Sim starts, merge not

src/scgenerator/__init__.py

@@ -5,4 +5,5 @@ from .physics.simulate import RK4IP, parallel_RK4IP, run_simulation
 from .plotting import mean_values_plot, plot_spectrogram, propagation_plot, single_position_plot
 from .spectra import Pulse, Spectrum
 from .utils import Paths, open_config, parameter
-from .utils.parameter import Configuration, Parameters, PlotRange
+from .utils.parameter import Configuration, Parameters
+from .utils.utils import PlotRange

src/scgenerator/physics/simulate.py

@@ -11,7 +11,7 @@ from send2trash import send2trash
 
 from .. import env, utils
 from ..logger import get_logger
-from ..utils.parameter import Configuration, Parameters, format_variable_list
+from ..utils.parameter import Configuration, Parameters
 from . import pulse
 from .fiber import create_non_linear_op, fast_dispersion_op
 
@@ -466,14 +466,14 @@ class Simulations:
 
         self.configuration = configuration
 
-        self.name = self.configuration.final_path
-        self.sim_dir = self.configuration.final_sim_dir
+        self.name = self.configuration.name
+        self.sim_dir = self.configuration.final_path
         self.configuration.save_parameters()
 
         self.sim_jobs_per_node = 1
 
     def finished_and_complete(self):
-        for sim in self.configuration.all_configs_dict.values():
+        for sim in self.configuration.all_configs.values():
             if (
                 self.configuration.sim_status(sim.output_path)[0]
                 != self.configuration.State.COMPLETE
@@ -487,7 +487,7 @@ class Simulations:
 
     def _run_available(self):
         for variable, params in self.configuration:
-            v_list_str = format_variable_list(variable, add_iden=True)
+            v_list_str = variable.formatted_descriptor(True)
             utils.save_parameters(params.prepare_for_dump(), Path(params.output_path))
 
             self.new_sim(v_list_str, params)
@@ -526,7 +526,9 @@ class SequencialSimulations(Simulations, priority=0):
     def __init__(self, configuration: Configuration, task_id):
         super().__init__(configuration, task_id=task_id)
         self.pbars = utils.PBars(
-            self.configuration.total_num_steps, "Simulating " + self.configuration.final_path, 1
+            self.configuration.total_num_steps,
+            "Simulating " + self.configuration.final_path.name,
+            1,
         )
         self.configuration.skip_callback = lambda num: self.pbars.update(0, num)
 
@@ -569,7 +571,7 @@ class MultiProcSimulations(Simulations, priority=1):
         self.p_worker = multiprocessing.Process(
             target=utils.progress_worker,
             args=(
-                self.configuration.final_path,
+                self.configuration.final_path.name,
                 self.sim_jobs_per_node,
                 self.configuration.total_num_steps,
                 self.progress_queue,
@@ -716,7 +718,7 @@ def run_simulation(
 
     sim = new_simulation(config, method)
     sim.run()
-    path_trees = utils.build_path_trees(config.sim_dirs[-1])
+    path_trees = utils.build_path_trees(config.fiber_paths[-1])
 
     final_name = env.get(env.OUTPUT_PATH)
     if final_name is None:
@@ -724,7 +726,7 @@ def run_simulation(
 
     utils.merge(final_name, path_trees)
     try:
-        send2trash(config.sim_dirs)
+        send2trash(config.fiber_paths)
     except (PermissionError, OSError):
         get_logger(__name__).error("Could not send temporary directories to trash")
 

src/scgenerator/plotting.py

@@ -14,7 +14,8 @@ from .const import PARAM_SEPARATOR
 from .defaults import default_plotting as defaults
 from .math import abs2, span
 from .physics import pulse, units
-from .utils.parameter import Parameters, PlotRange, sort_axis
+from .utils.parameter import Parameters
+from .utils.utils import PlotRange, sort_axis
 
 RangeType = tuple[float, float, Union[str, Callable]]
 NO_LIM = object()

src/scgenerator/scripts/__init__.py

@@ -16,9 +16,8 @@ from ..utils import auto_crop, open_config, save_toml, translate_parameters
 from ..utils.parameter import (
     Configuration,
     Parameters,
-    pretty_format_from_sim_name,
-    pretty_format_value,
 )
+from ..utils.variationer import VariationDescriptor
 
 
 def fingerprint(params: Parameters):
@@ -46,7 +45,7 @@ def plot_all(sim_dir: Path, limits: list[str], show=False, **opts):
                 path, fig, ax = plot_setup(
                     pulse.path.parent
                     / (
-                        pretty_format_from_sim_name(pulse.path.name)
+                        pulse.path.name
                         + PARAM_SEPARATOR
                         + f"{left:.1f}{PARAM_SEPARATOR}{right:.1f}{PARAM_SEPARATOR}{unit}"
                     )

src/scgenerator/spectra.py

@@ -16,7 +16,8 @@ from .plotting import (
     single_position_plot,
     transform_2D_propagation,
 )
-from .utils.parameter import Parameters, PlotRange
+from .utils.parameter import Parameters
+from .utils.utils import PlotRange
 from .utils import load_spectrum
 
