v1.2.2 #73

hover2pi · 2022-08-08T20:50:30Z

Improvements and bug fixes:

Update uncertainty estimation using bootstrapping method
Fix model isochrone ingestion and interpolation
Support for Jansky units
Support for specutils.Spectrum1D
Try/except statement to get around broken bokeh image save

hover2pi · 2022-08-10T13:53:05Z

Hmmm, I replaced the sum approximation method used for the integral (to measure fbol) with a bootstrapping method @ECGonzales and I spoke about, but I get a VERY similar answer. I wonder if it's just correct given we are integrating a curve with SNR=100. This propagates down to a Teff ~ 2000 with an error of ~ Teff*0.01, or just a few tens of Kelvin, as we're seeing.

Here's the old code:

fbol_units = self.flux_units * self.wave_units
fbol_unc = np.sqrt(np.nansum((unc * np.gradient(wave) * fbol_units)**2))

And here's the bootstrapping code:

# Bootstrap errors
uvals = []
for n in range(1000):
    usamp = np.random.normal(flux, unc)
    err = (np.trapz(usamp, x=wave) * fbol_units)
    uvals.append(err.value - fbol.value)

# Get 1-sigma of distribution
fbol_unc = np.mean(abs(np.asarray(uvals))) * fbol_units

@ECGonzales what does your method look like that get's more realistic errors?

ECGonzales · 2022-08-10T16:31:50Z

So I just add the upper and lower limits to the photometry and create two text files with that data. I then run MakeSED on each and just take the difference between the upper and lower Lbols as the uncertainty. Lastly, I rerun the Teff function to get out better Teff uncertainties.

```
def get_teff(Lbol, sig_Lbol, r, sig_r):
    """
  Returns the effective temperature in Kelvin given the bolometric luminosity, radius, and uncertanties.

  Parameters
  ----------
  Lbol: astropy.quantity
    The bolometric luminosity in solar units
  sig_Lbol: astropy.quantity
    The uncertainty in the bolometric luminosity
  r: astropy.quantity
    The radius of the source in units of R_Jup
  sig_r: astropy.quantity
    The uncertainty in the radius

  Returns
  -------
  The effective temperature and uncertainty in Kelvin

  """
    # Lbol, r = (ac.L_sun*10**Lbol).to(q.W) if solar_units else Lbol, ac.R_jup*r
    # sig_Lbol, sig_r = (ac.L_sun*sig_Lbol/(Lbol*np.log(10))).to(q.W) if solar_units else sig_Lbol, ac.R_jup*sig_r
    # Convert R_jup to meters
    r, sig_r = ac.R_jup * r, ac.R_jup * sig_r
    # Get Lbol into watts: take out of log and convert to Watts
    Lbol_watt=10**(Lbol)*ac.L_sun
    sig_Lbol_watt = Lbol_watt*np.log(10)*sig_Lbol
    T = np.sqrt(np.sqrt(((Lbol_watt)/ (4 * np.pi * ac.sigma_sb * r ** 2)).to(q.K ** 4)))
    sig_T = T * np.sqrt((sig_Lbol_watt / Lbol_watt).value** 2 + (2 * sig_r / r).value ** 2) / 4. # This uses the ratio in as what comes out in SEDkit for Lbol and sig_Lbol
    return T.round(0), sig_T.round(0)
```

I've attached some example code that I used for the Trappist paper.

import pandas as pd
from astrodbkit import astrodb
import numpy as np

# Load up the database ( a real and fake version)
db = astrodb.Database('/Users/eileengonzales/Dropbox/BDNYC/BDNYCdb_copy/BDNYCdevdb/bdnycdev.db')
db_fake = astrodb.Database('/Users/eileengonzales/Dropbox/BDNYC/BDNYCdb_copy/Fake_database/bdnycdev_fake.db')

