diff --git a/PySDM/attributes/isotopes/bolin_numbers.py b/PySDM/attributes/isotopes/bolin_numbers.py index 8c63d4d409..dd7a770ecb 100644 --- a/PySDM/attributes/isotopes/bolin_numbers.py +++ b/PySDM/attributes/isotopes/bolin_numbers.py @@ -1,6 +1,6 @@ """ Derived droplet attribute representing the Bolin number for heavy -water isotopologues (D, 17O, 18O substitutions). +water isotopologues (2H, 17O, 18O substitutions). The Bolin number is a dimensionless coefficient relating the heavy-isotope mole tendency to the bulk liquid-water mass tendency @@ -24,6 +24,7 @@ def __init__(self, builder, *, heavy_isotope: str): heavy_isotope : str Heavy isotopologue identifier (entry of ``HEAVY_ISOTOPES``). """ + self.isotope = heavy_isotope self.moles_heavy = builder.get_attribute(f"moles_{heavy_isotope}") self.moles_light = builder.get_attribute("moles light water") # TODO #1787 self.cell_id = builder.get_attribute("cell id") @@ -41,9 +42,12 @@ def recalculate(self): self.particulator.backend.bolin_number( output=self.data, cell_id=self.cell_id.data, + isotope=self.isotope, relative_humidity=self.particulator.environment["RH"], temperature=self.particulator.environment["T"], - density_dry_air=self.particulator.environment["dry_air_density"], + water_vapour_mixing_ratio=self.particulator.environment[ + "water_vapour_mixing_ratio" + ], moles_light_molecule=self.moles_light.data, moles_heavy=self.moles_heavy.data, molality_in_dry_air=self.molality_in_dry_air, diff --git a/PySDM/attributes/physics/diffusional_growth_mass_change.py b/PySDM/attributes/physics/diffusional_growth_mass_change.py index d68093fa58..b9251eb977 100644 --- a/PySDM/attributes/physics/diffusional_growth_mass_change.py +++ b/PySDM/attributes/physics/diffusional_growth_mass_change.py @@ -25,12 +25,10 @@ def __init__(self, builder): def setup(self): self.old = self.water_mass.data.data.copy() - self.data.data[:] = 0 def notify(self): - new = self.water_mass.data.data - self.data.data[:] = new - self.old - self.old[:] = new + self.old[:] = self.water_mass.data.data def recalculate(self): - pass + new = self.water_mass.data.data + self.data.data[:] = new - self.old diff --git a/PySDM/backends/impl_numba/methods/isotope_methods.py b/PySDM/backends/impl_numba/methods/isotope_methods.py index 4d17cb204f..cdf5a75be8 100644 --- a/PySDM/backends/impl_numba/methods/isotope_methods.py +++ b/PySDM/backends/impl_numba/methods/isotope_methods.py @@ -5,6 +5,7 @@ from functools import cached_property import numba +import numpy as np from PySDM.backends.impl_common.backend_methods import BackendMethods @@ -67,22 +68,26 @@ def body( ): # pylint: disable=too-many-locals for sd_id in range(multiplicity.shape[0]): Bo = bolin_number[sd_id] + # Explicit Euler applied to logarithm of mass to ensure positive mole number in droplet mass_ratio_heavy_to_total = ( moles_heavy_molecule[sd_id] * molar_mass_heavy_molecule ) / signed_water_mass[sd_id] - if Bo == 0: + if abs(Bo) < 1e-5: # TODO dm_heavy = 0 else: dm_heavy = dm_total[sd_id] / Bo * mass_ratio_heavy_to_total - dn_heavy_molecule = dm_heavy / molar_mass_heavy_molecule - moles_heavy_molecule[sd_id] += dn_heavy_molecule - mass_of_dry_air = ( - dry_air_density[cell_id[sd_id]] * cell_volume[cell_id[sd_id]] + dn_heavy_molecule = moles_heavy_molecule[sd_id] * ( + np.exp(dm_heavy / moles_heavy_molecule[sd_id]) - 1 ) + moles_heavy_molecule[sd_id] += dn_heavy_molecule + mass_of_dry_air = dry_air_density[cell_id[sd_id]] * cell_volume molality_in_dry_air[cell_id[sd_id]] -= ( dn_heavy_molecule * multiplicity[sd_id] / mass_of_dry_air ) + # + # assert molality_in_dry_air[cell_id[sd_id]] >= 0 + return body def isotopic_fractionation( @@ -102,7 +107,7 @@ def isotopic_fractionation( """Update heavy-isotope composition during droplet growth/evaporation.""" self._isotopic_fractionation_body( cell_id=cell_id.data, - cell_volume=cell_volume.data, + cell_volume=cell_volume, multiplicity=multiplicity.data, dm_total=dm_total.data, signed_water_mass=signed_water_mass.data, @@ -133,9 +138,10 @@ def body( *, output, cell_id, + isotope, relative_humidity, temperature, - density_dry_air, + water_vapour_mixing_ratio, moles_light_molecule, moles_heavy, molality_in_dry_air, @@ -145,32 +151,42 @@ def body( pvs_water = ff.saturation_vapour_pressure__pvs_water(T) moles_heavy_atom = moles_heavy[i] moles_light_isotope = moles_light_molecule[i] # TODO #1787 - conc_vap_total = ( - pvs_water * relative_humidity[cell_id[i]] / ff.constants.R_str / T - ) - rho_v = pvs_water / T / ff.constants.Rv - isotopic_fraction = ff.trivia__isotopic_fraction( - molality_in_dry_air=molality_in_dry_air[cell_id[i]], - density_dry_air=density_dry_air[cell_id[i]], - total_vap_concentration=conc_vap_total, - ) - D_ratio_heavy_to_light = ( - ff.isotope_diffusivity_ratios__ratio_2H_heavy_to_light(T) + isotopic_fraction = ( # TODO calculations (do not use trivia, rhod not needed + molality_in_dry_air[cell_id[i]] + / water_vapour_mixing_ratio[cell_id[i]] + * ff.constants.Mv ) + + if isotope == "2H": + D_ratio = ff.isotope_diffusivity_ratios__ratio_2H_heavy_to_light(T) + alpha = ff.isotope_equilibrium_fractionation_factors__alpha_l_2H(T) + if isotope == "3H": + D_ratio = ff.isotope_diffusivity_ratios__ratio_3H_heavy_to_light(T) + alpha = ff.isotope_equilibrium_fractionation_factors__alpha_l_3H(T) + elif isotope == "18O": + D_ratio = ff.isotope_diffusivity_ratios__ratio_18O_heavy_to_light(T) + alpha = ff.isotope_equilibrium_fractionation_factors__alpha_l_18O(T) + elif isotope == "17O": + D_ratio = ff.isotope_diffusivity_ratios__ratio_17O_heavy_to_light(T) + alpha = ff.isotope_equilibrium_fractionation_factors__alpha_l_17O(T) + output[i] = ff.isotope_relaxation_timescale__bolin_number( - D_ratio_heavy_to_light=D_ratio_heavy_to_light, - alpha=ff.isotope_equilibrium_fractionation_factors__alpha_l_2H(T), - D_light=ff.constants.D0, + D_ratio_heavy_to_light=D_ratio, + alpha=alpha, Fk=ff.drop_growth__Fk( T=T, K=ff.constants.K0, lv=ff.constants.l_tri ), + Fd=ff.drop_growth__Fd( + T=T, + D=ff.constants.D0, + pvs=pvs_water, + ), R_vap=ff.trivia__isotopic_ratio_assuming_single_heavy_isotope( isotopic_fraction ), R_liq=moles_heavy_atom / moles_light_isotope, relative_humidity=relative_humidity[cell_id[i]], - rho_v=rho_v, ) return body @@ -180,9 +196,10 @@ def bolin_number( *, output, cell_id, + isotope, relative_humidity, temperature, - density_dry_air, + water_vapour_mixing_ratio, moles_light_molecule, moles_heavy, molality_in_dry_air, @@ -191,9 +208,10 @@ def bolin_number( self._bolin_number_body( output=output.data, cell_id=cell_id.data, + isotope=isotope, relative_humidity=relative_humidity.data, temperature=temperature.data, - density_dry_air=density_dry_air.data, + water_vapour_mixing_ratio=water_vapour_mixing_ratio.data, moles_light_molecule=moles_light_molecule.data, moles_heavy=moles_heavy.data, molality_in_dry_air=molality_in_dry_air.data, diff --git a/PySDM/backends/impl_thrust_rtc/methods/isotope_methods.py b/PySDM/backends/impl_thrust_rtc/methods/isotope_methods.py index 1b04b33ec1..28b281f3b4 100644 --- a/PySDM/backends/impl_thrust_rtc/methods/isotope_methods.py +++ b/PySDM/backends/impl_thrust_rtc/methods/isotope_methods.py @@ -63,7 +63,7 @@ def __isotopic_fractionation(self): moles_heavy_molecule[i] += dn_heavy; - real_type mass_of_dry_air = dry_air_density[cid] * cell_volume[cid]; + real_type mass_of_dry_air = dry_air_density[cid] * cell_volume; atomicAdd((real_type*) &molality_in_dry_air[cid], -dn_heavy * multiplicity[i] / mass_of_dry_air); """.replace("real_type", self._get_c_type()), @@ -87,7 +87,7 @@ def isotopic_fractionation( n=len(multiplicity), args=( cell_id.data, - cell_volume.data, + self._get_floating_point(cell_volume), multiplicity.data, dm_total.data, signed_water_mass.data, diff --git a/PySDM/dynamics/isotopic_fractionation.py b/PySDM/dynamics/isotopic_fractionation.py index a27580d0b4..0767525408 100644 --- a/PySDM/dynamics/isotopic_fractionation.py +++ b/PySDM/dynamics/isotopic_fractionation.py @@ -41,8 +41,10 @@ def register(self, builder): raise AssertionError( f"Isotopic fractionation not implemented for {isotope}" ) - builder.request_attribute(f"moles_{isotope}") builder.request_attribute(f"Bolin number for {isotope}") + for isotope in HEAVY_ISOTOPES: + builder.request_attribute(f"moles_{isotope}") + def __call__(self): self.particulator.isotopic_fractionation(self.isotopes) diff --git a/PySDM/environments/parcel.py b/PySDM/environments/parcel.py index ff628526da..163098ccd2 100644 --- a/PySDM/environments/parcel.py +++ b/PySDM/environments/parcel.py @@ -2,7 +2,7 @@ Zero-dimensional adiabatic parcel framework """ -from typing import List, Optional, Union +from typing import Sequence, Optional, Union import numpy as np @@ -27,14 +27,14 @@ def __init__( w: Union[float, callable], z0: float = 0, mixed_phase=False, - variables: Optional[List[str]] = None, + variables: Optional[Sequence[str]] = None, initial_water_vapour_mixing_ratio: float = None, initial_relative_humidity: float = None, ): assert (initial_water_vapour_mixing_ratio is not None) ^ ( initial_relative_humidity is not None ) - variables = (variables or []) + ["rhod", "z"] + variables = list(variables or []) + ["rhod", "z"] super().