 

src/scgenerator/utils/__init__.py

@@ -161,29 +161,35 @@ def save_toml(path: os.PathLike, dico):
     return dico
 
 
-def load_config_sequence(final_config_path: os.PathLike) -> tuple[list[dict[str, Any]], str]:
-    loaded_config = open_config(final_config_path)
-    final_name = loaded_config.get("name")
-    fiber_list = loaded_config.pop("Fiber")
-    configs = []
-    if fiber_list is not None:
-        master_variable = loaded_config.get("variable", {})
-        for i, params in enumerate(fiber_list):
-            params.setdefault("variable", master_variable if i == 0 else {})
-            if i == 0:
-                params["variable"] |= master_variable
-            configs.append(loaded_config | params)
-    else:
-        configs.append(loaded_config)
-        while "previous_config_file" in configs[0]:
-            configs.insert(0, open_config(configs[0]["previous_config_file"]))
-        configs[0].setdefault("variable", {})
-        for pre, nex in zip(configs[:-1], configs[1:]):
-            variable = nex.pop("variable", {})
-            nex.update({k: v for k, v in pre.items() if k not in nex})
-            nex["variable"] = variable
+def load_config_sequence(path: os.PathLike) -> tuple[Path, list[dict[str, Any]]]:
+    """loads a configuration file
 
-    return configs, final_name
+    Parameters
+    ----------
+    path : os.PathLike
+        path to the config toml file
+
+    Returns
+    -------
+    final_path : Path
+        output name of the simulation
+    list[dict[str, Any]]
+        one config per fiber
+
+    """
+    loaded_config = open_config(path)
+
+    fiber_list: list[dict[str, Any]] = loaded_config.pop("Fiber")
+    if len(fiber_list) == 0:
+        raise ValueError(f"No fiber in config {path}")
+    final_path = loaded_config.get("name")
+    configs = []
+    for i, params in enumerate(fiber_list):
+        params.setdefault("variable", {})
+        configs.append(loaded_config | params)
+    configs[0]["variable"] = loaded_config.get("variable", {}) | configs[0]["variable"]
+
+    return Path(final_path), configs
 
 
 def save_parameters(

src/scgenerator/utils/parameter.py

@@ -11,14 +11,18 @@ from dataclasses import asdict, dataclass, fields
 from functools import cache, lru_cache
 from pathlib import Path
 from typing import Any, Callable, Generator, Iterable, Literal, Optional, Sequence, TypeVar, Union
+
 import numpy as np
 from numpy.lib import isin
+from scgenerator.utils import ensure_folder, variationer
 
 from .. import math, utils
 from ..const import PARAM_FN, PARAM_SEPARATOR, __version__
 from ..errors import EvaluatorError, NoDefaultError
 from ..logger import get_logger
 from ..physics import fiber, materials, pulse, units
+from ..utils.variationer import VariationDescriptor, Variationer
+from .utils import func_rewrite, _mock_function, get_arg_names
 
 T = TypeVar("T")
 
@@ -256,7 +260,7 @@ class Parameter:
         ----------
         tpe : type
             type of the paramter
-        validators : Callable[[str, Any], None]
+        validator : Callable[[str, Any], None]
             signature : validator(name, value)
             must raise a ValueError when value doesn't fit the criteria checked by
             validator. name is passed to validator to be included in the error message
@@ -290,7 +294,6 @@ class Parameter:
         if isinstance(value, Parameter):
             defaut = None if self.default is None else copy(self.default)
             instance.__dict__[self.name] = defaut
-            # instance.__dict__[self.name] = None
         else:
             if value is not None:
                 if self.converter is not None:
@@ -768,9 +771,11 @@ class Configuration:
     obj with the output path of the simulation saved in its output_path attribute.
     """
 
-    master_configs: list[dict[str, Any]]
-    sim_dirs: list[Path]
+    fiber_configs: list[dict[str, Any]]
+    master_config: dict[str, Any]
+    fiber_paths: list[Path]
     num_sim: int
+    num_fibers: int
     repeat: int
     z_num: int
     total_num_steps: int
@@ -778,19 +783,17 @@ class Configuration:
     parallel: bool
     overwrite: bool
     final_path: str
-    all_configs_dict: dict[tuple[tuple[int, ...], ...], "Configuration.__SimConfig"]
-    all_configs_list: list[list["Configuration.__SimConfig"]]
+    all_configs: dict[tuple[tuple[int, ...], ...], "Configuration.__SimConfig"]
 
     @dataclass(frozen=True)
     class __SimConfig:
-        vary_list: list[tuple[str, Any]]
+        descriptor: VariationDescriptor
         config: dict[str, Any]
         output_path: Path
-        index: tuple[tuple[int, ...], ...]
 
