def lstats(x, n):
'''
n is the number of the lratios you wish to include, starting at L2 to exclude mean
the function return L2,L3,L4 if n=4
'''
lratios = lm.lmom_ratios(x, nmom=n)
return lratios[1:n]
def test_n50_nmom3(self):
start_i = 0
for i in range(10000):
l1, l2, t3 = lm.lmom_ratios(self.record[start_i:start_i + self.n],
nmom=3)
t2 = l2 / l1
start_i += self.n
def test_samlmu(self):
testdata = [2.0, 3.0, 4.0, 2.4, 5.5, 1.2, 5.4, 2.2, 7.1, 1.3, 1.5]
expected = [
3.23636364, 1.14181818, 0.27388535, 0.02335456, -0.04246285
]
result = lm.lmom_ratios(testdata)
assert_almost_equal(result, expected)
def smrstat(x, vmin=-np.inf, vmax=np.inf):
'''
Summary Statistics
Input: one array of interest
Output: mean, variance, skewness, kurtosis, min, max, percentile80, percentile90, percentile95, percentile99, percentile99.9, LCV, Lskew, Lkurtosis
'''
ind=np.where((np.isnan(x)==False) & (x>vmin) & (x<vmax))
if np.any(ind):
x_0=[ind[0]]
else:
sys.exit(' Array without valid numbers.')
ferr=np.zeros((14),'f')*np.nan
ferr[0] = np.mean(x_0)
ferr[1] = np.var(x_0)
ferr[2] = scipy.stats.skew(x_0)
ferr[3] = scipy.stats.kurtosis(x_0)
ferr[4] = np.min(x_0)
ferr[5] = np.max(x_0)
ferr[6] = np.percentile(x_0,80)
ferr[7] = np.percentile(x_0,90)
ferr[8] = np.percentile(x_0,95)
ferr[9] = np.percentile(x_0,99)
ferr[10] = np.percentile(x_0,99.9)
# Hosking & Wallis L-moment ratios
# pip install git+https://github.com/OpenHydrology/lmoments3.git
hwlm = lm.lmom_ratios(x_0, nmom=5)
ferr[11] = hwlm[1]/hwlm[0]
ferr[12] = hwlm[2]
ferr[13] = hwlm[3]
return ferr
def get_initial_params_using_lm(vals):
from lmoments3 import distr
sorted_vals = list(sorted(vals))
the_moments = lmoments3.lmom_ratios(sorted_vals, nmom=3)
ksi, mu, sigma = distr.gev.lmom_fit(sorted_vals, the_moments[0:3]).values()
return [sigma, mu, -ksi] # -ksi because they are using -ksi convention
def get_initial_params_using_lm(vals):
from lmoments3 import distr
sorted_vals = list(sorted(vals))
the_moments = lmoments3.lmom_ratios(sorted_vals, nmom=3)
ksi, mu, sigma = distr.gev.lmom_fit(sorted_vals, the_moments[0:3]).values()
return [sigma, mu, -ksi] # -ksi because they are using -ksi convention
def test_lm():
x = [
360.228515625, 513.506103515625, 273.85031127929688,
340.94839477539062, 244.13925170898438, 283.414306640625,
394.42819213867188, 284.3604736328125, 281.26956176757812,
241.46173095703125, 489.75482177734375, 236.31536865234375,
407.55133056640625, 244.6295166015625, 432.40670776367188,
260.501953125, 517.23052978515625, 317.6553955078125,
407.61935424804688, 275.0709228515625, 330.369140625,
285.92086791992188, 247.9954833984375, 344.34811401367188,
379.55596923828125, 330.80569458007812, 312.35330200195312,
251.79550170898438, 372.66928100585938, 239.72474670410156
]
# print(get_initial_params_using_lm(x))
print(np.mean(x))
pars = [128.28104749, 578.4927539, 0.62410911]
data = [
588.4747314453125, 693.6640625, 519.03155517578125, 716.58013916015625,
686.29168701171875, 432.65786743164062, 682.72113037109375,
