def makeFit_logratiosValues(df,numGenes,fractionImportant,noise): normNum=int(np.ceil(numGenes*(1-fractionImportant))) phenNum = numGenes-normNum c1Vals = skewnorm.rvs(-2, scale=0.99, size=normNum,random_state=random_state) c1PhenoVals = skewnorm.rvs(0, scale=noise, size=phenNum,random_state=random_state) c2Vals = list(map(add, c1Vals, skewnorm.rvs(0, scale=noise*2, size=normNum,random_state=random_state))) c2PhenoVals = skewnorm.rvs(-20, scale=4, size=phenNum,random_state=random_state) df['setAS2 c1']=np.append(c1Vals,c1PhenoVals) df['setAS3 c2']=np.append(c2Vals,c2PhenoVals) return(df)
def gen_remainder(self, height, width, time, y, x):
"""
Finds the values for the Dren and Vaelf Magics depending on the Magic
values from the previous time step.
:param height: The number of points in the y-dimension of the Map.
:param width: The number of points in the x-dimension of the Map.
:param time: The current time.
:param y: The y-location of the given point.
:param x: The x-location of the given point.
"""
if time == 0:
# Want higher end to be at 3.3; exp(1.1939) = 3.3
# Want lowest end to be at 0.7; exp(-0.3567) = 0.7
# Therefore use (1.1939 + 0.3567) / width
decay_loc = np.exp(1.1939 - ((1.5506 * x) / width))
growth_loc = np.exp(-0.3567 + ((1.5506 * x) / width))
# Want higher end to be at 0.5; exp(-0.6931) = 0.5
# Want lowest end to be at 0.25; exp(-1.3863) = 0.25
# Therefore use (-1.3863 + 0.6931) / height
decay_scale = np.exp(-0.6931 - ((0.6932 * x) / width))
growth_scale = np.exp(-1.3863 + ((0.6932 * x) / width))
self.magics[time, 8, y, x] = np.round(skewnorm.rvs(0,
loc=growth_loc,
scale=growth_scale))
self.magics[time, 11, y, x] = np.round(skewnorm.rvs(0,
loc=decay_loc,
scale=decay_scale))
else:
log_avg = self.find_LR_average(height, time-1, 8, y, x)
exp_avg = self.find_LR_average(height, time-1, 11, y, x)
decay_loc = np.exp(1.1939 - ((1.5506 * x) / width))
growth_loc = np.exp(-0.3567 + ((1.5506 * x) / width))
decay_scale = np.exp(-0.6931 - ((0.6932 * x) / width))
growth_scale = np.exp(-1.3863 + ((0.6932 * x) / width))
decay_skew = 3 * (log_avg - decay_loc)
growth_skew = 3 * (exp_avg - growth_loc)
self.magics[time, 8, y, x] = np.round(skewnorm.rvs(growth_skew,
loc=growth_loc,
scale=growth_scale))
self.magics[time, 11, y, x] = np.round(skewnorm.rvs(decay_skew,
loc=decay_loc,
scale=decay_scale))
def data_simulator4(ntrain, ntest, p, seeding, return_mean=False, corr=0):
nobs = ntrain + ntest
np.random.seed(seeding)
if corr != 0:
cov = gen_ar1_corr_matrix(corr, p)
mvnorm_x = np.random.multivariate_normal([0]*p, cov, nobs)
X = norm.cdf(mvnorm_x)
else:
X = np.random.uniform(size=(nobs, p))
if not return_mean:
Y = (10*np.sin(2*np.pi*X[:,0]*X[:,1])
+ 10*X[:,3] + 20*np.square((X[:,2] - 0.5))
+ 5*X[:,4]) + skewnorm.rvs(-5, size=nobs)
else:
Y = (10*np.sin(2*np.pi*X[:,0]*X[:,1])
+ 10*X[:,3] + 20*np.square((X[:,2] - 0.5))
+ 5*X[:,4])
Y = Y.reshape(-1,1)
TrainX = X[:ntrain,:]
TrainY = Y[:ntrain,:]
TestX = X[ntrain:,:]
TestY = Y[ntrain:,:]
return TrainX, TrainY, TestX, TestY
def rand_frag_size(dist_string):
dist_list = dist_string.split(',')
if dist_list[0] == 'skewed-normal':
rand_size = abs(
int(
round(
skewnormdist.rvs(float(dist_list[3]),
loc=float(dist_list[1]),
scale=float(dist_list[2])))))
elif dist_list[0] == 'normal':
rand_size = abs(
int(
round(
normdist.rvs(loc=float(dist_list[1]),
scale=float(dist_list[2])))))
elif dist_list[0] == 'uniform':
rand_size = abs(
int(
round(
uniformdist.rvs(loc=float(dist_list[1]),
scale=float(dist_list[2])))))
elif dist_list[0] == 'truncated-normal':
rand_size = abs(
int(
round(
truncnormdist.rvs(float(dist_list[3]),
float(dist_list[4]),
loc=float(dist_list[1]),
scale=float(dist_list[2])))))
return (rand_size)
def get_data(n):
data = np.concatenate(
(expon.rvs(scale=1, size=n // 2), skewnorm.rvs(5, loc=3, size=n // 2)))
#getting exp dist data
#now shuffle the data
np.random.shuffle(data)
