def test_numpy_argmax(self):
# See GH#16830
data = np.arange(1, 11)
s = Series(data, index=data)
result = np.argmax(s)
expected = np.argmax(data)
assert result == expected
result = s.argmax()
assert result == expected
msg = "the 'out' parameter is not supported"
with pytest.raises(ValueError, match=msg):
np.argmax(s, out=data)
def peak_frequency(self, min_frequency=0.0, max_frequency=None,
column=None, run=None, label=None):
"""Return frequency with peak magnitude.
Parameters
----------
min_frequency : float
Minimum frequency to search for peak from
max_frequency : float
Maximum frequency to search for peak from
column : str, optional
Electrode to use for finding peak. If None then the mean of the
magnitude across all electrodes is used.
run : Index, optional
Index for the run
label : Index, optional
Index for the label
Returns
-------
freq : float
Peak frequency
Examples
--------
>>> spectrum = Spectra4D({'baseline': {'Trial 1':
... {'Cz': [1, 2, 3, 1, 2, 10, 3, 4, 1]}}})
>>> spectrum.peak_frequency()
5
"""
min_frequency = max(0.0, min_frequency)
if max_frequency is None:
max_frequency = max(self.major_axis)
indices = ((self.major_axis >= min_frequency) &
(self.major_axis <= max_frequency))
if column is None and run is None and label is None:
magnitudes = np.abs(self[:, :, indices, :]).values.mean(
axis=(0, 1, 3))
magnitudes = Series(magnitudes, index=self.major_axis[indices])
else:
magnitudes = np.abs(self[label, run, indices, column])
# TODO: Combination when some of the indices are not parameters.
peak_frequency = magnitudes.argmax()
return peak_frequency
def test_numpy_argmax_deprecated(self):
# See GH#16830
data = np.arange(1, 11)
s = Series(data, index=data)
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
# The deprecation of Series.argmax also causes a deprecation
# warning when calling np.argmax. This behavior is temporary
# until the implementation of Series.argmax is corrected.
result = np.argmax(s)
assert result == 10
with tm.assert_produces_warning(FutureWarning):
# argmax is aliased to idxmax
result = s.argmax()
assert result == 10
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
msg = "the 'out' parameter is not supported"
with pytest.raises(ValueError, match=msg):
np.argmax(s, out=data)
def classify(freq,Yabs,filt,tolerance):
'''
取可疑极大值点左右两个极小值点，求出两个极小值点的均值作为这附近功率谱的基准。
'''
N=5
Yabs = np.convolve(Yabs, np.ones((N,))/N, mode='same')
y = Series(Yabs ,index = freq)
if filt:
yFilt= y[filt[0]:filt[1]]
else:
yFilt = y
maximum = yFilt.max()
mean = yFilt.mean()
threshold = 4
if compute_SNR(y, y) > threshold and maximum < 15:
# Threshold
maxF = y.argmax()
# print("Max freq at %f" % maxF)
# Inspect half frequency of max frequency with a tolerance
halfMaxF = maxF/2
halfY = y[halfMaxF-tolerance:halfMaxF+tolerance]
halfSNR = compute_SNR(halfY,y)
doubleMaxF = maxF*2
doubleY = y[doubleMaxF-2*tolerance:doubleMaxF+2*tolerance]
doubleSNR = compute_SNR(doubleY,y)
if halfSNR <= threshold:
if doubleSNR > threshold/2:
print('Stimuli at %f' % yFilt.argmax())
else:
print('SNR on double frequency too low')
# print('Max freq at %f' % yFilt.argmax())
else:
print('Stimuli at %f' % halfY.argmax())
else:
print('SNR too low')
return y
def test_numpy_argmax_deprecated(self):
# See GH#16830
data = np.arange(1, 11)
s = Series(data, index=data)
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
# The deprecation of Series.argmax also causes a deprecation
# warning when calling np.argmax. This behavior is temporary
# until the implementation of Series.argmax is corrected.
result = np.argmax(s)
assert result == 10
with tm.assert_produces_warning(FutureWarning):
# argmax is aliased to idxmax
result = s.argmax()
assert result == 10
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
msg = "the 'out' parameter is not supported"
with pytest.raises(ValueError, match=msg):
np.argmax(s, out=data)
# longest and shortest times
np.set_printoptions(threshold=175)
print np.unique(in_df.PoliceDistrict)
in_df.PoliceDistrict.value_counts() # Freq
policeDistricts_list = np.unique(in_df.PoliceDistrict)
dist_resp_times = SR(np.zeros(len(policeDistricts_list)))
for i in policeDistricts_list:
mask = (in_df.PoliceDistrict == i) & (response_time1 > 0)
dist_resp_times[i] = response_time1[mask].mean()
print 'District ', i,
print_response_time('Mean', dist_resp_times[i])
print 'Longest avg. resp time ', dist_resp_times.max(
), 'for District', dist_resp_times.argmax()
print 'Shortest avg. resp time ', min(
dist_resp_times), 'for District', dist_resp_times.argmin()
print_response_time('Difference in avg.',
max(dist_resp_times) - min(dist_resp_times))
#------------------------------------------------------------------------------
# Event types that occur more often in a district
print np.unique(in_df.Type_)
in_df.Type_.value_counts() ## 21 has the highest freq
in_df.TypeText[in_df.Type_ == '21'] ## Corresponds to 'COMPLAINT OTHER'
in_df.TypeText.value_counts()
tmp_df1 = in_df.groupby(['PoliceDistrict', 'Type_',
'TypeText']).size().reset_index(name='Times')
