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How to use by_operation method in localstack
Best Python code snippet using localstack_python
main.py
Source:main.py 
1import numpy as np2import matplotlib.pyplot as plt3import scipy.interpolate as interpolate4from scipy.optimize import curve_fit5import sys6class AsymmetricData:7 def __init__(self, mu=10.0, sigma_n=1.0, sigma_p=1.0, N=10000, confidence=1.0, creation_type='by_constructor', data=[]):8 """9 :param mu: Mode of the distribution, the most probable value10 :param sigma_n: Pegative sigma11 :param sigma_p: Positive sigma12 :param N: Sample size13 """14 self.mu = mu15 self.confidence = confidence # sigma16 self.sigma_n, self.sigma_p = sigma_n, sigma_p17 self.sigma2_n, self.sigma2_p = None, None18 self.sigma3_n, self.sigma3_p = None, None19 self.N = int(N)20 self.creation_type = creation_type21 if not any(data):22 self.data = np.asarray([])23 else:24 self.data = np.asarray(data)25 #self.creation_type = 'by_operation'26 self.bin_value = 5027 if str(self.creation_type) == 'by_constructor':28 if confidence == 1.0:29 self.sigma_n, self.sigma_p = sigma_n, sigma_p30 self.sigma2_n, self.sigma2_p = self.convert_from_1_sigma(self.mu, sigma_n, sigma_p, 2.0)31 self.sigma3_n, self.sigma3_p = self.convert_from_1_sigma(self.mu, sigma_n, sigma_p, 3.0)32 elif confidence == 2.0:33 self.sigma_n, self.sigma_p = self.convert_to_1_sigma(self.mu, sigma_n, sigma_p, 2.0)34 self.sigma2_n, self.sigma2_p = sigma_n, sigma_p35 self.sigma3_n, self.sigma3_p = self.convert_from_1_sigma(self.mu, self.sigma_n, self.sigma_p, 3.0)36 elif confidence == 3.0:37 self.sigma_n, self.sigma_p = self.convert_to_1_sigma(self.mu, sigma_n, sigma_p, 3.0)38 self.sigma2_n, self.sigma2_p = self.convert_from_1_sigma(self.mu, self.sigma_n, self.sigma_p, 2.0)39 self.sigma3_n, self.sigma3_p = sigma_n, sigma_p40 else:41 raise ValueError42 self.x_limits = [self.mu - 5.0*self.sigma_n, self.mu + 5.0*self.sigma_p]43 self.x_values = np.linspace(self.x_limits[0], self.x_limits[1], self.N)44 self.norm = 1.045 self.norm = self.calculate_norm()46 self.pdf_values = np.asarray(self.pdf(self.x_values))47 self.cdf_values = self.calculate_cdf_values()48 self.cdf = self.calculate_cdf()49 self.inverse_cdf = self.calculate_inverse_cdf()50 self.log_likelihood_values = self.log_likelihood(self.x_values)51 self.generate()52 elif str(self.creation_type) == 'by_operation':53 self.N = self.data.size54 self.fit()55 #self.sigma_n, self.sigma_p = self.estimate()56 self.sigma2_n, self.sigma2_p = self.convert_from_1_sigma(self.mu, self.sigma_n, self.sigma_p, 2.0)57 self.sigma3_n, self.sigma3_p = self.convert_from_1_sigma(self.mu, self.sigma_n, self.sigma_p, 3.0)58 self.x_limits = [self.mu - 5.0*self.sigma_n, self.mu + 5.0*self.sigma_p]59 self.x_values = np.linspace(self.x_limits[0], self.x_limits[1], self.N)60 self.norm = 1.061 self.norm = self.calculate_norm()62 self.pdf_values = np.asarray(self.pdf(self.x_values))63 self.cdf_values = self.calculate_cdf_values()64 self.cdf = self.calculate_cdf()65 self.inverse_cdf = self.calculate_inverse_cdf()66 def __str__(self):67 output = f"Value = {self.mu:.4f} (-{self.sigma_n:.4f}, +{self.sigma_p:.4f}) (1 sigma)"68 output2 = f"Value = {self.mu:.4f} (-{self.sigma2_n:.4f}, +{self.sigma2_p:.4f}) (2 sigma)"69 output3 = f"Value = {self.mu:.4f} (-{self.sigma3_n:.4f}, +{self.sigma3_p:.4f}) (3 sigma)"70 result = "{}\n{}\n{}".format(output, output2, output3)71 return result72 @classmethod73 def new(cls, mu=10.0, sigma_n=1.0, sigma_p=1.0, N=10000):74 return cls(mu, sigma_n, sigma_p, N)75 def