integrated scgenerator to remove dependency

.gitignore

@@ -1,3 +1,5 @@
+.conda-env
+dispersion_config.toml
 pyrightconfig.json
 .DS_Store
 

README.md

@@ -1,7 +1,3 @@
-# Crash course on Python installation, with a few small side tracks
-
-
-
 # Installation
 1. If not already done so, create a virtual environment to contain all the packages required. Python 3.10 is recommended. The command below will create a new environment named 'app-env' with the latest version of Python 3.10.
 
@@ -11,6 +7,39 @@ conda create -y -n app-env python=3.10
 
 conda activate app-env
 
-3. The prompt should now read '(app-env)' on the left. The app is not published on Github or anywhere else. The link below points to my own personnal home server (I didn't find any way of getting a direct download link  You are now ready to install everything with this command:
+3. The prompt should now read '(app-env)' on the left. The app is not published on Github or anywhere else. The link below points to the webserver running on the FibNas server. You are now ready to install everything with this command:
+
+pip install http://130.92.113.172/dispersionapp.zip
+
+# Usage
+
+1. Make sure your environment is activated (step 1 above)
+
+2. call the `dispersionapp` command
+
+dispersionapp
+
+2*. this can be also done via calling python first (`-m` tells python to open the specified module)
+
+python -m dispersionapp
+
+# Interface
+
+At the very top is a summary of the configuration as well as a few physical properties derived from the current parameters.
+
+Below, you will find the parameter list. Each parameter has a couple elements. From left to right :
+- the name of the paramter
+- a slider to quickly explore values
+- arrow buttons to move from one value to the next.
+- a text box where you can input your desired value. The closest possible value will then be selected. Possible values are hard-coded. I might add an option for custom values later on.
+
+To navigate the plot:
+- left-click+drag to move around
+- right-click+drag to zoom
+- scroll wheel to zoom
+- for each of these movement, you can do it while the cursor is hovering over one of the axis. In that case, only that axis is affected.
+- to go back to a view that fits everything, click on the small "A" at the bottom left. This is not ideal for the dispersion plot because of all the resonances, so you can find another reset button in the parameter list.
+- to hide/show a line, click on it in the legend. You can also click+drag on the legend to move it around.
+- click+drag on the purble title bar to move plots around (it's a bit buggy)
+- double click on the purple title bar (left side or top) to pop out the plot in its own window.
 
-pip install https://bao.dedebenui.me/s/f3KgiqMq7giN73i/download

pyproject.toml

@@ -1,17 +1,19 @@
 [project]
 name = "dispersionapp"
-version = "0.1.0"
+version = "0.2.2"
 description = "Model hollow capillary and revolver fiber interactively"
-authors = [{ name = "Benoît Sierro", email = "benoit.sierro@unibe.ch" }]
+authors = [{ name = "Dedebenui", email = "dedebenui@protonmail.com" }]
+requires-python = ">=3.13"
 
 dependencies = [
-    "scgenerator @ git+https://github.com/bsierro/scgenerator.git",
     "click",
-    "pydantic",
+    "pydantic >= 2",
     "tomli",
     "tomli_w",
     "PySide6 >= 6.4.0",
     "pyqtgraph >= 0.13.1",
+    "numba>=0.62.1",
+    "scipy>=1.16.3",
 ]
 
 license = { file = "LICENSE" }
@@ -25,5 +27,17 @@ classifiers = [
 dispersionapp = "dispersionapp.__main__:main"
 
 [build-system]
-build-backend = "setuptools.build_meta"
+requires = ["hatchling"]
-requires = ["setuptools >= 65.0.0"]
+build-backend = "hatchling.build"
+
+[tool.pyright]
+exclude = [".venv"]
+venvPath = "."
+venv = ".venv"
+typeCheckingMode = "off"
+
+[dependency-groups]
+dev = [
+    "matplotlib>=3.10.7",
+]
+

src/dispersionapp/__main__.py

@@ -3,7 +3,7 @@ import sys
 
 import click
 
-from dispersionapp.core import DEFAULT_CONFIG_FILE
+from dispersionapp.core import DEFAULT_CONFIG_FILE, Config
 from dispersionapp.gui import app
 
 
@@ -11,12 +11,17 @@ from dispersionapp.gui import app
 @click.option(
     "-c",
     "--config",
+    "config_file",
     default=DEFAULT_CONFIG_FILE,
     help="configuration file in TOML format",
     type=click.Path(file_okay=True, dir_okay=False, resolve_path=True),
 )
+@click.option("-g", "--gas", help="Gas. Must be one of ", type=str)
 @click.version_option()
-def main(config: os.PathLike):
+def main(config_file: os.PathLike, gas: str | None = None):
+    config = Config.load(config_file)
+    if gas is not None:
+        config.gas = gas.lower()
     app(config)
 
 

src/dispersionapp/core.py

@@ -1,33 +1,39 @@
 from __future__ import annotations
+import os
 
 import sys
 from pathlib import Path
-from typing import  NamedTuple
+from typing import Annotated, NamedTuple, Self
 
 import numpy as np
-import scgenerator as sc
 import tomli
 import tomli_w
-from pydantic import BaseModel, PrivateAttr, ValidationError, confloat, conint
+from pydantic import BaseModel, PrivateAttr, ValidationError, Field
 
-DEFAULT_CONFIG_FILE = "dispersion_config.toml"
+import dispersionapp.physics.units as units
+import dispersionapp.physics.materials as materials
+import dispersionapp.physics.pulse as pulse
+import dispersionapp.physics.fiber as fiber
+import dispersionapp.math as math
+
+DEFAULT_CONFIG_FILE = Path.home() / "dispersion_config.toml"
 
 
 class CurrentState(BaseModel):
     core_diameter_um: float
     pressure_mbar: float
     wall_thickness_um: float
+    num_resonances: Annotated[int, Field(ge=6, le=40)] = 6
     n_tubes: int
     gap_um: float
     t_fwhm_fs: float
 
 
 class Config(BaseModel):
-    wl_min: confloat(ge=100, le=1000) = 160
+    wl_min: Annotated[float, Field(ge=100, le=1000)] = 160
-    wl_max: confloat(ge=500, le=6000) = 1600
+    wl_max: Annotated[float, Field(ge=500, le=6000)] = 1600
-    wl_pump: confloat(ge=200, le=6000) = 800
+    wl_pump: Annotated[float, Field(ge=200, le=6000)] = 800
-    rep_rate: confloat(gt=0) = 8e3
+    rep_rate: Annotated[float, Field(gt=0)] = 8e3
-    num_resonances: conint(ge=6, le=20) = 6
     gas: str = "argon"
     safety_factor: float = 10.0
     current_state: CurrentState | None = None
@@ -35,7 +41,7 @@ class Config(BaseModel):
     _file_name: Path = PrivateAttr()
 
     @classmethod
-    def load(cls, config_file: str | None = None) -> Config:
+    def load(cls, config_file: os.PathLike | None = None) -> Self:
         config_file = Path(config_file or DEFAULT_CONFIG_FILE)
         if not config_file.exists():
             config_file.touch()
@@ -45,7 +51,7 @@ class Config(BaseModel):
         try:
             out = cls(**d)
         except ValidationError as e:
-            s = f"invalid input in config file {config_file}:\n{e}"
+            s = f"invalid input in config file {config_file}:\n{e}\ncreating new config"
             print(s)
             sys.exit(1)
         out._file_name = Path(config_file)
@@ -54,16 +60,24 @@ class Config(BaseModel):
     def save(self):
         tmp = self._file_name.parent / f"{self._file_name.name}.tmp"
         with open(tmp, "wb") as file:
-            tomli_w.dump(self.dict(), file)
+            tomli_w.dump(self.model_dump(), file)
         tmp.replace(self._file_name)
 
     def update_current(
-        self, core_diameter_um, pressure_mbar, wall_thickness_um, n_tubes, gap_um, t_fwhm_fs
+        self,
+        core_diameter_um: float,
+        pressure_mbar: float,
+        wall_thickness_um: float,
+        num_resonances: int,
+        n_tubes: int,
+        gap_um: float,
+        t_fwhm_fs: float,
     ):
         self.current_state = CurrentState(
             core_diameter_um=core_diameter_um,
             pressure_mbar=pressure_mbar,
             wall_thickness_um=wall_thickness_um,
+            num_resonances=num_resonances,
             n_tubes=n_tubes,
             gap_um=gap_um,
             t_fwhm_fs=t_fwhm_fs,
@@ -80,12 +94,16 @@ class LimitValues(NamedTuple):
 
 def b2(w, n_eff):
     dw = w[1] - w[0]
-    beta = sc.fiber.beta(w, n_eff)
+    beta = fiber.beta(w, n_eff)
-    return sc.math.differentiate_arr(beta, 2, 4, dw)
+    return math.differentiate_arr(beta, 2, 4, dw)
 
 
 def N_sf_max(
-    wl: np.ndarray, t0: float, wl_zero_disp: float, gas: sc.materials.Gas, safety: float = 10.0
+    wl: np.ndarray,
+    t0: float,
+    wl_zero_disp: float,
+    gas: materials.Gas,
+    safety: float = 10.0,
 ) -> np.ndarray:
     """
     maximum soliton number according to self focusing
@@ -97,30 +115,34 @@ def N_sf_max(
 
 
 def N_ion_max(
-    wl: np.ndarray, t0: float, wl_zero_disp: float, gas: sc.materials.Gas, safety: float = 10.0
+    wl: np.ndarray,
+    t0: float,
+    wl_zero_disp: float,
+    gas: materials.Gas,
+    safety: float = 10.0,
 ) -> np.ndarray:
     """
     eq. S16 in Travers2019
     """
-    ind = sc.math.argclosest(wl, wl_zero_disp)
+    ind = math.argclosest(wl, wl_zero_disp)
     f = np.gradient(np.gradient(gas.sellmeier.chi(wl), wl), wl)
-    factor = (sc.math.u_nm(1, 1) / sc.units.c) ** 2 * (0.5 * wl / np.pi) ** 3
+    factor = (math.u_nm(1, 1) / units.c) ** 2 * (0.5 * wl / np.pi) ** 3
     delta = factor * (f / f[ind] - 1)
     denom = safety * np.pi * wl * np.abs(delta) * f[ind]
-    return t0 * sc.math.u_nm(1, 1) * np.sqrt(gas.n2() * gas.barrier_suppression / denom)
+    return t0 * math.u_nm(1, 1) * np.sqrt(gas.n2() * gas.barrier_suppression / denom)
 
 
 def solition_num(
     t0: float, w0: float, beta2: float, n2: float, core_radius: float, peak_power: float
 ) -> float:
-    gamma = sc.fiber.gamma_parameter(n2, w0, sc.fiber.A_eff_marcatili(core_radius))
+    gamma = fiber.gamma_parameter(n2, w0, fiber.effective_area_marcatili(core_radius))
-    ld = sc.pulse.L_D(t0, beta2)
+    ld = pulse.dispersion_length(t0, beta2)
     return np.sqrt(gamma * ld * peak_power)
 
 
 def energy(
     t0: float, w0: float, beta2: float, n2: float, core_radius: float, solition_num: float
 ) -> float:
-    gamma = sc.fiber.gamma_parameter(n2, w0, sc.fiber.A_eff_marcatili(core_radius))
+    gamma = fiber.gamma_parameter(n2, w0, fiber.effective_area_marcatili(core_radius))
     peak_power = solition_num**2 * abs(beta2) / (t0**2 * gamma)
-    return sc.pulse.P0_to_E0(peak_power, t0, "sech")
+    return pulse.P0_to_E0(peak_power, t0, "sech")

src/dispersionapp/data/README.md

@@ -0,0 +1,10 @@
+# `materials.json`
+Contains material property like Sellmeir coefficients, nonlinear refractive index and Van der Waals constants.
+
+Sources are embeded in the document and are completed as such:
+Van der Waals constants : https://en.wikipedia.org/wiki/Van_der_Waals_constants_(data_page)
+Chi3 : Wahlstrand 2012
+
+# `raman_response.csv`
+Raman impulse response recovered from measured gain spectrum. This is used then `raman_type` is set to `"measured"` (Stolen1989).
+