return data
def asymmetric_samples(mean, plus, minus, size=5000):
if (plus == 0.0) and (minus == 0.0):
samples = np.ones(size)*mean
return samples
x = np.array([mean-minus, mean, mean+plus])
data = np.array([0.16, 0.5, 0.84])
params = Parameters()
params.add('mu', value=mean)
params.add('sigma', value=np.mean([minus, plus]))
params.add('alpha', value=0.0)
try:
minner = Minimizer(fnc2min, params, fcn_args=(x, data))
result = minner.minimize()
mu = result.params['mu'].value
sigma = result.params['sigma'].value
alpha = result.params['alpha'].value
samples = skewnorm.rvs(a=alpha, loc=mu, scale=sigma, size=size)
except ValueError:
print("Problem producing the distribution ot sampling from it.")
print("\nReversing to a Normal Distribution")
print("\nwith scale given by the average between the maximum and minimum errors.")
samples = norm.rvs(loc=mean, scale=np.mean([minus, plus]), size=size)
return samples
def old():
n, d = 10000, 100
random = check_random_state(0)
bg_weight = random.beta(1, 10, size=d)
bg_weight /= np.linalg.norm(bg_weight)
fg_weight = random.beta(1, 5, size=d)
fg_weight /= np.linalg.norm(fg_weight)
alpha = .5
skew = 0
loc = n / 5 * 4
scale = n / 10 * 3 / 4
bg_ts = uniform.rvs(0, n, size=int(n * (1 - alpha)), random_state=random)
fg_ts = skewnorm.rvs(skew,
loc,
scale,
size=int(n * alpha),
random_state=random)
bg_X = random.normal(scale=bg_weight, size=(len(bg_ts), len(bg_weight)))
fg_X = random.normal(scale=fg_weight, size=(len(fg_ts), len(fg_weight)))
ts = np.concatenate((bg_ts, fg_ts))
X = np.concatenate((bg_X, fg_X), axis=0)
order = np.argsort(ts)
ts = ts[order]
X = X[order]
np.savetxt('ts.csv', ts, fmt='%.2f')
np.savetxt('X.csv', X, fmt='%.5f')
def init_event_occuring(self,max_pos_skew = -5, max_neg_skew = 5):
"""
Initialise probabilities of event occurring. Generates a matrix of
probability distributions. Each row represents number of days passed due
date to visit location. Each row contains Gaussian distribution of
probability values form 0-1 where as the number of days passed due date
increases so the probability distribution becomes more negatively skewed
to higher probability values indicating increasing urgency of visiting
a location
"""
# Define list of distributions relating to the number of frames since last visited
# so let's say we base it over 100 frames
skewness_values = np.linspace(max_neg_skew, max_pos_skew, 100)
# generate 1000 random values for each skewness setting
dist_sample_size = 1000
# initialize the _sm_probabilities numpy array
self._probabilities = np.zeros((dist_sample_size, len(skewness_values)))
# Populate numpy array with probabilities changing the skewness of the prob distribution depending on how long it is since the agent last visited a specific destination
for idx, skewness in enumerate(skewness_values):
# generate _sm_probabilities distributions for each time frame
self._probabilities[:,idx] = skewnorm.rvs(a = skewness,loc=100, size=dist_sample_size)
# shift values so that lowest value is 0
self._probabilities[:,idx] = self._probabilities[:,idx] - np.min(self._probabilities[:,idx])
# standardise all values between 0 and 1
self._probabilities[:,idx] = self._probabilities[:,idx] / np.max(self._probabilities[:,idx])
return self._probabilities
def create_pdf(sd, mean, alfa):
#invertire il segno di alfa
x = skewnorm.rvs(alfa, size=1000000)
def calc(k, sd, mean):
return (k * sd) + mean
x = calc(x, sd, mean) #standard distribution
def _get_age_dist_values(total_users, *, seed=None):
"""Return Numpy array of age values, given total_users"""
if seed:
np.random.seed(seed)
# negative skewnorm dist - age range of 16 to 60 ish
return ((27 + 10 * skewnorm.rvs(a=2, size=total_users)).astype(int).clip(
min=16))
def gen_cold_and_ice(self, height, width, time, y, x):
"""
Determines the value of both Cold and Ice Magic at a point given the
values of the points nearby in the previous time step.