# For the real database add the PS photometry for the objects of intrest Cross matching comes from Best+18
data = list()
data.append(['bibcode','shortname', 'DOI', 'description'])
data.append(['2018ApJS..234....1B','Best18', '10.3847/1538-4365/aa9982', 'Photometry and Proper Motions of M, L, and T Dwarfs from the Pan-STARRS1 3pi Survey'])
db.add_data(data, 'publications')

data = list()
data.append(['source_id', 'band', 'magnitude', 'magnitude_unc', 'publication_shortname', 'comments'])
# vb8
data.append([1371,'PS_g',17.4262, 0.0042,'Cham16'])    #17.43 0.01
data.append([1371,'PS_r',16.0220,	0.0071,'Cham16'])  #16.02 0.01
#data.append([1371,'PS_i',13.2570,	0.0049,'Cham16'])
#data.append([1371,'PS_z',12.0603,	0.0100,'Cham16'])
#data.append([1371,'PS_y',11.7421,	0.0847,'Cham16'])
# vb10
#data.append([300,'PS_g',18.2231, 0.0114,'Cham16'])
data.append([300,'PS_r',16.5893,0.0030,'Cham16']) #16.59 0.01
#data.append([300,'PS_i',13.9473,0.0011,'Cham16'])
#data.append([300,'PS_z',12.6794,0.0648,'Cham16'])
#data.append([300,'PS_y',11.7284,0.0164,'Cham16'])
#0320
data.append([320,'PS_g',19.7975,0.0292,'Cham16']) #19.80 0.03
data.append([320,'PS_r',18.2044,0.0100,'Cham16']) #18.20 0.01
data.append([320,'PS_i',15.6026,0.0073,'Cham16']) #15.60 0.01
data.append([320,'PS_z',14.2388,0.0040,'Cham16']) # 14.24 0.01
data.append([320,'PS_y',13.4329,0.0056,'Cham16']) #13.43 0.01
#LHS 3003
#Will doesn't have these at all. He has g and r 17.65	0.01	16.21	0.01
# data.append([126,'PS_i',13.4577,0.0134,'Cham16'])
# data.append([126,'PS_z',12.2627,0.0010,'Cham16'])
# data.append([126,'PS_y',11.5639,0.0010,'Cham16'])
# --- NEW ----- 2MASS J18353790+3259545
data.append([82,'PS_g',	19.0248,	0.0121,'Cham16']) #19.02 0.01
data.append([82,'PS_r',17.1756,	0.0094,'Cham16']) #17.18 0.01
# data.append([82,'PS_i',14.4478,	0.0006,'Cham16'])
# data.append([82,'PS_z',12.9888,	0.0014,'Cham16'])
# data.append([82,'PS_y',12.0515,	0.0038,'Cham16'])
# None 1048-39
# --- NEW ----- 2MASS J08533619-0329321
data.append([355,'PS_g',19.7983,	0.0418,'Cham16']) #19.80 0.04
data.append([355,'PS_r',17.8464,	0.0084,'Cham16']) #17.85 0.01
data.append([355,'PS_i',15.5047,	0.0014,'Cham16']) #15.50 0.01
data.append([355,'PS_z',14.0634,	0.0047,'Cham16']) #14.06 0.01
data.append([355,'PS_y',13.1045,	0.0018,'Cham16']) #13.10 0.01
#SSSPM J1013-1356
data.append([1333,'PS_g',20.9931, 0.0730,'Cham16']) #20.99 0.07
data.append([1333,'PS_r',19.2511,0.0095,'Cham16'])  #19.25 0.01
data.append([1333,'PS_i',17.1840,0.0005,'Cham16'])  #17.18 0.01
data.append([1333,'PS_z',16.2469,0.0018,'Cham16']) #16.25 0.01
data.append([1333,'PS_y',15.9044,0.0027,'Cham16']) #15.90 0.01
# GJ660.1b
data.append([1476,'PS_y',14.40,0.018,'Best18'])
#1256
data.append([1357,'PS_r',21.7783,0.1857,'Cham16'])
data.append([1357,'PS_i',19.4728,0.0448,'Cham16']) #19.47 0.04
data.append([1357,'PS_z',18.0028,0.0264,'Cham16']) #18.00 0.03
data.append([1357,'PS_y',17.5395,0.0120,'Cham16']) #17.54 0.01
#1444
data.append([1454,'PS_r',19.31,0.03,'Best18'])
data.append([1454,'PS_i',16.09,0.05,'Best18'])
data.append([1454,'PS_y',14.06,0.02,'Best18'])
# Non 1247-38
#0608
data.append([413,'PS_r',20.9394,0.0241,'Cham16']) #20.94 0.02
data.append([413,'PS_i',18.1406,0.0037,'Cham16']) #18.14 0.01