src/dispersionapp/data/materials.json

@@ -0,0 +1,449 @@
+{
+  "air":
+  {
+    "a": 0.1358,
+    "atomic_mass": 4.809e-26,
+    "atomic_number": 7.14,
+    "b": 3.64e-5,
+    "kerr":
+    {
+      "P0": 101325,
+      "T0": 293.15,
+      "n2": 4e-23,
+      "source": "Nibbering, E., Grillon, G., Franco, M.A., Prade, B., Mysyrowicz, A., 1997. JOSA B 14, 650–660."
+    },
+    "sellmeier":
+    {
+      "B":
+      [
+        57921050000,
+        1679170000
+      ],
+      "C":
+      [
+        2.380185e+14,
+        5.7362e+13
+      ],
+      "P0": 101325,
+      "T0": 288.15,
+      "kind": 2
+    }
+  },
+  "argon":
+  {
+    "a": 0.1355,
+    "atomic_mass": 6.6338e-26,
+    "atomic_number": 18,
+    "b": 3.201e-5,
+    "ionization_energy": 2.5249661793774e-18,
+    "kerr":
+    {
+      "P0": 30400,
+      "T0": 273.15,
+      "n2": 9.7e-24,
+      "source": "Wahlstrand, J. K., Cheng, Y. H., & Milchberg, H. M. (2012). High field optical nonlinearity and the Kramers-Kronig relations. Physical review letters, 109(11), 113904."
+    },
+    "sellmeier":
+    {
+      "B":
+      [
+        2501410000,
+        500283000,
+        52234300000
+      ],
+      "C":
+      [
+        9.1012e+13,
+        8.7892e+13,
+        2.1402e+14
+      ],
+      "P0": 101325,
+      "T0": 273.15,
+      "kind": 2,
+      "source": "A. Bideau-Mehu, Y. Guern, R. Abjean, A. Johannin-Gilles. Measurement of refractive indices of neon, argon, krypton and xenon in the 253.7-140.4 nm wavelength range. Dispersion relations and estimated oscillator strengths of the resonance lines. J. Quant. Spectrosc. Rad. Transfer 25, 395-402 (1981)"
+    }
+  },
+  "argon_alt":
+  {
+    "a": 0.1355,
+    "atomic_mass": 6.6338e-26,
+    "atomic_number": 18,
+    "b": 3.201e-5,
+    "kerr":
+    {
+      "P0": 30400,
+      "T0": 273.15,
+      "n2": 9.7e-24,
+      "source": "Wahlstrand, J. K., Cheng, Y. H., & Milchberg, H. M. (2012). High field optical nonlinearity and the Kramers-Kronig relations. Physical review letters, 109(11), 113904."
+    },
+    "sellmeier":
+    {
+      "B":
+      [
+        0.0002033229,
+        0.0003445831
+      ],
+      "C":
+      [
+        2.0612e-16,
+        8.066e-15
+      ],
+      "P0": 101325,
+      "T0": 273.15,
+      "kind": 1,
+      "source": "A. Börzsönyi, Z. Heiner, M. P. Kalashnikov, A. P. Kovács, and K. Osvay, Dispersion measurement of inert gases and gas mixtures at 800 nm, Appl. Opt. 47, 4856-4863 (2008)"
+    }
+  },
+  "argon_alt2":
+  {
+    "a": 0.1355,
+    "b": 3.201e-5,
+    "kerr":
+    {
+      "P0": 30400,
+      "T0": 273.15,
+      "n2": 9.7e-24,
+      "source": "Wahlstrand, J. K., Cheng, Y. H., & Milchberg, H. M. (2012). High field optical nonlinearity and the Kramers-Kronig relations. Physical review letters, 109(11), 113904."
+    },
+    "sellmeier":
+    {
+      "B":
+      [
+        0.030182943
+      ],
+      "C":
+      [
+        1.44e+14
+      ],
+      "P0": 101325,
+      "T0": 273.15,
+      "const": 6.7867e-5,
+      "kind": 2,
+      "source": "E. R. Peck and D. J. Fisher. Dispersion of argon, J. Opt. Soc. Am. 54, 1362-1364 (1964)"
+    }
+  },
+  "d-zlaf52la":
+  {
+    "sellmeier":
+    {
+      "B":
+      [
+        -8487502830,
+        3.1753525,
+        3.71301651e-14,
+        -1.09609062e-27,
+        2.4418582e-40,
+        -8.94583294e-54
+      ],
+      "C": [],
+      "kind": 3,
+      "reference":
+      [
+        "CDGM Zemax catalog 2017-09 (obtained from http://www.cdgmgd.com)"
+      ]
+    }
+  },
+  "e7":
+  {
+    "sellmeier":
+    {
+      "B":
+      [
+        0,
+        7.2e-15,
+        3e-28
+      ],
+      "C": [],
+      "kind": 3,
+      "reference":
+      [
+        "J. Li, C. H. Wen, S. Gauza, R. Lu and S. T. Wu. Refractive indices of liquid crystals for display applications, J. Disp. Technol. 1, 51-61, 2005"
+      ]
+    }
+  },
+  "helium":
+  {
+    "a": 0.00346,
+    "atomic_mass": 6.646477e-27,
+    "atomic_number": 2,
+    "b": 2.38e-5,
+    "ionization_energy": 3.9393356074281e-18,
+    "kerr":
+    {
+      "P0": 30400,
+      "T0": 273.15,
+      "n2": 3.1e-25,
+      "source": "Wahlstrand, J. K., Cheng, Y. H., & Milchberg, H. M. (2012). High field optical nonlinearity and the Kramers-Kronig relations. Physical review letters, 109(11), 113904."
+    },
+    "sellmeier":
+    {
+      "B":
+      [
+        2.16463842e-5,
+        2.10561127e-7,
+        4.7509272e-5
+      ],
+      "C":
+      [
+        -6.80769781e-16,
+        5.13251289e-15,
+        3.18621354e-15
+      ],
+      "P0": 101325,
+      "T0": 273.15,
+      "kind": 1,
+      "source": "A. Ermolov, K. F. Mak, M. H. Frosz, J. C. Travers, P. St. J. Russell, Supercontinuum generation in the vacuum ultraviolet through dispersive-wave and soliton-plasma interaction in a noble-gas-filled hollow-core photonic crystal fiber, Phys. Rev. A 92, 033821 (2015)"
+    }
+  },
+  "helium_alt":
+  {
+    "a": 0.00346,
+    "b": 2.38e-5,
+    "kerr":
+    {
+      "P0": 30400,
+      "T0": 273.15,
+      "n2": 3.1e-25
+    },
+    "sellmeier":
+    {
+      "B":
+      [
+        14755297000
+      ],
+      "C":
+      [
+        4.262974e+14
+      ],
+      "P0": 101325,
+      "T0": 273.15,
+      "kind": 2,
+      "source": " C. Cuthbertson and M. Cuthbertson. The refraction and dispersion of neon and helium. Proc. R. Soc. London A 135, 40-47 (1936)"
+    }
+  },
+  "hydrogen":
+  {
+    "a": 0.02453,
+    "atomic_mass": 1.674e-27,
+    "b": 2.651e-5,
+    "kerr":
+    {
+      "P0": 30400,
+      "T0": 273.15,
+      "n2": 6.36e-24,
+      "source": "Shelton, D. P., & Rice, J. E. (1994). Measurements and calculations of the hyperpolarizabilities of atoms and small molecules in the gas phase. Chemical Reviews, 94(1), 3-29"
+    },
+    "sellmeier":
+    {
+      "B":
+      [
+        0.0148956,
+        0.0049037
+      ],
+      "C":
+      [
+        1.807e-10,
+        9.2e-11
+      ],
+      "P0": 101325,
+      "T0": 273.15,
+      "kind": 2,
+      "source": "E. R. Peck and S. Hung. Refractivity and dispersion of hydrogen in the visible and near infrared, J. Opt. Soc. Am. 67, 1550-1554 (1977)"
+    }
+  },
+  "krypton":
+  {
+    "a": 0.2349,
+    "atomic_mass": 1.3915e-25,
+    "atomic_number": 36,
+    "b": 3.978e-5,
+    "ionization_energy": 2.2429831039374e-18,
+    "kerr":
+    {
+      "P0": 30400,
+      "T0": 273.15,
+      "n2": 2.2e-23,
+      "source": "Wahlstrand, J. K., Cheng, Y. H., & Milchberg, H. M. (2012). High field optical nonlinearity and the Kramers-Kronig relations. Physical review letters, 109(11), 113904."
+    },
+    "sellmeier":
+    {
+      "B":
+      [
+        2536370000,
+        2736490000,
+        62080200000
+      ],
+      "C":
+      [
+        6.54742e+13,
+        7.3698e+13,
+        1.8108e+14
+      ],
+      "P0": 101325,
+      "T0": 273.15,
+      "kind": 2
+    }
+  },
+  "nbk7":
+  {
+    "sellmeier":
+    {
+      "B":
+      [
+        1.03961212,
+        0.231792344,
+        1.01046945
+      ],
+      "C":
+      [
+        6.00069867e-15,
+        2.00179144e-14,
+        1.03560653e-10
+      ],
+      "kind": 1,
+      "reference":
+      [
+        "SCHOTT Zemax catalog 2017-01-20b (obtained from http://www.schott.com)"
+      ]
+    }
+  },
+  "neon":
+  {
+    "a": 0.02135,
+    "atomic_mass": 3.35092e-26,
+    "atomic_number": 10,
+    "b": 1.709e-5,
+    "ionization_energy": 3.45501365359425e-18,
+    "kerr":
+    {
+      "P0": 30400,
+      "T0": 273.15,
+      "n2": 8.7e-25,
+      "source": "Wahlstrand, J. K., Cheng, Y. H., & Milchberg, H. M. (2012). High field optical nonlinearity and the Kramers-Kronig relations. Physical review letters, 109(11), 113904."
+    },
+    "sellmeier":
+    {
+      "B":
+      [
+        1281450000,
+        22048600000
+      ],
+      "C":
+      [
+        1.84661e+14,
+        3.7684e+14
+      ],
+      "P0": 101325,
+      "T0": 273.15,
+      "kind": 2
+    }
+  },
+  "nitrogen":
+  {
+    "a": 0.137,
+    "atomic_mass": 2.3259e-26,
+    "atomic_number": 7,
+    "b": 1.709e-5,
+    "kerr":
+    {
+      "P0": 30400,
+      "T0": 273.15,
+      "n2": 2.2e-23,
+      "source": "Wahlstrand, J. K., Cheng, Y. H., & Milchberg, H. M. (2012). High field optical nonlinearity and the Kramers-Kronig relations. Physical review letters, 109(11), 113904."
+    },
+    "sellmeier":
+    {
+      "B":
+      [
+        32431570000
+      ],
+      "C":
+      [
+        1.44e+14
+      ],
+      "P0": 101325,
+      "T0": 273.15,
+      "const": 6.8552e-5,
+      "kind": 2
+    }
+  },
+  "silica":
+  {
+    "sellmeier":
+    {
+      "B":
+      [
+        0.6961663,
+        0.4079426,
+        0.8974794
+      ],
+      "C":
+      [
+        4.67914826e-15,
+        1.35120631e-14,
+        9.79340025e-11
+      ],
+      "kind": 1,
+      "reference":
+      [
+        "I. H. Malitson. Interspecimen comparison of the refractive index of fused silica, J. Opt. Soc. Am. 55, 1205-1208 (1965)",
+        "C. Z. Tan. Determination of refractive index of silica glass for infrared wavelengths by IR spectroscopy, J. Non-Cryst. Solids 223, 158-163 (1998)"
+      ]
+    }
+  },
+  "vacuum":
+  {
+    "a": 0,
+    "atomic_mass": 0,
+    "atomic_number": 1,
+    "b": 0,
+    "kerr":
+    {
+      "P0": 30400,
+      "T0": 273.15,
+      "n2": 0,
+      "source": "none"
+    },
+    "sellmeier":
+    {
+      "B": [],
+      "C": [],
+      "P0": 101325,
+      "T0": 273.15,
+      "kind": 1
+    }
+  },
+  "xenon":
+  {
+    "a": 0.425,
+    "atomic_mass": 2.18017e-25,
+    "atomic_number": 54,
+    "b": 5.105e-5,
+    "ionization_energy": 1.94342415157935e-18,
+    "kerr":
+    {
+      "P0": 30400,
+      "T0": 273.15,
+      "n2": 5.8e-23,
+      "source": "Wahlstrand, J. K., Cheng, Y. H., & Milchberg, H. M. (2012). High field optical nonlinearity and the Kramers-Kronig relations. Physical review letters, 109(11), 113904."
+    },
+    "sellmeier":
+    {
+      "B":
+      [
+        3228690000,
+        3553930000,
+        60676400000
+      ],
+      "C":
+      [
+        4.6301e+13,
+        5.9578e+13,
+        1.1274e+14
+      ],
+      "P0": 101325,
+      "T0": 273.15,
+      "kind": 2
+    }
+  }
+}

src/dispersionapp/gui.py

@@ -1,43 +1,54 @@
 from __future__ import annotations
 
-import os
 import warnings
 from functools import cache
 
 import numpy as np
-import scgenerator as sc
 
+import dispersionapp.math as math
+import dispersionapp.physics.fiber as fiber
+import dispersionapp.physics.materials as materials
+import dispersionapp.physics.pulse as pulse
+import dispersionapp.physics.units as units
 from dispersionapp.core import Config, LimitValues, N_ion_max, N_sf_max, b2, energy
-from dispersionapp.plotapp import PlotApp
+from dispersionapp.plotapp import PlotApp, QtWidgets
 
 
-def app(config_file: os.PathLike | None = None):
+def app(config: Config):
-    config = Config.load(config_file)
     # grid stuff
-    w = sc.w_from_wl(config.wl_min, config.wl_max, 2048)
+    w = units.w_from_wl(config.wl_min, config.wl_max, 2048)
-    wl = sc.units.m.inv(w)
+    wl = units.m_rads(w)
-    wl_ind = sc.math.argclosest(wl, config.wl_pump * 1e-9)
+    wl_ind = math.argclosest(wl, config.wl_pump * 1e-9)
-    w0 = w[wl_ind]
+    w0: float = w[wl_ind]
 
-    gas = sc.materials.Gas(config.gas)
+    gas = materials.Gas(config.gas)
 
     with PlotApp(
         f"Dispersion design with {config.gas.title()}",
-        core_diameter_um=np.linspace(50, 300),
+        core_diameter_um=np.arange(5, 301, dtype=float),
         pressure_mbar=np.geomspace(1, 2000),
-        wall_thickness_um=np.geomspace(0.01, 10),
+        wall_thickness_um=np.geomspace(0.01, 10, 201),
+        num_resonances=np.arange(6, 41),
         n_tubes=np.arange(6, 16),
-        gap_um=np.linspace(1, 15),
+        gap_um=np.arange(1, 15.5, 0.5),
-        t_fwhm_fs=np.linspace(10, 200, 96),
+        t_fwhm_fs=np.arange(10, 201, dtype=float),
     ) as app:
         # initial setup
-        app[0].horizontal_line("reference", 0, color="gray")
+        beta_ax = app["Dispersion plot"]
-        app[0].set_xlabel("wavelength (nm)")
+        beta_ax.horizontal_line("reference", 0, color="gray")
-        app[0].set_ylabel("beta2 (fs^2/cm)")
+        beta_ax.set_xlabel("wavelength (nm)")
+        beta_ax.set_ylabel("beta2 (fs^2/cm)")
+
+        n_ax = app["Refractive index (real)"]
+        n_ax.link_x(beta_ax)
+        n_ax.set_xlabel("wavelength (nm)")
+        n_ax.set_ylabel("n")
+
         if config.current_state is not None:
             app.params["core_diameter_um"].value = config.current_state.core_diameter_um
             app.params["pressure_mbar"].value = config.current_state.pressure_mbar
             app.params["wall_thickness_um"].value = config.current_state.wall_thickness_um
+            app.params["num_resonances"].value = config.current_state.num_resonances
             app.params["n_tubes"].value = config.current_state.n_tubes
             app.params["gap_um"].value = config.current_state.gap_um
             app.params["t_fwhm_fs"].value = config.current_state.t_fwhm_fs
@@ -45,10 +56,19 @@ def app(config_file: os.PathLike | None = None):
             app.params["core_diameter_um"].value = 100
             app.params["pressure_mbar"].value = 500
             app.params["wall_thickness_um"].value = 1
+            app.params["num_resonances"].value = 6
             app.params["n_tubes"].value = 7
             app.params["gap_um"].value = 5
             app.params["t_fwhm_fs"].value = 100
-        app[0].set_lim(ylim=(-4, 2))
+
+        def reset_view():
+            # reseting beta_ax resets n_ax as well since they're linked
+            beta_ax.set_lim(ylim=(-4, 2))
+
+        reset_button = QtWidgets.QPushButton("Reset axes")
+        reset_button.clicked.connect(reset_view)
+        app.params_layout.addWidget(reset_button)
+        reset_view()
 
         app.update(config.update_current)
 