         @property
         def sim_num(self) -> int:
-            return len(self.index)
+            return len(self.descriptor.index)
 
     class State(enum.Enum):
         COMPLETE = enum.auto()
@@ -810,48 +813,48 @@ class Configuration:
     ):
         self.logger = get_logger(__name__)
 
-        self.master_configs, self.final_path = utils.load_config_sequence(final_config_path)
-        if self.final_path is None:
-            self.final_path = Parameters.name.default
-        self.name = Path(self.final_path).name
+        self.overwrite = overwrite
+        self.final_path, self.fiber_configs = utils.load_config_sequence(final_config_path)
+        self.final_path = utils.ensure_folder(
+            self.final_path, mkdir=False, prevent_overwrite=not self.overwrite
+        )
+        self.master_config = self.fiber_configs[0]
+        self.name = self.final_path.name
         self.z_num = 0
         self.total_num_steps = 0
-        self.sim_dirs = []
-        self.overwrite = overwrite
+        self.fiber_paths = []
+        self.all_configs = {}
         self.skip_callback = skip_callback
-        self.worker_num = self.master_configs[0].get("worker_num", max(1, os.cpu_count() // 2))
-        self.repeat = self.master_configs[0].get("repeat", 1)
+        self.worker_num = self.master_config.get("worker_num", max(1, os.cpu_count() // 2))
+        self.repeat = self.master_config.get("repeat", 1)
+        self.variationer = Variationer()
 
-        names = set()
-        for i, config in enumerate(self.master_configs):
+        fiber_names = set()
+        self.num_fibers = 0
+        for i, config in enumerate(self.fiber_configs):
+            config.setdefault("name", Parameters.name.default)
             self.z_num += config["z_num"]
-            config.setdefault("name", f"{Parameters.name.default} {i}")
-            given_name = config["name"]
-            fn_i = 0
-            while config["name"] in names:
-                config["name"] = given_name + f"_{fn_i}"
-                fn_i += 1
-            names.add(config["name"])
-
-            self.sim_dirs.append(
+            fiber_names.add(config["name"])
+            self.variationer.append(config.pop("variable"))
+            self.fiber_paths.append(
                 utils.ensure_folder(
-                    Path("_".join(["_", self.name, Path(config["name"]).name, "_"])),
+                    self.fiber_path(i, config),
                     mkdir=False,
                     prevent_overwrite=not self.overwrite,
                 )
             )
             self.__validate_variable(config)
-        self.__compute_sim_dirs()
-        [Evaluator.evaluate_default(c[0].config, True) for c in self.all_configs_list]
-        self.num_sim = len(self.all_configs_list[-1])
+            self.num_fibers += 1
+            Evaluator.evaluate_default(config, True)
+        self.num_sim = self.variationer.var_num()
         self.total_num_steps = sum(
-            config["z_num"] * len(self.all_configs_list[i])
-            for i, config in enumerate(self.master_configs)
+            config["z_num"] * self.variationer.var_num(i)
+            for i, config in enumerate(self.fiber_configs)
         )
-        self.final_sim_dir = utils.ensure_folder(
-            Path(self.master_configs[-1]["name"]), mkdir=False, prevent_overwrite=not self.overwrite
-        )
-        self.parallel = self.master_configs[0].get("parallel", Parameters.parallel.default)
+        self.parallel = self.master_config.get("parallel", Parameters.parallel.default)
+
+    def fiber_path(self, i: int, full_config: dict[str, Any]) -> Path:
+        return self.final_path / PARAM_SEPARATOR.join([format(i), self.name, full_config["name"]])
 
     def __validate_variable(self, config: dict[str, Any]):
         for k, v in config.get("variable", {}).items():
@@ -862,76 +865,62 @@ class Configuration:
             if len(v) == 0:
                 raise ValueError(f"variable parameter {k!r} must not be empty")
 