data.append([413,'PS_z',16.5489,0.0055,'Cham16']) #16.55 0.01
data.append([413,'PS_y',15.5418,0.0067,'Cham16']) #15.54 0.01
# none 2000-75
#none 1207-39
#0443+0002 -- ADDED         #WILL: 21.51	0.07	19.66	0.02	16.96	0.01	15.38	0.01	14.40	0.01
# 0518
#data.append([91,'PS_r',21.8871,	0.1550,'Cham16'])
data.append([91,'PS_i',19.9933,	0.0232,'Cham16']) #19.99	0.02	18.49	0.01	17.50	0.01
data.append(['source_id', 'band', 'magnitude', 'magnitude_unc', 'publication_shortname', 'comments'])
data.append([91,'PS_z',18.49,	0.01,'Cham16', 'Best18 unc'])
data.append([91,'PS_y',17.50,	0.01,'Cham16', 'Best18 unc'])
#None 2235-59
#0714
data.append([849,'PS_g',20.1524,	0.0257,'Cham16']) #20.15	0.03	18.61	0.01	15.86	0.01	14.48	0.01	13.66	0.01
data.append([849,'PS_r',18.6059,	0.0085,'Cham16'])
data.append([849,'PS_i',15.8585,	0.0021,'Cham16'])
data.append([849,'PS_z',14.4771,	0.0049,'Cham16'])
data.append([849,'PS_y',13.6642,	0.0027,'Cham16'])
#0953
data.append([415,'PS_r',20.4710,	0.0282,'Cham16']) #20.47	0.03	18.00	0.01	16.47	0.01	15.44	0.01
data.append([415,'PS_i',17.9995,	0.0044,'Cham16'])
data.append([415,'PS_z',16.4657,	0.0028,'Cham16'])
data.append([415,'PS_y',15.4396,	0.0048,'Cham16'])
#2352
data.append([146,'PS_g',20.5741,	0.0248,'Cham16']) #20.57	0.02	19.18	0.01	16.49	0.01	15.23	0.01	14.49	0.01
data.append([146,'PS_r',19.1830,	0.0142,'Cham16'])
data.append([146,'PS_i',16.4937,	0.0019,'Cham16'])
data.append([146,'PS_z',15.2285,	0.0019,'Cham16'])
data.append([146,'PS_y',14.4897,	0.0022,'Cham16'])
#2341
data.append([669,'PS_g',21.1287,	0.0323,'Cham16'])  # These are not in Will's paper at all. Keep???? I kept and updated uncs
data.append([669,'PS_r',19.8407,	0.0181,'Cham16'])
data.append([669,'PS_i',17.1363,	0.0025,'Cham16'])
data.append([669,'PS_z',15.8644,	0.0024,'Cham16'])
data.append([669,'PS_y',15.1312,	0.0049,'Cham16'])
#None ---LHS 132
#0532
data.append([1304,'PS_z',18.0747,	0.0096,'Cham16']) # 18.07	0.01	16.92	0.03
data.append([1304,'PS_y',16.9173,	0.0260,'Cham16'])
# LHS 377
data.append([1452,'PS_g',18.9919, 0.0124,'Cham16']) #18.99	0.02	17.69	0.01	15.68	0.01	14.84	0.01	14.48	0.01
data.append([1452,'PS_r',17.6872,0.0116,'Cham16'])
data.append([1452,'PS_i',15.5951,0.0140,'Cham16'])
data.append([1452,'PS_z',14.8256,0.0027,'Cham16'])
data.append([1452,'PS_y',14.4663,0.0129,'Cham16'])
#1610
data.append([1591,'PS_g',20.1155,	0.0963,'Cham16']) #20.12	0.10	18.08	0.01	15.90	0.01	14.86	0.01	14.41	0.02
data.append([1591,'PS_r',18.0825,	0.0037,'Cham16'])
data.append([1591,'PS_i',15.8974,	0.0007,'Cham16'])
data.append([1591,'PS_z',14.8636,	0.0058,'Cham16'])
data.append([1591,'PS_y',14.4070,	0.0154,'Cham16'])
data.append([1592,'PS_g',20.1155,	0.0963,'Cham16'])
data.append([1592,'PS_r',18.0825,	0.0037,'Cham16'])
data.append([1592,'PS_i',15.8974,	0.0007,'Cham16'])
data.append([1592,'PS_z',14.8636,	0.0058,'Cham16'])
data.append([1592,'PS_y',14.4070,	0.0154,'Cham16'])
#2036
data.append([1490,'PS_g',19.6098,	0.0150,'Cham16']) #19.61	0.02	18.14	0.01	16.13	0.01	15.24	0.01	14.89	0.01
data.append([1490,'PS_r',18.1371,	0.0092,'Cham16'])
data.append([1490,'PS_i',16.1318,	0.0012,'Cham16'])
data.append([1490,'PS_z',15.2421,	0.0040,'Cham16'])
data.append([1490,'PS_y',14.8904,	0.0074,'Cham16'])

