stderr_tail":"","stdout_sha256":"1941da257560b386877de154b0a739f2cc04a43e37b64555f28116c9b739ad75","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-12.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:43:55.964963Z","duration_s":0.00296,"timed_out":false,"image_before":"70aa53fe-d945-4acc-9127-48dc3b1caf5f","image_after":"03bd5403-d931-4c3b-bad8-512368c14b8f","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-13","seq":13,"branch_id":"agent1","kind":"command","cmd":"sed -n '200,280p' /PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py","path":null,"exit_code":0,"stdout_tail":"    @property\r\n    def _physical_values(self) -> List[List[float]]:\r\n        \"\"\"\r\n        Lists of physical values for each grid square\r\n        \"\"\"\r\n        return [\r\n            [\r\n                prior.value_for(unit_value)\r\n                for prior, unit_value in zip(\r\n                    self.perturb_model.priors_ordered_by_id, unit_values\r\n                )\r\n            ]\r\n            for unit_values in self._lists\r\n        ]\r\n\r\n    @property\r\n    def _headers(self) -> Generator[str, None, None]:\r\n        \"\"\"\r\n        A name for each of the perturb priors\r\n        \"\"\"\r\n        for path, _ in self.perturb_model.prior_tuples:\r\n            yield path\r\n\r\n    @property\r\n    def _labels(self) -> Generator[str, None, None]:\r\n        \"\"\"\r\n        One label for each perturbation, used to distinguish\r\n        fits for each perturbation by placing them in separate\r\n        directories.\r\n        \"\"\"\r\n        for list_ in self._lists:\r\n            strings = list()\r\n            for value, prior_tuple in zip(list_, self.perturb_model.prior_tuples):\r\n                path, prior = prior_tuple\r\n                value = prior.value_for(value)\r\n                strings.append(f\"{path}_{value}\")\r\n            yield \"_\".join(strings)\r\n\r\n    @property\r\n    def _perturb_instances(self) -> Generator[ModelInstance, None, None]:\r\n        \"\"\"\r\n        A list of instances each of which defines a perturbation to\r\n        be applied to the image.\r\n        \"\"\"\r\n        for list_ in self._lists:\r\n            yield self.perturb_model.instance_from_unit_vector(list_)\r\n\r\n    @property\r\n    def _perturb_models(self) -> Generator[AbstractPriorModel, None, None]:\r\n        \"\"\"\r\n        A list of models representing a perturbation at each grid square.\r\n\r\n        By default models have priors with limits at the edges of a grid square.\r\n        These limits can be scaled using the limit_scale variable. If the variable\r\n        is 2 then the priors will have width twice the step size.\r\n        \"\"\"\r\n        half_step = self.limit_scale * self.step_size / 2\r\n        for list_ in self._lists:\r\n            limits = [\r\n                (\r\n                    prior.value_for(max(0.0, centre - half_step)),\r\n                    prior.value_for(min(1.0, centre + half_step)),\r\n                )\r\n                for centre, prior in zip(list_, self.perturb_model.priors_ordered_by_id)\r\n            ]\r\n            yield self.perturb_model.with_limits(limits)\r\n\r\n    def _make_jobs(self) -> Generator[Job, None, None]:\r\n        \"\"\"\r\n        Create a list of jobs to be run on separate processes.\r\n\r\n        Each job fits a perturb image with the original model\r\n        and a model which includes a perturbation.\r\n        \"\"\"\r\n        for number, (perturb_instance, perturb_model, label) in enumerate(\r\n            zip(self._perturb_instances, self._perturb_models, self._labels)\r\n        ):\r\n\r\n            if self.perturb_model_prior_func is not None:\r\n                perturb_model = self.perturb_model_prior_func(\r\n                    perturb_instance=perturb_instance,\r\n","stderr_tail":"","stdout_sha256":"9c521fbc21c01fd86c78aa1039dd32f5dfbbc0f62501088121a8449110cfcc1c","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-13.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:43:57.849843Z","duration_s":0.003124,"timed_out":false,"image_before":"03bd5403-d931-4c3b-bad8-512368c14b8f","image_after":"9d8dd5e2-976c-4444-b34a-fb32ce3de155","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-14","seq":14,"branch_id":"agent1","kind":"command","cmd":"sed -n '1,100p' /PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py","path":null,"exit_code":0,"stdout_tail":"import csv\r\nimport logging\r\nimport os\r\nfrom copy import copy\r\nfrom pathlib import Path\r\nfrom typing import List, Generator, Callable, ClassVar, Optional, Union, Tuple\r\n\r\nfrom autoconf.dictable import to_dict\r\nfrom autofit.mapper.model import ModelInstance\r\nfrom autofit.mapper.prior_model.abstract import AbstractPriorModel\r\nfrom autofit.non_linear.grid.grid_search import make_lists, Sequential\r\nfrom autofit.non_linear.grid.sensitivity.job import Job\r\nfrom autofit.non_linear.grid.sensitivity.job import JobResult\r\nfrom autofit.non_linear.grid.sensitivity.result import SensitivityResult\r\nfrom autofit.non_linear.parallel import Process\r\nfrom autofit.text.text_util import padding\r\n\r\nclass Sensitivity:\r\n    def __init__(\r\n        self,\r\n        base_model: AbstractPriorModel,\r\n        perturb_model: AbstractPriorModel,\r\n        simulation_instance,\r\n        paths,\r\n        simulate_cls: Callable,\r\n        base_fit_cls: Callable,\r\n        perturb_fit_cls: Callable,\r\n        job_cls: ClassVar = Job,\r\n        perturb_model_prior_func : Optional[Callable] = None,\r\n        number_of_steps: Union[Tuple[int, ...], int] = 4,\r\n        number_of_cores: int = 2,\r\n        limit_scale: int = 1,\r\n    ):\r\n        \"\"\"\r\n        Perform sensitivity mapping to evaluate whether a perturbation\r\n        can be detected if it occurs in different parts of an image.\r\n\r\n        For a range from 0 to 1 with step_size, for each dimension of the\r\n        perturb_model, a perturbation is created and used in conjunction\r\n        with the instance to create an image.\r\n\r\n        For each of these images, a fit is run with just the model and with both\r\n        the model and perturb_model to compare how much better the image\r\n        can be fit if the perturbation is included.\r\n\r\n        Parameters\r\n        ----------\r\n        base_model\r\n            A model that fits the instance well\r\n        perturb_model\r\n            A model which provides a perturbations to be applied to the instance\r\n            before creating images\r\n        simulation_instance\r\n            An instance of a model to which perturbations are applied prior to\r\n            images being generated\r\n        simulate_cls\r\n            A class which simulates images from each perturb instance that sensitivity mapping is performed on.\r\n        base_fit_cls\r\n            The class which fits the base model to each simulated dataset of the sensitivity map.\r\n        perturb_fit_cls\r\n            The class which fits the perturb model to each simulated dataset of the sensitivity map.\r\n        number_of_steps\r\n            The number of steps for each dimension of the sensitivity grid. If input as a float the dimensions are\r\n            all that value. If input as a tuple of length the number of dimensions, each tuple value is the number of\r\n            steps in that dimension.\r\n        number_of_cores\r\n            How many cores does this computer have?\r\n        limit_scale\r\n            Scales the priors for each perturbation model.\r\n                A scale of 1 means priors have limits the same size as the grid square.\r\n                A scale of 2 means priors have limits larger than the grid square with\r\n                    width twice a grid square.\r\n                A scale of 0.5 means priors have limits smaller than the grid square\r\n                    with width half a grid square.\r\n        \"\"\"\r\n        self.logger = logging.getLogger(f\"Sensitivity ({paths.name})\")\r\n\r\n        self.logger.info(\"Creating\")\r\n\r\n        self.instance = simulation_instance\r\n        self.model = base_model\r\n        self.perturb_model = perturb_model\r\n\r\n        self.paths = paths\r\n\r\n        self.simulate_cls = simulate_cls\r\n        self.base_fit_cls = base_fit_cls\r\n        self.perturb_fit_cls = perturb_fit_cls\r\n\r\n        self.perturb_model_prior_func = perturb_model_prior_func\r\n\r\n        self.job_cls = job_cls\r\n\r\n        self.number_of_steps = number_of_steps\r\n        self.number_of_cores = number_of_cores\r\n\r\n        self.limit_scale = limit_scale\r\n\r\n    def run(self) -> SensitivityResult:\r\n        \"\"\"\r\n","stderr_tail":"","stdout_sha256":"7f3597f09c967455a383ed924fcca1e3dd8d40a31a6e8596061d102da97a9a90","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-14.