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How to use stddev method in pytest-benchmark

Best Python code snippet using pytest-benchmark

main.py

Source:main.py

...31        32        elif(file == "standard deviation"):33            file2=str(input("Please enter concentration number\n"))34            if(file2 == '1'):35                stddev(loc1, 1)36            elif(file2 == '2'):37                stddev(loc1, 2)38            elif(file2 == '3'):39                stddev(loc1, 3)40            elif(file2 == '4'):41                stddev(loc1, 4)42            elif(file2 == '5'):43                stddev(loc1, 5)44            elif(file2 == '6'):45                stddev(loc1, 6)46            elif(file2 == '7'):47                stddev(loc1, 7)48            elif(file2 == '8'):49                stddev(loc1, 8)50                51        52        elif(file == "polynomial regression"):53            approxP(loc1)54        elif(file == "single"):55            singleWell(loc1)56        elif(file == "all"):57            allWells(loc1)58        elif(file == "groups"):59            groupWells(loc1)60        elif(file == "concentrations"):61            concenWells(loc1)62        elif(file == "triplicate"):63            tripWells(loc1)64        elif(file == "everything"):65            singleWell(loc1)66            allWells(loc1)67            groupWells(loc1)68            concenWells(loc1)69            tripWells(loc1)70        else:71            print('Invalid. Try again')72            73    elif(file == '2'):74        file=str(input("Please enter which graph you would like to see\n"))75        76        if(file == 'sigmoid'):77            x = pd.read_excel(loc2, usecols="B")78            x = np.array(x).reshape(-1)79            g2 = pd.read_excel(loc2, usecols="C:CT")80            #sigresults holds: [a1, a2, b1, b2, c1, c2, error1, error2]81            sigresults = train(x, g2)82            test(x, g2, sigresults)83        84        85        elif(file == "standard deviation"):86            file2=str(input("Please enter concentration number\n"))87            if(file2 == '1'):88                stddev(loc2, 1)89            elif(file2 == '2'):90                stddev(loc2, 2)91            elif(file2 == '3'):92                stddev(loc2, 3)93            elif(file2 == '4'):94                stddev(loc2, 4)95            elif(file2 == '5'):96                stddev(loc2, 5)97            elif(file2 == '6'):98                stddev(loc2, 6)99            elif(file2 == '7'):100                stddev(loc2, 7)101            elif(file2 == '8'):102                stddev(loc2, 8)103                104        elif(file == "polynomial regression"):105            approxP(loc2)106        elif(file == "single"):107            singleWell(loc2)108        elif(file == "all"):109            allWells(loc2)110        elif(file == "groups"):111            groupWells(loc2)112        elif(file == "concentrations"):113            concenWells(loc2)114        elif(file == "triplicate"):115            tripWells(loc2)116        elif(file == "everything"):117            singleWell(loc2)118            allWells(loc2)119            groupWells(loc2)120            concenWells(loc2)121            tripWells(loc2)122        else:123            print('Invalid. Try again')124            125    elif(file == '3'):126        file=str(input("Please enter which graph you would like to see\n"))127        128        if(file == 'sigmoid'):129            x = pd.read_excel(loc3, usecols="B")130            x = np.array(x).reshape(-1)131            g2 = pd.read_excel(loc3, usecols="C:CT")132            #sigresults holds: [a1, a2, b1, b2, c1, c2, error1, error2]133            sigresults = train(x, g2)134            test(x, g2, sigresults)135        136        elif(file == "standard deviation"):137            file2=str(input("Please enter concentration number\n"))138            if(file2 == '1'):139                stddev(loc3, 1)140            elif(file2 == '2'):141                stddev(loc3, 2)142            elif(file2 == '3'):143                stddev(loc3, 3)144            elif(file2 == '4'):145                stddev(loc3, 4)146            elif(file2 == '5'):147                stddev(loc3, 5)148            elif(file2 == '6'):149                stddev(loc3, 6)150            elif(file2 == '7'):151                stddev(loc3, 7)152            elif(file2 == '8'):153                stddev(loc3, 8)154        155        elif(file == "polynomial regression"):156            approxP(loc3)        157        elif(file == "single"):158            singleWell(loc3)159        elif(file == "all"):160            allWells(loc3)161        elif(file == "groups"):162            groupWells(loc3)163        elif(file == "concentrations"):164            concenWells(loc3)165        elif(file == "triplicate"):166            tripWells(loc3)167        elif(file == "everything"):168            singleWell(loc3)169            allWells(loc3)170            groupWells(loc3)171            concenWells(loc3)172            tripWells(loc3)173        else:174            print('Invalid. Try again')175            176    elif(file == '4'):177        file=str(input("Please enter which graph you would like to see\n"))178        179        if(file == 'sigmoid'):180            x = pd.read_excel(loc4, usecols="B")181            x = np.array(x).reshape(-1)182            g2 = pd.read_excel(loc4, usecols="C:CT")183            #sigresults holds: [a1, a2, b1, b2, c1, c2, error1, error2]184            sigresults = train(x, g2)185            test(x, g2, sigresults)186            187        elif(file == "standard deviation"):188            file2=str(input("Please enter concentration number\n"))189            if(file2 == '1'):190                stddev(loc4, 1)191            elif(file2 == '2'):192                stddev(loc4, 2)193            elif(file2 == '3'):194                stddev(loc4, 3)195            elif(file2 == '4'):196                stddev(loc4, 4)197            elif(file2 == '5'):198                stddev(loc4, 5)199            elif(file2 == '6'):200                stddev(loc4, 6)201            elif(file2 == '7'):202                stddev(loc4, 7)203            elif(file2 == '8'):204                stddev(loc4, 8)205        206        elif(file == "polynomial regression"):207            approxP(loc4)        208        elif(file == "single"):209            singleWell(loc4)210        elif(file == "all"):211            allWells(loc4)212        elif(file == "groups"):213            groupWells(loc4)214        elif(file == "concentrations"):215            concenWells(loc4)216        elif(file == "triplicate"):217            tripWells(loc4)218        elif(file == "everything"):219            singleWell(loc4)220            allWells(loc4)221            groupWells(loc4)222            concenWells(loc4)223            tripWells(loc4)224        else:225            print('Invalid. Try again')226        227    elif(file == '5'):228        file=str(input("Please enter which graph you would like to see\n"))229        230        if(file == 'sigmoid'):231            x = pd.read_excel(loc5, usecols="B")232            x = np.array(x).reshape(-1)233            g2 = pd.read_excel(loc5, usecols="C:CT")234            #sigresults holds: [a1, a2, b1, b2, c1, c2, error1, error2]235            sigresults = train(x, g2)236            test(x, g2, sigresults)237        238        elif(file == "standard deviation"):239            file2=str(input("Please enter concentration number\n"))240            if(file2 == '1'):241                stddev(loc5, 1)242            elif(file2 == '2'):243                stddev(loc5, 2)244            elif(file2 == '3'):245                stddev(loc5, 3)246            elif(file2 == '4'):247                stddev(loc5, 4)248            elif(file2 == '5'):249                stddev(loc5, 5)250            elif(file2 == '6'):251                stddev(loc5, 6)252            elif(file2 == '7'):253                stddev(loc5, 7)254            elif(file2 == '8'):255                stddev(loc5, 8)256        257        elif(file == "polynomial regression"):258            approxP(loc5)        259        elif(file == "single"):260            singleWell(loc5)261        elif(file == "all"):262            allWells(loc5)263        elif(file == "groups"):264            groupWells(loc5)265        elif(file == "concentrations"):266            concenWells(loc5)267        elif(file == "triplicate"):268            tripWells(loc5)269        elif(file == "everything"):270            singleWell(loc5)271            allWells(loc5)272            groupWells(loc5)273            concenWells(loc5)274            tripWells(loc5)275        else:276            print('Invalid. Try again')277        278    elif(file == '6'):279        file=str(input("Please enter which graph you would like to see\n"))280        281        if(file == 'sigmoid'):282            x = pd.read_excel(loc6, usecols="B")283            x = np.array(x).reshape(-1)284            g2 = pd.read_excel(loc6, usecols="C:CT")285            #sigresults holds: [a1, a2, b1, b2, c1, c2, error1, error2]286            sigresults = train(x, g2)287            test(x, g2, sigresults)288        289        elif(file == "standard deviation"):290            file2=str(input("Please enter concentration number\n"))291            if(file2 == '1'):292                stddev(loc6, 1)293            elif(file2 == '2'):294                stddev(loc6, 2)295            elif(file2 == '3'):296                stddev(loc6, 3)297            elif(file2 == '4'):298                stddev(loc6, 4)299            elif(file2 == '5'):300                stddev(loc6, 5)301            elif(file2 == '6'):302                stddev(loc6, 6)303            elif(file2 == '7'):304                stddev(loc6, 7)305            elif(file2 == '8'):306                stddev(loc6, 8)307         308        elif(file == "polynomial regression"):309            approxP(loc6)              310        elif(file == "single"):311            singleWell(loc6)312        elif(file == "all"):313            allWells(loc6)314        elif(file == "groups"):315            groupWells(loc6)316        elif(file == "concentrations"):317            concenWells(loc6)318        elif(file == "triplicate"):319            tripWells(loc6)320        elif(file == "everything"):321            singleWell(loc6)322            allWells(loc6)323            groupWells(loc6)324            concenWells(loc6)325            tripWells(loc6)326        else:327            print('Invalid. Try again')328            329    elif(file == '7'):330        file=str(input("Please enter which graph you would like to see\n"))331        332        if(file == 'sigmoid'):333            x = pd.read_excel(loc7, usecols="B")334            x = np.array(x).reshape(-1)335            g2 = pd.read_excel(loc7, usecols="C:CT")336            #sigresults holds: [a1, a2, b1, b2, c1, c2, error1, error2]337            sigresults = train(x, g2)338            test(x, g2, sigresults)339        340        elif(file == "standard deviation"):341            file2=str(input("Please enter concentration number\n"))342            if(file2 == '1'):343                stddev(loc7, 1)344            elif(file2 == '2'):345                stddev(loc7, 2)346            elif(file2 == '3'):347                stddev(loc7, 3)348            elif(file2 == '4'):349                stddev(loc7, 4)350            elif(file2 == '5'):351                stddev(loc7, 5)352            elif(file2 == '6'):353                stddev(loc7, 6)354            elif(file2 == '7'):355                stddev(loc7, 7)356            elif(file2 == '8'):357                stddev(loc7, 8)358        359        elif(file == "polynomial regression"):360            approxP(loc7)        361        elif(file == "single"):362            singleWell(loc7)363        elif(file == "all"):364            allWells(loc7)365        elif(file == "groups"):366            groupWells(loc7)367        elif(file == "concentrations"):368            concenWells(loc7)369        elif(file == "triplicate"):370            tripWells(loc7)371        elif(file == "everything"):...

