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davidwhogg / Joaquin_W_Hogg.ipynb
Last active June 25, 2022 18:43
Testing components of Joaquin
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@davidwhogg
davidwhogg / play_with_MH.ipynb
Created June 23, 2022 00:42
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davidwhogg / XP-APOGEE.ipynb
Last active June 23, 2022 00:23
A notebook for the Gaia Hike
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davidwhogg / ImagePhaseSandbox.ipynb
Created October 26, 2019 16:44 — forked from adrn/ImagePhaseSandbox.ipynb
ImagePhaseSandbox.ipynb
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davidwhogg / Simple-Linear-Nonlinear-Inference.ipynb
Created October 5, 2019 14:00 — forked from adrn/Simple-Linear-Nonlinear-Inference.ipynb
hq/notebooks/Simple-Linear-Nonlinear-Inference.ipynb
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davidwhogg / Galah-demo.ipynb
Created July 18, 2018 07:32 — forked from adrn/Galah-demo.ipynb
Galah-for-Hogg.ipynb
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davidwhogg / Get_all_the_TGAS_data_by_curl.sh
Created September 14, 2016 10:56
Get all the TGAS data by curl
#!/bin/bash
curl -O http://cdn.gea.esac.esa.int/Gaia/tgas_source/fits/TgasSource_000-000-000.fits
curl -O http://cdn.gea.esac.esa.int/Gaia/tgas_source/fits/TgasSource_000-000-001.fits
curl -O http://cdn.gea.esac.esa.int/Gaia/tgas_source/fits/TgasSource_000-000-002.fits
curl -O http://cdn.gea.esac.esa.int/Gaia/tgas_source/fits/TgasSource_000-000-003.fits
curl -O http://cdn.gea.esac.esa.int/Gaia/tgas_source/fits/TgasSource_000-000-004.fits
curl -O http://cdn.gea.esac.esa.int/Gaia/tgas_source/fits/TgasSource_000-000-005.fits
curl -O http://cdn.gea.esac.esa.int/Gaia/tgas_source/fits/TgasSource_000-000-006.fits
curl -O http://cdn.gea.esac.esa.int/Gaia/tgas_source/fits/TgasSource_000-000-007.fits
curl -O http://cdn.gea.esac.esa.int/Gaia/tgas_source/fits/TgasSource_000-000-008.fits
@davidwhogg
davidwhogg / weighted_median.py
Created June 7, 2016 21:09
import numpy as np
from scipy.interpolate import interp1d
def weighted_median(xs, ws=None):
if ws is None:
ws = np.ones_like(xs)
totalw = np.sum(ws)
sindx = np.argsort(xs) # expensive
cs = np.cumsum(ws[sindx]) / totalw
symcumsum = 0.5 * (cs + np.append(0., cs[0:-1]))
@davidwhogg
davidwhogg / hoggsumexp
Created June 6, 2016 18:57
import numpy as np
def hoggsumexp(qns, dqn_dams, diag=False):
"""
# purpose:
- Computes L = log(sum(exp(qns, axis=-1))).
- Also computes its M-dimensional gradient components dL / da_m.
# input
- qns: ndarray of shape [n1, n2, n3, ..., nD, N]
@davidwhogg
davidwhogg / make random hashes for M-S DSE Summit
Last active September 25, 2015 14:35
import numpy as np
def int_string(x, base, digits):
xx = x + 0
str = ""
for i in range(digits):
str = (xx % base).__str__() + str
xx /= base
return str