-    def __compute_sim_dirs(self):
-        self.all_configs_dict = {}
-        self.all_configs_list = []
-        self.master_configs[0]["variable"]["num"] = list(
-            range(self.master_configs[0].get("repeat", 1))
-        )
-        dp = DataPather([c["variable"] for c in self.master_configs])
-        for i, conf in enumerate(self.master_configs):
-            self.all_configs_list.append([])
-            for sim_index, prev_path, this_path, this_vary in dp.all_vary_list(i):
-                this_conf = conf.copy()
+    def __iter__(self) -> Generator[tuple[VariationDescriptor, Parameters], None, None]:
+        for i in range(self.num_fibers):
+            for sim_config in self.iterate_single_fiber(i):
+
                 if i > 0:
-                    prev_path = utils.ensure_folder(
-                        self.sim_dirs[i - 1] / prev_path, not self.overwrite, False
+                    sim_config.config["prev_data_dir"] = str(
+                        self.fiber_paths[i - 1] / sim_config.descriptor[:i].formatted_descriptor()
                     )
-                    this_conf["prev_data_dir"] = str(prev_path)
-
-                this_path = utils.ensure_folder(
-                    self.sim_dirs[i] / this_path, not self.overwrite, False
-                )
-                this_conf.pop("variable")
-                conf_to_use = {k: v for k, v in this_vary if k != "num"} | this_conf
-                self.all_configs_dict[sim_index] = self.__SimConfig(
-                    this_vary, conf_to_use, this_path, sim_index
-                )
-                self.all_configs_list[i].append(self.all_configs_dict[sim_index])
-
-    def __iter__(self) -> Generator[tuple[list[tuple[str, Any]], Parameters], None, None]:
-        for i, sim_config_list in enumerate(self.all_configs_list):
-            for sim_config, params in self.__iter_1_sim(sim_config_list):
+                params = Parameters(**sim_config.config)
+                params.compute()
                 fiber_map = []
                 for j in range(i + 1):
-                    this_conf = self.all_configs_dict[sim_config.index[: j + 1]].config
+                    this_conf = self.all_configs[sim_config.descriptor.index[: j + 1]].config
                     if j > 0:
-                        prev_conf = self.all_configs_dict[sim_config.index[:j]].config
+                        prev_conf = self.all_configs[sim_config.descriptor.index[:j]].config
                         length = prev_conf["length"] + fiber_map[j - 1][0]
                     else:
                         length = 0.0
                     fiber_map.append((length, this_conf["name"]))
-                params.output_path = str(sim_config.output_path)
                 params.fiber_map = fiber_map
-                yield sim_config.vary_list, params
+                yield sim_config.descriptor, params
 
-    def __iter_1_sim(
-        self, configs: list["Configuration.__SimConfig"]
-    ) -> Generator[tuple["Configuration.__SimConfig", Parameters], None, None]:
+    def iterate_single_fiber(
+        self, index: int
+    ) -> Generator["Configuration.__SimConfig", None, None]:
         """iterates through the parameters of only one fiber. It takes care of recovering partially
         completed simulations, skipping complete ones and waiting for the previous fiber to finish
 
         Parameters
         ----------
-        configs : list[__SimConfig]
-            list of configuration obj
+        index : int
+            which fiber to iterate over
 
         Yields
         -------
         __SimConfig
             configuration obj
-        Parameters
-            computed Parameters obj
         """
-        sim_dict: dict[Path, Configuration.__SimConfig] = {s.output_path: s for s in configs}
+        sim_dict: dict[Path, self.__SimConfig] = {}
+        for descr in self.variationer.iterate(index):
+            cfg = descr.update_config(self.fiber_configs[index])
+            p = ensure_folder(
+                self.fiber_paths[index] / descr.formatted_descriptor(),
+                not self.overwrite,
+                False,
+            )
+            cfg["output_path"] = str(p)
+            sim_config = self.__SimConfig(descr, cfg, p)
+            sim_dict[p] = sim_config
+            self.all_configs[sim_config.descriptor.index] = sim_config
         while len(sim_dict) > 0:
             for data_dir, sim_config in sim_dict.items():
                 task, config_dict = self.__decide(sim_config)
                 if task == self.Action.RUN:
                     sim_dict.pop(data_dir)
-                    p = Parameters(**config_dict)
-                    p.compute()
-                    yield sim_config, p
+                    yield sim_config
                     if "recovery_last_stored" in config_dict and self.skip_callback is not None:
                         self.skip_callback(config_dict["recovery_last_stored"])
                     break
@@ -956,7 +945,7 @@ class Configuration:
 
         Returns
         -------
-        str : {'run', 'wait', 'skip'}
+        str : Configuration.Action
             what to do
         config_dict : dict[str, Any]
             config dictionary. The only key possibly modified is 'prev_data_dir', which
@@ -1012,7 +1001,7 @@ class Configuration:
             raise ValueError(f"Too many spectra in {data_dir}")
 
     def save_parameters(self):
-        for config, sim_dir in zip(self.master_configs, self.sim_dirs):
+        for config, sim_dir in zip(self.fiber_configs, self.fiber_paths):
             os.makedirs(sim_dir, exist_ok=True)
             utils.save_toml(sim_dir / f"initial_config.toml", config)
 