db.add_data(data, 'photometry')
db.add_changelog('Eileen Gonzales', 'Photometry', 'Added PS photometry for TRAPPIST-1 Referee report')
# ---------Save and pr before moving on. Move tabledata over. ----------
 # ---------- HERE IS Where I need to pick up for new values -----
# Then query the real db to create individual dataframes of the photometry for each source
x_1371 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=1371", fmt='pandas')
x_300 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=300", fmt='pandas')
#x_300 = x_300.drop(x_300['magnitude_unc']=="None")
x_320 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=320", fmt='pandas')
#x_320 = x_320.drop(16) # drop by index
x_126 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=126", fmt='pandas')
x_82 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=82", fmt='pandas')
x_130 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=130", fmt='pandas')
#x_130 = x_130.drop([3,4,5])
x_355 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=355", fmt='pandas')
#x_355 = x_355.drop(14)
x_1333 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=1333", fmt='pandas')
#x_1333 = x_1333.drop(6)
x_1476 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=1476", fmt='pandas')
x_1357 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=1357", fmt='pandas')
#x_1357 = x_1357.drop([5,8,9])
x_1454 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=1454", fmt='pandas')
#x_1454 = x_1454.drop(6)
x_1940 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=1940", fmt='pandas')
x_413 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=413", fmt='pandas')
#x_413 = x_413.drop(6)
x_759 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=759", fmt='pandas')
x_475 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=475", fmt='pandas')
x_1928 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=1928", fmt='pandas')
x_751 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=751", fmt='pandas')
#x_751= x_751.drop(0)
x_91 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=91", fmt='pandas')
x_2107 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=2107", fmt='pandas')
x_849 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=849", fmt='pandas')
x_415 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=415", fmt='pandas')
x_146 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=146", fmt='pandas')
#x_146=x_146.drop(0)
x_669 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=669", fmt='pandas')
x_107 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=107", fmt='pandas')
x_1304 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=1304", fmt='pandas')
x_1452 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=1452", fmt='pandas')
#x_1452 = x_1452.drop(16)
x_1591 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=1591", fmt='pandas')
#x_1591 = x_1591.drop(9)
x_1490 = db.query("Select source_id, band, magnitude, magnitude_unc from photometry where source_id=1490", fmt='pandas')