:param height: The number of points in the y-dimension of the Map.
:param width: The number of points in the x-dimension of the Map.
:param time: The value of time since the Map started its weather
tracking.
:param y: The y-location of the given point.
:param x: The x-location of the given point.
"""
if time == 0:
# Want higher end to be at 3.3; exp(1.1939) = 3.3
# Want lowest end to be at 0.7; exp(-0.3567) = 0.7
# Therefore use (1.1939 + 0.3567) / width
growth_loc = np.exp(-0.3567 + ((1.5506 * y) / height))
# Want higher end to be at 0.5; exp(-0.6931) = 0.5
# Want lowest end to be at 0.25; exp(-1.3863) = 0.25
# Therefore use (-1.3863 + 0.6931) / height
growth_scale = np.exp(-1.3863 + ((0.6932 * y) / height))
self.magics[time, 5, y, x] = np.round(skewnorm.rvs(0, loc=growth_loc,
scale=growth_scale))
self.magics[time, 7, y, x] = np.round(skewnorm.rvs(0, loc=growth_loc,
scale=growth_scale))
else:
average1 = self.find_BT_average(width, time-1, 4, y, x)
average2 = self.find_BT_average(width, time-1, 6, y, x)
growth_loc = np.exp(-0.3567 + ((1.5506 * y) / height))
skew_value1 = 3 * (average1 - growth_loc)
skew_value2 = 3 * (average2 - growth_loc)
growth_scale = np.exp(-1.3863 + ((0.6932 * y) / height))
seasonal_shift = 0.7 + 0.3 * np.cos(np.pi + 8.7266 * (10**-4)
* (time % 7200))
self.magics[time, 5, y, x] = np.round(skewnorm.rvs(skew_value1,
loc=growth_loc * seasonal_shift,
scale=growth_scale))
self.magics[time, 7, y, x] = np.round(skewnorm.rvs(skew_value2,
loc=growth_loc,
scale=growth_scale))
def get_skew_distribution(a, num_vertices):
# A is the skew, and num_vertices tells how many samples to take
r = skewnorm.rvs(a, size=num_vertices)
lower, upper = skewnorm.interval(.999, a)
# shifted = [x - lower for x in r]
diff = upper - lower
rescaled = [abs((x - lower) / diff) / 2 for x in r]
return rescaled
def conditional_sample_func4(X, nobs, seeding=1234):
loc1 = (10*np.sin(2*np.pi*X[0]*X[1]) + 10*X[3] + 20*np.square((X[2]-0.5))
+ 5*X[4])
np.random.seed(seed=seeding)
samples = loc1 + skewnorm.rvs(-5, size=nobs)
return samples
def test_joint_entropyND():
"""
Test that our implemented function to return the entropy corresponds
to Scipy's entropy method in multiple dimensions.
"""
gridpoints = 10 # for KDE estimation
## Test 2D joint entropy:
X = skewnorm.rvs(size=1000, a=-3, loc=0, scale=2)
Y = skewnorm.rvs(size=1000, a=-3, loc=0, scale=2)
data = pd.DataFrame({'X':X, 'Y':Y})
## So this is valid if test_get_pdf passes
pdf = get_pdf(data,gridpoints=gridpoints)