@@ -1022,144 +1011,6 @@ class Configuration:
             return param
 
 
-@dataclass(frozen=True)
-class PlotRange:
-    left: float = Parameter(type_checker(int, float))
-    right: float = Parameter(type_checker(int, float))
-    unit: Callable[[float], float] = Parameter(units.is_unit, converter=units.get_unit)
-    conserved_quantity: bool = Parameter(boolean, default=True)
-
-    def __str__(self):
-        return f"{self.left:.1f}-{self.right:.1f} {self.unit.__name__}"
-
-    def sort_axis(self, axis: np.ndarray) -> tuple[np.ndarray, np.ndarray, tuple[float, float]]:
-        return sort_axis(axis, self)
-
-    def __iter__(self):
-        yield self.left
-        yield self.right
-        yield self.unit.__name__
-
-
-def sort_axis(
-    axis: np.ndarray, plt_range: PlotRange
-) -> tuple[np.ndarray, np.ndarray, tuple[float, float]]:
-    """
-    given an axis, returns this axis cropped according to the given range, converted and sorted
-
-    Parameters
-    ----------
-    axis : 1D array containing the original axis (usual the w or t array)
-    plt_range : tupple (min, max, conversion_function) used to crop the axis
-
-    Returns
-    -------
-    cropped : the axis cropped, converted and sorted
-    indices : indices to use to slice and sort other array in the same fashion
-    extent : tupple with min and max of cropped
-
-    Example
-    -------
-    w = np.append(np.linspace(0, -10, 20), np.linspace(0, 10, 20))
-    t = np.linspace(-10, 10, 400)
-    W, T = np.meshgrid(w, t)
-    y = np.exp(-W**2 - T**2)
-
-    # Define ranges
-    rw = (-4, 4, s)
-    rt = (-2, 6, s)
-
-    w, cw = sort_axis(w, rw)
-    t, ct = sort_axis(t, rt)
-
-    # slice y according to the given ranges
-    y = y[ct][:, cw]
-    """
-    if isinstance(plt_range, tuple):
-        plt_range = PlotRange(*plt_range)
-    r = np.array((plt_range.left, plt_range.right), dtype="float")
-
-    indices = np.arange(len(axis))[
-        (axis <= np.max(plt_range.unit(r))) & (axis >= np.min(plt_range.unit(r)))
-    ]
-    cropped = axis[indices]
-    order = np.argsort(plt_range.unit.inv(cropped))
-    indices = indices[order]
-    cropped = cropped[order]
-    out_ax = plt_range.unit.inv(cropped)
-
-    return out_ax, indices, (out_ax[0], out_ax[-1])
-
-
-def get_arg_names(func: Callable) -> list[str]:
-    spec = inspect.getfullargspec(func)
-    args = spec.args
-    if spec.defaults is not None and len(spec.defaults) > 0:
-        args = args[: -len(spec.defaults)]
-    return args
-
-
-def validate_arg_names(names: list[str]):
-    for n in names:
-        if re.match(r"^[^\s\-'\(\)\"\d][^\(\)\-\s'\"]*$", n) is None:
-            raise ValueError(f"{n} is an invalid parameter name")
-
-
-def func_rewrite(func: Callable, kwarg_names: list[str], arg_names: list[str] = None) -> Callable:
-    if arg_names is None:
-        arg_names = get_arg_names(func)
-    else:
-        validate_arg_names(arg_names)
-    validate_arg_names(kwarg_names)
-    sign_arg_str = ", ".join(arg_names + kwarg_names)
-    call_arg_str = ", ".join(arg_names + [f"{s}={s}" for s in kwarg_names])
-    tmp_name = f"{func.__name__}_0"
-    func_str = f"def {tmp_name}({sign_arg_str}):\n    return __func__({call_arg_str})"
-    scope = dict(__func__=func)
-    exec(func_str, scope)
-    out_func = scope[tmp_name]
-    out_func.__module__ = "evaluator"
-    return out_func
-
-
-@cache
-def _mock_function(num_args: int, num_returns: int) -> Callable:
-    if not isinstance(num_args, int) and isinstance(num_returns, int):
-        raise TypeError(f"num_args and num_returns must be int")
-    arg_str = ", ".join("a" * (n + 1) for n in range(num_args))
-    return_str = ", ".join("True" for _ in range(num_returns))
-    func_name = f"__mock_{num_args}_{num_returns}"
-    func_str = f"def {func_name}({arg_str}):\n    return {return_str}"
-    scope = {}
-    exec(func_str, scope)
-    out_func = scope[func_name]
-    out_func.__module__ = "evaluator"
-    return out_func
-
-
-def pretty_format_from_sim_name(name: str) -> str:
-    """formats a pretty version of a simulation directory
-
-    Parameters
-    ----------
-    name : str
-        name of the simulation (directory name)
-
-    Returns
-    -------
-    str
-        prettier name
-    """
-    s = name.split(PARAM_SEPARATOR)
-    out = []
-    for key, value in zip(s[::2], s[1::2]):
-        try:
-            out += [key.replace("_", " "), getattr(Parameters, key).display(float(value))]
-        except (AttributeError, ValueError):
-            out.append(key + PARAM_SEPARATOR + value)
-    return PARAM_SEPARATOR.join(out)
-
-
 default_rules: list[Rule] = [
     # Grid
     *Rule.deduce(