__init__(dt, Mesh.mesh_0d(), variables, mixed_phase=mixed_phase) self.p0 = p0 diff --git a/PySDM/initialisation/isotopic_equilibrium.py b/PySDM/initialisation/isotopic_equilibrium.py new file mode 100644 index 0000000000..464090415c --- /dev/null +++ b/PySDM/initialisation/isotopic_equilibrium.py @@ -0,0 +1 @@ +# TODO diff --git a/PySDM/particulator.py b/PySDM/particulator.py index 7d787dd9e7..d48c383285 100644 --- a/PySDM/particulator.py +++ b/PySDM/particulator.py @@ -456,7 +456,7 @@ def isotopic_fractionation(self, heavy_isotopes: tuple): multiplicity=self.attributes["multiplicity"], dm_total=self.attributes["diffusional growth mass change"], signed_water_mass=self.attributes["signed water mass"], - dry_air_density=self.environment["dry_air_density"], + dry_air_density=self.environment["rhod"], molar_mass_heavy_molecule=getattr( self.formulae.constants, { @@ -467,7 +467,9 @@ def isotopic_fractionation(self, heavy_isotopes: tuple): }[isotope], ), moles_heavy_molecule=self.attributes[f"moles_{isotope}"], # TODO #1787 - molality_in_dry_air=self.environment[f"molality {isotope} in dry air"], + molality_in_dry_air=self.environment.get_predicted( + f"molality {isotope} in dry air" + ), bolin_number=self.attributes[f"Bolin number for {isotope}"], ) self.attributes.mark_updated(f"moles_{isotope}") diff --git a/PySDM/physics/isotope_diffusivity_ratios/grahams_law.py b/PySDM/physics/isotope_diffusivity_ratios/grahams_law.py index 7079a1848f..b380781fae 100644 --- a/PySDM/physics/isotope_diffusivity_ratios/grahams_law.py +++ b/PySDM/physics/isotope_diffusivity_ratios/grahams_law.py @@ -18,3 +18,15 @@ def ratio_3H_heavy_to_light(const, temperature): # pylint: disable=unused-argum return ( (2 * const.M_1H + const.M_16O) / (const.M_3H + const.M_1H + const.M_16O) ) ** const.ONE_HALF + + @staticmethod + def ratio_17O_heavy_to_light(const, temperature): # pylint: disable=unused-argument + return ( + (2 * const.M_1H + const.M_16O) / (2 * const.M_1H + const.M_17O) + ) ** const.ONE_HALF + + @staticmethod + def ratio_18O_heavy_to_light(const, temperature): # pylint: disable=unused-argument + return ( + (2 * const.M_1H + const.M_16O) / (2 * const.M_1H + const.M_18O) + ) ** const.ONE_HALF diff --git a/PySDM/physics/isotope_ratio_evolution/__init__.py b/PySDM/physics/isotope_ratio_evolution/__init__.py index 44b0ec42f3..fd021d375b 100644 --- a/PySDM/physics/isotope_ratio_evolution/__init__.py +++ b/PySDM/physics/isotope_ratio_evolution/__init__.py @@ -4,3 +4,4 @@ from .merlivat_and_jouzel_1979 import MerlivatAndJouzel1979 from .rayleigh_distillation import RayleighDistillation from .gedzelman_and_arnold_1994 import GedzelmanAndArnold1994 +from .zaba_et_al import ZabaEtAl diff --git a/PySDM/physics/isotope_ratio_evolution/zaba_et_al.py b/PySDM/physics/isotope_ratio_evolution/zaba_et_al.py new file mode 100644 index 0000000000..381cf2232f --- /dev/null +++ b/PySDM/physics/isotope_ratio_evolution/zaba_et_al.py @@ -0,0 +1,25 @@ +""" +saturation when no change in isotopic ratio +in liquid and in vapour +""" + + +# pylint: disable=too-few-public-methods +class ZabaEtAl: + def __init__(self, _): + pass + + # pylint: disable=too-many-arguments + @staticmethod + def saturation_for_zero_dR_condition( + _, + diff_rat_heavy_to_light, + iso_ratio_x, + iso_ratio_r, + iso_ratio_v, + alpha, + Fd, + Fk, + ): + A = alpha / diff_rat_heavy_to_light * iso_ratio_x / iso_ratio_r - 1 + return A / ((1 + Fk / Fd) * (1 - alpha * iso_ratio_v / iso_ratio_r) + A) diff --git a/PySDM/physics/isotope_relaxation_timescale/zaba_et_al.py b/PySDM/physics/isotope_relaxation_timescale/zaba_et_al.py index 21770e9587..d75311eef0 100644 --- a/PySDM/physics/isotope_relaxation_timescale/zaba_et_al.py +++ b/PySDM/physics/isotope_relaxation_timescale/zaba_et_al.py @@ -22,15 +22,13 @@ def tau( @staticmethod def bolin_number( - const, + relative_humidity, D_ratio_heavy_to_light, alpha, - D_light, - Fk, R_vap, R_liq, - relative_humidity, - rho_v, + Fd, + Fk, ): # pylint: disable=too-many-arguments,too-many-positional-arguments # TODO #1809 Numba can't compile when * in bolin_number """Heavy to total isotopic-timescales ratio (tau_heavy/tau_total). @@ -43,9 +41,7 @@ def bolin_number( * (1 - relative_humidity) / D_ratio_heavy_to_light / ( - (1 + rho_v * D_light * Fk / const.rho_w) - * relative_humidity - * (1 - alpha * R_vap / R_liq) + (1 + Fk / Fd) * relative_humidity * (1 - alpha * R_vap / R_liq) - relative_humidity + 1 ) diff --git a/examples/PySDM_examples/Gedzelman_and_Arnold_1994/fig_2.ipynb b/examples/PySDM_examples/Gedzelman_and_Arnold_1994/fig_2.ipynb index 6f54294838..f98e516cfe 100644 --- a/examples/PySDM_examples/Gedzelman_and_Arnold_1994/fig_2.ipynb +++ b/examples/PySDM_examples/Gedzelman_and_Arnold_1994/fig_2.ipynb @@ -29,9 +29,7 @@ "https://doi.org/10.1029/93JD03518\n", "\n", "**Description** \n", - "Theoretical curves from the paper are reproduced as in Fig. 2, separately for liquid and vapour isotopic content (black lines in Figure 1 in this notebook).\n", - "The figure illustrates the change in Deuterium isotopic composition in both liquid and vapour phases following an arbitrary water mass change, relative to the theoretical lines.\n", - "Figure 1 was submitted for 2026 AMS Madison Summit (17th Conference on Cloud Physics).\n", + "Theoretical curves from the paper are reproduced as in Fig. 2, separately for liquid and vapour isotopic content. The figure illustrates the change in Deuterium isotopic composition in both liquid and vapour phases following an arbitrary water mass change, relative to the theoretical lines.\n", "\n", "**Notes** \n", "- Parameter *b* in Eq. (18) of the paper appears to be missing a multiplication by $\\rho_\\text{s}$. Including $\\rho_\\text{s}$ corrects the units and aligns the reproduced plot with expectations. This suggests a likely typographical error in the original equation.\n", @@ -51,8 +49,8 @@ "id": "2088324ebcd34d62", "metadata": { "ExecuteTime": { - "end_time": "2026-04-17T20:13:26.803639Z", - "start_time": "2026-04-17T20:13:26.798354Z" + "end_time": "2026-05-20T21:53:17.136264Z", + "start_time": "2026-05-20T21:53:17.121561Z" } }, "source": [ @@ -71,19 +69,15 @@ "id": "50b690c4d6b87266", "metadata": { "ExecuteTime": { - "end_time": "2026-04-17T20:13:29.707976Z", - "start_time": "2026-04-17T20:13:26.815111Z" + "end_time": "2026-05-20T21:53:18.906803Z", + "start_time": "2026-05-20T21:53:17.137059Z" } }, "source": [ - "# pylint: disable=redefined-outer-name\n", - "\n", - "from dataclasses import dataclass\n", "from collections import namedtuple\n", "import numpy as np\n", "from matplotlib import pyplot\n", "from matplotlib.lines import Line2D\n", - "\n", "from open_atmos_jupyter_utils import show_plot\n", "\n", "from PySDM import Formulae\n", @@ -125,8 +119,8 @@ "id": "cb7fd1c2a10c76c5", "metadata": { "ExecuteTime": { - "end_time": "2026-04-17T20:13:29.742776Z", - "start_time": "2026-04-17T20:13:29.712137Z" + "end_time": "2026-05-20T21:53:18.943961Z", + "start_time": "2026-05-20T21:53:18.912511Z" } }, "source": [ @@ -176,8 +170,8 @@ "id": "c8f7e3e23a33b364", "metadata": { "ExecuteTime": { - "end_time": "2026-04-17T20:13:29.760101Z", - "start_time": "2026-04-17T20:13:29.749307Z" + "end_time": "2026-05-20T21:53:18.965833Z", + "start_time": "2026-05-20T21:53:18.950295Z" } }, "source": [ @@ -192,10 +186,9 @@ " \"vsmow\",\n", " ]\n", ")\n", - "\n", "Commons = namedtuple(\"Commons\", [\"f\", \"c\", \"iso\", \"T\", \"params\"])\n", "\n", - "class CommonsFactory:\n", + "class CommonsFactory: #pylint: disable=too-few-public-methods\n", " \"\"\"Compute and assemble common isotope quantities.\"\"\"\n", " def __init__(self, formulae):\n", " self._f = formulae\n", @@ -212,6 +205,7 @@ " )\n", "\n", " def _build_params(self, ctx):\n", + " \"\"\"Calculate parameters from formulae\"\"\"\n", " vsmow = getattr(self._const, f\"VSMOW_R_{ctx.iso}\")\n", " diff_fun = getattr(\n", " self._f.isotope_diffusivity_ratios,\n", @@ -225,12 +219,12 @@ " )\n", " )\n", " pvs = self._f.saturation_vapour_pressure.pvs_water(ctx.T)\n", - " Fk = self._f.drop_growth.Fk(\n", + " fk = self._f.drop_growth.Fk(\n", " T=ctx.T,\n", " K=self._const.K0,\n", " lv=self._f.latent_heat_vapourisation.lv(ctx.T),\n", " )\n", - " Fd = self._f.drop_growth.Fd(\n", + " fd = self._f.drop_growth.Fd(\n", " pvs=pvs,\n", " T=ctx.T,\n", " D=self._const.D0,\n", @@ -239,13 +233,13 @@ " return Parameters(\n", " D_ratio=D_ratio,\n", " f_ratio=ventilation_ratio,\n", - " alpha_w=self.alpha_w(ctx),\n", - " Fk_plus_Fd=Fk + Fd,\n", - " b=rho_s * Fk / self._const.rho_w * self._const.D0,\n", + " alpha_w=self._alpha_w(ctx),\n", + " Fk_plus_Fd=fk + fd,\n", + " b=rho_s * fk / self._const.rho_w * self._const.D0,\n", " vsmow=vsmow,\n", " )\n", " \n", - " def alpha_w(self, ctx: IsotopeContext) -> float:\n", + " def _alpha_w(self, ctx: IsotopeContext) -> float:\n", " \"\"\"Calculate isotopic fractionation factor for specified isotope\"\"\"\n", " alpha_fun = getattr(\n", " self._f.isotope_equilibrium_fractionation_factors,\n", @@ -260,8 +254,7 @@ " return alpha_fun(np.nan, alpha_l_18o)\n", " return alpha_fun(ctx.T)\n", " \n", - "C2K = Formulae().trivia.C2K\n", - "K2C = Formulae().trivia.K2C" + "C2K = Formulae().trivia.C2K" ], "outputs": [], "execution_count": 4 @@ -269,23 +262,27 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-04-17T20:13:29.772306Z", - "start_time": "2026-04-17T20:13:29.769495Z" + "end_time": "2026-05-20T21:53:18.973036Z", + "start_time": "2026-05-20T21:53:18.968371Z" } }, "cell_type": "code", "source": [ - "def no_fractionation_saturation(x, iso_ratio_v, cmn, phase):\n", - " iso_ratio_r = x * cmn.params.vsmow\n", - " return cmn.f.isotope_ratio_evolution.saturation_for_zero_dR_condition(\n", - " iso_ratio_x=iso_ratio_r if phase=='liquid' else iso_ratio_v,\n", + "def no_fractionation_saturation(iso_ratio_over_vsmow, iso_ratio_vap, commons, water_phase):\n", + " \"\"\"\n", + " Wrapper for saturation_for_zero_dR_condition formula\n", + " utilising precomputed parameters from commons\n", + " \"\"\"\n", + " iso_ratio_liq = iso_ratio_over_vsmow * commons.params.vsmow\n", + " return commons.f.isotope_ratio_evolution.saturation_for_zero_dR_condition(\n", + " iso_ratio_x=iso_ratio_liq if water_phase=='liquid' else iso_ratio_vap,\n", " diff_rat_light_to_heavy=(\n", - " cmn.params.f_ratio / cmn.params.D_ratio\n", + " commons.params.f_ratio / commons.params.D_ratio\n", " ),\n", - " b=cmn.params.b,\n", - " alpha_w=cmn.params.alpha_w,\n", - " iso_ratio_r=iso_ratio_r,\n", - " iso_ratio_v=iso_ratio_v,\n", + " b=commons.params.b,\n", + " alpha_w=commons.params.alpha_w,\n", + " iso_ratio_r=iso_ratio_liq,\n", + " iso_ratio_v=iso_ratio_vap,\n", " )" ], "id": "134c3082c2f45ee1", @@ -295,15 +292,14 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-04-17T20:34:54.750883Z", - "start_time": "2026-04-17T20:34:54.379790Z" + "end_time": "2026-05-20T21:53:19.306773Z", + "start_time": "2026-05-20T21:53:18.973434Z" } }, "cell_type": "code", "source": [ - "@dataclass(frozen=True)\n", - "class Paper:\n", - " \"\"\"Configuration reproducing the paper setup.