730.12603759765625, 698.971923828125, 491.75332641601562,
597.258544921875, 487.13619995117188, 482.33123779296875,
573.57861328125, 801.67169189453125, 616.41668701171875,
690.954833984375, 671.31646728515625, 680.87554931640625,
534.18414306640625, 427.86019897460938, 236.22953796386719,
691.40972900390625, 599.84637451171875, 545.3563232421875,
553.059814453125, 549.1295166015625, 658.3983154296875,
719.122802734375, 636.84906005859375
]
import lmoments3
from lmoments3 import distr
the_moments = lmoments3.lmom_ratios(sorted(data), 5)
pars = distr.gev.lmom_fit(sorted(data), lmom_ratios=the_moments)
print("Fitted params using lmoments: ", pars)
xi, mu, sigma = pars.values()
print(objective_function_stationary_high([sigma, mu, -xi], data))
print("Fitted using MLE: ", distr.gev.fit(sorted(data)))
print(
"Fitted using custom method (Huziy et al 2013), not using l-moments: ",
optimize_stationary_for_period(np.array(sorted(data))))
print(
"Fitted using custom method (Huziy et al 2013), using l-moments: ",
optimize_stationary_for_period(np.array(sorted(data)),
use_lmoments=True))
from scipy.stats import genextreme
print("Fitted using scipy.stats.genextreme: ",
genextreme.fit(np.array(sorted(data))))
print("10 year high flow return level: ",
get_high_ret_level_stationary([sigma, mu, -xi, 0], 10))
print("10 year high flow return level: ",
get_high_ret_level_stationary([sigma, mu, -0.5, 0], 10))
def _l_cv_and_skew(self, catchment):
"""
Calculate L-CV and L-SKEW for a gauged catchment. Uses `lmoments3` library.
Methodology source: Science Report SC050050, para. 6.7.5
"""
z = self._dimensionless_flows(catchment)
l1, l2, t3 = lm.lmom_ratios(z, nmom=3)
return l2 / l1, t3
def _l_cv_and_skew(self, catchment):
"""
Calculate L-CV and L-SKEW for a gauged catchment. Uses `lmoments3` library.
Methodology source: Science Report SC050050, para. 6.7.5
"""
z = self._dimensionless_flows(catchment)
l1, l2, t3 = lm.lmom_ratios(z, nmom=3)
return l2 / l1, t3
def hudgins_features(emg_vec, config):
""" Extract the four hudgin's features
Parameters
----------
data : dictionary
epoched emg data
config : yaml
configuration file
Return
------
Data : dictionary
dictionary of feature and label data from all the subjects
"""
Data = collections.defaultdict(dict)
# A 3d array with dimensions representing trial_samples x emg_channels x epochs
data_shape = emg_vec.shape
# initialize the feature array - samples x features
features1 = np.zeros(
(data_shape[0], config['n_electrodes'] * config['n_features']))
features2 = np.zeros(
(data_shape[0], config['n_electrodes'] * (config['n_features'] + 1)))
for i in range(data_shape[0]):
for j in range(data_shape[1]):
rawEMGSignal = emg_vec[i, j, :]
# feature set 1
features1[i,
4 * j] = pysiology.electromyography.getWL(rawEMGSignal)
features1[i, 4 * j + 1] = pysiology.electromyography.getZC(
rawEMGSignal, config['threshold'])
features1[i, 4 * j + 2] = pysiology.electromyography.getSSC(
rawEMGSignal, config['threshold'])
features1[i, 4 * j +
3] = pysiology.electromyography.getMAV(rawEMGSignal)
