diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..6bcfafb --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ +slurm/ +Private_Results/ +notebooks/ +ClusterOutput/ \ No newline at end of file diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 0000000..306f58e --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,16 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python: Current File", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": true + } + ] +} \ No newline at end of file diff --git a/CLI_test/Input_2025-01-12_01-23-04.csv b/CLI_test/Input_2025-01-12_01-23-04.csv new file mode 100644 index 0000000..899dbbf --- /dev/null +++ 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+ddm,400,200,0.1,1.18,0.5,0.5,0.3,0.33,0.3,0.25,0,0.1,0.05,500,1 +ddm,560,200,0.1,1.18,0.5,0.5,0.3,0.33,0.3,0.25,0,0.1,0.05,500,1 +ddm,720,200,0.1,1.18,0.5,0.5,0.3,0.33,0.3,0.25,0,0.1,0.05,500,1 diff --git a/ClusterInput/InputFile_GD_DDM.xlsx b/ClusterInput/InputFile_GD_DDM.xlsx new file mode 100644 index 0000000..5d56b0b Binary files /dev/null and b/ClusterInput/InputFile_GD_DDM.xlsx differ diff --git a/ClusterInput/InputFile_IC_DDM.csv b/ClusterInput/InputFile_IC_DDM.csv new file mode 100644 index 0000000..9db14aa --- /dev/null +++ b/ClusterInput/InputFile_IC_DDM.csv @@ -0,0 +1,76 @@ +model,ntrials,npp,mean_v,mean_a,mean_z,mean_t,std_v,std_a,std_z,std_t,tau,nreps,full_speed +ddm,40,20,0.1,1.18,0.5,0.5,0.1,0.33,0.1,0.25,0.8,500,1 +ddm,60,20,0.1,1.18,0.5,0.5,0.1,0.33,0.1,0.25,0.8,500,1 +ddm,80,20,0.1,1.18,0.5,0.5,0.1,0.33,0.1,0.25,0.8,500,1 +ddm,100,20,0.1,1.18,0.5,0.5,0.1,0.33,0.1,0.25,0.8,500,1 +ddm,120,20,0.1,1.18,0.5,0.5,0.1,0.33,0.1,0.25,0.8,500,1 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+ddm,100,100,0.1,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0.8,500,1 +ddm,120,100,0.1,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0.8,500,1 diff --git a/ClusterInput/ProCor/ProCor_InputFile_IC_DDM.csv b/ClusterInput/ProCor/ProCor_InputFile_IC_DDM.csv new file mode 100644 index 0000000..836529c --- /dev/null +++ b/ClusterInput/ProCor/ProCor_InputFile_IC_DDM.csv @@ -0,0 +1,10 @@ +model,ntrials,npp,mean_v,mean_a,mean_z,mean_t,std_v,std_a,std_z,std_t,tau,nreps,full_speed +ddm,100,20,2.4,1.18,0.5,0.5,0.2,0.33,0.1,0.25,0.8,500,1 +ddm,100,20,2.1,1.18,0.5,0.5,0.2,0.33,0.1,0.25,0.8,500,1 +ddm,100,20,1.8,1.18,0.5,0.5,0.2,0.33,0.1,0.25,0.8,500,1 +ddm,100,20,1.5,1.18,0.5,0.5,0.2,0.33,0.1,0.25,0.8,500,1 +ddm,100,20,1.2,1.18,0.5,0.5,0.2,0.33,0.1,0.25,0.8,500,1 +ddm,100,20,0.9,1.18,0.5,0.5,0.2,0.33,0.1,0.25,0.8,500,1 +ddm,100,20,0.6,1.18,0.5,0.5,0.2,0.33,0.1,0.25,0.8,500,1 +ddm,100,20,0.3,1.18,0.5,0.5,0.2,0.33,0.1,0.25,0.8,500,1 +ddm,100,20,0,1.18,0.5,0.5,0.2,0.33,0.1,0.25,0.8,500,1 diff --git a/ClusterInput/old/InputFile_EC_DDM.csv b/ClusterInput/old/InputFile_EC_DDM.csv new file mode 100644 index 0000000..433e5d8 --- /dev/null +++ b/ClusterInput/old/InputFile_EC_DDM.csv @@ -0,0 +1,76 @@ +model,ntrials,npp,mean_v,mean_a,mean_z,mean_t,std_v,std_a,std_z,std_t,par_ind,True_correlation,TypeIerror,nreps,full_speed +ddm,80,40,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0,0.1,0.05,500,1 +ddm,240,40,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0,0.1,0.05,500,1 +ddm,400,40,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0,0.1,0.05,500,1 +ddm,560,40,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0,0.1,0.05,500,1 +ddm,720,40,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0,0.1,0.05,500,1 +ddm,80,80,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0,0.1,0.05,500,1 +ddm,240,80,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0,0.1,0.05,500,1 +ddm,400,80,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0,0.1,0.05,500,1 +ddm,560,80,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0,0.1,0.05,500,1 +ddm,720,80,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0,0.1,0.05,500,1 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b/ClusterOutput/cluster_outputs_0/data_EC/PowerEC0P0.3SD0.1TC240T160N500M.csv @@ -0,0 +1,3 @@ +,v,tau,true_pValue,conventional_power,model,ntrials,npp,mean_v,mean_a,mean_z,mean_t,std_v,std_a,std_z,std_t,par_ind,True_correlation,TypeIerror,nreps,full_speed +0,0.028,0.1552253313040799,0.10416885312176027,0.24396537089781423,,,,,,,,,,,,,,,, +16,,,,,ddm,240,160,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0,0.1,0.05,500,1 diff --git a/ClusterOutput/cluster_outputs_0/data_EC/PowerEC0P0.3SD0.1TC240T200N500M.csv b/ClusterOutput/cluster_outputs_0/data_EC/PowerEC0P0.3SD0.1TC240T200N500M.csv new file mode 100644 index 0000000..9d5f95f --- /dev/null +++ b/ClusterOutput/cluster_outputs_0/data_EC/PowerEC0P0.3SD0.1TC240T200N500M.csv @@ -0,0 +1,3 @@ +,v,tau,true_pValue,conventional_power,model,ntrials,npp,mean_v,mean_a,mean_z,mean_t,std_v,std_a,std_z,std_t,par_ind,True_correlation,TypeIerror,nreps,full_speed +0,0.018,0.1387888539150407,0.07943485244720527,0.2927653024723611,,,,,,,,,,,,,,,, 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likelihood(parameter_set, data): - """ - - Parameters - ---------- - parameter_set : numpy array, shape = (2,) - Contains the current estimates for each parameter used to calculate the likelihood of the data given this parameter set. - Contains two values: parameter_set[0] = learning rate, parameter_set[1] = inverse_temperature - data : csv file name = (ntrials X 4) - Data that will be used to estimate the likelihood of the data given the current parameter set. The data should contain one row per trial and 4 columns, - Its columns are: [Trial, Stimulus, Response, Reward]. - The Trial column should contain a value indicating the trial number in increasing order. - The Stimulus column should contain an integer value of 0 to Nstim-1 for each trial, indicating which stimulus is presented - The Response column should contain an integer value of 0 to Nresp-1 for each trial, indicating which response was given by the participant - The Reward column contains a value for the reward that is given to the participant on each trial. - Returns - ------- - -summed_logL : float - The negative summed log likelihood of the data given the current parameter set. This value will be used to select - the next parameter set that will be evaluated. The goal is to find the most optimal parameters given the data, - the parameters for which the -summed_logL of all responses is minimal. - - Description - ----------- - Function to estimate the likelihood of the parameter set under consideration (learning_rate and inverse_temperature) given the data: L(parameter set|data). - The design is exactly the same as the design used to simulate_data for this hypothetical participant, but now the simulated responses are included as well. - On each trial: L(parameter set|current response) = P(current response|parameter set). This probability is calculated using the softmax choice rule: - P(responseX) = exp(value_responseX*inverse_temperature) / (exp(value_response0*inverse_temperature) + exp(value_response1*inverse_temperature)) with X = current response (0 or 1). - This probability depends on the LR since this defines the value_responseX and on the inverse_temperature since this is part of the softmax function. - Over trials: summed log likelihood = sum(log(L(parameter set | current response))) with the best fitting parameter set yielding the highest summed logL. - The function returns -summed_LogL because the optimization function that will be used to find the most likely parameters given the data searches for the minimum value for this likelihood function. - """ -#First retransform the transformedLR to the originalLR - retransformed_LR = LR_retransformation(parameter_set[0]) - retransformed_invT = InverseT_retransformation(parameter_set[1]) - - df = pd.read_csv(data) #Read data - ntrials = df.shape[0] #Extract number of trials - - nstim = len(np.unique(df.iloc[:,1])) #Extract number of stimuli - nresp = len(np.unique(df.iloc[:,2])) #Extract number of responses - -#Prepare the likelihood estimation process: make sure all relevant variables are defined - # the start values for each stimulus-response pair: a-priori, every response is equally likely and the values are scaled with the max reward - values = (np.ones((nstim, nresp))/nresp )* np.max(df.iloc[:,3]) - -#Start the likelihood estimation process: summed_logL = log(L(parameter set|data)) - # log(L(parameter set|data)) = sum( log( L(parameter set|response) ) for trial in trials) - summed_logL = 0 # this is calculated by summing over trials the log( L(parameter set|response on that trial) ) - - # trial-loop: calculate log(L(parameter set|response)) on each trial - for trial in range(ntrials): - #Define the variables that are important for the likelihood estimation process - stimulus = int(df.iloc[trial,1]) #the stimulus shown on this trial - response = int(df.iloc[trial,2]) #the response given on this trial - reward_this_trial = int(df.iloc[trial,3]) #the reward given on this trial - - #Calculate the loglikelihood: log(L(parameter set|response)) = log(P(response|parameter set)) - #select the correct response_values given the stimulus on this trial - stimulus_weights = values[stimulus, :] - - # this to ensure no overflows are encountered in the estimation process - if np.abs(retransformed_invT) > 99: - # loglikelihoods = np.array([-999, -999]) - summed_logL = -9999 - break - else: loglikelihoods = np.log(softmax(stimulus_weights, retransformed_invT)) - - #then select the probability of the actual response given the parameter set - current_loglikelihood = loglikelihoods[response] - - #Add L(parameter set|current response) to the total log likelihood - summed_logL = summed_logL + current_loglikelihood - #Values are updated with current_learning_rate - - #Update the value for the relevant stimulus-response pair using the delta rule - # (since this influences the probability of the responses on the next trials) - PE, updated_value = delta_rule(previous_value = values[stimulus, response], - obtained_reward = reward_this_trial, - LR = retransformed_LR) - values[stimulus, response] = updated_value - return -summed_logL - -def simulate_responses(simulation_LR = 0.5, simulation_inverseTemp = 1, data = "filename"): - """ - - Parameters - ---------- - simulation_LR : float, optional - Value for the learning rate parameter that will be used to simulate data for this participant. The default is 0.5. - simulation_inverseTemp : float, optional - Value for the inverse temperature parameter that will be used to simulate data for this participant. The default is 1. - design : csv file name = (ntrials X 4) - Data that will be used to estimate the likelihood of a response and prediction error given the current parameter set. The data should contain one row per trial and 4 columns, - Its columns are: [Trial, Stimulus, Response, Reward]. - The Trial column should contain a value indicating the trial number in increasing order. - The Stimulus column should contain an integer value of 0 to Nstim-1 for each trial, indicating which stimulus is presented - The Response column should contain an integer value of 0 to Nresp-1 for each trial, indicating which response was given by the participant - The Reward column contains a value for the reward that is given to the participant on each trial. - Returns - ------- - responses : numpy array (with elements of type integer), shape = (ntrials,) - Array containing the responses simulated by the model for this participant. - - Description - ----------- - Function to simulate a response on each trial for a given participant with LR = simulation_LR and inverseTemperature = simulation_inverseTemp. - The design used for data generation for this participant should also be used for parameter estimation for this participant when running the function 'likelihood_estimation'.""" - - df = pd.read_csv(data) #Read data - ntrials = df.shape[0] #Extract number of trials - - nstim = len(np.unique(df.iloc[:,1])) #Extract number of stimuli - nresp = len(np.unique(df.iloc[:,2])) #Extract number of responses - - # the start values for each stimulus-response pair: a-priori, every response is equally likely and the values are scaled with the max reward - values = (np.ones((nstim, nresp))/nresp )* np.max(df.iloc[:,3]) - - # A column list for the output file: we add two columns (Response_likelihood and PE_estimate) - column_list = ["Trial", "Stimulus", "Response", "Reward", "Response_likelihood", "PE_estimate"] - simulated_data = pd.DataFrame(columns=column_list) - - # trial-loop: generate a response on each trial sequentially - for trial in range(ntrials): - - #Define the variables you'll need for this trial (take them from the design) - stimulus = int(df.iloc[trial,1]) #the stimulus shown on this trial - response = int(df.iloc[trial,2]) #the response given on this trial - reward_this_trial = int(df.iloc[trial,3]) #the reward on this trial - - #Simulate the response given by the hypothetical participant. Depending on the value for each response and the inverse_temperature parameter. - - # define which weights are of importance on this trial: depends on which stimulus "appears" - stimulus_weights = values[stimulus, :] - # compute probability of each action on this trial (using the weights for each action with the stimulus of this trial) - response_probabilities = softmax(values = stimulus_weights, inverse_temperature = simulation_inverseTemp) - # define which action is actually chosen (based on the probabilities) - response_likelihood = response_probabilities[response] - #compute the PE and the updated value for this trial (and this stimulus-response pair) - # to compute the PE & updated value, just work with whether reward was present or not - PE, updated_value = delta_rule(previous_value=values[stimulus, response], - obtained_reward=reward_this_trial, LR=simulation_LR) - - #update the value of the stimulus-response pair that was used this trial - values[stimulus, response] = updated_value - - #Store trial data - simulated_data.loc[trial] = [int(df.iloc[trial,0]) , stimulus, response, reward_this_trial, response_likelihood, PE] - - #Write to output file - simulated_data.to_csv(data[0:-4]+"_Simulated.csv", columns = column_list, float_format ='%.3f') - return - -if __name__ == '__main__': - #Extract path to data folder - directory = sys.argv[1:] - assert len(directory) == 1 - directory = directory[0] - - #Get a list of files in the folder and filter on csv files that are not previous fitting results - filelist = os.listdir(directory) - filtered_filelist = [x for x in filelist if x[-3::]=="csv"] - filtered_filelist = [x for x in filtered_filelist if x != "Fitting_results.csv"] - filtered_filelist = [x for x in filtered_filelist if x[-13::] != "Simulated.csv"] - - #Go to that directory - os.chdir(directory) - - #Define the starting parameters for the likelihood - start_params = np.random.uniform(-4.5, 4.5), np.random.uniform(-4.6, 2) - - #Define columns for output file - column_list = ["Subject_ID", "Estimated_LR", "Estimated_InvTemp", "Negative_LogL"] - estimated_data = pd.DataFrame(columns=column_list) - - idx = -1 - for file in filtered_filelist: - idx +=1 - #Get subject ID - sub = file.split("_")[2][0:-4] - - print("*** Started minimizing negative log likelihood of subject: {} ***\n".format(sub)) - #Minimize negative loglikelihood - optimization_output = optimize.minimize(likelihood, start_params, args =(tuple([file])), method = 'Nelder-Mead', options = {'maxfev':10000, 'xatol':0.00001, 'return_all':0}) - - #Get minimum log likelihood and parameter estimations - LL = optimization_output['fun'] - estimated_parameters = optimization_output['x'] - lr = LR_retransformation(estimated_parameters[0]) - inv_temp = InverseT_retransformation(estimated_parameters[1]) - - print("estimated learning rate is: {0} and estimated inverse temperature is: {1}.\n\n".format(lr, inv_temp)) - #Store everything in output file - estimated_data.loc[idx] = [sub, lr, inv_temp, LL] - - #Simulate with fitted parameters - simulate_responses(lr, inv_temp, file) - - print("Simulated data") - - #Write results of parameter fitting - estimated_data.to_csv("Fitting_results.csv", columns = column_list, float_format ='%.3f') - print("End of fitting procedure") diff --git a/Functions_DDM.py b/Functions_DDM.py new file mode 100644 index 0000000..2091d2a --- /dev/null +++ b/Functions_DDM.py @@ -0,0 +1,636 @@ + +# -*- coding: utf-8 -*- +""" +Created on Wed Oct 13 17:09:53 2021 +@author: Maud +""" +import ssms +import numpy as np +import pandas as pd +import os, sys, time +import math +from scipy import optimize +from scipy import stats as stat +import matplotlib.pyplot as plt +from Likelihoods import neg_likelihood +from ParameterEstimation import MLE + +#This is to avoid warnings being printed to the terminal window +import warnings +warnings.filterwarnings('ignore') + + +def generate_parameters_DDM(means,stds, param_bounds, npp = 150, multivariate = False, par_ind = 0 , + corr = False): + """ + Parameters + ---------- + means : 1 * n array + Means of parameters in an array. n: number of parameters + stds : 1 * n array + The standard deviation of the normal distribution from which parameters are drawn. The default is 0.1. + param_bounds: 2 * n array + Min and max of parameters + npp: int + Sample size of participants and parameters + multivariate: boolean, optional + Put to true for the external correlation criterion such that values are drawn from multivariate normal distribution. The default is False. + par_ind: int, optional (only used when multivariate = True) + The index of parameter which is hypothetically correlated with an external measure + corr: boolean or float, optional + The correlation for the external correlation criterion. For other criterions this is ignored. The default is False. + Returns + ------- + parameters : npp * len(means) array or npp * 2 array (when multivariate = True) + Array with shape ('size',) containing the parameters drawn from the normal distribution. + When multivariate = True, it returns correlated parameters generated from a multivariate normal distribution. + Its covariance matrix is defined by the assumed correlation coeffient and the std of the parameter of interest + The column of 0 index is the correlated parameters. The column of of 1 index is the external measure assumly correlated to the parameters. + + Description + ----------- + Function to draw 'npp' parameters from a normal distribution with mean 'mean' and standard deviation 'std'. + Function is used to generate parameters for each participant. + When the criterion is external correlation, the parameter of interest and the external measure are drawn from a multivariate normal distribution. + Here, the correlation is specified in the covariance matrix.""" + + if multivariate: + mean = means[par_ind] + std = stds[par_ind] + # draw 'npp' values from multivariate normal distribution with mean 'mean', standard deviation 'std' and correlation 'cor' + parameters = np.random.multivariate_normal([mean, 0], np.array([[corr*std, std], [std, corr*std]]), npp) + # while-loop: ensure no learning rate parameters get a value smaller than or equal to 0 + while max(parameters[:,0])>param_bounds[1][par_ind] or min(parameters[:,0])= param_bounds[1][par_ind]) + parameters[outerID] = np.random.multivariate_normal([mean, 0], np.array([[corr*std, std], [std, corr*std]]), size = sum(outerID)) + else: + # sample all parameters + parameters = np.zeros((npp, len(means))) + + + for p in range(len(means)): + # draw 'npp' values from normal distribution with mean 'mean' and standard deviation 'std' + parameters[:,p] = np.random.normal(loc = means[p], scale = stds[p], size = npp) + + while max(parameters[:,p])>param_bounds[1][p] or min(parameters[:,p])= param_bounds[1][p]) + parameters[outerID,p] = np.random.normal(loc = means[p], scale = stds[p], size = sum(outerID)) + + + return parameters # shape ('npp',) +def simulate_responses_DDM(theta = np.array([0,1.6,0.5,1,0.6]), DDM_id = 'angle',n_samples = 250): + """ + + simulate_responses_DDM + ---------- + theta : 1 * n array, n: number of parameters + Parameters in an array to generate responses from ssms package. + DDM_id: string + Index of DDM model which should be matched with ssms package + n_samples: int + Number of trials that will be simulated + + + Returns + ------- + responses : Dataframe shape = (ntrials,2) + Dataframe containing the responses simulated by the model for this participant, with 2 columns. + The 1st col: RTs; the 2nd col: choices denoted by -1 or 1 + + Description + ----------- + Function to simulate a response on each trial for one given participant with thetas, which contains input parameters with the same order as in ssms. + """ + from ssms.basic_simulators.simulator import simulator + + sim_out = simulator(theta = theta, + model = DDM_id, + n_samples = n_samples) + + responses = pd.DataFrame(np.zeros((n_samples, 2), + dtype = np.float32), + columns = ['rts', 'choices']) + + responses['rts'] = sim_out['rts'] + responses['choices'] = sim_out['choices'] + + return responses + +def Incorrelation_repetition_DDM(means,stds , + param_bounds, + npp = 150,ntrials = 450, + DDM_id = "ddm", method = "Differential_Evolution",rep=1, nreps = 250, ncpu = 6,show = 0): + """ + + Parameters + ---------- + means: 1 * n array + Means of distribution from which true parameters are sampled + stds: 1 * n array + Stds of distribution from which true parameters are sampled + param_bounds: 2 * n array + Range of parameters, the 0th row corresponds to lower bound, the 1st corresponds to upper bound + npp : integer + Number of participants in the study. + ntrials : integer + Number of trials that will be used to do the parameter recovery analysis for each participant. + DDM_id: string + Index of DDM model which should be matched with SSMS package + method: string + Method of optimization, either "Nelder-Mead" or "Brute" + rep : integer + Which repetition of the power estimation process is being executed. + nreps: integer + The total amount of repetitions. + ncpu: + The amount of cpu available. + + Returns + ------- + Statistic_Proficienct : array, shape = (number of parameters + 2,), saved in output file + Index 0 to (number of parameters-1): The correlation found between the true and recovered parameters this repetition. + Index end-1: average ACC of all participants. ACC was calculated by whether the sign of drift rate and final decision are of the same sign. + Index end: average RT of all participants. + True_Par: array, not saved in output file + True parameters sampled from the distributions defined in the input file. + Esti_Par: array, not saved in output file + Estimated parameters of generated behavioral data. + + Description + ----------- + Function to execute the parameter recovery analysis (Internal correlation criterion) once. + This criterion prescribes that resources