\"\"\"\n", + "class Paper: #pylint: disable=too-few-public-methods\n", + " \"\"\"Configuration reproducing the paper setup. Instance is not needed.\"\"\"\n", " formulae = Formulae(\n", " isotope_equilibrium_fractionation_factors=\"MerlivatAndNief1967+Majoube1971\",\n", " isotope_kinetic_fractionation_factors=\"JouzelAndMerlivat1984\",\n", @@ -322,13 +318,13 @@ ], "id": "84ac9d90edbfffa6", "outputs": [], - "execution_count": 11 + "execution_count": 6 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-04-17T20:34:56.826201Z", - "start_time": "2026-04-17T20:34:54.763002Z" + "end_time": "2026-05-20T21:53:21.902485Z", + "start_time": "2026-05-20T21:53:19.307408Z" } }, "cell_type": "code", @@ -336,38 +332,53 @@ "cmn = CommonsFactory(Paper.formulae).build(\n", " IsotopeContext(iso=Paper.isotope, T=Paper.temperature)\n", ")\n", - "RH, R_liq = np.meshgrid(\n", - " np.linspace(0.01, 1.1, 32),\n", - " np.linspace(*Paper.xlim, 32) * cmn.params.vsmow\n", - ")\n", - "f = cmn.f\n", + "CMN_FOR_TEST = cmn" + ], + "id": "b3ae06a4bb0527ea", + "outputs": [], + "execution_count": 7 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-20T21:53:22.091202Z", + "start_time": "2026-05-20T21:53:21.909942Z" + } + }, + "cell_type": "code", + "source": [ + "RH = np.linspace(0.01, 1.1, 32)\n", + "R_liq_over_vsmow = np.linspace(*Paper.xlim, 32)\n", + "\n", + "XX, YY = np.meshgrid(R_liq_over_vsmow, RH)\n", + "\n", "T = cmn.T\n", "rho_v = cmn.f.saturation_vapour_pressure.pvs_water(cmn.T) / cmn.T / cmn.c.Rv\n", "Fk = cmn.f.drop_growth.Fk(T=cmn.T, K=cmn.c.K0, lv=cmn.c.l_tri)\n", + "Fd = cmn.f.drop_growth.Fd(T=cmn.T, D=cmn.c.D0, pvs=cmn.f.saturation_vapour_pressure.pvs_water(cmn.T))\n", + "\n", "ISO_RATIO_V = cmn.f.trivia.isotopic_delta_2_ratio(Paper.delta_v, cmn.params.vsmow)\n", - "X_eq = ISO_RATIO_V * cmn.params.alpha_w /cmn.params.vsmow\n", + "X_eq = ISO_RATIO_V * cmn.params.alpha_w / cmn.params.vsmow\n", "Y_eq = in_unit(1, Paper.rh_unit)\n", - "\n", "Bolin_number = cmn.f.isotope_relaxation_timescale.bolin_number(\n", " D_ratio_heavy_to_light=cmn.params.D_ratio,\n", " alpha=cmn.params.alpha_w,\n", - " D_light=cmn.c.D0,\n", + " Fd=Fd,\n", " Fk=Fk,\n", " R_vap=ISO_RATIO_V,\n", - " R_liq=R_liq,\n", - " relative_humidity=RH,\n", - " rho_v=rho_v,\n", - ").T" + " R_liq=XX * cmn.params.vsmow,\n", + " relative_humidity=YY,\n", + ")" ], "id": "2a5f9b367fe6e519", "outputs": [], - "execution_count": 12 + "execution_count": 8 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-04-17T20:34:56.857253Z", - "start_time": "2026-04-17T20:34:56.854076Z" + "end_time": "2026-05-20T21:53:22.101421Z", + "start_time": "2026-05-20T21:53:22.096512Z" } }, "cell_type": "code", @@ -380,23 +391,24 @@ " \"linestyle\": \"-\",\n", " },\n", " \"vapour\": {\n", - " \"eqs\": R_liq.T / ISO_RATIO_V - Bolin_number,\n", + " \"eqs\": XX * cmn.params.vsmow / ISO_RATIO_V - Bolin_number,\n", " \"label\": \"$\\\\bf{{(B)}}$ \"\n", " + \"$\\\\frac{\\\\text{R}_\\\\text{liq}}{\\\\text{R}_\\\\text{vap}} - \\\\text{Bo}$ [1]\",\n", " \"center\": 0,\n", " \"linestyle\": \"--\",\n", " }\n", - "}" + "}\n", + "plots = {\"liquid\": {}, \"vapour\": {}}" ], "id": "c08afc2d451f991f", "outputs": [], - "execution_count": 13 + "execution_count": 9 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-04-17T20:35:01.391423Z", - "start_time": "2026-04-17T20:34:59.049604Z" + "end_time": "2026-05-20T21:53:23.248319Z", + "start_time": "2026-05-20T21:53:22.101871Z" } }, "cell_type": "code", @@ -404,27 +416,28 @@ "fig, axs = pyplot.subplots(\n", " 1, 2,\n", " figsize=(10,4),\n", - " constrained_layout=True,\n", ")\n", - "PLOT_LINE = {'liquid': None, 'vapour': None}\n", - "PCM = {'liquid': None, 'vapour': None}\n", - "\n", - "X, Y = R_liq[:, 0] / cmn.params.vsmow, in_unit(RH[0, :], Paper.rh_unit)\n", "v = .2\n", - "for i, c in enumerate(cases.items()):\n", - " axs[i].set_title(f\"{c[1]['label']}\\n\")\n", - " pcm = axs[i].pcolormesh(X, Y, c[1][\"eqs\"],\n", - " cmap='RdBu_r', vmin=-v + c[1]['center'], vmax=v + c[1]['center'])\n", - " cb = fig.colorbar(pcm, ax=axs[i])\n", - " cb.ax.set_title('[1]')\n", - " PCM[c[0]] = pcm\n", - " for phase in cases:\n", - " x = np.linspace(X_eq, 1.1, 500)\n", - " y = no_fractionation_saturation(x, ISO_RATIO_V, cmn, phase)\n", - " ls = \"-\" if phase == c[0] else \":\"\n", - " PLOT_LINE[phase] = axs[i].plot(x, y / PER_CENT, c='k', label=rf\"$S_\\text{{{phase}}}^\\text{{eq}}$\", ls=ls, lw=2)\n", - " \n", - " axs[i].plot(X, np.full_like(X, Y_eq), c='r', label='phase equilibrium')\n", + "for i, (phase, params) in enumerate(cases.items()):\n", + " pcm = axs[i].pcolormesh(R_liq_over_vsmow, in_unit(RH, Paper.rh_unit), params[\"eqs\"],\n", + " cmap='RdBu_r', vmin=-v + params['center'], vmax=v + params['center'])\n", + " cases[phase][\"pcolormesh\"] = pcm\n", + " cb = fig.colorbar(pcm, ax=axs[i], orientation='horizontal', location='top', aspect=30, fraction=0.05)\n", + " cb.set_label(f\"{params['label']}\\n\", fontsize=12)\n", + " \n", + " for p in cases:\n", + " x = np.linspace(0, 1.05, 2000)\n", + " y = no_fractionation_saturation(x, ISO_RATIO_V, cmn, p)\n", + " plots[p][\"x\"] = x\n", + " plots[p][\"y\"] = y\n", + " y_masked = np.where((0.0 < y) & (y < 1.05), y / PER_CENT, np.nan)\n", + " axs[i].plot(\n", + " x, y_masked,\n", + " label=rf\"$S_\\text{{{p}}}^\\text{{eq}}$\",\n", + " ls=\"-\" if p[0] == 'l' else \":\", lw=2, c='k',\n", + " )\n", + " \n", + " axs[i].plot(R_liq_over_vsmow, np.full_like(R_liq_over_vsmow, Y_eq), c='r', label='phase eq')\n", " axs[i].annotate('phase & isotopic\\nequilibria', xy=(X_eq, Y_eq), color='white',\n", " arrowprops={\"arrowstyle\": '->', \"color\": 'white', \"lw\": 1},\n", " textcoords='offset points',xytext=(40, -25),)\n", @@ -432,7 +445,7 @@ " \n", "for ax in axs:\n", " ax.set_xlabel(\"R$_\\\\text{liq}$ / R$_\\\\text{SMOW}$ [1]\")\n", - " ax.set_xlim(X[0], X[-1])\n", + " ax.set_xlim(R_liq_over_vsmow[0], R_liq_over_vsmow[-1])\n", " ax.set_ylabel(\"S=RH [%]\")\n", " ax.set_ylim(0, 105)\n", " ax.legend(loc=\"lower right\")\n", @@ -442,19 +455,19 @@ "the following condition must be met: $\\left(\\frac{{R_{{\\text{{liq}}}}}}{{R_{{\\text{{vap}}}}}} - \\text{{Bo}} = 0\\right)$. The isotopologue considered in both plots is $^1\\mathrm{{H}}^2\\mathrm{{H}}^{{16}}\\mathrm{{O}}$.\n", "Black lines: isotopic equilibrium curves from Gedzelman & Arnold (1994, Fig. 2);\n", "red line: phase equilibrium (RH = 100%); white dot: phase and isotopic equilibrium, $R_{{\\text{{liq}}}} = \\alpha(T)\\,R_{{\\text{{vap}}}}$.\n", - "The environmental setting follows that of the paper: $T = {K2C(Paper.temperature):.0f}^\\circ\\mathrm{{C}}$, $\\delta_{{^2\\mathrm{{H}}}} = \\frac{{R_{{\\text{{vap}}}}}}{{R_{{\\text{{SMOW}}}}}} - 1 = {in_unit(Paper.delta_v, PER_MILLE):.0f}\\,‰$.\n", + "The environmental setting follows that of the paper: $T = {Paper.temperature-273.15:.0f}^\\circ\\mathrm{{C}}$, $\\delta_{{^2\\mathrm{{H}}}} = \\frac{{R_{{\\text{{vap}}}}}}{{R_{{\\text{{SMOW}}}}}} - 1 = {in_unit(Paper.delta_v, PER_MILLE):.0f}\\,‰$.\n", "\"\"\"\n", "fig.text(0.5, -0.5, text, ha=\"center\", fontsize=12)\n", "show_plot(\"Bo\")" ], - "id": "25685d6ad0b6e1df", + "id": "800c6c40056f462e", "outputs": [ { "data": { "text/plain": [ "
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" + "image/svg+xml": "\n\n\n \n \n \n \n 2026-05-20T23:53:23.091040\n image/svg+xml\n \n \n Matplotlib v3.10.8, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, "metadata": {}, "output_type": "display_data" @@ -467,41 +480,40 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "cdb721f0a9f6431faebaac659c56aca8" + "model_id": "708560d2fa814d97a29d39ea8b40e6bd" } }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 14 + "execution_count": 10 }, { "metadata": {}, "cell_type": "markdown", "source": "# Extensions", - "id": "699cbeeb8afafbba" + "id": "895f732d8e02384d" }, { - "cell_type": "code", - "id": "cd19a4efb40c3b22", "metadata": { "ExecuteTime": { - "end_time": "2026-04-17T20:13:40.413436Z", - "start_time": "2026-04-17T20:13:36.675594Z" + "end_time": "2026-05-20T21:53:26.338800Z", + "start_time": "2026-05-20T21:53:23.260065Z" } }, + "cell_type": "code", "source": [ - "formulae = Formulae(\n", + "f = Formulae(\n", " isotope_equilibrium_fractionation_factors=\"BarkanAndLuz2005+HoritaAndWesolowski1994\",\n", " isotope_diffusivity_ratios=\"HellmannAndHarvey2020\",\n", " isotope_meteoric_water_line=\"BarkanAndLuz2007+Dansgaard1964\",\n", " isotope_ratio_evolution=\"GedzelmanAndArnold1994\",\n", " isotope_ventilation_ratio=\"Neglect\",\n", ")\n", - "commons_factory = CommonsFactory(formulae)\n", + "commons_factory = CommonsFactory(f)\n", "\n", - "iso_mwl = formulae.isotope_meteoric_water_line\n", + "iso_mwl = f.isotope_meteoric_water_line\n", "d2H = (-300, -200, -100)\n", "deltas_2H = [x * PER_MILLE for x in d2H]\n", "deltas_18O = [iso_mwl.d18O_of_d2H(d) for d in deltas_2H]\n", @@ -515,26 +527,27 @@ " for j_delta, delta in enumerate((deltas_2H, deltas_18O, deltas_17O)[idx_iso]):\n", " ax = axs[idx_iso][j_delta]\n", " for t in Ts:\n", - " commons = commons_factory.build(IsotopeContext(iso, t))\n", - " iso_ratio_v = commons.f.trivia.isotopic_delta_2_ratio(delta, commons.params.vsmow)\n", + " cmn = commons_factory.build(IsotopeContext(iso, t))\n", + " iso_ratio_v = cmn.f.trivia.isotopic_delta_2_ratio(delta, cmn.params.vsmow)\n", " x = np.linspace(\n", - " commons.params.alpha_w * iso_ratio_v / commons.params.vsmow, 1.1, 200\n", + " cmn.params.alpha_w * iso_ratio_v / cmn.params.vsmow, 1.1, 200\n", " )\n", - " y_liq = no_fractionation_saturation(x, iso_ratio_v, commons, 'liquid')\n", - " y_vap = no_fractionation_saturation(x, iso_ratio_v, commons, 'vapour')\n", + " y_liq = no_fractionation_saturation(x, iso_ratio_v, cmn, 'liquid')\n", + " y_vap = no_fractionation_saturation(x, iso_ratio_v, cmn, 'vapour')\n", "\n", - " color = (\n", + " c = (\n", " (t - Ts[0]) / (Ts[-1] - Ts[0]),\n", " 0.333,\n", " 1 - (t - Ts[0]) / (Ts[-1] - Ts[0]),\n", " )\n", " if t in (Ts[0], Ts[-1]):\n", " ax.annotate(\n", - " f\" {K2C(t):g}°C\", (x_lim[iso][1], y_liq[-1]), fontsize=8, ha='left', va='center'\n", + " f\"{Formulae().trivia.K2C(t):g}°C\",\n", + " (x_lim[iso][1], y_liq[-1] - 0.015),\n", + " fontsize=8\n", " )\n", - "\n", - " ax.plot(x, y_liq, color=color, linestyle=\"-\")\n", - " ax.plot(x, y_vap, color=color, linestyle=\":\")\n", + " ax.plot(x, y_liq, c=c, ls=\"-\")\n", + " ax.plot(x, y_vap, c=c, ls=\":\")\n", "\n", " ax.set(\n", " title=f\"ambient vapour $\\\\delta^{{{iso[:-1]}}}{iso[-1]}$ = {in_unit(delta, PER_MILLE):.3g}‰\",\n", @@ -561,13 +574,14 @@ "fig.subplots_adjust(top=0.88, bottom=0)\n", "show_plot(\"plot_grid.pdf\")" ], + "id": "5f52ac488de12c95", "outputs": [ { "data": { "text/plain": [ "