## The estimated entropy should correspond to scipy's value (to 5 d.p.)
assert_almost_equal(get_entropy(data,gridpoints=gridpoints), entropy(pdf.flatten(),base=2), 5)
def ABCsimulation(param): #param = [om, w0]
if param[0] < 0.0 or param[0] > 1.0:
return [None]*len(zbins)
else:
model_1_class = DistanceCalc(param[0],0,1-param[0],0,[param[1],0],0.7) #om,ok,ol,wmodel,de_params,h0
data_abc = np.zeros(len(zbins))
for i in range(len(zbins)):
data_abc[i] = model_1_class.mu(zbins[i]) + skewnorm.rvs(a, loc=e, scale=w, size=1)
return data_abc
def skewnorm_distribution_maker(bins, skew, focus=1.0, size=10000):
distro = skewnorm.rvs(skew, 1, 1, size=10000)
distro_histro = np.histogram(distro,
bins=bins,
range=(0, 10),
density=True)[0]
distro_histro = [i**0.25 for i in distro_histro]
distro_histro /= np.sum(distro_histro)
return distro_histro
def scale_obj(obj, mean_size=60, max_size=103, size_real=0, distance=0):
"""
Inputs: obj - object to work with
distance - distance in mm from camera, randomized if not set
size_real - length in mm of object, if not set this is randomized
based on species mean and maximum sizes
mean_size - common length of species
max_size - maximum length of species
Assuming a horizontal object, we scale it to the correct pixel size
based on distance from camera and known fields of view.
"""
if (distance == 0):
# cross section is trapezoid, height increases towards back of image.
# we want distribution to reflect this
max_distance = 700
min_distance = 200
s = skewnorm.rvs(-5, size=1, loc=650, scale=120)
s2 = (int)(np.clip(s, 200, 700))
distance = np.random.choice(s2)
if (size_real == 0):
# add some variance
var_size = (max_size - mean_size) / 3
#print(max_size, mean_size, var_size)
size_real = np.random.normal(mean_size, var_size)
#print('mean size = ' + str(mean_size) + ', var size = ' + str(var_size))
# in front of image, size is 1392px/339mm
# in back of image, size is 1392px/1190mm
pix_per_mm = (1392 / (339 + (distance - 200) * ((1190 - 339) /
(max_distance - 200))))
new_length = (int)(pix_per_mm * size_real)
ratio = new_length / (int)(np.shape(obj)[1])
#print('distance ' +str(distance))
#print('pix per mm ', str(pix_per_mm))
#print('ratio ' + str(ratio))
#print('new length ' + str(new_length) + 'px, ' + str(size_real) + ' mm')
new_height = (int)(np.shape(obj)[0] * ratio)
#print('new height ' + str(new_height))
# ensure nothing is resized to 0
if (new_length < 1):
new_length = 1
if (new_height < 1):
new_height = 1
new_obj = obj.resize((new_length, new_height))
return new_obj, distance
def test_error_when_nan_introduced_during_transform():
# test error when NA are introduced during the discretisation.
rng = default_rng()
# create dataframe with 2 variables, 1 normal and 1 skewed
random = skewnorm.rvs(a=-50, loc=4, size=100)
random = random - min(
random) # Shift so the minimum value is equal to zero.
train = pd.concat(
[
pd.Series(rng.standard_normal(100)),
pd.Series(random),
],
axis=1,
)
train.columns = ["var_a", "var_b"]
# create a dataframe with 2 variables normally distributed
test = pd.concat(
[
pd.Series(rng.standard_normal(100)),
pd.Series(rng.standard_normal(100)),
],
axis=1,
)
test.columns = ["var_a", "var_b"]
msg = ("During the discretisation, NaN values were introduced "
"in the feature(s) var_b.")
limits_dict = {"var_a": [-5, -2, 0, 2, 5], "var_b": [0, 2, 5]}
# check for warning when errors equals 'ignore'
with pytest.warns(UserWarning) as record:
transformer = ArbitraryDiscretiser(binning_dict=limits_dict,
errors="ignore")
transformer.fit(train)
transformer.transform(test)
# check that only one warning was returned
assert len(record) == 1
# check that message matches
assert record[0].message.args[0] == msg
# check for error when errors equals 'raise'
with pytest.raises(ValueError) as record:
transformer = ArbitraryDiscretiser(binning_dict=limits_dict,
errors="raise")
transformer.fit(train)
transformer.transform(test)
# check that error message matches
assert str(record.value) == msg
def skewNormGen(s1, xi_popts, ome_popts, alp_popts):
"""
skewNormGen
Generated S1 events using the effective marginalized pdf in S1.
inputs: s1, xi optimums, omega optimums, alpha optimums
"""
xi = FUNC_EPS(s1, *xi_popts)
omega = FUNC_OME(s1, *ome_popts)
alpha = FUNC_ALP(s1, *alp_popts)
return skewnorm.rvs(alpha, loc=xi, scale=omega)
def get_samples_from_percentiles(val,
ehi,
elo,
Nsamp=1e3,
add_p5_p95=True,
pltt=False):
'''Given the 16, 50, and 84 percentiles of a parameter's distribution,
fit a Skew normal CDF and sample it.'''