are sufficient when: correlation(true parameters, recovered parameters) >= certain cut-off. + Thus, the statistic of interest is: correlation(ttrue parameters, recovered parameters). This statistic is returned for execution of this function (thus for each repetition). + In order to calculate the statistic the parameter recovery analysis has to be completed. This analysis consists of several steps: + 1. Create ONE hypothetical participants by defining a parameter set. + A parameter set consists of values required by SSMS package. Parameters are sampled from the Gaussian distribution defined in the input file. + + 2. Simulate data for ONE hypothetical participant (thus one parameter set). + This is done by simulating responses using the DDM model from SSMS package with the values of the free parameters = the parameter set of this hypothetical participant. + + 3. Test the performance of this hypothetical participant. + if ACC <= 0.50 or ACC >= 0.95 or RT >= 10, then resample the parameter set from the defined distributions. + + 3. Estimate the best fitting parameter set given the simulated data: best fitting parameter set = 'Esti_Par'. + This is done using the Maximum log-Likelihood estimation process: iteratively estimating the log-likelihood of different parameter values given the data. + The parameter set with the highest log-likelihood given the data is selected. For more details on the likelihood estimation process see function 'likelihood'. + For standard DDM: analytical likelihood function is used. + + 4. Back to step 1. for next hypothetical participants, until all npp participants were finished. + + 5. Calculate the Statistic of interest for this repetition of the parameter recovery analysis. + The statistic that is calculated here is correlation(true parameters, recovered parameters). + 6. Average ACC and RT were calculated and saved into output file along with Statistics. + If this function is repeated a number of times and the value of the Statistic is stored each time, we can evaluate later on the power or probability to meet the proposed parameter recovery criterion (the internal correlation criterion) in a single study. + + NOTE: RESCALING BOUNDARY PARAMETER "A" + As the parameter a was unmatched scaled in wfpt likelihood functions, this function includes corrected scales of this parameter explicitly. + It is achieved by: + (1) double optimization range of 'a', i.e., param_bounds_Opti[:,1] = param_bounds_Opti[:,1]*2 + (2) take 1/2 of estimated parameter "a" + """ + + if rep == 0: + t0 = time.time() + + ####PART 1: parameter generation for all participants#### + # Define the True params that will be used for each pp in this rep + + # True_Par = generate_parameters(means = means, stds = stds, + # param_bounds = param_bounds, npp = npp) + # True_Par.columns = ssms.config.model_config[DDM_id]['params'] + + True_Par = pd.DataFrame(np.empty((npp,len(means)))) + True_Par.columns = ssms.config.model_config[DDM_id]['params'] + + Esti_Par = pd.DataFrame(np.empty((npp,len(means)))) + Esti_Par.columns = ssms.config.model_config[DDM_id]['params'] + + ACC_out = np.empty((npp,1)) + RT_out = np.empty((npp,1)) + + waste_counter = 0 + + ########################################### + # RESCALE THE RANGE OF A # + ########################################### + param_bounds_Opti = np.array(ssms.config.model_config[DDM_id]['param_bounds']) + if DDM_id == "ddm": + param_bounds_Opti[:,1] = param_bounds_Opti[:,1]*2 + else: + print('TBC rescaling for other models') + + + for pp in range(npp): # loop for participants + ACC = 0 + ####Part 2: Data simulation for this participant#### + while ACC <= 0.5 or ACC >= 0.95 or RT >= 10: + # generate the responses for this participant + + True_Par.iloc[pp,:] = generate_parameters_DDM(means = means, stds = stds, + param_bounds = param_bounds, npp = 1) + + + + responses = simulate_responses_DDM(np.array(True_Par.iloc[pp,:]),DDM_id,ntrials) + responses = np.array(responses['rts'] * responses['choices']) + # validation of parameters + + ACC = np.mean(responses * True_Par.iloc[pp,0] > 0) + RT = np.mean(np.abs(responses)) + + if ACC <= 0.5 or ACC >= 0.95 or RT >= 10: + waste_counter = waste_counter+1 + + ACC_out[pp] = ACC + + RT_out[pp] = RT + + + + ####Part 3: parameter estimation for this participant#### + fun = neg_likelihood + arg = (responses,DDM_id) + # method = "Nelder-Mead" or method=="Brute" + + Esti_Par.iloc[pp,:] = MLE(fun,arg,param_bounds_Opti,method,show = 0) + if show: + print("Final results:", Esti_Par.iloc[pp,:],neg_likelihood(Esti_Par.iloc[pp,:],arg)) + print("True parameters:", np.array(True_Par.loc[pp]),neg_likelihood(True_Par.loc[pp],arg)) + + + # re-scaling parameter a + Esti_Par['a'] = Esti_Par['a']/2 + ####Part 4: correlation between true & estimated learning rates#### + # if the estimation failed for a certain participant, delete this participant from the correlation estimation for this repetition + ACC_average = round(float(sum(ACC_out))/len(ACC_out),3) + RT_average = round(float(sum(RT_out))/len(RT_out),3) + Statistic = np.empty((1,len(means))) + for p in range(len(means)): + #Statistic[0,p] = np.round(np.corrcoef(True_Par.iloc[:,p], Esti_Par.iloc[:,p])[0,1], 3) + Statistic[0,p] = np.corrcoef(True_Par.iloc[:,p], Esti_Par.iloc[:,p])[0, 1] + print("Sample: {}/{}, Statistic of parameter {}: r = {:.3f}".format(rep+1,nreps,ssms.config.model_config[DDM_id]['params'][p],Statistic[0,p])) + + # Statistic_Proficienct = np.append(Statistic,ACC_average) + # Statistic_Proficienct = np.append(Statistic_Proficienct,RT_average) + Statistic = np.append(Statistic,ACC_average) + Statistic = np.append(Statistic,RT_average) + + if rep == 0: + t1 = time.time() - t0 + estimated_seconds = t1 * np.ceil(nreps / ncpu) + estimated_time = np.ceil(estimated_seconds / 60) + print("\nThe power analysis will take ca. {} minutes".format(estimated_time)) + # return proportion_failed_estimates, Statistic + return Statistic, True_Par, Esti_Par + +def Excorrelation_repetition_DDM(means,stds , param_bounds, par_ind,DDM_id,true_correlation, + npp, ntrials,rep, nreps, ncpu=6, method = "Differential_Evolution"): + """ + + Parameters + ---------- + means: 1 * n array + Means of distribution from which true parameters are sampled + stds: 1 * n array + Stds of distribution from which true parameters are sampled + param_bounds: 2 * n array + range of parameters, the 0th row corresponds to lower bound, the 1st corresponds to upper bound + par_ind: int + The index of parameter of interest, start + DDM_id: string + Index of DDM model which should be matched with SSMS package + true_correlation: + Defines the hypothesized correlation between the parameter of interest and an external parameter. + npp : integer + Number of participants in the study. + ntrials : integer + Number of trials that will be used to do the parameter recovery analysis for each participant. + rep : integer + Which repetition of the power estimation process is being executed. + nreps: integer + The total amount of repetitions. + ncpu: int + The amount of cpu available. + method: string + Method of optimization, either "Nelder-Mead" or "Brute" + + Returns + ------- + Esti_r: float + The Pearson correlation coeffient of the estimated parameters and the true external external measures + Esti_pValue: float + The p-value of Esti_r + True_r: float + The Pearson correlation coeffient of the generated true parameters and the true external external measures + True_pValue: float + The p-value of True_pValue + ACC_average: + Average ACC of all participants. ACC was calculated by whether the sign of drift rate and final decision are of the same sign. + RT_average: + Average RT of all participants. + Description + ----------- + Function to execute the external correlation statistic once. + This criterion prescribes that resources are sufficient when: correlation(external measure, recovered parameter) >= certain cut-off. + Thus, the statistic of interest is: correlation(measure, recovered parameter). The correlation is statistically significant when the p-value is smaller than or equal to a specified cut_off. + In order to calculate the statistic the parameter recovery analysis has to be completed. This analysis consists of several steps: + 1. Generate correlated true parameter of interest and external measure based on defined mean, std of that parameter distribution and true correlation. + npp of parameters and external measure pairs will be generated and fixed through out simulation. + This is designed to enable resample other parameters, which are not assumed to be correlated with an external measure, to achienve a validate ACC and RT + but remain the correlation between the parameter of interest and external measure. + + 2. Sample other parameters required for SSMS package models for one hypothetical participant, which are not assumed to be correlated with an external measure. + (Start looping for npp hypothetical participants.) + + 3. Given all parameters requireed, simulate behavioral data for this hypothetical participant. + Validate behavioral performance of this participant. If ACC <= 0.50 or ACC >= 0.95 or RT >= 10, then go back to step 2. + + 4. Estimate the best fitting parameter set given the simulated data: best fitting parameter set = 'Esti_Par'. + This is done using the Maximum log-Likelihood estimation process: iteratively estimating the log-likelihood of different parameter values given the data. + The parameter set with the highest log-likelihood given the data is selected. For more details on the likelihood estimation process see function 'likelihood'. + For standard DDM: analytical likelihood function is used. + + 5. Back to step 2. for next hypothetical participants, until all npp participants were finished. + + 6. Calculate and return the statistics for this repetition of the analysis, including: + correlation(external measure, recovered parameter of interest), and its p-value; + correlation(external measure, true parameter of interest), and its p-value; + average ACC and RT + + NOTE: RESCALING BOUNDARY PARAMETER "A" + As the parameter a was unmatched scaled in wfpt likelihood functions, this function includes corrected scales of this parameter explicitly. + It is achieved by: + (1) double optimization range of 'a', i.e., param_bounds_Opti[:,1] = param_bounds_Opti[:,1]*2 + (2) take 1/2 of estimated parameter "a" + + """ + + if rep == 0: + t0 = time.time() + + ####PART 1: parameter generation for all participants#### + # Define the True params that will be used for each pp in this rep + True_Par = pd.DataFrame(np.empty((npp,len(means)))) + True_Par.columns = ssms.config.model_config[DDM_id]['params'] + # Generate the correlated parameter and the external measure as true parameters + correlated_values = generate_parameters_DDM(means, stds, param_bounds, npp = npp, + multivariate = True, par_ind = par_ind,corr = true_correlation) + True_Par.iloc[:,par_ind] = correlated_values[:,0] + Theta = correlated_values[:,1] # the external measure + + Esti_Par = pd.DataFrame(np.empty((npp,len(means)))) + Esti_Par.columns = ssms.config.model_config[DDM_id]['params'] + + # rescale optimizing range of parameter 'a' + param_bounds_Opti = np.array(ssms.config.model_config[DDM_id]['param_bounds']) + if DDM_id =="ddm": + param_bounds_Opti[:,1] = param_bounds_Opti[:,1]*2 + + Col_UncPar = np.delete(range(len(means)),par_ind) + waste_counter = 0 + ACC_out = np.empty((npp,1)) + RT_out = np.empty((npp,1)) + + for pp in range(npp): + # AF-TODO ADDED + ##print(pp) + ACC = 0 + ####Part 2: Data simulation for this participant#### + # AF MADE Change + while (ACC <= 0.50 or ACC >= 0.95 or RT >= 10): + # generate the responses for this participant + + True_Par.iloc[pp, Col_UncPar] = generate_parameters_DDM(means = means[Col_UncPar], stds = stds[Col_UncPar], param_bounds = param_bounds, npp = 1) + + responses = simulate_responses_DDM(theta=True_Par.iloc[pp,:].values, DDM_id = DDM_id, n_samples = ntrials) + # fill in the responses of this participant into the start design, in order to use this later in param. estimation + responses = np.array(responses['rts'] * responses['choices']) + # validation of parameters + + if True_Par.iloc[pp, 0] == 0: + ACC = np.mean( (responses * (True_Par.iloc[pp,0] + 0.001)) > 0) + else: + ACC = np.mean( (responses * True_Par.iloc[pp,0]) > 0) + + RT = np.mean(np.abs(responses)) + + if ACC <= 0.50 or ACC >= 0.95 or RT >= 10: + waste_counter = waste_counter+1 + if waste_counter >= 99: + print('Accuracy is: {}'.format(ACC)) + print('RT is: {}'.format(RT)) + print('Parameters: {}'.format(True_Par.iloc[pp,:].values)) + print("waster_counter reached {}, something is probably not right!".format(waste_counter)) + #sys.stdout.fush() + + ####Part 3: parameter estimation for this participant#### + fun = neg_likelihood + arg = (responses,DDM_id) + # method = "Nelder-Mead" or method=="Brute" + Esti_Par.iloc[pp,:] = MLE(fun,arg,param_bounds_Opti,method,show = 0) + + ACC_out[pp] = ACC + RT_out[pp] = RT + + # print(Esti_Par[pp],neg_likelihood(Esti_Par[pp],arg)) + # print(np.array(True_Par.loc[pp]),neg_likelihood(True_Par.loc[pp],arg)) + + # Take average of Accuracy and RT since this is across subjects + ACC_average = float(sum(ACC_out))/len(ACC_out) + RT_average = float(sum(RT_out))/len(RT_out) + + # re-scaling parameter a + Esti_Par['a'] = Esti_Par['a']/2 + + ####Part 4: correlation between true & estimated learning rates#### + # if the estimation failed for a certain participant, delete this participant from the correlation estimation for this repetition + True_r = stat.pearsonr(Theta, True_Par.iloc[:,par_ind])[0] + True_pValue = stat.pearsonr(Theta, True_Par.iloc[:,par_ind])[1] + Stat = stat.pearsonr(Theta, Esti_Par.iloc[:,par_ind]) + Esti_r = Stat[0] + Esti_pValue = Stat[1] + + print('sample: {}/{}, statistics: r = {:.3f}, p = {:.3f}'.format(rep+1,nreps,Esti_r,Esti_pValue)) + + if rep == 0: + t1 = time.time() - t0 + estimated_seconds = t1 * np.ceil(nreps / ncpu) + estimated_time = np.ceil(estimated_seconds / 60) + print("\nThe power analysis will take ca. {} minutes".format(estimated_time)) + + # return proportion_failed_estimates, Statistic + return Esti_r, Esti_pValue, True_r, True_pValue, ACC_average, RT_average + +def Groupdifference_repetition_DDM(means_g1, stds_g1,means_g2, stds_g2,DDM_id, par_ind,param_bounds, + npp_per_group, ntrials, rep, nreps, ncpu, method = "Differential_Evolution"): + """ + + Parameters + ---------- + means_g1: array + Means of the distribution from which samples true parameters of group 1 + means_g2: array + Means of the distribution from which samples true parameters of group 2 + stds_g1: array + Stds of the distribution from which samples true parameters of group 1 + stds_g2: array + Stds of the distribution from which samples true parameters of group 2 + DDM_id: string + Index of DDM model which should be matched with ssms package + par_ind: int + The index of parameter of interest, start from 0 + param_bounds: 2 * n array + Range of parameters, the 0th row corresponds to lower bound, the 1st corresponds to upper bound + npp_per_group : integer + Number of participants in each group that will be used in the parameter recovery analysis. + ntrials : integer + Number of trials that will be used to do the parameter recovery analysis for each participant. + rep : integer + Which repetition of the power estimation process is being executed. + nreps: integer + The total amount of repetitions. + ncpu: + The amount of cpu available. + method: string + Method of optimization, either "Nelder-Mead" or "Brute" + + + Returns + ------- + Statistic: float + t-value of a two-sample t-test comparing the recovered parameters of interest for group 1 and group 2. + pValue: float + p-value of the t-value. + ACC_g1: float + average ACC of group 1 + ACC_g2: float + average ACC of group 2 + RT_g1: float + average RT of group 1 + RT_g2: float + average RT of group 2 + + + Description + ----------- + Function to execute the group difference statistic once. + This criterion prescribes that resources are sufficient when a significant group difference is found using the recovered parameters for all participants. + Thus, the statistic of interest is the p-value returned by a two-sample t-test comparing the recovered parameters of group 1 with the recovered parameters of group 2. + The group difference is statistically significant when the p-value is smaller than or equal to a specified cut_off (we use a one-sided t-test). + In order to calculate the statistic the parameter recovery analysis has to be completed. This analysis consists of several steps: + + 1. Create ONE hypothetical participants by defining a parameter set. + A parameter set consists of values required by SSMS package. Parameters are sampled from the Gaussian distribution defined in the input file. + If number of samples exceeds npp_per_group, then sampple true parameters from the group 2 distribution. + + 2. Simulate data for ONE hypothetical participant (thus one parameter set). + This is done by simulating responses using the DDM model from SSMS package with the values of the free parameters = the parameter set of this hypothetical participant. + + 3. Test the performance of this hypothetical participant. + if ACC <= 0.50 or ACC >= 0.95 or RT >= 10, then resample the parameter set from the defined distributions. + + 3. Estimate the best fitting parameter set given the simulated data: best fitting parameter set = 'Esti_Par'. + This is done using the Maximum log-Likelihood estimation process: iteratively estimating the log-likelihood of different parameter values given the data. + The parameter set with the highest log-likelihood given the data is selected. For more details on the likelihood estimation process see function 'likelihood'. + For standard DDM: analytical likelihood function is used. + + 4. Back to step 1. for next hypothetical participants, until all "npp_per_group*2" participants were finished. + + 5. Calculate the Statistic of interest for this repetition of the parameter recovery analysis. + The statistic that is calculated here is the p-value associated with the T-statistic which is obtained by a two-sample t-test comparing the recovered parameter of interest for group 1 and group 2. + + NOTE: RESCALING BOUNDARY PARAMETER "A" + As the parameter a was unmatched scaled in wfpt likelihood functions, this function includes corrected scales of this parameter explicitly. + It is achieved by: + (1) double optimization range of 'a', i.e., param_bounds_Opti[:,1] = param_bounds_Opti[:,1]*2 + (2) take 1/2 of estimated parameter "a" + """ + if rep == 0: + t0 = time.time() + + # create array that will contain the final LRestimate for each participant this repetition + # LRestimations = np.empty([2, npp_per_group]) + # InvTestimations = np.empty([2, npp_per_group]) + + + cn = ["group",]+ssms.config.model_config[DDM_id]['params'] + + True_Par = pd.DataFrame(np.empty([npp_per_group*2,len(means_g1)+1])) + True_Par.columns = cn + Esti_Par = pd.DataFrame(np.empty((npp_per_group*2,len(means_g1)+1))) + Esti_Par.columns = cn + ACC_out = np.empty((npp_per_group*2,2)) + RT_out = np.empty((npp_per_group*2,2)) + + param_bounds_Opti = np.array(ssms.config.model_config[DDM_id]['param_bounds']) + if DDM_id == "ddm": + param_bounds_Opti[:,1] = param_bounds_Opti[:,1]*2 # rescale parameter a + waste_counter = 0 + + # loop over all pp. to do the data generation and parameter estimation + for pp in range(npp_per_group*2): + if pp <= npp_per_group-1: + group = 1 + means = means_g1 + stds = stds_g1 + else: + group = 2 + means = means_g2 + stds = stds_g2 + + ACC = 0 + ####Part 2: Data simulation for this participant#### + while ACC <= 0.50 or ACC >= 0.95 or RT >= 10: + # generate the responses for this participant + True_Par.iloc[pp,0] = group + True_Par.iloc[pp, 1:] = generate_parameters_DDM(means = means, stds = stds, + param_bounds = param_bounds, npp = 1) + + responses = simulate_responses_DDM(theta=True_Par.iloc[pp,1:].values, DDM_id = DDM_id, n_samples = ntrials) + # fill in the responses of this participant into the start design, in order to use this later in param. estimation + responses = np.array(responses['rts'] * responses['choices']) + # validation of parameters + + ACC = np.mean(responses * True_Par['v'][pp] > 0) # [pp,1]:index of v, drfit rate + RT = np.mean(np.abs(responses)) + + if ACC <= 0.50 or ACC >= 0.95 or RT >= 10: + waste_counter = waste_counter+1 + + ACC_out[pp,group-1] = ACC + RT_out[pp,group-1] = RT + + ####Part 3: parameter estimation for this participant#### + fun = neg_likelihood + arg = (responses,DDM_id) + Esti_Par.iloc[pp,0] = group + Esti_Par.iloc[pp,1:] = MLE(fun,arg,param_bounds_Opti,method,show = 0) + + # print(Esti_Par[pp],neg_likelihood(Esti_Par[pp],arg)) + # print(np.array(True_Par.loc[pp]),neg_likelihood(True_Par.loc[pp],arg)) + # re-scaling parameter a + Esti_Par['a'] = Esti_Par['a']/2 + + # use two-sided then divide by two, this way we can use the same formula for HPC and non HPC + + g1_df = Esti_Par[Esti_Par['group'] == 1] + g2_df = Esti_Par[Esti_Par['group'] == 2] + # default: alternative = two-sided + Statistic, pValue = stat.ttest_ind(g1_df.iloc[:,par_ind+1], g2_df.iloc[:,par_ind+1]) + # because alternative = less does not exist in scipy version 1.4.0, yet we want a one-sided test + pValue = pValue/2 + + print('sample: {}/{}, statistics: t = {:.3f}, p = {:.3f}'.format(rep+1,nreps,Statistic,pValue)) + + if rep == 0: + t1 = time.time() - t0 + estimated_seconds = t1 * nreps / ncpu + estimated_time = np.ceil(estimated_seconds / 60) + print("\nThe power analysis will take ca. {} minutes".format(estimated_time)) + ACC_g1 = np.mean(ACC_out, axis=0)[0] + ACC_g2 = np.mean(ACC_out, axis=0)[1] + RT_g1 = np.mean(RT_out, axis=0)[0] + RT_g2 = np.mean(RT_out, axis=0)[1] + + return Statistic, pValue, ACC_g1,ACC_g2,RT_g1,RT_g2 \ No newline at end of file diff --git a/InputFile_EC.csv b/InputFile_EC.csv deleted file mode 100644 index e2e5293..0000000 --- a/InputFile_EC.csv +++ /dev/null @@ -1,2 +0,0 @@ -ntrials,nreversals,npp,meanLR,sdLR,meanInverseTemperature,sdInverseTemperature,True_correlation,reward_probability,TypeIerror,nreps,full_speed,output_folder -200,25,50,0.55,0.1,1.5,0.5,0.2,0.9,0.05,250,1,/Users/pieter/Desktop/Compass_results \ No newline at end of file diff --git a/InputFile_EC_DDM.csv b/InputFile_EC_DDM.csv new file mode 100644 index 0000000..72e67fd --- /dev/null +++ b/InputFile_EC_DDM.csv @@ -0,0 +1,8 @@ +model,ntrials,npp,mean_v,mean_a,mean_z,mean_t,std_v,std_a,std_z,std_t,par_ind,True_correlation,TypeIerror,nreps,full_speed +ddm,100,20,0,1.18,0.5,0.5,0.3,0.33,0.3,0.25,0,0.3,0.05,50,1 +ddm,100,20,0.4,1.18,0.5,0.5,0.3,0.33,0.3,0.25,0,0.3,0.05,50,1 +ddm,100,20,0.8,1.18,0.5,0.5,0.3,0.33,0.3,0.25,0,0.3,0.05,50,1 +ddm,100,20,1.2,1.18,0.5,0.5,0.3,0.33,0.3,0.25,0,0.3,0.05,50,1 +ddm,100,20,1.6,1.18,0.5,0.5,0.3,0.33,0.3,0.25,0,0.3,0.05,50,1 +ddm,100,20,2,1.18,0.5,0.5,0.3,0.33,0.3,0.25,0,0.3,0.05,50,1 +ddm,100,20,2.4,1.18,0.5,0.5,0.3,0.33,0.3,0.25,0,0.3,0.05,50,1 diff --git a/InputFile_EC_DDM_sampleMStd.csv b/InputFile_EC_DDM_sampleMStd.csv new file mode 100644 index 0000000..3c6d83b --- /dev/null +++ b/InputFile_EC_DDM_sampleMStd.csv @@ -0,0 +1,10 @@ +model,ntrials,npp,mean_v,mean_a,mean_z,mean_t,std_v,std_a,std_z,std_t,par_ind,True_correlation,TypeIerror,nreps,full_speed,output_folder +ddm,20,40,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0,0.5,0.05,20,1,D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM/results/test5_EC/ +ddm,40,40,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0,0.5,0.05,20,1,D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM/results/test5_EC/ +ddm,60,40,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0,0.5,0.05,20,1,D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM/results/test5_EC/ +ddm,20,60,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0,0.5,0.05,20,1,D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM/results/test5_EC/ +ddm,40,60,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0,0.5,0.05,20,1,D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM/results/test5_EC/ +ddm,60,60,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0,0.5,0.05,20,1,D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM/results/test5_EC/ +ddm,20,20,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0,0.5,0.05,20,1,D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM/results/test5_EC/ +ddm,40,20,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0,0.5,0.05,20,1,D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM/results/test5_EC/ +ddm,60,20,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0,0.5,0.05,20,1,D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM/results/test5_EC/ diff --git a/InputFile_GD.csv b/InputFile_GD.csv deleted file mode 100644 index 67ce57f..0000000 --- a/InputFile_GD.csv +++ /dev/null @@ -1,4 +0,0 @@ -ntrials,nreversals,npp_group,meanLR_g1,sdLR_g1,meanLR_g2,sdLR_g2,meanInverseTemperature_g1,sdInverseTemperature_g1,meanInverseTemperature_g2,sdInverseTemperature_g2,reward_probability,TypeIerror,nreps,full_speed,output_folder -100,5,20,0.5,0.25,0.75,0.25,1.5,0.5,1.5,0.5,0.8,0.05,250,1,/Users/pieter/Documents/Compass_tutorial -500,25,20,0.5,0.25,0.75,0.25,1.5,0.5,1.5,0.5,0.8,0.05,250,1,/Users/pieter/Documents/Compass_tutorial -500,25,50,0.5,0.25,0.75,0.25,1.5,0.5,1.5,0.5,0.8,0.05,250,1,/Users/pieter/Documents/Compass_tutorial \ No newline at end of file diff --git a/InputFile_GD_DDM.csv b/InputFile_GD_DDM.csv new file mode 100644 index 0000000..e6ad3da --- /dev/null +++ b/InputFile_GD_DDM.csv @@ -0,0 +1,4 @@ +model,ntrials,npp_group,mean_v,mean_a,mean_z,mean_t,std_v,std_a,std_z,std_t,par_ind,TypeIerror,nreps,full_speed,output_folder +ddm,5,5,"0,0.06",1.18,0.5,0.5,"0.3,0.3",0.33,0.1,0.25,0,0.05,3,1,/users/afengler/data/proj_compass_ddm/ +ddm,5,5,"0,0.15",1.18,0.5,0.5,"0.3,0.3",0.33,0.1,0.25,0,0.05,3,1,/users/afengler/data/proj_compass_ddm/ +ddm,5,5,"0,0.24",1.18,0.5,0.5,"0.3,0.3",0.33,0.1,0.25,0,0.05,3,1,/users/afengler/data/proj_compass_ddm/ diff --git a/InputFile_GD_DDM_sampleMStd.csv b/InputFile_GD_DDM_sampleMStd.csv new file mode 100644 index 0000000..2aa8ed7 --- /dev/null +++ b/InputFile_GD_DDM_sampleMStd.csv @@ -0,0 +1,2 @@ +model,ntrials,npp_group,mean_v,mean_a,mean_z,mean_t,std_v,std_a,std_z,std_t,par_ind,TypeIerror,nreps,full_speed,output_folder +ddm,60,40,0,1.18,"0.5,0.55",0.5,1,0.33,"0.1,0.1",0.25,2,0.05,250,1,D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM/results/test6_GD/ diff --git a/InputFile_IC.csv b/InputFile_IC.csv deleted file mode 100644 index f4076a0..0000000 --- a/InputFile_IC.csv +++ /dev/null @@ -1,3 +0,0 @@ -ntrials,nreversals,npp,meanLR,sdLR,meanInverseTemperature,sdInverseTemperature,reward_probability,tau,nreps,full_speed,output_folder -450,25,150,0.55,0.1,1.5,0.5,0.9,0.5,250,1,/Users/pieter/Desktop/Compass_results -450,25,30,0.55,0.1,1.5,0.5,0.9,0.5,250,1,/Users/pieter/Desktop/Compass_results \ No newline at end of file diff --git a/InputFile_IC_DDM.csv b/InputFile_IC_DDM.csv new file mode 100644 index 0000000..fb567f3 --- /dev/null +++ b/InputFile_IC_DDM.csv @@ -0,0 +1,3 @@ +model,ntrials,npp,mean_v,mean_a,mean_z,mean_t,std_v,std_a,std_z,std_t,tau,nreps,full_speed +ddm,100,40,2.4,1.18,0.5,0.5,0.2,0.33,0.1,0.25,0.8,5,1 +ddm,200,40,2.4,1.18,0.5,0.5,0.2,0.33,0.1,0.25,0.8,5,1 diff --git a/InputFile_IC_DDM_sampleMStd.csv b/InputFile_IC_DDM_sampleMStd.csv new file mode 100644 index 0000000..e305ab9 --- /dev/null +++ b/InputFile_IC_DDM_sampleMStd.csv @@ -0,0 +1,10 @@ +model,ntrials,npp,mean_v,mean_a,mean_z,mean_t,std_v,std_a,std_z,std_t,tau,nreps,full_speed,output_folder +ddm,20,20,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0.8,20,1,D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM/results/test3/ +ddm,40,20,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0.8,20,1,D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM/results/test3/ +ddm,60,20,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0.8,20,1,D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM/results/test3/ +ddm,20,40,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0.8,20,1,D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM/results/test3/ +ddm,40,40,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0.8,20,1,D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM/results/test3/ +ddm,60,40,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0.8,20,1,D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM/results/test3/ +ddm,20,60,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0.8,20,1,D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM/results/test3/ +ddm,40,60,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0.8,20,1,D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM/results/test3/ +ddm,60,60,0,1.18,0.5,0.5,0.3,0.33,0.1,0.25,0.8,20,1,D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM/results/test3/ diff --git a/LAN/TrainedLANs/248c94cca33e11ecb947ac1f6bfea5a4_training_data_angle_torch__network_config.pickle b/LAN/TrainedLANs/248c94cca33e11ecb947ac1f6bfea5a4_training_data_angle_torch__network_config.pickle new file mode 100644 index 0000000..8496a5b Binary files /dev/null and b/LAN/TrainedLANs/248c94cca33e11ecb947ac1f6bfea5a4_training_data_angle_torch__network_config.pickle differ diff --git a/LAN/TrainedLANs/248c94cca33e11ecb947ac1f6bfea5a4_training_data_angle_torch_state_dict.pt b/LAN/TrainedLANs/248c94cca33e11ecb947ac1f6bfea5a4_training_data_angle_torch_state_dict.pt new file mode 100644 index 0000000..9b497f0 Binary files /dev/null and b/LAN/TrainedLANs/248c94cca33e11ecb947ac1f6bfea5a4_training_data_angle_torch_state_dict.pt differ diff --git a/LAN/infLAN.py b/LAN/infLAN.py new file mode 100644 index 0000000..42a8e0e --- /dev/null +++ b/LAN/infLAN.py @@ -0,0 +1,190 @@ +if __name__ == '__main__': +# Load necessary packages + import ssms + import lanfactory + import os + import numpy as np + from copy import deepcopy + import torch + import pandas as pd + import matplotlib.pyplot as plt + + import torch + import uuid + # from .torch_config import TorchConfig + # from .mlp_mo del_class import TorchMLP + # import hddm + + class TorchMLP(nn.Module): + def __init__( + self, + network_config=None, + input_shape=10, + save_folder=None, + generative_model_id="ddm", + ): + super(TorchMLP, self).__init__() + if generative_model_id is not None: + self.model_id = uuid.uuid1().hex + "_" + generative_model_id + else: + self.model_id = None + + self.save_folder = save_folder + self.input_shape = input_shape + self.network_config = network_config + self.activations = {"relu": torch.nn.ReLU(), "tanh": torch.nn.Tanh()} + self.layers = nn.ModuleList() + + self.layers.append( + nn.Linear(input_shape, self.network_config["layer_sizes"][0]) + ) + self.layers.append(self.activations[self.network_config["activations"][0]]) + # print(self.network_config['activations'][0]) + for i in range(len(self.network_config["layer_sizes"]) - 1): + self.layers.append( + nn.Linear( + self.network_config["layer_sizes"][i], + self.network_config["layer_sizes"][i + 1], + ) + ) + # print(self.network_config['activations'][i + 1]) + if i < (len(self.network_config["layer_sizes"]) - 2): + self.layers.append( + self.activations[self.network_config["activations"][i + 1]] + ) + else: + # skip last activation since + pass + self.len_layers = len(self.layers) + + def forward(self, x): + for i in range(self.len_layers - 1): + x = self.layers[i](x) + return self.layers[-1](x) + + # except: + # print( + # "Error loading pytorch capabilities. Neural network functionality cannot be used." + # ) + + + class LoadTorchMLPInfer: + def __init__(self, model_file_path=None, network_config=None, input_dim=None): + torch.backends.cudnn.benchmark = True + self.dev = ( + torch.device("cuda") + if torch.cuda.is_available() + else torch.device("cpu") + ) + self.model_file_path = model_file_path + self.network_config = network_config + self.input_dim = input_dim + + self.net = TorchMLP( + network_config=self.network_config, + input_shape=self.input_dim, + generative_model_id=None, + ) + self.net.load_state_dict( + torch.load(self.model_file_path, map_location=self.dev) + ) + self.net.to(self.dev) + # AF Q: IS THIS REDUNDANT NOW? + + self.net.eval() + + @torch.no_grad() + def predict_on_batch(self, x=None): + return self.net(torch.from_numpy(x).to(self.dev)).cpu().numpy() + + def load_torch_mlp(model=None): + cfg = TorchConfig(model=model) + infer_model = LoadTorchMLPInfer( + model_file_path=cfg.network_path, + network_config=cfg.network_config, + input_dim=len(hddm.model_config.model_config[model]["params"]) + 2, + ) + + return infer_model + +# except: +# print("HDDM: pytorch module seems missing. No LAN functionality can be loaded.") + + ddm_model = "angle" + n_ddm_parameter = len(ssms.config.model_config[ddm_model]['params']) + path = 'train_LAN/{}/data/'.format(ddm_model) + + network_path_list = os.listdir(path) + network_file_path = [path + file_ for file_ in network_path_list if 'state_dict' in file_][0] + + network_config = lanfactory.config.network_configs.network_config_mlp + + network = lanfactory.trainers.LoadTorchMLPInfer(model_file_path = network_file_path, + network_config = network_config, + input_dim = n_ddm_parameter + 2) + + #%% 9 + # Two ways to call the network + # Direct call --> need tensor input + # direct_out = network(torch.from_numpy(np.array([1, 0.5, 0.5, 0.5, 1, 0.65, 1], dtype = np.float32))) + # print('direct call out: ', direct_out) + + # predict_on_batch method + # predict_on_batch_out = network.predict_on_batch(np.array([1, 1.5, 0.5, 1.0, 0.1, 0.65, 1], dtype = np.float32)) + # print('predict_on_batch out: ', predict_on_batch_out) + #%% 10 + # show likeliood + if ddm_model == "ddm" : + data_col = ['v', 'a', 'z', 't', 'rt', 'choice'] + + data = pd.DataFrame(np.zeros((2000, n_ddm_parameter + 2), + dtype = np.float32), + columns = data_col) + data['v'] = 0.5 # drift rate + data['a'] = 0.75 # threshold + data['z'] = 0.5 # bias + data['t'] = 0.2 # nondesicion time + data['rt'].iloc[:1000] = np.linspace(5, 0, 1000) + data['rt'].iloc[1000:] = np.linspace(0, 5, 1000) + data['choice'].iloc[:1000] = -1 + data['choice'].iloc[1000:] = 1 + print(data['choice']) + + elif ddm_model == "angle": + data = pd.DataFrame(np.zeros((2000, n_ddm_parameter + 2), + dtype = np.float32), + columns = ['v', 'a', 'z', 't', 'theta','rt', 'choice']) + data['v'] = 0.5 # drift rate + data['a'] = 0.75 # threshold + data['z'] = 0.5 # bias + data['t'] = 0.2 # nondesicion time + data['theta'] = 0.1 # linear boundary, the angle of boundary + data['rt'].iloc[:1000] = np.linspace(5, 0, 1000) + data['rt'].iloc[1000:] = np.linspace(0, 5, 1000) + data['choice'].iloc[:1000] = -1 + data['choice'].iloc[1000:] = 1 + print(data['choice']) + + #%% 11 + # Network predictions + predict_on_batch_out = network.predict_on_batch(data.values.astype(np.float32)) + + # Simulations + from ssms.basic_simulators import simulator + sim_out = simulator(model = ddm_model, + theta = data.values[0, :-2], + n_samples = 2000) + #%% 12 + data_rt = np.array(data['rt'] * data['choice']) + # Plot network predictions + # plt.plot(data['rt'] * data['choice'], np.exp(predict_on_batch_out), color = 'black', label = 'network') + plt.plot(data_rt, np.exp(predict_on_batch_out), color = 'black', label = 'network') + # Plot simulations + plt.hist(sim_out['rts'] * sim_out['choices'], bins = 30, histtype = 'step', label = 'simulations', color = 'blue', density = True) + plt.legend(fontsize=18) + plt.title('SSM likelihood: {}'.format(ddm_model), fontsize=22) + plt.xticks(fontsize=18) + plt.yticks(fontsize=18) + plt.xlabel('rt', fontsize=18) + plt.ylabel('likelihod', fontsize=18) + plt.show() diff --git a/LAN/test.ipynb b/LAN/test.ipynb new file mode 100644 index 0000000..e812277 --- /dev/null +++ b/LAN/test.ipynb @@ -0,0 +1,299 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "if __name__ == '__main__':\n", + "# Load necessary packages\n", + " import ssms\n", + " import lanfactory \n", + " import os\n", + " import numpy as np\n", + " from copy import deepcopy\n", + " import torch\n", + " import pandas as pd\n", + " import matplotlib.pyplot as plt \n", + "\n", + " import torch\n", + " import torch.nn as nn\n", + " import uuid\n", + " # from .torch_config import TorchConfig\n", + " # from .mlp_model_class import TorchMLP\n", + " # import hddm\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "class TorchMLP(nn.Module):\n", + " def __init__(\n", + " self,\n", + " network_config=None,\n", + " input_shape=10,\n", + " save_folder=None,\n", + " generative_model_id=\"ddm\",\n", + " ):\n", + " super(TorchMLP, self).__init__()\n", + " if generative_model_id is not None:\n", + " self.model_id = uuid.uuid1().hex + \"_\" + generative_model_id\n", + " else:\n", + " self.model_id = None\n", + "\n", + " self.save_folder = save_folder\n", + " self.input_shape = input_shape\n", + " self.network_config = network_config\n", + " self.activations = {\"relu\": torch.nn.ReLU(), \"tanh\": torch.nn.Tanh()}\n", + " self.layers = nn.ModuleList()\n", + "\n", + " self.layers.append(\n", + " nn.Linear(input_shape, self.network_config[\"layer_sizes\"][0])\n", + " )\n", + " self.layers.append(self.activations[self.network_config[\"activations\"][0]])\n", + " # print(self.network_config['activations'][0])\n", + " for i in range(len(self.network_config[\"layer_sizes\"]) - 1):\n", + " self.layers.append(\n", + " nn.Linear(\n", + " self.network_config[\"layer_sizes\"][i],\n", + " self.network_config[\"layer_sizes\"][i + 1],\n", + " )\n", + " )\n", + " # print(self.network_config['activations'][i + 1])\n", + " if i < (len(self.network_config[\"layer_sizes\"]) - 2):\n", + " self.layers.append(\n", + " self.activations[self.network_config[\"activations\"][i + 1]]\n", + " )\n", + " else:\n", + " # skip last activation since\n", + " pass\n", + " self.len_layers = len(self.layers)\n", + "\n", + " def forward(self, x):\n", + " for i in range(self.len_layers - 1):\n", + " x = self.layers[i](x)\n", + " return self.layers[-1](x)\n", + "\n", + "# except:\n", + "# print(\n", + "# \"Error loading pytorch capabilities. Neural network functionality cannot be used.\"\n", + "# )\n", + "\n", + "\n", + "class LoadTorchMLPInfer:\n", + " def __init__(self, model_file_path=None, network_config=None, input_dim=None):\n", + " torch.backends.cudnn.benchmark = True\n", + " self.dev = (\n", + " torch.device(\"cuda\")\n", + " if torch.cuda.is_available()\n", + " else torch.device(\"cpu\")\n", + " )\n", + "\n", + " print('dev:', self.dev)\n", + "\n", + " self.model_file_path = model_file_path\n", + " self.network_config = network_config\n", + " self.input_dim = input_dim\n", + "\n", + " self.net = TorchMLP(\n", + " network_config=self.network_config,\n", + " input_shape=self.input_dim,\n", + " generative_model_id=None,\n", + " )\n", + " self.net.load_state_dict(\n", + " torch.load(self.model_file_path, map_location=self.dev)\n", + " )\n", + " self.net.to(self.dev)\n", + " # AF Q: IS THIS REDUNDANT NOW?\n", + "\n", + " self.net.eval()\n", + "\n", + " @torch.no_grad()\n", + " def predict_on_batch(self, x=None):\n", + " return self.net(torch.from_numpy(x).to(self.dev)).cpu().numpy()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dev: cpu\n", + "0 -1.0\n", + "1 -1.0\n", + "2 -1.0\n", + "3 -1.0\n", + "4 -1.0\n", + " ... \n", + "1995 1.0\n", + "1996 1.0\n", + "1997 1.0\n", + "1998 1.0\n", + "1999 1.0\n", + "Name: choice, Length: 2000, dtype: float32\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\horiz\\AppData\\Local\\Temp\\ipykernel_16592\\3292323189.py:56: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['rt'].iloc[:1000] = np.linspace(5, 0, 1000)\n", + "C:\\Users\\horiz\\AppData\\Local\\Temp\\ipykernel_16592\\3292323189.py:58: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['choice'].iloc[:1000] = -1\n", + "C:\\Users\\horiz\\AppData\\Local\\Temp\\ipykernel_16592\\3292323189.py:59: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['choice'].iloc[1000:] = 1\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# except:\n", + "# print(\"HDDM: pytorch module seems missing. No LAN functionality can be loaded.\")\n", + "\n", + "ddm_model = \"angle\"\n", + "n_ddm_parameter = len(ssms.config.model_config[ddm_model]['params'])\n", + "# path = 'train_LAN/{}/data/'.format(ddm_model)\n", + "\n", + "path = 'TrainedLANs/'\n", + "\n", + "network_path_list = os.listdir(path)\n", + "network_file_path = [path + file_ for file_ in network_path_list if 'state_dict' in file_][0]\n", + "\n", + "import pickle \n", + "network_config = pickle.load(open('TrainedLANs/248c94cca33e11ecb947ac1f6bfea5a4_training_data_angle_torch__network_config.pickle', 'rb'))\n", + "\n", + "network = LoadTorchMLPInfer(model_file_path = network_file_path,\n", + " network_config = network_config,\n", + " input_dim = n_ddm_parameter + 2)\n", + "\n", + "#%% 9\n", + "# Two ways to call the network\n", + "# Direct call --> need tensor input\n", + "# direct_out = network(torch.from_numpy(np.array([1, 0.5, 0.5, 0.5, 1, 0.65, 1], dtype = np.float32)))\n", + "# print('direct call out: ', direct_out)\n", + "\n", + "# predict_on_batch method\n", + "# predict_on_batch_out = network.predict_on_batch(np.array([1, 1.5, 0.5, 1.0, 0.1, 0.65, 1], dtype = np.float32))\n", + "# print('predict_on_batch out: ', predict_on_batch_out)\n", + "#%% 10\n", + "# show likeliood\n", + "if ddm_model == \"ddm\" :\n", + " data_col = ['v', 'a', 'z', 't', 'rt', 'choice']\n", + "\n", + " data = pd.DataFrame(np.zeros((2000, n_ddm_parameter + 2), \n", + " dtype = np.float32), \n", + " columns = data_col)\n", + " data['v'] = 0.5 # drift rate\n", + " data['a'] = 0.75 # threshold\n", + " data['z'] = 0.5 # bias\n", + " data['t'] = 0.2 # nondesicion time\n", + " data['rt'].iloc[:1000] = np.linspace(5, 0, 1000)\n", + " data['rt'].iloc[1000:] = np.linspace(0, 5, 1000)\n", + " data['choice'].iloc[:1000] = -1\n", + " data['choice'].iloc[1000:] = 1\n", + " print(data['choice'])\n", + "\n", + "elif ddm_model == \"angle\":\n", + " data = pd.DataFrame(np.zeros((2000, n_ddm_parameter + 2), \n", + " dtype = np.float32), \n", + " columns = ['v', 'a', 'z', 't', 'theta','rt', 'choice'])\n", + " data['v'] = 0.5 # drift rate\n", + " data['a'] = 0.75 # threshold\n", + " data['z'] = 0.5 # bias\n", + " data['t'] = 0.2 # nondesicion time\n", + " data['theta'] = 0.1 # linear boundary, the angle of boundary\n", + " data['rt'].iloc[:1000] = np.linspace(5, 0, 1000)\n", + " data['rt'].iloc[1000:] = np.linspace(0, 5, 1000)\n", + " data['choice'].iloc[:1000] = -1\n", + " data['choice'].iloc[1000:] = 1\n", + " print(data['choice'])\n", + "\n", + "#%% 11\n", + "# Network predictions\n", + "predict_on_batch_out = network.predict_on_batch(data.values.astype(np.float32))\n", + "\n", + "# Simulations\n", + "from ssms.basic_simulators import simulator \n", + "sim_out = simulator(model = ddm_model, \n", + " theta = data.values[0, :-2],\n", + " n_samples = 2000)\n", + "#%% 12\n", + "data_rt = np.array(data['rt'] * data['choice'])\n", + "# Plot network predictions\n", + "# plt.plot(data['rt'] * data['choice'], np.exp(predict_on_batch_out), color = 'black', label = 'network')\n", + "plt.plot(data_rt, np.exp(predict_on_batch_out), color = 'black', label = 'network')\n", + "# Plot simulations\n", + "plt.hist(sim_out['rts'] * sim_out['choices'], bins = 30, histtype = 'step', label = 'simulations', color = 'blue', density = True)\n", + "plt.legend(fontsize=18)\n", + "plt.title('SSM likelihood: {}'.format(ddm_model), fontsize=22)\n", + "plt.xticks(fontsize=18)\n", + "plt.yticks(fontsize=18)\n", + "plt.xlabel('rt', fontsize=18)\n", + "plt.ylabel('likelihod', fontsize=18)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pyPower", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/LAN/trainLAN.py b/LAN/trainLAN.py new file mode 100644 index 0000000..9f93ad3 --- /dev/null +++ b/LAN/trainLAN.py @@ -0,0 +1,162 @@ +if __name__ == '__main__': +# Load necessary packages + import ssms + import lanfactory + import os + import numpy as np + from copy import deepcopy + import torch + import pandas as pd + import matplotlib.pyplot as plt + + # os.chdir('D:horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM/') + #%% Generate training data + # MAKE CONFIGS + + # Initialize the generator config (for MLP LANs) + # generator_config = deepcopy(ssms.config.data_generator_config['lan']['mlp']) + + + generator_config = deepcopy(ssms.config.data_generator_config['lan']) + + # Specify generative model (one from the list of included models mentioned above) + ddm_model = "angle" + generator_config['dgp_list'] = ddm_model + # Specify number of parameter sets to simulate + generator_config['n_parameter_sets'] = 100 # suggest not change + # Specify how many samples a simulation run should entail + generator_config['n_samples'] = 100 + # Specify folder in which to save generated data + generator_config['output_folder'] = 'train_LAN/{}/data/'.format(ddm_model) + # save config path + save_configs_path = 'train_LAN/{}/config/'.format(ddm_model) + # 'data/lan_mlp/sets'+str(generator_config['n_parameter_sets'])+'samples'+str(generator_config['n_samples'])+"/" + print("generate data saving at: "+generator_config['output_folder'] ) + + # Make model config dict + model_config = ssms.config.model_config[ddm_model] + + + #%% MAKE DATA + my_dataset_generator = ssms.dataset_generators.data_generator(generator_config = generator_config, + model_config = model_config) + # save data + training_data = my_dataset_generator.generate_data_training_uniform(save = True) + # 2 Prepare for training + # LAN uses custom dataloaders. We expect to receive a bunch of training data so + # We need dataloader to spits out batches of data from a specific