@@ -59,24 +79,28 @@ def app(config_file: os.PathLike | None = None):
             t_fwhm = 1e-15 * t_fwhm_fs
             pressure = 1e2 * pressure_mbar
             core_radius = 0.5e-6 * core_diameter_um
-            t0 = sc.pulse.fwhm_to_T0_fac["sech"] * t_fwhm
+            t0 = pulse.fwhm_to_T0_fac["sech"] * t_fwhm
 
             disp = compute_capillary(core_diameter_um, pressure_mbar)
-            wl_zero_disp = sc.math.all_zeros(wl, disp)
+            wl_zero_disp = math.all_zeros(wl, disp)
             if len(wl_zero_disp) == 0:
-                return
+                raise ValueError("No zero dispersion found")
-            wl_zero_disp = wl_zero_disp[0]
+            single_wl_zero_disp: float = wl_zero_disp[0]
 
             with warnings.catch_warnings():
                 warnings.simplefilter("ignore")
-                ion_limit = N_ion_max(wl, t0, wl_zero_disp, gas, config.safety_factor)[wl_ind]
+                ion_limit: float = N_ion_max(
-                sf_limit = N_sf_max(wl, t0, wl_zero_disp, gas, config.safety_factor)[wl_ind]
+                    wl, t0, single_wl_zero_disp, gas, config.safety_factor
+                )[wl_ind]
+                sf_limit: float = N_sf_max(wl, t0, single_wl_zero_disp, gas, config.safety_factor)[
+                    wl_ind
+                ]
 
             beta2 = disp[wl_ind]
             n2 = gas.n2(pressure=pressure)
 
             return LimitValues(
-                wl_zero_disp,
+                single_wl_zero_disp,
                 ion_limit,
                 sf_limit,
                 energy(t0, w0, beta2, n2, core_radius, ion_limit),
@@ -84,29 +108,36 @@ def app(config_file: os.PathLike | None = None):
             )
 
         @cache
-        def compute_vincetti(
+        def compute_n_eff_vincetti(
             wall_thickness_um: float,
             core_diameter_um: float,
             pressure_mbar: float,
+            num_resonances: int,
             n_tubes: int,
             gap_um: float,
-        ) -> np.ndarray:
+        ):
             core_diameter = core_diameter_um * 1e-6
             wall_thickness = wall_thickness_um * 1e-6
             gap = gap_um * 1e-6
             pressure = pressure_mbar * 1e2
 
-            tr = sc.fiber.tube_radius_from_gap(core_diameter / 2, gap, n_tubes)
+            tr = fiber.tube_radius_from_gap(core_diameter / 2, gap, n_tubes)
             n_gas_2 = gas.sellmeier.n_gas_2(wl, None, pressure)
-            n_eff_vinc = sc.fiber.n_eff_vincetti(
+            return fiber.n_eff_vincetti(
-                wl,
+                wl, 800e-9, n_gas_2, wall_thickness, tr, gap, n_tubes, n_terms=num_resonances
-                800e-9,
+            )
-                n_gas_2,
+
-                wall_thickness,
+        @cache
-                tr,
+        def compute_vincetti_beta(
-                gap,
+            wall_thickness_um: float,
-                n_tubes,
+            core_diameter_um: float,
-                n_terms=config.num_resonances,
+            pressure_mbar: float,
+            num_resonances: int,
+            n_tubes: int,
+            gap_um: float,
+        ) -> np.ndarray:
+            n_eff_vinc = compute_n_eff_vincetti(
+                wall_thickness_um, core_diameter_um, pressure_mbar, num_resonances, n_tubes, gap_um
             )
             return b2(w, n_eff_vinc)
 
@@ -115,42 +146,64 @@ def app(config_file: os.PathLike | None = None):
             pressure = pressure_mbar * 1e2
             core_diameter = core_diameter_um * 1e-6
             n_gas_2 = gas.sellmeier.n_gas_2(wl, None, pressure)
-            n_eff_cap = sc.fiber.n_eff_marcatili(wl, n_gas_2, core_diameter / 2)
+            n_eff_cap = fiber.n_eff_marcatili(wl, n_gas_2, core_diameter / 2)
             return b2(w, n_eff_cap)
 
         @app.update
-        def draw_vincetty(
+        def draw_vincetty_n_eff(
             wall_thickness_um: float,
             core_diameter_um: float,
             pressure_mbar: float,
+            num_resonances: int,
             n_tubes: int,
             gap_um: float,
         ):
-            b2 = compute_vincetti(
+            n_eff = compute_n_eff_vincetti(
-                wall_thickness_um, core_diameter_um, pressure_mbar, n_tubes, gap_um
+                wall_thickness_um, core_diameter_um, pressure_mbar, num_resonances, n_tubes, gap_um
             )
-            app[0].set_line_data("Vincetti", wl * 1e9, sc.units.beta2_fs_cm_inv(b2))
+            n_ax.set_line_data("Vincetti", wl * 1e9, n_eff)
+
+        @app.update
+        def draw_vincetty_beta(
+            wall_thickness_um: float,
+            core_diameter_um: float,
+            pressure_mbar: float,
+            num_resonances: int,
+            n_tubes: int,
+            gap_um: float,
+        ):
+            b2 = compute_vincetti_beta(
+                wall_thickness_um, core_diameter_um, pressure_mbar, num_resonances, n_tubes, gap_um
+            )
+            beta_ax.set_line_data("Vincetti", wl * 1e9, units.beta2_fs_cm_inv(b2))
 
         @app.update
         def draw_capillary(core_diameter_um: float, pressure_mbar: float):
             b2 = compute_capillary(core_diameter_um, pressure_mbar)
-            app[0].set_line_data("Capillary", wl * 1e9, sc.units.beta2_fs_cm_inv(b2))
+            beta_ax.set_line_data("Capillary", wl * 1e9, units.beta2_fs_cm_inv(b2))
 
         @app.update
         def draw_energy_limit(core_diameter_um: float, pressure_mbar: float, t_fwhm_fs: float):
-            lim = compute_max_energy(core_diameter_um, pressure_mbar, t_fwhm_fs)
-            zdw = lim.wl_zero_disp * 1e9
-            app[0].set_line_data("zdw", [zdw, zdw], [-3, 3])
-
             info_lines = [
                 f"gas = {config.gas.title()}",
                 f"rep rate = {config.rep_rate * 1e-3}kHz",
-                f"ZDW = {zdw:.0f}nm",
             ]
+
+            try:
+                lim = compute_max_energy(core_diameter_um, pressure_mbar, t_fwhm_fs)
+            except ValueError:
+                info_lines.append("No ZDW found")
+                app.info_label.setText("\n".join(info_lines))
+                return
+
+            zdw = lim.wl_zero_disp * 1e9
+            beta_ax.set_line_data("zdw", [zdw, zdw], [-3, 3])
+
+            info_lines.append(f"ZDW = {zdw:.0f}nm")
             if lim.ion_lim > lim.sf_lim:
                 power = lim.sf_lim * 1e3 * config.rep_rate
                 info_lines += [
-                    f"max energy = {power:.0f}mW",
+                    f"max power = {power:.0f}mW",
                     f"N = {lim.soliton_sf_limit:.1f}",
                     "limited by self-focusing",
                 ]
@@ -161,6 +214,6 @@ def app(config_file: os.PathLike | None = None):
                     f"N = {lim.soliton_sf_limit:.1f}",
                     "limited by ionization",
                 ]
-            app.info_label.setText("\n".join(info_lines))
+            app.info_label.setText("    ".join(info_lines))
 
     config.save()