# Concatenate queries into one dataframe
df = pd.concat([x_1371, x_300, x_320, x_126, x_82, x_130, x_355, x_1333, x_1476, x_1357, x_1454, x_1940, x_413, x_759,
               x_475, x_1928, x_751, x_91, x_2107, x_849, x_415, x_146, x_669, x_107, x_1304, x_1452, x_1591, x_1490],
               ignore_index=True)
# Drop the upper limits
df = df.dropna()

# Add columns for upper and lower limits and uncertainites, make sure all are floats and round to 4 decimals
df['Upper'] = df['magnitude'] + df['magnitude_unc']
df['Upper_unc'] = df['magnitude_unc']
df['Lower'] = df['magnitude'] - df['magnitude_unc']
df['Lower_unc'] = df['magnitude_unc']
df['pub'] = 'Missing'
df['comments'] = 'Fake Upper'
df['comments1'] = 'Fake lower'

df.Upper.astype(float)
df.Upper_unc.astype(float)
df.Lower.astype(float)
df.Lower_unc.astype(float)

decimals = 4
df['Upper'] = df['Upper'].apply(lambda x: round(x, decimals))
df['Upper_unc'] = df['Upper_unc'].apply(lambda x: round(x, decimals))
df['Lower'] = df['Lower'].apply(lambda x: round(x, decimals))
df['Lower_unc'] = df['Lower_unc'].apply(lambda x: round(x, decimals))


# write to two files for easy adding to the FAKE database to make the uppers and lowers one at a time
df.to_csv('/Users/eileengonzales/Dropbox/BDNYC/BDNYCdb_copy/Fake_database/Fake_phot_upper.txt',columns=['source_id', 'band', 'Upper', 'Upper_unc', 'pub', 'comments'], index=False)
df.to_csv('/Users/eileengonzales/Dropbox/BDNYC/BDNYCdb_copy/Fake_database/Fake_phot_lower.txt',columns=['source_id', 'band', 'Lower', 'Lower_unc', 'pub', 'comments1'], index=False)

# Now remove orginial photometry from the FAKE db and add upper/lower to FAKE database
db_fake.modify("Delete from photometry where source_id in (1371, 300, 320, 126, 82, 130, 355, 1476, 1357, 1454, 1940, \
413, 759, 475, 1928, 751, 91, 2107, 849, 415, 146, 669, 107, 1304, 1452, 1591, 1490)")

# Renmae header info to match db input and add
db_fake.add_data('Fake_database/Fake_phot_upper.txt','photometry', delimiter=',')

# Add for Trappist list of fake Upper limits
# Code to calculate upper and lower limits.
up=14.1     +    0.01
down = 14.1  -       0.01
print up, down

data  =list()
data.append(['source_id', 'band', 'magnitude', 'magnitude_unc', 'publication_shortname', 'comments'])
data.append([137, '2MASS_H', 10.739,0.021, 'Missing', 'Fake Upper'])
data.append([137, '2MASS_J', 11.376,0.022, 'Missing', 'Fake Upper'])
data.append([137, '2MASS_Ks', 10.319,0.023, 'Missing', 'Fake Upper'])
data.append([137, 'Gaia_BP', 19.046,0.048, 'Missing', 'Fake Upper'])
data.append([137, 'Gaia_RP', 14.11,0.01, 'Missing', 'Fake Upper'])
data.append([137, 'PS_g', 19.35,0.01, 'Missing', 'Fake Upper'])
data.append([137, 'PS_r', 17.8864,0.0061, 'Missing', 'Fake Upper'])
data.append([137, 'PS_i', 15.1139,0.0017, 'Missing', 'Fake Upper'])
data.append([137, 'PS_z', 13.7777,0.0126, 'Missing', 'Fake Upper'])
data.append([137, 'PS_y', 12.9933,0.0067, 'Missing', 'Fake Upper'])
data.append([137, 'WISE_W1', 10.065,0.023, 'Missing', 'Fake Upper'])
data.append([137, 'WISE_W2', 9.819,0.02, 'Missing', 'Fake Upper'])
data.append([137, 'WISE_W3', 9.569,0.041, 'Missing', 'Fake Upper'])
db_fake.add_data(data, 'photometry')