training data file, and keep checking when to load a new file + + # List of datafiles (here only one) + # folder_ = 'data/lan_mlp/traindata_angle/' + folder_ = generator_config['output_folder'] + file_list_ = [folder_ + file_ for file_ in os.listdir(folder_) if 'training_data' in file_] + # network_file_path = [generator_config['output_folder'] + file_ for file_ in network_path_list if 'state_dict' in file_][0] + # Training dataset + torch_training_dataset = lanfactory.trainers.DatasetTorch(file_ids = file_list_, + batch_size = 128) + + torch_training_dataloader = torch.utils.data.DataLoader(torch_training_dataset, + shuffle = True, + batch_size = None, + num_workers = 1, + pin_memory = True) + + # Validation dataset + torch_validation_dataset = lanfactory.trainers.DatasetTorch(file_ids = file_list_, + batch_size = 128) + + torch_validation_dataloader = torch.utils.data.DataLoader(torch_validation_dataset, + shuffle = True, + batch_size = None, + num_workers = 1, + pin_memory = True) + + network_config = lanfactory.config.network_configs.network_config_mlp + + print('Network config: ') + print(network_config) + + train_config = lanfactory.config.network_configs.train_config_mlp + + train_config["n_epochs"] = 10 + + print('Train config: ') + print(train_config) +#%% 3 + ################################## + ################################## + ################################## + # LOAD NETWORK + net = lanfactory.trainers.TorchMLP(network_config = deepcopy(network_config), + input_shape = torch_training_dataset.input_dim) + # save_folder = '/data/torch_models/', + # generative_model_id = 'angle') + + # SAVE CONFIGS + lanfactory.utils.save_configs(model_id =ddm_model, + save_folder = save_configs_path, + network_config = network_config, + train_config = train_config, + allow_abs_path_folder_generation = True) + # TRAIN MODEL # need to change name? + model_trainer=lanfactory.trainers.ModelTrainerTorchMLP(train_config=train_config, + train_dl=torch_training_dataloader, + valid_dl=torch_validation_dataloader, + model=net) + model_trainer.train_and_evaluate(save_history = True, + save_model = True, + output_folder= generator_config['output_folder'], + verbose = 0) + #%% 8 + # network_path_list = os.listdir(generator_config['output_folder']+"/") + # network_file_path = [generator_config['output_folder'] + file_ for file_ in network_path_list if 'state_dict' in file_][0] + + # network = lanfactory.trainers.LoadTorchMLPInfer(model_file_path = network_file_path, + # network_config = network_config, + # input_dim = torch_training_dataset.input_dim) + + # #%% 9 + # # Two ways to call the network + # # Direct call --> need tensor input + # # direct_out = network(torch.from_numpy(np.array([1, .5, 0.5, 1.0, 0.1, 0.65, 1], dtype = np.float32))) + # direct_out = network(torch.from_numpy(np.array([1, 0.5, 0.5, 0.5, 0.65, 1], dtype = np.float32))) + # print('direct call out: ', direct_out) + + # # predict_on_batch method + # # predict_on_batch_out = network.predict_on_batch(np.array([1, 1.5, 0.5, 1.0, 0.1, 0.65, 1], dtype = np.float32)) + # # print('predict_on_batch out: ', predict_on_batch_out) + # #%% 10 + # # show likeliood + # data = pd.DataFrame(np.zeros((2000, 7), + # dtype = np.float32), + # columns = ['v', 'a', 'z', 't', 'theta', 'rt', 'choice']) + # data['v'] = 0.5 # drift rate + # data['a'] = 0.75 # threshold + # data['z'] = 0.5 # bias + # data['t'] = 0.2 # nondesicion time + # # data['theta'] = 0.1 # linear boundary, the angle of boundary + # data['rt'].iloc[:1000] = np.linspace(5, 0, 1000) + # data['rt'].iloc[1000:] = np.linspace(0, 5, 1000) + # data['choice'].iloc[:1000] = -1 + # data['choice'].iloc[1000:] = 1 + # print(data['choice']) + # #%% 11 + # # Network predictions + # predict_on_batch_out = network.predict_on_batch(data.values.astype(np.float32)) + + # # Simulations + # from ssms.basic_simulators import simulator + # sim_out = simulator(model = 'angle', + # theta = data.values[0, :-2], + # n_samples = 2000) + # #%% 12 + # data_rt = np.array(data['rt'] * data['choice']); + # # Plot network predictions + # # plt.plot(data['rt'] * data['choice'], np.exp(predict_on_batch_out), color = 'black', label = 'network') + # plt.plot(data_rt, np.exp(predict_on_batch_out), color = 'black', label = 'network') + # # Plot simulations + # plt.hist(sim_out['rts'] * sim_out['choices'], bins = 30, histtype = 'step', label = 'simulations', color = 'blue', density = True) + # plt.legend() + # plt.title('SSM likelihood') + # plt.xlabel('rt') + # plt.ylabel('likelihod') diff --git a/Likelihoods.py b/Likelihoods.py new file mode 100644 index 0000000..09d11b5 --- /dev/null +++ b/Likelihoods.py @@ -0,0 +1,49 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Jul 19 23:03:42 2023 + +@author: horiz +""" + +import math +from wfpt import wiener_like +from wfpt_n.wfpt_n import wiener_like_n +# from wfpt_n import wiener_like_n +import numpy as np + +def neg_likelihood(param,arg): + """ + neg_likelihood(param,arg): + ---------- + theta : 1 * n array, n: number of parameters + parameters in an array to generate responses from ssms package. + arg: tuple + arg[0]: DDM_id, string, should match those from the SSMS package. + arg[1]: data, array, behavioral data to estimate parameters, which is denoted by responses['rts'] * responses['choices'] + + Returns + ------- + -llh : float + negative log-likelihood of generate the given data with provided parameters. + + Description + ----------- + Function to generate negative log-likelihood for estimate parameters of ddm models. + """ + err = 10 ** (-10) + data = arg[0] + DDM_id = arg[1] + if DDM_id =="ddm": + llh = wiener_like_n(data, + param[0],0, + param[1], + param[2],0, + param[3],0,err) +# ============================================================================= +# parameters of wienr_like +# def wiener_like(np.ndarray[double, ndim=1] x, double v, double sv, double a, double z, double sz, double t, +# double st, double err, int n_st=10, int n_sz=10, bint use_adaptive=1, double simps_err=1e-8, +# double p_outlier=0, double w_outlier=0.1): +# ============================================================================= + + return -llh \ No newline at end of file diff --git a/ParameterEstimation.py b/ParameterEstimation.py new file mode 100644 index 0000000..a3f91f6 --- /dev/null +++ b/ParameterEstimation.py @@ -0,0 +1,104 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Jul 19 16:46:54 2023 + +@author: horiz +""" + +from scipy import optimize +import numpy as np + +def MLE(fun,arg,param_bounds,method = "Differential_Evolution", show = 0, n_ini = 2): + """ + + Parameters + ---------- + fun: function + Negative log-likelihood function to be minimized + arg: tuple + Arg to fun, including (responses,DDM_id) + param_bounds: 2 * n array + range of parameters, the 0th row corresponds to lower bound, the 1st corresponds to upper bound + method: str + Algorithm for optimizing, including "Nelder-Mead" and "Brute" + show: bool + Whether show initial guesses, start points, end points in "Nelder-Mead" method + + Returns + ------- + estimated_parameters : array, shape = (number of parameters) + estimated parameters given behavioral responses + + Description + ------- + This function minimize the negative log-likelihood fucntion to estimated parameters that generate a given behavioral data. + Optimization can be accomplished by two methods: + "Brute": using gird research in the parameter range to estimate parameters + "Nelder-Mead": this method loops for 10 * num_par times to get different initial guesses, then select the parameter set with minimum negative log-likelihood as return + + + """ + # define rangs of parameters + + ranges = () + for p in range(param_bounds.shape[1]) : + ranges = ranges+(tuple(param_bounds.T[p]),) + + num_par = param_bounds.shape[1] + if method=="Brute": + # grid research method + estimated_parameters = optimize.brute(fun, ranges, args = (arg,) ,finish=None) + + elif method=="Nelder-Mead": + + # for r in range(10 * num_par): # loop for multiple initial guesses + start_params = np.random.uniform(param_bounds[0],param_bounds[1]) + if show : + print("initial guess:",r) + print('start point',start_params,fun(start_params,arg)) + # start_params = True_Par.loc[pp] + np.random.rand() + + # llh_test = fun(start_params,responses,DDM_id) + optimization_output = optimize.minimize(fun, start_params,args = (arg,), + method="Nelder-Mead", + bounds= ranges, + # bounds= tuple(param_bounds.T.reshape(num_par,2)), + options = {'maxfev':1000, 'xatol':0.01, 'return_all':1}) + estimated_parameters_SinIni = optimization_output['x'] + if show : + print('end point',estimated_parameters_SinIni,fun(estimated_parameters_SinIni,arg)) + # estimated_parameters_MulIni[r,0:num_par] = estimated_parameters_SinIni + # estimated_parameters_MulIni[r,num_par] = fun(estimated_parameters_SinIni,arg) + # estimated_parameters = estimated_parameters_MulIni[np.argmin(list(estimated_parameters_MulIni[:,-1])),0:num_par] + estimated_parameters = estimated_parameters_SinIni + elif method=="Nelder-Mead_MulIni": + estimated_parameters_MulIni = np.empty((n_ini * param_bounds.shape[1],param_bounds.shape[1]+1)) + for r in range(n_ini * num_par): # loop for multiple initial guesses + start_params = np.random.uniform(param_bounds[0],param_bounds[1]) + if show : + print("initial guess:",r) + # print('start point',start_params,fun(start_params,arg)) + # start_params = True_Par.loc[pp] + np.random.rand() + + # llh_test = fun(start_params,responses,DDM_id) + optimization_output = optimize.minimize(fun, start_params,args = (arg,), + method="Nelder-Mead", + bounds= ranges, + # bounds= tuple(param_bounds.T.reshape(num_par,2)), + options = {'maxfev':1000, 'xatol':0.01, 'return_all':1}) + estimated_parameters_SinIni = optimization_output['x'] + if show : + print('end point',estimated_parameters_SinIni,fun(estimated_parameters_SinIni,arg)) + estimated_parameters_MulIni[r,0:num_par] = estimated_parameters_SinIni + estimated_parameters_MulIni[r,num_par] = fun(estimated_parameters_SinIni,arg) + + estimated_parameters = estimated_parameters_MulIni[np.argmin(list(estimated_parameters_MulIni[:,-1])),0:num_par] + # estimated_parameters = estimated_parameters_SinIni + elif method=="Differential_Evolution": + estimated_parameters_DE = optimize.differential_evolution(fun, ranges, args = (arg,)) + estimated_parameters = estimated_parameters_DE.x + if show : + print('estimation from DE',estimated_parameters,fun(estimated_parameters,arg)) + + return estimated_parameters + \ No newline at end of file diff --git a/Plot_ProPow.ipynb b/Plot_ProPow.ipynb new file mode 100644 index 0000000..ea05cac --- /dev/null +++ b/Plot_ProPow.ipynb @@ -0,0 +1,189 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import ssms\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from scipy import stats\n", + "import os,sys\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "\n", + "def ImPlot_plot(title,x,xlabel,y,ylabel,type = \"plot\",titlesize=35,xylabelsize=25,labelsize = 25,linewidth = 5):\n", + " # corr_coef = np.corrcoef(x, y)[0, 1]\n", + " # coefficients = np.polyfit(x, y, 1) # 线性回归，拟åˆä¸€æ¬¡å¤šé¡¹å¼ï¼ˆä¸€æ¬¡ç›´çº¿ï¼‰\n", + " # poly = np.poly1d(coefficients)\n", + " # x_fit = np.linspace(min(x), max(x)) # 创建拟åˆçº¿çš„ x 值\n", + " # y_fit = poly(x_fit) \n", + "\n", + " plt.figure(figsize=(12, 12))\n", + " plt.title(title,fontdict={\"fontsize\" : titlesize})\n", + " if type == \"plot\":\n", + " plt.plot(x,y,linewidth=linewidth)\n", + " elif type == \"scatter\":\n", + " plt.scatter(x,y,s=linewidth)\n", + " # plt.plot(x_fit, y_fit, color='red', label='Regression line') # 回归线\n", + "\n", + "\n", + " plt.xlabel(xlabel,fontdict={\"fontsize\" : xylabelsize})\n", + " plt.tick_params(labelsize=labelsize)\n", + " plt.ylabel(ylabel,fontdict={\"fontsize\" : xylabelsize})\n", + " return plt\n", + "\n", + "def MatchPrefix (criterion, ntrials,npp, nreps, par_ind = None, sd = None,es = None):\n", + " if criterion == \"GD\":\n", + " prefix_to_match = 'OutputGD{}P{}SD{}T{}N{}M{}ES'.format(par_ind,np.round(sd,2),ntrials, \n", + " npp, nreps,np.round(es,2))\n", + " elif criterion == \"EC\": \n", + " prefix_to_match = 'OutputEC{}P{}SD{}TC{}T{}N{}M'.format(par_ind,sd, es, ntrials,\n", + " npp, nreps) \n", + " elif criterion == \"IC\": \n", + " prefix_to_match = 'OutputIC{}T{}N{}M'.format(ntrials,npp, nreps)\n", + " return prefix_to_match" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plots for Proficiency & Power\n", + "# X-ACC, y-power, lable-settings of distributions\n", + "\n", + "## load data\n", + "### find the results\n", + "\n", + "\n", + "ResultPath = \"\\\\Private_Results\\\\ProCor\"\n", + "DDM_id = \"ddm\"\n", + "ntrials = 100\n", + "npp = 20\n", + "nreps = 50\n", + "criterion = \"IC\"\n", + "Data_folder = os.getcwd()+ResultPath\n", + "results_files = [f for f in os.listdir(Data_folder) if os.path.isfile(os.path.join(Data_folder, f)) and f.lower().endswith('.csv')]\n", + "save = 0\n", + "p_list = ['v','a','z','t']\n", + "v_mean = ['0',\"0.6\",'0.8','1','1.2',\"2.4\"] \n", + "Alldata_list = []\n", + "## plot\n", + "\n", + "title_pre = \"Correlation between ACC \\nand recovery of \"\n", + "titlesize = 25\n", + "linewidth =8\n", + "##\n", + "for n_p in range(len(p_list)):\n", + " for file in results_files:\n", + " if file.startswith(MatchPrefix (criterion, ntrials,npp, nreps, par_ind = None, sd = None,es = None)) : # check prefix\n", + "\n", + " OutputFile_path = Data_folder+ '\\\\' + file\n", + " OutputResults = pd.read_csv(OutputFile_path, delimiter = ',')\n", + " Alldata_list.append(OutputResults) \n", + "\n", + " Alldata = pd.concat(Alldata_list,ignore_index=True)\n", + " plt.figure(figsize=(12, 12))\n", + " title = title_pre + p_list[n_p]\n", + " plt.title(title,fontdict={\"fontsize\" : titlesize})\n", + "\n", + " unique_input_rows = np.sort(Alldata['Input_row'].unique())\n", + " colors = plt.cm.jet(np.linspace(0, 1, len(unique_input_rows)))\n", + "\n", + " for input_row, color in zip(unique_input_rows, colors):\n", + "\n", + " subset = Alldata[Alldata['Input_row'] == input_row]\n", + "\n", + " plt.scatter(subset['ACC_average'], subset[ p_list[n_p]], color=color, label=f'v_mean = {v_mean[input_row]}')\n", + "\n", + " plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)\n", + " plt.xlabel(\"ACC\")\n", + " plt.ylabel(\"Correlation Coeffients\")\n", + " # sns.regplot(x=Alldata[p_list[n_p]], y=Alldata[\"ACC_average\"])\n", + " corr_coef, p_value = stats.pearsonr(Alldata[\"ACC_average\"],Alldata[p_list[n_p]])\n", + " annotation = f\"Correlation coefficient: {corr_coef:.2f}\\nP-value: {p_value:.4f}\"\n", + " plt.annotate(annotation, xy=(0.1, 0.85), xycoords='axes fraction', fontsize=20)\n", + "\n", + "\n", + " if save:\n", + " file_name = '\\ProPowIC{}T{}N{}M_{}.png'.format(ntrials,npp,nreps,p_list[n_p])\n", + " plt.savefig(ResultPath+file_name,bbox_inches='tight')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pyPower", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/PowerAnalysis.py b/PowerAnalysis.py index cee0471..996bb2f 100644 --- a/PowerAnalysis.py +++ b/PowerAnalysis.py @@ -1,61 +1,66 @@ -# -*- coding: utf-8 -*- + """ Created on Tue Dec 14 11:04:23 2021 +MOdified on june 2023 -@author: maudb +@author: maudb, Luning He """ -HPC = False +CLIarg = 1 +import os,sys +import argparse +# os.chdir('D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DDM') + +#%% +# import matplotlib +# import matplotlib.pyplot as plt import numpy as np import pandas as pd from multiprocessing import Pool, cpu_count -from Functions import create_design, Incorrelation_repetition, groupdifference_repetition, check_input_parameters, Excorrelation_repetition +from Functions_DDM import Incorrelation_repetition_DDM, Groupdifference_repetition_DDM, Excorrelation_repetition_DDM from scipy import stats as stat from datetime import datetime +import ssms +import seaborn as sns +import matplotlib.pyplot as plt -if HPC == False: - import seaborn as sns - import matplotlib.pyplot as plt - -#This is to avoid warnings being printed to the terminal window +#This is to avoid warnings being printed to the terminal window import warnings warnings.filterwarnings('ignore') +#%% RL functions -def power_estimation_Incorrelation(npp = 30, ntrials = 480, nreversals = 12, cut_off = 0.7, high_performance = False, - nreps = 100, reward_probability = 0.8, mean_LRdistribution = 0.5, SD_LRdistribution = 0.1, - mean_inverseTempdistribution = 2.0, SD_inverseTempdistribution = 1.0): - """ +#%% DDM functions +def power_estimation_Incorrelation_DDM(means = None, stds=None, DDM_id = "ddm", + npp = 30, ntrials = 30, nreps = 6, ncpu=6, + cut_off = 0.7, method = "Differential_Evolution"): + + + """ Parameters ---------- + means: array + Means of distribution from which true parameters are sampled + stds: array + Stds of distribution from which true parameters are sampled + DDM_id: string + Index of DDM model which should be matched with ssms package npp : integer Number of participants in the study. ntrials : integer Number of trials that will be used to do the parameter recovery analysis for each participant. - nreversals : integer - The number of rule-reversals that will occur in the experiment. Should be smaller than ntrials. - cut_off : float - Critical value that will be used to evaluate whether the repetition was successful. - high_performance : bool (True or False) - Defines whether multiple cores on the computer will be used in order to estimate the power. nreps : integer Number of repetitions that will be used for the parameter estimation process. - reward_probability : float (element within [0, 1]), optional - The probability that reward will be congruent with the current stimulus-response mapping rule. The default is 0.8. - mean_LRdistribution: float - Mean for the normal distribution to sample learning rates from. - SD_LRdistribution: float - Standard deviation for the normal distribution to sample learning rates from. - mean_inverseTempdistribution: float - Mean for the normal distribution to sample inverse temperatures from. - SD_inverseTempdistribution: float - Standard deviation for the normal distribution to sample inverse temperatures from. + cut_off : float + Critical value that will be used to evaluate whether the repetition was successful. + method: string + Method of optimization, including: "Differential_Evolution", "Nelder-Mead_MulIni", "Nelder-Mead" and "Brute" Returns ------- - allreps_output : TYPE - Pandas dataframe containing the correlation value on each repetition. + allreps_output : DataFrame + Pandas dataframe containing statistics from each nrep of all parameters power_estimate: float [0, 1] The power estimation: number of reps for which the parameter recovery was successful (correlation > significance_cutoff) divided by the total number of reps. @@ -65,123 +70,155 @@ def power_estimation_Incorrelation(npp = 30, ntrials = 480, nreversals = 12, cut Parameter estimates are considered to be adequate if their correlation with the true parameters is minimum the cut_off. Power is calculated using a Monte Carlo simulation-based approach. """ - start_design = create_design(ntrials = ntrials, nreversals = nreversals, reward_probability = reward_probability) - if HPC == True: n_cpu = cpu_count() - elif high_performance == True: n_cpu = cpu_count() - 2 - else: n_cpu = 1 #divide process over multiple cores - pool = Pool(processes = n_cpu) - LR_distribution = np.array([mean_LRdistribution, SD_LRdistribution]) - inverseTemp_distribution = np.array([mean_inverseTempdistribution, SD_inverseTempdistribution]) - out = pool.starmap(Incorrelation_repetition, [(inverseTemp_distribution, LR_distribution, npp, ntrials, - start_design, rep, nreps, n_cpu) for rep in range(nreps)]) - pool.close() - pool.join() - - allreps_output = pd.DataFrame(out, columns = ['correlations']) + pool = Pool(processes = ncpu) + + # Parameter distribution + +# ============================================================================= +# # Incorrelation_repetition(means ,stds , +# param_bounds , +# npp = 150, +# ntrials = 450, DDM_id = "angle", rep=1, nreps = 250, ncpu = 6): +# ============================================================================= + - power_estimate = np.mean((allreps_output['correlations'] >= cut_off)*1) - print(str("\nProbability to obtain a correlation(true_param, param_estim) >= {}".format(cut_off) - + " with {} trials and {} participants: {}%".format(ntrials, npp, power_estimate*100))) + param_bounds = np.array(ssms.config.model_config[DDM_id]['param_bounds']) + print("Optimization Method:",method) + out_AllReturns = pool.starmap(Incorrelation_repetition_DDM, [(means,stds, param_bounds, + npp, ntrials,DDM_id,method, + rep, nreps, ncpu) for rep in range(nreps)]) + pool.close() + pool.join() + # allreps_output = pd.DataFrame(out_AllReturns, columns = ['Statistic','True_Par','Esti_Par', 'ACC_out', 'RT_out']) + + out = [IC_Sta[0] for IC_Sta in out_AllReturns] + True_out = [IC_Sta[1] for IC_Sta in out_AllReturns] + cn = ssms.config.model_config[DDM_id]['params']+["ACC_average","RT_average"] + allreps_output = pd.DataFrame(np.array(out).reshape(nreps,len(means)+2),columns=cn) + power_estimate = pd.DataFrame(np.empty((1,len(means))),columns=ssms.config.model_config[DDM_id]['params']) + + for p in range(len(means)): + power_estimate.iloc[0,p] = np.mean((allreps_output.iloc[:,p] >= cut_off)*1) +# print(str("\nProbability to obtain a correlation(true_param, param_estim) >= {}".format(cut_off) +# + " with {} trials and {} participants: {}%".format(ntrials, npp, power_estimate*100))) return allreps_output, power_estimate -def power_estimation_Excorrelation(npp = 100, ntrials = 480, nreversals = 12, typeIerror = 0.05, high_performance = False, - nreps = 100, reward_probability = 0.8, mean_LRdistribution = 0.5, SD_LRdistribution = 0.1, - mean_inverseTempdistribution = 2.0, SD_inverseTempdistribution = 1.0, True_correlation = .5): +def power_estimation_Excorrelation_DDM(means,stds,par_ind,DDM_id,true_correlation = 0.5, + npp = 100, ntrials = 480, nreps = 100, + typeIerror = 0.05, ncpu = 6, method = "Differential_Evolution"): """ Parameters ---------- + means: array + Means of distribution from which true parameters are sampled + stds: array + Stds of distribution from which true parameters are sampled + par_ind: integer + Parameter index of the parameter of interest, according to the order from ssms. + START FROM 0. E.g., par_ind = 0, corresponding to "v" + DDM_id: + Index of DDM model which should be matched with ssms package + True_correlation: float + The hypothesized correlation between the learning rate and the external measure theta. npp : integer Number of participants in the study. ntrials : integer Number of trials that will be used to do the parameter recovery analysis for each participant. - nreversals : integer - The number of rule-reversals that