" ], - "image/svg+xml": "\n\n\n \n \n \n \n 2026-04-17T22:13:40.249268\n image/svg+xml\n \n \n Matplotlib v3.10.8, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" + "image/svg+xml": "\n\n\n \n \n \n \n 2026-05-20T23:53:26.220090\n image/svg+xml\n \n \n Matplotlib v3.10.8, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, "metadata": {}, "output_type": "display_data" @@ -580,27 +594,27 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "87967ac5a0f34639b76ff61653214427" + "model_id": "00b9d730534347279f035ce3add5b203" } }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 10 + "execution_count": 11 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-04-17T20:13:40.543694Z", - "start_time": "2026-04-17T20:13:40.532349Z" + "end_time": "2026-05-20T21:53:26.386722Z", + "start_time": "2026-05-20T21:53:26.351705Z" } }, "cell_type": "code", "source": "", - "id": "2d3e41abde5dba54", + "id": "a6c0fffbdc6f74e9", "outputs": [], - "execution_count": null + "execution_count": 11 } ], "metadata": { diff --git a/examples/PySDM_examples/Graf_et_al_2019/figure_4.ipynb b/examples/PySDM_examples/Graf_et_al_2019/figure_4.ipynb index 9b84f81caa..5965b6c907 100644 --- a/examples/PySDM_examples/Graf_et_al_2019/figure_4.ipynb +++ b/examples/PySDM_examples/Graf_et_al_2019/figure_4.ipynb @@ -21,9 +21,13 @@ "source": "based on [Graf et al. 2019](https://doi.org/10.5194/acp-19-747-2019)" }, { + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T08:44:42.409696Z", + "start_time": "2026-05-21T08:44:42.391628Z" + } + }, "cell_type": "code", - "id": "b2c87898f6737935", - "metadata": {}, "source": [ "import os, sys\n", "os.environ['NUMBA_THREADING_LAYER'] = 'workqueue' # PySDM & PyMPDATA don't work with TBB; OpenMP has extra dependencies on macOS\n", @@ -32,19 +36,18 @@ " from open_atmos_jupyter_utils import pip_install_on_colab\n", " pip_install_on_colab('PySDM-examples', 'PySDM')" ], + "id": "6d6aeebd9e3fd12", "outputs": [], "execution_count": 1 }, { - "cell_type": "code", - "id": "2432d43d15e97625", "metadata": { - "collapsed": false, "ExecuteTime": { - "end_time": "2026-05-05T08:13:17.012497Z", - "start_time": "2026-05-05T08:13:13.795346Z" + "end_time": "2026-05-21T08:44:44.002229Z", + "start_time": "2026-05-21T08:44:42.410485Z" } }, + "cell_type": "code", "source": [ "import math\n", "import numpy as np\n", @@ -56,32 +59,34 @@ "from PySDM.environments import Parcel\n", "from PySDM.backends import CPU\n", "from PySDM import Builder, Formulae\n", - "from PySDM.dynamics import AmbientThermodynamics, Condensation\n", + "from PySDM.dynamics import AmbientThermodynamics, Condensation, IsotopicFractionation\n", "from PySDM.initialisation.spectra import Lognormal\n", "from PySDM.initialisation.sampling import spectral_sampling\n", "from PySDM.initialisation.hygroscopic_equilibrium import equilibrate_wet_radii\n", "from PySDM.initialisation import discretise_multiplicities\n", - "from PySDM import products" + "from PySDM import products\n", + "from PySDM.dynamics.isotopic_fractionation import HEAVY_ISOTOPES\n", + "from PySDM.products.impl import register_product, MoistEnvironmentProduct" ], + "id": "e0459f9328d3ed4e", "outputs": [], "execution_count": 2 }, { - "cell_type": "code", - "id": "81d815991be0cc9", "metadata": { - "collapsed": false, "ExecuteTime": { - "end_time": "2026-05-05T08:13:19.491413Z", - "start_time": "2026-05-05T08:13:17.023531Z" + "end_time": "2026-05-21T08:44:45.696421Z", + "start_time": "2026-05-21T08:44:44.007493Z" } }, + "cell_type": "code", "source": [ "formulae = Formulae(\n", - " isotope_equilibrium_fractionation_factors=\"Majoube1971+MerlivatAndNief1967+Majoube1970\",\n", + " isotope_equilibrium_fractionation_factors=\"Majoube1971+MerlivatAndNief1967+Majoube1970+VanHook1968\",\n", " isotope_kinetic_fractionation_factors=\"JouzelAndMerlivat1984\",\n", - " isotope_diffusivity_ratios=\"Stewart1975\",\n", + " isotope_diffusivity_ratios=\"Stewart1975+GrahamsLaw\",\n", " isotope_meteoric_water_line=\"Dansgaard1964\",\n", + " isotope_relaxation_timescale=\"ZabaEtAl\",\n", ")\n", "const = formulae.constants\n", "pvs_water = formulae.saturation_vapour_pressure.pvs_water\n", @@ -90,13 +95,15 @@ "p0 = 950 * si.hPa\n", "T0 = const.T0 + 12 * si.K\n", "\n", - "delta0_2H = -150 * const.PER_MILLE\n", - "R0_2H = formulae.trivia.isotopic_delta_2_ratio(\n", - " delta0_2H, const.VSMOW_R_2H\n", + "delta0_2H_vap = -150 * const.PER_MILLE\n", + "R0_2H_vap = formulae.trivia.isotopic_delta_2_ratio(\n", + " delta0_2H_vap, const.VSMOW_R_2H\n", ")\n", - "delta0_18O = formulae.isotope_meteoric_water_line.d18O_of_d2H(delta0_2H)\n", - "R0_18O = formulae.trivia.isotopic_delta_2_ratio(\n", - " delta0_18O,\n", + "\n", + "R0_2H_liq = formulae.isotope_equilibrium_fractionation_factors.alpha_l_2H(T0) * R0_2H_vap# TODO from saturation_for_zero_dR_condition()\n", + "delta0_18O_vap = formulae.isotope_meteoric_water_line.d18O_of_d2H(delta0_2H_vap)\n", + "R0_18O_vap = formulae.trivia.isotopic_delta_2_ratio(\n", + " delta0_18O_vap,\n", " const.VSMOW_R_18O,\n", ")\n", "\n", @@ -108,21 +115,22 @@ "\n", "n_sd = 1\n", "dt = 10 * si.s\n", - "vertical_velocity = 2.5 * si.m / si.s" + "vertical_velocity = 2.5 * si.m / si.s\n", + "\n", + "ISOTOPE_CONSIDERED = \"2H\"" ], + "id": "d9c86406b9121339", "outputs": [], "execution_count": 3 }, { - "cell_type": "code", - "id": "dada2ae5b4083f0e", "metadata": { - "collapsed": false, "ExecuteTime": { - "end_time": "2026-05-05T08:13:19.509029Z", - "start_time": "2026-05-05T08:13:19.506750Z" + "end_time": "2026-05-21T08:44:45.708428Z", + "start_time": "2026-05-21T08:44:45.698054Z" } }, + "cell_type": "code", "source": [ "env = Parcel(\n", " # given in the paper\n", @@ -133,39 +141,48 @@ " w=vertical_velocity,\n", " dt=dt,\n", " mass_of_dry_air=1e3 * si.kg,\n", + " variables=(f'molality {ISOTOPE_CONSIDERED} in dry air',),\n", ")" ], + "id": "3c61485d0f2f42bf", "outputs": [], "execution_count": 4 }, { - "cell_type": "code", - "id": "47f3f883e4e67ed", "metadata": { - "collapsed": false, "ExecuteTime": { - "end_time": "2026-05-05T08:13:21.984619Z", - "start_time": "2026-05-05T08:13:19.512552Z" + "end_time": "2026-05-21T08:44:47.107428Z", + "start_time": "2026-05-21T08:44:45.710642Z" } }, + "cell_type": "code", "source": [ "builder = Builder(backend=CPU(formulae=formulae), n_sd=n_sd, environment=env)\n", "builder.add_dynamic(AmbientThermodynamics())\n", - "builder.add_dynamic(Condensation())\n" + "builder.add_dynamic(Condensation())\n", + "builder.add_dynamic(IsotopicFractionation(isotopes=(ISOTOPE_CONSIDERED,)))\n" + ], + "id": "c238b623182caa6c", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/agnieszkazaba/PycharmProjects/PySDM/PySDM/backends/numba.py:57: UserWarning: Disabling Numba threading due to ARM64 CPU (atomics do not work yet)\n", + " warnings.warn(\n" + ] + } ], - "outputs": [], "execution_count": 5 }, { - "cell_type": "code", - "id": "f98070a3d550d65", "metadata": { - "collapsed": false, "ExecuteTime": { - "end_time": "2026-05-05T08:13:21.991081Z", - "start_time": "2026-05-05T08:13:21.988828Z" + "end_time": "2026-05-21T08:44:47.117045Z", + "start_time": "2026-05-21T08:44:47.108437Z" } }, + "cell_type": "code", "source": [ "spectrum = Lognormal(norm_factor=1e4 / si.mg, m_mode=50 * si.nm, s_geom=1.5)\n", "kappa = 0.5 * si.dimensionless\n", @@ -173,6 +190,7 @@ "output_interval = 4\n", "output_points = 40" ], + "id": "46541f69106d3a62", "outputs": [], "execution_count": 6 }, @@ -182,8 +200,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2026-05-05T08:13:21.996379Z", - "start_time": "2026-05-05T08:13:21.994049Z" + "end_time": "2026-05-21T08:44:47.122854Z", + "start_time": "2026-05-21T08:44:47.117675Z" } }, "source": [ @@ -200,8 +218,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2026-05-05T08:13:22.320028Z", - "start_time": "2026-05-05T08:13:21.999037Z" + "end_time": "2026-05-21T08:44:47.319692Z", + "start_time": "2026-05-21T08:44:47.123296Z" } }, "source": [ @@ -211,15 +229,13 @@ "execution_count": 8 }, { - "cell_type": "code", - "id": "28897f499330f699", "metadata": { - "collapsed": false, "ExecuteTime": { - "end_time": "2026-05-05T08:13:25.280363Z", - "start_time": "2026-05-05T08:13:22.323650Z" + "end_time": "2026-05-21T08:44:49.034828Z", + "start_time": "2026-05-21T08:44:47.320305Z" } }, + "cell_type": "code", "source": [ "r_wet = equilibrate_wet_radii(\n", " r_dry=r_dry,\n", @@ -227,19 +243,18 @@ " kappa_times_dry_volume=kappa * v_dry,\n", ")" ], + "id": "48a79f47ce72709a", "outputs": [], "execution_count": 9 }, { - "cell_type": "code", - "id": "8cf40dfb18e7e082", "metadata": { - "collapsed": false, "ExecuteTime": { - "end_time": "2026-05-05T08:13:25.299021Z", - "start_time": "2026-05-05T08:13:25.296127Z" + "end_time": "2026-05-21T08:44:49.251339Z", + "start_time": "2026-05-21T08:44:49.035605Z" } }, + "cell_type": "code", "source": [ "attributes = {\n", " \"multiplicity\": discretise_multiplicities(\n", @@ -247,21 +262,33 @@ " ),\n", " \"dry volume\": v_dry,\n", " \"kappa times dry volume\": kappa * v_dry,\n", - " \"volume\": formulae.trivia.volume(radius=r_wet),\n", - "}" + " \"signed water mass\": formulae.particle_shape_and_density.radius_to_mass(radius=r_wet),\n", + "}\n", + "for isotope in HEAVY_ISOTOPES:\n", + " if isotope == ISOTOPE_CONSIDERED:\n", + " attributes[f'moles_{isotope}'] = formulae.trivia.isotopic_fraction_assuming_single_heavy_isotope(isotopic_ratio=R0_2H_liq) * attributes[\"signed water mass\"] / const.Mv # TODO shouldn't we use computed Mv?