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:43:59.823498Z","duration_s":0.002984,"timed_out":false,"image_before":"9d8dd5e2-976c-4444-b34a-fb32ce3de155","image_after":"01ece0fa-893d-4897-9310-678808149b5e","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-15","seq":15,"branch_id":"agent1","kind":"command","cmd":"grep -n \"step_size\" /PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py","path":null,"exit_code":0,"stdout_tail":"38:        For a range from 0 to 1 with step_size, for each dimension of the\r\n176:    def step_size(self) -> Union[float, Tuple]:\r\n180:        step_size\r\n198:        return make_lists(self.perturb_model.prior_count, step_size=self.step_size)\r\n256:        half_step = self.limit_scale * self.step_size / 2\r\n","stderr_tail":"","stdout_sha256":"09ab8423f6bcfd69214d999980412cffc0cbbffa0dfb8ddb193790204a1156db","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-15.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:01.623330Z","duration_s":0.002894,"timed_out":false,"image_before":"01ece0fa-893d-4897-9310-678808149b5e","image_after":"4b4c72ed-02dd-4560-8fc8-76d93278cc83","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-16","seq":16,"branch_id":"agent1","kind":"command","cmd":"sed -n '170,200p' /PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py","path":null,"exit_code":0,"stdout_tail":"        if isinstance(self.number_of_steps, tuple):\r\n            return self.number_of_steps\r\n\r\n        return tuple(self.number_of_steps for _ in range(self.perturb_model.prior_count))\r\n\r\n    @property\r\n    def step_size(self) -> Union[float, Tuple]:\r\n        \"\"\"\r\n        Returns\r\n        -------\r\n        step_size\r\n            The size of a step in any given dimension in hyper space.\r\n        \"\"\"\r\n        if isinstance(self.number_of_steps, tuple):\r\n            return tuple(1 / number_of_steps for number_of_steps in self.number_of_steps)\r\n        return 1 / self.number_of_steps\r\n\r\n    @property\r\n    def results_path(self) -> Path:\r\n        return self.paths.output_path / \"results.csv\"\r\n\r\n    @property\r\n    def _lists(self) -> List[List[float]]:\r\n        \"\"\"\r\n        A list of hypercube vectors, used to instantiate\r\n        the perturb_model and create the individual\r\n        perturbations.\r\n        \"\"\"\r\n        return make_lists(self.perturb_model.prior_count, step_size=self.step_size)\r\n\r\n    @property\r\n","stderr_tail":"","stdout_sha256":"9d2aed31dcc1634a2a99e5a431cfebcf8db031ccbe355aee51176ab5d69ed9c9","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-16.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:04.173076Z","duration_s":0.002676,"timed_out":false,"image_before":"4b4c72ed-02dd-4560-8fc8-76d93278cc83","image_after":"92c728f6-faea-414e-a035-0a4a5b6c62a8","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-17","seq":17,"branch_id":"agent1","kind":"command","cmd":"find /PyAutoFit -path \"*/test*sensitivity*\" -name \"*.py\" 2>/dev/null","path":null,"exit_code":0,"stdout_tail":"/PyAutoFit/test_autofit/non_linear/grid/test_sensitivity/__init__.py\n/PyAutoFit/test_autofit/non_linear/grid/test_sensitivity/conftest.py\n/PyAutoFit/test_autofit/non_linear/grid/test_sensitivity/test_results.py\n/PyAutoFit/test_autofit/non_linear/grid/test_sensitivity/test_run.py\n/PyAutoFit/test_autofit/non_linear/grid/test_sensitivity/test_functionality.py\n","stderr_tail":"","stdout_sha256":"553f4fc02cbfde09086c640841d47955a39ce1e7b5bbda24896feb79d200ad4e","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-17.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:06.854465Z","duration_s":0.008794,"timed_out":false,"image_before":"92c728f6-faea-414e-a035-0a4a5b6c62a8","image_after":"65a27db2-9b5a-4b5f-a44a-0bb1d0b190d1","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-18","seq":18,"branch_id":"agent1","kind":"command","cmd":"grep -n \"tuple_steps\\|test_models_with_tuple_steps\\|test_jobs_with_tuple_steps\" /PyAutoFit/test_autofit/non_linear/grid/test_sensitivity/test_functionality.py","path":null,"exit_code":0,"stdout_tail":"137:def test_models_with_tuple_steps(tuple_sensitivity):\n142:def test_jobs_with_tuple_steps(tuple_sensitivity):\n","stderr_tail":"","stdout_sha256":"01a20ad431b2078da4ff8d44c2ac5bfe4f5acbf74005f4a8171a35396f8dc513","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-18.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:08.734872Z","duration_s":0.002784,"timed_out":false,"image_before":"65a27db2-9b5a-4b5f-a44a-0bb1d0b190d1","image_after":"8cbb6222-5105-407a-a668-47f350a61e6a","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-19","seq":19,"branch_id":"agent1","kind":"command","cmd":"sed -n '130,160p' /PyAutoFit/test_autofit/non_linear/grid/test_sensitivity/test_functionality.py","path":null,"exit_code":0,"stdout_tail":"\n@pytest.fixture(name=\"tuple_sensitivity\")\ndef make_tuple_sensitivity(sensitivity):\n    sensitivity.number_of_steps = (2, 2, 4)\n    return sensitivity\n\n\ndef test_models_with_tuple_steps(tuple_sensitivity):\n    tuple_sensitivity.number_of_steps = (2, 2, 4)\n    assert len(list(tuple_sensitivity._perturb_models)) == 16\n\n\ndef test_jobs_with_tuple_steps(tuple_sensitivity):\n    assert len(list(tuple_sensitivity._make_jobs())) == 16\n","stderr_tail":"","stdout_sha256":"03e41a54612947fa1e3da4a9b8373031dbc76f2e51ef074254e5662a169b74c1","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-19.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:11.355342Z","duration_s":0.003387,"timed_out":false,"image_before":"8cbb6222-5105-407a-a668-47f350a61e6a","image_after":"883fb49e-9e27-45ef-b988-fc366690b6a1","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-20","seq":20,"branch_id":"agent1","kind":"command","cmd":"grep -n \"def make_lists\" /PyAutoFit/autofit/non_linear/grid/grid_search.py","path":null,"exit_code":2,"stdout_tail":"","stderr_tail":"grep: /PyAutoFit/autofit/non_linear/grid/grid_search.py: No such file or directory\n","stdout_sha256":"e75203626e5c83fcab99084aa8ded636108c273549ff859f530ff5a023ba6e2c","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-20.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:15.022960Z","duration_s":0.002971,"timed_out":false,"image_before":"883fb49e-9e27-45ef-b988-fc366690b6a1","image_after":"8c7ac47f-5290-4ad4-8ec2-a9dfb8220d8c","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-21","seq":21,"branch_id":"agent1","kind":"command","cmd":"find /PyAutoFit -name \"grid_search.py\" -type f","path":null,"exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-21.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:16.750515Z","duration_s":0.008717,"timed_out":false,"image_before":"8c7ac47f-5290-4ad4-8ec2-a9dfb8220d8c","image_after":"c6b4f53b-f2aa-4ac9-b975-cb364b9c62c1","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-22","seq":22,"branch_id":"agent1","kind":"command","cmd":"find /PyAutoFit -path \"*/grid_search*\" -name \"*.py\" type f","path":null,"exit_code":1,"stdout_tail":"","stderr_tail":"find: paths must precede expression: `type'\n","stdout_sha256":"b720d581bcebdbc9a50e644a05a2e26ebb8b403fbde602fed7f3a06fc53dfed8","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-22.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:18.547446Z","duration_s":0.003249,"timed_out":false,"image_before":"c6b4f53b-f2aa-4ac9-b975-cb364b9c62c1","image_after":"b7aa05de-263b-4ad2-b4b7-1432d2cd8163","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-23","seq":23,"branch_id":"agent1","kind":"command","cmd":"find /PyAutoFit -name \"grid_search.py\"","path":null,"exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-23.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:20.267144Z","duration_s":0.008901,"timed_out":false,"image_before":"b7aa05de-263b-4ad2-b4b7-1432d2cd8163","image_after":"df36eaf3-1ec8-499c-93cd-9291a87e9958","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-24","seq":24,"branch_id":"agent1","kind":"command","cmd":"ls -la /PyAutoFit/autofit/non_linear/grid/grid_search/ 2>/dev/null","path":null,"exit_code":0,"stdout_tail":"total 36\ndrwxrwxrwx 1 root root    86 Apr 30 15:12 .\ndrwxrwxrwx 1 root root   118 Apr 30 15:12 ..\n-rw-r--r-- 1 root root 12415 Feb 21  2026 __init__.py\n-rw-r--r-- 1 root root  1817 Feb 21  2026 job.py\n-rw-r--r-- 1 root root  8999 Feb 21  2026 result.py\n-rw-r--r-- 1 root root  2863 Feb 21  2026 result_builder.py\n","stderr_tail":"","stdout_sha256":"91f13063a8f137654d2ce79e1ea6507bc59b74ef864a3d0a2c74c268ac921b4c","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-24.