# New feature set 2 based on the paper by Phinyomark, Angkoon, Rami N Khushaba, and Erik Scheme.
# "Feature extraction and selection for myoelectric control based on wearable EMG sensors." Sensors 18.5 (2018): 1615.
features2[i, 5 * j] = pysiology.electromyography.getWAMP(
rawEMGSignal, config['threshold']) # Willison amplitude
features2[i, 5 * j + 1] = np.mean(
np.sqrt(
np.absolute(rawEMGSignal))) # Mean value of square root
features2[i, 5 * j + 2] = np.log10(
np.sqrt(np.absolute(np.sum(np.square(
np.diff(rawEMGSignal)))))) # Maximum fractal length
features2[i, 5 * j + 3:5 * j + 5] = np.array(
lm.lmom_ratios(rawEMGSignal,
nmom=2)).reshape(1, 2) # L-moments with order 2
return features1, features2
def test_lm():
x = [360.228515625, 513.506103515625, 273.85031127929688, 340.94839477539062,
244.13925170898438, 283.414306640625, 394.42819213867188, 284.3604736328125,
281.26956176757812, 241.46173095703125, 489.75482177734375, 236.31536865234375,
407.55133056640625, 244.6295166015625, 432.40670776367188, 260.501953125,
517.23052978515625, 317.6553955078125, 407.61935424804688, 275.0709228515625,
330.369140625, 285.92086791992188, 247.9954833984375, 344.34811401367188,
379.55596923828125, 330.80569458007812, 312.35330200195312, 251.79550170898438,
372.66928100585938, 239.72474670410156]
# print(get_initial_params_using_lm(x))
print(np.mean(x))
pars = [128.28104749, 578.4927539, 0.62410911]
data = [588.4747314453125, 693.6640625, 519.03155517578125, 716.58013916015625,
686.29168701171875, 432.65786743164062, 682.72113037109375, 730.12603759765625,
698.971923828125, 491.75332641601562, 597.258544921875, 487.13619995117188, 482.33123779296875,
573.57861328125, 801.67169189453125, 616.41668701171875, 690.954833984375, 671.31646728515625,
680.87554931640625, 534.18414306640625, 427.86019897460938, 236.22953796386719, 691.40972900390625,
599.84637451171875,
545.3563232421875, 553.059814453125, 549.1295166015625, 658.3983154296875, 719.122802734375,
636.84906005859375]
import lmoments3
from lmoments3 import distr
the_moments = lmoments3.lmom_ratios(sorted(data), 5)
pars = distr.gev.lmom_fit(sorted(data), lmom_ratios=the_moments)
print("Fitted params using lmoments: ", pars)
xi, mu, sigma = pars.values()
print(objective_function_stationary_high([sigma, mu, -xi], data))
print("Fitted using MLE: ", distr.gev.fit(sorted(data)))
print("Fitted using custom method (Huziy et al 2013), not using l-moments: ", optimize_stationary_for_period(
np.array(sorted(data))))
print("Fitted using custom method (Huziy et al 2013), using l-moments: ",
optimize_stationary_for_period(np.array(sorted(data)), use_lmoments=True))
from scipy.stats import genextreme
print("Fitted using scipy.stats.genextreme: ", genextreme.fit(np.array(sorted(data))))
print("10 year high flow return level: ", get_high_ret_level_stationary([sigma, mu, -xi, 0], 10))
print("10 year high flow return level: ", get_high_ret_level_stationary([sigma, mu, -0.5, 0], 10))
def __run_a_simulation(self):
simulatedDurationStatisticsDF = pd.DataFrame()
for row in self.__regionalDurationStatisticsDF.itertuples():
years = row.nyears
# Generate synethic AMS
ams = self.__kapDist.rvs(years)
ratios = lm.lmom_ratios(ams, nmom=5)
data = {'nyears':years,
'l1':ratios[0],
'l2':ratios[1],
't':ratios[1]/ratios[0],
't3':ratios[2],
't4':ratios[3],
't5':ratios[4]}
simulatedDurationStatisticsDF = simulatedDurationStatisticsDF.append(data, ignore_index=True)
# Calculate the regionalized moments
simulatedRlmoments = getWeightedLMoments(simulatedDurationStatisticsDF)
v = self.get_V1(simulatedDurationStatisticsDF, simulatedRlmoments)
self.__v_array.append(v)
def lmom_fit(self, data=[], lmom_ratios=[]):
"""
Fit the distribution function to the given data or given L-moments.
