v0.0.13

README.md

@@ -30,7 +30,7 @@ systems availalbe from the atmosphere model.
 When using the `RBFInterpolator`, we strongly encourage the use of scipy 1.9+
 which provides a speed gain of O(100) times.
 
-v0.0.12 or below only supports numpy v1.
+Supports NumPy v1 and v2 (>=1.23,<3). For best performance with RBFInterpolator, use SciPy 1.9+.
 
 ## Documentation
 

example/compare_ps_cooling_rates.py

@@ -63,46 +63,39 @@
     # format the data into matplotlib.tricontour readable format
     _x.append(BP(logg_i, Mbol) - RP(logg_i, Mbol))
     _y.append(G(logg_i, Mbol))
-    _z1.append(wdlf.cooling_rate_interpolator(logL, m_valid))
-    _z2.append(wdlf_nps.cooling_rate_interpolator(logL, m_valid))
+    _z1.append(-wdlf.cooling_rate_interpolator(logL, m_valid))
+    _z2.append(-wdlf_nps.cooling_rate_interpolator(logL, m_valid))
 
 x = np.concatenate(_x)
 y = np.concatenate(_y)
 z1 = np.log10(np.concatenate(_z1))
 z2 = np.log10(np.concatenate(_z2))
 
-# masking the non-finite values
-mask1 = np.isfinite(z1)
-mask2 = np.isfinite(z2)
+# masking the non-finite values across x, y, and z
+mask1 = np.isfinite(x) & np.isfinite(y) & np.isfinite(z1)
+mask2 = np.isfinite(x) & np.isfinite(y) & np.isfinite(z2)
+mask12 = mask1 & mask2
 CMAP = "RdBu_r"
 LEVELS = 50
 
 # make the contour lines
-ax1.tricontour(
-    x[mask1], y[mask1], z1[mask1], levels=LEVELS, linewidths=0.5, colors="k"
-)
-ax2.tricontour(
-    x[mask2], y[mask2], z1[mask2], levels=LEVELS, linewidths=0.5, colors="k"
-)
+ax1.tricontour(x[mask1], y[mask1], z1[mask1], levels=LEVELS, linewidths=0.5, colors="k")
+ax2.tricontour(x[mask2], y[mask2], z2[mask2], levels=LEVELS, linewidths=0.5, colors="k")
 ax3.tricontour(
-    x[mask1 & mask2],
-    y[mask1 & mask2],
-    (z1 / z2)[mask1 & mask2],
+    x[mask12],
+    y[mask12],
+    (z1 / z2)[mask12],
     levels=LEVELS,
     linewidths=0.5,
     colors="k",
 )
 # make the contour shades
-contour1 = ax1.tricontourf(
-    x[mask1], y[mask1], z1[mask1], levels=LEVELS, cmap=CMAP
-)
-contour2 = ax2.tricontourf(
-    x[mask2], y[mask2], z2[mask2], levels=LEVELS, cmap=CMAP
-)
+contour1 = ax1.tricontourf(x[mask1], y[mask1], z1[mask1], levels=LEVELS, cmap=CMAP)
+contour2 = ax2.tricontourf(x[mask2], y[mask2], z2[mask2], levels=LEVELS, cmap=CMAP)
 contour3 = ax3.tricontourf(
-    x[mask1 & mask2],
-    y[mask1 & mask2],
-    (z1 / z2)[mask1 & mask2],
+    x[mask12],
+    y[mask12],
+    (z1 / z2)[mask12],
     levels=LEVELS,
     cmap=CMAP,
 )
@@ -127,6 +120,4 @@
 
 plt.suptitle("log(dL/dt) contour plot")
 plt.subplots_adjust(wspace=0.0, left=0.075, right=0.975)
-plt.savefig(
-    os.path.join(HERE, "example_output", "compare_ps_cooling_rates.png")
-)
+plt.savefig(os.path.join(HERE, "example_output", "compare_ps_cooling_rates.png"))

example/converting_from_gaia_to_sdss.py

@@ -16,7 +16,7 @@
 except NameError:
     HERE = os.path.dirname(os.path.realpath(__name__))
 
-_hdu = fits.open(os.path.join(HERE, "GaiaEDR3_WD_SDSSspec.FITS"))[1]
+_hdu = fits.open(os.path.join(HERE, "GaiaEDR3_WD_SDSSspec.fits"))[1]
 data = _hdu.data
 size = len(data)
 
@@ -51,38 +51,20 @@
 r_itp_teff = atm.interp_am(dependent="r_sdss", independent=["G3", "Teff"])
 