src/scgenerator/utils/utils.py

@@ -1,4 +1,23 @@
+import inspect
+import re
+from functools import cache
 from string import printable as str_printable
+from typing import Callable
+
+import numpy as np
+from pydantic import BaseModel
+
+from ..physics.units import get_unit
+
+
+class HashableBaseModel(BaseModel):
+    """Pydantic BaseModel that's immutable and can be hashed"""
+
+    def __hash__(self) -> int:
+        return hash(type(self)) + sum(hash(v) for v in self.__dict__.values())
+
+    class Config:
+        allow_mutation = False
 
 
 def to_62(i: int) -> str:
@@ -10,3 +29,118 @@ def to_62(i: int) -> str:
         i, value = divmod(i, 62)
         arr.append(str_printable[value])
     return "".join(reversed(arr))
+
+
+class PlotRange(HashableBaseModel):
+    left: float
+    right: float
+    unit: Callable[[float], float]
+    conserved_quantity: bool = True
+
+    def __init__(self, left, right, unit, **kwargs):
+        super().__init__(left=left, right=right, unit=get_unit(unit), **kwargs)
+
+    def __str__(self):
+        return f"{self.left:.1f}-{self.right:.1f} {self.unit.__name__}"
+
+    def sort_axis(self, axis: np.ndarray) -> tuple[np.ndarray, np.ndarray, tuple[float, float]]:
+        return sort_axis(axis, self)
+
+    def __iter__(self):
+        yield self.left
+        yield self.right
+        yield self.unit.__name__
+
+
+def sort_axis(
+    axis: np.ndarray, plt_range: PlotRange
+) -> tuple[np.ndarray, np.ndarray, tuple[float, float]]:
+    """
+    given an axis, returns this axis cropped according to the given range, converted and sorted
+
+    Parameters
+    ----------
+    axis : 1D array containing the original axis (usual the w or t array)
+    plt_range : tupple (min, max, conversion_function) used to crop the axis
+
+    Returns
+    -------
+    cropped : the axis cropped, converted and sorted
+    indices : indices to use to slice and sort other array in the same fashion
+    extent : tupple with min and max of cropped
+
+    Example
+    -------
+    w = np.append(np.linspace(0, -10, 20), np.linspace(0, 10, 20))
+    t = np.linspace(-10, 10, 400)
+    W, T = np.meshgrid(w, t)
+    y = np.exp(-W**2 - T**2)
+
+    # Define ranges
+    rw = (-4, 4, s)
+    rt = (-2, 6, s)
+
+    w, cw = sort_axis(w, rw)
+    t, ct = sort_axis(t, rt)
+
+    # slice y according to the given ranges
+    y = y[ct][:, cw]
+    """
+    if isinstance(plt_range, tuple):
+        plt_range = PlotRange(*plt_range)
+    r = np.array((plt_range.left, plt_range.right), dtype="float")
+
+    indices = np.arange(len(axis))[
+        (axis <= np.max(plt_range.unit(r))) & (axis >= np.min(plt_range.unit(r)))
+    ]
+    cropped = axis[indices]
+    order = np.argsort(plt_range.unit.inv(cropped))
+    indices = indices[order]
+    cropped = cropped[order]
+    out_ax = plt_range.unit.inv(cropped)
+
+    return out_ax, indices, (out_ax[0], out_ax[-1])
+
+
+def get_arg_names(func: Callable) -> list[str]:
+    spec = inspect.getfullargspec(func)
+    args = spec.args
+    if spec.defaults is not None and len(spec.defaults) > 0:
+        args = args[: -len(spec.defaults)]
+    return args
+
+
+def validate_arg_names(names: list[str]):
+    for n in names:
+        if re.match(r"^[^\s\-'\(\)\"\d][^\(\)\-\s'\"]*$", n) is None:
+            raise ValueError(f"{n} is an invalid parameter name")
+
+
+def func_rewrite(func: Callable, kwarg_names: list[str], arg_names: list[str] = None) -> Callable:
+    if arg_names is None:
+        arg_names = get_arg_names(func)
+    else:
+        validate_arg_names(arg_names)
+    validate_arg_names(kwarg_names)
+    sign_arg_str = ", ".join(arg_names + kwarg_names)
+    call_arg_str = ", ".join(arg_names + [f"{s}={s}" for s in kwarg_names])
+    tmp_name = f"{func.__name__}_0"
+    func_str = f"def {tmp_name}({sign_arg_str}):\n    return __func__({call_arg_str})"
+    scope = dict(__func__=func)
+    exec(func_str, scope)
+    out_func = scope[tmp_name]
+    out_func.__module__ = "evaluator"
+    return out_func
+
+
+@cache
+def _mock_function(num_args: int, num_returns: int) -> Callable:
+    arg_str = ", ".join("a" * (n + 1) for n in range(num_args))
+    return_str = ", ".join("True" for _ in range(num_returns))
+    func_name = f"__mock_{num_args}_{num_returns}"
+    func_str = f"def {func_name}({arg_str}):\n    return {return_str}"
+    scope = {}
+    exec(func_str, scope)
+    out_func = scope[func_name]
+    out_func.__module__ = "evaluator"
+    return out_func

