def test_hist_addition():
bins = [-3, -2, -1, 0, 1, 2, 3]
bounds = IntervalSet.from_list_of_edges(bins)
contents = np.ones(len(bins) - 1)
hh1 = Histogram(bounds, contents, is_poisson=True)
hh2 = hh1+hh1
assert np.all(hh2.contents == 2*hh1.contents)
hh3 = Histogram(bounds, contents, errors=contents)
hh4 = hh3 + hh3
with pytest.raises(AssertionError):
hh3 + hh1
hh5 = Histogram(bounds, contents, errors=contents, sys_errors=contents)
hh6 = hh5 +hh5
hh7 = hh5 + hh3
hh8 = hh3 + hh5
def from_numpy_histogram(cls,hist,errors=None,sys_errors=None,is_poisson=False,**kwargs):
"""
create a Histogram from a numpy histogram.
Example:
r = np.random.randn(1000)
np_hist = np.histogram(r)
hist = Histogram.from_numpy_histogram(np_hist)
:param hist: a np.histogram tuple
:param errors: list of errors for each bin in the numpy histogram
:param sys_errors: list of systematic errors for each bin in the numpy histogram
:param is_poisson: if the data is Poisson distributed or not
:param kwargs: any kwargs to pass along
:return: a Histogram object
"""
# extract the contents and the bin edges
contents = hist[0] #type: np.ndarray
edges = hist[1] #type: np.ndarray
# convert the edges to an interval set
bounds = IntervalSet.from_list_of_edges(edges) #type: IntervalSet
# return a histogram
return cls(list_of_intervals=bounds,
contents=contents,
errors=errors,
sys_errors=sys_errors,
is_poisson=is_poisson,
**kwargs)
def _calculate_fwhm(self):
"""
calculate the FWHM of the filters
:return:
"""
wmin = []
wmax = []
# go through each filter
# and find the non-gaussian FWHM bounds
for filter in self._filters:
response = filter.response
max_response = response.max()
idx_max = response.argmax()
half_max = 0.5 * max_response
idx1 = abs(response[:idx_max] - half_max).argmin()
idx2 = abs(response[idx_max:] - half_max).argmin() + idx_max
# have to grab the private member here
# bc the library does not expose it!
w1 = filter._wavelength[idx1]
w2 = filter._wavelength[idx2]
wmin.append(w1)
wmax.append(w2)
self._wavebounds = IntervalSet.from_starts_and_stops(wmin, wmax)
def test_hist_addition():
bins = [-3, -2, -1, 0, 1, 2, 3]
bounds = IntervalSet.from_list_of_edges(bins)
contents = np.ones(len(bins) - 1)
hh1 = Histogram(bounds, contents, is_poisson=True)
hh2 = hh1 + hh1
assert np.all(hh2.contents == 2 * hh1.contents)
hh3 = Histogram(bounds, contents, errors=contents)
hh4 = hh3 + hh3
with pytest.raises(AssertionError):
hh3 + hh1
hh5 = Histogram(bounds, contents, errors=contents, sys_errors=contents)
hh6 = hh5 + hh5
hh7 = hh5 + hh3
hh8 = hh3 + hh5
def test_hist_constructor():
with within_directory(__this_dir__):
bins=[-3,-2,-1,0,1,2,3]
bounds = IntervalSet.from_list_of_edges(bins)
contents = np.ones(len(bins) - 1)
hh1 = Histogram(bounds, contents, is_poisson=True)
assert hh1.is_poisson == True
assert len(hh1) == len(bins)-1
hh1.display()
# rnum = np.loadtxt('test_hist_data.txt')
#
#
# #rnum = np.random.randn(1000)
# hrnum = np.histogram(rnum, bins=bins, normed=False)
# hh2 = Histogram.from_numpy_histogram(hrnum, is_poisson=True)
# hh3 = Histogram.from_entries(bounds,rnum)
#
# assert hh2==hh3
hh4 = Histogram(bounds, contents, errors=contents)
assert hh4.is_poisson == False
with pytest.raises(AssertionError):
hh4 = Histogram(bounds, contents, errors=contents,is_poisson=True)
def _calculate_fwhm(self):
"""
calculate the FWHM of the filters
:return:
"""
wmin = []
wmax = []
# go through each filter
# and find the non-gaussian FWHM bounds
for filter in self._filters:
response = filter.response
max_response = response.max()
idx_max = response.argmax()
half_max = 0.5 * max_response
idx1 = abs(response[:idx_max] -
half_max).argmin()
idx2 = abs(response[idx_max:] -
half_max).argmin() + idx_max
# have to grab the private member here
# bc the library does not expose it!
w1 = filter._wavelength[idx1]
w2 = filter._wavelength[idx2]
wmin.append(w1)
wmax.append(w2)
self._wavebounds = IntervalSet.from_starts_and_stops(wmin,wmax)
def test_hist_constructor():
with within_directory(__this_dir__):
bins = [-3, -2, -1, 0, 1, 2, 3]
bounds = IntervalSet.from_list_of_edges(bins)
contents = np.ones(len(bins) - 1)
hh1 = Histogram(bounds, contents, is_poisson=True)
assert hh1.is_poisson == True
assert len(hh1) == len(bins) - 1
hh1.display()
# rnum = np.loadtxt('test_hist_data.txt')
#
#
# #rnum = np.random.randn(1000)
# hrnum = np.histogram(rnum, bins=bins, normed=False)
# hh2 = Histogram.from_numpy_histogram(hrnum, is_poisson=True)
# hh3 = Histogram.from_entries(bounds,rnum)
#
# assert hh2==hh3
hh4 = Histogram(bounds, contents, errors=contents)
assert hh4.is_poisson == False
with pytest.raises(AssertionError):
hh4 = Histogram(bounds, contents, errors=contents, is_poisson=True)