:param data: Data to use in calculating the distribution parameters
:type data: array_like
:param lmom_ratios: L-moments (ratios) l1, l2, t3, t4, .. to use in calculating the distribution parameters
:type lmom_ratios: array_like
:returns: Distribution parameters in `scipy` order, e.g. scale, loc, shape
:rtype: :class:`OrderedDict`
"""
n_min = self.numargs + 2
if len(data) > 0:
if len(data) <= n_min:
raise ValueError("At least {} data points must be provided.".format(n_min))
lmom_ratios = lm.lmom_ratios(data, nmom=n_min)
elif not lmom_ratios:
raise Exception("Either `data` or `lmom_ratios` must be provided.")
elif len(lmom_ratios) < n_min:
raise ValueError("At least {} number of L-moments must be provided.".format(n_min))
return self._lmom_fit(lmom_ratios)
def lmom_fit(self, data=[], lmom_ratios=[]):
"""
Fit the distribution function to the given data or given L-moments.
:param data: Data to use in calculating the distribution parameters
:type data: array_like
:param lmom_ratios: L-moments (ratios) l1, l2, t3, t4, .. to use in calculating the distribution parameters
:type lmom_ratios: array_like
:returns: Distribution parameters in `scipy` order, e.g. scale, loc, shape
:rtype: :class:`OrderedDict`
"""
n_min = self.numargs + 2
if len(data) > 0:
if len(data) <= n_min:
raise ValueError("At least {} data points must be provided.".format(n_min))
lmom_ratios = lm.lmom_ratios(data, nmom=n_min)
elif not lmom_ratios:
raise Exception("Either `data` or `lmom_ratios` must be provided.")
elif len(lmom_ratios) < n_min:
raise ValueError("At least {} number of L-moments must be provided.".format(n_min))
return self._lmom_fit(lmom_ratios)
def lscale(x):
return lm.lmom_ratios(x)[1]
def setUpClass(cls):
cls.testdata = [2.0, 3.0, 4.0, 2.4, 5.5, 1.2, 5.4, 2.2, 7.1, 1.3, 1.5]
cls.lmu = lm.lmom_ratios(cls.testdata)
if cls.dist:
cls.distr_f = getattr(distr, cls.dist)
super(DistributionTestCase, cls).setUpClass()
def mean(x):
return lm.lmom_ratios(x)[0]
def setUpClass(cls):
cls.testdata = [2.0, 3.0, 4.0, 2.4, 5.5, 1.2, 5.4, 2.2, 7.1, 1.3, 1.5]
cls.lmu = lm.lmom_ratios(cls.testdata)
if cls.dist:
cls.distr_f = getattr(distr, cls.dist)
super(DistributionTestCase, cls).setUpClass()
def test_n50_nmom4(self):
start_i = 0
for i in range(10000):
l1, l2, t3, t4 = lm.lmom_ratios(self.record[start_i:start_i + self.n], nmom=4)
t2 = l2 / l1
start_i += self.n
def lskewness(x):
return lm.lmom_ratios(x)[2]
def extract_emg_features(data, config, scale=False):
""" Load the EMG data and extract the features
Parameters
----------
data : dictionary
epoched emg data
config : yaml
configuration file
scale : bool
use min-max scaling if scale=True
Return
------
Data : dictionary
dictionary of feature and label data from all the subjects
"""
Data = collections.defaultdict(dict)
for subject in tqdm(config['subjects']):
emg_vec = data['subject_' + subject]['EMG']
labels = data['subject_' + subject]['labels']
# A 3d array with dimensions representing trial_samples x emg_channels x epochs
data_shape = emg_vec.shape
# initialize the feature array - samples x features
features1 = np.zeros(
