def set_target():
from random import random
outfile1 = open("ideal_wrap.dat","w")
dx = 200.0/10000.0
dx = dx*1e-6
for x in range(10000):
#print(' '.join(str(x) for x in [x*dx*1e6, refCylin(x*dx), "\n"]))
outfile1.write(' '.join(str(x) for x in [x*dx*1e6, refCylin_wrapped(x*dx), "\n"]))
outfile1.close()
outfile2 = open("ideal_unwrap.dat","w")
for x in range(10000):
#print(' '.join(str(x) for x in [x*dx*1e6, refCylin(x*dx), "\n"]))
outfile2.write(' '.join(str(x) for x in [x*dx*1e6, refCylin(x*dx), "\n"]))
outfile2.close()
peaks = find_peaks("ideal_wrap.dat")
return peaks
import data_in_direct
import smooth_curve
import find_peaks
import get_weight
import data_out
def get_accel_data(data):
return [x[0] for x in data]
def get_weight_data(data):
return [x[1] for x in data]
data = data_in_direct.start_collection()
accel_data = get_accel_data(data)
weight_data = get_weight_data(data)
smooth_data = smooth_curve.smoothListGaussian(accel_data, 35)
peaks = find_peaks.find_peaks(smooth_data)
weight = get_weight.get_weight(weight_data)
data = { "reps": len(peaks), "weight": weight }
print len(peaks)
print weight
data_out.start_server(data)
plt.title('Convolution')
plt.savefig('convolution.png')
map_positive = map_filt - map_filt.min()
map_normal = map_positive/map_positive.max()
my_map = map_normal
if trace ==1:
fidx=fidx+1
plt.figure(fidx)
plt.imshow(my_map/my_map.max())
plt.title('maplot(), map image')
plt.savefig('map_image.png')
plt.show()
[peakInf_node_isLeaf_sort_am,peakInf_node_isLeaf_sort_energy_sum,peakInf_node_isLeaf,peakInf_node_all] =find_peaks.find_peaks(my_map)
#
# Found the highest point in the image
# [x0_pix, y0_pix] is the axis of highest point, unit:pixel, axis original point locate left-up corner
# my_map_max = max(my_map(:));
# [c r] = find(my_map>=my_map_max);
# y0_pix= c(1);
# x0_pix= r(1);
centerPos = peakInf_node_isLeaf_sort_am[0]['centerPos']
y0_pix = centerPos[0]
x0_pix = centerPos[1]
#%%
map_positive = map_filt - map_filt.min()
map_normal = map_positive / map_positive.max()
my_map = map_normal
if trace == 1:
fidx = fidx + 1
plt.figure(fidx)
plt.imshow(my_map / my_map.max())
plt.title('maplot(), map image')
plt.savefig('map_image.png')
plt.show()
[
peakInf_node_isLeaf_sort_am, peakInf_node_isLeaf_sort_energy_sum,
peakInf_node_isLeaf, peakInf_node_all
] = find_peaks.find_peaks(my_map)
#
# Found the highest point in the image
# [x0_pix, y0_pix] is the axis of highest point, unit:pixel, axis original point locate left-up corner
# my_map_max = max(my_map(:));
# [c r] = find(my_map>=my_map_max);
# y0_pix= c(1);
# x0_pix= r(1);
centerPos = peakInf_node_isLeaf_sort_am[0]['centerPos']
y0_pix = centerPos[0]
x0_pix = centerPos[1]
#%%
#% change the axis[x0_mas,y0_mas] unit: mas,axis original point in the image center
# This implements a phase vocoder for time-stretching/compression
band = 'r'
chisq_min = 1e4
lcs = l2_service.get_SN_candidate_lcs(band, chisq_min)
mjds = l2_service.mjds[band]
print("# SNe candidates:", len(lcs))
# Define a +/-30 day window around the peak flux.
dt = 30
outfile = 'tmp.txt'
with open(outfile, 'w') as output:
output.write('#objectId chisq ndof z t0 x0 x1 c\n')
for objectId, fluxes in lcs.items()[:5]:
peak_indexes = find_peaks(fluxes)[0][0]
lc = lc_factory.create(objectId)
for ipeak in peak_indexes:
print("fitting object %i, peak index %i" % (objectId, ipeak))
mjd_peak = mjds[ipeak]
mask = np.where((lc.data['mjd'] > mjd_peak - dt)
& (lc.data['mjd'] < mjd_peak + dt)
& (lc.data['bandpass'] != 'lsstu'))
sn_data = lc.data[mask]
model = sncosmo.Model(source='salt2-extended')
model.set(t0=mjd_peak, z=0.2)
try:
res, fitted_model =\
sncosmo.fit_lc(sn_data, model, 'z t0 x0 x1 c'.split(),
bounds=dict(z=(0.01, 1.)))
except (RuntimeError, sncosmo.fitting.DataQualityError):