src/dispersionapp/io.py

@@ -0,0 +1,432 @@
+from __future__ import annotations
+
+import datetime
+import importlib.resources
+import json
+import os
+import re
+from dataclasses import dataclass
+from functools import cache
+from io import BytesIO
+from pathlib import Path
+from typing import Any, BinaryIO, Protocol, Sequence, overload
+from zipfile import ZipFile
+
+import numpy as np
+
+
+class TimedMessage:
+    def __init__(self, interval: float = 10.0):
+        self.interval = datetime.timedelta(seconds=interval)
+        self.next = datetime.datetime.now()
+
+    def ready(self) -> bool:
+        t = datetime.datetime.now()
+        if self.next <= t:
+            self.next = t + self.interval
+            return True
+        return False
+
+
+def data_file(path: str) -> Path:
+    """returns a `Path` object pointing to the desired data file included in `scgenerator`"""
+    return Path(importlib.resources.files("dispersionapp") / "data" / path)
+
+
+class CustomEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, (datetime.date, datetime.datetime)):
+            return obj.isoformat()
+        elif isinstance(obj, np.ndarray):
+            return tuple(obj)
+        elif isinstance(obj, DataFile):
+            return obj.__json__()
+
+
+def _decode_datetime(s: str) -> datetime.date | datetime.datetime | str:
+    try:
+        return datetime.datetime.fromisoformat(s)
+    except Exception:
+        pass
+    try:
+        return datetime.date.fromisoformat(s)
+    except Exception:
+        pass
+    return s
+
+
+def custom_decode_hook(obj):
+    """
+    Some simple, non json-compatible objects are encoded as str, this function reconstructs the
+    original object and sets it in the decoded json structure
+    """
+    for k, v in obj.items():
+        if isinstance(v, str):
+            obj[k] = _decode_datetime(v)
+        elif isinstance(v, list):
+            obj[k] = tuple(v)
+    return obj
+
+
+@cache
+def load_material_dico(name: str) -> dict[str, Any]:
+    """
+    loads a material dictionary
+
+    Parameters
+    ----------
+    name : str
+        name of the material
+
+    Returns
+    -------
+    material_dico : dict
+    """
+    return json.loads(data_file("materials.json").read_text())[name]
+
+
+class PropagationIOHandler(Protocol):
+    def __len__(self) -> int: ...
+
+    def keys(self) -> list[str]: ...
+
+    def save_spectrum(self, index: int, spectrum: np.ndarray): ...
+
+    def load_spectrum(self, index: int) -> np.ndarray: ...
+
+    def save_data(self, name: str, data: bytes): ...
+
+    def load_data(self, name: str) -> bytes: ...
+
+    def clear(self): ...
+
+
+class MemoryIOHandler:
+    spectra: dict[int, np.ndarray]
+    data: dict[str, bytes]
+
+    def __init__(self):
+        self.spectra = {}
+        self.data = {}
+
+    def __len__(self) -> int:
+        return len(self.spectra)
+
+    def keys(self) -> list[str]:
+        return list(self.data.keys())
+
+    def save_spectrum(self, index: int, spectrum: np.ndarray):
+        self.spectra[index] = spectrum
+
+    def load_spectrum(self, index: int) -> np.ndarray:
+        return self.spectra[index]
+
+    def save_data(self, name: str, data: bytes):
+        self.data[name] = data
+
+    def load_data(self, name: str) -> bytes:
+        return self.data[name]
+
+    def clear(self):
+        self.spectra = {}
+        self.data = {}
+
+
+class ZipFileIOHandler:
+    file: BinaryIO
+    SPECTRUM_FN = "spectra/spectrum_{}.npy"
+
+    def __init__(self, file: os.PathLike):
+        """
+        Create a IO handler to be used in Propagation.
+        This handler saves spectra as numpy `.npy` files.
+
+        Parameters
+        ----------
+        file : os.PathLike
+            path to the desired zip file. Will be created if it doesn't exist.
+            Passing in an already opened file-like object is not supported at the moment
+        """
+        self.file = Path(file)
+        if not self.file.exists():
+            ZipFile(self.file, "w").close()
+
+    def __len__(self) -> int:
+        with ZipFile(self.file, "r") as zip_file:
+            i = 0
+            while True:
+                try:
+                    zip_file.open(self.SPECTRUM_FN.format(i))
+                except KeyError:
+                    return i
+                i += 1
+
+    def keys(self) -> list[str]:
+        with ZipFile(self.file, "r") as zip_file:
+            return zip_file.namelist()
+
+    def save_spectrum(self, index: int, spectrum: np.ndarray):
+        with (
+            ZipFile(self.file, "a") as zip_file,
+            zip_file.open(self.SPECTRUM_FN.format(index), "w") as file,
+        ):
+            np.lib.format.write_array(file, spectrum, allow_pickle=False)
+
+    def load_spectrum(self, index: int) -> np.ndarray:
+        with (
+            ZipFile(self.file, "r") as zip_file,
+            zip_file.open(self.SPECTRUM_FN.format(index), "r") as file,
+        ):
+            return np.load(file)
+
+    def save_data(self, name: str, data: bytes):
+        with ZipFile(self.file, "a") as zip_file, zip_file.open(name, "w") as file:
+            file.write(data)
+
+    def load_data(self, name: str) -> bytes:
+        with ZipFile(self.file, "r") as zip_file, zip_file.open(name, "r") as file:
+            return file.read()
+
+    def clear(self):
+        self.file.unlink(missing_ok=True)
+
+
+@dataclass
+class DataFile:
+    """
+    Holds information about external files necessary for a simulation.
+    In the current implementation, only reading data from the file is supported
+    """
+
+    prefix: str | None
+    path: str
+    io: PropagationIOHandler
+
+    PREFIX_SEP = "::"
+
+    @classmethod
+    def from_str(cls, s: str, io: PropagationIOHandler | None = None) -> DataFile:
+        if cls.PREFIX_SEP in s:
+            prefix, path = s.split(cls.PREFIX_SEP, 1)
+        else:
+            prefix = None
+            path = s
+        return cls(prefix, path, io)
+
+    @classmethod
+    def validate(cls, name: str, data: str | DataFile) -> DataFile:
+        """To be used to automatically construct a DataFile when creating a Parameters obj"""
+        if isinstance(data, cls):
+            return data
+        elif isinstance(data, str):
+            return cls.from_str(data)
+        elif isinstance(data, Path):
+            return cls.from_str(os.fspath(data))
+        else:
+            raise TypeError(
+                f"{name!r} must be a path or a bundled file specifier, not a {type(data)!r}"
+            )
+
+    def __json__(self) -> str:
+        if self.prefix is None:
+            return os.fspath(Path(self.path))
+        return self.prefix + self.PREFIX_SEP + self.path
+
+    def load_bytes(self) -> bytes:
+        if self.prefix is not None and self.io is None:
+            raise ValueError(
+                f"a bundled file prefixed with {self.prefix} "
+                "must have a PropagationIOHandler attached"
+            )
+        # a DataFile obj may have a useless io obj attached to it
+        if self.prefix is not None:
+            return self.io.load_data(self.path)
+        else:
+            return Path(self.path).read_bytes()
+
+    @overload
+    def load_arrays(self, labels: str | tuple[str, ...]) -> tuple[np.ndarray]: ...
+    @overload
+    def load_arrays(
+        self, labels: str | tuple[str, ...], labels2: str | tuple[str, ...]
+    ) -> tuple[np.ndarray, np.ndarray]: ...
+    def load_arrays(self, *labels: str | tuple[str, ...]) -> tuple[np.ndarray, ...]:
+        raw_data = self.load_bytes()
+        extension = self.path.lower().split(".")[-1]
+        return _parse_raw_data(raw_data, extension, *labels)
+
+    def similar_to(self, other: DataFile) -> bool:
+        return Path(self.path).name == Path(other.path).name
+
+
+def open_data_file(path: os.PathLike, *labels: str) -> tuple[np.ndarray, ...]:
+    path = Path(path)
+    raw_data = path.read_bytes()
+    extension = path.suffix.lower()
+    return _parse_raw_data(raw_data, extension, *labels)
+
+
+def _parse_raw_data(data: bytes, extension: str, *keys) -> tuple[np.ndarray, ...]:
+    if extension.endswith("npz"):
+        df = np.load(BytesIO(data))
+        return _parse_npz_data(df, *keys)
+    elif extension.endswith("npy"):
+        return tuple(np.load(BytesIO(data)))
+    else:
+        return _parse_text_data(data.decode())
+
+
+def _parse_text_data(s: str) -> tuple[np.ndarray, ...]:
+    lines = s.splitlines()
+    for delimiter in ", \t;":
+        try:
+            return tuple(np.loadtxt(lines, delimiter=delimiter).T)
+        except ValueError:
+            continue
+    raise ValueError("Could not load text data as numpy array")
+
+
+def _parse_npz_data(
+    df: np.lib.npyio.NpzFile, *labels: str | tuple[str, ...]
+) -> tuple[np.ndarray, ...]:
+    if not labels:
+        return tuple(df.values())
+    out = []
+    for key in labels:
+        if not isinstance(key, (list, tuple, set)):
+            out.append(df[key])
+            continue
+        for possible_key in key:
+            try:
+                out.append(df[possible_key])
+            except KeyError:
+                continue
+            else:
+                break
+        else:
+            raise KeyError(f"no key of {key!r} present in {df}")
+    return tuple(out)
+
+
+def unique_name(base_name: str, existing: set[str]) -> str:
+    name = base_name
+    p = Path(base_name)
+    base = p.stem
+    ext = p.suffix
+    i = 0
+    while name in existing:
+        name = f"{base}_{i}{ext}"
+        i += 1
+    return name
+
+
+def format_graph(left_elements: Sequence[str], middle: str, right_elements: Sequence[str]):
+    if len(left_elements) == 0:
+        left_elements = [""]
+    if len(right_elements) == 0:
+        right_elements = [""]
+    mid_elements = ["."[:i] + el for i, el in enumerate(middle.split("."))]
+    left_height = len(left_elements)
+    right_height = len(right_elements)
+
+    max_left = max(len(el) for el in left_elements) + 1
+    max_right = max(len(el) for el in right_elements) + 1
+    max_mid = max(len(el) for el in mid_elements) + 2
+
+    right_elements = [fit_right(el, max_right) for el in right_elements][::-1]
+    left_elements = [fit_left(el, max_left) for el in left_elements][::-1]
+    mid_elements = ["─" * max_mid] + [fit_mid(el, max_mid) for el in mid_elements][::-1]
+
+    total_height = max(len(left_elements), len(right_elements), len(mid_elements))
+
+    line_pos = total_height - len(mid_elements)
+
+    left = left_col(
+        left_elements, line_pos, total_height, max_left, start=max(0, line_pos - left_height + 1)
+    )
+    right = right_col(
+        right_elements, line_pos, total_height, max_right, start=max(0, line_pos - right_height + 1)
+    )
+    middle = mid_col(mid_elements, line_pos, total_height, max_mid)
+
+    final = "\n".join(l + m + r for l, m, r in zip(left, middle, right))
+    return final
+
+
+def fit_left(el, length, line=1, pad=1):
+    return f"{' ' * length}{el}{' ' * pad}{'─' * line}"[-(length + 1) :]
+
+
+def fit_right(el, length, line=1, pad=1):
+    return f"{'─' * line}{' ' * pad}{el}{' ' * length}"[: length + 1]
+
+
+def fit_mid(el, length, pad=1):
+    return f"{' ' * pad}{el}{' ' * length}"[:length]
+
+
+def mid_col(mid_els, line_pos, height, pad_length):
+    out = [" " * pad_length] * line_pos + mid_els
+    while len(out) < height:
+        out.append(" " * pad_length)
+    return out[::-1]
+
+
+def left_col(left_els, line_pos, height, pad_length, start=0):
+    out = [" " * (pad_length + 1)] * start
+    for rel_i, el in enumerate(left_els):
+        abs_j = rel_i + start
+        out.append(el + get_symb(False, rel_i, abs_j, line_pos, len(left_els) - 1))
+    while len(out) < height:
+        out.append(" " * (pad_length + 2))
+    return out[::-1]
+
+
+def right_col(right_els, line_pos, height, pad_length, start=0):
+    out = [" " * (pad_length + 1)] * start
+    for rel_i, el in enumerate(right_els):
+        abs_j = rel_i + start
+        out.append(get_symb(True, rel_i, abs_j, line_pos, len(right_els) - 1) + el)
+    while len(out) < height:
+        out.append(" " * (pad_length + 2))
+    return out[::-1]
+
+
+def get_symb(right: bool, rel_i, abs_j, line_pos, max_ind):
+    if max_ind == 0:
+        return "─"
+    elif rel_i == 0:
+        if abs_j < line_pos:
+            return "╯╰"[right]
+        elif abs_j == line_pos:
+            return "┴"
+        else:
+            raise ValueError("bottom of left columns cannot be above line")
+    elif rel_i < max_ind:
+        if abs_j == line_pos:
+            return "┼"
+        else:
+            return "┤├"[right]
+    else:
+        if abs_j == line_pos:
+            return "┬"
+        elif abs_j > line_pos:
+            return "╮╭"[right]
+        else:
+            raise ValueError("top of left columns cannot be below line")
+
+
+def _to_num(s: str) -> str | float | int:
+    for f in int, float:
+        try:
+            return f(s)
+        except ValueError:
+            pass
+    return s
+
+
+NUM_REGEX = r"[+-]?[\d]+(?:\.?[\d]+)?(?:[eE][+-]?\d+)?"
+
+
+def logical_sort_key(s: os.PathLike | str) -> tuple:
+    return tuple(_to_num(el) for el in re.split(f"({NUM_REGEX})", str(s)) if el)

