Skip to content

remove deprecated median_absolute_deviation #188

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
jeremyneveu wants to merge 1 commit into
base: master
Choose a base branch
from
Open

remove deprecated median_absolute_deviation #188

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
22 changes: 11 additions & 11 deletions runMultiSpectra.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@


def filter_data(file_names): # pragma: no cover
from scipy.stats import median_absolute_deviation
from scipy.stats import median_abs_deviation
D = []
chi2 = []
dx = []
Expand All @@ -34,20 +34,20 @@ def filter_data(file_names): # pragma: no cover
plt.plot(k, D)
# plt.plot(k, np.polyval(np.polyfit(k, reg, deg=1), k))
plt.axhline(np.median(D))
plt.axhline(np.median(D) + 3 * median_absolute_deviation(D))
plt.axhline(np.median(D) - 3 * median_absolute_deviation(D))
plt.axhline(np.median(D) + 3 * median_abs_deviation(D, scale='normal'))
plt.axhline(np.median(D) - 3 * median_abs_deviation(D, scale='normal'))
plt.grid()
plt.title("D2CCD")
plt.show()
filter_indices = np.logical_and(D > np.median(D) - 3 * median_absolute_deviation(D),
D < np.median(D) + 3 * median_absolute_deviation(D))
filter_indices = np.logical_and(D > np.median(D) - 3 * median_abs_deviation(D, scale='normal'),
D < np.median(D) + 3 * median_abs_deviation(D, scale='normal'))
if len(chi2) > 0:
filter_indices *= np.logical_and(chi2 > np.median(chi2) - 3 * median_absolute_deviation(chi2),
chi2 < np.median(chi2) + 3 * median_absolute_deviation(chi2))
filter_indices *= np.logical_and(dx > np.median(dx) - 3 * median_absolute_deviation(dx),
dx < np.median(dx) + 3 * median_absolute_deviation(dx))
filter_indices *= np.logical_and(regs > np.median(regs) - 3 * median_absolute_deviation(regs),
regs < np.median(regs) + 3 * median_absolute_deviation(regs))
filter_indices *= np.logical_and(chi2 > np.median(chi2) - 3 * median_abs_deviation(chi2, scale='normal'),
chi2 < np.median(chi2) + 3 * median_abs_deviation(chi2, scale='normal'))
filter_indices *= np.logical_and(dx > np.median(dx) - 3 * median_abs_deviation(dx, scale='normal'),
dx < np.median(dx) + 3 * median_abs_deviation(dx, scale='normal'))
filter_indices *= np.logical_and(regs > np.median(regs) - 3 * median_abs_deviation(regs, scale='normal'),
regs < np.median(regs) + 3 * median_abs_deviation(regs, scale='normal'))
plt.plot(k, D)
plt.title("D2CCD")
plt.plot(k[filter_indices], D[filter_indices], "ko")
Expand Down
Loading