src/scgenerator/utils/variationer.py

@@ -1,45 +1,14 @@
-from pydantic import BaseModel, validator
-from typing import Union, Iterable, Generator, Any
-from collections.abc import Sequence, MutableMapping
+from math import prod
 import itertools
+from collections.abc import MutableMapping, Sequence
+from pathlib import Path
+from typing import Any, Callable, Generator, Iterable, Union
+
+import numpy as np
+from pydantic import validator
+
 from ..const import PARAM_SEPARATOR
 from . import utils
-import numpy as np
-from pathlib import Path
-
-
-def format_value(name: str, value) -> str:
-    if value is True or value is False:
-        return str(value)
-    elif isinstance(value, (float, int)):
-        try:
-            return getattr(Parameters, name).display(value)
-        except AttributeError:
-            return format(value, ".9g")
-    elif isinstance(value, (list, tuple, np.ndarray)):
-        return "-".join([str(v) for v in value])
-    elif isinstance(value, str):
-        p = Path(value)
-        if p.exists():
-            return p.stem
-    return str(value)
-
-
-def pretty_format_value(name: str, value) -> str:
-    try:
-        return getattr(Parameters, name).display(value)
-    except AttributeError:
-        return name + PARAM_SEPARATOR + str(value)
-
-
-class HashableBaseModel(BaseModel):
-    """Pydantic BaseModel that's immutable and can be hashed"""
-
-    def __hash__(self) -> int:
-        return hash(type(self)) + sum(hash(v) for v in self.__dict__.values())
-
-    class Config:
-        allow_mutation = False
 
 
 class VariationSpecsError(ValueError):
@@ -67,23 +36,20 @@ class Variationer:
     all_indices: list[list[int]]
     all_dicts: list[list[dict[str, list]]]
 
-    def __init__(self, variables: Iterable[Union[list[MutableMapping], MutableMapping]]):
+    def __init__(self, variables: Iterable[Union[list[MutableMapping], MutableMapping]] = None):
         self.all_indices = []
         self.all_dicts = []
-        for i, el in enumerate(variables):
-            if not isinstance(el, Sequence):
-                el = [{k: v} for k, v in el.items()]
-            else:
-                el = list(el)
-            self.append(el)
+        if variables is not None:
+            for i, el in enumerate(variables):
+                self.append(el)
 
-    def append(self, var_list: list[dict[str, list]]):
+    def append(self, var_list: Union[list[MutableMapping], MutableMapping]):
         """append a list of variable parameter sets
         each call to append creates a new group of parameters
 
         Parameters
         ----------
-        var_list : list[dict[str, list]]
+        var_list : Union[list[MutableMapping], MutableMapping]
             each dict in the list is treated as an independent parameter
             this means that if for one dict, len > 1, the lists of possible values
             must be the same length
@@ -100,6 +66,10 @@ class Variationer:
         VariationSpecsError
             raised when possible values lists in a same dict are not the same length
         """
+        if not isinstance(var_list, Sequence):
+            var_list = [{k: v} for k, v in var_list.items()]
+        else:
+            var_list = list(var_list)
         num_vars = []
         for d in var_list:
             values = list(d.values())
@@ -114,30 +84,43 @@ class Variationer:
         self.all_indices.append(num_vars)
         self.all_dicts.append(var_list)
 