will occur in the experiment. Should be smaller than ntrials. - typeIerror : float - Critical value for p-values. From this also the cut-off for the correlation statistic can be determined. - high_performance : bool (True or False) - Defines whether multiple cores on the computer will be used in order to estimate the power. nreps : integer Number of repetitions that will be used for the parameter estimation process. - reward_probability : float (element within [0, 1]), optional - The probability that reward will be congruent with the current stimulus-response mapping rule. The default is 0.8. - mean_LRdistribution: float - Mean for the normal distribution to sample learning rates from. - SD_LRdistribution: float - Standard deviation for the normal distribution to sample learning rates from. - mean_inverseTempdistribution: float - Mean for the normal distribution to sample inverse temperatures from. - SD_inverseTempdistribution: float - Standard deviation for the normal distribution to sample inverse temperatures from. - True_correlation: float - The hypothesized correlation between the learning rate and the external measure theta. + typeIerror : float + Critical value for p-values. From this also the cut-off for the correlation statistic can be determined. + ncpu: integer + Number of cpu used + Returns ------- - allreps_output : TYPE - Pandas dataframe containing the correlation value on each repetition, the p-value and the p-value if estimates would be perfect. - power_estimate: float [0, 1] - The power estimation: number of reps for which the parameter recovery was successful (correlation > significance_cutoff) divided by the total number of reps. - + allreps_output : datafram + Pandas dataframe containing estimated and statistics of the parameter of interest: estimated correlation coefficient, estimated p-value, true correlation coefficient, true p-value + power_estimate: dataframe + Results of power analysis, including: the name of parameter calulated, correlation cut-off value(i.e., tau), trure p-value, conventional power. + Description ----------- Function that actually calculates the probability to obtain significant correlations with external measures. Parameter estimates are considered to be adequate if correctly reveal a significant correlation when a significant correlation. Power is calculated using a Monte Carlo simulation-based approach. """ - start_design = create_design(ntrials = ntrials, nreversals = nreversals, reward_probability = reward_probability) - if HPC == True: n_cpu = cpu_count() - elif high_performance == True: n_cpu = cpu_count() - 2 - else: n_cpu = 1 - + #Use beta_distribution to determine the p-value for the hypothesized correlation beta_distribution = stat.beta((npp/2)-1, (npp/2)-1, loc = -1, scale = 2) true_pValue = 1-beta_distribution.cdf(True_correlation) tau = -beta_distribution.ppf(typeIerror/2) + + + # Specifically, beta.pdf(x, a, b, loc, scale) is identically equivalent to beta.pdf(y, a, b) / scale with y = (x - loc) / scale. + # Note that shifting the location of a distribution does not make it a “noncentral†distribution; + # noncentral generalizations of some distributions are available in separate classes. #compute conventional power noncentral_beta = stat.beta((npp/2)-1, (npp/2)-1, loc = -1+True_correlation, scale = 2) conventional_power = 1-noncentral_beta.cdf(tau) + print(str("\nProbability to obtain a significant correlation under conventional power implementation: {}%".format(np.round(conventional_power*100,2)))) print(str("\nThe correlation cut-off value is: {}".format(np.round(tau,2)))) print(str("\np-value for true correlation is :{}".format(np.round(true_pValue,5)))) - print(str("\nProbability to obtain a significant correlation under conventional power implementation: {}%".format(np.round(conventional_power*100,2)))) #divide process over multiple cores - pool = Pool(processes = n_cpu) - LR_distribution = np.array([mean_LRdistribution, SD_LRdistribution]) - inverseTemp_distribution = np.array([mean_inverseTempdistribution, SD_inverseTempdistribution]) - out = pool.starmap(Excorrelation_repetition, [(inverseTemp_distribution, LR_distribution, True_correlation, npp, ntrials, - start_design, rep, nreps, n_cpu) for rep in range(nreps)]) + pool = Pool(processes = ncpu) + + param_bounds = np.array(ssms.config.model_config[DDM_id]['param_bounds']) + print("Optimization Method:",method) + + out = pool.starmap(Excorrelation_repetition_DDM, [(means,stds, + param_bounds,par_ind, DDM_id,true_correlation, + npp, ntrials, rep, nreps, ncpu,method) for rep in range(nreps)]) + + pool.close() pool.join() - allreps_output = pd.DataFrame(out, columns = ['Statistic','estimated_pValue', 'True_pValue']) + allreps_output = pd.DataFrame(out, columns = ['Esti_r', 'Esti_pValue', 'True_r', 'True_pValue', "ACC_average", "RT_average"]) #Compute power if estimates would be perfect. - #power_true = np.mean((allreps_output['True_pValue'] <= typeIerror/2)*1) - #print(str("\nProbability to obtain a significant correlation under conventional power implementation: {}%".format(np.round(power_true*100,2)))) - #Compute power for correlation with estimated parameter values. - power_estimate = np.mean((allreps_output['estimated_pValue'] <= typeIerror/2)*1) - print(str("\nProbability to obtain a significant correlation between model parameter and an external measure that is {} correlated".format(True_correlation) - + " with {} trials and {} participants: {}%".format(ntrials, npp, np.round(power_estimate*100,2)))) + power_estimate = pd.DataFrame(np.empty((1,4)), columns=["power_"+ssms.config.model_config[DDM_id]['params'][par_ind],"tau",'true_pValue','conventional_power']) + power_estimate.iloc[0,0] = np.mean((allreps_output['Esti_pValue'] <= typeIerror/2)*1) + power_estimate.iloc[0]['tau'] = tau + power_estimate.iloc[0]['true_pValue'] = true_pValue + power_estimate.iloc[0]['conventional_power'] = conventional_power + + print(str("\nProbability to obtain a significant correlation between model parameter {} and an external measure that is {} correlated".format(ssms.config.model_config[DDM_id]['params'][par_ind],True_correlation) + + " with {} trials and {} participants: {}%".format(ntrials, npp, np.round(power_estimate.iloc[0,0]*100,2)))) + return allreps_output, power_estimate -def power_estimation_groupdifference(npp_per_group = 20, ntrials = 480, nreps = 100, typeIerror = 0.05, - high_performance = False, nreversals = 12, reward_probability = 0.8, - mean_LRdistributionG1 = 0.5, SD_LRdistributionG1 = 0.1, - mean_LRdistributionG2 = 0.5, SD_LRdistributionG2 = 0.1, - mean_inverseTempdistributionG1 = 2.0, SD_inverseTempdistributionG1 = 1.0, - mean_inverseTempdistributionG2 = 2.0, SD_inverseTempdistributionG2 = 1.0): +def power_estimation_groupdifference_DDM(cohens_d, means_g1,means_g2,stds_g1,stds_g2,DDM_id, par_ind, + npp_per_group = 40, ntrials = 100, ncpu=6, + nreps = 250, typeIerror = 0.05, method = "Differential_Evolution"): + """ Parameters ---------- + means_g1: array + Means of the distribution from which samples true parameters of group 1 + means_g2: array + Means of the distribution from which samples true parameters of group 2 + stds_g1: array + Stds of the distribution from which samples true parameters of group 1 + stds_g2: array + Stds of the distribution from which samples true parameters of group 2 + DDM_id: string + Index of DDM model which should be matched with ssms package + par_ind: + Parameter index of the parameter of interest, according to the order from ssms. + START FROM 0. E.g., par_ind = 0, corresponding to "v" npp_per_group : integer Number of participants per group in the study. ntrials : integer @@ -190,33 +227,11 @@ def power_estimation_groupdifference(npp_per_group = 20, ntrials = 480, nreps = Number of repetitions that will be used for the parameter estimation process. typeIerror : float Critical value for p-values. From this also the cut-off for the correlation statistic can be determined. - high_performance : bool (True or False) - Defines whether multiple cores on the computer will be used in order to estimate the power. - nreversals : integer - The number of rule-reversals that will occur in the experiment. Should be smaller than ntrials. - reward_probability : float (element within [0, 1]), optional - The probability that reward will be congruent with the current stimulus-response mapping rule. The default is 0.8. - mean_LRdistributionG1: float - Mean for the normal distribution to sample learning rates for group 1. - SD_LRdistributioG1: float - Standard deviation for the normal distribution to sample learning rates for group 1. - mean_inverseTempdistributionG1: float - Mean for the normal distribution to sample inverse temperatures for group 1. - SD_inverseTempdistributionG1: float - Standard deviation for the normal distribution to sample inverse temperatures for group 1. - mean_LRdistributionG2: float - Mean for the normal distribution to sample learning rates for group 2. - SD_LRdistributioG2: float - Standard deviation for the normal distribution to sample learning rates for group 2. - mean_inverseTempdistributionG2: float - Mean for the normal distribution to sample inverse temperatures for group 2. - SD_inverseTempdistributionG2: float - Standard deviation for the normal distribution to sample inverse temperatures for group 2. - + Returns ------- - allreps_output : TYPE - Pandas dataframe containing the p-value on each repetition. + allreps_output : dataframe + Pandas dataframe containing the t-value and p-value based on t-test of estimated parameters on each repetition. power_estimate: float [0, 1] The power estimation: number of reps for which a significant group difference was found divided by the total number of reps. @@ -227,174 +242,400 @@ def power_estimation_groupdifference(npp_per_group = 20, ntrials = 480, nreps = Power is calculated using a Monte Carlo simulation-based approach. """ - start_design = create_design(ntrials = ntrials, nreversals = nreversals, reward_probability = reward_probability) - if high_performance == True: n_cpu = cpu_count() - 2 - else: n_cpu = 1 - if __name__ == '__main__': - - #Use t_distribution to determine the p-value for the hypothesized cohen's d - true_pValue = 1-stat.t.cdf(cohens_d*np.sqrt(npp_per_group), (npp_per_group-1)*2) - tau = -stat.t.ppf(typeIerror/2, (npp_per_group-1)*2) - - #Compute conventional power - conventional_power = 1-stat.nct.cdf(tau, (npp_per_group-1)*2, cohens_d*np.sqrt(npp_per_group)) - - print(str("\nThe t-distribution cut-off value is: {}".format(np.round(tau,2)))) - print(str("\np-value for given cohen's d is :{}".format(np.round(true_pValue,5)))) - print("\nProbability to obtain a significant group difference under conventional power implementation: {}%".format(np.round(conventional_power*100,2))) - - #divide process over multiple cores - if mean_LRdistributionG1 > mean_LRdistributionG2: - LR_distributions = np.array([[mean_LRdistributionG1, SD_LRdistributionG1], [mean_LRdistributionG2, SD_LRdistributionG2]]) - else: - LR_distributions = np.array([[mean_LRdistributionG2, SD_LRdistributionG2], [mean_LRdistributionG1, SD_LRdistributionG1]]) - - inverseTemp_distributions = np.array([[mean_inverseTempdistributionG1, SD_inverseTempdistributionG1], - [mean_inverseTempdistributionG2, SD_inverseTempdistributionG2]]) - pool = Pool(processes = n_cpu) - out = pool.starmap(groupdifference_repetition, [(inverseTemp_distributions, LR_distributions, npp_per_group, - ntrials, start_design, rep, nreps, n_cpu, False) for rep in range(nreps)]) - # before calling pool.join(), should call pool.close() to indicate that there will be no new processing - pool.close() - pool.join() - - allreps_output = pd.DataFrame(out, columns = ['Statistic', 'PValue']) - - # check for which % of repetitions the group difference was significant - # note that we're working with a one-sided t-test (if interested in two-sided need to divide the p-value obtained at each rep with 2) - power_estimate = np.mean((allreps_output['PValue'] <= typeIerror/2)) - print(str("\nProbability to detect a significant group difference when the estimated effect size d = {}".format(np.round(cohens_d,3)) - + " with {} trials and {} participants per group: {}%".format(ntrials, - npp_per_group, np.round(power_estimate*100,2)))) - return allreps_output, power_estimate + #Use t_distribution to determine the p-value for the hypothesized cohen's d + true_pValue = 1-stat.t.cdf(cohens_d*np.sqrt(npp_per_group), (npp_per_group-1)*2) + tau = -stat.t.ppf(typeIerror/2, (npp_per_group-1)*2) + + #Compute conventional power + conventional_power = 1-stat.nct.cdf(tau, (npp_per_group-1)*2, cohens_d*np.sqrt(npp_per_group)) -#%% -import os, sys + + print("\nProbability to obtain a significant group difference under conventional power implementation: {}%".format(np.round(conventional_power*100,2))) + print(str("\nEstimated effect size d = : {}".format(np.round(cohens_d,3)))) + print(str("\nThe t-distribution cut-off value is: {}".format(np.round(tau,3)))) + print(str("\np-value for given cohen's d is :{}".format(np.round(true_pValue,5)))) + #divide process over multiple cores + + pool = Pool(processes = ncpu) + param_bounds = np.array(ssms.config.model_config[DDM_id]['param_bounds']) + print("Optimization Method:",method) + out = pool.starmap(Groupdifference_repetition_DDM, [(means_g1, stds_g1,means_g2, stds_g2,DDM_id, par_ind,param_bounds, + npp_per_group, ntrials, rep, nreps, ncpu , method) for rep in range(nreps)]) + # before calling pool.join(), should call pool.close() to indicate that there will be no new processing + pool.close() + pool.join() + + allreps_output = pd.DataFrame(out, columns = ['Statistic', 'PValue','ACC_g1','ACC_g2','RT_g1','RT_g2']) + + # check for which % of repetitions the group difference was significant + # note that we're working with a one-sided t-test (if interested in two-sided need to divide the p-value obtained at each rep with 2) + power_estimate = pd.DataFrame(np.empty((1,4)), columns=[ssms.config.model_config[DDM_id]['params'][par_ind],"tau",'true_pValue','conventional_power']) + power_estimate.iloc[0,0] = np.mean((allreps_output['PValue'] <= typeIerror/2)) + power_estimate.iloc[0]['tau'] = tau + power_estimate.iloc[0]['true_pValue'] = true_pValue + power_estimate.iloc[0]['conventional_power'] = conventional_power + print(str("\nProbability to detect a significant group difference when the estimated effect size d = {}".format(np.round(cohens_d,3)) + + " with {} trials and {} participants per group: {}%".format(ntrials, + npp_per_group, np.round(power_estimate.iloc[0,0]*100,2)))) + return allreps_output, power_estimate + +#%% +def GetMeansStd(InputDictionary , row): + """ + Parameters + ---------- + InputDictionary: Dictionary + Configures from the input csv file + + Returns + ------- + means : array + Each element is an extracted mean of a parameter in input filem, with the same order as defined in the csv + stds: array + Each element is an extracted std of a parameter in input filem, with the same order as defined in the csv + + Description + ----------- + Function that extract means and stds from the input file based on cols with "mean_" and "std_". + """ + means = [] + stds = [] +# ============================================================================= +# # NOTE: the order of parameters should be corresponding to that in ssms +# to see parameters included and its order: ssms.config.model_config[DDM_id]['params'] +# ============================================================================= + for DtbName, DtbValues in InputDictionary.items(): + if ("mean" in DtbName): + means.append(DtbValues[row]) + if ("std" in DtbName): + stds.append(DtbValues[row]) + means = np.array(means) + stds = np.array(stds) + return means,stds + +def none_or_str(value): + print('none_or_str') + print(value) + print(type(value)) + if value == 'None': + return None + return value + +def none_or_int(value): + print('none_or_int') + print(value) + print(type(value)) + #print('printing type of value supplied to non_or_int ', type(int(value))) + if value == 'None': + return None + return int(value) + + +def proportion_within_boundaries(mean, std, lower_bound, upper_bound): + # Calculate the Z-scores for the boundaries + z_lower = (lower_bound - mean) / std + z_upper = (upper_bound - mean) / std + + # Calculate the proportion within boundaries using the CDF + prop_within = stat.norm.cdf(z_upper) - stat.norm.cdf(z_lower) + + return prop_within if __name__ == '__main__': - criterion = sys.argv[1:] - assert len(criterion) == 1 - criterion = criterion[0] + CLI = argparse.ArgumentParser() + CLI.add_argument("--input_file", + type = none_or_str, + default = "InputFile_IC_DDM.csv") + CLI.add_argument("--output_folder", + type = none_or_str, + default = "CLI_test") + CLI.add_argument("--criterion", + type = none_or_str, + default = "IC") + CLI.add_argument("--id", + type = none_or_int, + default = None) + CLI.add_argument("--multiprocess", + type=int, + default=1) + CLI.add_argument("--show_plots", + type=int, + default=0) + args = CLI.parse_args() + + if args.multiprocess: + ncpu = cpu_count() - 2 + else: ncpu = 1 + + # TODO: Specify reasonable defaults + # criterion = sys.argv[1:] + # if not criterion: + # criterion = ["GD_DDM"] + + # assert len(criterion) == 1 + # criterion = criterion[0] + # criterion = criterion[0] + + # InputFile_name = "InputFile_{}.csv".format(args.criterion) + + # + if CLIarg: + InputFile_path = os.path.join(os.getcwd(), args.input_file) + output_folder = os.path.join(os.getcwd(), args.output_folder) + criterion = args.criterion + show_plots = args.show_plots + - InputFile_name = "InputFile_{}.csv".format(criterion) - InputFile_path = os.path.join(os.getcwd(), InputFile_name) InputParameters = pd.read_csv(InputFile_path, delimiter = ',') if InputParameters.shape[1] == 1: InputParameters = pd.read_csv(InputFile_path, delimiter = ';') # depending on how you save the csv-file, the delimiter should be "," or ";". - This if-statement ensures that the correct delimiter is used. + + if isinstance(args.id, int): + print("THE ID PASSED TO THE PROCESS IS: ", args.id) + valid_ids = [args.id] + InputParameters = InputParameters.iloc[args.id, :].to_frame().transpose() + #InputParameters = InputParameters.iloc[args.id, :] + else: + print("THE ID PASSED TO THE PROCESS IS: None") + valid_ids = list(np.arange(InputParameters.shape[0])) + InputDictionary = InputParameters.to_dict() - for row in range(InputParameters.shape[0]): + time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + InputParameters.to_csv(os.path.join(os.getcwd(),output_folder, 'Input_{}.csv'.format(time))) + + + for row in valid_ids: + #Calculate how long it takes to do a power estimation start_time = datetime.now() print("Power estimation started at {}.".format(start_time)) #Extract all values that are the same regardless of the criterion used - ntrials = InputDictionary['ntrials'][row] - nreversals = InputDictionary['nreversals'][row] - reward_probability = InputDictionary['reward_probability'][row] - nreps = InputDictionary['nreps'][row] - full_speed = InputDictionary['full_speed'][row] - output_folder = InputDictionary['output_folder'][row] - variables_fine = check_input_parameters(ntrials, nreversals, reward_probability, full_speed, criterion, output_folder) - if variables_fine == 0: quit() + ntrials = int(InputDictionary['ntrials'][row]) + nreps = int(InputDictionary['nreps'][row]) + #full_speed = InputDictionary['full_speed'][row] + + # if args.output_folder is None: + # output_folder = InputDictionary['output_folder'][row] + # else: + # output_folder = args.output_folder + - #if not os.path.isdir(output_folder): - # print('output_folder does not exist, please adapt the csv-file') - # quit() - + if not os.path.isdir(output_folder): + print('output_folder does not exist, please adapt the csv-file') + # quit() + sys.exit(0) +# IC if criterion == "IC": - npp = InputDictionary['npp'][row] - meanLR, sdLR = InputDictionary['meanLR'][row], InputDictionary['sdLR'][row] - meanInverseT, sdInverseT = InputDictionary['meanInverseTemperature'][row], InputDictionary['sdInverseTemperature'][row] + npp = int(InputDictionary['npp'][row]) tau = InputDictionary['tau'][row] - s_pooled = sdLR - - output, power_estimate = power_estimation_Incorrelation(npp = npp, ntrials = ntrials, nreps = nreps, - cut_off = tau, - high_performance = full_speed, nreversals = nreversals, - reward_probability = reward_probability, mean_LRdistribution = meanLR, - SD_LRdistribution = sdLR, mean_inverseTempdistribution = meanInverseT, - SD_inverseTempdistribution = sdInverseT) - output.to_csv(os.path.join(output_folder, 'OutputIC{}SD{}T{}R{}N{}M.csv'.format(s_pooled, ntrials, - nreversals, - npp, nreps))) - if HPC == False: - fig, axes = plt.subplots(nrows = 1, ncols = 1) - sns.kdeplot(output["correlations"], label = "Correlations", ax = axes) - fig.suptitle("Pr(Correlation >= {}) \nwith {} pp, {} trials)".format(tau, npp, ntrials), fontweight = 'bold') - axes.set_title("Power = {}% \nbased on {} reps".format(np.round(power_estimate*100, 2), nreps)) - axes.axvline(x = tau, lw = 2, linestyle ="dashed", color ='k', label ='tau') - - elif criterion == "GD": - npp_pergroup = InputDictionary['npp_group'][row] - npp = npp_pergroup*2 - meanLR_g1, sdLR_g1 = InputDictionary['meanLR_g1'][row], InputDictionary['sdLR_g1'][row] - meanLR_g2, sdLR_g2 = InputDictionary['meanLR_g2'][row], InputDictionary['sdLR_g2'][row] - meanInverseT_g1, sdInverseT_g1 = InputDictionary['meanInverseTemperature_g1'][row], InputDictionary['sdInverseTemperature_g1'][row] - meanInverseT_g2, sdInverseT_g2 = InputDictionary['meanInverseTemperature_g2'][row], InputDictionary['sdInverseTemperature_g2'][row] - typeIerror = InputDictionary['TypeIerror'][row] - # Calculate tau based on the typeIerror and the df - tau = -stat.t.ppf(typeIerror/2, npp-1) - s_pooled = np.sqrt((sdLR_g1**2 + sdLR_g2**2) / 2) - cohens_d = np.abs(meanLR_g1-meanLR_g2)/s_pooled - - output, power_estimate = power_estimation_groupdifference(npp_per_group = npp_pergroup, ntrials = ntrials, - nreps = nreps, typeIerror = typeIerror, high_performance = full_speed, - nreversals = nreversals, reward_probability = reward_probability, - mean_LRdistributionG1 = meanLR_g1, SD_LRdistributionG1 = sdLR_g1, - mean_LRdistributionG2 = meanLR_g2, SD_LRdistributionG2=sdLR_g2, - mean_inverseTempdistributionG1 = meanInverseT_g1, SD_inverseTempdistributionG1 = sdInverseT_g1, - mean_inverseTempdistributionG2 = meanInverseT_g2, SD_inverseTempdistributionG2 = sdInverseT_g2) - output.to_csv(os.path.join(output_folder, 'OutputGD{}SD{}T{}R{}N{}M{}ES.csv'.format(np.round(s_pooled,2), - ntrials, - nreversals, - npp, nreps, np.round(cohens_d,2)))) - if HPC == False: - fig, axes = plt.subplots(nrows = 1, ncols = 1) - sns.kdeplot(output["Statistic"], label = "T-statistic", ax = axes) - fig.suptitle("Pr(T-statistic > {}) \nconsidering a type I error of {} \nwith {} pp, {} trials".format(np.round(tau,2), typeIerror, npp_pergroup, ntrials), fontweight = 'bold') - axes.set_title("Power = {}% \nbased on {} reps with Cohen's d = {}".format(np.round(power_estimate*100, 2), nreps, np.round(cohens_d,2))) + DDM_id = InputDictionary['model'][row] + means, stds = GetMeansStd(InputDictionary, row = row) + + lower_bound = np.array(ssms.config.model_config[DDM_id]['param_bounds'])[0,:] + upper_bound = np.array(ssms.config.model_config[DDM_id]['param_bounds'])[1,:] + # print propotion within boundries + print("\nCheck parameter range:") + for p in range(len(means)): + prop = proportion_within_boundaries(means[p], stds[p], lower_bound[p], upper_bound[p]) + print("Parameter:{}, mean: {}, std:{}, propotion within boundries:{}".format(ssms.config.model_config[DDM_id]["params"][p], + means[p],stds[p],round(prop,2))) + if prop < 0.7: + print("!!! Warning: distribution of parameter {} is trimmed".format(ssms.config.model_config[DDM_id]["params"][p])) + + + print("\nStart IC analysis for DDM model\n") + print("model: {}".format(DDM_id)) + print("trials: {}".format(ntrials)) + print("participants: {}".format(npp)) + print("tau: {}".format(tau)) + + # MAIN FUNCTION CALL + output, power_estimate = power_estimation_Incorrelation_DDM(means = means, stds=stds, DDM_id = DDM_id, + npp = npp, ntrials = ntrials, nreps = nreps, + ncpu = ncpu, + cut_off = tau) + + # POST PROCESS + # output = pd.concat([output,InputParameters]) + power_estimate['Input_row'] = row + output['Input_row'] = row + time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + + output.to_csv(os.path.join(output_folder, 'OutputIC{}T{}N{}M_{}.csv'.format(ntrials,npp, nreps,time))) + # power_estimate = pd.concat([power_estimate,InputParameters]) + power_estimate.to_csv(os.path.join(output_folder, 'PowerIC{}T{}N{}M_{}.csv'.format(ntrials,npp, nreps,time))) + + # PLOTTING + Parameters = ssms.config.model_config[DDM_id]["params"] + for p in Parameters : + + fig, axes = plt.subplots(nrows = 1, ncols = 1,figsize=(8, 8)) + # sns.set_theme(style = "white",font_scale=1.4) + sns.kdeplot(output[p].dropna(axis = 0), ax = axes) + fig.suptitle("Pr(Correlation >= {}) with {} pp, {} trials)".format(tau, npp, ntrials), fontweight = 'bold',fontsize = 25) + + power_value = power_estimate[p].dropna(axis = 0).values[0] + axes.set_title("Power = {}% based on {} reps".format(np.round(power_value*100, 2), nreps),fontsize = 25) axes.axvline(x = tau, lw = 2, linestyle ="dashed", color ='k', label ='tau') - + axes.tick_params(labelsize=20) + axes.set_xlabel('Correlations of '+p,fontsize = 20) + axes.set_ylabel('Density',fontsize = 20) + plt.tight_layout() + time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + file_name = 'PowerIC{}T{}N{}M_{}_{}.png'.format(ntrials,npp,nreps,p,time) + plt.savefig(os.path.join(output_folder, file_name), bbox_inches='tight') + if show_plots: + plt.show(block=False) + plt.close() + plt.clf() + +# EC elif criterion == "EC": npp = InputDictionary['npp'][row] - meanLR, sdLR = InputDictionary['meanLR'][row], InputDictionary['sdLR'][row] - meanInverseT, sdInverseT = InputDictionary['meanInverseTemperature'][row], InputDictionary['sdInverseTemperature'][row] + means,stds = GetMeansStd(InputDictionary, row = row) True_correlation = InputDictionary['True_correlation'][row] typeIerror = InputDictionary['TypeIerror'][row] - s_pooled = sdLR + par_ind = InputDictionary['par_ind'][row] + + s_pooled = stds[par_ind] beta_distribution = stat.beta((npp/2)-1, (npp/2)-1, loc = -1, scale = 2) tau = -beta_distribution.ppf(typeIerror/2) - output, power_estimate = power_estimation_Excorrelation(npp = npp, ntrials = ntrials, nreps = nreps, - typeIerror = typeIerror, - high_performance = full_speed, nreversals = nreversals, - reward_probability = reward_probability, mean_LRdistribution = meanLR, - SD_LRdistribution = sdLR, mean_inverseTempdistribution = meanInverseT, - SD_inverseTempdistribution = sdInverseT, True_correlation = True_correlation) - output.to_csv(os.path.join(output_folder, 'OutputEC{}SD{}TC{}T{}R{}N{}M.csv'.format(s_pooled, True_correlation, ntrials, - nreversals, - npp, nreps))) - if HPC == False: - fig, axes = plt.subplots(nrows = 1, ncols = 1) - sns.kdeplot(output["Statistic"], label = "Correlation", ax = axes) - fig.suptitle("Pr(Correlation > {}) \nconsidering a type I error of {} \nwith {} pp, {} trials".format(np.round(tau,2), typeIerror, npp, ntrials), fontweight = 'bold') - axes.set_title("Power = {}% \nbased on {} reps with true correlation {}".format(np.round(power_estimate*100, 2), nreps, True_correlation)) - axes.axvline(x = tau, lw = 2, linestyle ="dashed", color ='k', label ='tau') - + DDM_id = InputDictionary['model'][row] + param_bounds = np.array(ssms.config.model_config[DDM_id]['param_bounds']) + + print("\nStart EC analysis for DDM model\n") + print("model: {}".format(DDM_id)) + print("trials: {}".format(ntrials)) + print("participants: {}".format(npp)) + print("parameter index: {}".format(par_ind)) + print("True_correlation: {}".format(True_correlation)) + + # MAIN FUNCTION CALL + output, power_estimate = power_estimation_Excorrelation_DDM(means,stds,par_ind,DDM_id, + True_correlation,npp, ntrials, nreps, + typeIerror, ncpu = ncpu) + + # output = pd.concat([output,InputParameters]) + power_estimate['Input_row'] = row + output['Input_row'] = row + time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + # InputParameters.to_csv(os.path.join(output_folder, 'InputEC{}P{}SD{}TC{}T{}N{}M_{}.csv'.format(par_ind,s_pooled, True_correlation, ntrials, + # npp, nreps,time))) + output.to_csv(os.path.join(output_folder, 'OutputEC{}P{}SD{}TC{}T{}N{}M_{}.csv'.format(par_ind,s_pooled, True_correlation, ntrials, + npp, nreps,time))) + + # power_estimate = pd.concat([pd.DataFrame(power_estimate),InputParameters]) + power_estimate.to_csv(os.path.join(output_folder, 'PowerEC{}P{}SD{}TC{}T{}N{}M_{}.csv'.format(par_ind,s_pooled, True_correlation, ntrials, + npp, nreps,time))) + + # TODO: AF - THIS IS JUST FOR DEBUGGING - UNCOMMENT AGAIN! + # PLOTTING + p_n = ssms.config.model_config[DDM_id]['params'][par_ind] + fig, axes = plt.subplots(nrows = 1, ncols = 1,figsize=(8, 8)) + sns.kdeplot(output["Esti_r"].dropna(axis = 0),label = "Correlation", ax = axes) + fig.suptitle("Pr(Correlation > {}) considering a type I error of {} \nwith {} pp, {} trials".format(np.round(tau,2), typeIerror, npp, ntrials), fontweight = 'bold',fontsize = 25) + power_value = power_estimate["power_"+p_n].dropna(axis = 0).values[0] + + axes.set_title("Power = {} % based on {} reps with true correlation {}".format(np.round(power_value*100, 2), nreps, True_correlation),fontsize = 25) + axes.axvline(x = tau, lw = 2, linestyle ="dashed", color ='k', label ='tau') + axes.tick_params(labelsize=20) + axes.set_xlabel('Correlations of '+ p_n, fontsize = 20) + axes.set_ylabel('Density',fontsize = 20) + plt.tight_layout() + time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + file_name = 'PowerEC{}P{}SD{}TC{}T{}N{}M_{}.png'.format(par_ind,s_pooled, True_correlation, ntrials, + npp, nreps,time) + plt.savefig(os.path.join(output_folder, file_name), bbox_inches='tight') + if show_plots: + plt.show(block=False) + else: + plt.close() + plt.clf() + # GD + elif criterion == "GD": + npp_pergroup = InputDictionary['npp_group'][row] + npp = npp_pergroup*2 + par_ind = InputDictionary['par_ind'][row] + typeIerror = InputDictionary['TypeIerror'][row] + means,stds = GetMeansStd(InputDictionary, row = row) + + DDM_id = InputDictionary['model'][row] + param_bounds = np.array(ssms.config.model_config[DDM_id]['param_bounds']) + + def GetGroupDistribution(raw_DistributionPar,par_ind,group): + NotCompare_ind = np.ones(raw_DistributionPar.shape, dtype=bool) + NotCompare_ind[par_ind] = False + try : + Compare_ms_g = float(raw_DistributionPar[par_ind].split(",")[group-1]) + except: + print("Check the distribution and index of parameter you want to compare") + sys.exit(0) + else: + + ms_g = raw_DistributionPar[NotCompare_ind].astype(float) + ms_g = np.insert(ms_g, par_ind, Compare_ms_g) + return ms_g + + means_g1 = GetGroupDistribution(means,par_ind,group = 1) + means_g2 = GetGroupDistribution(means,par_ind,group = 2) + stds_g1 = GetGroupDistribution(stds,par_ind,group = 1) + stds_g2 = GetGroupDistribution(stds,par_ind,group = 2) + # Calculate tau based on the typeIerror and the df + tau = -stat.t.ppf(typeIerror/2, npp-1) + s_pooled = np.sqrt((stds_g1[par_ind]**2 + stds_g2[par_ind]**2) / 2) + cohens_d = np.abs(means_g1[par_ind]-means_g2[par_ind])/s_pooled + + + print("\nStart GD analysis for DDM model\n") + print("model: {}".format(DDM_id)) + print("trials: {}".format(ntrials)) + print("participants: {}".format(npp)) + print("parameter index: {}".format(par_ind)) + print("Means of group 1: {}, stds of group 1: {}".format(means_g1,stds_g1)) + print("Means of group 2: {}, stds of group 2: {}".format(means_g2,stds_g2)) + + output, power_estimate = power_estimation_groupdifference_DDM(cohens_d,means_g1,means_g2,stds_g1,stds_g2,DDM_id, par_ind, + npp_per_group = npp_pergroup, ntrials = ntrials, + nreps = nreps, typeIerror = typeIerror, ncpu=ncpu) + + # output = pd.concat([output,InputParameters]) + time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + power_estimate['Input_row'] = row + output['Input_row'] = row + + output.to_csv(os.path.join(output_folder, 'OutputGD{}P{}SD{}T{}N{}M{}ES_{}.csv'.format(par_ind,np.round(s_pooled,2), + ntrials,npp_pergroup, nreps, np.round(cohens_d,2),time))) + # power_estimate = pd.concat([pd.DataFrame(power_estimate),InputParameters]) + power_estimate.to_csv(os.path.join(output_folder, 'PowerGD{}P{}SD{}T{}N{}M{}_{}ES.csv'.format(par_ind,np.round(s_pooled,2),ntrials, + npp_pergroup, nreps,np.round(cohens_d,2),time))) + + p_n = ssms.config.model_config[DDM_id]['params'][par_ind] + fig, axes = plt.subplots(nrows = 1, ncols = 1,figsize=(10, 10)) + sns.kdeplot(output["Statistic"].dropna(axis = 0),label = "Correlation", ax = axes) + fig.suptitle("Pr(T-statistic > {}) considering a type I error of {} \nwith {} pp, {} trials".format(np.round(tau,2), typeIerror, npp_pergroup, ntrials), fontweight = 'bold',fontsize = 25) + power_value = power_estimate[p_n].dropna(axis = 0).values[0] + axes.set_title("Power = {} % based on {} reps with Cohen's d = {}".format(np.round(power_value*100, 2), nreps, np.round(cohens_d,2)),fontsize = 25) + axes.axvline(x = tau, lw = 2, linestyle ="dashed", color ='k', label ='tau') + axes.tick_params(labelsize=20) + axes.set_xlabel('T-statistics of '+ p_n, fontsize = 20) + axes.set_ylabel('Density',fontsize = 20) + plt.tight_layout() + time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + file_name = 'PowerGD{}P{}SD{}T{}N{}M{}ES_{}.png'.format(par_ind,np.round(s_pooled,2),ntrials, + npp_pergroup, nreps,np.round(cohens_d,2),time) + plt.savefig(os.path.join(output_folder, file_name), bbox_inches='tight') + if show_plots: + plt.show(block=False) + else: + plt.close() + plt.clf() else: print("Criterion not found") - #final adaptations to the output figure & store the figure - if HPC == False: - fig.legend(loc = 'center right') - fig.tight_layout() - fig.savefig(os.path.join(output_folder, 'Plot{}{}T{}R{}N{}M{}.jpg'.format(criterion, - np.round(s_pooled, 2), - ntrials, nreversals, - npp, nreps))) - - # measure how long the power estimation lasted + # # measure how long the power estimation lasted end_time = datetime.now() - print("\nPower analysis ended at {}; run lasted {} hours.".format(end_time, end_time-start_time)) + print("\nPower analysis ended at {}; run lasted {} hours.".format(end_time, end_time-start_time)) \ No newline at end of file diff --git a/ReadMe.md b/ReadMe.md index 5a07b8f..5422ca7 100644 --- a/ReadMe.md +++ b/ReadMe.md @@ -1,259 +1,77 @@ [![Uni](https://img.shields.io/badge/University-Ghent%20University-brightgreen)](https://img.shields.io/badge/University-Ghent%20University-brightgreen) [![Date](https://img.shields.io/badge/Last%20update-2023-yellow)](https://img.shields.io/badge/Last%20update-2023-yellow) -# COmputational Power Analysis using Simulations "COMPASS" toolbox +# Computational Power Analysis using Simulations "COMPASS" toolbox on DDM + +Expanding COMPASS toolbox to DDM models. + +Additional to the original COMPASS, packages required by this extension for DDM: + +1.ssms: Python Package which collects simulators for Sequential Sampling Models. +For more details, see: https://github.com/AlexanderFengler/ssm-simulators + +## The workflow of COMPASS DDM + +Similar to the original COMPASS, COMPASS DDM follows these steps: + +1. Sample true parameters from the distributions defined by a .csv input file, with ranges defined in ssms package. +2. Generate behavioral data by the true parameters sampled. Behavioral data includes both choices (denoted by 1 or -1) and reaction times. +3. Validate performance of generated behavioral data. If performance exceeds normal range, then go back to step 1 + Performance validation: + A participant as well as its true parameter can be accepted only when the mean ACC is within 50% to 95% AND mean RT is within 0 to 10 s. + Here mean ACC and mean RT of one participant are used as measure of performance. For one choice, ACC is defined by the sign of drift parameter and the sign of that choice. For example, if the option denoted by -1 is chosen and the drift parameter is also a negative value, then it is a correct choice. If the drift parameter equals to zero, the parameters will be resampled. +5. Estimate the best fitting parameters for each participant given the simulated behavioral data. +6. Compute statistics on one sample and compare to the cut-off. Statistics including: + Internal_correlation: correlation coefficients between sampled and estimated parameter values. + External_correlation: correlation coefficients between an external measurement (which is assumed to be Gaussian distributed) and estimated parameter values. + Groupdifference: t-values measuring a group difference between estimated parameters of two groups +7. Repeat sampling and evaluate the proportion of statistics reached the cut-off value + +## Steps to compute power of DDM +### 1.Define the distributions from which true parameters are sampled +Creat a csv file, with name of "InputFile_IC_DDM", "InputFile_EC_DDM", or "InputFile_GD_DDM" corresponding to each criterion. + +For IC criterion, you should define: + * model: index of DDM model which should be matched with ssms package + * ntrials: number of trials that will be used to do the parameter recovery analysis for each participant. + * npp: integer, number of participants in the study. + * “mean_{}â€s: means of true parameter distribution. The order of parameters MUST BE MATCH with that in ssms. e.g., for ddm model, the order must be: mean_v, mean_a, mean_z, mean_t + * “std_{}â€s: stds of true parameter distribution. The order of parameters MUST BE MATCH with that in ssms. e.g., for ddm model, the order must be: std_v, std_a, std_z, std_t + * tau: the cut-off value of correlation coef + * nreps: number of samples to calculate proportion(probability) of which statistics exceed cut-off value + * full_speed: defines whether multiple cores on the computer will be used in order to estimate the power. + * output_folder: path to save results + +For EC criterion, you should define: + * model: index of DDM model which should be matched with ssms package + * ntrials: number of trials that will be used to do the parameter recovery analysis for each participant. + * npp: integer, number of participants in the study. + * “mean_{}â€s: means of true parameter distribution. The order of parameters MUST BE MATCH with that in ssms. e.g., for ddm model, the order must be: mean_v, mean_a, mean_z, mean_t + * “std_{}â€s: stds of true parameter distribution. The order of parameters MUST BE MATCH with that in ssms. e.g., for ddm model, the order must be: std_v, std_a, std_z, std_t + * par_ind: parameter index of the parameter of interest, according to the order from ssms. START FROM 0. E.g., par_ind = 0, corresponding to "v" + * True_correlation: the hypothesized correlation between the learning rate and the external measure theta. + * TypeIerror: critical value for p-values. From this also the cut-off for the correlation statistic can be determined. + * nreps: number of samples to calculate proportion(probability) of which statistics exceed cut-off value + * full_speed: defines whether multiple cores on the computer will be used in order to estimate the power. + * output_folder: path to save results + +For GD criterion, you should define: + * model: index of DDM model which should be matched with ssms package + * ntrials: number of trials that will be used to do the parameter recovery analysis for each participant. + * npp: integer, number of participants in the study. + * “mean_{}â€s: means of true parameter distribution. There should be TWO values separated by COMMA in the cell corresponding to the parameter you want to compare. The order of parameters MUST BE MATCH with that in ssms. e.g., for ddm model, the order must be: mean_v, mean_a, mean_z, mean_t + * “std_{}â€s: stds of true parameter distribution. There should be TWO values separated by COMMA in the cell corresponding to the parameter you want to compare. The order of parameters MUST BE MATCH with that in ssms. e.g., for ddm model, the order must be: stds_v, stds_a, stds_z, stds_t + * par_ind: parameter index of the parameter of interest, according to the order from ssms. START FROM 0. E.g., par_ind = 0, corresponding to "v" + * True_correlation: the hypothesized correlation between the learning rate and the external measure theta. + * TypeIerror: critical value for p-values. From this also the cut-off for the correlation statistic can be determined. + * nreps: number of samples to calculate proportion(probability) of which statistics exceed cut-off value + * full_speed: defines whether multiple cores on the computer will be used in order to estimate the power. + +### 2. run COMPASS +1. Open Anaconda prompt, type:conda activate pyPower +2. Run PowerAnalysis.py with criterion as input: python PowerAnalysis.py IC_DDM, python PowerAnalysis.py GD_DDM, python PowerAnalysis.py EC_DDM depending on the criterion that you want to use. + + +### Output files +PowerIC{}T{}N{}M_{}.png -This toolbox has been developed to evaluate statistical power when using parameter estimates from computational models. - -In the current version, use is limited to the Rescorla-Wagner (RW) model in two-armed bandit tasks. - -More details can be found in the manuscript: *https://10.31234/osf.io/dexyk* - -**We now also provide a script to easily fit the RW model on your own empirical data!!!** - -More details are provided at the end of this page. - -## Installation guide -*Step 1:* Downloading all code and storing them locally on your own pc. - -*Step 2:* Creating the PyPower environment - - Normally, power analyses with COMPASS should be possible in a basic Python environment. - Nevertheless, to control for version issues, we provide environment files for windows and mac users. - * Install Anaconda 3 by following their [installation guide](https://docs.anaconda.com/anaconda/install/windows/) - * When the installation is complete, open an Anaconda prompt - * Go to the directory where the COMPASS files are stored using ```cd``` - * For windows, run: ```conda env create --file environment_windows.yml``` and for mac, run ```conda env create --file environment_mac.yml``` - * Allow the installation of all required packages - -## The model and task currently implemented in COMPASS -### The RW model - -```To specify by the user: meanLR, sdLR, meanInverseTemperature and sdInverseTemperature``` - -The RW model is used to fit participants’ behaviour in this task. -The core of the model is formed by the delta-learning rule and the softmax choice rule. -The model has two free parameters: the learning rate and the inverse temperature (see manuscript for details). -The population distribution of these parameters can be specified by the user in the csv files by completing *meanLR, sdLR, meanInverseTemperature* and *sdInverseTemperature*. - -### Two-armed bandit task - -```To specify by the user: ntrials, nreversals, reward_probability``` - -Based on the parameters that are implemented in the csv files, a task design is created. -In this task design, there are two stimuli/bandits and two possible actions (selecting bandit 1 or 2). -One bandit is more optimal since it has a higher probability of reward (specified by *reward_probability*). -For simplicity, we implement that Pr(Reward | optimal_bandit) = 1- Pr(Reward | suboptimal_bandit). -As in classic reversal learning tasks, we provide the option that the identity of the optimal bandit can reverse. Here, one has to specify the frequency of rule reversals (*nreversals*). If set to zero, there are no reversals. -The design is created for *ntrials*. As demonstrated in the manuscript, this is a crucial variable for obtaining reliable parameter estimates and high statistical power. - -## Important note: the required computational time. - -```To specify by the user: npp, nreps, full_speed``` - -As we perform parameter estimations for *nreps* Monte Carlo repetitions, computational time can increase exponentially. -The computational time strongly depends on the number of participants (*npp*) and the number of Monte Carlo repetitions (*nreps*). We recommend to set *nreps* to 250. Smaller numbers can be used as well but then power computations will be less precise. -Notably, also increasing the number of trials in the task design (*ntrials*) can significantly increase the power computation time in COMPASS. - -As a partial solution for this computation time, the option is included to run the power analysis on multiple cores. This happens when the user defines the *full_speed* option as 1; if this option is activated, all minus two cores on the computer will be used for power computations. - -When running COMPASS it will asap provide an estimate of how long it will take to calculate the power for each power computation (each row of the csv files specify one power computation). -This estimate is based on the time it takes to execute a single repetition and calculated by multiplying the total number of repetitions by the time required for a single repetition, divided by the number of cores that are used in the power analysis. - -If you want to stop the process whilst running, you can use 'ctrl + C' in the anaconda prompt shell. This will stop the execution of the script. - -## Runnig power computations with COMPASS -As described in the manuscript, three criteria for power computations are specified. -For each criterion (IC, EC or GD), we provide a csv file which holds the power variables that should be specified by the user. -For all criteria, power is specified as - - ```power = Pr(Statistic > cut-off | Hypothesis)``` - -Here, the statistic differs across criteria and the cut-off and hypothesis should be specified by the user. - -Power computations consist of the following five steps: - 1. Sample *npp* participants from the population. - This sampling process is guided by the hypothesis that is specified by the user in the csv files. (population distribution of parameter values (for IC), true correlation (for EC) or difference between groups (for GD)) - 2. Simulate data for each participant. - 3. Estimate the best fitting parameters for each participant given the simulated data. - These are the ‘estimated parameters’. - 4. Compute statistics. - The statistic differs across criteria. - - _internal_correlation_: correlation between sampled and estimated parameter values. - - _external_correlation_: correlation between estimated parameter values and external measure (e.g., questionnaire score). - - _group_difference_: T-statistic of difference in parameter values between two groups. - 5. Evaluate which proportion of statistics reached the cut-off value. - -### The steps for the user -1. Make sure that COMPASS is installed correctly (see Installation guide above). - -2. Choose a criterion and specify variables in the corresponding csv file. -*Notice that multiple rows can be specified in the csv files, power computations will be performed for each row that is completed by the user* - - 2a) Internal Correlation (IC): Correlation between sampled and estimated parameter values. - - Open the InputFile_IC and specify - * _ntrials_: integer 𜖠[5, +∞[ - **number of trials within the experiment (minimal 5)** - * _nreversals_: integer 𜖠[0, ntrials[ - **number of rule reversals within the experiment** - * _npp_: integer 𜖠[5, +∞[ - **total number of participants within the experiment (minimal 5)** - * _meanLR_: float 𜖠[0, 1] - **mean of the assumed population distribution of learning rates** - * _sdLR_: float 𜖠[0, 1] - **sd of the assumed population distribution of learning rates** - * _meanInverseTemperature_: float 𜖠[0, 1] - **mean of the assumed population distribution of inverse temperatures** - * _sdInverseTemperature_: float 𜖠[0, 1] - **sd of the assumed population distribution of inverse temperatures** - * _reward_probability_: float 𜖠[0, 1] - **The probability of reward for the optimal bandit in the two-arm bandit task.