src/dispersionapp/math.py

@@ -0,0 +1,800 @@
+"""
+collection of purely mathematical function
+"""
+
+import math
+import warnings
+from dataclasses import dataclass
+from functools import cache
+from typing import Callable, Sequence, overload
+
+import numba
+import numpy as np
+from scipy import fft as sfft
+from scipy.interpolate import interp1d, lagrange
+from scipy.special import jn_zeros
+
+pi = np.pi
+c = 299792458.0
+
+
+def fft_functions(
+    full_field: bool,
+) -> tuple[Callable[[np.ndarray], np.ndarray], Callable[[np.ndarray], np.ndarray]]:
+    if full_field:
+        return sfft.rfft, sfft.irfft
+    else:
+        return sfft.fft, sfft.ifft
+
+
+def expm1_int(y: np.ndarray, dx: float) -> np.ndarray:
+    """evaluates 1 - exp( -∫func(y(x))dx ) from x=-inf to x"""
+    return -np.expm1(-cumulative_simpson(y) * dx)
+
+
+def span(*vec: np.ndarray) -> tuple[float, float]:
+    """returns the min and max of whatever array-like is given. can accept many args"""
+    out = (np.inf, -np.inf)
+    if len(vec) == 0 or len(vec[0]) == 0:
+        raise ValueError("did not provide any value to span")
+    for x in vec:
+        x = np.atleast_1d(x)
+        out = (min(np.min(x), out[0]), max(np.max(x), out[1]))
+    return out
+
+
+def total_extent(*vec: np.ndarray) -> float:
+    """measure the distance between the min and max value of all given arrays"""
+    left, right = span(*vec)
+    return right - left
+
+
+def span_above(arr: np.ndarray, threshold: float) -> tuple[int, int]:
+    """returns the first and last index where the array is above the specified threshold"""
+    ind = np.where(arr >= threshold)[-1]
+    return np.min(ind), np.max(ind)
+
+
+@overload
+def argclosest(array: np.ndarray, target: float | int) -> int: ...
+@overload
+def argclosest(array: np.ndarray, target: Sequence[float | int] | np.ndarray) -> np.ndarray: ...
+
+
+def argclosest(
+    array: np.ndarray, target: float | int | Sequence[float | int] | np.ndarray
+) -> int | np.ndarray:
+    """
+    returns the index/indices corresponding to the closest matches of target in array
+
+    Parameters
+    ----------
+    array : np.ndarray, shape (n,)
+        array of values
+    target : number | np.ndarray
+        find the closest value to target in `array`. The index of the closest match is returned.
+
+    Returns
+    -------
+    int | np.ndarray
+        index / indices of the closest match
+
+    """
+    min_dist = np.inf
+    index = 0
+    if isinstance(target, (list, tuple, np.ndarray)):
+        return np.array([argclosest(array, t) for t in target])
+    for k, val in enumerate(array):
+        dist = abs(val - target)
+        if dist < min_dist:
+            min_dist = dist
+            index = k
+
+    return index
+
+
+def extent(x):
+    return np.max(x) - np.min(x)
+
+
+def power_fact(x, n):
+    """
+    returns x ^ n / n!
+    """
+    if isinstance(x, (int, float)):
+        return _power_fact_single(x, n)
+
+    elif isinstance(x, np.ndarray):
+        return _power_fact_array(x, n)
+    else:
+        raise TypeError(f"type {type(x)} of x not supported.")
+
+
+def _power_fact_single(x, n):
+    result = 1.0
+    for k in range(n):
+        result = result * x / (n - k)
+    return result
+
+
+def _power_fact_array(x, n):
+    """returns x^2/n!"""
+    result = np.ones(len(x), dtype=np.float64)
+    for k in range(n):
+        result = result * x / (n - k)
+    return result
+
+
+@numba.njit()
+def abs2(z: np.ndarray) -> np.ndarray:
+    return z.real**2 + z.imag**2
+
+
+def normalized(z: np.ndarray) -> np.ndarray:
+    ab = abs2(z)
+    return ab / ab.max()
+
+
+def autocrop(
+    x, y, z, threshold: float = 1e-4, x_only: bool = False
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    thr = z.max()
+    yind, xind = np.where(z > thr * threshold)
+    xmin, xmax = xind.min(), xind.max()
+    if x_only:
+        ymin, ymax = 0, len(y) - 1
+    else:
+        ymin, ymax = yind.min(), yind.max()
+    return x[xmin : xmax + 1], y[ymin : ymax + 1], z[ymin : ymax + 1][:, xmin : xmax + 1]
+
+
+def to_dB(arr: np.ndarray, ref=None, axis=None, default: float | None = None) -> np.ndarray:
+    """
+    converts unitless values in dB
+
+    Parameters
+    ----------
+    arr : np.ndarray
+        array of non-negative values. Any values 0 or below will be mapped to the minimum
+        positive value in dB
+    ref : float, optional
+        reference value corresponding to 0dB (default : max(arr))
+    axis : int | None, optional
+        on which axis to apply the transformation. If `ref` is given, this has no effect
+
+    Returns
+    ----------
+    np.ndarray
+        array in dB
+    """
+    if axis is not None and arr.ndim > 1 and ref is None:
+        return np.apply_along_axis(to_dB, axis, arr, default=default)
+
+    out = np.ones_like(arr)
+    if default is not None and math.isfinite(default):
+        out[:] *= default
+    else:
+        out *= np.nan
+
+    if ref is None:
+        ref = np.max(arr)
+    above_0 = arr > 0
+    if not np.any(above_0) or ref <= 0:
+        warnings.warn(f"invalid array to convert to dB, returning {default} instead")
+        if default is None:
+            out *= np.nan
+        return out
+    m = arr / ref
+    if default is None:
+        out *= 10 * np.log10(m[above_0].min())
+    return 10 * np.log10(m, out=out, where=above_0)
+
+
+def u_nm(n, m):
+    """
+    returns the mth zero of the Bessel function of order n-1
+
+    Parameters
+    ----------
+    n-1 : order of the Bessel function
+    m : order of the zero
+
+    Returns
+    ----------
+        float
+    """
+    return jn_zeros(n - 1, m)[-1]
+
+
+def all_zeros(x: np.ndarray, y: np.ndarray) -> np.ndarray:
+    """find all the x values such that y(x)=0 with linear interpolation"""
+    pos = np.argwhere(y[1:] * y[:-1] < 0)[:, 0]
+    m = (y[pos + 1] - y[pos]) / (x[pos + 1] - x[pos])
+    return -y[pos] / m + x[pos]
+
+
+def wspace(t: float | np.ndarray, t_num=0):
+    """
+    frequency array such that x(t) <-> np.fft(x)(w)
+
+    Parameters
+    ----------
+    t : float | np.ndarray
+        float : total width of the time window
+        array : time array
+    t_num : int-
+        if t is a float, specifies the number of points
+
+    Returns
+    -------
+    w : array
+        linspace of frencies corresponding to t
+    """
+    if isinstance(t, (np.ndarray, list, tuple)):
+        dt = t[1] - t[0]
+        t_num = len(t)
+    else:
+        dt = t / t_num
+    return np.fft.fftfreq(t_num, dt) * 2 * np.pi
+
+
+def tspace(time_window: float | None = None, t_num: int | None = None, dt: float | None = None):
+    """
+    returns a time array centered on 0
+
+    Parameters
+    ----------
+    time_window : float
+        total time spanned
+    t_num : int
+        number of points
+    dt : float
+        time resolution
+
+    at least 2 arguments must be given. They are prioritize as such
+    t_num > time_window > dt
+
+    Returns
+    -------
+    t : array
+        a linearily spaced time array
+    Raises
+    ------
+    TypeError
+        missing at least 1 argument
+    """
+    if t_num is not None:
+        if isinstance(time_window, (float, int)):
+            return np.linspace(-time_window / 2, time_window / 2, int(t_num))
+        elif isinstance(dt, (float, int)):
+            time_window = (t_num - 1) * dt
+            return np.linspace(-time_window / 2, time_window / 2, t_num)
+    elif isinstance(time_window, (float, int)) and isinstance(dt, (float, int)):
+        t_num = int(time_window / dt) + 1
+        return np.linspace(-time_window / 2, time_window / 2, t_num)
+    raise TypeError("not enough parameter to determine time vector")
+
+
+def irfftfreq(freq: np.ndarray, retstep: bool = False):
+    """
+    Given an array of positive only frequency, this returns the corresponding time array centered
+    around 0 that will be aligned with the `numpy.fft.irfft` of a spectrum aligned with `freq`.
+    if `retstep` is True, the sample spacing is returned as well
+    """
+    df = freq[1] - freq[0]
+    nt = (len(freq) - 1) * 2
+    period = 1 / df
+    dt = period / nt
+
+    t = np.linspace(-(period - dt) / 2, (period - dt) / 2, nt)
+    if retstep:
+        return t, dt
+    else:
+        return t
+
+
+def iwspace(w: np.ndarray, retstep: bool = False):
+    """invserse of wspace: recovers the (symmetric) time array corresponsding to `w`"""
+    df = (w[1] - w[0]) * 0.5 / np.pi
+    nt = len(w)
+    period = 1 / df
+    dt = period / nt
+    t = np.linspace(-(period - dt) / 2, (period - dt) / 2, nt)
+    if retstep:
+        return t, dt
+    else:
+        return t
+
+
+def dt_from_min_wl(wl_min: float, wavelength: float) -> float:
+    return 0.5 / c / (1 / wl_min - 1 / wavelength)
+
+
+def min_wl_from_dt(dt: float, wavelength: float) -> float:
+    return wavelength / (1 + wavelength / (2 * c * dt))
+
+
+def build_envelope_w_grid(t: np.ndarray, w0: float) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """
+    convenience function to
+
+    Parameters
+    ----------
+    t : np.ndarray, shape (t_num,)
+        time array
+    w0 : float
+        center frequency
+
+    Returns
+    -------
+    w_c : np.ndarray, shape (n, )
+        centered angular frequencies in rad/s where 0 is the pump frequency
+    w : np.ndarray, shape (n, )
+        angular frequency grid
+    w_order : np.ndarray, shape (n,)
+        arrays of indices such that w[w_order] is sorted
+    """
+    w_c = wspace(t)
+    w = w_c + w0
+    w_order = np.argsort(w)
+    return w_c, w, w_order
+
+
+def build_full_field_w_grid(t_num: int, dt: float) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """
+    Paramters
+    ---------
+    t_num : int
+        number of temporal sampling points
+    dt : float
+        uniform spacing between sample points
+
+    Returns
+    -------
+    w : np.ndarray, shape (n, )
+        angular frequency grid
+    w_order : np.ndarray, shape (n,)
+        arrays of indices such that w[w_order] is sorted
+    """
+    w = 2 * pi * np.fft.rfftfreq(t_num, dt)
+    w_order = np.argsort(w)
+    ind = w != 0
+    wl = np.zeros(len(w))
+    wl[ind] = 2 * pi * c / w[ind]
+    if any(ind):
+        wl[~ind] = 2 * pi * c / (np.abs(w[ind]).min())
+    return w, w_order, wl
+
+
+def build_z_grid(z_num: int, length: float) -> np.ndarray:
+    return np.linspace(0, length, z_num)
+
+
+def build_t_grid(
+    time_window: float | None = None, t_num: int | None = None, dt: float | None = None
+) -> tuple[np.ndarray, float, float, int]:
+    """
+
+    Returns
+    -------
+    t : np.ndarray, shape (t_num, )
+        temporal points in s
+    time_window : float
+        total width of the temporal grid in s, by default None
+    dt : float
+        spacing of the temporal grid in s, by default None
+    t_num : int
+        number of temporal grid points, by default None
+    """
+    t = tspace(time_window, t_num, dt)
+    time_window = t.max() - t.min()
+    dt = t[1] - t[0]
+    t_num = len(t)
+    return t, time_window, dt, t_num
+
+
+def polynom_extrapolation(x: np.ndarray, y: np.ndarray, degree: int) -> np.ndarray:
+    """
+    performs a polynomial extrapolation on both ends of the support
+
+    Parameters
+    ----------
+    x : np.ndarray (n,)
+        x values
+    y : np.ndarray (n,)
+        y values. The shape must correspond to that of x.
+    degree : int
+        degree of the polynom.
+
+    Returns
+    -------
+    np.ndarray, shape (n,)
+        y array with zero values on either side replaces with polynomial extrapolation
+
+    Example
+
+
+
+    """
+    out = y.copy()
+    order = np.argsort(x)
+    left_ind, *_, right_ind = np.nonzero(out[order])[0]
+    return _polynom_extrapolation_in_place(out[order], left_ind, right_ind, degree)[order.argsort]
+
+
+def _polynom_extrapolation_in_place(y: np.ndarray, left_ind: int, right_ind: int, degree: float):
+    """
+    extrapolates IN PLACE linearily on both side of the support
+
+    Parameters
+    ----------
+    y : np.ndarray
+        array
+    left_ind : int
+        first value we want to keep (extrapolate to the left of that)
+    right_ind : int
+        last value we want to keep (extrapolate to the right of that)
+    """
+    r_left = (1 + np.arange(left_ind))[::-1] ** degree
+    r_right = np.arange(len(y) - right_ind) ** degree
+    y[:left_ind] = r_left * (y[left_ind] - y[left_ind + 1]) + y[left_ind]
+    y[right_ind:] = r_right * (y[right_ind] - y[right_ind - 1]) + y[right_ind]
+    return y
+
+
+def interp_2d(
+    old_x: np.ndarray,
+    old_y: np.ndarray,
+    z: np.ndarray,
+    new_x: np.ndarray | tuple,
+    new_y: np.ndarray | tuple,
+    kind="linear",
+) -> np.ndarray:
+    if isinstance(new_x, tuple):
+        new_x = np.linspace(*new_x)
+    if isinstance(new_y, tuple):
+        new_y = np.linspace(*new_y)
+    z = interp1d(old_y, z, axis=0, kind=kind, bounds_error=False, fill_value=0)(new_y)
+    return interp1d(old_x, z, kind=kind, bounds_error=False, fill_value=0)(new_x)
+
+
+@numba.jit(nopython=True)
+def linear_interp_2d(old_x: np.ndarray, old_y: np.ndarray, new_x: np.ndarray):
+    new_vals = np.zeros((len(old_y), len(new_x)))
+    interpolable = (new_x > old_x[0]) & (new_x <= old_x[-1])
+    equal = new_x == old_x[0]
+    inds = np.searchsorted(old_x, new_x[interpolable])
+    for i, val in enumerate(old_y):
+        new_vals[i][interpolable] = val[inds - 1] + (new_x[interpolable] - old_x[inds - 1]) * (
+            val[inds] - val[inds - 1]
+        ) / (old_x[inds] - old_x[inds - 1])
+        new_vals[i][equal] = val[0]
+    return new_vals
+
+
+@numba.jit(nopython=True)
+def linear_interp_1d(old_x: np.ndarray, old_y: np.ndarray, new_x: np.ndarray):
+    new_vals = np.zeros(len(new_x))
+    interpolable = (new_x > old_x[0]) & (new_x <= old_x[-1])
+    inds = np.searchsorted(old_x, new_x[interpolable])
+    new_vals[interpolable] = old_y[inds - 1] + (new_x[interpolable] - old_x[inds - 1]) * (
+        old_y[inds] - old_y[inds - 1]
+    ) / (old_x[inds] - old_x[inds - 1])
+    new_vals[new_x == old_x[0]] = old_y[0]
+    return new_vals
+
+
+def envelope_ind(
+    signal: np.ndarray, dmin: int = 1, dmax: int = 1, split: bool = False
+) -> tuple[np.ndarray, np.ndarray]:
+    """
+    Attempts to separate the envolope from a periodic signal and return the indices of the
+    top/bottom envelope of a signal
+
+    Parameters
+    ----------
+    signal : np.ndarray, shape (n,)
+        signal array (must be sorted)
+    dmin, dmax : int, optional
+        size of chunks for lower/upper envelope
+    split: bool, optional
+        split the signal in half along its mean, might help to generate the envelope in some cases
+        this has the effect of forcing the envlopes to be on either side of the dc signal
+        by default False
+
+    Returns
+    -------
+    np.ndarray, shape (m,), m <= n
+        lower envelope indices
+    np.ndarray, shape (l,), l <= n
+        upper envelope indices
+    """
+
+    local_min = (np.diff(np.sign(np.diff(signal))) > 0).nonzero()[0] + 1
+    local_max = (np.diff(np.sign(np.diff(signal))) < 0).nonzero()[0] + 1
+
+    if split:
+        dc_value = np.mean(signal)
+        local_min = local_min[signal[local_min] < dc_value]
+        local_max = local_max[signal[local_max] > dc_value]
+
+    if dmin > 1:
+        local_min = local_min[
+            [i + np.argmin(signal[local_min[i : i + dmin]]) for i in range(0, len(local_min), dmin)]
+        ]
+    if dmax > 1:
+        local_max = local_max[
+            [i + np.argmax(signal[local_max[i : i + dmax]]) for i in range(0, len(local_max), dmax)]
+        ]
+
+    return local_min, local_max
+
+
+def envelope_2d(x: np.ndarray, values: np.ndarray) -> np.ndarray:
+    """
+    returns the envelope of a 2d propagation-like array
+
+    Parameters
+    ----------
+    x : np.ndarray, shape (nt,)
+        x axis
+    values : np.ndarray, shape (nz, nt)
+        values of which to find the envelope
+
+    Returns
+    -------
+    np.ndarray, shape (nz, nt)
+        interpolated values
+    """
+    return np.array([envelope_1d(x, y) for y in values])
+
+
+def envelope_1d(y: np.ndarray, x: np.ndarray | None = None) -> np.ndarray:
+    if x is None:
+        x = np.arange(len(y))
+    _, hi = envelope_ind(y)
+    return interp1d(x[hi], y[hi], kind="cubic", fill_value=0, bounds_error=False)(x)
+
+
+@dataclass(frozen=True)
+class LobeProps:
+    left_pos: float
+    left_fwhm: float
+    center: float
+    right_fwhm: float
+    right_pos: float
+    interp: interp1d
+
+    @property
+    @cache
+    def x(self) -> np.ndarray:
+        return np.array(
+            [self.left_pos, self.left_fwhm, self.center, self.right_fwhm, self.right_pos]
+        )
+
+    @property
+    @cache
+    def y(self) -> np.ndarray:
+        return self.interp(self.x)
+
+    @property
+    @cache
+    def fwhm(self) -> float:
+        return abs(self.right_fwhm - self.left_fwhm)
+
+    @property
+    @cache
+    def width(self) -> float:
+        return abs(self.right_pos - self.left_pos)
+
+
+def measure_lobe(x_in, y_in, /, lobe_pos: int | None = None, thr_rel: float = 1 / 50) -> LobeProps:
+    """
+    given a fairly smooth signal, finds the highest lobe and returns its position as well
+    as its fwhm points
+
+    Parameters
+    ----------
+    x_in : np.ndarray, shape (n,)
+        x values
+    y_in : np.ndarray, shape (n,)
+        y values
+    lobe_pos : int, optional
+        index of the desired lobe, by default None (take highest peak)
+    thr_rel : float, optional
+
+
+    Returns
+    -------
+    np.ndarray
+        (left limit, left half maximum, maximum position, right half maximum, right limit)
+    """
+    interp = interp1d(x_in, y_in)
+    lobe_index = lobe_pos or np.argmax(y_in)
+    maxi = y_in[lobe_index]
+    maxi2 = maxi / 2
+    thr_abs = maxi * thr_rel
+    half_max_left: float = all_zeros(x_in[:lobe_index], y_in[:lobe_index] - maxi2)[-1]
+    half_max_right: float = all_zeros(x_in[lobe_index:], y_in[lobe_index:] - maxi2)[0]
+
+    poly = lagrange((half_max_left, x_in[lobe_index], half_max_right), (maxi2, maxi2 * 2, maxi2))
+    parabola_left, parabola_right = sorted(poly.roots)
+
+    r_cand = x_in > half_max_right
+    x_right = x_in[r_cand]
+    y_right = y_in[r_cand]
+
+    l_cand = x_in < half_max_left
+    x_left = x_in[l_cand][::-1]
+    y_left = y_in[l_cand][::-1]
+
+    d = {}
+    for x, y, central_parabola_root, sign in [
+        (x_left, y_left, parabola_left, 1),
+        (x_right, y_right, parabola_right, -1),
+    ]:
+        candidates = []
+        slope = sign * np.gradient(y, x)
+
+        for y_test, num_to_take in [
+            (sign * np.gradient(slope, x), 2),
+            (y - thr_abs, 1),
+            (slope, 3),
+        ]:
+            candidates.extend(all_zeros(x, y_test)[:num_to_take])
+        candidates = np.array(sorted(candidates))
+
+        side_parabola_root = x[0] - 2 * y[0] / (sign * slope[0])
+        weights = (
+            np.abs(candidates - side_parabola_root)
+            + np.abs(candidates - central_parabola_root)
+            + interp(candidates) / thr_abs
+        )
+        d[sign] = candidates[np.argmin(weights)]
+
+    return LobeProps(d[1], half_max_left, x_in[lobe_index], half_max_right, d[-1], interp)
+
+
+@numba.jit(nopython=True)
+def cumulative_simpson(x: np.ndarray) -> np.ndarray:
+    out = np.zeros_like(x)
+    c1 = 1 / 3
+    c2 = 4 / 3
+    out[1] = (x[1] + x[0]) * 0.5
+    for i in range(2, len(x)):
+        out[i] = out[i - 2] + x[i - 2] * c1 + x[i - 1] * c2 + c1 * x[i]
+    return out
+
+
+@numba.jit(nopython=True)
+def cumulative_boole(x: np.ndarray) -> np.ndarray:
+    out = np.zeros_like(x)
+    c1 = 14 / 45
+    c2 = 64 / 45
+    c3 = 24 / 45
+    c = np.array([c1, c2, c3, c2, c1])
+    ind = np.arange(5)
+    out[ind] = cumulative_simpson(x[ind])
+    for i in range(4, len(x)):
+        out[i] = out[i - 4] + np.sum(c * x[i - 4 : i + 1])
+    return out
+
+
+def stencil_coefficients(stencil_points: Sequence, order: int) -> np.ndarray:
+    """
+    Reference
+    ---------
+    https://en.wikipedia.org/wiki/Finite_difference_coefficient#Arbitrary_stencil_points
+    """
+    mat = np.power.outer(stencil_points, np.arange(len(stencil_points))).T
+    d = np.zeros(len(stencil_points))
+    d[order] = math.factorial(order)
+    return np.linalg.solve(mat, d)
+
+
+@cache
+def central_stencil_coefficients(n: int, order: int) -> np.ndarray:
+    """
+    returns the coefficients of a centered finite difference scheme
+
+    Parameters
+    ----------
+    n : int
+        number of points
+    order : int
+        order of differentiation
+    """
+    return stencil_coefficients(np.arange(n * 2 + 1) - n, order)
+
+
+@cache
+def stencil_coefficients_set(n: int, order: int) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """
+    coefficients for a forward, centered and backward finite difference differentation scheme of
+    order `order` that extends `n` points away from the evaluation point (`x_0`)
+    """
+    central = central_stencil_coefficients(n, order)
+    left = stencil_coefficients(np.arange(n + 1), order)
+    right = stencil_coefficients(-np.arange(n + 1)[::-1], order)
+    return left, central, right
+
+
+def differentiate_arr(
+    values: np.ndarray,
+    diff_order: int,
+    extent: int | None = None,
+    h: float = 1.0,
+    correct_edges=True,
+) -> np.ndarray:
+    """
+    takes a derivative of order `diff_order` using equally spaced values
+
+    Parameters
+    ---------
+    values : ndarray
+        equally spaced values
+    diff_order : int
+        order of differentiation
+    extent : int, optional
+        how many points away from the center the scheme uses. This determines accuracy.
+        example: extent=6 means that 13 (6 on either side + center) points are used to evaluate the
+        derivative at each point. by default diff_order + 2
+    h : float, optional
+        step size, by default 1.0
+    correct_edges : bool, optional
+        avoid artifacts by using forward/backward schemes on the edges, by default True
+
+    Reference
+    ---------
+    https://en.wikipedia.org/wiki/Finite_difference_coefficient
+
+    """
+    if extent is None:
+        extent = diff_order + 2
+    n_points = (diff_order + extent) // 2
+
+    if not correct_edges:
+        central_coefs = central_stencil_coefficients(n_points, diff_order)
+        result = np.convolve(values, central_coefs[::-1], mode="same")
+    else:
+        left_coefs, central_coefs, right_coefs = stencil_coefficients_set(n_points, diff_order)
+        result = np.concatenate(
+            (
+                np.convolve(values[: 2 * n_points], left_coefs[::-1], mode="valid"),
+                np.convolve(values, central_coefs[::-1], mode="valid"),
+                np.convolve(values[-2 * n_points :], right_coefs[::-1], mode="valid"),
+            )
+        )
+    return result / h**diff_order
+
+
+def mean_angle(values: np.ndarray, axis: int = 0):
+    """
+    computes the mean angle of an array along a given axis
+
+    Parameters
+    ----------
+    spectra : np.ndarray
+        complex values from which to compute the mean phase
+    axis : int, optional
+        axis on which to take the mean, by default 0
+
+    Returns
+    ----------
+    np.ndarray
+        array of complex numbers of unit length representing the mean phase, decimated along the
+        specified axis
+
+    Example
+    ----------
+    >>> x = np.array([[1 + 1j, 0 + 2j, -3 - 1j],
+                      [1 + 0j, 2 + 3j, -3 + 1j]])
+    >>> mean_angle(x)
+    array([ 0.92387953+0.38268343j,  0.28978415+0.95709203j,  -1.        +0.j        ])
+
+    """
+    values = np.divide(
+        values, np.abs(values), out=np.zeros(values.shape, dtype=values.dtype), where=values != 0
+    )
+    total_phase = np.sum(values, axis=axis)
+    return np.divide(total_phase, np.abs(total_phase), where=total_phase != 0, out=total_phase)

