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Fix indexing with int array #18

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merged 3 commits into from
Jun 27, 2016
Merged

Fix indexing with int array #18

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aa8571d
Fix indexing with int array
Jun 13, 2016
90d235e
Fix of bad index offsets in clean_idwinterp
Jun 22, 2016
45d00fe
Test to check whether median cleaning updates mask.
Jun 22, 2016
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7 changes: 4 additions & 3 deletions astroscrappy/astroscrappy.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -338,7 +338,8 @@ def detect_cosmics(indat, inmask=None, float sigclip=4.5, float sigfrac=0.3,
# otherwise clean the image and iterate
if cleantype == 'median':
# Unmasked median filter
cleanarr[crmask] = m5[crmask]
cindices = crmask.nonzero()
cleanarr[cindices] = m5[cindices]
del m5
# Masked mean filter
elif cleantype == 'meanmask':
Expand Down Expand Up @@ -601,8 +602,8 @@ cdef void clean_idwinterp(float[:, ::1] cleanarr, bool[:, ::1] crmask,
for k in range(-2, 3):
x = i + k
if not (crmask[y, x] or mask[y, x]):
val = val + weights[l, k] * cleanarr[y, x]
wsum = wsum + weights[l, k]
val = val + weights[l+2, k+2] * cleanarr[y, x]
wsum = wsum + weights[l+2, k+2]
if wsum < 1e-6:
cleanarr[j, i] = background_level
else:
Expand Down
66 changes: 66 additions & 0 deletions astroscrappy/tests/test_cleanmedian.py
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@@ -0,0 +1,66 @@
# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
from ..astroscrappy import detect_cosmics
imdata = np.array([ 2369, 2404, 2387, 2406, 2448, 2470, 2454, 2460,
2494, 2491, 2448, 2411, 2413, 2393, 2408, 2378,
2364, 2365, 2331, 2304, 2362, 2353, 2381, 2405,
2458, 2437, 2426, 2442, 2484, 2418, 2444, 2444,
2398, 2388, 2395, 2373, 2352, 2332, 2322, 2310,
2338, 2353, 2370, 2396, 2367, 2432, 2419, 2433,
2420, 2432, 2413, 2398, 2405, 2356, 2376, 2383,
2338, 2331, 2290, 2284, 2302, 2337, 2329, 2364,
2391, 2375, 2441, 2386, 2421, 2398, 2370, 2414,
2407, 2349, 2391, 2360, 2326, 2335, 2336, 2290,
2334, 2333, 2320, 2324, 2372, 2365, 2392, 2398,
2361, 2367, 2348, 2357, 2361, 2345, 2335, 2310,
2337, 2310, 2307, 2323, 2304, 2324, 2327, 2338,
2365, 2338, 2336, 2319, 2357, 2378, 2391, 2345,
2342, 2342, 2325, 2304, 2292, 2336, 2329, 2307,
2309, 2287, 2309, 2283, 2344, 2318, 2312, 2334,
2330, 2336, 2328, 2304, 2334, 2339, 2298, 2291,
2321, 2315, 2306, 2320, 2308, 2293, 2317, 2333,
2326, 2313, 2356, 2327, 2298, 2337, 2328, 2320,
2316, 2297, 2313, 2332, 2310, 2307, 2319, 2302,
2333, 2338, 2322, 2303, 2316, 2279, 2324, 2318,
2290, 2322, 2325, 2342, 2336, 2292, 2301, 2299,
2287, 2315, 2301, 2318, 2301, 2306, 2313, 2325,
2313, 2313, 2294, 2302, 2309, 2306, 2333, 2327,
2310, 2257, 2304, 2315, 2303, 2309, 2314, 2315,
2331, 2321, 2310, 2307, 2317, 2299, 2324, 2315,
2323, 2336, 2295, 2316, 2330, 2309, 2306, 2314,
2272, 2347, 2319, 2310, 2301, 2320, 2321, 2408,
2703, 2649, 2295, 2329, 2321, 2307, 2321, 2320,
2312, 2298, 2312, 2311, 2298, 2301, 2296, 2317,
2306, 2332, 2307, 2334, 2310, 2339, 2658, 2658,
2643, 2660, 2523, 2322, 2284, 2322, 2322, 2338,
2302, 2287, 2309, 2307, 2305, 2317, 2316, 2310,
2306, 2306, 2344, 2310, 2285, 2308, 2421, 2679,
2675, 2701, 2657, 2421, 2303, 2331, 2307, 2307,
2317, 2297, 2304, 2319, 2327, 2300, 2331, 2338,
2302, 2323, 2303, 2305, 2308, 2313, 2322, 2554,
2654, 2670, 2678, 2638, 2316, 2302, 2322, 2299,
2339, 2305, 2321, 2291, 2280, 2304, 2324, 2318,
2282, 2281, 2301, 2306, 2295, 2284, 2288, 2313,
2294, 2301, 2324, 2344, 2330, 2304, 2312, 2317,
2326, 2306, 2326, 2318, 2329, 2318, 2300, 2287,
2268, 2294, 2293, 2319, 2279, 2322, 2298, 2298,
2330, 2296, 2326, 2331, 2310, 2303, 2314, 2329,
2315, 2322, 2296, 2307, 2272, 2309, 2310, 2315,
2325, 2314, 2264, 2293, 2312, 2305, 2308, 2318,
2287, 2267, 2314, 2302, 2310, 2277, 2310, 2307,
2304, 2315, 2314, 2313, 2318, 2309, 2315, 2305,
2312, 2289, 2268, 2310, 2315, 2294, 2314, 2312,
2286, 2311, 2305, 2320, 2299, 2293, 2283, 2302]).reshape(20,20)

imdata = imdata.astype('f4')

def test_medclean():
mask1, _clean = detect_cosmics(imdata, fsmode='median',
cleantype='median', niter = 1)

mask2, _clean = detect_cosmics(imdata, fsmode='median', cleantype='median', niter = 2)

assert (mask1.sum() < mask2.sum())