parameters are no longer auto-computed

README.md

@@ -230,12 +230,15 @@ soliton_num: float
 field_file : str
     if you have an initial field to use, convert it to a npz file with time (key : 'time') in s and electric field (key : 'field') in sqrt(W) (can be complex). You the use it with this config key. You can then scale it by settings any 1 of mean_power, energy and peak_power (priority is in this order)
 
-quantum_noise: bool
+quantum_noise : bool
     whether or not one-photon-per-mode quantum noise is activated. default : False
 
-intensity_noise: float
+intensity_noise : float
     relative intensity noise
 
+noise_correlation : float
+    correlation between intensity noise and pulse width noise. a negative value means anti-correlation
+
 shape: str {"gaussian", "sech"}
     shape of the pulse. default : gaussian
 

src/scgenerator/cli/cli.py

@@ -118,6 +118,13 @@ def create_parser():
     )
     init_plot_parser.set_defaults(func=plot_init)
 
+    convert_parser = subparsers.add_parser(
+        "convert",
+        help="convert parameter files that have been saved with an older version of the program",
+    )
+    convert_parser.add_argument("config", help="path to config/parameter file")
+    convert_parser.set_defaults(func=translate_parameters)
+
     return parser
 
 
@@ -224,5 +231,10 @@ def plot_dispersion(args):
     scripts.plot_dispersion(args.config, lims)
 
 
+def translate_parameters(args):
+    path = args.config
+    scripts.convert_params(path)
+
+
 if __name__ == "__main__":
     main()

src/scgenerator/const.py

@@ -1,4 +1,4 @@
-__version__ = "0.2.1rules"
+__version__ = "0.2.2rules"
 
 
 from typing import Any

src/scgenerator/physics/simulate.py

@@ -1,4 +1,3 @@
-from send2trash import send2trash
 import multiprocessing
 import multiprocessing.connection
 import os
@@ -8,6 +7,7 @@ from pathlib import Path
 from typing import Any, Generator, Type
 
 import numpy as np
+from send2trash import send2trash
 
 from .. import env, utils
 from ..logger import get_logger
@@ -638,19 +638,15 @@ class RaySimulations(Simulations, priority=2):
             )
         )
 
-        self.propagator = ray.remote(RayRK4IP).options(runtime_env=dict(env_vars=env.all_environ()))
+        self.propagator = ray.remote(RayRK4IP)
 
         self.update_cluster_frequency = 3
         self.jobs = []
         self.pool = ray.util.ActorPool(self.propagator.remote() for _ in range(self.sim_jobs_total))
         self.num_submitted = 0
         self.rolling_id = 0
-        self.p_actor = (
-            ray.remote(utils.ProgressBarActor)
-            .options(runtime_env=dict(env_vars=env.all_environ()))
-            .remote(
-                self.configuration.name, self.sim_jobs_total, self.configuration.total_num_steps
-            )
+        self.p_actor = ray.remote(utils.ProgressBarActor).remote(
+            self.configuration.name, self.sim_jobs_total, self.configuration.total_num_steps
         )
         self.configuration.skip_callback = lambda num: ray.get(self.p_actor.update.remote(0, num))
 

src/scgenerator/scripts/__init__.py

@@ -1,4 +1,5 @@
 import itertools
+import os
 from itertools import cycle
 from pathlib import Path
 from typing import Any, Iterable, Optional
@@ -9,11 +10,11 @@ from cycler import cycler
 from tqdm import tqdm
 
 from .. import env, math
-from ..const import PARAM_SEPARATOR
+from ..const import PARAM_FN, PARAM_SEPARATOR
 from ..physics import fiber, units
 from ..plotting import plot_setup
 from ..spectra import Pulse
-from ..utils import auto_crop, load_toml
+from ..utils import auto_crop, load_toml, save_toml, translate_parameters
 from ..utils.parameter import (
     Configuration,
     Parameters,
@@ -262,3 +263,18 @@ def plot_helper(config_path: Path) -> Iterable[tuple[dict, list[str], Parameters
     for style, (variables, params) in zip(cc, pseq):
         lbl = [pretty_format_value(name, value) for name, value in variables[1:-1]]
         yield style, lbl, params
+
+
+def convert_params(params_file: os.PathLike):
+    p = Path(params_file)
+    if p.name == PARAM_FN:
+        d = load_toml(params_file)
+        d = translate_parameters(d)
+        save_toml(params_file, d)
+        print(f"converted {p}")
+    else:
+        for pp in p.glob(PARAM_FN):
+            convert_params(pp)
+        for pp in p.glob("fiber*"):
+            if pp.is_dir():
+                convert_params(pp)

src/scgenerator/spectra.py

@@ -150,6 +150,7 @@ class Pulse(Sequence):
             raise FileNotFoundError(f"Folder {self.path} does not exist")
 
         self.params = Parameters.load(self.path / "params.toml")
+        self.params.compute(["t", "l", "w_c", "w0", "z_targets"])
 
         try:
             self.z = np.load(os.path.join(path, "z.npy"))

src/scgenerator/utils/__init__.py

@@ -14,7 +14,6 @@ import re
 import shutil
 import threading
 from collections import abc
-from copy import deepcopy
 from io import StringIO
 from pathlib import Path
 from string import printable as str_printable
@@ -26,8 +25,7 @@ import toml
 from tqdm import tqdm
 
 from ..const import PARAM_FN, PARAM_SEPARATOR, SPEC1_FN, SPECN_FN, Z_FN, __version__
-from ..env import TMP_FOLDER_KEY_BASE, data_folder, pbar_policy
-from ..errors import IncompleteDataFolderError
+from ..env import pbar_policy
 from ..logger import get_logger
 