\n", + " else:\n", + " attributes[f'moles_{isotope}'] = 0" ], + "id": "a865f08cbb54be9e", "outputs": [], "execution_count": 10 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-05-05T08:13:25.489919Z", - "start_time": "2026-05-05T08:13:25.302428Z" + "end_time": "2026-05-21T08:44:49.322156Z", + "start_time": "2026-05-21T08:44:49.256836Z" } }, "cell_type": "code", "source": [ + "@register_product()\n", + "class MolalityInDryAir(MoistEnvironmentProduct): # TODO products: delta_isotope; excess (from 2H and 18O vs meteoric water line), initial value; check get_predicted\n", + " def __init__(self, name=\"molality_in_dry_air\", unit=\"1/kg\", isotope=None):\n", + " assert isotope is not None\n", + " super().__init__(name=name, unit=unit, var=f\"molality {isotope} in dry air\")\n", + " \n", "particulator = builder.build(\n", " attributes=attributes,\n", " products=(\n", @@ -271,18 +298,28 @@ " products.AmbientWaterVapourMixingRatio(\n", " name=\"w\", var=\"water_vapour_mixing_ratio\"\n", " ),\n", + " products.AmbientDryAirDensity(name=\"rhod\"),\n", + " MolalityInDryAir(name=\"molality_in_dry_air\", isotope=ISOTOPE_CONSIDERED),\n", " ),\n", - ")" + ")\n", + "rhod = particulator.environment[\"rhod\"].to_ndarray()\n", + "molality = formulae.trivia.molality_in_dry_air(\n", + " isotopic_fraction=formulae.trivia.isotopic_fraction_assuming_single_heavy_isotope(isotopic_ratio=R0_2H_vap),\n", + " density_dry_air=rhod,\n", + " total_vap_concentration=initial_water_vapour_mixing_ratio * rhod / const.Mv, # TODO shouldn't we use computed Mv?\n", + ")\n", + "particulator.environment._tmp[f\"molality {ISOTOPE_CONSIDERED} in dry air\"][:] = molality\n", + "particulator.environment[f\"molality {ISOTOPE_CONSIDERED} in dry air\"][:] = molality" ], - "id": "21160fa1832ffd83", + "id": "75f3e679646a399f", "outputs": [], "execution_count": 11 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-05-05T08:13:53.165325Z", - "start_time": "2026-05-05T08:13:25.501437Z" + "end_time": "2026-05-21T08:45:05.368336Z", + "start_time": "2026-05-21T08:44:49.323004Z" } }, "cell_type": "code", @@ -306,15 +343,262 @@ " if levels[\"0C\"] is None and output[\"T\"][-1] < const.T0:\n", " levels[\"0C\"] = 0.5 * (output[\"z\"][-1] + output[\"z\"][-2])" ], - "id": "b291cb19e4740ad9", - "outputs": [], + "id": "96feee33491fea01", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "predicted: \n", + "5.12122068389895e-05\n", + "predicted: \n", + "5.12122060046895e-05\n", + "predicted: \n", + 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"start_time": "2026-05-21T08:45:05.369647Z" } }, "cell_type": "code", @@ -367,7 +651,7 @@ "for iso in (\"2H\", \"18O\"):\n", " a_eql_l = getattr(formulae.isotope_equilibrium_fractionation_factors, f\"alpha_l_{iso}\")\n", " a_eql_i = getattr(formulae.isotope_equilibrium_fractionation_factors, f\"alpha_i_{iso}\")\n", - " r0 = R0_2H if iso == \"2H\" else R0_18O\n", + " r0 = R0_2H_vap if iso == \"2H\" else R0_18O_vap\n", " vsmow = getattr(const, f\"VSMOW_R_{iso}\")\n", " deltas_Rayleigh[iso]['eql'] = formulae.trivia.isotopic_ratio_2_delta(\n", " Rayleigh_distillation(r0, a_eql_l, a_eql_i), vsmow\n", @@ -388,15 +672,77 @@ "\n", "z = np.asarray(output[\"z\"]) + alt_initial" ], - "id": "c02c523d26caa354", + "id": "1e736e8c84d3be74", "outputs": [], "execution_count": 13 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-05-05T08:13:56.975643Z", - "start_time": "2026-05-05T08:13:56.212050Z" + "end_time": "2026-05-21T08:45:06.240216Z", + "start_time": "2026-05-21T08:45:06.131382Z" + } + }, + "cell_type": "code", + "source": [ + "rhod = np.asarray(output[\"rhod\"])\n", + "isotopic_ratio = formulae.trivia.isotopic_ratio_assuming_single_heavy_isotope(\n", + " isotopic_fraction=formulae.trivia.isotopic_fraction(\n", + " molality_in_dry_air=np.asarray(output[\"molality_in_dry_air\"]),\n", + " density_dry_air=rhod,\n", + " total_vap_concentration=np.asarray(output[\"w\"]) * rhod / const.Mv\n", + " )\n", + ")\n", + "# print(isotopic_ratio)\n", + "delta_2H_vap = formulae.trivia.isotopic_ratio_2_delta(isotopic_ratio, const.VSMOW_R_2H)\n", + "print(np.asarray(output[\"molality_in_dry_air\"]))" + ], + "id": "ea02d11f5a86f4aa", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 5.12122068e-005 5.12122060e-005 5.12122060e-005 5.12122075e-005\n", + " 5.12122116e-005 5.12122204e-005 5.12122377e-005 5.12122710e-005\n", + " 5.12123355e-005 5.12124648e-005 5.12127370e-005 5.12133493e-005\n", + " 5.12148503e-005 5.12189419e-005 5.12316523e-005 5.12780082e-005\n", + " 5.14840488e-005 5.26554523e-005 6.17349029e-005 1.65754087e-004\n", + " 1.97639571e-003 6.30491467e-003 -3.79402151e+000 -1.31374068e+002\n", + " -1.16787626e+004 -9.28927380e+005 -6.58153370e+007 -4.22189376e+009\n", + " -2.48496844e+011 -1.26038574e+013 -5.96642060e+014 -2.64693181e+016\n", + " -1.10622316e+018 -4.37491631e+019 -1.64359973e+021 -5.88519339e+022\n", + " -2.01425145e+024 -6.60617571e+025 -2.08083315e+027 -6.30718006e+028\n", + " -1.84295053e+030 -5.19954228e+031 -1.41845566e+033 -3.74657534e+034\n", + " -9.59268276e+035 -2.38345866e+037 -5.75272326e+038 -1.35001883e+040\n", + " -3.08303428e+041 -6.85699033e+042 -1.48636475e+044 -3.14233105e+045\n", + " -6.48322722e+046 -1.30618094e+048 -2.57117617e+049 -4.94772228e+050\n", + " -9.31190768e+051 -1.71488171e+053 -3.09159472e+054 -5.45838138e+055\n", + " -9.44165945e+056 -1.60065230e+058 -2.66049546e+059 -4.33700234e+060\n", + " -6.93610837e+061 -1.08860871e+063 -1.67718799e+064 -2.53725440e+065\n", + " -3.76991337e+066 -5.50289643e+067 -7.89308059e+068 -1.11274325e+070\n", + " -1.54216301e+071 -2.10152833e+072 -2.92539262e+073 -4.00296385e+074\n", + " -5.19822792e+075 -6.69717123e+076 -8.70441425e+077 -1.11735578e+079\n", + " -1.40521724e+080 -1.74041174e+081 -2.13346526e+082 -2.56253459e+083\n", + " -3.04991739e+084 -3.57571307e+085 -4.11299462e+086 -4.54181903e+087\n", + " -4.90944089e+088 -5.26886091e+089 -5.71774430e+090 -5.71374593e+091\n", + " -5.57170899e+092 -5.41654154e+093 -5.13924531e+094 -4.96857566e+095\n", + " -4.57772971e+096 -4.21093318e+097 -3.94314177e+098 -3.51461603e+099\n", + " -3.20828029e+100 -2.78312838e+101 -2.46697952e+102 -2.15645448e+103\n", + " -1.80246854e+104 -1.53556894e+105 -1.29329959e+106 -1.03572158e+107\n", + " -8.19883401e+107 -6.41720688e+108 -4.89032014e+109 -3.67551927e+110\n", + " -2.72673439e+111 -2.08174669e+112 -1.52821349e+113 -1.09215112e+114\n", + " -8.09061480e+114 -5.65522526e+115 -4.08355759e+116 -2.76836320e+117]\n" + ] + } + ], + "execution_count": 14 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T08:45:06.719106Z", + "start_time": "2026-05-21T08:45:06.240884Z" } }, "cell_type": "code", @@ -417,12 +763,13 @@ "\n", "xyc = axs[1, 0]\n", "xlabel = \"$\\\\delta^2\\\\mathrm{H}$\"\n", - "for delta_2H, label, ls in zip(\n", + "for delta_2H_Rayleigh, label, ls in zip(\n", " (deltas_Rayleigh['2H']['eql'], deltas_Rayleigh['2H']['kin']),\n", " (f\"{xlabel} eql\", f\"{xlabel} kin\"),\n", " (\"-\", \"--\"),\n", "):\n", - " xyc.plot(in_unit(delta_2H, const.PER_MILLE), z, label=label, ls=ls)\n", + " xyc.plot(in_unit(delta_2H_Rayleigh, const.PER_MILLE), z, label=label, ls=ls)\n", + "xyc.plot(in_unit(delta_2H_vap, const.PER_MILLE), z, label=xlabel + \" PySDM\")\n", "\n", "xyc.set_xlabel(f\"{xlabel} [‰]\")\n", "xyc.set_xlim(-250, -50)\n", @@ -440,20 +787,17 @@ "\n", "show_plot(\"fig_4.pdf\")" ], - "id": "4c0691246300b852", + "id": "be0fb118f40e0e49", "outputs": [ { "data": { "text/plain": [ "
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } + "output_type": "display_data" }, { "data": { @@ -463,30 +807,27 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "210e49ad844e48479864ed1235000abc" + "model_id": "8c72c90b50434161a2be1c73a85f443b" } }, "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } + "output_type": "display_data" } ], - "execution_count": 14 + "execution_count": 15 }, { "metadata": { "ExecuteTime": { - "end_time": "2026-05-05T08:13:56.996110Z", - "start_time": "2026-05-05T08:13:56.992781Z" + "end_time": "2026-05-21T08:45:06.724995Z", + "start_time": "2026-05-21T08:45:06.720130Z" } }, "cell_type": "code", "source": "", - "id": "652a377137f61167", + "id": "2591f6add22c6609", "outputs": [], - "execution_count": null + "execution_count": 15 } ], "metadata": { diff --git a/examples/PySDM_examples/Zaba_et_al/isotopic_ratios_temp.ipynb b/examples/PySDM_examples/Zaba_et_al/isotopic_ratios_temp.ipynb new file mode 100644 index 0000000000..d7cda9df8c --- /dev/null +++ b/examples/PySDM_examples/Zaba_et_al/isotopic_ratios_temp.ipynb @@ -0,0 +1,522 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Isotopic ratios — simplified PySDM reproduction notebook\n", + "\n", + "This is a minimal, functional rewrite of the Zaba et al. isotopic ratios example.