v2.0.5

docs/ObservationProperties.md

@@ -46,7 +46,7 @@
   Cal Bench filter 1 position. Throughput = 10^-OD. Values: OD 0.1, OD 1.0, OD 1.3, OD 2.0, OD 3.0, OD 4.0
 
 **CalND2**: `str`
-  Cal Bench filter 2 position. Throughput = 10^-OD. Values: OD 0.1, OD 0.3, OD 0.5, OD 0.8, OD 1.0, OD 4.0
+  Cal Bench filter 2 position. Throughput = 10^-OD. Values: OD 0.1, OD 0.3, OD 0.5, OD 1.0, OD 1.3, OD 2.0
 
 **NodN**: `float`
   [arcseconds] Distance to nod the telescope North before starting exposure.

docs/ZeroPointAnalysis/1-PrepareDataFile/1-PrepareDataFile.md

@@ -0,0 +1,166 @@
+# Examine Properties of Data Set
+
+This data set includes (anonymized) results of the first few years of KPF observations.  This contains select FITS header keyworsds from the DRP's L1 output file.  We're primarily interested in the DRP's estimate of the signal to noise at various wavelengths (the `SNRSCnnn` values).  These are the typical signal to noise for one order around the specified wavelength value in nm.
+
+In this notebook, we anonymize the data then filter out data with certain known issues.  After filtering we have over 12,000 individual observations (FITS files).  Finally, we examine the properties of the targets (effective temperature and Gaia G magnituide), of the observations (time of observation), and of the DRP reductions (DRP version used).
+
+
+```python
+from pathlib import Path
+import numpy as np
+from astropy.table import Table, MaskedColumn
+from astropy.time import Time
+
+from matplotlib import pyplot as plt
+```
+
+
+```python
+# Read raw data
+raw_data_file = Path('data/Feb2026.csv')
+```
+
+
+```python
+if raw_data_file.exists():
+    t = Table.read(raw_data_file, format='ascii.csv')
+    t.remove_columns(['ObsID', 'OBJECT', 'TARGNAME', 'TOTCNTS', 'CCFRV_MPS', 'CCD1RV_MPS', 'CCD2RV_MPS', 'CCFERV_MPS', 'CCFBJD', 'CCFERV'])
+    # Separate out TOTCORR values to separate columns
+    TOTCORR_1 = MaskedColumn(data=np.zeros(len(t)), name='TOTCORR_1')
+    TOTCORR_2 = MaskedColumn(data=np.zeros(len(t)), name='TOTCORR_2')
+    TOTCORR_3 = MaskedColumn(data=np.zeros(len(t)), name='TOTCORR_3')
+    TOTCORR_4 = MaskedColumn(data=np.zeros(len(t)), name='TOTCORR_4')
+    TOTCORR_SUM = MaskedColumn(data=np.zeros(len(t)), name='TOTCORR_SUM')
+    for i,row in enumerate(t):
+        masked = t['TOTCORR'].mask[i]
+        if not masked:
+            TOTCORR_1[i] = t['TOTCORR'][i].split()[0]
+            TOTCORR_2[i] = t['TOTCORR'][i].split()[1]
+            TOTCORR_3[i] = t['TOTCORR'][i].split()[2]
+            TOTCORR_4[i] = t['TOTCORR'][i].split()[3]
+            TOTCORR_SUM[i] = np.sum([float(v) for v in t['TOTCORR'][i].split()])
+    TOTCORR_1.mask = t['TOTCORR'].mask
+    TOTCORR_2.mask = t['TOTCORR'].mask
+    TOTCORR_3.mask = t['TOTCORR'].mask
+    TOTCORR_4.mask = t['TOTCORR'].mask
+    TOTCORR_SUM.mask = t['TOTCORR'].mask
+    t.add_columns([TOTCORR_1, TOTCORR_2, TOTCORR_3, TOTCORR_4, TOTCORR_SUM])