 T_ = TypeVar("T_")
@@ -126,8 +124,11 @@ def load_config_sequence(final_config_path: os.PathLike) -> tuple[list[dict[str,
     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", loaded_config.get("variable", {}) if i == 0 else {})
+            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)
@@ -618,11 +619,21 @@ def auto_crop(x: np.ndarray, y: np.ndarray, rel_thr: float = 0.01) -> np.ndarray
 
 
 def translate_parameters(d: dict[str, Any]) -> dict[str, Any]:
-    old_names = dict(interp_degree="interpolation_degree")
+    old_names = dict(
+        interp_degree="interpolation_degree",
+        beta="beta2_coefficients",
+        interp_range="interpolation_range",
+    )
+    deleted_names = {"lower_wavelength_interp_limit", "upper_wavelength_interp_limit"}
+    defaults_to_add = dict(repeat=1)
     new = {}
     for k, v in d.items():
-        if isinstance(v, MutableMapping):
+        if k == "error_ok":
+            new["tolerated_error" if d.get("adapt_step_size", True) else "step_size"] = v
+        elif k in deleted_names:
+            continue
+        elif isinstance(v, MutableMapping):
             new[k] = translate_parameters(v)
         else:
             new[old_names.get(k, k)] = v
-    return new
+    return defaults_to_add | new

src/scgenerator/utils/parameter.py

@@ -67,6 +67,7 @@ VALID_VARIABLE = {
     "step_size",
     "interpolation_degree",
     "ideal_gas",
+    "length",
 }
 
 MANDATORY_PARAMETERS = [
@@ -91,6 +92,7 @@ MANDATORY_PARAMETERS = [
     "dynamic_dispersion",
     "recovery_last_stored",
     "output_path",
+    "repeat",
 ]
 
 
@@ -428,11 +430,11 @@ class Parameters:
         param["version"] = __version__
         return param
 
-    def __post_init__(self):
+    def compute(self, to_compute: list[str] = MANDATORY_PARAMETERS):
         param_dict = {k: v for k, v in asdict(self).items() if v is not None}
         evaluator = Evaluator.default()
         evaluator.set(**param_dict)
-        for p_name in MANDATORY_PARAMETERS:
+        for p_name in to_compute:
             evaluator.compute(p_name)
         valid_fields = self.all_parameters()
         for k, v in evaluator.params.items():
@@ -447,6 +449,12 @@ class Parameters:
     def load(cls, path: os.PathLike) -> "Parameters":
         return cls(**utils.load_toml(path))
 
+    @classmethod
+    def load_and_compute(cls, path: os.PathLike) -> "Parameters":
+        p = cls.load(path)
+        p.compute()
+        return p
+
     @staticmethod
     def strip_params_dict(dico: dict[str, Any]) -> dict[str, Any]:
         """prepares a dictionary for serialization. Some keys may not be preserved
@@ -753,7 +761,6 @@ class Configuration:
     num_sim: int
     repeat: int
     z_num: int
-    total_length: float
     total_num_steps: int
     worker_num: int
     parallel: bool
@@ -789,7 +796,6 @@ class Configuration:
         if self.name is None:
             self.name = Parameters.name.default
         self.z_num = 0
-        self.total_length = 0.0
         self.total_num_steps = 0
         self.sim_dirs = []
         self.overwrite = overwrite
@@ -800,7 +806,6 @@ class Configuration:
         names = set()
         for i, config in enumerate(self.configs):
             self.z_num += config["z_num"]
-            self.total_length += config["length"]
             config.setdefault("name", f"{Parameters.name.default} {i}")
             given_name = config["name"]
             i = 0
@@ -858,8 +863,8 @@ class Configuration:
                 )
                 self.data_dirs[i].append(this_path)
                 this_conf.pop("variable")
-                this_conf.update({k: v for k, v in this_vary if k != "num"})
-                self.all_required[i].append((this_vary, this_conf))
+                conf_to_use = {k: v for k, v in this_vary if k != "num"} | this_conf
+                self.all_required[i].append((this_vary, conf_to_use))
 
     def __iter__(self) -> Generator[tuple[list[tuple[str, Any]], Parameters], None, None]:
         for sim_paths, fiber in zip(self.data_dirs, self.all_required):
@@ -897,7 +902,9 @@ class Configuration:
                 task, config_dict = self.__decide(data_dir, config_dict)
                 if task == self.Action.RUN:
                     sim_dict.pop(data_dir)
-                    yield variable_list, data_dir, Parameters(**config_dict)
+                    p = Parameters(**config_dict)
+                    p.compute()
+                    yield variable_list, data_dir, p
                     if "recovery_last_stored" in config_dict and self.skip_callback is not None:
                         self.skip_callback(config_dict["recovery_last_stored"])
                     break