-    def iterate(self, index: int = -1) -> Generator["SimulationDescriptor", None, None]:
-        if index < 0:
-            index = len(self.all_indices) + index + 1
-        flattened_indices = sum(self.all_indices[:index], [])
-        index_positions = np.cumsum([0] + [len(i) for i in self.all_indices[:index]])
+    def iterate(self, index: int = -1) -> Generator["VariationDescriptor", None, None]:
+        index = self.__index(index)
+        flattened_indices = sum(self.all_indices[: index + 1], [])
+        index_positions = np.cumsum([0] + [len(i) for i in self.all_indices[: index + 1]])
         ranges = [range(i) for i in flattened_indices]
         for r in itertools.product(*ranges):
             out: list[list[tuple[str, Any]]] = []
+            indicies: list[list[int]] = []
             for i, (start, end) in enumerate(zip(index_positions[:-1], index_positions[1:])):
                 out.append([])
+                indicies.append([])
                 for value_index, var_d in zip(r[start:end], self.all_dicts[i]):
                     for k, v in var_d.items():
                         out[-1].append((k, v[value_index]))
-            yield SimulationDescriptor(raw_descr=out)
+                        indicies[-1].append(value_index)
+            yield VariationDescriptor(raw_descr=out, index=indicies)
+
+    def __index(self, index: int) -> int:
+        if index < 0:
+            index = len(self.all_indices) + index
+        return index
+
+    def var_num(self, index: int = -1) -> int:
+        index = self.__index(index)
+        return max(1, prod(prod(el) for el in self.all_indices[: index + 1]))
 
 
-class SimulationDescriptor(HashableBaseModel):
+class VariationDescriptor(utils.HashableBaseModel):
     raw_descr: tuple[tuple[tuple[str, Any], ...], ...]
+    index: tuple[tuple[int, ...], ...]
     separator: str = "fiber"
+    _format_registry: dict[str, Callable[..., str]] = {}
 
     def __str__(self) -> str:
-        return self.descriptor(add_identifier=False)
+        return self.formatted_descriptor(add_identifier=False)
 
-    def descriptor(self, add_identifier=False) -> str:
+    def formatted_descriptor(self, add_identifier=False) -> str:
         """formats a variable list into a str such that each simulation has a unique
         directory name. A u_XXX unique identifier and b_XXX (ignoring repeat simulations)
         branch identifier can added at the beginning.
@@ -156,7 +139,7 @@ class SimulationDescriptor(HashableBaseModel):
 
         for p_name, p_value in self.flat:
             ps = p_name.replace("/", "").replace("\\", "").replace(PARAM_SEPARATOR, "")
-            vs = format_value(p_name, p_value).replace("/", "").replace(PARAM_SEPARATOR, "")
+            vs = self.format_value(p_name, p_value).replace("/", "").replace(PARAM_SEPARATOR, "")
             str_list.append(ps + PARAM_SEPARATOR + vs)
         tmp_name = PARAM_SEPARATOR.join(str_list)
         if not add_identifier:
@@ -165,6 +148,34 @@ class SimulationDescriptor(HashableBaseModel):
             self.identifier + PARAM_SEPARATOR + self.branch.identifier + PARAM_SEPARATOR + tmp_name
         )
 
+    @classmethod
+    def register_formatter(cls, p_name: str, func: Callable[..., str]):
+        cls._format_registry[p_name] = func
+
+    def format_value(self, name: str, value) -> str:
+        if value is True or value is False:
+            return str(value)
+        elif isinstance(value, (float, int)):
+            try:
+                return self._format_registry[name](value)
+            except KeyError:
+                return format(value, ".9g")
+        elif isinstance(value, (list, tuple, np.ndarray)):
+            return "-".join([str(v) for v in value])
+        elif isinstance(value, str):
+            p = Path(value)
+            if p.exists():
+                return p.stem
+        return str(value)
+
+    def __getitem__(self, key) -> "VariationDescriptor":
+        return VariationDescriptor(
+            raw_descr=self.raw_descr[key], index=self.index[key], separator=self.separator
+        )
+
+    def update_config(self, cfg: dict[str, Any]):
+        return cfg | {k: v for k, v in self.raw_descr[-1]}
+
     @property
     def flat(self) -> list[tuple[str, Any]]:
         out = []
@@ -177,17 +188,27 @@ class SimulationDescriptor(HashableBaseModel):
 
     @property
     def branch(self) -> "BranchDescriptor":
-        return SimulationDescriptor(raw_descr=self.raw_descr, separator=self.separator)
+        for i in reversed(range(len(self.raw_descr))):
+            for j in reversed(range(len(self.raw_descr[i]))):
+                if self.raw_descr[i][j][0] == "num":
+                    del self.raw_descr[i][j]
+        return VariationDescriptor(
+            raw_descr=self.raw_descr, index=self.index, separator=self.separator
+        )
 
     @property
     def identifier(self) -> str:
         return "u_" + utils.to_62(hash(str(self.flat)))
 
 
-class BranchDescriptor(SimulationDescriptor):
+class BranchDescriptor(VariationDescriptor):
+    __ids: dict[int, int] = {}
+
     @property
     def identifier(self) -> str:
-        return "b_" + utils.to_62(hash(str(self.flat)))
+        branch_id = hash(str(self.flat))
+        self.__ids.setdefault(branch_id, len(self.__ids))
+        return str(self.__ids[branch_id])
 
     @validator("raw_descr")
     def validate_raw_descr(cls, v):