(data_shape[0], config['n_electrodes'] * config['n_features']))
features2 = np.zeros(
(data_shape[0],
config['n_electrodes'] * (config['n_features'] + 1)))
for i in range(data_shape[0]):
for j in range(data_shape[1]):
rawEMGSignal = emg_vec[i, j, :]
# feature set 1
features1[i, 4 *
j] = pysiology.electromyography.getWL(rawEMGSignal)
features1[i, 4 * j + 1] = pysiology.electromyography.getZC(
rawEMGSignal, config['threshold'])
features1[i, 4 * j + 2] = pysiology.electromyography.getSSC(
rawEMGSignal, config['threshold'])
features1[i, 4 * j +
3] = pysiology.electromyography.getMAV(rawEMGSignal)
# Previously used - feature set 2
# The getHIST feature returns ZC and WAMP for each bin. The 'WAMP' from all the bins are added and selected as a single feature
# temp_Hist = pysiology.electromyography.getHIST(rawEMGSignal, nseg=9, threshold=config['threshold'])
# Hist_sum = 0
# for val in temp_Hist:
# Hist_sum += temp_Hist[val]['WAMP']
# features2[i,4*j] = pysiology.electromyography.getRMS(rawEMGSignal)
# features2[i,4*j+1] = pysiology.electromyography.getWL(rawEMGSignal)
# features2[i,4*j+2] = sampEntropy(rawEMGSignal)
# features2[i,4*j+3] = Hist_sum
# New feature set 2 based on the paper by Phinyomark, Angkoon, Rami N Khushaba, and Erik Scheme.
# "Feature extraction and selection for myoelectric control based on wearable EMG sensors." Sensors 18.5 (2018): 1615.
features2[i, 5 * j] = pysiology.electromyography.getWAMP(
rawEMGSignal, config['threshold']) # Willison amplitude
features2[i, 5 * j + 1] = np.mean(
np.sqrt(np.absolute(
rawEMGSignal))) # Mean value of square root
features2[i, 5 * j + 2] = np.log10(
np.sqrt(
np.absolute(np.sum(np.square(np.diff(
rawEMGSignal)))))) # Maximum fractal length
features2[i, 5 * j + 3:5 * j + 5] = np.array(
lm.lmom_ratios(rawEMGSignal, nmom=2)).reshape(
1, 2) # L-moments with order 2
# features[i,6*j+5] = sampen2(normalize_data(copy_rawEMGSignal))[2][1] # m = 2, r = 0.2 * std
if scale:
# print('Min-Max scaling the emg-features')
# Min-Max scaling
min_max_scaler = preprocessing.MinMaxScaler()
features1 = min_max_scaler.fit_transform(features1)
features2 = min_max_scaler.fit_transform(features2)
Data['subject_' + subject]['features1'] = features1
Data['subject_' + subject]['features2'] = features2
Data['subject_' + subject]['labels'] = labels
return Data
636,
343,
586,
221,
298,
2200,
237,
275,
980,
)
import matplotlib.pyplot as plt
plt.plot(x, y, '.')
#%%
l1, l2, l3, l4 = lm.lmom_ratios(x, nmom=4)
t3 = l3 / l2
t4 = l4 / l2
#%%
from scipy.stats import pearson3, kappa4
import matplotlib.pyplot as plt
import numpy as np
p = kappa4(h=l4, loc=l1, k=l3, scale=l2)
x = sorted(x)
vals1 = np.linspace(0, 1, 31)
vals = np.linspace(0, 1, 1000)
ys = p.ppf(vals)
plt.plot(vals, ys, vals1, x)
def test_samlmu(self):
testdata = [2.0, 3.0, 4.0, 2.4, 5.5, 1.2, 5.4, 2.2, 7.1, 1.3, 1.5]
expected = [3.23636364, 1.14181818, 0.27388535, 0.02335456, -0.04246285]
result = lm.lmom_ratios(testdata)
assert_almost_equal(result, expected)
def lkurtosis(x):
return lm.lmom_ratios(x)[3]