integrate(self):76 delta_x = self.x_limits[1] - self.x_limits[0]77 c = delta_x / (self.N - 1)78 # x_values = np.linspace(self.x_limits[0], self.x_limits[1], self.N, dtype=float)79 area = np.sum(c * self.pdf(self.x_values))80 return area81 def calculate_norm(self):82 area = self.integrate()83 norm = 1/area84 return norm85 def pdf(self, x):86 par_1 = (2.0 * self.sigma_p * self.sigma_n) / (self.sigma_p + self.sigma_n)87 par_2 = (self.sigma_p - self.sigma_n) / (self.sigma_p + self.sigma_n)88 par_3 = (-1.0/2.0) * ((self.mu - x)/(par_1 + par_2*(x - self.mu)))**2.089 par_4 = self.norm / (2.0 * np.pi)**0.590 value = par_4 * np.exp(par_3)91 return value92 def log_likelihood(self, x):93 par_1 = (2.0 * self.sigma_p * self.sigma_n) / (self.sigma_p + self.sigma_n)94 par_2 = (self.sigma_p - self.sigma_n) / (self.sigma_p + self.sigma_n)95 value = (-1.0/2.0) * ((self.mu - x)/(par_1 + par_2*(x - self.mu)))**2.096 return value97 def calculate_cdf_values(self):98 delta_x = self.x_limits[1] - self.x_limits[0]99 c = delta_x / (self.N - 1)100 area = 0.0101 cdf_values = np.asarray([])102 for i in range(self.N):103 area += self.pdf_values[i] * c104 cdf_values = np.append(cdf_values, area)105 return cdf_values106 def calculate_cdf(self):107 cdf = interpolate.interp1d(self.x_values, self.cdf_values, kind='nearest')108 return cdf109 def calculate_inverse_cdf(self):110 inverse_cdf = interpolate.interp1d(self.cdf_values, self.x_values, kind='nearest')111 return inverse_cdf112 def generate(self):113 rnd_prob = np.random.uniform(0, 1, self.N)114 self.data = self.inverse_cdf(rnd_prob)115 @staticmethod116 def fit_func(x, norm, mu, sigma_n, sigma_p):117 par_1 = (2.0 * sigma_p * sigma_n) / (sigma_p + sigma_n)118 par_2 = (sigma_p - sigma_n) / (sigma_p + sigma_n)119 par_3 = (-1.0 / 2.0) * ((mu - x) / (par_1 + par_2 * (x - mu))) ** 2.0120 par_4 = norm / (2.0 * np.pi) ** 0.5121 value = par_4 * np.exp(par_3)122 return value123 def fit(self, expected_values=None):124 y, x, _ = plt.hist(self.data, bins=int(self.N/250))125 plt.clf()126 x = (x[1:] + x[:-1]) / 2 # for len(x)==len(y)127 mod = None128 max_y = max(y)129 for i in range(len(y)):130 if y[i] == max_y:131 mod = x[i]132 #print("mod", mod)133 #print(len(self.data))134 min_data = min(self.data)135 max_data = max(self.data)136 norm = 1000.0137 if not expected_values:138 expected_values = norm, mod, (mod - min_data) * 0.1, (max_data - mod) * 0.1139 expected = (expected_values[0], expected_values[1], expected_values[2], expected_values[3])140 params, cov = curve_fit(self.fit_func, x, y, expected, method='trf')141 self.norm = params[0]142 self.mu = params[1]143 #print("params", params)144 if params[2] > 0.0:145 self.sigma_n = (params[2])146 self.sigma_p = (params[3])147 else:148 self.sigma_n = (params[3])149 self.sigma_p = (params[2])150 def estimate(self, confidence=1.0):151 target_likelihood = -0.5 * float(confidence)152 delta_steps = 1e-5153 current_value = self.mu154 delta = abs(self.mu - self.sigma_p) * delta_steps155 current_likelihood = self.log_likelihood(current_value)156 while abs(current_likelihood) < abs(target_likelihood):157 current_value += delta158 current_likelihood = self.log_likelihood(current_value)159 positive_limit = current_value160 current_value = self.mu161 delta = abs(self.mu - self.sigma_n) * delta_steps162 current_likelihood = self.log_likelihood(current_value)163 while abs(current_likelihood) < abs(target_likelihood):164 current_value -= delta165 current_likelihood = self.log_likelihood(current_value)166 negative_limit = current_value167 print("interval found")168 