\n" + ], + "id": "28d6a6dddda9d900" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T07:40:31.127212Z", + "start_time": "2026-05-21T07:40:29.198080Z" + } + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot\n", + "from matplotlib.ticker import FuncFormatter\n", + "\n", + "from PySDM import Builder\n", + "from PySDM.environments import Box\n", + "from PySDM.backends import CPU\n", + "from PySDM.dynamics import Condensation, IsotopicFractionation\n", + "from PySDM import Formulae\n", + "from PySDM.physics.constants import si, PER_MILLE, in_unit, PER_CENT\n", + "from PySDM.dynamics.isotopic_fractionation import HEAVY_ISOTOPES" + ], + "id": "d68833cfdc5fad16", + "outputs": [], + "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T07:40:31.137572Z", + "start_time": "2026-05-21T07:40:31.128580Z" + } + }, + "cell_type": "code", + "source": [ + "class Simulation:\n", + "\n", + " @staticmethod\n", + " def make_particulator(\n", + " *,\n", + " ff,\n", + " molecular_isotopic_ratio,\n", + " initial_R_vap,\n", + " attributes,\n", + " n_sd,\n", + " dv,\n", + " dt,\n", + " relative_humidity,\n", + " T,\n", + " isotope,\n", + " ):\n", + " const = ff.constants\n", + "\n", + " if isotope == \"2H\":\n", + " molar_mass_heavy = const.M_2H_1H_16O\n", + " else:\n", + " molar_mass_heavy = getattr(const, f\"M_1H2_{isotope}\")\n", + "\n", + " for iso in HEAVY_ISOTOPES:\n", + " attributes[f\"moles_{iso}\"] = 0.0\n", + " attributes[f\"moles_{isotope}\"] = ff.trivia.moles_heavy_atom(\n", + " mass_total=attributes[\"signed water mass\"],\n", + " mass_other_heavy_isotopes=0,\n", + " molar_mass_light_molecule=const.M_1H2_16O,\n", + " molar_mass_heavy_molecule=molar_mass_heavy,\n", + " molecular_isotopic_ratio=molecular_isotopic_ratio,\n", + " atoms_per_heavy_molecule=1,\n", + " )\n", + "\n", + " builder = Builder(\n", + " n_sd=n_sd,\n", + " backend=CPU(formulae=ff),\n", + " environment=Box(dv=dv, dt=dt),\n", + " )\n", + "\n", + " builder.add_dynamic(Condensation())\n", + " builder.add_dynamic(IsotopicFractionation(isotopes=(isotope,)))\n", + "\n", + " rho_d = const.p_STP / const.Rd / T\n", + " \n", + " initial_conc_vap = (\n", + " ff.saturation_vapour_pressure.pvs_water(T)\n", + " * relative_humidity\n", + " / const.R_str\n", + " / T\n", + " )\n", + "\n", + " molality = ff.trivia.molality_in_dry_air(\n", + " isotopic_fraction=ff.trivia.isotopic_fraction_assuming_single_heavy_isotope(isotopic_ratio=initial_R_vap),\n", + " density_dry_air=rho_d,\n", + " total_vap_concentration=initial_conc_vap,\n", + " )\n", + " \n", + " for iso in HEAVY_ISOTOPES:\n", + " if iso == isotope:\n", + " builder.particulator.environment[f\"molality {iso} in dry air\"] = (\n", + " molality\n", + " )\n", + " else:\n", + " builder.particulator.environment[f\"molality {iso} in dry air\"] = 0\n", + " builder.particulator.environment[\"rhod\"] = rho_d\n", + " builder.particulator.environment[\"RH\"] = relative_humidity\n", + " builder.particulator.environment[\"T\"] = T\n", + "\n", + " builder.request_attribute(f\"delta_{isotope}\")\n", + " # builder.request_attribute(f\"Bolin number for {isotope}\")\n", + "\n", + " particulator = builder.build(attributes=attributes, products=())\n", + " \n", + " return particulator, initial_conc_vap\n", + "\n", + " @staticmethod\n", + " def do_one_step(\n", + " *,\n", + " ff,\n", + " particulator,\n", + " dm_dt_per_droplet,\n", + " init_m_R_liq,\n", + " initial_conc_vap,\n", + " isotope,\n", + " ):\n", + "\n", + " if isotope == \"2H\":\n", + " light_isotope = \"1H\"\n", + " atoms_per_light = 2\n", + " else:\n", + " light_isotope = \"16O\"\n", + " atoms_per_light = 1\n", + "\n", + " isotopic_fraction = ff.trivia.isotopic_fraction(\n", + " particulator.environment[f\"molality {isotope} in dry air\"][0],\n", + " particulator.environment[\"rhod\"][0],\n", + " initial_conc_vap,\n", + " )\n", + "\n", + " R_vap = ff.trivia.isotopic_ratio_assuming_single_heavy_isotope(\n", + " isotopic_fraction=isotopic_fraction\n", + " )\n", + "\n", + " initial_R_liq = (\n", + " particulator.attributes[f\"moles_{isotope}\"][0]\n", + " / particulator.attributes[f\"moles_{light_isotope}\"][0]\n", + " )\n", + " \n", + " # np.testing.assert_approx_equal(\n", + " # initial_R_liq,\n", + " # init_m_R_liq / atoms_per_light,\n", + " # significant=4,\n", + " # )\n", + "\n", + " init_Bo = particulator.attributes[f\"Bolin number for {isotope}\"][0]\n", + "\n", + " particulator.attributes[\"diffusional growth mass change\"].data[:] = (\n", + " dm_dt_per_droplet * particulator.dt\n", + " )\n", + " particulator.dynamics[\"IsotopicFractionation\"]()\n", + " \n", + " total_vap_conc = (\n", + " initial_conc_vap\n", + " - dm_dt_per_droplet\n", + " * particulator.dt\n", + " * particulator.attributes[\"multiplicity\"][0]\n", + " / ff.constants.Mv\n", + " / particulator.environment.mesh.dv\n", + " )\n", + " new_isotopic_fraction = ff.trivia.isotopic_fraction(\n", + " particulator.environment[f\"molality {isotope} in dry air\"][0],\n", + " particulator.environment[\"rhod\"][0],\n", + " total_vap_conc,\n", + " )\n", + "\n", + " new_R_vap = ff.trivia.isotopic_ratio_assuming_single_heavy_isotope(\n", + " isotopic_fraction=new_isotopic_fraction\n", + " )\n", + " \n", + " new_R_liq = (\n", + " particulator.attributes[f\"moles_{isotope}\"][0]\n", + " / particulator.attributes[f\"moles_{light_isotope}\"][0]\n", + " )\n", + " dR_dt_vap = (new_R_vap - R_vap) / particulator.dt\n", + " dR_dt_liq = (new_R_liq - initial_R_liq) / particulator.dt\n", + " return dR_dt_vap, dR_dt_liq, init_Bo\n" + ], + "id": "60c6411250e91a50", + "outputs": [], + "execution_count": 2 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T07:40:31.143672Z", + "start_time": "2026-05-21T07:40:31.138154Z" + } + }, + "cell_type": "code", + "source": [ + "def run_cell(ff, ATTR, rh, m_R_liq, volume, T, iso, n_sd):\n", + " delta_v = -200 * PER_MILLE\n", + " const = ff.constants\n", + " ISO_RATIO_V = ff.trivia.isotopic_delta_2_ratio(delta_v, const.VSMOW_R_2H)\n", + " part, Rv0 = Simulation.make_particulator(\n", + " ff=ff,\n", + " molecular_isotopic_ratio=m_R_liq,\n", + " initial_R_vap=ISO_RATIO_V,\n", + " attributes=ATTR,\n", + " n_sd=n_sd,\n", + " dv=volume,\n", + " dt=6 * si.s,\n", + " relative_humidity=rh,\n", + " T=T,\n", + " isotope=iso,\n", + " )\n", + " pvs = ff.saturation_vapour_pressure.pvs_water(T)\n", + " Fk = ff.drop_growth.Fk(\n", + " T=T,\n", + " K=const.K0,\n", + " lv=ff.latent_heat_vapourisation.lv(T),\n", + " )\n", + " Fd = ff.drop_growth.Fd(\n", + " pvs=pvs,\n", + " T=T,\n", + " D=const.D0,\n", + " )\n", + " c = (\n", + " 4 * np.pi * const.rho_w\n", + " / (Fk + Fd)\n", + " )\n", + " return Simulation.do_one_step(\n", + " ff=ff,\n", + " particulator=part,\n", + " dm_dt_per_droplet=c * radius * (rh - 1),\n", + " init_m_R_liq=m_R_liq,\n", + " initial_conc_vap=Rv0,\n", + " isotope=iso,\n", + " )\n", + "\n", + "\n", + "def run_grid(ff, ATTR, grid_rh, grid_R, volume, T, iso, n_sd):\n", + " nn = len(grid_rh)\n", + " vap = np.zeros((nn, nn))\n", + " liq = np.zeros((nn, nn))\n", + " Bo = np.zeros((nn, nn))\n", + "\n", + " for i, rh in enumerate(grid_rh):\n", + " for j, R in enumerate(grid_R):\n", + " vap[i, j], liq[i, j], Bo[i, j] = run_cell(\n", + " ff, ATTR, rh, R, volume, T, iso, n_sd\n", + " )\n", + "\n", + " return vap, liq, Bo\n" + ], + "id": "37435e37b6c29428", + "outputs": [], + "execution_count": 3 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T07:40:39.216400Z", + "start_time": "2026-05-21T07:40:31.144493Z" + } + }, + "cell_type": "code", + "source": [ + "iso = \"2H\"\n", + "T = 283.15\n", + "ff = Formulae(\n", + " isotope_diffusivity_ratios=\"HellmannAndHarvey2020+GrahamsLaw\",\n", + " isotope_equilibrium_fractionation_factors=\"VanHook1968\",\n", + " isotope_relaxation_timescale=\"ZabaEtAl\",\n", + ")\n", + "\n", + "grid_rh = np.linspace(0.0, 1.05, 32)\n", + "grid_R = np.linspace(0.8, 1.05, 32)\n", + "R_liq = grid_R * ff.constants.VSMOW_R_2H\n", + "\n", + "radius = 0.5 * si.mm\n", + "volume = 1 * si.dm**3\n", + "\n", + "\n", + "ATTR = {\"multiplicity\": np.ones(1), \"dry volume\": np.nan, \"kappa times dry volume\": np.nan, \"signed water mass\": (\n", + " 4 / 3 * np.pi * radius ** 3 * 1000.0\n", + ")}\n", + "\n", + "\n", + "vap_pristine, liq_pristine, Bo_pristine = run_grid(\n", + " ff, ATTR,\n", + " grid_rh=grid_rh,\n", + " grid_R=R_liq,\n", + " volume=volume,\n", + " T=T,\n", + " iso=iso,\n", + " n_sd=10,\n", + ")\n", + "\n", + "vap_polluted, liq_polluted, Bo_polluted = run_grid(\n", + " ff, ATTR,\n", + " grid_rh=grid_rh,\n", + " grid_R=R_liq,\n", + " volume=volume,\n", + " T=T,\n", + " iso=iso,\n", + " n_sd=30,\n", + ")\n" + ], + "id": "f45042cf05e557ce", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/agnieszkazaba/PycharmProjects/PySDM/PySDM/backends/numba.py:57: UserWarning: Disabling Numba threading due to ARM64 CPU (atomics do not work yet)\n", + " warnings.warn(\n" + ] + } + ], + "execution_count": 4 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T07:40:39.243747Z", + "start_time": "2026-05-21T07:40:39.228260Z" + } + }, + "cell_type": "code", + "source": [ + "\n", + "def plot_pristine_vs_polluted(\n", + " vap_pristine, liq_pristine,\n", + " vap_polluted, liq_polluted,\n", + " grid_rh, grid_R,\n", + "):\n", + " XX, YY = np.meshgrid(grid_R, grid_rh)\n", + "\n", + " all_liq = np.concatenate([liq_pristine.ravel(), liq_polluted.ravel()])\n", + " all_vap = np.concatenate([vap_pristine.ravel(), vap_polluted.ravel()])\n", + "\n", + " liq_vmin, liq_vmax = np.nanmin(all_liq), np.nanmax(all_liq)\n", + " vap_vmin, vap_vmax = np.nanmin(all_vap), np.nanmax(all_vap)\n", + "\n", + " fig, axs = pyplot.subplots(\n", + " 1, 2,\n", + " figsize=(11, 6),\n", + " subplot_kw={'projection': '3d'},\n", + " constrained_layout=True,\n", + " )\n", + "\n", + " fig.suptitle(\n", + " \"Pristine vs Polluted isotope evolution (shared scale)\"\n", + " )\n", + "\n", + " def format_ax(ax):\n", + " ax.view_init(elev=45, azim=-65)\n", + " ax.tick_params(axis='y', pad=-2)\n", + " ax.tick_params(axis='z', pad=6)\n", + "\n", + " ax.yaxis.set_major_formatter(\n", + " FuncFormatter(lambda x, _: f\"{x:.2f}\")\n", + " )\n", + " ax.zaxis.set_major_formatter(\n", + " FuncFormatter(lambda x, _: f\"{x:.2e}\")\n", + " )\n", + "\n", + " ax.set_xlabel(\"R/R_VSMOW\")\n", + " ax.set_ylabel(\"RH\")\n", + "\n", + " ax = axs[0]\n", + " format_ax(ax)\n", + "\n", + " ax.set_title(r\"Liquid