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:22.074837Z","duration_s":0.0037730000000000003,"timed_out":false,"image_before":"df36eaf3-1ec8-499c-93cd-9291a87e9958","image_after":"095feb8f-659e-4f0f-8951-186566c0602c","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-25","seq":25,"branch_id":"agent1","kind":"command","cmd":"grep -n \"def make_lists\" /PyAutoFit/autofit/non_linear/grid/grid_search/__init__.py","path":null,"exit_code":0,"stdout_tail":"105:    def make_lists(self, grid_priors):\n378:def make_lists(\n","stderr_tail":"","stdout_sha256":"c0724f9e01830d4b12fcca80214d9dde11c97b96bf5e5915a3b07a249333f40c","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-25.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:24.713340Z","duration_s":0.003,"timed_out":false,"image_before":"095feb8f-659e-4f0f-8951-186566c0602c","image_after":"a05b4261-a696-42f0-903e-f8668bb12caa","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-26","seq":26,"branch_id":"agent1","kind":"command","cmd":"sed -n '378,430p' /PyAutoFit/autofit/non_linear/grid/grid_search/__init__.py","path":null,"exit_code":0,"stdout_tail":"def make_lists(\n    no_dimensions: int, step_size: Union[Tuple[float], float], centre_steps=True\n):\n    \"\"\"\n        Returns a list of lists of floats covering every combination across no_dimensions of points of integer step size\n    between 0 and 1 inclusive.\n\n    Parameters\n    ----------\n    no_dimensions\n        The number of dimensions, that is the length of the lists\n    step_size\n        The step size. This can be a float or a tuple with the same number of dimensions\n    centre_steps\n\n    Returns\n    -------\n    lists: [[float]]\n        A list of lists\n    \"\"\"\n    if isinstance(step_size, float):\n        step_size = tuple(step_size for _ in range(no_dimensions))\n\n    if no_dimensions == 0:\n        return [[]]\n\n    sub_lists = make_lists(no_dimensions - 1, step_size[1:], centre_steps=centre_steps)\n    step_size = step_size[0]\n    return [\n        [step_size * value + (0.5 * step_size if centre_steps else 0)] + sub_list\n        for value in range(int((1 / step_size)))\n        for sub_list in sub_lists\n    ]\n","stderr_tail":"","stdout_sha256":"51fa16eeb17bdd18ec9f4c1480b08e35889e81d84773325e684a49220952f337","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-26.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:27.146755Z","duration_s":0.00323,"timed_out":false,"image_before":"a05b4261-a696-42f0-903e-f8668bb12caa","image_after":"c5ba23e6-e67d-41fa-ae0e-a63c0b2b7e40","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-27","seq":27,"branch_id":"agent1","kind":"command","cmd":"sed -n '230,260p' /PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py","path":null,"exit_code":0,"stdout_tail":"        for list_ in self._lists:\r\n            strings = list()\r\n            for value, prior_tuple in zip(list_, self.perturb_model.prior_tuples):\r\n                path, prior = prior_tuple\r\n                value = prior.value_for(value)\r\n                strings.append(f\"{path}_{value}\")\r\n            yield \"_\".join(strings)\r\n\r\n    @property\r\n    def _perturb_instances(self) -> Generator[ModelInstance, None, None]:\r\n        \"\"\"\r\n        A list of instances each of which defines a perturbation to\r\n        be applied to the image.\r\n        \"\"\"\r\n        for list_ in self._lists:\r\n            yield self.perturb_model.instance_from_unit_vector(list_)\r\n\r\n    @property\r\n    def _perturb_models(self) -> Generator[AbstractPriorModel, None, None]:\r\n        \"\"\"\r\n        A list of models representing a perturbation at each grid square.\r\n\r\n        By default models have priors with limits at the edges of a grid square.\r\n        These limits can be scaled using the limit_scale variable. If the variable\r\n        is 2 then the priors will have width twice the step size.\r\n        \"\"\"\r\n        half_step = self.limit_scale * self.step_size / 2\r\n        for list_ in self._lists:\r\n            limits = [\r\n                (\r\n                    prior.value_for(max(0.0, centre - half_step)),\r\n","stderr_tail":"","stdout_sha256":"887e4359944fc0aa0f1722b6096728b9fc55dadf4e4ea3780fe00b10b0373f04","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-27.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:30.232083Z","duration_s":0.003004,"timed_out":false,"image_before":"c5ba23e6-e67d-41fa-ae0e-a63c0b2b7e40","image_after":"9ba25e65-30d1-4ef8-b3a6-f3a1cae7e916","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-28","seq":28,"branch_id":"agent1","kind":"command","cmd":"sed -n '244,262p' /PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py","path":null,"exit_code":0,"stdout_tail":"        for list_ in self._lists:\r\n            yield self.perturb_model.instance_from_unit_vector(list_)\r\n\r\n    @property\r\n    def _perturb_models(self) -> Generator[AbstractPriorModel, None, None]:\r\n        \"\"\"\r\n        A list of models representing a perturbation at each grid square.\r\n\r\n        By default models have priors with limits at the edges of a grid square.\r\n        These limits can be scaled using the limit_scale variable. If the variable\r\n        is 2 then the priors will have width twice the step size.\r\n        \"\"\"\r\n        half_step = self.limit_scale * self.step_size / 2\r\n        for list_ in self._lists:\r\n            limits = [\r\n                (\r\n                    prior.value_for(max(0.0, centre - half_step)),\r\n                    prior.value_for(min(1.0, centre + half_step)),\r\n                )\r\n","stderr_tail":"","stdout_sha256":"cca30593c2d268269d6775b392f40cb947ad572be14a5619a2e04eab4ce8c3b7","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-28.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:35.145582Z","duration_s":0.003067,"timed_out":false,"image_before":"9ba25e65-30d1-4ef8-b3a6-f3a1cae7e916","image_after":"7fdc9e70-0cd6-4278-8274-4a86d9046664","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-29","seq":29,"branch_id":"agent1","kind":"command","cmd":"sed -n '244,270p' /PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py","path":null,"exit_code":0,"stdout_tail":"        for list_ in self._lists:\r\n            yield self.perturb_model.instance_from_unit_vector(list_)\r\n\r\n    @property\r\n    def _perturb_models(self) -> Generator[AbstractPriorModel, None, None]:\r\n        \"\"\"\r\n        A list of models representing a perturbation at each grid square.\r\n\r\n        By default models have priors with limits at the edges of a grid square.\r\n        These limits can be scaled using the limit_scale variable. If the variable\r\n        is 2 then the priors will have width twice the step size.\r\n        \"\"\"\r\n        half_step = self.limit_scale * self.step_size / 2\r\n        for list_ in self._lists:\r\n            limits = [\r\n                (\r\n                    prior.value_for(max(0.0, centre - half_step)),\r\n                    prior.value_for(min(1.0, centre + half_step)),\r\n                )\r\n                for centre, prior in zip(list_, self.perturb_model.priors_ordered_by_id)\r\n            ]\r\n            yield self.perturb_model.with_limits(limits)\r\n\r\n    def _make_jobs(self) -> Generator[Job, None, None]:\r\n        \"\"\"\r\n        Create a list of jobs to be run on separate processes.\r\n\r\n","stderr_tail":"","stdout_sha256":"ae5805256d29df8275f0650b89de6235d7f70757dd6e37a5c03dcc1ff97510cb","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-29.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:37.567143Z","duration_s":0.0031520000000000003,"timed_out":false,"image_before":"7fdc9e70-0cd6-4278-8274-4a86d9046664","image_after":"51fda598-a238-4ab2-9819-774a202cc1c2","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-30","seq":30,"branch_id":"agent1","kind":"command","cmd":"sed -n '244,262p' /PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py | cat -A","path":null,"exit_code":0,"stdout_tail":"        for list_ in self._lists:^M$\n            yield self.perturb_model.instance_from_unit_vector(list_)^M$\n^M$\n    @property^M$\n    def _perturb_models(self) -> Generator[AbstractPriorModel, None, None]:^M$\n        \"\"\"^M$\n        A list of models representing a perturbation at each grid square.^M$\n^M$\n        By default models have priors with limits at the edges of a grid square.^M$\n        These limits can be scaled using the limit_scale variable. If the variable^M$\n        is 2 then the priors will have width twice the step size.