figure15_helpers.py

Source:figure15_helpers.py

...10        lst = [float(s)]11    elif type(s) == list:12        lst = s13    return sum(lst)/len(lst)14def stddev(s):15    '''Compute stddev of list or string of values. Adapted from avg().'''16    if ',' in s:17        lst = [float(f) for f in s.split(',')]18    elif type(s) == str:19        lst = [float(s)]20    elif type(s) == list:21        lst = s22    return np.std(lst)23def figure15a_paper_data():24    '''Populate data struct for plot, using data from figure 15a in paper.25    For plotBarClusters() below. Fill in "Mininet ..." fields with test data.26    Extracted manually from paper using this tool:27    http://arohatgi.info/WebPlotDigitizer/app/28    '''...

noise.py

Source:noise.py

1import numpy as np2class AdaptiveParamNoiseSpec(object):3    def __init__(self, initial_stddev=0.1, desired_action_stddev=0.1, adoption_coefficient=1.01):4        self.initial_stddev = initial_stddev5        self.desired_action_stddev = desired_action_stddev6        self.adoption_coefficient = adoption_coefficient7        self.current_stddev = initial_stddev8    def adapt(self, distance):9        if distance > self.desired_action_stddev:10            # Decrease stddev.11            self.current_stddev /= self.adoption_coefficient12        else:13            # Increase stddev.14            self.current_stddev *= self.adoption_coefficient15    def get_stats(self):16        stats = {17            'param_noise_stddev': self.current_stddev,18        }19        return stats20    def __repr__(self):21        fmt = 'AdaptiveParamNoiseSpec(initial_stddev={}, desired_action_stddev={}, adoption_coefficient={})'22        return fmt.format(self.initial_stddev, self.desired_action_stddev, self.adoption_coefficient)23class ActionNoise(object):24    def reset(self):25        pass26class NormalActionNoise(ActionNoise):27    def __init__(self, mu, sigma):28        self.mu = mu29        self.sigma = sigma30    def __call__(self):31        return np.random.normal(self.mu, self.sigma)32    def __repr__(self):33        return 'NormalActionNoise(mu={}, sigma={})'.format(self.mu, self.sigma)34# Based on http://math.stackexchange.com/questions/1287634/implementing-ornstein-uhlenbeck-in-matlab35class OrnsteinUhlenbeckActionNoise(ActionNoise):36    def __init__(self, mu, sigma, theta=.15, dt=1e-2, x0=None):37        self.theta = theta38        self.mu = mu39        self.sigma = sigma40        self.dt = dt41        self.x0 = x042        self.reset()43    def __call__(self):44        x = self.x_prev + self.theta * (self.mu - self.x_prev) * self.dt + self.sigma * np.sqrt(self.dt) * np.random.normal(size=self.mu.shape)45        self.x_prev = x46        return x47    def reset(self):48        self.x_prev = self.x0 if self.x0 is not None else np.zeros_like(self.mu)49    def __repr__(self):...

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