# don't do anything if NaNs are input
if np.any(np.isnan([val, ehi, elo])):
return np.nan, np.nan, np.repeat(np.nan, int(Nsamp))
# get percentiles
p16, med, p84 = float(val - elo), float(val), float(val + ehi)
assert p16 < med
assert med < p84
# add approximate percentiles to help with fitting the wings
# otherwise the resulting fitting distritubions tend to
if add_p5_p95:
p5_approx = med - 2 * (med - p16)
p95_approx = med + 2 * (p84 - med)
xin = [p5_approx, p16, med, p84, p95_approx]
yin = [.05, .16, .5, .84, .95]
else:
xin, yin = [p16, med, p84], [.16, .5, .84]
# make initial parameter guess
mu, sig = med, np.mean([abs(p16), abs(p84)])
a = (abs(p16) - abs(p84)) / sig
p0 = a, mu, sig
popt, pcov = curve_fit(Skewnorm_CDF_func,
xin,
yin,
p0=p0,
sigma=np.repeat(.01, len(yin)),
absolute_sigma=False)
# sample the fitted pdf
samples = skewnorm.rvs(*popt, size=int(Nsamp))
# plot distribution if desired
if pltt:
plt.hist(samples,
bins=30,
normed=True,
label='Sampled parameter posterior')
plt.plot(np.sort(samples),
skewnorm.pdf(np.sort(samples), *popt),
'-',
label='Skew-normal fit: a=%.3f, m=%.3f, s=%.3f' % tuple(popt))
plt.xlabel('Parameter values'), plt.legend(loc='upper right')
plt.show()
return p0, popt, samples
def generate(self) -> datetime:
"""
Generate a random date taken from a Skewed Normal Distribution
:return: datetime
"""
timestamp = round(self.center.timestamp())
generated = skewnorm.rvs(self.skew,
loc=timestamp,
scale=self.dispersion)
return datetime.fromtimestamp(generated)
def next_transaction(self):
# price
rand_delta = skewnorm.rvs(self.skewness, size=1)[0]
price = self.average_price * (rand_delta + 1)
price = max(self.min_gas_price, price)
if self.max_gas_price:
price = min(self.max_gas_price, price)
# gas consumed
# 1 <= gas_consumed <= 2* block_size / txs_per_block
gas = random.randint(1,
2 * int(self.block_gas_size / self.txs_per_block))
return Tx(gas, price)
def sim_data(chronological=True):
a = random.choice([2., 3., 4.])
ys = skewnorm.rvs(a, size=1000)
outliers = [
random.choice([-1., 1.]) * 10. if np.random.rand() < 0.05 else 0.
for _ in ys
]
xs = np.cumsum(0.1 * np.random.randn(1000))
zs = [
x + y * random.choice([-1., 1.]) + o
for x, y, o in zip(xs, ys, outliers)
]
return zs if chronological else list(reversed(zs))
def skewed_dist(max_value=10,
min_value=0,
num_values=10000,
skewness=5,
integers=False):
""" generate skewed distribution """
# Negative skewness values are left skewed (long right tail), positive values are right skewed (left tail).
random_list = skewnorm.rvs(a=skewness, loc=max_value, size=num_values)
return scale_a_distribution(random_list,
integers=integers,
max_value=max_value,
min_value=min_value)
def skew():
fig, ax = plt.subplots(1, 1) # This function creates a figure and a grid of subplots
a = 5 # skewness parameter, when a = 0 the distribution is identical to a normal distribution
# mean, variance, skew, kurt = skewnorm.stats ( a, moments='mvsk' )
d = skewnorm.rvs(a, size=350000)
ax.hist(d, density=True, histtype='stepfilled', alpha=0.2) # histogram of numbers generated
plt.show()
return d # return all the numbers generated with skew distribution
def rand_normal(loc=0, sigma=1, size=1, skew=0, decimals='all'):
# Normal distribution centered at loc
data = skewnorm.rvs(a=skew, loc=loc, scale=sigma, size=size)
if decimals != 'all':
data = np.around(data, decimals)
if decimals == 0:
data = data.astype(int)
if size == 1:
return data[0]
else:
return data
def generate_random_c_x1(self):
'''Function to generate random c and x1 from either Normal or skew normal distributions'''
if bench:
print 'Drawing color and stretch rvs...'