^M$\n        \"\"\"^M$\n        half_step = self.limit_scale * self.step_size / 2^M$\n        for list_ in self._lists:^M$\n            limits = [^M$\n                (^M$\n                    prior.value_for(max(0.0, centre - half_step)),^M$\n                    prior.value_for(min(1.0, centre + half_step)),^M$\n                )^M$\n","stderr_tail":"","stdout_sha256":"f8284cf968c0170a2be45f8311a0252bfdd0b6f2e051d747f460972e4f7c4094","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-30.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:42.057718Z","duration_s":0.004674,"timed_out":false,"image_before":"51fda598-a238-4ab2-9819-774a202cc1c2","image_after":"3eea97fb-1f69-46f1-9f00-3328a181e9bd","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-31","seq":31,"branch_id":"agent1","kind":"command","cmd":"sed -i '253s/.*/        if isinstance(self.step_size, tuple):\\n            half_step = tuple(\\n                self.limit_scale * step / 2 for step in self.step_size\\n        )\\n        else:\\n            half_step = self.limit_scale * self.step_size / 2/' /PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py","path":null,"exit_code":1,"stdout_tail":"","stderr_tail":"sed: -e expression #1, char 132: unknown option to `s'\n","stdout_sha256":"308f96506ce70e04f7916578882ad24591bae935c5d3a5b09da3b70ef34059c0","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-31.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:44.945947Z","duration_s":0.00287,"timed_out":false,"image_before":"3eea97fb-1f69-46f1-9f00-3328a181e9bd","image_after":"dcaac885-6384-4416-b7af-50cdb6f4c747","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-32","seq":32,"branch_id":"agent1","kind":"command","cmd":"python3 << 'EOF'\nwith open('/PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py', 'r') as f:\n    lines = f.readlines()\n\n# Find the line with \"half_step = self.limit_scale * self.step_size / 2\"\n# It should be around line 253 (0-indexed: 252)\nfor i, line in enumerate(lines):\n    if 'half_step = self.limit_scale * self.step_size / 2' in line:\n        print(f\"Found at line {i+1}: {repr(line)}\")\n        # Replace this line and the next few lines\n        # Original lines 253-261 (1-indexed):\n        # 253:         half_step = self.limit_scale * self.step_size / 2\n        # 254:         for list_ in self._lists:\n        # 255:             limits = [\n        # 256:                 (\n        # 257:                     prior.value_for(max(0.0, centre - half_step)),\n        # 258:                     prior.value_for(min(1.0, centre + half_step)),\n        # 259:                 )\n        # 260:                 for centre, prior in zip(list_, self.perturb_model.priors_ordered_by_id)\n        # 261:             ]\n        # 262:             yield self.perturb_model.with_limits(limits)\n        \n        new_code = '''        if isinstance(self.step_size, tuple):\n            half_step = tuple(\n                self.limit_scale * step / 2 for step in self.step_size\n            )\n        else:\n            half_step = self.limit_scale * self.step_size / 2\n        for list_ in self._lists:\n            limits = [\n                (\n                    prior.value_for(max(0.0, centre - half_step[i])),\n                    prior.value_for(min(1.0, centre + half_step[i])),\n                )\n                for i, (centre, prior) in enumerate(zip(list_, self.perturb_model.priors_ordered_by_id))\n            ]\n            yield self.perturb_model.with_limits(limits)'''\n        \n        # Replace lines 253-262 (0-indexed: 252-261)\n        lines[252:262] = [new_code + '\\n']\n        break\n\nwith open('/PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py', 'w') as f:\n    f.writelines(lines)\n\nprint(\"Done!\")\nEOF","path":null,"exit_code":0,"stdout_tail":"Found at line 256: '        half_step = self.limit_scale * self.step_size / 2\\n'\nDone!\n","stderr_tail":"","stdout_sha256":"39335b4fd56be90ff2e54b5899c1685d06ba28643ec72efcb0f25bef7b56330e","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-32.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:48.765747Z","duration_s":0.023319,"timed_out":false,"image_before":"dcaac885-6384-4416-b7af-50cdb6f4c747","image_after":"2b00bea4-6313-48b6-beff-94a3e919f1a2","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-33","seq":33,"branch_id":"agent1","kind":"command","cmd":"sed -n '250,275p' /PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py","path":null,"exit_code":0,"stdout_tail":"        A list of models representing a perturbation at each grid square.\n\n        By default models have priors with limits at the edges of a grid square.\n        if isinstance(self.step_size, tuple):\n            half_step = tuple(\n                self.limit_scale * step / 2 for step in self.step_size\n            )\n        else:\n            half_step = self.limit_scale * self.step_size / 2\n        for list_ in self._lists:\n            limits = [\n                (\n                    prior.value_for(max(0.0, centre - half_step[i])),\n                    prior.value_for(min(1.0, centre + half_step[i])),\n                )\n                for i, (centre, prior) in enumerate(zip(list_, self.perturb_model.priors_ordered_by_id))\n            ]\n            yield self.perturb_model.with_limits(limits)\n                for centre, prior in zip(list_, self.perturb_model.priors_ordered_by_id)\n            ]\n            yield self.perturb_model.with_limits(limits)\n\n    def _make_jobs(self) -> Generator[Job, None, None]:\n        \"\"\"\n        Create a list of jobs to be run on separate processes.\n\n","stderr_tail":"","stdout_sha256":"6d6d24064e70e3ef4a6cbe6287440f9658b7a25e7776d458f5e931441003b087","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-33.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:50.628273Z","duration_s":0.00302,"timed_out":false,"image_before":"2b00bea4-6313-48b6-beff-94a3e919f1a2","image_after":"3520a72f-5c4f-46c8-be71-e7342a80680f","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-34","seq":34,"branch_id":"agent1","kind":"command","cmd":"sed -n '244,280p' /PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py","path":null,"exit_code":0,"stdout_tail":"        for list_ in self._lists:\n            yield self.perturb_model.instance_from_unit_vector(list_)\n\n    @property\n    def _perturb_models(self) -> Generator[AbstractPriorModel, None, None]:\n        \"\"\"\n        A list of models representing a perturbation at each grid square.\n\n        By default models have priors with limits at the edges of a grid square.\n        if isinstance(self.step_size, tuple):\n            half_step = tuple(\n                self.limit_scale * step / 2 for step in self.step_size\n            )\n        else:\n            half_step = self.limit_scale * self.step_size / 2\n        for list_ in self._lists:\n            limits = [\n                (\n                    prior.value_for(max(0.0, centre - half_step[i])),\n                    prior.value_for(min(1.0, centre + half_step[i])),\n                )\n                for i, (centre, prior) in enumerate(zip(list_, self.perturb_model.priors_ordered_by_id))\n            ]\n            yield self.perturb_model.with_limits(limits)\n                for centre, prior in zip(list_, self.perturb_model.priors_ordered_by_id)\n            ]\n            yield self.perturb_model.with_limits(limits)\n\n    def _make_jobs(self) -> Generator[Job, None, None]:\n        \"\"\"\n        Create a list of jobs to be run on separate processes.\n\n        Each job fits a perturb image with the original model\n        and a model which includes a perturbation.\n        \"\"\"\n        for number, (perturb_instance, perturb_model, label) in enumerate(\n            zip(self._perturb_instances, self._perturb_models, self._labels)\n","stderr_tail":"","stdout_sha256":"43ccf61fd442b97e1971a2d2aa3da9ecb5db34808c502797c55102b470a4bd9f","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-34.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:52.620755Z","duration_s":0.003038,"timed_out":false,"image_before":"3520a72f-5c4f-46c8-be71-e7342a80680f","image_after":"b06bb143-df7e-4699-b158-e6cd54044a91","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-35","seq":35,"branch_id":"agent1","kind":"command","cmd":"git -C /PyAutoFit checkout -- autofit/non_linear/grid/sensitivity/__init__.py 2>/dev/null || echo \"Not a git repo or file not tracked\"","path":null,"exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-35.