return [self.mu - negative_limit, positive_limit - self.mu]169 def plot_pdf(self, show=True, save=False):170 plt.clf()171 plt.plot(self.x_values, self.pdf_values, color="blue")172 plt.xlabel("x")173 plt.ylabel("prob")174 if save:175 plt.savefig("plot_pdf.png", dpi=300)176 if show:177 plt.show()178 def plot_log_likelihood(self, show=True, save=False):179 plt.clf()180 plt.plot(self.x_values, self.log_likelihood(self.x_values))181 plt.ylim([-5, 1.5])182 plt.xlabel("x")183 plt.ylabel("ln L")184 plt.axhline(y=-0.5, color="black", ls="--", lw="2.0", label=f"Value = {self.mu:.4f} (-{self.sigma_n:.4f}, +{self.sigma_p:.4f}) (1 sigma)")185 plt.axhline(y=-2.0, color="black", ls="--", lw="1.5", label=f"Value = {self.mu:.4f} (-{self.sigma2_n:.4f}, +{self.sigma2_p:.4f}) (2 sigma)")186 plt.axhline(y=-4.5, color="black", ls="--", lw="1.0", label=f"Value = {self.mu:.4f} (-{self.sigma3_n:.4f}, +{self.sigma3_p:.4f}) (3 sigma)")187 plt.legend()188 if save:189 plt.savefig("plot_log_likelihood.png", dpi=300)190 if show:191 plt.show()192 def plot_cdf(self, show=True, save=False):193 plt.plot(self.x_values, self.cdf(self.x_values))194 if save:195 plt.savefig("plot_cdf.png", dpi=300)196 if show:197 plt.show()198 def plot_data(self, bins=None, show=True, save=False):199 if not bins:200 bins = self.bin_value201 plt.clf()202 plt.hist(self.data, bins=bins, density=True, color="green", alpha=0.7)203 if save:204 plt.savefig("plot_data.png", dpi=300)205 if show:206 plt.show()207 def plot_data_and_pdf(self, bins=None, show=True, save=False):208 if not bins:209 bins = self.bin_value210 plt.clf()211 plt.hist(self.data, bins=bins, density=True, color="green", alpha=0.6)212 plt.plot(self.x_values, self.pdf_values, color="blue")213 plt.xlabel("x")214 plt.ylabel("Prob.")215 plt.axvline(x=self.mu - self.sigma_n, color="black", ls="--", lw="1.5",216 label=f"Value = {self.mu:.4f} (-{self.sigma_n:.4f}, +{self.sigma_p:.4f}) (1 sigma)")217 plt.axvline(x=self.mu + self.sigma_p, color="black", ls="--", lw="1.5")218 plt.axvline(x=self.mu - self.sigma2_n, color="black", ls="--", lw="1.0",219 label=f"Value = {self.mu:.4f} (-{self.sigma2_n:.4f}, +{self.sigma2_p:.4f}) (2 sigma)")220 plt.axvline(x=self.mu + self.sigma2_p, color="black", ls="--", lw="1.0")221 plt.axvline(x=self.mu - self.sigma3_n, color="black", ls="--", lw="0.5",222 label=f"Value = {self.mu:.4f} (-{self.sigma3_n:.4f}, +{self.sigma3_p:.4f}) (3 sigma)")223 plt.axvline(x=self.mu + self.sigma3_p, color="black", ls="--", lw="0.5")224 plt.legend()225 if save:226 plt.savefig("plot_data_and_pdf.png", dpi=300)227 if show:228 plt.show()229 def plot_pdf_cdf(self, show=True, save=False):230 plt.plot(self.x_values, self.cdf_values)231 plt.plot(self.x_values, self.pdf_values)232 if save:233 plt.savefig("plot_pdf_cdf.png", dpi=300)234 if show:235 plt.show()236 def __add__(self, other):237 if isinstance(other, self.__class__):238 add = self.data + other.data239 print(len(add))240 elif isinstance(other, (int, float)):241 add = self.data + float(other)242 else:243 print("Unindentified input type! ({}, {})".format(other, type(other)))244 sys.exit()245 temp_obj = AsymmetricData(creation_type='by_operation', data=add)246 return temp_obj247 def __radd__(self, other):248 if isinstance(other, self.__class__):249 add = other.data + self.data250 elif isinstance(other, (int, float)):251 add = float(other) + self.data252 else:253 print("Unindentified input type! ({}, {})".format(other, type(other)))254 sys.exit()255 temp_obj = AsymmetricData(creation_type='by_operation', data=add)256 return temp_obj257 def __sub__(self, other):258 if isinstance(other, self.