if self.c_pdf == 'Normal':
self.simc = np.random.normal(loc=self.c_params[0],
scale=self.c_params[1],
size=len(self.totz))
elif self.c_pdf == 'SkewNormal':
self.simc = skewnorm.rvs(self.c_params[2],
loc=self.c_params[0],
scale=self.c_params[1],
size=len(self.totz))
if self.x1_pdf == 'Normal':
self.simx1 = np.random.normal(loc=self.x1_params[0],
scale=self.x1_params[1],
size=len(self.totz))
elif self.x1_pdf == 'SkewNormal':
self.simx1 = skewnorm.rvs(self.x1_params[2],
loc=self.x1_params[0],
scale=self.x1_params[1],
size=len(self.totz))
def gen_waxing_burst(self, time, y, x):
"""
Generates points where Waxing Magic is extremely strong.
:param time: The value of time since the Map started its weather
tracking.
:param y: The y-location of the given point.
:param x: The x-location of the given point.
"""
self.magics[time, 3, y, x] = np.round(skewnorm.rvs(4, loc=0, scale=1.4))
if self.magics[time, 3, y, x] < 4:
self.magics[time, 3, y, x] = 0
def simulate_PDF(median, lower_err, upper_err, size=1, plot=True):
'''
Simulates a draw of posterior samples from a value and asymmetric errorbars
by assuming the underlying distribution is a skewed normal distribution.
Developed to estimate PDFs from literature exoplanet parameters that did not report their MCMC chains.
Inputs:
-------
median : float
the median value that was reported
lower_err : float
the lower errorbar that was reported
upper_err : float
the upper errorbar that was reported
size : int
the number of samples to be drawn
Returns:
--------
samples : array of float
the samples drawn from the simulated skrewed normal distribution
'''
sigma, omega, alpha = calculate_skewed_normal_params(
median, lower_err, upper_err)
samples = skewnorm.rvs(alpha, loc=sigma, scale=omega, size=size)
if plot == False:
return samples
else:
lower_err = np.abs(lower_err)
upper_err = np.abs(upper_err)
x = np.arange(median - 4 * lower_err, median + 4 * upper_err, 0.01)
fig = plt.figure()
for i in range(3):
plt.axvline([median - lower_err, median, median + upper_err][i],
color='k',
lw=2)
plt.plot(x, skewnorm.pdf(x, alpha, loc=sigma, scale=omega), 'r-', lw=2)
fit_percentiles = skewnorm.ppf([0.16, 0.5, 0.84],
alpha,
loc=sigma,
scale=omega)
for i in range(3):
plt.axvline(fit_percentiles[i], color='r', ls='--', lw=2)
plt.hist(samples, density=True, color='red', alpha=0.5)
return samples, fig
def draw_strain_skewstep(self, x, iter, beta):
q = x.copy()
lqxy = 0
a = 2
s = 1
diff = skewnorm.rvs(a, scale=s)
q[self.pnames.index(
'log10_h')] = x[self.pnames.index('log10_h')] - diff
lqxy = skewnorm.logpdf(-diff, a, scale=s) - skewnorm.logpdf(
diff, a, scale=s)
return q, float(lqxy)
""" ############################################################################### # By default, ChainConsumer uses maximum likelihood statistics. Thus you do not # need to explicitly enable maximum likelihood statistics. If you want to # anyway, the keyword is `"max"`. import numpy as np from scipy.stats import skewnorm from chainconsumer import ChainConsumer # Lets create some data here to set things up np.random.seed(0) data = skewnorm.rvs(5, size=(1000000, 2)) parameters = ["$x$", "$y$"] # Now the normal way of giving data is passing a numpy array and parameter separately c = ChainConsumer().add_chain(data, parameters=parameters).configure(statistics="max") fig = c.plotter.plot() fig.set_size_inches(4.5 + fig.get_size_inches()) # Resize fig for doco. You don't need this. ############################################################################### # Or we can enable cumulative statistics c = ChainConsumer().add_chain(data, parameters=parameters).configure(statistics="cumulative") fig = c.plotter.plot() fig.set_size_inches(4.5 + fig.get_size_inches()) # Resize fig for doco. You don't need this.