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:54.577665Z","duration_s":0.004768,"timed_out":false,"image_before":"b06bb143-df7e-4699-b158-e6cd54044a91","image_after":"5c4017fd-9a5d-4bcd-97ee-5144364f350a","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-36","seq":36,"branch_id":"agent1","kind":"command","cmd":"git -C /PyAutoFit status 2>&1 | head -20","path":null,"exit_code":0,"stdout_tail":"Not currently on any branch.\nnothing to commit, working tree clean\n","stderr_tail":"","stdout_sha256":"d9550dfc5513b6c1ee9d2c6c474cf804a5505f8897f3894c4499b385eab3d812","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-36.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:56.400567Z","duration_s":0.020702,"timed_out":false,"image_before":"5c4017fd-9a5d-4bcd-97ee-5144364f350a","image_after":"725c9092-e227-492c-9c07-82dc341f9bb4","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-37","seq":37,"branch_id":"agent1","kind":"command","cmd":"git -C /PyAutoFit log --oneline -5","path":null,"exit_code":0,"stdout_tail":"d564f29a receipts snapshot\nb0ec41b3 Merge pull request #887 from rhayes777/feature/parent_id\nb74d8378 Merge pull request #886 from rhayes777/feature/sensitivity_result\nbbb93ba7 refview complete -> samples no longer optional\n10f3bfe2 remove parent from grid search\n","stderr_tail":"","stdout_sha256":"52ce7e77b4660d51672c6491d723f6d8a1edba4c35e23274ded60c74b4f7715a","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-37.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:44:58.502106Z","duration_s":0.004466,"timed_out":false,"image_before":"725c9092-e227-492c-9c07-82dc341f9bb4","image_after":"6f25794a-ee56-400b-9a90-8a955b6ceeb5","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-38","seq":38,"branch_id":"agent1","kind":"command","cmd":"git -C /PyAutoFit restore autofit/non_linear/grid/sensitivity/__init__.py","path":null,"exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-38.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:45:00.870726Z","duration_s":0.004594,"timed_out":false,"image_before":"6f25794a-ee56-400b-9a90-8a955b6ceeb5","image_after":"0e7a26b3-b70b-4a4a-8b73-772b29c2bfd1","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-39","seq":39,"branch_id":"agent1","kind":"command","cmd":"sed -n '244,265p' /PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py","path":null,"exit_code":0,"stdout_tail":"        for list_ in self._lists:\r\n            yield self.perturb_model.instance_from_unit_vector(list_)\r\n\r\n    @property\r\n    def _perturb_models(self) -> Generator[AbstractPriorModel, None, None]:\r\n        \"\"\"\r\n        A list of models representing a perturbation at each grid square.\r\n\r\n        By default models have priors with limits at the edges of a grid square.\r\n        These limits can be scaled using the limit_scale variable. If the variable\r\n        is 2 then the priors will have width twice the step size.\r\n        \"\"\"\r\n        half_step = self.limit_scale * self.step_size / 2\r\n        for list_ in self._lists:\r\n            limits = [\r\n                (\r\n                    prior.value_for(max(0.0, centre - half_step)),\r\n                    prior.value_for(min(1.0, centre + half_step)),\r\n                )\r\n                for centre, prior in zip(list_, self.perturb_model.priors_ordered_by_id)\r\n            ]\r\n            yield self.perturb_model.with_limits(limits)\r\n","stderr_tail":"","stdout_sha256":"4f3138552f010ac49c806dee0473ec0dc42ebc57f8e1a972f99f8524a27b0c03","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-39.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:45:03.241582Z","duration_s":0.003443,"timed_out":false,"image_before":"0e7a26b3-b70b-4a4a-8b73-772b29c2bfd1","image_after":"b9420961-c408-41be-9777-93ab733f1e7c","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-40","seq":40,"branch_id":"agent1","kind":"command","cmd":"python3 << 'PYEOF'\nwith open('/PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py', 'r') as f:\n    content = f.read()\n\n# The old code to replace\nold_code = '''        half_step = self.limit_scale * self.step_size / 2\n        for list_ in self._lists:\n            limits = [\n                (\n                    prior.value_for(max(0.0, centre - half_step)),\n                    prior.value_for(min(1.0, centre + half_step)),\n                )\n                for centre, prior in zip(list_, self.perturb_model.priors_ordered_by_id)\n            ]\n            yield self.perturb_model.with_limits(limits)'''\n\n# The new code\nnew_code = '''        if isinstance(self.step_size, tuple):\n            half_step = tuple(\n                self.limit_scale * step / 2 for step in self.step_size\n            )\n        else:\n            half_step = self.limit_scale * self.step_size / 2\n        for list_ in self._lists:\n            limits = [\n                (\n                    prior.value_for(max(0.0, centre - half_step[i])),\n                    prior.value_for(min(1.0, centre + half_step[i])),\n                )\n                for i, (centre, prior) in enumerate(zip(list_, self.perturb_model.priors_ordered_by_id))\n            ]\n            yield self.perturb_model.with_limits(limits)'''\n\nif old_code in content:\n    new_content = content.replace(old_code, new_code)\n    with open('/PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py', 'w') as f:\n        f.write(new_content)\n    print(\"Successfully replaced the code\")\nelse:\n    print(\"Could not find the old code pattern\")\n    # Let's debug what's actually there\n    idx = content.find('half_step = self.limit_scale * self.step_size / 2')\n    if idx != -1:\n        print(f\"Found at index {idx}\")\n        print(repr(content[idx:idx+200]))\nPYEOF","path":null,"exit_code":0,"stdout_tail":"Successfully replaced the code\n","stderr_tail":"","stdout_sha256":"3cdf0e2b5a9591a4dfabe03e0b591e2f6f1b8c1a1e18e856420980e8016480e9","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-40.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:45:07.269581Z","duration_s":0.024771,"timed_out":false,"image_before":"b9420961-c408-41be-9777-93ab733f1e7c","image_after":"7300eb4b-eb09-4559-9f7e-0844b49a158b","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-41","seq":41,"branch_id":"agent1","kind":"command","cmd":"sed -n '244,275p' /PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py","path":null,"exit_code":0,"stdout_tail":"        for list_ in self._lists:\n            yield self.perturb_model.instance_from_unit_vector(list_)\n\n    @property\n    def _perturb_models(self) -> Generator[AbstractPriorModel, None, None]:\n        \"\"\"\n        A list of models representing a perturbation at each grid square.\n\n        By default models have priors with limits at the edges of a grid square.\n        These limits can be scaled using the limit_scale variable. If the variable\n        is 2 then the priors will have width twice the step size.\n        \"\"\"\n        if isinstance(self.step_size, tuple):\n            half_step = tuple(\n                self.limit_scale * step / 2 for step in self.step_size\n            )\n        else:\n            half_step = self.limit_scale * self.step_size / 2\n        for list_ in self._lists:\n            limits = [\n                (\n                    prior.value_for(max(0.0, centre - half_step[i])),\n                    prior.value_for(min(1.0, centre + half_step[i])),\n                )\n                for i, (centre, prior) in enumerate(zip(list_, self.perturb_model.priors_ordered_by_id))\n            ]\n            yield self.perturb_model.with_limits(limits)\n\n    def _make_jobs(self) -> Generator[Job, None, None]:\n        \"\"\"\n        Create a list of jobs to be run on separate processes.\n\n","stderr_tail":"","stdout_sha256":"546bfbb4144a4e955f77f35741057bfea68b26364ecbdd0bf76e89a25b8e917e","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-41.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:45:09.818432Z","duration_s":0.003464,"timed_out":false,"image_before":"7300eb4b-eb09-4559-9f7e-0844b49a158b","image_after":"7a74891c-77fb-48c7-a4a3-3ed4d4a6dec4","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-42","seq":42,"branch_id":"agent1","kind":"command","cmd":"cd /PyAutoFit && python -m pytest test_autofit/non_linear/grid/test_sensitivity/test_functionality.py::test_models_with_tuple_steps test_autofit/non_linear/grid/test_sensitivity/test_functionality.py::test_jobs_with_tuple_steps -v 2>&1","path":null,"exit_code":0,"stdout_tail":"============================= test session starts ==============================\nplatform linux -- Python 3.10.19, pytest-9.0.2, pluggy-1.6.0 -- /usr/local/bin/python\ncachedir: .pytest_cache\nrootdir: /PyAutoFit\nplugins: cov-7.0.0\ncollecting ... collected 2 items\n\ntest_autofit/non_linear/grid/test_sensitivity/test_functionality.py::test_models_with_tuple_steps PASSED [ 50%]\ntest_autofit/non_linear/grid/test_sensitivity/test_functionality.py::test_jobs_with_tuple_steps PASSED [100%]\n\n=============================== warnings summary ===============================\nautofit/mapper/prior/gaussian.py:19\n  /PyAutoFit/autofit/mapper/prior/gaussian.py:19: DeprecationWarning: invalid escape sequence '\\m'\n    \"\"\"\n\nautofit/mapper/operator.py:20\n  /PyAutoFit/autofit/mapper/operator.py:20: DeprecationWarning: invalid escape sequence '\\ '\n    \"\"\"Implements the functionality of a linear operator.