 # Getting the interpolation function depending on G3_BP and Teff
-u_itp_bp_teff = atm.interp_am(
-    dependent="u_sdss", independent=["G3_BP", "Teff"]
-)
-g_itp_bp_teff = atm.interp_am(
-    dependent="g_sdss", independent=["G3_BP", "Teff"]
-)
-r_itp_bp_teff = atm.interp_am(
-    dependent="r_sdss", independent=["G3_BP", "Teff"]
-)
+u_itp_bp_teff = atm.interp_am(dependent="u_sdss", independent=["G3_BP", "Teff"])
+g_itp_bp_teff = atm.interp_am(dependent="g_sdss", independent=["G3_BP", "Teff"])
+r_itp_bp_teff = atm.interp_am(dependent="r_sdss", independent=["G3_BP", "Teff"])
 
 # Getting the interpolation function depending on G3_RP and Teff
-u_itp_rp_teff = atm.interp_am(
-    dependent="u_sdss", independent=["G3_RP", "Teff"]
-)
-g_itp_rp_teff = atm.interp_am(
-    dependent="g_sdss", independent=["G3_RP", "Teff"]
-)
-r_itp_rp_teff = atm.interp_am(
-    dependent="r_sdss", independent=["G3_RP", "Teff"]
-)
+u_itp_rp_teff = atm.interp_am(dependent="u_sdss", independent=["G3_RP", "Teff"])
+g_itp_rp_teff = atm.interp_am(dependent="g_sdss", independent=["G3_RP", "Teff"])
+r_itp_rp_teff = atm.interp_am(dependent="r_sdss", independent=["G3_RP", "Teff"])
 
 
 # Getting the interpolation function depending on G3_RP and Teff
-u_itp_logg_teff = atm.interp_am(
-    dependent="u_sdss", independent=["logg", "Teff"]
-)
-g_itp_logg_teff = atm.interp_am(
-    dependent="g_sdss", independent=["logg", "Teff"]
-)
-r_itp_logg_teff = atm.interp_am(
-    dependent="r_sdss", independent=["logg", "Teff"]
-)
+u_itp_logg_teff = atm.interp_am(dependent="u_sdss", independent=["logg", "Teff"])
+g_itp_logg_teff = atm.interp_am(dependent="g_sdss", independent=["logg", "Teff"])
+r_itp_logg_teff = atm.interp_am(dependent="r_sdss", independent=["logg", "Teff"])
 
 
 # converting into SDSS photometry
@@ -117,9 +99,7 @@
 # Plot the (u-g) vs (g-r) colour-colour diagram
 # Top row is based on interpolation of (logg, Gaia filter)
 # Bottom row is based on interpolation of (Teff, Gaia filter)
-fig, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(
-    2, 3, sharex=True, figsize=(15, 15)
-)
+fig, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(2, 3, sharex=True, figsize=(15, 15))
 ax1.scatter(u_sdss - g_sdss, g_sdss - r_sdss, s=1, c=loggH)
 ax2.scatter(u_sdss_bp - g_sdss_bp, g_sdss_bp - r_sdss_bp, s=1, c=loggH)
 ax3.scatter(u_sdss_rp - g_sdss_rp, g_sdss_rp - r_sdss_rp, s=1, c=loggH)
@@ -176,12 +156,8 @@
 ax3.set_title(r"$\{G3_{\mathrm{RP}}, \log(g)\} \rightarrow \{u, g, r\}$")
 
 ax4.set_title(r"$\{G3, T_{\mathrm{eff}}\} \rightarrow \{u, g, r\}$")
-ax5.set_title(
-    r"$\{G3_{\mathrm{BP}}, T_{\mathrm{eff}}\} \rightarrow \{u, g, r\}$"
-)
-ax6.set_title(
-    r"$\{G3_{\mathrm{RP}}, T_{\mathrm{eff}}\} \rightarrow \{u, g, r\}$"
-)
+ax5.set_title(r"$\{G3_{\mathrm{BP}}, T_{\mathrm{eff}}\} \rightarrow \{u, g, r\}$")
+ax6.set_title(r"$\{G3_{\mathrm{RP}}, T_{\mathrm{eff}}\} \rightarrow \{u, g, r\}$")
 
 ax1.grid()
 ax2.grid()
@@ -190,21 +166,15 @@
 ax5.grid()
 ax6.grid()
 
-plt.subplots_adjust(
-    top=0.95, bottom=0.075, left=0.08, right=0.975, wspace=0.0, hspace=0.1
-)
+plt.subplots_adjust(top=0.95, bottom=0.075, left=0.08, right=0.975, wspace=0.0, hspace=0.1)
 
-plt.savefig(
-    os.path.join(HERE, "example_output", "gaia_to_sdss_cc_diagram.png")
-)
+plt.savefig(os.path.join(HERE, "example_output", "gaia_to_sdss_cc_diagram.png"))
 