phase\")\n", + "\n", + " ax.plot_surface(\n", + " XX, YY, liq_pristine,\n", + " color=\"grey\", alpha=0.18, edgecolor=None, zorder=1\n", + " )\n", + " ax.plot_wireframe(\n", + " XX, YY, liq_pristine,\n", + " color=\"black\", alpha=0.25, linewidth=0.6, zorder=2\n", + " )\n", + "\n", + " surf = ax.plot_surface(\n", + " XX, YY, liq_polluted,\n", + " cmap=\"viridis\",\n", + " vmin=liq_vmin, vmax=liq_vmax,\n", + " alpha=0.65, edgecolor=None, zorder=3\n", + " )\n", + " ax.plot_wireframe(\n", + " XX, YY, liq_polluted,\n", + " color=\"darkgreen\", alpha=0.35, linewidth=0.5, zorder=4\n", + " )\n", + "\n", + " ax.set_zlim(liq_vmin, liq_vmax)\n", + " ax.set_zlabel(\"liq signal\")\n", + "\n", + " ax = axs[1]\n", + " format_ax(ax)\n", + "\n", + " ax.set_title(r\"Vapour phase\")\n", + "\n", + " ax.plot_surface(\n", + " XX, YY, vap_pristine,\n", + " color=\"grey\", alpha=0.18, edgecolor=None, zorder=1\n", + " )\n", + " ax.plot_wireframe(\n", + " XX, YY, vap_pristine,\n", + " color=\"black\", alpha=0.25, linewidth=0.6, zorder=2\n", + " )\n", + "\n", + " surf = ax.plot_surface(\n", + " XX, YY, vap_polluted,\n", + " cmap=\"plasma\",\n", + " vmin=vap_vmin, vmax=vap_vmax,\n", + " alpha=0.65, edgecolor=None, zorder=3\n", + " )\n", + " ax.plot_wireframe(\n", + " XX, YY, vap_polluted,\n", + " color=\"darkred\", alpha=0.35, linewidth=0.5, zorder=4\n", + " )\n", + "\n", + " ax.set_zlim(vap_vmin, vap_vmax)\n", + " ax.set_zlabel(\"vap signal\")\n", + "\n", + " pyplot.show()" + ], + "id": "d6a3c3f901ca045a", + "outputs": [], + "execution_count": 5 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T07:40:39.815718Z", + "start_time": "2026-05-21T07:40:39.244637Z" + } + }, + "cell_type": "code", + "source": [ + "plot_pristine_vs_polluted(\n", + " vap_pristine, liq_pristine,\n", + " vap_polluted, liq_polluted,\n", + " grid_rh, grid_R,\n", + ")" + ], + "id": "4bb327485e61c177", + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 6 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T07:40:39.897632Z", + "start_time": "2026-05-21T07:40:39.816724Z" + } + }, + "cell_type": "code", + "source": [ + "pyplot.imshow(Bo_polluted, vmin=0.5, vmax=1)\n", + "pyplot.colorbar()" + ], + "id": "8aa26e1d72e75d29", + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 7 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.x" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/PySDM_examples/Zaba_et_al/simulation.py b/examples/PySDM_examples/Zaba_et_al/simulation.py new file mode 100644 index 0000000000..e442305367 --- /dev/null +++ b/examples/PySDM_examples/Zaba_et_al/simulation.py @@ -0,0 +1,150 @@ +import numpy as np + +from PySDM import Builder +from PySDM.environments import Box +from PySDM.backends import CPU +from PySDM.dynamics import Condensation, IsotopicFractionation +from PySDM.dynamics.isotopic_fractionation import HEAVY_ISOTOPES + + +class Simulation: + @staticmethod + def make_particulator( + *, + ff, + molecular_isotopic_ratio, + initial_R_vap, + attributes, + n_sd, + dv, + dt, + relative_humidity, + T, + isotope, + ): # pylint: disable=too-many-arguments + """needed setup for simulation""" + const = ff.constants + if isotope == "2H": + molar_mass_heavy = const.M_2H_1H_16O + atoms_per_heavy_molecule = 1 + elif isotope[-1] == "O": + molar_mass_heavy = getattr(const, f"M_1H2_{isotope}") + atoms_per_heavy_molecule = 1 + else: + molar_mass_heavy = 0 + atoms_per_heavy_molecule = 0 + attributes[f"moles_{isotope}"] = ff.trivia.moles_heavy_atom( + mass_total=attributes["signed water mass"], + mass_other_heavy_isotopes=0, + molar_mass_light_molecule=const.M_1H2_16O, + molar_mass_heavy_molecule=molar_mass_heavy, + molecular_isotopic_ratio=molecular_isotopic_ratio, + atoms_per_heavy_molecule=atoms_per_heavy_molecule, + ) + builder = Builder( + n_sd=n_sd, + backend=CPU( + formulae=ff, + ), + environment=Box(dv=dv, dt=dt), + ) + builder.add_dynamic(Condensation()) + builder.add_dynamic(IsotopicFractionation(isotopes=(isotope,))) + + rho_d = const.p_STP / const.Rd / T + builder.particulator.environment["rhod"] = rho_d + + builder.particulator.environment["RH"] = relative_humidity + builder.particulator.environment["T"] = T + initial_conc_vap = ( + ff.saturation_vapour_pressure.pvs_water(T) + * relative_humidity + / const.R_str + / T + ) + + molality_in_dry_air = ff.trivia.molality_in_dry_air( + isotopic_fraction=ff.trivia.isotopic_fraction_assuming_single_heavy_isotope( + isotopic_ratio=initial_R_vap + ), + density_dry_air=rho_d, + total_vap_concentration=initial_conc_vap, + ) + + for iso in HEAVY_ISOTOPES: + if iso == isotope: + builder.particulator.environment[f"molality {iso} in dry air"] = ( + molality_in_dry_air + ) + else: + builder.particulator.environment[f"molality {iso} in dry air"] = 0 + builder.request_attribute(f"delta_{isotope}") + builder.request_attribute(f"Bolin number for {isotope}") + return builder.build(attributes=attributes, products=()), initial_conc_vap + + @staticmethod + def do_one_step( + *, + ff, + particulator, + dm_dt_per_droplet, + init_m_R_liq, + initial_conc_vap, + isotope, + ): + """perform one step of simulation""" + if isotope[-1] == "O": + light_isotope = "16O" + atoms_per_light_molecule = 1 + elif isotope == "2H": + light_isotope = "1H" + atoms_per_light_molecule = 2 + + isotopic_fraction = ff.trivia.isotopic_fraction( + particulator.environment[f"molality {isotope} in dry air"][0], + particulator.environment["rhod"][0], + initial_conc_vap, + ) + R_vap = ff.trivia.isotopic_ratio_assuming_single_heavy_isotope( + isotopic_fraction=isotopic_fraction + ) + + initial_R_liq = ( + particulator.attributes[f"moles_{isotope}"][0] + / particulator.attributes[f"moles_{light_isotope}"][0] + ) + + np.testing.assert_approx_equal( + initial_R_liq, init_m_R_liq / atoms_per_light_molecule, significant=4 + ) + init_Bo = particulator.attributes[f"Bolin number for {isotope}"][0] + + particulator.attributes["diffusional growth mass change"].data[:] = ( + dm_dt_per_droplet * particulator.dt + ) + particulator.dynamics["IsotopicFractionation"]() + + total_vap_conc = ( + initial_conc_vap + - dm_dt_per_droplet + * particulator.dt + * particulator.attributes["multiplicity"][0] + / ff.constants.Mv + / particulator.environment.mesh.dv[0] + ) + new_isotopic_fraction = ff.trivia.isotopic_fraction( + particulator.environment[f"molality {isotope} in dry air"][0], + particulator.environment["rhod"][0], + total_vap_conc, + ) + + new_R_vap = ff.trivia.isotopic_ratio_assuming_single_heavy_isotope( + isotopic_fraction=new_isotopic_fraction + ) + new_R_liq = ( + particulator.attributes[f"moles_{isotope}"][0] + / particulator.attributes[f"moles_{light_isotope}"][0] + ) + dR_dt_vap = (new_R_vap - R_vap) / particulator.dt + dR_dt_liq = (new_R_liq - initial_R_liq) / particulator.dt + return dR_dt_vap, dR_dt_liq, init_Bo diff --git a/tests/smoke_tests/no_env/gedzelman_and_arnold_1994/test_fig_2.py b/tests/smoke_tests/no_env/gedzelman_and_arnold_1994/test_fig_2.py index e7a32c479a..9704119461 100644 --- a/tests/smoke_tests/no_env/gedzelman_and_arnold_1994/test_fig_2.py +++ b/tests/smoke_tests/no_env/gedzelman_and_arnold_1994/test_fig_2.py @@ -10,8 +10,6 @@ from open_atmos_jupyter_utils import notebook_vars from PySDM_examples import Gedzelman_and_Arnold_1994 -from PySDM.physics.constants import PER_CENT - PLOT = False @@ -43,8 +41,8 @@ def test_fig_2(notebook_variables, x, expected_y, phase): """ # arrange - xy_data = notebook_variables["PLOT_LINE"][phase][0].get_xydata() - plot_x, plot_y = xy_data[:, 0], xy_data[:, 1] * PER_CENT + plot_x = notebook_variables["plots"][phase]["x"] + plot_y = notebook_variables["plots"][phase]["y"] plot_x_eps = (plot_x[1] - plot_x[0]) / 2 plot_y_eps = np.max(abs(np.diff(plot_y))) / 2 @@ -56,37 +54,37 @@ def test_fig_2(notebook_variables, x, expected_y, phase): np.testing.assert_allclose( actual=sut, desired=expected_y, - atol=plot_y_eps / plot_x_eps, + rtol=plot_y_eps, ) @pytest.mark.parametrize( - "phase, condition, atol", + "phase, condition, rtol, eps", ( - ("vapour", 0, 0.2), - ("liquid", 1, 0.02), + ("vapour", 0.0, 0.1, 1e-3), + ("liquid", 1.0, 0.1, 1e-2), ), ) -def test_dR_zero_condition(notebook_variables, phase, condition, atol): +def test_dR_zero_condition(notebook_variables, phase, condition, rtol, eps): """Test values plotted with color in Fig 1. Points (x, y) for which z equals condition should match theoretical lines.""" # arrange - cmn = notebook_variables["cmn"] - - X_eq = notebook_variables["X_eq"] + cmn = notebook_variables["CMN_FOR_TEST"] iso_ratio_v = notebook_variables["ISO_RATIO_V"] - X, Y = np.meshgrid(notebook_variables["X"], notebook_variables["Y"] * PER_CENT) - pcm_data = notebook_variables["PCM"][phase].get_array() + Y = notebook_variables["YY"] + X = notebook_variables["XX"] - # act + pcm_data = notebook_variables["cases"][phase]["pcolormesh"].get_array() within = ( - (condition - 0.005 < pcm_data) & (pcm_data < condition + 0.005) & (X >= X_eq) + (condition - eps < pcm_data) + & (pcm_data < condition + eps) + & (X > notebook_variables["X_eq"]) ) + assert np.sum(within) > 0 - x_to_check = X[within] - y_to_check = Y[within] - iso_ratio_r = x_to_check * cmn.params.vsmow + # act + iso_ratio_r = X[within] * cmn.params.vsmow expected_y = cmn.f.isotope_ratio_evolution.saturation_for_zero_dR_condition( iso_ratio_x=iso_ratio_r if phase == "liquid" else iso_ratio_v, diff_rat_light_to_heavy=(cmn.params.f_ratio / cmn.params.D_ratio), @@ -97,5 +95,4 @@ def test_dR_zero_condition(notebook_variables, phase, condition, atol): ) # assert - assert np.sum(within) > 0 - np.testing.assert_allclose(y_to_check, expected_y, atol=atol) + np.testing.assert_allclose(Y[within], expected_y, rtol=rtol) diff --git a/tests/unit_tests/attributes/test_diffusional_growth_mass_change.py b/tests/unit_tests/attributes/test_diffusional_growth_mass_change.py index c16843788e..5c1dfd79b5 100644 --- a/tests/unit_tests/attributes/test_diffusional_growth_mass_change.py +++ b/tests/unit_tests/attributes/test_diffusional_growth_mass_change.py @@ -73,10 +73,12 @@ def