+```
+
+
+```python
+# Remove data with known bad parameters
+if raw_data_file.exists():
+    # Remove Data with MJD-OBS Before KPF was Installed
+    t = t[t['MJD-OBS'] > 59200]
+    print(len(t))
+    # Remove Data with Gmag<=0
+    t = t[t['GAIAMAG'] > 0]
+    print(len(t))
+    #because of missing labeling in OBs, this was a default value, so remove all stars with this value as the data is not usable.
+    t = t[(t['GAIAMAG'] < 9.2) | (t['GAIAMAG'] > 9.5)]
+    print(len(t))
+    # Remove very short exposures (removes start state errors)
+    t = t[t['ELAPSED'] > 7]
+    print(len(t))
+```
+
+    13547
+    12700
+    12118
+    12058
+
+
+
+```python
+anonymized_data_file = Path('data/Feb2026_anonymized.csv')
+if raw_data_file.exists():
+    t.write(anonymized_data_file, overwrite=True)
+else:
+    t = Table.read(anonymized_data_file, format='ascii.csv')
+```
+
+# Examine Properties of Data Set
+
+
+```python
+# KPFERA 1.0 is before SM1
+SM1start = Time('2024-02-03')
+SM1end = Time('2024-02-23')
+# KPFERA 2.0 is between SM1 and SM2
+SM2start = Time('2024-10-31')
+SM2end = Time('2024-11-20')
+# KPFERA 2.5/2.6 is between SM2 and SM3
+SM3start = Time('2025-03-28')
+SM3end = Time('2025-04-23')
+# KPFERA 3.0 is between SM3 and SM4
+SM4start = Time('2025-08-31')
+SM4end = Time('2025-10-27')
+# KPFERA 4.0 is after SM4
+```
+
+
+```python
+plt.figure(figsize=(8,8))
+
+plt.subplot(3,1,1)
+plt.title('Histogram of Teff and Gmag Values in the Data Set')
+bins=np.arange(2700,10000,100)
+plt.hist(t['TARGTEFF'], bins=bins, log=True)
+plt.axvline(2700, color='r', label='KPF-etc Limits')
+plt.axvline(6600, color='r')
+plt.xlabel('Teff')
+plt.legend(loc='best')
+
+plt.subplot(3,1,2)
+plt.hist(t['GAIAMAG'], bins=40, log=True)
+plt.axvline(2, color='r', label='KPF-etc Limits')
+plt.axvline(19, color='r')
+plt.xlabel('Gmag')
+
+plt.subplot(3,1,3)
+nd = max(t['MJD-OBS'])-min(t['MJD-OBS'])
+plt.hist(t['MJD-OBS'], bins=int(nd/7))
+plt.axvspan(SM1start.mjd, SM1end.mjd, color='k', alpha=0.3, label='Servicing Mission')
+plt.axvspan(SM2start.mjd, SM2end.mjd, color='k', alpha=0.3)
+plt.axvspan(SM3start.mjd, SM3end.mjd, color='k', alpha=0.3)
+plt.axvspan(SM4start.mjd, SM4end.mjd, color='k', alpha=0.3)
+plt.xlabel('MJD')
+
+plt.show()
+```
+
+
+
+![png](output_8_0.png)
+
+
+
+
+```python
+drpvs = set([str(v) for v in t['DRPTAGL2']])
+print("DRPTAGL2   : N Files")
+print("____________________")
+total = 0
+for v in sorted(drpvs):
+    if v == '--':
+        Nv = np.sum(np.array(t['DRPTAGL2'].mask, dtype=int))
+    else:
+        tv = t[t['DRPTAGL2'] == v]
+        Nv = len(tv)
+    total += Nv
+    print(f"{v:11s}: {Nv}")
+print(f"TOTAL = {total} ({len(t)})")
+```
+
+    DRPTAGL2   : N Files
+    ____________________
+    --         : 38
+    v2.10.3    : 3216
+    v2.11.1    : 23
+    v2.12.0rc1 : 4369
+    v2.5.3     : 35
+    v2.8.2     : 2030
+    v2.9.1     : 2347
+    TOTAL = 12058 (12058)
+