src/dispersionapp/physics/materials.py

@@ -0,0 +1,445 @@
+from __future__ import annotations
+
+import warnings
+from dataclasses import dataclass, field
+from functools import cache
+from typing import Any, overload
+
+import numpy as np
+
+import dispersionapp.io as io
+import dispersionapp.math as math
+import dispersionapp.physics.units as units
+from dispersionapp.physics.units import NA, c, epsilon0, kB
+
+type Num = np.floating | np.integer | float | int
+
+
+@dataclass
+class Sellmeier:
+    B: list[float] = field(default_factory=list)
+    C: list[float] = field(default_factory=list)
+    pressure_ref: float = 101325
+    temperature_ref: float = 273.15
+    kind: int = 2
+    constant: float = 0
+
+    @classmethod
+    @cache
+    def load(cls, name: str) -> Sellmeier:
+        mat_dico = io.load_material_dico(name)
+        s = mat_dico.get("sellmeier", {})
+        return cls(
+            **{
+                newk: s.get(k, None)
+                for newk, k in zip(
+                    ["B", "C", "pressure_ref", "temperature_ref", "kind", "constant"],
+                    ["B", "C", "P0", "T0", "kind", "const"],
+                )
+                if k in s
+            }
+        )
+
+    @overload
+    def chi(
+        self, wl: np.ndarray, temperature: float | None = None, pressure: float | None = None
+    ) -> np.ndarray: ...
+    @overload
+    def chi(
+        self, wl: Num, temperature: float | None = None, pressure: float | None = None
+    ) -> Num: ...
+    def chi(self, wl, temperature: float | None = None, pressure: float | None = None):
+        """n^2 - 1"""
+        if isinstance(wl, np.ndarray):
+            chi = np.zeros_like(wl)  # = n^2 - 1
+        else:
+            chi = 0
+        if self.kind == 1:
+            for b, c_ in zip(self.B, self.C):
+                chi += wl**2 * b / (wl**2 - c_)
+        elif self.kind == 2:  # gives n-1
+            for b, c_ in zip(self.B, self.C):
+                chi += b / (c_ - 1 / wl**2)
+            chi += self.constant
+            chi = (chi + 1) ** 2 - 1
+        elif self.kind == 3:  # Schott formula
+            for i, b in reversed(list(enumerate(self.B))):
+                chi += b * wl ** (-2 * (i - 1))
+            chi = chi - 1
+        else:
+            raise ValueError(f"kind {self.kind} is not recognized.")
+
+        if temperature is not None:
+            chi *= self.temperature_ref / temperature
+
+        if pressure is not None:
+            chi *= pressure / self.pressure_ref
+        return chi
+
+    @overload
+    def n_gas_2(
+        self, wl: np.ndarray, temperature: float | None = None, pressure: float | None = None
+    ) -> np.ndarray: ...
+    @overload
+    def n_gas_2(
+        self, wl: Num, temperature: float | None = None, pressure: float | None = None
+    ) -> float: ...
+    def n_gas_2(self, wl, temperature: float | None = None, pressure: float | None = None):
+        return self.chi(wl, temperature, pressure) + 1
+
+    @overload
+    def n(
+        self, wl: np.ndarray, temperature: float | None = None, pressure: float | None = None
+    ) -> np.ndarray: ...
+    @overload
+    def n(
+        self, wl: Num, temperature: float | None = None, pressure: float | None = None
+    ) -> float: ...
+    def n(self, wl, temperature: float | None = None, pressure: float | None = None):
+        return np.sqrt(self.n_gas_2(wl, temperature, pressure))
+
+    def delta(self, wl: np.ndarray, wl_zero_disp: float) -> np.ndarray:
+        """
+        'delta' quantity that describes the gas dispersion according to eq. S7 in [1]
+
+        Parameters
+        ----------
+        wl : ndarray
+            wavelength in m
+        wl_zero_disp : float
+            zero dispersion wavelength in m
+
+        Reference
+        ---------
+        [1] TRAVERS, John C., GRIGOROVA, Teodora F., BRAHMS, Christian, et al. High-energy
+            pulse self-compression and ultraviolet generation through soliton dynamics in hollow
+            capillary fibres. Nature Photonics, 2019, vol. 13, no 8, p. 547-554.
+        """
+        factor = (math.u_nm(1, 1) / c) ** 2 * (0.5 * wl / np.pi) ** 3
+        f = np.gradient(np.gradient(self.chi(wl), wl), wl)
+        ind = math.argclosest(wl, wl_zero_disp)
+        return factor * (f / f[ind] - 1)
+
+
+class Gas:
+    name: str
+    sellmeier: Sellmeier
+    atomic_number: int | float
+    atomic_mass: float
+    chi3_0: float
+    ionization_energy: float | None
+
+    _raw_sellmeier: dict[str, Any]
+    _kerr_wl: np.ndarray
+
+    def __init__(self, gas_name: str):
+        self.name = gas_name
+        self._raw_sellmeier = io.load_material_dico(gas_name)
+        self.atomic_mass = self._raw_sellmeier["atomic_mass"]
+        self.atomic_number = self._raw_sellmeier["atomic_number"]
+        self.ionization_energy = self._raw_sellmeier.get("ionization_energy")
+
+        s = self._raw_sellmeier.get("sellmeier", {})
+        self.sellmeier = Sellmeier(
+            **{
+                newk: s.get(k, None)
+                for newk, k in zip(
+                    ["B", "C", "pressure_ref", "temperature_ref", "kind", "constant"],
+                    ["B", "C", "P0", "T0", "kind", "const"],
+                )
+                if k in s
+            }
+        )
+        kerr = self._raw_sellmeier["kerr"]
+        n2_0 = kerr["n2"]
+        self._kerr_wl = kerr.get("wavelength", 800e-9)
+        self.chi3_0 = (
+            4
+            / 3
+            * units.epsilon0
+            * units.c
+            * self.sellmeier.n_gas_2(self._kerr_wl, kerr["T0"], kerr["P0"])
+            * n2_0
+        )
+
+    def pressure_from_relative_density(
+        self, density: float, temperature: float | None = None
+    ) -> float:
+        temperature = temperature or self.sellmeier.temperature_ref
+        return self.sellmeier.temperature_ref / temperature * density * self.sellmeier.pressure_ref
+
+    def density_factor(self, temperature: float, pressure: float, ideal_gas: bool) -> float:
+        """
+        returns the number density relative to reference values
+
+        Parameters
+        ----------
+        temperature : float
+            target temperature in K
+        pressure : float
+            target pressure in Pa
+        ideal_gas : bool
+            whether to use ideal gas law
+
+        Returns
+        -------
+        float
+            N / N_0
+        """
+        if ideal_gas:
+            return (
+                pressure
+                / self.sellmeier.pressure_ref
+                * self.sellmeier.temperature_ref
+                / temperature
+            )
+        else:
+            return self.number_density_van_der_waals(
+                pressure, temperature
+            ) / self.number_density_van_der_waals(
+                self.sellmeier.pressure_ref,
+                self.sellmeier.temperature_ref,
+            )
+
+    def number_density(
+        self,
+        temperature: float | None = None,
+        pressure: float | None = None,
+        ideal_gas: bool = False,
+    ) -> float:
+        """
+        returns the number density in 1/m^3 using van der Waals equation
+
+        Parameters
+        ----------
+        temperature : float, optional
+            temperature in K, by default None
+        pressure : float, optional
+            pressure in Pa, by default None
+
+        Returns
+        -------
+        float
+            number density in 1/m^3
+        """
+        pressure = pressure or self.sellmeier.pressure_ref
+        temperature = temperature or self.sellmeier.temperature_ref
+        if ideal_gas:
+            return pressure / temperature / kB
+        else:
+            return self.number_density_van_der_waals(pressure, temperature)
+
+    def number_density_van_der_waals(
+        self, pressure: float | None = None, temperature: float | None = None
+    ):
+        """
+        returns the number density of a gas
+
+        Parameters
+        ----------
+        pressure : float, optional
+            pressure in Pa, by default the reference pressure
+        temperature : float, optional
+            temperature in K, by default the reference temperature
+
+        Returns
+        ----------
+        the numbers density (/m^3)
+
+        Raises
+        ----------
+        ValueError : Since the Van der Waals equation is a cubic one, there could be more than one
+        real, positive solution
+        """
+
+        if pressure == 0:
+            return 0
+        a = self._raw_sellmeier.get("a", 0)
+        b = self._raw_sellmeier.get("b", 0)
+        pressure = (
+            self._raw_sellmeier["sellmeier"].get("P0", 101325) if pressure is None else pressure
+        )
+        temperature = (
+            self._raw_sellmeier["sellmeier"].get("T0", 273.15)
+            if temperature is None
+            else temperature
+        )
+        ap = a / NA**2
+        bp = b / NA
+
+        # setup van der Waals equation for the number density
+        p3 = -ap * bp
+        p2 = ap
+        p1 = -(pressure * bp + kB * temperature)
+        p0 = pressure
+
+        # filter out unwanted matches
+        roots = np.roots([p3, p2, p1, p0])
+        roots = roots[np.isreal(roots)].real
+        roots = roots[roots > 0]
+        if len(roots) != 1:
+            s = f"Van der Waals eq with parameters P={pressure}, T={temperature}, a={a}, b={b}"
+            s += f", There is more than one possible number density : {roots}."
+            s += f", {np.min(roots)} was returned"
+            warnings.warn(s)
+        return np.min(roots)
+
+    def chi3(self, temperature: float | None = None, pressure: float | None = None) -> float:
+        """nonlinear susceptibility"""
+
+        # if pressure and/or temperature are specified, adjustment is made
+        # according to number density ratio
+        if pressure is not None or temperature is not None:
+            N0 = self.number_density_van_der_waals()
+            N = self.number_density_van_der_waals(pressure, temperature)
+            ratio = N / N0
+        else:
+            ratio = 1
+        return ratio * self.chi3_0
+
+    def n2(self, temperature: float | None = None, pressure: float | None = None) -> float:
+        sellmeier = self.sellmeier.n_gas_2(self._kerr_wl, temperature, pressure)
+        chi3 = self.chi3(temperature, pressure)
+        return 0.75 * chi3 / (units.epsilon0 * units.c * sellmeier)
+
+    @property
+    def ionic_charge(self):
+        return 1
+
+    @property
+    def barrier_suppression(self) -> float:
+        """
+        Compute the barrier suppression threshold (W/m^2)
+        """
+        field_max = self.barrier_suppression_field
+        return 0.5 * units.c * units.epsilon0 * field_max**2
+
+    @property
+    def barrier_suppression_field(self) -> float:
+        """
+        Barrier suppression threshold electric field
+
+        Reference
+        ---------
+        [1] KRAINOV, V. P. Theory of barrier-suppression ionization of atoms. Journal of Nonlinear
+            Optical Physics & Materials, 1995, vol. 4, no 04, p. 775-798.
+        [2] BETHE, H. A. et SALPETER, E. E. Quantum mechanics of one-and two-electron atoms. 1977.
+        """
+        # from [1-2] :
+        # field_max = 0.0625 / self.ionic_charge * (self.ionization_energy / 2.1787e-18) ** 2
+        # return field_max = 5.14220670712125e11
+
+        # from [3] :
+        if self.ionization_energy is None:
+            raise ValueError(f"no ionization_energy for {self.name}")
+        Z = self.ionic_charge
+        Ip_au = self.ionization_energy / 4.359744650021498e-18
+        ns = Z / np.sqrt(2 * Ip_au)
+        return Z**3 / (16 * ns**4) * 5.14220670712125e11
+
+    def get(self, key, default=None):
+        return self._raw_sellmeier.get(key, default)
+
+    def __getitem__(self, key):
+        return self._raw_sellmeier[key]
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}({self.name!r})"
+
+
+def n_gas_2(
+    wl: np.ndarray, gas_name: str, pressure: float | None, temperature: float | None
+) -> np.ndarray:
+    """Returns the sqare of the index of refraction of the specified gas"""
+    return Sellmeier.load(gas_name).n_gas_2(wl, temperature, pressure)
+
+
+def pressure_from_gradient(ratio: float, p0: float, p1: float) -> float:
+    """
+    returns the pressure as function of distance with eq. 20 in Markos et al. (2017)
+
+    Parameters
+    ----------
+        ratio : relative position in the fiber (0 = start, 1 = end)
+        p0 : pressure at the start
+        p1 : pressure at the end
+
+    Returns
+    -------
+        the pressure (float)
+    """
+    return np.sqrt(p0**2 - ratio * (p0**2 - p1**2))
+
+
+def delta_gas(w: np.ndarray, gas: Gas) -> np.ndarray:
+    """
+    returns the value delta_t (eq. 24 in Markos(2017))
+
+    Parameters
+    ----------
+        w : np.ndarray
+            angular frequency array
+        gas : Gas
+
+    Returns
+    -------
+        delta_t
+        since 2 gradients are computed, it is recommended to exclude the 2 extremum values
+    """
+    chi = gas.sellmeier.chi(units.m_rads(w))
+    N0 = gas.number_density_van_der_waals()
+
+    dchi_dw = np.gradient(chi, w)
+    return 1 / (N0 * c) * (dchi_dw + w / 2 * np.gradient(dchi_dw, w))
+
+
+def gas_n2(gas_name: str, pressure: float, temperature: float) -> float:
+    """returns the nonlinear refractive index
+
+    Parameters
+    ----------
+    gas_name : str
+        gas name
+    pressure : float
+        pressure in Pa
+    temperature : float
+        temperature in K
+
+    Returns
+    -------
+    float
+        n2 in m2/W
+    """
+    return Gas(gas_name).n2(temperature, pressure)
+
+
+def gas_chi3(gas_name: str, wavelength: float, pressure: float, temperature: float) -> float:
+    """
+    returns the chi3 of a particular material
+
+    Parameters
+    ----------
+    gas_name : str
+        [description]
+    pressure : float
+        [description]
+    temperature : float
+        [description]
+
+    Returns
+    -------
+    float
+        [description]
+    """
+    gas = Gas(gas_name)
+    n = gas.sellmeier.n(wavelength, temperature, pressure)
+    n2 = gas.n2(temperature, pressure)
+    return n2_to_chi3(n2, n)
+
+
+def n2_to_chi3(n2: float, n0: float) -> float:
+    return n2 * 4 * epsilon0 * n0**2 * c / 3
+
+
+def chi3_to_n2(chi3: float, n0: float) -> float:
+    return 3.0 * chi3 / (4.0 * epsilon0 * c * n0**2)