\n\nautofit/messages/transform.py:37\n  /PyAutoFit/autofit/messages/transform.py:37: DeprecationWarning: invalid escape sequence '\\i'\n    \"\"\"\n\nautofit/mapper/prior/log_gaussian.py:21\n  /PyAutoFit/autofit/mapper/prior/log_gaussian.py:21: DeprecationWarning: invalid escape sequence '\\m'\n    \"\"\"\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n======================== 2 passed, 4 warnings in 0.23s =========================\n","stderr_tail":"","stdout_sha256":"e696b426f167e84acb2844732a56d1390b6644f4361f7d19f641709ed2536e42","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-42.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:45:11.805341Z","duration_s":2.529543,"timed_out":false,"image_before":"7a74891c-77fb-48c7-a4a3-3ed4d4a6dec4","image_after":"8bad2cca-336e-41e3-bf40-a21a843ab545","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-43","seq":43,"branch_id":"agent1","kind":"command","cmd":"cd /PyAutoFit && python -m pytest test_autofit/non_linear/grid/test_sensitivity/ -v 2>&1","path":null,"exit_code":1,"stdout_tail":".py:101: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nautofit/non_linear/grid/sensitivity/__init__.py:279: in _make_jobs\n    for number, (perturb_instance, perturb_model, label) in enumerate(\nautofit/non_linear/grid/sensitivity/__init__.py:263: in _perturb_models\n    limits = [\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\n.0 = <enumerate object at 0x7fffcbb1ea00>\n\n    limits = [\n        (\n>           prior.value_for(max(0.0, centre - half_step[i])),\n            prior.value_for(min(1.0, centre + half_step[i])),\n        )\n        for i, (centre, prior) in enumerate(zip(list_, self.perturb_model.priors_ordered_by_id))\n    ]\nE   TypeError: 'float' object is not subscriptable\n\nautofit/non_linear/grid/sensitivity/__init__.py:265: TypeError\n---------------------------- Captured stdout setup -----------------------------\n2026-10-03 21:45:20,428 - Sensitivity () - INFO - Creating\n------------------------------ Captured log setup ------------------------------\nINFO     Sensitivity ():__init__.py:78 Creating\n_______________________________ test_sensitivity _______________________________\n\nsensitivity = <autofit.non_linear.grid.sensitivity.Sensitivity object at 0x7fffcba9c400>\n\n    @with_config(\"general\", \"model\", \"ignore_prior_limits\", value=True)\n    def test_sensitivity(sensitivity):\n>       results = sensitivity.run()\n\ntest_autofit/non_linear/grid/test_sensitivity/test_run.py:6: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nautofit/non_linear/grid/sensitivity/__init__.py:119: in run\n    for result in process_class.run_jobs(\nautofit/non_linear/parallel/process.py:162: in run_jobs\n    for job in jobs:\nautofit/non_linear/grid/sensitivity/__init__.py:279: in _make_jobs\n    for number, (perturb_instance, perturb_model, label) in enumerate(\nautofit/non_linear/grid/sensitivity/__init__.py:263: in _perturb_models\n    limits = [\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\n.0 = <enumerate object at 0x7fffcbaf8a40>\n\n    limits = [\n        (\n>           prior.value_for(max(0.0, centre - half_step[i])),\n            prior.value_for(min(1.0, centre + half_step[i])),\n        )\n        for i, (centre, prior) in enumerate(zip(list_, self.perturb_model.priors_ordered_by_id))\n    ]\nE   TypeError: 'float' object is not subscriptable\n\nautofit/non_linear/grid/sensitivity/__init__.py:265: TypeError\n---------------------------- Captured stdout setup -----------------------------\n2026-10-03 21:45:20,468 - Sensitivity () - INFO - Creating\n------------------------------ Captured log setup ------------------------------\nINFO     Sensitivity ():__init__.py:78 Creating\n----------------------------- Captured stdout call -----------------------------\n2026-10-03 21:45:20,468 - Sensitivity () - INFO - Running\n2026-10-03 21:45:20,483 - process 0 - INFO - created\n------------------------------ Captured log call -------------------------------\nINFO     Sensitivity ():__init__.py:104 Running\nINFO     process 0:process.py:70 created\n_________________________________ test_serial __________________________________\n\nsensitivity = <autofit.non_linear.grid.sensitivity.Sensitivity object at 0x7fffcba9d930>\n\n    def test_serial(sensitivity):\n        sensitivity.number_of_cores = 1\n    \n>       results = sensitivity.run()\n\ntest_autofit/non_linear/grid/test_sensitivity/test_run.py:27: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \nautofit/non_linear/grid/sensitivity/__init__.py:119: in run\n    for result in process_class.run_jobs(\nautofit/non_linear/grid/grid_search/__init__.py:21: in run_jobs\n    for job_ in jobs:\nautofit/non_linear/grid/sensitivity/__init__.py:279: in _make_jobs\n    for number, (perturb_instance, perturb_model, label) in enumerate(\nautofit/non_linear/grid/sensitivity/__init__.py:263: in _perturb_models\n    limits = [\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\n.0 = <enumerate object at 0x7fffcb9ad840>\n\n    limits = [\n        (\n>           prior.value_for(max(0.0, centre - half_step[i])),\n            prior.value_for(min(1.0, centre + half_step[i])),\n        )\n        for i, (centre, prior) in enumerate(zip(list_, self.perturb_model.priors_ordered_by_id))\n    ]\nE   TypeError: 'float' object is not subscriptable\n\nautofit/non_linear/grid/sensitivity/__init__.py:265: TypeError\n---------------------------- Captured stdout setup -----------------------------\n2026-10-03 21:45:20,495 - Sensitivity () - INFO - Creating\n------------------------------ Captured log setup ------------------------------\nINFO     Sensitivity ():__init__.py:78 Creating\n----------------------------- Captured stdout call -----------------------------\n2026-10-03 21:45:20,495 - Sensitivity () - INFO - Running\n------------------------------ Captured log call -------------------------------\nINFO     Sensitivity ():__init__.py:104 Running\n=========================== short test summary info ============================\nFAILED test_autofit/non_linear/grid/test_sensitivity/test_functionality.py::TestPerturbationModels::test_perturb_models[1.0-0.0-0.5-0.5-1.0]\nFAILED test_autofit/non_linear/grid/test_sensitivity/test_functionality.py::TestPerturbationModels::test_perturb_models[2.0-0.0-0.75-0.25-1.0]\nFAILED test_autofit/non_linear/grid/test_sensitivity/test_functionality.py::TestPerturbationModels::test_perturb_models[4.0-0.0-1.0-0.0-1.0]\nFAILED test_autofit/non_linear/grid/test_sensitivity/test_functionality.py::TestPerturbationModels::test__perturb_models__prior_overwrite_via_perturb_model_prior_func\nFAILED test_autofit/non_linear/grid/test_sensitivity/test_functionality.py::TestPerturbationModels::test_physical\nFAILED test_autofit/non_linear/grid/test_sensitivity/test_run.py::test_sensitivity\nFAILED test_autofit/non_linear/grid/test_sensitivity/test_run.py::test_serial\n========================= 7 failed, 11 passed in 0.40s =========================\n","stderr_tail":"","stdout_sha256":"cdb8f7713dbbb5cbd14cd8a728d7f4ad3ca4333a016ab423cdbc152dfc5a87ac","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-43.