__class__):259 add = self.data - other.data260 elif isinstance(other, (int, float)):261 add = self.data - float(other)262 else:263 print("Unindentified input type! ({}, {})".format(other, type(other)))264 sys.exit()265 temp_obj = AsymmetricData(creation_type='by_operation', data=add)266 return temp_obj267 def __rsub__(self, other):268 if isinstance(other, self.__class__):269 add = other.data - self.data270 elif isinstance(other, (int, float)):271 add = float(other) - self.data272 else:273 print("Unindentified input type! ({}, {})".format(other, type(other)))274 sys.exit()275 temp_obj = AsymmetricData(creation_type='by_operation', data=add)276 return temp_obj277 def __mul__(self, other):278 if isinstance(other, self.__class__):279 add = self.data * other.data280 elif isinstance(other, (int, float)):281 add = self.data * float(other)282 else:283 print("Unindentified input type! ({}, {})".format(other, type(other)))284 sys.exit()285 temp_obj = AsymmetricData(creation_type='by_operation', data=add)286 return temp_obj287 def __rmul__(self, other):288 if isinstance(other, self.__class__):289 add = other.data * self.data290 elif isinstance(other, (int, float)):291 add = float(other) * self.data292 else:293 print("Unindentified input type! ({}, {})".format(other, type(other)))294 sys.exit()295 temp_obj = AsymmetricData(creation_type='by_operation', data=add)296 return temp_obj297 def __truediv__(self, other):298 if isinstance(other, self.__class__):299 add = self.data / other.data300 elif isinstance(other, (int, float)):301 add = self.data / float(other)302 else:303 print("Unindentified input type! ({}, {})".format(other, type(other)))304 sys.exit()305 temp_obj = AsymmetricData(creation_type='by_operation', data=add)306 return temp_obj307 def __rtruediv__(self, other):308 if isinstance(other, self.__class__):309 add = other.data / self.data310 elif isinstance(other, (int, float)):311 add = float(other) / self.data312 else:313 print("Unindentified input type! ({}, {})".format(other, type(other)))314 sys.exit()315 temp_obj = AsymmetricData(creation_type='by_operation', data=add)316 return temp_obj317 def __pow__(self, other):318 if isinstance(other, self.__class__):319 add = self.data ** other.data320 elif isinstance(other, (int, float)):321 add = self.data ** float(other)322 else:323 print("Unindentified input type! ({}, {})".format(other, type(other)))324 sys.exit()325 temp_obj = AsymmetricData(creation_type='by_operation', data=add)326 return temp_obj327 def __rpow__(self, other):328 if isinstance(other, self.__class__):329 add = other.data ** self.data330 elif isinstance(other, (int, float)):331 add = float(other) ** self.data332 else:333 print("Unindentified input type! ({}, {})".format(other, type(other)))334 sys.exit()335 temp_obj = AsymmetricData(creation_type='by_operation', data=add)336 return temp_obj337 @staticmethod338 def lnL(x, mu, sigma_n, sigma_p):339 par_1 = (2.0 * sigma_p * sigma_n) / (sigma_p + sigma_n)340 par_2 = (sigma_p - sigma_n) / (sigma_p + sigma_n)341 value = (-1.0 / 2.0) * ((mu - x) / (par_1 + par_2 * (x - mu))) ** 2.0342 return value343 @staticmethod344 def residual(params1, mu, n3, p3, confidence):345 n1, p1 = params1346 if confidence == 1.0:347 target_likelihood = -0.5348 elif confidence == 2.0:349 target_likelihood = -2.0350 elif confidence == 3.0:351 target_likelihood = -4.5352 else:353 target_likelihood = -0.5354 print("Something went wrong!")355 resid = (AsymmetricData.lnL(mu - n3, mu, n1, p1) - target_likelihood) ** 2.0 + (356 