src/dispersionapp/physics/units.py

@@ -0,0 +1,460 @@
+# series of functions to convert different values to angular frequencies
+# For example, nm(X) means "I give the number X in nm, figure out the ang. freq."
+# to be used especially when giving plotting ranges : (400, 1400, nm), (-4, 8, ps), ...
+
+from __future__ import annotations
+
+from typing import Callable, Protocol, Sequence, Union, overload
+
+import numpy as np
+from numpy import pi
+
+c = 299792458.0
+h = 6.62607015e-34
+hbar = 0.5 * h / np.pi
+NA = 6.02214076e23
+R = 8.31446261815324
+kB = 1.380649e-23
+epsilon0 = 8.85418781e-12
+me = 9.1093837015e-31
+e = 1.602176634e-19
+
+
+prefix = dict(P=1e12, G=1e9, M=1e6, k=1e3, d=1e-1, c=1e-2, m=1e-3, u=1e-6, n=1e-9, p=1e-12, f=1e-15)
+
+"""
+Below are common units. You can define your own unit function
+provided you decorate it with @unit and provide at least a type and a label
+types are "WL", "FREQ", "AFREQ", "TIME", "PRESSURE", "TEMPERATURE", "OTHER"
+"""
+type _UT[T] = Callable[[T], T]
+PRIMARIES = dict(WL="m", FREQ="Rad/s", AFREQ="Rad/s", TIME="s", PRESSURE="Pa", TEMPERATURE="K")
+
+
+class Unit(Protocol):
+    @overload
+    def __call__(self, v: np.ndarray) -> np.ndarray: ...
+    @overload
+    def __call__(self, v: float | int | np.floating | np.integer) -> float: ...
+    def __call__(self, v): ...
+    @overload
+    def inv(self, v: np.ndarray) -> np.ndarray: ...
+    @overload
+    def inv(self, v: float | int | np.floating | np.integer) -> float: ...
+    def inv(self, v): ...
+    def inverse(self, f: _UT) -> Unit: ...
+
+
+class UnitMap(dict):
+    def __setitem__(self, new_name, new_func):
+        super().__setitem__(new_name, new_func)
+        already_here = [name for name in self if isinstance(name, str)]
+        for old_name in already_here:
+            super().__setitem__((old_name, new_name), self._chain(old_name, new_name))
+            super().__setitem__((new_name, old_name), self._chain(new_name, old_name))
+
+    def _chain(self, name_1: str, name_2: str) -> _UT:
+        c1 = self[name_1]
+        c2 = self[name_2]
+
+        def chained_function[T](x: T) -> T:
+            return c2.inv(c1(x))
+
+        chained_function.name = f"{name_1}_to_{name_2}"
+        chained_function.__name__ = f"{name_1}_to_{name_2}"
+        chained_function.__doc__ = f"converts x from {name_1} to {name_2}"
+
+        return chained_function
+
+
+units_map: dict[Union[str, tuple[str, str]], _UT] = UnitMap()
+
+
+class To:
+    def __init__(self, name: str):
+        self.name = name
+
+    def __getstate__(self):
+        """make pickle happy"""
+        return self.name
+
+    def __setstate__(self, name):
+        """make pickle happy"""
+        self.name = name
+
+    def __getattr__(self, key: str):
+        try:
+            return units_map[self.name, key]
+        except KeyError:
+            raise KeyError(f"no registered unit named {key!r}") from None
+
+
+def W_to_Vm(n0: float, effective_area: float) -> float:
+    """returns the factor to convert from [|E|²] = W to [|E|] = V/m
+
+    Parameters
+    ----------
+    n : float
+        refractive index
+
+    Returns
+    -------
+    float
+        p such that E_sqrt(W) * p = W_Vm
+    """
+    return 1.0 / np.sqrt(effective_area * 0.5 * epsilon0 * c * n0)
+
+
+def unit_formatter(
+    unit: str, decimals: int = 3, vmin: float | None = 1e-28
+) -> Callable[[float | int], str]:
+    if not unit:
+
+        def formatter(val):
+            return f"{val:.{decimals}g}"
+
+    elif unit == "%":
+
+        def formatter(val):
+            return f"{100 * val:.{decimals}g}%"
+
+    else:
+
+        def formatter(val):
+            if vmin is not None and abs(val) < vmin:
+                return f"0{unit}"
+            base, true_exp = format(val, f".{3 + decimals}e").split("e")
+            true_exp = int(true_exp)
+            exp, mult = divmod(true_exp, 3)
+            if exp < -8:
+                mult -= -3 * (8 + exp)
+                exp = -8
+            elif exp > 8:
+                mult += 3 * (exp - 8)
+                exp = 8
+
+            prefix = "yzafpnµm kMGTPEZY"[8 + exp] if exp else ""
+            return f"{float(base) * 10**mult:.{decimals}g}{prefix}{unit}"
+
+    def _format(val):
+        if isinstance(val, (Sequence, np.ndarray)):
+            return f"({', '.join(_format(el) for el in val)})"
+        else:
+            return formatter(val)
+
+    def _format_mpl(val, _):
+        return _format(val)
+
+    setattr(_format, "for_mpl", _format_mpl)
+
+    return _format
+
+
+class unit:
+    inv = None
+
+    def __init__(self, tpe: str, label: str):
+        self.type = tpe
+        self.label = label
+
+    def __call__(self, func) -> Unit:
+        self.name = func.__name__
+        self.func = func
+        self.inv = func
+
+        units_map[self.name] = self.func
+
+        func.to = To(self.name)
+        func.inv = func
+        func.inverse = self.inverse
+        func.type = self.type
+        func.label = self.label
+        func.name = self.name
+        func.__doc__ = f"Transform x from {self.name!r} to {PRIMARIES.get(self.type)!r}"
+        return func
+
+    def inverse(self, func: _UT):
+        if func.__name__ == self.name:
+            raise ValueError(
+                f"inverse function of {self.name} must be named something else than {self.name}"
+            )
+        self.inv = func
+        self.func.inv = func
+        return func
+
+
+@unit("WL", r"Wavelength λ (m)")
+def m(wl):
+    return wl
+
+
+@unit("WL", r"Wavelength λ (nm)")
+def nm(wl):
+    return wl * 1e-9
+
+
+@nm.inverse
+def nm_inv(wl):
+    return wl * 1e9
+
+
+@unit("WL", r"Wavelength λ (μm)")
+def um(wl):
+    return wl * 1e-6
+
+
+@um.inverse
+def um_inv(wl):
+    return wl * 1e6
+
+
+@unit("FREQ", r"Frequency $f$ (Hz)")
+def Hz(f):
+    return 2 * pi * f
+
+
+@Hz.inverse
+def Hz_inv(w):
+    return w / (2 * pi)
+
+
+@unit("FREQ", r"Frequency $f$ (THz)")
+def THz(f):
+    return 1e12 * 2 * pi * f
+
+
+@THz.inverse
+def THz_inv(w):
+    return w / (1e12 * 2 * pi)
+
+
+@unit("FREQ", r"Frequency $f$ (PHz)")
+def PHz(f):
+    return 1e15 * 2 * pi * f
+
+
+@PHz.inverse
+def PHz_inv(w):
+    return w / (1e15 * 2 * pi)
+
+
+@unit("AFREQ", r"Angular frequency $\omega$ ($\frac{\mathrm{rad}}{\mathrm{s}}$)")
+def rad_s(w):
+    return w
+
+
+@unit("AFREQ", r"Angular frequency $\omega$ ($\frac{\mathrm{Prad}}{\mathrm{s}}$)")
+def Prad_s(w):
+    return w * 1e15
+
+
+@Prad_s.inverse
+def Prad_s_inv(w):
+    return w * 1e-15
+
+
+@unit("TIME", r"relative time ${\tau}/{\tau_\mathrm{0, FWHM}}$")
+def rel_time(t):
+    return t
+
+
+@unit("FREQ", r"relative angular freq. $(\omega - \omega_0)/\Delta\omega_0$")
+def rel_freq(f):
+    return f
+
+
+@unit("TIME", r"Time $t$ (s)")
+def s(t):
+    return t
+
+
+@unit("TIME", r"Time $t$ (us)")
+def us(t):
+    return t * 1e-6
+
+
+@us.inverse
+def us_inv(t):
+    return t * 1e6
+
+
+@unit("TIME", r"Time $t$ (ns)")
+def ns(t):
+    return t * 1e-9
+
+
+@ns.inverse
+def ns_inv(t):
+    return t * 1e9
+
+
+@unit("TIME", r"Time $t$ (ps)")
+def ps(t):
+    return t * 1e-12
+
+
+@ps.inverse
+def ps_inv(t):
+    return t * 1e12
+
+
+@unit("TIME", r"Time $t$ (fs)")
+def fs(t):
+    return t * 1e-15
+
+
+@fs.inverse
+def fs_inv(t):
+    return t * 1e15
+
+
+@unit("WL", "inverse")
+def inv(x):
+    return 1 / x
+
+
+@unit("PRESSURE", "Pressure (bar)")
+def bar(p):
+    return 1e5 * p
+
+
+@bar.inverse
+def bar_inv(p):
+    return p * 1e-5
+
+
+@unit("PRESSURE", "Pressure (mbar)")
+def mbar(p):
+    return 1e2 * p
+
+
+@mbar.inverse
+def mbar_inv(p):
+    return 1e-2 * p
+
+
+@unit("OTHER", r"$\beta_2$ (fs$^2$/cm)")
+def beta2_fs_cm(b2):
+    return 1e-28 * b2
+
+
+@beta2_fs_cm.inverse
+def beta2_fs_cm_inv(b2):
+    return 1e28 * b2
+
+
+@unit("OTHER", r"$\beta_2$ (ps$^2$/km)")
+def beta2_ps_km(b2):
+    return 1e-27 * b2
+
+
+@beta2_ps_km.inverse
+def beta2_ps_km_inv(b2):
+    return 1e27 * b2
+
+
+@unit("OTHER", r"$D$ (ps/(nm km))")
+def D_ps_nm_km(D):
+    return 1e-6 * D
+
+
+@D_ps_nm_km.inverse
+def D_ps_nm_km_inv(D):
+    return 1e6 * D
+
+
+@unit("OTHER", r"a.u.")
+def unity(x):
+    return x
+
+
+@unit("TEMPERATURE", r"Temperature (K)")
+def K(t):
+    return t
+
+
+@unit("TEMPERATURE", r"Temperature (°C)")
+def C(t_C):
+    return t_C + 272.15
+
+
+@C.inverse
+def C_inv(t_K):
+    return t_K - 272.15
+
+
+@unit("OTHER", r"a.u")
+def no_unit(x):
+    return x
+
+
+def nm_rads(nm):
+    return 2e9 * np.pi * c / nm
+
+
+def nm_hz(nm):
+    return 1e9 * c / nm
+
+
+def um_rads(um):
+    return 2e6 * np.pi * c / um
+
+
+def um_hz(um):
+    return 1e6 * c / um
+
+
+def m_rads(m):
+    return 2 * np.pi * c / m
+
+
+def m_hz(m):
+    return c / m
+
+
+def get_unit(unit: Union[str, Callable]) -> Callable[[float], float]:
+    if isinstance(unit, str):
+        return units_map[unit]
+    return unit
+
+
+def beta2_coef(beta2_coefficients):
+    fac = 1e27
+    out = np.zeros_like(beta2_coefficients)
+    for i, b in enumerate(beta2_coefficients):
+        out[i] = fac * b
+        fac *= 1e12
+    return out
+
+
+def to_WL(spectrum: np.ndarray, lambda_: np.ndarray) -> np.ndarray:
+    """
+    rescales the spectrum because of uneven binning when going from freq to wl
+
+    Parameters
+    ----------
+    spectrum : np.ndarray, shape (..., n)
+        intensity spectrum
+    lambda_ : np.ndarray, shape (n, )
+        wavelength in m
+
+    Returns
+    -------
+    np.ndarray, shape (n, )
+        intensity spectrum correctly scaled
+    """
+    m = 2 * pi * c / (lambda_**2) * spectrum
+    return m
+
+
+def w_from_wl(wl_min_nm: float, wl_max_nm: float, n: int) -> np.ndarray:
+    return np.linspace(nm_rads(wl_max_nm), nm_rads(wl_min_nm), n)
+
+
+def normalize_wavelengths(wl: np.ndarray) -> np.ndarray:
+    """attempts to guess the units of wl and return the same array in base SI units (m)"""
+    if np.all((wl > 0.01) & (wl < 20)):
+        return wl * 1e-6
+    elif np.all(wl >= 20):
+        return wl * 1e-9
+    return wl