 # Plot the residual of the catalogue value to converted values
 # Top row is the residual in u from the interpolation of G_BP, G & G_RP
 # Middle row is the residual in g from the interpolation of G_BP, G & G_RP
 # Bottom row is the residual in r from the interpolation of G_BP, G & G_RP
-fig, ((ax1, ax2, ax3), (ax4, ax5, ax6), (ax7, ax8, ax9)) = plt.subplots(
-    3, 3, sharex=True, figsize=(15, 15)
-)
+fig, ((ax1, ax2, ax3), (ax4, ax5, ax6), (ax7, ax8, ax9)) = plt.subplots(3, 3, sharex=True, figsize=(15, 15))
 
 ax1.scatter(TeffH, data["umag"] - u_sdss_bp_teff, s=1)
 ax2.scatter(TeffH, data["umag"] - u_sdss_teff, s=1)
@@ -218,29 +188,17 @@
 ax8.scatter(TeffH, data["rmag"] - r_sdss_teff, s=1)
 ax9.scatter(TeffH, data["rmag"] - r_sdss_rp_teff, s=1)
 
-ax1.set_title(
-    r"$u_{\mathrm{catalogue}} - u(T_{\mathrm{eff}}, G_{\mathrm{BP}})$"
-)
+ax1.set_title(r"$u_{\mathrm{catalogue}} - u(T_{\mathrm{eff}}, G_{\mathrm{BP}})$")
 ax2.set_title(r"$u_{\mathrm{catalogue}} - u(T_{\mathrm{eff}}, G)$")
-ax3.set_title(
-    r"$u_{\mathrm{catalogue}} - u(T_{\mathrm{eff}}, G_{\mathrm{RP}})$"
-)
+ax3.set_title(r"$u_{\mathrm{catalogue}} - u(T_{\mathrm{eff}}, G_{\mathrm{RP}})$")
 
-ax4.set_title(
-    r"$g_{\mathrm{catalogue}} - g(T_{\mathrm{eff}}, G_{\mathrm{BP}})$"
-)
+ax4.set_title(r"$g_{\mathrm{catalogue}} - g(T_{\mathrm{eff}}, G_{\mathrm{BP}})$")
 ax5.set_title(r"$g_{\mathrm{catalogue}} - g(T_{\mathrm{eff}}, G)$")
-ax6.set_title(
-    r"$g_{\mathrm{catalogue}} - g(T_{\mathrm{eff}}, G_{\mathrm{RP}})$"
-)
+ax6.set_title(r"$g_{\mathrm{catalogue}} - g(T_{\mathrm{eff}}, G_{\mathrm{RP}})$")
 
-ax7.set_title(
-    r"$r_{\mathrm{catalogue}} - r(T_{\mathrm{eff}}, G_{\mathrm{BP}})$"
-)
+ax7.set_title(r"$r_{\mathrm{catalogue}} - r(T_{\mathrm{eff}}, G_{\mathrm{BP}})$")
 ax8.set_title(r"$r_{\mathrm{catalogue}} - r(T_{\mathrm{eff}}, G)$")
-ax9.set_title(
-    r"$r_{\mathrm{catalogue}} - r(T_{\mathrm{eff}}, G_{\mathrm{RP}})$"
-)
+ax9.set_title(r"$r_{\mathrm{catalogue}} - r(T_{\mathrm{eff}}, G_{\mathrm{RP}})$")
 
 ax2.set_yticklabels([""])
 ax3.set_yticklabels([""])
@@ -280,19 +238,13 @@
 
 ax1.set_xscale("log")
 
-plt.subplots_adjust(
-    top=0.95, bottom=0.07, left=0.08, right=0.975, wspace=0.0, hspace=0.15
-)
+plt.subplots_adjust(top=0.95, bottom=0.07, left=0.08, right=0.975, wspace=0.0, hspace=0.15)
 
-plt.savefig(
-    os.path.join(HERE, "example_output", "gaia_to_sdss_ugr_residual.png")
-)
+plt.savefig(os.path.join(HERE, "example_output", "gaia_to_sdss_ugr_residual.png"))
 
 
 # Plot the residual of the catalogue value to derived values from (logg, Teff)
-fig, (ax1, ax2, ax3) = plt.subplots(
-    1, 3, sharex=True, sharey=True, figsize=(15, 10)
-)
+fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharex=True, sharey=True, figsize=(15, 10))
 
 ax1.scatter(TeffH, data["umag"] - u_model, s=1)
 ax2.scatter(TeffH, data["gmag"] - g_model, s=1)
@@ -317,12 +269,6 @@
 
 ax1.set_xscale("log")
 
-plt.subplots_adjust(
-    top=0.95, bottom=0.075, left=0.075, right=0.975, wspace=0.0, hspace=0.15
-)
+plt.subplots_adjust(top=0.95, bottom=0.075, left=0.075, right=0.975, wspace=0.0, hspace=0.15)
 
-plt.savefig(
-    os.path.join(
-        HERE, "example_output", "gaia_to_sdss_ugr_residual_logg_teff.png"
-    )
-)
+plt.savefig(os.path.join(HERE, "example_output", "gaia_to_sdss_ugr_residual_logg_teff.png"))

example/example_output/10.000Gyr_burst_PARSECz0017_C08_montreal_co_da_20_montreal_co_da_20_montreal_co_da_20.csv

@@ -0,0 +1,100 @@
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