AsymmetricData.lnL(mu + p3, mu, n1, p1) - target_likelihood) ** 2.0357 return resid358 @staticmethod359 def convert_to_1_sigma(mu, n, p, confidence):360 N = 500361 n_range = np.linspace(1e-5, n, N)362 p_range = np.linspace(1e-5, p, N)363 np_matrix = np.zeros([n_range.shape[0], p_range.shape[0]])364 for i in range(n_range.shape[0]):365 for j in range(p_range.shape[0]):366 np_matrix[i, j] = np.log(AsymmetricData.residual([n_range[i], p_range[j]], mu, n, p, confidence))367 min_val = np_matrix.min()368 index_n, index_p = np.where(np_matrix == min_val)369 n_new, p_new = n_range[index_n[0]], p_range[index_p[0]]370 #print("")371 #print("# Converting to 1 sigma")372 #print("# {} (-{},+{}) ({} sigma) -> {} (-{},+{}) ({} sigma)".format(mu, n, p, confidence, mu, n_new, p_new, 1.0))373 return [n_new, p_new]374 @staticmethod375 def convert_from_1_sigma(mu, sigma_n, sigma_p, confidence):376 if confidence == 1.0:377 target_likelihood = -0.5378 elif confidence == 2.0:379 target_likelihood = -2.0380 elif confidence == 3.0:381 target_likelihood = -4.5382 else:383 target_likelihood = -0.5384 delta_steps = 1e-4385 current_value = mu386 delta = abs(mu - sigma_p) * delta_steps387 current_likelihood = AsymmetricData.lnL(current_value, mu, sigma_n, sigma_p)388 while abs(current_likelihood) < abs(target_likelihood):389 current_value += delta390 current_likelihood = AsymmetricData.lnL(current_value, mu, sigma_n, sigma_p)391 positive_limit = current_value392 current_value = mu393 delta = abs(mu - sigma_n) * delta_steps394 current_likelihood = AsymmetricData.lnL(current_value, mu, sigma_n, sigma_p)395 while abs(current_likelihood) < abs(target_likelihood):396 current_value -= delta397 current_likelihood = AsymmetricData.lnL(current_value, mu, sigma_n, sigma_p)398 negative_limit = current_value399 n_new, p_new = mu - negative_limit, positive_limit - mu400 #print("")401 #print("# Converting from 1 sigma")402 #print("# {} (-{},+{}) ({} sigma) -> {} (-{},+{}) ({} sigma)".format(mu, sigma_n, sigma_p, 1.0, mu, n_new, p_new, confidence))...
histogram-input.py
Source:histogram-input.py
1from soad import AsymmetricData as asyd2import numpy as np3N=500004data_input = list(np.random.normal(10,1,N))5a = asyd(creation_type='by_operation', data=data_input)6print(a)7a.plot_data_and_pdf(show=True, save=False)8a.plot_pdf()9#******* Ignore this part **********10#c=a**0.111#print("print c", c)12#c.plot_data_and_pdf()13#plt.hist(a.data, bins=80, density=True, color='red', alpha=0.5)14#plt.hist(b.data, bins=80, density=True, color='blue', alpha=0.5)15#plt.hist(c.data, bins=100, density=True, color='cyan', alpha=0.5)16#plt.show()17#a = asyd(10,1,1.2,N=N)18#b = asyd(10,1,1.2,N=N)19"""20c = asyd(10,1,1.2,N=N)21d = asyd(10,1,1.2,N=N)22e = asyd(10,1,1.2,N=N)23f = asyd(10,1,1.2,N=N)24g = asyd(10,1,1.2,N=N)25h = asyd(10,1,1.2,N=N)26print(a,b,c,d,e,f,g,h,sep="\n")27data_input = np.concatenate((a.data, b.data, c.data, d.data, e.data, f.data, g.data, h.data))28"""29#data_input = np.concatenate((a.data, b.data))30#x = asyd(creation_type='by_operation', data=data_input)31#x = (a+b+c+d+e+f+g+h+a+b+c+d+e+f+g+h+a+b+c+d+e+f+g+h+a+b+c+d+e+f+g+h)/32.032#x = (a+b)/2.033#print(x)34#print("asym a: {}".format((a.sigma_p-a.sigma_n)/(a.sigma_p+a.sigma_n)))35#print("asym x: {}".format((x.sigma_p-x.sigma_n)/(x.sigma_p+x.sigma_n)))36#x.plot_data_and_pdf()37#means = []38#for i in range(5000):39# temp = asyd(10,1.0,1.5,500)40# temp_mean = np.sum(temp.data)/temp.N41# means.append(temp_mean)42#plt.hist(means, bins=30)...
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