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:45:17.156661Z","duration_s":2.450777,"timed_out":false,"image_before":"8bad2cca-336e-41e3-bf40-a21a843ab545","image_after":"271915dd-b01c-4c16-bd56-6d9f0d1f99ee","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-44","seq":44,"branch_id":"agent1","kind":"command","cmd":"python3 << 'PYEOF'\nwith open('/PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py', 'r') as f:\n    content = f.read()\n\n# The current code to replace\nold_code = '''        if isinstance(self.step_size, tuple):\n            half_step = tuple(\n                self.limit_scale * step / 2 for step in self.step_size\n            )\n        else:\n            half_step = self.limit_scale * self.step_size / 2\n        for list_ in self._lists:\n            limits = [\n                (\n                    prior.value_for(max(0.0, centre - half_step[i])),\n                    prior.value_for(min(1.0, centre + half_step[i])),\n                )\n                for i, (centre, prior) in enumerate(zip(list_, self.perturb_model.priors_ordered_by_id))\n            ]\n            yield self.perturb_model.with_limits(limits)'''\n\n# New code that handles both cases\nnew_code = '''        if isinstance(self.step_size, tuple):\n            half_step = tuple(\n                self.limit_scale * step / 2 for step in self.step_size\n            )\n        else:\n            half_step = self.limit_scale * self.step_size / 2\n        for list_ in self._lists:\n            if isinstance(half_step, tuple):\n                limits = [\n                    (\n                        prior.value_for(max(0.0, centre - half_step[i])),\n                        prior.value_for(min(1.0, centre + half_step[i])),\n                    )\n                    for i, (centre, prior) in enumerate(zip(list_, self.perturb_model.priors_ordered_by_id))\n                ]\n            else:\n                limits = [\n                    (\n                        prior.value_for(max(0.0, centre - half_step)),\n                        prior.value_for(min(1.0, centre + half_step)),\n                    )\n                    for centre, prior in zip(list_, self.perturb_model.priors_ordered_by_id)\n                ]\n            yield self.perturb_model.with_limits(limits)'''\n\nif old_code in content:\n    new_content = content.replace(old_code, new_code)\n    with open('/PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py', 'w') as f:\n        f.write(new_content)\n    print(\"Successfully replaced the code\")\nelse:\n    print(\"Could not find the old code pattern\")\nPYEOF","path":null,"exit_code":0,"stdout_tail":"Successfully replaced the code\n","stderr_tail":"","stdout_sha256":"3cdf0e2b5a9591a4dfabe03e0b591e2f6f1b8c1a1e18e856420980e8016480e9","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-44.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:45:27.703008Z","duration_s":0.022997999999999998,"timed_out":false,"image_before":"271915dd-b01c-4c16-bd56-6d9f0d1f99ee","image_after":"0a228f7e-c584-4bf8-ad54-6e156843a29f","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-45","seq":45,"branch_id":"agent1","kind":"command","cmd":"sed -n '244,280p' /PyAutoFit/autofit/non_linear/grid/sensitivity/__init__.py","path":null,"exit_code":0,"stdout_tail":"        for list_ in self._lists:\n            yield self.perturb_model.instance_from_unit_vector(list_)\n\n    @property\n    def _perturb_models(self) -> Generator[AbstractPriorModel, None, None]:\n        \"\"\"\n        A list of models representing a perturbation at each grid square.\n\n        By default models have priors with limits at the edges of a grid square.\n        These limits can be scaled using the limit_scale variable. If the variable\n        is 2 then the priors will have width twice the step size.\n        \"\"\"\n        if isinstance(self.step_size, tuple):\n            half_step = tuple(\n                self.limit_scale * step / 2 for step in self.step_size\n            )\n        else:\n            half_step = self.limit_scale * self.step_size / 2\n        for list_ in self._lists:\n            if isinstance(half_step, tuple):\n                limits = [\n                    (\n                        prior.value_for(max(0.0, centre - half_step[i])),\n                        prior.value_for(min(1.0, centre + half_step[i])),\n                    )\n                    for i, (centre, prior) in enumerate(zip(list_, self.perturb_model.priors_ordered_by_id))\n                ]\n            else:\n                limits = [\n                    (\n                        prior.value_for(max(0.0, centre - half_step)),\n                        prior.value_for(min(1.0, centre + half_step)),\n                    )\n                    for centre, prior in zip(list_, self.perturb_model.priors_ordered_by_id)\n                ]\n            yield self.perturb_model.with_limits(limits)\n\n","stderr_tail":"","stdout_sha256":"ecd8aa3e2e0cc4bdce74b3779f580bb29017e590fff6733cc8b226f501b12510","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-45.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:45:29.773845Z","duration_s":0.003019,"timed_out":false,"image_before":"0a228f7e-c584-4bf8-ad54-6e156843a29f","image_after":"82f40e6b-4190-4d2f-8d77-e106aeeccaef","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-46","seq":46,"branch_id":"agent1","kind":"command","cmd":"cd /PyAutoFit && python -m pytest test_autofit/non_linear/grid/test_sensitivity/ -v 2>&1","path":null,"exit_code":0,"stdout_tail":"============================= test session starts ==============================\nplatform linux -- Python 3.10.19, pytest-9.0.2, pluggy-1.6.0 -- /usr/local/bin/python\ncachedir: .pytest_cache\nrootdir: /PyAutoFit\nplugins: cov-7.0.0\ncollecting ... collected 18 items\n\ntest_autofit/non_linear/grid/test_sensitivity/test_functionality.py::test_lists PASSED [  5%]\ntest_autofit/non_linear/grid/test_sensitivity/test_functionality.py::test_tuple_step_size PASSED [ 11%]\ntest_autofit/non_linear/grid/test_sensitivity/test_functionality.py::test_labels PASSED [ 16%]\ntest_autofit/non_linear/grid/test_sensitivity/test_functionality.py::test_perform_job PASSED [ 22%]\ntest_autofit/non_linear/grid/test_sensitivity/test_functionality.py::TestPerturbationModels::test_perturb_models[1.0-0.0-0.5-0.5-1.0] PASSED [ 27%]\ntest_autofit/non_linear/grid/test_sensitivity/test_functionality.py::TestPerturbationModels::test_perturb_models[2.0-0.0-0.75-0.25-1.0] PASSED [ 33%]\ntest_autofit/non_linear/grid/test_sensitivity/test_functionality.py::TestPerturbationModels::test_perturb_models[4.0-0.0-1.0-0.0-1.0] PASSED [ 38%]\ntest_autofit/non_linear/grid/test_sensitivity/test_functionality.py::TestPerturbationModels::test__perturb_models__prior_overwrite_via_perturb_model_prior_func PASSED [ 44%]\ntest_autofit/non_linear/grid/test_sensitivity/test_functionality.py::TestPerturbationModels::test_physical PASSED [ 50%]\ntest_autofit/non_linear/grid/test_sensitivity/test_functionality.py::TestPerturbationModels::test_model_with_limits PASSED [ 55%]\ntest_autofit/non_linear/grid/test_sensitivity/test_functionality.py::TestPerturbationModels::test_prior_with_limits PASSED [ 61%]\ntest_autofit/non_linear/grid/test_sensitivity/test_functionality.py::TestPerturbationModels::test_existing_limits PASSED [ 66%]\ntest_autofit/non_linear/grid/test_sensitivity/test_functionality.py::test_models_with_tuple_steps PASSED [ 72%]\ntest_autofit/non_linear/grid/test_sensitivity/test_functionality.py::test_jobs_with_tuple_steps PASSED [ 77%]\ntest_autofit/non_linear/grid/test_sensitivity/test_results.py::test_job_result PASSED [ 83%]\ntest_autofit/non_linear/grid/test_sensitivity/test_results.py::test_result PASSED [ 88%]\ntest_autofit/non_linear/grid/test_sensitivity/test_run.py::test_sensitivity PASSED [ 94%]\ntest_autofit/non_linear/grid/test_sensitivity/test_run.py::test_serial PASSED [100%]\n\n============================== 18 passed in 0.63s ==============================\n","stderr_tail":"","stdout_sha256":"73662b59714c3c17e7594732fa4c76d84907e061a1f1be8f1f8ac25d829238b8","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-46.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:45:32.221251Z","duration_s":2.7609909999999998,"timed_out":false,"image_before":"82f40e6b-4190-4d2f-8d77-e106aeeccaef","image_after":"68371ed9-b729-4c9c-8b83-3b13f6c545bb","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-47","seq":47,"branch_id":"agent1","kind":"command","cmd":"cd /PyAutoFit && python -m pytest test_autofit/non_linear/grid/test_sensitivity/test_functionality.py::test_models_with_tuple_steps test_autofit/non_linear/grid/test_sensitivity/test_functionality.py::test_jobs_with_tuple_steps -v 2>&1","path":null,"exit_code":0,"stdout_tail":"============================= test session starts ==============================\nplatform linux -- Python 3.10.19, pytest-9.0.2, pluggy-1.6.0 -- /usr/local/bin/python\ncachedir: .pytest_cache\nrootdir: /PyAutoFit\nplugins: cov-7.0.0\ncollecting ... collected 2 items\n\ntest_autofit/non_linear/grid/test_sensitivity/test_functionality.py::test_models_with_tuple_steps PASSED [ 50%]\ntest_autofit/non_linear/grid/test_sensitivity/test_functionality.py::test_jobs_with_tuple_steps PASSED [100%]\n\n============================== 2 passed in 0.20s ===============================\n","stderr_tail":"","stdout_sha256":"89db564be103574bc079e8cf54d73e9a33ae0ae01c63fb523e742d75fb820cb0","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-47.