Remove some astropy usage

artistools/inputmodel/slice1dfromconein3dmodel.py

@@ -176,7 +176,7 @@ def make_1d_model_files(args):
 # cone = cone.loc[cone['rho_model'] > 0.0]
 
 
-def make_plot(args):
+def make_plot(args) -> None:
     cone = make_cone(args)
 
     cone = cone.loc[cone["rho_model"] > 0.0002]  # cut low densities (empty cells?) from plot

artistools/lightcurve/plotlightcurve.py

@@ -19,7 +19,6 @@
 import numpy as np
 import pandas as pd
 import polars as pl
-from astropy import constants as const
 
 import artistools as at
 
@@ -440,7 +439,8 @@ def plot_artis_lightcurve(
 
         if args.magnitude:
             # convert to bol magnitude
-            lcdata["mag"] = 4.74 - (2.5 * np.log10(lcdata["lum"] / const.L_sun.to("erg/s").value))
+            Lsun_to_erg_s = 3.828e33
+            lcdata["mag"] = 4.74 - (2.5 * np.log10(lcdata["lum"] / Lsun_to_erg_s))
             ycolumn = "mag"
         else:
             ycolumn = "lum"

artistools/misc.py

@@ -1219,9 +1219,6 @@ def get_nprocs(modelpath: Path) -> int:
 @lru_cache(maxsize=8)
 def get_inputparams(modelpath: Path) -> dict[str, t.Any]:
     """Return parameters specified in input.txt."""
-    from astropy import constants as const
-    from astropy import units as u
-
     params: dict[str, t.Any] = {}
     with Path(modelpath, "input.txt").open("r", encoding="utf-8") as inputfile:
         params["pre_zseed"] = int(readnoncommentline(inputfile).split("#")[0])
@@ -1234,8 +1231,9 @@ def get_inputparams(modelpath: Path) -> dict[str, t.Any]:
 
         params["tmin"], params["tmax"] = (float(x) for x in readnoncommentline(inputfile).split("#")[0].split())
 
+        MeV_in_Hz = 2.417989242084918e20
         params["nusyn_min"], params["nusyn_max"] = (
-            (float(x) * u.MeV / const.h).to("Hz") for x in readnoncommentline(inputfile).split("#")[0].split()
+            float(x) * MeV_in_Hz for x in readnoncommentline(inputfile).split("#")[0].split()
         )
 
         # number of times for synthesis

artistools/nltepops/nltepops.py

@@ -9,7 +9,6 @@
 
 import pandas as pd
 import polars as pl
-from astropy import constants as const
 
 import artistools as at
 
@@ -66,7 +65,7 @@ def add_lte_pops(dfpop, adata, columntemperature_tuples, noprint=False, maxlevel
 
     columntemperature_tuples is a sequence of tuples of column name and temperature, e.g., ('mycolumn', 3000)
     """
-    k_b = const.k_B.to("eV / K").value
+    K_B = 8.617333262145179e-05  # eV / K
 
     for _, row in dfpop.drop_duplicates(["modelgridindex", "timestep", "Z", "ion_stage"]).iterrows():
         modelgridindex = int(row.modelgridindex)
@@ -104,7 +103,7 @@ def f_ltepop(x, T_exc: float, gsg: float, gse: float, ionlevels) -> float:
             ltepop = (
                 ionlevels["g"].item(int(x.level))
                 / gsg
-                * math.exp(-(ionlevels["energy_ev"].item(int(x.level)) - gse) / k_b / T_exc)
+                * math.exp(-(ionlevels["energy_ev"].item(int(x.level)) - gse) / K_B / T_exc)
             )
             assert isinstance(ltepop, float)
             return ltepop
@@ -133,7 +132,7 @@ def f_ltepop(x, T_exc: float, gsg: float, gse: float, ionlevels) -> float:
                 for columnname, T_exc in columntemperature_tuples:
                     superlevelpop = (
                         ionlevels[levelnumber_sl:]
-                        .select(pl.col("g") / gs_g * (-(pl.col("energy_ev") - gs_energy) / k_b / T_exc).exp())
+                        .select(pl.col("g") / gs_g * (-(pl.col("energy_ev") - gs_energy) / K_B / T_exc).exp())
                         .sum()
                         .item()
                     )

