add file to estimate spectral type by B-V

2024-12-02 08:22:33 +01:00
parent f150c02dfd
commit 6480c9fbee
1 changed files with 69 additions and 0 deletions
@@ -0,0 +1,69 @@
+from astroquery.simbad import Simbad
+import pandas as pd
+
+simbad = Simbad()
+
+bvStars = ["1RXS J064643.6-770027", "BD-08   995", "CPD-19   878", "PM J07058-5848", "TYC 1360-957-1", "TYC 4595-107-1"]
+jhcStars = ["2MASS J18524052+4156057", "2MASS J18534407+4208274", "2MASS J18535462+4135227", "2MASS J19335656+4010546"]
+
+filters = ["U", "B", "V", "R", "I", "J", "H", "K", "u", "g", "r", "i", "z", "G", "F150W", "F200W", "F444W"]
+for f in filters:
+    simbad.add_votable_fields(f"flux({f})")
+
+spTypeTable = pd.read_csv("cousins.cols")
+
+weights = {"SpType": 0.0,
+           "U-B": 2.0,
+           "B-V": 2.0,
+           "V-R": 0.8,
+           "V-I": 0.7,
+           "V-J": 0.6,
+           "V-H": 0.5,
+           "V-K": 0.4,
+           "V-L": 0.0,
+           "V-M": 0.0,
+           "V-N": 0.0}
+
+for star in bvStars:
+    star = simbad.query_object(star)
+    U_B = star["FLUX_U"] - star["FLUX_B"]
+    B_V = star["FLUX_B"] - star["FLUX_V"]
+    V_R = star["FLUX_V"] - star["FLUX_R"]
+    V_I = star["FLUX_V"] - star["FLUX_I"]
+    V_J = star["FLUX_V"] - star["FLUX_J"]
+    V_H = star["FLUX_V"] - star["FLUX_H"]
+    V_K = star["FLUX_V"] - star["FLUX_K"]
+
+    fluxDiff = {"U-B": U_B,
+                "B-V": B_V,
+                "V-R": V_R,
+                "V-I": V_I,
+                "V-J": V_J,
+                "V-H": V_H,
+                "V-K": V_K}
+    fluxDiff = pd.DataFrame(fluxDiff)
+    fluxDiffValColumns = fluxDiff.columns[fluxDiff.notna().iloc[0]]
+
+    matchingColumnsUBV = spTypeTable.columns.intersection(fluxDiffValColumns)
+    distancesUBV = spTypeTable[matchingColumnsUBV].sub(fluxDiff.iloc[0]).pow(2).sum(axis=1)
+    bestMatchIndexUBV = distancesUBV.idxmin()
+    bestMatchNameUBV = spTypeTable.iloc[bestMatchIndexUBV, 0]
+
+    distancesUBVweighted = (
+        spTypeTable[matchingColumnsUBV]
+        .sub(fluxDiff.iloc[0][matchingColumnsUBV])
+        .pow(2)
+        .multiply([weights[col] for col in matchingColumnsUBV], axis=1)
+        .sum(axis=1)
+    )
+    bestMatchIndexUBVweighted = distancesUBVweighted.idxmin()
+    bestMatchNameUBVweighted = spTypeTable.iloc[bestMatchIndexUBVweighted, 0]
+
+    print(f"{star['MAIN_ID'].value[0]}: {bestMatchNameUBV}/{bestMatchNameUBVweighted} type star")
+    print("Table entry: ")
+    print(spTypeTable.iloc[bestMatchIndexUBV:bestMatchIndexUBV+1])
+    print("Weighted Table entry:")
+    print(spTypeTable.iloc[bestMatchIndexUBVweighted:bestMatchIndexUBVweighted+1])
+    print("Star: ")
+    print(fluxDiff)
+    print("-------------------------------------------------------------------------------------")