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:45:42.058254Z","duration_s":2.278481,"timed_out":false,"image_before":"68371ed9-b729-4c9c-8b83-3b13f6c545bb","image_after":"9798fd96-5fa9-4afc-87a4-e32e9c46f220","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-48","seq":48,"branch_id":"agent1","kind":"command","cmd":"cd /PyAutoFit && git diff --stat","path":null,"exit_code":0,"stdout_tail":" autofit/non_linear/grid/sensitivity/__init__.py | 614 ++++++++++++------------\n 1 file changed, 314 insertions(+), 300 deletions(-)\n","stderr_tail":"","stdout_sha256":"baee1e5ee7b898cb25921af1fa7baf330321e350d08c8f77785a94dee76e84c7","log_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/logs/agent1/agent1-48.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T21:45:49.899598Z","duration_s":0.010933000000000002,"timed_out":false,"image_before":"9798fd96-5fa9-4afc-87a4-e32e9c46f220","image_after":"88511e78-5d7f-4813-9058-8bbb17d1bdc2","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-49","seq":49,"branch_id":"agent1","kind":"diff","cmd":"compute patch in executor staging agent1-8ba1646cd7134af4a49867a107dea542: /opt/homebrew/Cellar/git/2.53.0/bin/git -c core.hooksPath=/dev/null -c core.attributesFile=/dev/null -c diff.external= -c core.quotePath=false add -A && /opt/homebrew/Cellar/git/2.53.0/bin/git -c core.hooksPath=/dev/null -c core.attributesFile=/dev/null -c diff.external= -c core.quotePath=false diff --cached --binary --no-ext-diff --no-textconv --no-renames d564f29a393b75747f1ec3da7c51070d2e12cda1","path":null,"exit_code":0,"stdout_tail":"esult))\n+\n+        return result\n+\n+    @property\n+    def shape(self) -> Tuple[int, ...]:\n+        \"\"\"\n+        Returns the shape of the sensitivity grid.\n+\n+        The shape is the number of steps performed for each dimension of the `perturb_model`. If sensitivity mapping\n+        is performed in 3D, the shape will therefore be a tuple of length 3.\n+\n+        The `shape` can vary across dimensions if the `number_of_steps` parameter is input as a tuple.\n+\n+        Returns\n+        -------\n+        The shape of the sensitivity grid.\n+        \"\"\"\n+\n+        if isinstance(self.number_of_steps, tuple):\n+            return self.number_of_steps\n+\n+        return tuple(self.number_of_steps for _ in range(self.perturb_model.prior_count))\n+\n+    @property\n+    def step_size(self) -> Union[float, Tuple]:\n+        \"\"\"\n+        Returns\n+        -------\n+        step_size\n+            The size of a step in any given dimension in hyper space.\n+        \"\"\"\n+        if isinstance(self.number_of_steps, tuple):\n+            return tuple(1 / number_of_steps for number_of_steps in self.number_of_steps)\n+        return 1 / self.number_of_steps\n+\n+    @property\n+    def results_path(self) -> Path:\n+        return self.paths.output_path / \"results.csv\"\n+\n+    @property\n+    def _lists(self) -> List[List[float]]:\n+        \"\"\"\n+        A list of hypercube vectors, used to instantiate\n+        the perturb_model and create the individual\n+        perturbations.\n+        \"\"\"\n+        return make_lists(self.perturb_model.prior_count, step_size=self.step_size)\n+\n+    @property\n+    def _physical_values(self) -> List[List[float]]:\n+        \"\"\"\n+        Lists of physical values for each grid square\n+        \"\"\"\n+        return [\n+            [\n+                prior.value_for(unit_value)\n+                for prior, unit_value in zip(\n+                    self.perturb_model.priors_ordered_by_id, unit_values\n+                )\n+            ]\n+            for unit_values in self._lists\n+        ]\n+\n+    @property\n+    def _headers(self) -> Generator[str, None, None]:\n+        \"\"\"\n+        A name for each of the perturb priors\n+        \"\"\"\n+        for path, _ in self.perturb_model.prior_tuples:\n+            yield path\n+\n+    @property\n+    def _labels(self) -> Generator[str, None, None]:\n+        \"\"\"\n+        One label for each perturbation, used to distinguish\n+        fits for each perturbation by placing them in separate\n+        directories.\n+        \"\"\"\n+        for list_ in self._lists:\n+            strings = list()\n+            for value, prior_tuple in zip(list_, self.perturb_model.prior_tuples):\n+                path, prior = prior_tuple\n+                value = prior.value_for(value)\n+                strings.append(f\"{path}_{value}\")\n+            yield \"_\".join(strings)\n+\n+    @property\n+    def _perturb_instances(self) -> Generator[ModelInstance, None, None]:\n+        \"\"\"\n+        A list of instances each of which defines a perturbation to\n+        be applied to the image.\n+        \"\"\"\n+        for list_ in self._lists:\n+            yield self.perturb_model.instance_from_unit_vector(list_)\n+\n+    @property\n+    def _perturb_models(self) -> Generator[AbstractPriorModel, None, None]:\n+        \"\"\"\n+        A list of models representing a perturbation at each grid square.\n+\n+        By default models have priors with limits at the edges of a grid square.\n+        These limits can be scaled using the limit_scale variable. If the variable\n+        is 2 then the priors will have width twice the step size.\n+        \"\"\"\n+        if isinstance(self.step_size, tuple):\n+            half_step = tuple(\n+                self.limit_scale * step / 2 for step in self.step_size\n+            )\n+        else:\n+            half_step = self.limit_scale * self.step_size / 2\n+        for list_ in self._lists:\n+            if isinstance(half_step, tuple):\n+                limits = [\n+                    (\n+                        prior.value_for(max(0.0, centre - half_step[i])),\n+                        prior.value_for(min(1.0, centre + half_step[i])),\n+                    )\n+                    for i, (centre, prior) in enumerate(zip(list_, self.perturb_model.priors_ordered_by_id))\n+                ]\n+            else:\n+                limits = [\n+                    (\n+                        prior.value_for(max(0.0, centre - half_step)),\n+                        prior.value_for(min(1.0, centre + half_step)),\n+                    )\n+                    for centre, prior in zip(list_, self.perturb_model.priors_ordered_by_id)\n+                ]\n+            yield self.perturb_model.with_limits(limits)\n+\n+    def _make_jobs(self) -> Generator[Job, None, None]:\n+        \"\"\"\n+        Create a list of jobs to be run on separate processes.\n+\n+        Each job fits a perturb image with the original model\n+        and a model which includes a perturbation.\n+        \"\"\"\n+        for number, (perturb_instance, perturb_model, label) in enumerate(\n+            zip(self._perturb_instances, self._perturb_models, self._labels)\n+        ):\n+\n+            if self.perturb_model_prior_func is not None:\n+                perturb_model = self.perturb_model_prior_func(\n+                    perturb_instance=perturb_instance,\n+                    perturb_model=perturb_model\n+                )\n+\n+            simulate_instance = copy(self.instance)\n+            simulate_instance.perturb = perturb_instance\n+\n+            paths = self.paths.for_sub_analysis(\n+                label,\n+            )\n+\n+            yield self.job_cls(\n+                simulate_instance=simulate_instance,\n+                model=self.model,\n+                perturb_model=perturb_model,\n+                base_instance=self.instance,\n+                simulate_cls=self.simulate_cls,\n+                base_fit_cls=self.base_fit_cls,\n+                perturb_fit_cls=self.perturb_fit_cls,\n+                paths=paths,\n+                number=number,\n             )\n\\ No newline at end of file\n","stderr_tail":"","stdout_sha256":"9a3611f7bd4660903b1628c08d8d25ad5b2b1c149302bc2bfb3d15ed9bf08263","log_path":null,"tree_digest":"1459c61ea27c61b8763eb7d3d5cf39ddb309e94fb9c6f50a9f8cc3ada2f2c152","tree_download_s":3.792807999998331,"started_at":"2026-10-03T21:46:06.690667Z","duration_s":0.16795745794661343,"timed_out":false,"image_before":"88511e78-5d7f-4813-9058-8bbb17d1bdc2","image_after":"88511e78-5d7f-4813-9058-8bbb17d1bdc2","junit":null,"diff":{"files":[{"path":"autofit/non_linear/grid/sensitivity/__init__.py","added":314,"deleted":300,"category":"source","status":"modified"}],"patch_sha256":"9a3611f7bd4660903b1628c08d8d25ad5b2b1c149302bc2bfb3d15ed9bf08263","patch_path":"~/dev/nebius-hackathon/pilot/v2-arm-lightning/rhayes777__pyautofit-888/runs/pilot-rhayes777__pyautofit-888-1f7f86d866/artifacts/agent1/agent1-49.patch"},"patch_sha256":"9a3611f7bd4660903b1628c08d8d25ad5b2b1c149302bc2bfb3d15ed9bf08263","dropped_paths":[],"snapshot_sha":"d564f29a393b75747f1ec3da7c51070d2e12cda1"}