artistools/transitions.py

@@ -24,7 +24,7 @@ class IonTuple(t.NamedTuple):
 
 
 def get_kurucz_transitions() -> tuple[pd.DataFrame, list[IonTuple]]:
-    hc_evcm = (const.h * const.c).to("eV cm").value
+    hc_in_ev_cm = 0.0001239841984332003
     transitiontuple = namedtuple(
         "transitiontuple",
         "Z ion_stage lambda_angstroms A lower_energy_ev upper_energy_ev lower_statweight upper_statweight",
@@ -40,7 +40,7 @@ def get_kurucz_transitions() -> tuple[pd.DataFrame, list[IonTuple]]:
                     continue
                 lambda_angstroms = float(line[:12]) * 10
                 loggf = float(line[11:18])
-                lower_energy_ev, upper_energy_ev = hc_evcm * float(line[24:36]), hc_evcm * float(line[52:64])
+                lower_energy_ev, upper_energy_ev = hc_in_ev_cm * float(line[24:36]), hc_in_ev_cm * float(line[52:64])
                 lower_statweight, upper_statweight = 2 * float(line[36:42]) + 1, 2 * float(line[64:70]) + 1
                 fij = (10**loggf) / lower_statweight
                 A = fij / (1.49919e-16 * upper_statweight / lower_statweight * lambda_angstroms**2)
@@ -103,7 +103,7 @@ def generate_ion_spectrum(
         # contribute the Gaussian line profile to the discrete flux bins
 
         centre_index = round((line["lambda_angstroms"] - args.xmin) / plot_resolution)
-        sigma_angstroms = line["lambda_angstroms"] * args.sigma_v / const.c.to("km / s").value
+        sigma_angstroms = line["lambda_angstroms"] * args.sigma_v / 299792.458
         sigma_gridpoints = math.ceil(sigma_angstroms / plot_resolution)
         window_left_index = max(int(centre_index - args.gaussian_window * sigma_gridpoints), 0)
         window_right_index = min(int(centre_index + args.gaussian_window * sigma_gridpoints), len(xvalues))
@@ -205,7 +205,7 @@ def make_plot(
 def add_upper_lte_pop(
     dftransitions: pl.DataFrame, T_exc: float, ionpop: float, ltepartfunc: float, columnname: str | None = None
 ) -> pl.DataFrame:
-    K_B = const.k_B.to("eV / K").value
+    K_B = 8.617333262145179e-05  # eV / K
     scalefactor = ionpop / ltepartfunc
     if columnname is None:
         columnname = f"upper_pop_lte_{T_exc:.0f}K"
@@ -445,7 +445,7 @@ def main(args: argparse.Namespace | None = None, argsraw: Sequence[str] | None =
             print(f"  {len(pldftransitions)} plottable transitions")
 
             if args.atomicdatabase == "artis":
-                K_B = const.k_B.to("eV / K").value
+                K_B = 8.617333262145179e-05  # eV / K
                 T_exc = vardict["Te"]
                 ltepartfunc = (
                     ion["levels"].select(pl.col("g") * (-pl.col("energy_ev") / K_B / T_exc).exp()).sum().item()
@@ -500,7 +500,7 @@ def main(args: argparse.Namespace | None = None, argsraw: Sequence[str] | None =
                     T_exc = vardict[temperature]
                     popcolumnname = f"upper_pop_lte_{T_exc:.0f}K"
                     if args.atomicdatabase == "artis":
-                        K_B = const.k_B.to("eV / K").value
+                        K_B = 8.617333262145179e-05  # eV / K
                         ltepartfunc = (
                             ion["levels"].select(pl.col("g") * (-pl.col("energy_ev") / K_B / T_exc).exp()).sum().item()
                         )