src/dispersionapp/plotapp.py

@@ -29,30 +29,17 @@ MPL_COLORS = [
 key_type = Union[str, int]
 
 
-class Field(QtWidgets.QWidget):
+class SliderField(QtWidgets.QWidget):
     dtype: Type
     value: Any
     value_changed: QtCore.Signal
-    timer: QtCore.QTimer
-
-    @property
-    def value(self) -> Any:
-        raise NotImplementedError()
-
-    def values(self) -> list[Any]:
-        raise NotImplementedError()
-
-
-class SliderField(Field):
     dtype: Type
     possible_values: np.ndarray
     _slider_max = 100
-    _tuple_signal = QtCore.Signal(tuple)
     _int_signal = QtCore.Signal(int)
     _float_signal = QtCore.Signal(float)
-    _str_signal = QtCore.Signal(str)
 
-    def __init__(self, name: str, values: Iterable) -> None:
+    def __init__(self, name: str, values: Iterable[float] | Iterable[int]) -> None:
         super().__init__()
         self.__value = None
         self.slider = QtWidgets.QSlider(QtCore.Qt.Orientation.Horizontal)
@@ -64,16 +51,18 @@ class SliderField(Field):
         self.field.editingFinished.connect(self.field_changed)
 
         self.step_backward_button = QtWidgets.QPushButton("<")
+        self.step_backward_button.setFixedWidth(30)
         self.step_backward_button.clicked.connect(self.step_backward)
 
         self.step_forward_button = QtWidgets.QPushButton(">")
+        self.step_forward_button.setFixedWidth(30)
         self.step_forward_button.clicked.connect(self.step_forward)
 
         self._layout = QtWidgets.QHBoxLayout()
         self.setLayout(self._layout)
         self._layout.setContentsMargins(10, 0, 10, 0)
 
-        pretty_name = " ".join(s.title() for s in name.split("_"))
+        pretty_name = " ".join(s for s in name.split("_"))
         self.name_label = QtWidgets.QLabel(pretty_name + " :")
 
         self._layout.addWidget(self.name_label)
@@ -86,13 +75,13 @@ class SliderField(Field):
         self.value_changed = {
             int: self._int_signal,
             float: self._float_signal,
-            str: self._str_signal,
-            tuple: self._tuple_signal,
         }[self.dtype]
         self.value_changed.emit(self.__value)
 
     def set_values(self, values: Iterable):
-        self.possible_values = np.array(values)
+        self.possible_values = (
+            np.array(values) if not isinstance(values, np.ndarray) else values.copy()
+        )
         self.value_to_slider_map = {v: i for i, v in enumerate(self.possible_values)}
         self._slider_max = len(self.possible_values) - 1
         self.slider.setRange(0, self._slider_max)
@@ -115,7 +104,7 @@ class SliderField(Field):
         self.update_slider()
 
     @property
-    def value(self) -> float:
+    def value(self) -> float | int:
         return self.__value
 
     @value.setter
@@ -130,11 +119,8 @@ class SliderField(Field):
         self.value_changed.emit(new_value)
 
     def field_changed(self):
-        try:
         new_val = self.dtype(self.field.text())
-        except (ValueError, TypeError):
+        new_val = min(self.value_to_slider_map, key=lambda el: abs(el - new_val))
-            self.update_label()
-            return
         self.value = new_val
 
     def slider_changed(self):
@@ -168,7 +154,7 @@ class SliderField(Field):
         if self.dtype is int:
             return format(self.value)
         elif self.dtype is float:
-            return format(self.value, ".3g")
+            return format(self.value, ".5g")
         else:
             return format(self.value)
 
@@ -176,66 +162,6 @@ class SliderField(Field):
         return list(self.possible_values)
 
 
-class AnimatedSliderField(SliderField):
-    def __init__(self, name: str, values: Iterable) -> None:
-        super().__init__(name, values)
-        self.timer = QtCore.QTimer()
-        self.timer.timeout.connect(self.step_forward)
-
-        self.play_button = QtWidgets.QPushButton("⏯")
-        self.play_button.clicked.connect(self.toggle)
-        self.play_button.setMaximumWidth(30)
-        self.playing = False
-
-        self.interval_field = QtWidgets.QLineEdit()
-        self.interval_field.setMaximumWidth(60)
-        # self.interval_field.setValidator(QtGui.QIntValidator(1, 5000))
-        self.interval_field.editingFinished.connect(self.set_interval)
-        self.interval_field.inputRejected.connect(self.set_interval)
-        self.interval = 16
-        self.set_interval()
-
-        self._layout.addWidget(self.play_button)
-        self._layout.addWidget(self.interval_field)
-
-    def toggle(self):
-        if self.playing:
-            self.stop()
-            self.playing = False
-        else:
-            self.play()
-            self.playing = True
-
-    def play(self):
-        if self.slider.value() == self._slider_max:
-            self.slider.setValue(0)
-        self.timer.start(self.interval)
-        self.play_button.setStyleSheet("QPushButton {background-color: #DEF2DD;}")
-
-    def stop(self):
-        self.timer.stop()
-        self.play_button.setStyleSheet("QPushButton {background-color: none;}")
-
-    def set_interval(self):
-        try:
-            self.interval = max(1, int(self.interval_field.text()))
-        except ValueError:
-            self.interval_field.setText(str(self.interval))
-        if self.interval < 16:
-            self.increment = int(np.ceil(16 / self.interval))
-            self.interval *= self.increment
-        else:
-            self.increment = 1
-
-    def step_forward(self):
-        current = self.slider.value()
-        if current + self.increment <= self._slider_max:
-            self.slider.setValue(current + self.increment)
-        else:
-            self.slider.setValue(self._slider_max)
-            self.stop()
-
-
 class Plot:
     name: str
     dock: Dock
@@ -511,11 +437,12 @@ class PlotApp:
         self.params_widget = QtWidgets.QWidget()
         self.header_widget = QtWidgets.QWidget()
         self.info_label = QtWidgets.QLabel()
+        # self.info_label.setMaximumWidth(120)
         self.window.setCentralWidget(self.central_widget)
 
         self.central_layout = QtWidgets.QVBoxLayout()
-        self.params_layout = QtWidgets.QVBoxLayout()
+        self.params_layout = QtWidgets.QGridLayout()
-        self.header_layout = QtWidgets.QHBoxLayout()
+        self.header_layout = QtWidgets.QVBoxLayout()
 
         _pl = QtWidgets.QSizePolicy.Policy.Preferred
         info_sp = QtWidgets.QSizePolicy(_pl, _pl)
@@ -540,10 +467,10 @@ class PlotApp:
 
         self.__ran = False
         self.params = {}
-        for p_name, values in params.items():
+        for i, (p_name, values) in enumerate(params.items()):
-            field = AnimatedSliderField(p_name, values)
+            field = SliderField(p_name, values)
             self.params[p_name] = field
-            self.params_layout.addWidget(field)
+            self.params_layout.addWidget(field, *divmod(i, 2))
 
     def set_antialiasing(self, val: bool):
         pg.setConfigOptions(antialias=val)
@@ -562,7 +489,7 @@ class PlotApp:
                 opts = dict(zip(["v_min", "v_max", "v_num", "v_init"], opts))
             yield p_name, dtype, opts
 
-    def update(self, *p_names: str):
+    def update(self, first_param: str | Callable, *p_names: str):
         """
         use this as a decorator to connect the decorated function to
         the value_changed signal of some parameters
@@ -595,8 +522,12 @@ class PlotApp:
         changes and is given its new value as argument
         """
 
-        if len(p_names) == 1 and callable(p_names[0]):
+        if len(p_names) == 0 and callable(first_param) and not isinstance(first_param, str):
-            return self.update(*self._get_func_args(p_names[0]))(p_names[0])
+            return self.update(*self._get_func_args(first_param))(first_param)
+        elif not isinstance(first_param, str):
+            raise TypeError()
+
+        p_names = (first_param, *p_names)
 
         def wrapper(func):
             def to_call(v=None):
@@ -724,12 +655,10 @@ class PlotApp:
         self.app.exec()
 
     @overload
-    def __getitem__(self, key: tuple[key_type, key_type]) -> PlotDataItem:
+    def __getitem__(self, key: tuple[key_type, key_type]) -> PlotDataItem: ...
-        ...
 
     @overload
-    def __getitem__(self, key: key_type) -> Plot:
+    def __getitem__(self, key: key_type) -> Plot: ...
-        ...
 
     def __getitem__(self, key: key_type) -> Plot:
         if isinstance(key, tuple):

uv.lock

@@ -0,0 +1,704 @@
+version = 1
+revision = 3
+requires-python = ">=3.13"
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
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