test_methods(backend_class, steps): ) # act - particulator.run(steps=0) + if steps == 0: + particulator.run(steps=0) for _ in range(steps): - particulator.attributes["signed water mass"].data[:] += mass_delta particulator.run(steps=1) + particulator.attributes["signed water mass"].data[:] += mass_delta + particulator.attributes.mark_updated("signed water mass") # assert np.testing.assert_allclose( diff --git a/tests/unit_tests/attributes/test_isotopes.py b/tests/unit_tests/attributes/test_isotopes.py index 70fe03b69a..954bca5a9e 100644 --- a/tests/unit_tests/attributes/test_isotopes.py +++ b/tests/unit_tests/attributes/test_isotopes.py @@ -134,7 +134,7 @@ def test_bolin_number_attribute( any_positive_number = 44.0 ff = Formulae( isotope_relaxation_timescale=variant, - isotope_diffusivity_ratios="HellmannAndHarvey2020", + isotope_diffusivity_ratios="HellmannAndHarvey2020+GrahamsLaw", isotope_equilibrium_fractionation_factors="VanHook1968", ) n_sd = 1 @@ -165,7 +165,7 @@ def test_bolin_number_attribute( particulator = builder.build(attributes=attr) particulator.environment["RH"] = relative_humidity particulator.environment["T"] = any_positive_number - particulator.environment["dry_air_density"] = any_positive_number + particulator.environment["rhod"] = any_positive_number # act value = particulator.attributes[attribute_name].data diff --git a/tests/unit_tests/backends/test_isotope_methods.py b/tests/unit_tests/backends/test_isotope_methods.py index ea15203049..22ba7d14e4 100644 --- a/tests/unit_tests/backends/test_isotope_methods.py +++ b/tests/unit_tests/backends/test_isotope_methods.py @@ -3,6 +3,15 @@ """ import numpy as np +import math +import pytest +from PySDM.backends import CPU + +from PySDM import Formulae +from PySDM.environments import Parcel +from PySDM import Builder +from PySDM.physics import si +from PySDM.dynamics import Condensation, IsotopicFractionation, AmbientThermodynamics class TestIsotopeMethods: @@ -18,14 +27,14 @@ def test_isotopic_fractionation(backend_instance): moles_heavy = arr2storage(np.array([0.5])) molality_in_dry_air = arr2storage(np.array([0.1])) - expected_moles_heavy = 0.5 + 0.05 + expected_moles_heavy = 0.5 + 0.05 # TODO check and fix!!!! mass_of_dry_air = 1.0 * 2.0 expected_molality_air = 0.1 - (0.05 * 3.0 / mass_of_dry_air) # act backend.isotopic_fractionation( cell_id=arr2storage(np.array([0], dtype=int)), - cell_volume=arr2storage(np.array([2.0])), + cell_volume=2.0, multiplicity=arr2storage(np.array([3.0])), dm_total=arr2storage(np.array([0.2])), signed_water_mass=arr2storage(np.array([1.0])), @@ -40,6 +49,55 @@ def test_isotopic_fractionation(backend_instance): assert np.isclose(moles_heavy.to_ndarray()[0], expected_moles_heavy) assert np.isclose(molality_in_dry_air.to_ndarray()[0], expected_molality_air) + @staticmethod + def test_bolin_number(backend_class): + # arrange + if backend_class.__name__ == "ThrustRTC": + pytest.xfail("bolin_number not yet supported for ThrustRTC") + + backend = backend_class( + Formulae( + isotope_diffusivity_ratios="Stewart1975+GrahamsLaw", + isotope_relaxation_timescale="ZabaEtAl", + isotope_equilibrium_fractionation_factors="VanHook1968", + ) + ) + + arr2storage = backend.Storage.from_ndarray + + n_sd = 3 + n_cell = 2 + + output = arr2storage(np.empty(n_sd)) + + cell_id = arr2storage(np.zeros(n_sd, dtype=np.int64)) + relative_humidity = arr2storage(np.full(n_cell, 0.95)) + temperature = arr2storage(np.full(n_cell, 283.15)) + density_dry_air = arr2storage(np.full(n_cell, 1.2)) + moles_light_molecule = arr2storage(np.array([1e-3, 2e-3, 3e-3])) + moles_heavy = arr2storage(np.array([2e-6, 4e-6, 6e-6])) + molality_in_dry_air = arr2storage(np.full(n_cell, 1e-5)) + + # act + backend.bolin_number( + output=output, + cell_id=cell_id, + isotope="2H", # TODO + relative_humidity=relative_humidity, + temperature=temperature, + density_dry_air=density_dry_air, + moles_light_molecule=moles_light_molecule, + moles_heavy=moles_heavy, + molality_in_dry_air=molality_in_dry_air, + ) + + # assert + result = output.to_ndarray() + + assert result.shape == (n_sd,) + assert np.all(np.isfinite(result)) + assert np.all(result > 0) + @staticmethod def test_isotopic_delta(backend_instance): # arrange diff --git a/tests/unit_tests/dynamics/test_isotopic_fractionation.py b/tests/unit_tests/dynamics/test_isotopic_fractionation.py index 0f78d38362..51dd71f951 100644 --- a/tests/unit_tests/dynamics/test_isotopic_fractionation.py +++ b/tests/unit_tests/dynamics/test_isotopic_fractionation.py @@ -6,13 +6,16 @@ from contextlib import nullcontext import numpy as np +import math import pytest + from PySDM import Builder, Formulae -from PySDM.dynamics import Condensation, IsotopicFractionation -from PySDM.dynamics.isotopic_fractionation import HEAVY_ISOTOPES, LIGHT_ISOTOPES -from PySDM.environments import Box from PySDM.physics import si +from PySDM.environments import Box, Parcel +from PySDM.dynamics import Condensation, IsotopicFractionation, AmbientThermodynamics +from PySDM.dynamics.isotopic_fractionation import HEAVY_ISOTOPES, LIGHT_ISOTOPES +from PySDM.backends import CPU BASE_INITIAL_ATTRIBUTES = { "multiplicity": np.ones(1), @@ -34,7 +37,7 @@ def make_particulator( builder = Builder( n_sd=1, backend=backend_instance, - environment=Box(dv=np.ones(1), dt=1 * si.s), + environment=Box(dv=np.nan, dt=1 * si.s), ) for iso in isotopes_considered: if not attributes.get(f"moles_{iso}"): @@ -43,7 +46,7 @@ def make_particulator( builder.request_attribute(f"delta_{iso}") builder.particulator.environment["RH"] = np.array(rh) builder.particulator.environment["T"] = np.array(t) - builder.particulator.environment["dry_air_density"] = np.array(1) + builder.particulator.environment["rhod"] = np.array(1) builder.add_dynamic(Condensation()) builder.add_dynamic(IsotopicFractionation(isotopes=isotopes_considered)) @@ -252,3 +255,88 @@ def test_initial_condition_for_delta_isotopes( rtol=1e-10, atol=0, ) + + @staticmethod + @pytest.mark.parametrize("isotope", ("2H",)) + @pytest.mark.parametrize( + "ksi, initial_molality_in_dry_air, expected_sign_of_ambient_molality_change", + ( + (1, 1e-5 * si.mol / si.kg, -1), + (1, 0, +1), + (1e6, 1e-5 * si.mol / si.kg, -1), + (1e13, 1e-5 * si.mol / si.kg, -1), + (1e13, 0, +1), + # TODO: test case with both growth/evp + # TODO: case when trivial integration would yield negative molality + # TODO: case when ... yield negative isotope mass in droplet + ), + ) + def test_ambient_isotopes_in_parcel( + ksi, + isotope, + initial_molality_in_dry_air, + expected_sign_of_ambient_molality_change, + backend_class=CPU, + ): + # arrange + ambient_var = f"molality {isotope} in dry air" + builder = Builder( + environment=Parcel( + dt=1 * si.s, + mass_of_dry_air=1 * si.kg, + p0=1000 * si.hPa, + T0=300 * si.K, + initial_relative_humidity=0.99, + w=1 * si.m / si.s, + variables=(ambient_var,), + ), + backend=backend_class( + formulae=Formulae( + isotope_relaxation_timescale="ZabaEtAl", + isotope_equilibrium_fractionation_factors="MerlivatAndNief1967+VanHook1968", + isotope_diffusivity_ratios="Stewart1975+GrahamsLaw", + ), + ), + n_sd=(n_sd := 1), + ) + builder.add_dynamic(AmbientThermodynamics()) + builder.add_dynamic(Condensation()) + builder.add_dynamic(IsotopicFractionation((isotope,))) + particulator = builder.build( + attributes={ + "moles_2H": np.ones(n_sd) / 1e10, + "moles_3H": np.zeros(n_sd), + "moles_17O": np.zeros(n_sd), + "moles_18O": np.zeros(n_sd), + **builder.particulator.environment.init_attributes( + n_in_dv=ksi, + kappa=1, + r_dry=0.01 * si.micrometre, + ), + }, + products=(), + ) + initial_v_wet = particulator.attributes["volume"].to_ndarray()[0] + particulator.environment[ambient_var][:] = initial_molality_in_dry_air + + # act + particulator.run(steps=1) + + # assert + new_molality_in_dry_air = float( + particulator.environment[ambient_var].to_ndarray()[0] + ) + assert ( + particulator.attributes["volume"].to_ndarray()[0] != initial_v_wet + ), "wet volume did not change" + assert ( + new_molality_in_dry_air != initial_molality_in_dry_air + ), "molality in dry air did not change" + assert new_molality_in_dry_air > 0, "molality in dry air is negative" + assert ( + particulator.attributes[f"moles_{isotope}"].to_ndarray()[0] > 0 + ), "moles of heavy isotope in droplet is negative" + assert ( + math.copysign(1, new_molality_in_dry_air - initial_molality_in_dry_air) + == expected_sign_of_ambient_molality_change + ), "unexpected sign of ambient molality change" diff --git a/tests/unit_tests/physics/test_isotope_diffusivity_ratios.py b/tests/unit_tests/physics/test_isotope_diffusivity_ratios.py index 2a501474c0..0216855d5e 100644 --- a/tests/unit_tests/physics/test_isotope_diffusivity_ratios.py +++ b/tests/unit_tests/physics/test_isotope_diffusivity_ratios.py @@ -161,10 +161,11 @@ def test_all_on_one_plot(plot=False): pyplot.xlabel("temperature [K]") pyplot.grid() pyplot.ylabel("diffusivity ratio (heavy to light)") + pyplot.legend() if plot: pyplot.show() else: pyplot.clf() # assert - assert 0.985 > max_value > min_value > 0.968 + assert 0.985 > max_value > min_value > 0.948 diff --git a/tests/unit_tests/physics/test_isotope_relaxation_timescale.py b/tests/unit_tests/physics/test_isotope_relaxation_timescale.py index 90de14024f..8fef206c78 100644 --- a/tests/unit_tests/physics/test_isotope_relaxation_timescale.py +++ b/tests/unit_tests/physics/test_isotope_relaxation_timescale.py @@ -85,15 +85,13 @@ def test_bolin_number_unit(): # act value = sut( - const=Formulae().constants, D_ratio_heavy_to_light=any_number_except_one * si.dimensionless, - D_light=any_number_except_one * si.m**2 / si.s, alpha=any_number_except_one * si.dimensionless, R_vap=any_number_except_one * si.dimensionless, R_liq=any_number_except_one * si.dimensionless, relative_humidity=any_number_except_one * si.dimensionless, Fk=any_number_except_one * si.s / si.m**2, - rho_v=1 * si.g / si.m**3, + Fd=any_number_except_one * si.s / si.m**2, ) # assert