# Remove and add fake lowers
db_fake.modify("Delete from photometry where comments='Fake Upper'")

db_fake.add_data('Fake_database/Fake_phot_lower.txt','photometry', delimiter=',')
# Trapist fake lowers
data=list()
data.append(['source_id', 'band', 'magnitude', 'magnitude_unc', 'publication_shortname', 'comments'])
data.append([137, '2MASS_H', 10.697,0.021, 'Missing', 'Fake lower'])
data.append([137, '2MASS_J', 11.332,0.022, 'Missing', 'Fake lower'])
data.append([137, '2MASS_Ks', 10.273,0.023, 'Missing', 'Fake lower'])
data.append([137, 'Gaia_BP', 18.95,0.048, 'Missing', 'Fake lower'])
data.append([137, 'Gaia_RP', 14.09,0.01, 'Missing', 'Fake lower'])
data.append([137, 'PS_g', 19.3145,0.0189, 'Missing', 'Fake lower'])
data.append([137, 'PS_r', 17.8742,0.0061, 'Missing', 'Fake lower'])
data.append([137, 'PS_i', 15.1105,0.0017, 'Missing', 'Fake lower'])
data.append([137, 'PS_z', 13.7525,0.0126, 'Missing', 'Fake lower'])
data.append([137, 'PS_y', 12.9799,0.0067, 'Missing', 'Fake lower'])
data.append([137, 'WISE_W1', 10.019,0.023, 'Missing', 'Fake lower'])
data.append([137, 'WISE_W2', 9.779,0.02, 'Missing', 'Fake lower'])
data.append([137, 'WISE_W3', 9.487,0.041, 'Missing', 'Fake lower'])
db_fake.add_data(data, 'photometry')

db_fake.modify("Delete from photometry where comments='Fake lower' and source_id=137")

Fake_phot_lower2.txt
Fake_phot_upper2.txt

hover2pi · 2022-08-10T18:06:25Z

@ECGonzales ok, I see. I'm moving this discussion over to email!

kelle

Nice work! Just a couple comments about the Jy conversion.

https://docs.astropy.org/en/stable/units/equivalencies.html#spectral-flux-and-luminosity-density-units

kelle · 2022-08-11T12:05:38Z

sedkit/spectrum.py

+        if u.equivalent(flux_units, q.Jy) and u.equivalent(self.flux_units, u.FLAM):
+
+            # Convert native FLAM units to Jy
+            self._flux = self._flux * self.wave ** 2 * 3.34e-19


Hmm, is this converting to Jy? What are the numbers? Use astropy units instead?

kelle · 2022-08-11T12:06:03Z

sedkit/spectrum.py

+        elif u.equivalent(self.flux_units, q.Jy) and u.equivalent(flux_units, u.FLAM):
+
+            # Convert native Jy units to FLAM
+            self._flux = self._flux * 3e18 / (self.wave ** 2)


Use astropy units?

hover2pi added 3 commits August 8, 2022 16:47

Bumped to v1.2.2

7f64fcf

Add Fnu support

a59105b

Added test for Fnu support

6439aa4

hover2pi changed the title ~~Bumped to v1.2.2~~ v1.2.2 Aug 10, 2022

hover2pi added 3 commits August 11, 2022 01:28

Fixed infer_mass method by evaluating relations at local points only

dce14d1

Added support for specutils.spectrum1d.Spectrum1D class

136eadc

Removed broken plot save from export method for now

dff379c

hover2pi mentioned this pull request Aug 11, 2022

Coordinate with specutils? #71

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hover2pi merged commit 4f174c3 into master Aug 11, 2022

hover2pi deleted the v1.2.2 branch August 11, 2022 05:58

kelle reviewed Aug 11, 2022

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v1.2.2 #73

v1.2.2 #73

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hover2pi commented Aug 8, 2022 •

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hover2pi commented Aug 10, 2022

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v1.2.2 #73

v1.2.2 #73

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hover2pi commented Aug 8, 2022 •

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