** - * _tau_: float 𜖠[0, 1] - **the value against which the obtained statistic will be compared to define significance of the repetition.** - - correlation: cut_off = minimally desired correlation - recommended: 0.75 - * _full_speed_: integer (0 or 1) - **Define whether you want to do the power analysis at full speed.** - - 0 = only one core will be used (slow) - - 1 = (all-2) cores will be used (much faster, recommended unless you need your computer for other intensive tasks such as meetings) - * _nreps_: integer 𜖠[1, +∞[ - **Number of repetitions that will be conducted to estimate the power** - - Recommended number: 250 - * _output_folder_: string - **Path to the folder where the output-figure(s) will be stored** - - e.g. "C:\Users\maudb\Downloads" - - 2b) External Correlation (EC): Correlation between estimated parameter values and external measure (e.g., Questionnaire scores). - - Open the InputFile_EC and specify - * _ntrials_: integer 𜖠[5, +∞[ - **number of trials within the experiment (minimal 5)** - * _nreversals_: integer 𜖠[0, ntrials[ - **number of rule reversals within the experiment** - * _npp_: integer 𜖠[5, +∞[ - **total number of participants within the experiment (minimal 5)** - * _meanLR_: float 𜖠[0, 1] - **mean of the assumed population distribution of learning rates** - * _sdLR_: float 𜖠[0, 1] - **sd of the assumed population distribution of learning rates** - * _meanInverseTemperature_: float 𜖠[0, 1] - **mean of the assumed population distribution of inverse temperatures** - * _sdInverseTemperature_: float 𜖠[0, 1] - **sd of the assumed population distribution of inverse temperatures** - * _reward_probability_: float 𜖠[0, 1] - **The probability of reward for the optimal bandit in the two-arm bandit task.** - *_True_correlation_: float 𜖠[-1, 1] - **The hypothesized correlation between the learning rate parameter values and the external measure** - * _TypeIerror_: float 𜖠[0, 1] - **The allowed probability to make a type I error; the significance level** - - standard (and recommended) value: 0.05 - - correlation: cut_off = minimally desired correlation - recommended: 0.75 - * _full_speed_: integer (0 or 1) - **Define whether you want to do the power analysis at full speed.** - - 0 = only one core will be used (slow) - - 1 = (all-2) cores will be used (much faster, recommended unless you need your computer for other intensive tasks such as meetings) - * _nreps_: integer 𜖠[1, +∞[ - **Number of repetitions that will be conducted to estimate the power** - - Recommended number: 250 - * _output_folder_: string - **Path to the folder where the output-figure(s) will be stored** - - e.g. "C:\Users\maudb\Downloads" - - 2c) Group Difference (GD): T-statistic for difference between estimated parameter values of two groups. - - Open the InputFile_GD and specify - * _ntrials_: integer 𜖠[5, +∞[ - **number of trials within the experiment (minimal 5)** - * _nreversals_: integer 𜖠[0, ntrials[ - **number of rule reversals within the experiment** - * _npp_group_: integer 𜖠[5, +∞[ - **number of participants within the experiment (minimal 5)** - * _meanLR_g1_: float 𜖠[0, 1] - **mean of the assumed population distribution of learning rates for group 1** - * _sdLR_: float 𜖠[0, 1] - **sd of the assumed population distribution of learning rates for group 1** - * _meanLR_g2_: float 𜖠[0, 1] - **mean of the assumed population distribution of learning rates for group 2** - * _sdLR_g2_: float 𜖠[0, 1] - **sd of the assumed population distribution of learning rates for group 2** - * _meanInverseTemperature_g1_: float 𜖠[0, 1] - **mean of the assumed population distribution of inverse temperatures for group 1** - * _sdInverseTemperature_g1_: float 𜖠[0, 1] - **sd of the assumed population distribution of inverse temperatures for group 1** - * _meanInverseTemperature_g2_: float 𜖠[0, 1] - **mean of the assumed population distribution of inverse temperatures for group 2** - * _sdInverseTemperature_g2_: float 𜖠[0, 1] - **sd of the assumed population distribution of inverse temperatures for group 2** - * _reward_probability_: float 𜖠[0, 1] - **The probability of reward for the optimal bandit in the two-arm bandit task.** - * _TypeIerror_: float 𜖠[0, 1] - **The allowed probability to make a type I error; the significance level** - - standard (and recommended) value: 0.05 - * _full_speed_: integer (0 or 1) - **Define whether you want to do the power analysis at full speed.** - - 0 = only one core will be used (slow) - - 1 = (all-2) cores will be used (much faster, recommended unless you need your computer for other intensive tasks such as meetings) - * _nreps_: integer 𜖠[1, +∞[ - **Number of repetitions that will be conducted to estimate the power** - - Recommended number: 250 - * _output_folder_: string - **Path to the folder where the output-figure(s) will be stored** - - e.g. "C:\Users\maudb\Downloads" - -3. Run the PowerAnalysis.py script using the correct Anaconda 3 environment. - - If one followed the Installation guide above, a PyPower environment has been created. - - To use this environment: - * Open Anaconda prompt - * Now, run: ```conda activate pyPower``` - - To run COMPASS: - * Go to the directory where the COMPASS files are stored using ```cd``` - * Now, run: ```python PowerAnalysis.py IC```, ```python PowerAnalysis.py EC``` or ```python PowerAnalysis.py GD``` depending on the criterion that you want to use. - -4. Check the output in the shell & the stored figure(s) in the _output_folder_ - * _power estimate_: the probability to obtain adequate parameter estimates. - * _probability density plot of the Statistic of interest_: a plot visualising the obtained values for the Statistic of interest in all power recovery analyses - - x-axis: values for the statistic of interest (correlation or T-Statistic) - - y-axis: probability density for each value - - Example output (EC criterion): - - image - - image - -## Fitting the RW model on your data -### Requires a specifically structured folder with behavioural data files: - - Relevant files should follow this format: *Data_Subject_{SubjectID}.csv* - Here, {SubjectID} can be a number or string identifying that specific subject. - - Files should contain four columns: Trial, Stimulus, Response and Reward - - Stimulus and Response should be coded as integer going from 0 to the number of stimuli or responses (e.g., 0 and 1 if there are two response options) - - No other csv files should be in your folder! - -### Returns: - - *Fitting_results.csv* file, containing a row for each subject and four columns: *SubjectID, Estimated_LR, Estimated_InvTemp and Negative_LogL* - - Simulated files for each subject. A copy of each individual file is saved, containing two additional rows: *Response_likelihood and PE_estimate* - -### Running the fitting procedure: - -To use our environment: - * Open Anaconda prompt - * Now, run: ```conda activate pyPower``` - -To run the fitting procedure: - * Go to the directory where the COMPASS files are stored using ```cd``` - * Now, run: ```python Fit_data.py {"datafolder"}``` where {"datafolder"} represents a string indicating the path to the folder where your data is located - -# Contact -- Corresponding author: Pieter Verbeke - * [E-mail me at pjverbek (dot) Verbeke (at) UGent (dot) be](mailto:pjverbek.Verbeke@UGent.be) -- First author (internship student): Maud Beeckmans - * [E-mail me at Maud (dot) Beeckmans (at) UGent (dot) be](mailto:Maud.Beeckmans@UGent.be) -- Supervising PhD researcher: Pieter Huycke - * [E-mail me at Pieter (dot) Huycke (at) UGent (dot) be](mailto:Pieter.Huycke@UGent.be) -- Supervising PI: Tom Verguts - * [E-mail me at Tom (dot) Verguts (at) UGent (dot) be](mailto:Tom.Verguts@UGent.be) - -**Last edit: June 19th 2023** diff --git a/Functions.py b/__pycache__/Functions.cpython-39.pyc similarity index 53% rename from Functions.py rename to __pycache__/Functions.cpython-39.pyc index 9416177..59549f4 100644 Binary files a/Functions.py and b/__pycache__/Functions.cpython-39.pyc differ diff --git a/__pycache__/Functions_DDM.cpython-311.pyc b/__pycache__/Functions_DDM.cpython-311.pyc new file mode 100644 index 0000000..f1d2cd5 Binary files /dev/null and b/__pycache__/Functions_DDM.cpython-311.pyc differ diff --git a/__pycache__/Functions_DDM.cpython-36.pyc b/__pycache__/Functions_DDM.cpython-36.pyc new file mode 100644 index 0000000..e5e6084 Binary files /dev/null and b/__pycache__/Functions_DDM.cpython-36.pyc differ diff --git a/__pycache__/Functions_DDM.cpython-39.pyc b/__pycache__/Functions_DDM.cpython-39.pyc new file mode 100644 index 0000000..047f87f Binary files /dev/null and b/__pycache__/Functions_DDM.cpython-39.pyc differ diff --git a/__pycache__/Likelihoods.cpython-311.pyc b/__pycache__/Likelihoods.cpython-311.pyc new file mode 100644 index 0000000..f474136 Binary files /dev/null and b/__pycache__/Likelihoods.cpython-311.pyc differ diff --git a/__pycache__/Likelihoods.cpython-39.pyc b/__pycache__/Likelihoods.cpython-39.pyc new file mode 100644 index 0000000..64d4742 Binary files /dev/null and b/__pycache__/Likelihoods.cpython-39.pyc differ diff --git a/__pycache__/ParameterEstimation.cpython-311.pyc b/__pycache__/ParameterEstimation.cpython-311.pyc new file mode 100644 index 0000000..c8e3e02 Binary files /dev/null and b/__pycache__/ParameterEstimation.cpython-311.pyc differ diff --git a/__pycache__/ParameterEstimation.cpython-39.pyc b/__pycache__/ParameterEstimation.cpython-39.pyc new file mode 100644 index 0000000..b1649d1 Binary files /dev/null and b/__pycache__/ParameterEstimation.cpython-39.pyc differ diff --git a/__pycache__/test.cpython-39.pyc b/__pycache__/test.cpython-39.pyc new file mode 100644 index 0000000..0c6defd Binary files /dev/null and b/__pycache__/test.cpython-39.pyc differ diff --git a/af_change_record.md b/af_change_record.md new file mode 100644 index 0000000..53be47f --- /dev/null +++ b/af_change_record.md @@ -0,0 +1 @@ +- change nelder mead setting to 2 * number parameters (from 10 * number parameters) \ No newline at end of file diff --git a/bash_run.sh b/bash_run.sh new file mode 100644 index 0000000..c853a6f --- /dev/null +++ b/bash_run.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +# Set parameters +criterion=EC +input_file="ClusterInput/InputFile_"$criterion"_DDM.csv" +n_rows_input_file=75 +output_folder="/users/afengler/data/proj_compass_ddm/sbatch_output/data_"$criterion + +# Run sbatch script +# sbatch -p batch --account=carney-frankmj-condo --array=0-$n_rows_input_file sbatch_run.sh \ +# --input_file $input_file \ +# --output_folder $output_folder \ +# --criterion $criterion | cut -f 4 -d' ' + +sbatch -p batch --array=0-$n_rows_input_file sbatch_run.sh \ + --input_file $input_file \ + --output_folder $output_folder \ + --criterion $criterion | cut -f 4 -d' ' \ No newline at end of file diff --git a/local_run.sh b/local_run.sh new file mode 100644 index 0000000..018ce8c --- /dev/null +++ b/local_run.sh @@ -0,0 +1,42 @@ +#!/bin/bash + +# BASIC SETUP +# Read in arguments: +input_file=None +output_folder=/users/afengler/data/proj_compass_ddm/sbatch_output/ +criterion=None +id=None +multiprocess=1 +show_plots=0 +optimalgo=0 +while [ ! $# -eq 0 ] + do + case "$1" in + --input_file | -if) + input_file=$2 + ;; + --output_folder | -of) + output_folder=$2 + ;; + --criterion | -c) + criterion=$2 + ;; + --optimalgo | -o) + optimalgo=$2 + ;; + --show_plots | -s) + show_plots=$2 + ;; + --multiprocess | -mp) + multiprocess=$2 + ;; + esac + shift 2 + done + +python -u PowerAnalysis.py --input_file $input_file \ + --output_folder $output_folder \ + --criterion $criterion \ + --multiprocess $multiprocess \ + --show_plots $show_plots \ + --id $id \ \ No newline at end of file diff --git a/log.txt b/log.txt new file mode 100644 index 0000000..3cddf26 --- /dev/null +++ b/log.txt @@ -0,0 +1,68 @@ +Before computing power analysis on large size of samples, let's start from recovering parameters on one participant. + +HOW TO DO: set "parameter_recovery = 1" in the "test.py" and run the file + +### 3. set input file for power analysis + +You can specify multiple rows in your input file. + +For IC criterion, open InputFile_IC.csv and specify: + + * model: string, the DDM model of interest. model must be included by ssms package. + * ntrials: integer, the number of trials + * npp: integer, the number of participants + * mean_{}: float, means of parameters corresponding to the model type + NOTE: the ORDER of parameters must be the same as they are ordered in the ssms package. + NOTE: to see the order of parameters, use: ssms.config.model_config[model]['params'] + * std_{}: float, stds of parameters corresponding to the model type + NOTE: the ORDER of stds must correspond to the means + * tau: float 𜖠[0, 1] the value against which the obtained statistic will be compared to define significance of the repetition + * nreps: integer 𜖠[1, +∞] Number of repetitions that will be conducted to estimate the power + * full_speed: integer (0 or 1) Define whether you want to do the power analysis at full speed. + 0 = only one core will be used (slow) + 1 = (all-2) cores will be used (much faster, recommended unless you need your computer for other intensive tasks such as meetings) + * output_folder: string, path to the folder where the output-figure(s) will be stored + + +### 4. Run power computations for DDM + +Run the PowerAnalysis.py script using the correct Anaconda 3 environment. + +If one followed the Installation guide above, a PyPower environment has been created. + +To use this environment: + Open Anaconda prompt + Now, run: conda activate pyPower + +To run COMPASS: + Go to the directory where the COMPASS files are stored using cd + Now, run: python PowerAnalysis.py IC, python PowerAnalysis.py EC or python PowerAnalysis.py GD depending on the criterion that you want to use. + +### 5. Check the output in the shell & the stored figure(s) in the output_folder + +#### Power heatmap + +After computing power on multiple combinations of npp and ntrials, you can get a power heatmap to choose the optimal design of your research. + +HOW TO DO: +1. Specify the following variables of results in plot.py +* ResultPath：string, the path you gather results of power analysis +* DDM_id: string, correspond to the model in the input file, e.g., "ddm" +* tau: float, correspond to the tau in the input file +* nreps: integer 𜖠[1, +∞] Number of repetitions that will be conducted to estimate the power +* range_ntrials: list, list of trials of interest +* range_npp: list, list of trials of interest +* p_list: list, list of parameters + +2. Set "plot_heatmap = 1" +3. Run the file + +#### Distribution of statistics of one computation + +HOW TO DO: +1. Specify the "range_ntrials" and "range_npp" as the setting of computation of interest in plot.py + e.g., + range_ntrials = [60] + range_npp = [20] +2. Set "plot_single_setting = 1" +3. Run the file \ No newline at end of file diff --git a/notebooks/af_tests.ipynb b/notebooks/af_tests.ipynb new file mode 100644 index 0000000..3c2fea1 --- /dev/null +++ b/notebooks/af_tests.ipynb @@ -0,0 +1,2156 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "my_data_ic = pd.read_csv('../ClusterInput/InputFile_IC_DDM.csv')\n", + "my_data_ec = pd.read_csv('../ClusterInput/InputFile_EC_DDM.csv')\n", + "my_data_gd = pd.read_csv('../ClusterInput/InputFile_GD_DDM.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "my_data_gd = my_data_gd.rename(columns={\"npp\": \"npp_group\"})" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+ }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/plot_GD.py b/plot_GD.py new file mode 100644 index 0000000..352e3f7 --- /dev/null +++ b/plot_GD.py @@ -0,0 +1,170 @@ +import os,sys +import pandas as pd +import seaborn as sns +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import ssms +# sys.path.append(r"D:\horiz\IMPORTANT\0study_graduate\Pro_COMPASS\COMPASS_DDM\results\test3") +plot_heatmap_GD = 1 +c=1 +criterion = "IC" + +ResultPath = "\\results\Talk\IC\\" +DDM_id = "ddm" + +par_ind = [0] +nreps = 50 + +range_ntrials = [40,60,80,100,120] +range_npp = [20,40,60,80,100] + +s_pooled = 0.3 # see filename +if criterion=="GD": + ana_list = [0.2, 0.5, 0.8] # choen's d +elif criterion=="EC": + ana_list = [0.1,0.3,0.5] +elif criterion=="IC": + ana_list = [0.05, 0.1,0.3] + + +Data_folder = os.getcwd()+ResultPath +results_files = [f for f in os.listdir(Data_folder) if os.path.isfile(os.path.join(Data_folder, f))] + +# if multiple parameters +p_list = [] +for p in range(len(par_ind)): + p_list.append(ssms.config.model_config[DDM_id]["params"][par_ind[p]]) + +heatmap_p = np.zeros((len(range_ntrials),len(range_npp))) +heatmap_p_c = np.zeros((len(range_ntrials),len(range_npp))) + +def plot_MultiHeatmap(ax_fi,fi,fi_range,data,norm,title,xticklabels,yticklabels,xylabels): + y_visible = True + colbar = False + + ax=sns.heatmap(data, + xticklabels=xticklabels, + yticklabels=yticklabels, + annot=True, + ax = ax_fi, + cbar=colbar, + cmap = 'Blues', + norm=norm + ) + + ax.set_title(title, fontsize=18) + ax.set_xlabel(xylabels[0]) # x轴标题 + ax.set_ylabel(xylabels[1]) + ax.axes.yaxis.set_visible(y_visible) + ax.invert_yaxis() + figure = ax.get_figure() + + + if fi == fi_range-1: + colbar = True + plt.subplots_adjust(right=0.8) + cbar_ax = fig.add_axes([0.82, 0.15, 0.025, 0.7]) # 这里的数值å¯èƒ½éœ€è¦æ ¹æ®å®žé™…情况进行调整 + fig.colorbar(ax.collections[0], cax=cbar_ax) + return ax, figure +fig, axs = plt.subplots(1,len(ana_list)) + # gridspec_kw={ + # 'width_ratios': [1, 1,1,1.25] + # # 'height_ratios': [1, 1,1,1] + # }) + +for p in range(len(par_ind)): + for es_i in range(len(ana_list)): + for n_t in range(len(range_ntrials)): + for n_p in range(len(range_npp)): + ntrials = range_ntrials[n_t] + npp = range_npp[n_p] + for file in results_files: + if criterion == "GD": + prefix_to_match = 'PowerGD{}P{}SD{}T{}N{}M{}ES'.format(par_ind[p],np.round(s_pooled,2),ntrials, + npp, nreps,np.round(ana_list[es_i],2)) + elif criterion == "EC": + prefix_to_match = 'PowerEC{}P{}SD{}TC{}T{}N{}M'.format(par_ind[p],s_pooled, ana_list[es_i], ntrials, + npp, nreps) + if file.startswith(prefix_to_match) and file.endswith('.csv'): # check prefix + + PowerFile_path = os.getcwd()+ResultPath+file + + + PowerResults = pd.read_csv(PowerFile_path, delimiter = ',')["power_"+p_list[p]].dropna(axis = 0) + conven_cor = pd.read_csv(PowerFile_path, delimiter = ',')['conventional_power'].dropna(axis = 0) + + heatmap_p[n_t,n_p] = PowerResults.iloc[0] + heatmap_p_c[n_t,n_p] = conven_cor.iloc[0] + + Power_AllData = (heatmap_p,) + conven_AllData = (heatmap_p_c,) + + + fontsize = 20 + xylabels = ['participants','trials'] + xticklabels = range_npp + yticklabels = range_ntrials + y_visible = 1 + + if criterion == "GD": + fig.suptitle("Pr(T-statistic > tau) with p-value threshold = 0.05 ".format(par_ind[p]), fontsize=fontsize) + elif criterion == "EC": + fig.suptitle("Pr(External correlation > tau) with p-value threshold = 0.05 ".format(par_ind[p]), fontsize=fontsize) + + norm = matplotlib.colors.Normalize(vmin=0, vmax=1) + sns.set(font_scale=1.4) + + for i_p in range(len(par_ind)): + data=pd.DataFrame(Power_AllData[i_p]) + c_data = pd.DataFrame(conven_AllData[i_p]) + if criterion == "GD": + title = " Cohen's d = {}".format(ana_list[es_i]) + elif criterion == "EC": + title = " True correlation = {}".format(ana_list[es_i]) + if c == 1: + fig.suptitle("Conventional power with p-value threshold = 0.05 ".format(par_ind[p]), fontsize=fontsize) + ax,figure = plot_MultiHeatmap(axs[es_i],es_i,len(ana_list),c_data,norm,title,xticklabels,yticklabels,xylabels) + else: + ax,figure = plot_MultiHeatmap(axs[es_i],es_i,len(ana_list),data,norm,title,xticklabels,yticklabels,xylabels) + +plt.show() +print() + # fig[0]=sns.heatmap(data, + # xticklabels=xticklabels, + # yticklabels=yticklabels, + # annot=True, + # cbar=colbar, + # ax = ax_fi, + # cmap = 'Blues', + # norm=norm + # ) + + # ax.set_title("power of parameter {}".format(p_list[i_p]), fontsize=18) + # ax.set_xlabel(xylabels[0]) # x轴标题 + # ax.set_ylabel(xylabels[1]) + # ax.axes.yaxis.set_visible(y_visible) + # ax.invert_yaxis() + # figure = axs[0].get_figure() + # plt.show() + + # axs[1]=sns.heatmap(c_data, + # xticklabels=xticklabels, + # yticklabels=yticklabels, + # annot=True, + # cbar=colbar, + # cmap = 'Blues', + # norm=norm + # ) + + # axs[1].set_title("conventional power of parameter {}".format(p_list[i_p]), fontsize=18) + # axs[1].set_xlabel(xylabels[0]) # x轴标题 + # axs[1].set_ylabel(xylabels[1]) + # axs[1].axes.yaxis.set_visible(y_visible) + # axs[1].invert_yaxis() + # figure = axs[1].get_figure() + + # plt.show() + + # plt.show() + # a =0 \ No newline at end of file diff --git a/plot_IC.py b/plot_IC.py new file mode 100644 index 0000000..1ef8b0d --- /dev/null +++ b/plot_IC.py @@ -0,0 +1,190 @@ +import os,sys +import pandas as pd +import seaborn as sns +import matplotlib +from scipy import stats +import matplotlib.pyplot as plt +import numpy as np +import ssms +def ImPlot_plot(title,x,xlabel,y,ylabel,type = "plot",titlesize=35,xylabelsize=25,labelsize = 25,linewidth = 5): + # corr_coef = np.corrcoef(x, y)[0, 1] + # coefficients = np.polyfit(x, y, 1) # 线性回归，拟åˆä¸€æ¬¡å¤šé¡¹å¼ï¼ˆä¸€æ¬¡ç›´çº¿ï¼‰ + # poly = np.poly1d(coefficients) + # x_fit = np.linspace(min(x), max(x)) # 创建拟åˆçº¿çš„ x 值 + # y_fit = poly(x_fit) + + plt.figure(figsize=(12, 12)) + plt.title(title,fontdict={"fontsize" : titlesize}) + if type == "plot": + plt.plot(x,y,linewidth=linewidth) + elif type == "scatter": + plt.scatter(x,y,s=linewidth) + # plt.plot(x_fit, y_fit, color='red', label='Regression line') # 回归线 + + + plt.xlabel(xlabel,fontdict={"fontsize" : xylabelsize}) + plt.tick_params(labelsize=labelsize) + plt.ylabel(ylabel,fontdict={"fontsize" : xylabelsize}) + return plt + +def plot_ParameterRecovery(param_bounds,name,Est,Tru,fig_size = [10,10],xylabelsize=20,labelsize=20,titlesize=28): + # if len(ACC) ==1: + # ACC = np.ones((len(Est[name]),1)) + + min_lim = param_bounds[0] + max_lim = param_bounds[1] + fig = plt.figure() + fig.set_size_inches(fig_size[0],fig_size[1]) + plt.scatter(Est[name],y=Tru[name]) + # plt.scatter(Est[name],y=Tru[name],alpha=ACC) + plt.xlim(min_lim,max_lim) + plt.ylim(min_lim,max_lim) + l = np.arange(min_lim,max_lim,(max_lim-min_lim)/200) + plt.plot(l,l) + plt.title('Parameter Recovery of '+name,fontdict={"fontsize" : titlesize}) + plt.xlabel('estimated',fontdict={"fontsize" : xylabelsize}) + plt.ylabel('true',fontdict={"fontsize" : xylabelsize}) + + + plt.tick_params(labelsize=labelsize) + + plt.show() + +# sys.path.append(r"D:\horiz\IMPORTANT\0study_graduate\Pro_COMPASS\COMPASS_DDM\results\test3") + +plot_heatmap = 0 +plot_single_setting = 0 +plot_ProPow = 1 + +ResultPath = "results\\test_Cluster\\v_0.3" +DDM_id = "ddm" +range_ntrials = [100] +range_npp = [40] +tau = 0.8 +nreps = 80 + +p_list = ['v','a','z','t'] + +heatmap_v = np.zeros((len(range_ntrials),len(range_npp))) +heatmap_a = np.zeros((len(range_ntrials),len(range_npp))) +heatmap_z = np.zeros((len(range_ntrials),len(range_npp))) +heatmap_t = np.zeros((len(range_ntrials),len(range_npp))) + +for n_t in range(len(range_ntrials)): + for n_p in range(len(range_npp)): + ntrials = range_ntrials[n_t] + npp = range_npp[n_p] + + Parameters = ssms.config.model_config[DDM_id]["params"] + OutputFile_name = 'OutputIC{}T{}N{}M.csv'.format(ntrials,npp, nreps) + OutputFile_path = os.path.join(os.getcwd(), ResultPath, OutputFile_name) + out_cn = Parameters+["ACC","RT"] + OutputResults = pd.read_csv(OutputFile_path, delimiter = ',')[out_cn].dropna(axis = 0) + + + PowerFile_name = 'PowerIC{}T{}N{}M.csv'.format(ntrials,npp, nreps) + PowerFile_path = os.path.join(os.getcwd(), ResultPath, PowerFile_name) + PowerResults = pd.read_csv(PowerFile_path, delimiter = ',')[Parameters].dropna(axis = 0) + + + # tau = OutputResults['tau'].values[-1] + if plot_single_setting: + for p in Parameters : + plt.figure(figsize=(12, 12)) + fig, axes = plt.subplots(nrows = 1, ncols = 1) + sns.set_theme(style = "white",font_scale=1.4) + sns.kdeplot(OutputResults[p].dropna(axis = 0), ax = axes) + fig.suptitle("Pr(Correlation >= {}) with {} pp, {} trials)".format(tau, npp, ntrials), fontweight = 'bold') + + power_estimate = PowerResults[p].values + axes.set_title("Power = {} based on {} reps".format(np.round(power_estimate*100, 2), nreps)) + axes.axvline(x = tau, lw = 2, linestyle ="dashed", color ='k', label ='tau') + axes.tick_params(labelsize=20) + axes.set_xlabel('Correlations of '+p,fontsize = 20) + axes.set_ylabel('Density',fontsize = 20) + + plt.show() + if plot_ProPow: + for n_p in range(len(p_list)): + x = OutputResults[p_list[n_p]].values + y = OutputResults["ACC"].values + plt = ImPlot_plot("Correlation between ACC \nand recovery of "+p_list[n_p], + x,p_list[n_p],y ,'ACC', + type = "scatter",titlesize=35,xylabelsize=25,labelsize = 25,linewidth = 20) + sns.regplot(x=OutputResults[p_list[n_p]], y=OutputResults["ACC"]) + corr_coef, p_value = stats.pearsonr(x, y) + annotation = f"Correlation coefficient: {corr_coef:.2f}\nP-value: {p_value:.4f}" + plt.annotate(annotation, xy=(0.1, 0.85), xycoords='axes fraction', fontsize=20) + + file_name = '\ProPowIC{}T{}N{}M_{}.png'.format(ntrials,npp,nreps,p_list[n_p]) + plt.savefig(ResultPath+file_name,bbox_inches='tight') + + + + if plot_heatmap: + heatmap_v[n_t,n_p] = PowerResults['v'][0] + heatmap_a[n_t,n_p] = PowerResults['a'][0] + heatmap_z[n_t,n_p] = PowerResults['z'][0] + heatmap_t[n_t,n_p] = PowerResults['t'][0] + + + + + plt.tick_params(labelsize=labelsize) + + plt.show() + +Power_AllData = (heatmap_v,heatmap_a,heatmap_z,heatmap_t) + + +# plot heat map +if plot_heatmap: + fontsize = 20 + fig, axs = plt.subplots(1,len(p_list), + gridspec_kw={ + 'width_ratios': [1, 1,1,1.25] + # 'height_ratios': [1, 1,1,1] + }) + + fig.suptitle("Pr(internal correlation >= {}) with Nreps = {}".format(tau,nreps), fontsize=fontsize) + norm = matplotlib.colors.Normalize(vmin=0, vmax=1) + sns.set(font_scale=1.4) + def plot_MultiHeatmap(ax_fi,fi,fi_range,data,norm,title,xticklabels,yticklabels,xylabels, t = 0.5): + y_visible = False + colbar = False + if fi == fi_range[0]: + y_visible = True + if fi == fi_range[1]: + colbar = True + + ax=sns.heatmap(data, + xticklabels=xticklabels, + yticklabels=yticklabels, + annot=True, + ax = ax_fi, + cbar=colbar, + cmap = 'Blues', + norm=norm + ) + + ax.set_title(title, fontsize=18) + ax.set_xlabel(xylabels[0]) # x轴标题 + ax.set_ylabel(xylabels[1]) + ax.axes.yaxis.set_visible(y_visible) + ax.invert_yaxis() + figure = ax.get_figure() + return figure + + for i_p in range(len(p_list)): + + + data=pd.DataFrame(Power_AllData[i_p]) + power_plot = plot_MultiHeatmap(axs[i_p],i_p,[0,len(p_list)-1],data,title = "power of parameter {}".format(p_list[i_p]), + norm = norm, + xticklabels = range_npp,yticklabels = range_ntrials, + xylabels = ['participants','trials'], t = 0.5) + + + + plt.show() + diff --git a/plot_IC_SeFile.py b/plot_IC_SeFile.py new file mode 100644 index 0000000..2b2cb01 --- /dev/null +++ b/plot_IC_SeFile.py @@ -0,0 +1,177 @@ +import os,sys +import pandas as pd +import seaborn as sns +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import ssms +# sys.path.append(r"D:\horiz\IMPORTANT\0study_graduate\Pro_COMPASS\COMPASS_DDM\results\test3") +plot_heatmap_GD = 1 +c=0 +criterion = "IC" + +ResultPath = "\\results\Talk\IC\\" +DDM_id = "ddm" + +par_ind = [0] +pn = "drift rates" + +nreps = 50 + +range_ntrials = [40,60,80,100,120] +range_npp = [20,40,60,80,100] + +s_pooled = 0.3 # see filename +if criterion=="GD": + ana_list = [0.2, 0.5, 0.8] # choen's d +elif criterion=="EC": + ana_list = [0.1,0.3,0.5] +elif criterion=="IC": + ana_list = [0.1,0.2,0.3] + + +Data_folder = os.getcwd()+ResultPath + +# if multiple parameters +p_list = [] +for p in range(len(par_ind)): + p_list.append(ssms.config.model_config[DDM_id]["params"][par_ind[p]]) + +heatmap_p = np.zeros((len(range_ntrials),len(range_npp))) +heatmap_p_c = np.zeros((len(range_ntrials),len(range_npp))) + +def plot_MultiHeatmap(ax_fi,fi,fi_range,data,norm,title,xticklabels,yticklabels,xylabels): + y_visible = True + colbar = False + + ax=sns.heatmap(data, + xticklabels=xticklabels, + yticklabels=yticklabels, + annot=True, + ax = ax_fi, + cbar=colbar, + cmap = 'Blues', + norm=norm + ) + + ax.set_title(title, fontsize=18) + ax.set_xlabel(xylabels[0]) # x轴标题 + ax.set_ylabel(xylabels[1]) + ax.axes.yaxis.set_visible(y_visible) + ax.invert_yaxis() + figure = ax.get_figure() + + + if fi == fi_range-1: + colbar = True + plt.subplots_adjust(right=0.8) + cbar_ax = fig.add_axes([0.82, 0.15, 0.025, 0.7]) # 这里的数值å¯èƒ½éœ€è¦æ ¹æ®å®žé™…情况进行调整 + fig.colorbar(ax.collections[0], cax=cbar_ax) + return ax, figure +fig, axs = plt.subplots(1,len(ana_list)) + # gridspec_kw={ + # 'width_ratios': [1, 1,1,1.25] + # # 'height_ratios': [1, 1,1,1] + # }) + +for p in range(len(par_ind)): + for es_i in range(len(ana_list)): + Data_folder_IC = Data_folder+"SD"+str(ana_list[es_i])+"\\" + results_files = [f for f in os.listdir(Data_folder_IC) if os.path.isfile(os.path.join(Data_folder_IC, f))] + for n_t in range(len(range_ntrials)): + for n_p in range(len(range_npp)): + ntrials = range_ntrials[n_t] + npp = range_npp[n_p] + for file in results_files: + if criterion == "GD": + prefix_to_match = 'PowerGD{}P{}SD{}T{}N{}M{}ES'.format(par_ind[p],np.round(s_pooled,2),ntrials, + npp, nreps,np.round(ana_list[es_i],2)) + elif criterion == "EC": + prefix_to_match = 'PowerEC{}P{}SD{}TC{}T{}N{}M'.format(par_ind[p],s_pooled, ana_list[es_i], ntrials, + npp, nreps) + elif criterion == "IC": + prefix_to_match = 'PowerIC{}T{}N{}M'.format(ntrials,npp, nreps) + + if file.startswith(prefix_to_match) and file.endswith('.csv'): # check prefix + + PowerFile_path = Data_folder_IC+file + + + PowerResults = pd.read_csv(PowerFile_path, delimiter = ',')[p_list[p]].dropna(axis = 0) + + heatmap_p[n_t,n_p] = PowerResults.iloc[0] + + + Power_AllData = (heatmap_p,) + + + + fontsize = 20 + xylabels = ['participants','trials'] + xticklabels = range_npp + yticklabels = range_ntrials + y_visible = 1 + + if criterion == "GD": + fig.suptitle("Pr(T-statistic > tau) with p-value threshold = 0.05 ".format(par_ind[p]), fontsize=fontsize) + elif criterion == "EC": + fig.suptitle("Pr(External correlation > tau) with p-value threshold = 0.05 ".format(par_ind[p]), fontsize=fontsize) + elif criterion == "IC": + fig.suptitle("Pr(Internal correlation > 0.8) ".format(par_ind[p]), fontsize=fontsize) + + norm = matplotlib.colors.Normalize(vmin=0, vmax=1) + sns.set(font_scale=1.4) + + for i_p in range(len(par_ind)): + data=pd.DataFrame(Power_AllData[i_p]) + + if criterion == "GD": + title = " Cohen's d = {}".format(ana_list[es_i]) + elif criterion == "EC": + title = " True correlation = {}".format(ana_list[es_i]) + elif criterion == "IC": + title = " SD of {} = {}".format(pn,ana_list[es_i]) + + + ax,figure = plot_MultiHeatmap(axs[es_i],es_i,len(ana_list),data,norm,title,xticklabels,yticklabels,xylabels) + +plt.show() +print() + # fig[0]=sns.heatmap(data, + # xticklabels=xticklabels, + # yticklabels=yticklabels, + # annot=True, + # cbar=colbar, + # ax = ax_fi, + # cmap = 'Blues', + # norm=norm + # ) + + # ax.set_title("power of parameter {}".format(p_list[i_p]), fontsize=18) + # ax.set_xlabel(xylabels[0]) # x轴标题 + # ax.set_ylabel(xylabels[1]) + # ax.axes.yaxis.set_visible(y_visible) + # ax.invert_yaxis() + # figure = axs[0].get_figure() + # plt.show() + + # axs[1]=sns.heatmap(c_data, + # xticklabels=xticklabels, + # yticklabels=yticklabels, + # annot=True, + # cbar=colbar, + # cmap = 'Blues', + # norm=norm + # ) + + # axs[1].set_title("conventional power of parameter {}".format(p_list[i_p]), fontsize=18) + # axs[1].set_xlabel(xylabels[0]) # x轴标题 + # axs[1].set_ylabel(xylabels[1]) + # axs[1].axes.yaxis.set_visible(y_visible) + # axs[1].invert_yaxis() + # figure = axs[1].get_figure() + + # plt.show() + + # plt.show() + # a =0 \ No newline at end of file diff --git a/sbatch_run.sh b/sbatch_run.sh new file mode 100644 index 0000000..68fb01c --- /dev/null +++ b/sbatch_run.sh @@ -0,0 +1,78 @@ +#!/bin/bash + +# Default resources are 1 core with 2.8GB of memory per core. + +# job name: +#SBATCH -J compass + +# priority +# Unused because assumption is that this script is called via the batch_data_generation.sh script +##SBATCH --account=carney-frankmj-condo + +# output file +#SBATCH --output ../sbatch_output/slurm/compass_run_%A_%a.out + +# Request runtime, memory, cores +#SBATCH --time=48:00:00 +#SBATCH --mem=48G +#SBATCH -c 12 +#SBATCH -N 1 + +# Unused because assumption is that this script is called via the batch_data_generation.sh script +##SBATCH -p gpu --gres=gpu:1 +##SBATCH --array=1-75 +# -------------------------------------------------------------------------------------- + +# BASIC SETUP + +# Read in arguments: +input_file=None +output_folder=/users/afengler/data/proj_compass_ddm/sbatch_output/ +criterion=None +id=None +multiprocess=1 +show_plots=0 +while [ ! $# -eq 0 ] + do + case "$1" in + --input_file | -if) + input_file=$2 + ;; + --output_folder | -of) + output_folder=$2 + ;; + --criterion | -c) + criterion=$2 + ;; + esac + shift 2 + done + +# USER-INPUT NEEDED +source /users/afengler/.bashrc # NOTE: This file needs to contain conda initialization stuff + +# TODO: This double conda deactivate can be simplified further --> key is understanding how to handle .bashrc / .bash_profile correctly +conda deactivate +conda deactivate +conda activate compass_ddm + +echo "The input file passed is: $input_file" +echo "The output folder passed is: $output_folder" +echo "The criterion passed is: $criterion" + +if [ -z ${SLURM_ARRAY_TASK_ID} ]; +then + python -u PowerAnalysis.py --input_file $input_file \ + --output_folder $output_folder \ + --criterion $criterion \ + --multiprocess $multiprocess \ + --show_plots $show_plots \ + +else + python -u PowerAnalysis.py --input_file $input_file \ + --output_folder $output_folder \ + --criterion $criterion \ + --multiprocess $multiprocess \ + --show_plots $show_plots \ + --id $SLURM_ARRAY_TASK_ID +fi \ No newline at end of file diff --git a/wandb/debug-cli.horiz.log b/wandb/debug-cli.horiz.log new file mode 100644 index 0000000..e69de29 diff --git a/wandb/offline-run-20230626_170711-1ebgx1ch/files/conda-environment.yaml b/wandb/offline-run-20230626_170711-1ebgx1ch/files/conda-environment.yaml new file mode 100644 index 0000000..e69de29 diff --git a/wandb/offline-run-20230626_170711-1ebgx1ch/files/requirements.txt b/wandb/offline-run-20230626_170711-1ebgx1ch/files/requirements.txt new file mode 100644 index 0000000..9f6e3c0 --- /dev/null +++ b/wandb/offline-run-20230626_170711-1ebgx1ch/files/requirements.txt @@ -0,0 +1,252 @@ +absl-py==1.4.0 +alabaster==0.7.12 +appdirs==1.4.4 +argh==0.26.2 +argparse==1.4.0 +arrow==0.13.1 +arviz==0.13.0 +astroid==2.6.6 +async-generator==1.10 +atomicwrites==1.4.0 +attrs==21.2.0 +autopep8==1.5.7 +babel==2.9.1 +backcall==0.2.0 +bcrypt==3.2.0 +beautifulsoup4==4.12.2 +binaryornot==0.4.4 +black==19.10b0 +bleach==4.0.0 +boltons==23.0.0 +bottleneck==1.3.2 +brotlipy==0.7.0 +cached-property==1.5.2 +cachetools==4.2.2 +certifi==2023.5.7 +cffi==1.15.0 +cftime==1.6.2 +chardet==4.0.0 +charset-normalizer==2.0.4 +chex==0.1.7 +click==7.1.2 +cloudpickle==2.0.0 +colorama==0.4.4 +conda-build==3.25.0 +conda-index==0.2.3 +conda-package-handling==2.1.0 +conda-package-streaming==0.8.0 +conda==23.3.1 +cons==0.4.5 +cookiecutter==1.7.2 +cryptography==35.0.0 +cycler==0.10.0 +cython==0.29.35 +debugpy==1.5.1 +decorator==5.1.0 +defusedxml==0.7.1 +diff-match-patch==20200713 +dill==0.3.6 +dm-tree==0.1.8 +docker-pycreds==0.4.0 +docutils==0.17.1 +entrypoints==0.3 +etils==1.3.0 +etuples==0.3.9 +fastprogress==1.0.0 +filelock==3.12.2 +flake8==3.9.2 +flax==0.6.11 +fonttools==4.25.0 +frozendict==2.3.8 +gitdb==4.0.10 +gitpython==3.1.31 +glob2==0.7 +h5netcdf==1.2.0 +h5py==3.9.0 +idna==3.2 +imagesize==1.2.0 +importlib-metadata==4.8.1 +importlib-resources==5.12.0 +inflection==0.5.1 +intervaltree==3.1.0 +ipykernel==6.4.1 +ipython-genutils==0.2.0 +ipython==7.29.0 +isort==5.9.3 +jax==0.4.13 +jaxlib==0.4.13 +jedi==0.18.0 +jinja2-time==0.2.0 +jinja2==2.11.3 +joblib==1.2.0 +jsonpatch==1.32 +jsonpointer==2.1 +jsonschema==3.2.0 +jupyter-client==6.1.12 +jupyter-core==4.9.1 +jupyterlab-pygments==0.1.2 +kabuki==0.6.5 +keyring==23.1.0 +kiwisolver==1.3.1 +lanfactory==0.3.0.dev0 +lazy-object-proxy==1.6.0 +libarchive-c==2.9 +logical-unification==0.4.6 +markdown-it-py==3.0.0 +markupsafe==1.1.1 +matplotlib-inline==0.1.2 +matplotlib==3.4.3 +mccabe==0.6.1 +mdurl==0.1.2 +menuinst==1.4.19 +minikanren==1.0.3 +mistune==0.8.4 +mkl-fft==1.3.1 +mkl-random==1.2.2 +mkl-service==2.4.0 +ml-dtypes==0.2.0 +more-itertools==8.12.0 +mpmath==1.3.0 +msgpack==1.0.5 +multipledispatch==0.6.0 +munkres==1.1.4 +mypy-extensions==0.4.3 +nbclient==0.5.3 +nbconvert==6.1.0 +nbformat==5.1.3 +nest-asyncio==1.5.1 +netcdf4==1.6.2 +networkx==3.1 +numexpr==2.7.3 +numpy==1.22.4 +numpydoc==1.1.0 +olefile==0.46 +onnx==1.14.0 +openturns==1.21 +opt-einsum==3.3.0 +optax==0.1.5 +orbax-checkpoint==0.2.6 +packaging==21.3 +pandas==2.0.2 +pandocfilters==1.4.3 +paramiko==2.7.2 +parso==0.8.2 +pathspec==0.7.0 +pathtools==0.1.2 +patsy==0.5.3 +pexpect==4.8.0 +pickleshare==0.7.5 +pillow==8.4.0 +pip==21.2.4 +pkginfo==1.9.6 +pluggy==1.0.0 +poyo==0.5.0 +prompt-toolkit==3.0.20 +protobuf==4.23.3 +psutil==5.8.0 +ptyprocess==0.7.0 +pycodestyle==2.7.0 +pycosat==0.6.4 +pycparser==2.21 +pydocstyle==6.1.1 +pyflakes==2.3.1 +pygments==2.15.1 +pylint==2.9.6 +pyls-spyder==0.4.0 +pymc==5.0.1 +pynacl==1.4.0 +pyopenssl==21.0.0 +pyparsing==3.0.4 +pyqt5-plugins==5.15.4.2.2 +pyqt5-qt5==5.15.2 +pyqt5-sip==12.9.0 +pyqt5==5.15.4 +pyqtwebengine-qt5==5.15.2 +pyqtwebengine==5.15.5 +pyrsistent==0.18.0 +pysocks==1.7.1 +pytensor==2.12.3 +python-dateutil==2.8.2 +python-dotenv==0.19.2 +python-lsp-black==1.0.0 +python-lsp-jsonrpc==1.0.0 +python-lsp-server==1.2.4 +python-slugify==5.0.2 +pytz==2021.3 +pywin32-ctypes==0.2.0 +pywin32==228 +pyyaml==6.0 +pyzmq==22.2.1 +qdarkstyle==3.0.2 +qstylizer==0.1.10 +qt5-applications==5.15.2.2.2 +qt5-tools==5.15.2.1.2 +qtawesome==1.0.3 +qtconsole==5.1.1 +qtpy==1.10.0 +regex==2021.8.3 +requests==2.26.0 +rich==13.4.2 +rope==0.21.1 +rtree==0.9.7 +ruamel.yaml.clib==0.2.6 +ruamel.yaml==0.17.21 +scikit-learn==1.2.2 +scipy==1.10.1 +seaborn==0.11.2 +sentry-sdk==1.26.0 +setproctitle==1.3.2 +setuptools==68.0.0 +shiboken2==5.15.2 +sip==4.19.13 +six==1.16.0 +smmap==5.0.0 +snowballstemmer==2.1.0 +sortedcontainers==2.4.0 +soupsieve==2.4 +sphinx==4.2.0 +sphinxcontrib-applehelp==1.0.2 +sphinxcontrib-devhelp==1.0.2 +sphinxcontrib-htmlhelp==2.0.0 +sphinxcontrib-jsmath==1.0.1 +sphinxcontrib-qthelp==1.0.3 +sphinxcontrib-serializinghtml==1.1.5 +spyder-kernels==2.1.3 +spyder==5.1.5 +ssm-simulators==0.3.1 +statsmodels==0.13.1 +sympy==1.12 +tensorstore==0.1.39 +testpath==0.5.0 +text-unidecode==1.3 +textdistance==4.2.1 +threadpoolctl==3.1.0 +three-merge==0.1.1 +tinycss==0.4 +toml==0.10.2 +tomli==2.0.1 +toolz==0.12.0 +torch==2.0.1 +tornado==6.1 +tqdm==4.65.0 +traitlets==5.1.1 +typed-ast==1.4.3 +typing-extensions==4.6.3 +tzdata==2023.3 +ujson==4.0.2 +unidecode==1.2.0 +urllib3==1.26.16 +wandb==0.15.4 +watchdog==2.1.3 +wcwidth==0.2.5 +webencodings==0.5.1 +wheel==0.37.0 +whichcraft==0.6.1 +win-inet-pton==1.1.0 +wincertstore==0.2 +wrapt==1.12.1 +xarray-einstats==0.5.1 +xarray==2022.11.0 +yapf==0.31.0 +zipp==3.6.0 +zstandard==0.19.0 \ No newline at end of file diff --git a/wandb/offline-run-20230626_170711-1ebgx1ch/files/wandb-metadata.json b/wandb/offline-run-20230626_170711-1ebgx1ch/files/wandb-metadata.json new file mode 100644 index 0000000..a15c3e7 --- /dev/null +++ b/wandb/offline-run-20230626_170711-1ebgx1ch/files/wandb-metadata.json @@ -0,0 +1,42 @@ +{ + "os": "Windows-10-10.0.22621-SP0", + "python": "3.9.7", + "heartbeatAt": "2023-06-26T09:07:11.959827", + "startedAt": "2023-06-26T09:07:11.870924", + "docker": null, + "cuda": null, + "args": [], + "state": "running", + "program": "D:\\horiz\\IMPORTANT\\0study_graduate\\Pro_COMPASS\\COMPASS_DLC\\test.py", + "codePath": "test.py", + "git": { + "remote": "https://github.com/heeluning/COMPASS_DLC.git", + "commit": "f7a174c19e2a133a1678b9b865cb0a64c84a1589" + }, + "email": "heeluning@163.com", + "root": "D:/horiz/IMPORTANT/0study_graduate/Pro_COMPASS/COMPASS_DLC", + "host": "HLN_laptop", + "username": "horiz", + "executable": "C:\\ProgramData\\Anaconda3\\envs\\pyPower\\python.exe", + "cpu_count": 12, + "cpu_count_logical": 16, + "cpu_freq": { + "current": 2500.0, + "min": 0.0, + "max": 2500.0 + }, + "cpu_freq_per_core": [ + { + "current": 2500.0, + "min": 0.0, + "max": 2500.0 + } + ], + "disk": { + "total": 274.71483993530273, + "used": 113.10488891601562 + }, + "memory": { + "total": 15.731925964355469 + } +} diff --git a/wandb/offline-run-20230626_170711-1ebgx1ch/files/wandb-summary.json b/wandb/offline-run-20230626_170711-1ebgx1ch/files/wandb-summary.json new file mode 100644 index 0000000..69f879e --- /dev/null +++ b/wandb/offline-run-20230626_170711-1ebgx1ch/files/wandb-summary.json @@ -0,0 +1 @@ +{"_wandb": {"runtime": 77}} \ No newline at end of file diff --git a/wandb/offline-run-20230626_170711-1ebgx1ch/logs/debug-internal.log b/wandb/offline-run-20230626_170711-1ebgx1ch/logs/debug-internal.log new file mode 100644 index 0000000..ac03b01 --- /dev/null +++ b/wandb/offline-run-20230626_170711-1ebgx1ch/logs/debug-internal.log @@ -0,0 +1,233 @@ +2023-06-26 17:07:11,889 INFO StreamThr :4620 [internal.py:wandb_internal():86] W&B internal server running at pid: 4620, started at: 2023-06-26 17:07:11.888995 +2023-06-26 17:07:11,893 DEBUG HandlerThread:4620 [handler.py:handle_request():144] handle_request: status +2023-06-26 17:07:11,896 INFO WriterThread:4620 [datastore.py:open_for_write():85] open: D:\horiz\IMPORTANT\0study_graduate\Pro_COMPASS\COMPASS_DLC\wandb\offline-run-20230626_170711-1ebgx1ch\run-1ebgx1ch.wandb +2023-06-26 17:07:11,943 DEBUG HandlerThread:4620 [handler.py:handle_request():144] handle_request: run_start +2023-06-26 17:07:11,957 DEBUG HandlerThread:4620 [system_info.py:__init__():31] System info init +2023-06-26 17:07:11,957 DEBUG HandlerThread:4620 [system_info.py:__init__():46] System info init done +2023-06-26 17:07:11,957 INFO HandlerThread:4620 [system_monitor.py:start():181] Starting system monitor +2023-06-26 17:07:11,958 INFO HandlerThread:4620 [system_monitor.py:probe():201] Collecting system info +2023-06-26 17:07:11,958 INFO SystemMonitor:4620 [system_monitor.py:_start():145] Starting system asset monitoring threads +2023-06-26 17:07:11,959 DEBUG HandlerThread:4620 [system_info.py:probe():195] Probing system +2023-06-26 17:07:11,964 INFO SystemMonitor:4620 [interfaces.py:start():190] Started cpu monitoring +2023-06-26 17:07:11,966 INFO SystemMonitor:4620 [interfaces.py:start():190] Started disk monitoring +2023-06-26 17:07:11,967 INFO SystemMonitor:4620 [interfaces.py:start():190] Started memory monitoring +2023-06-26 17:07:11,979 INFO SystemMonitor:4620 [interfaces.py:start():190] Started network monitoring +2023-06-26 17:07:11,979 DEBUG HandlerThread:4620 [system_info.py:_probe_git():180] Probing git +2023-06-26 17:07:12,039 DEBUG HandlerThread:4620 [system_info.py:_probe_git():188] Probing git done +2023-06-26 17:07:12,040 DEBUG HandlerThread:4620 [system_info.py:probe():240] Probing 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{'total': 15.731925964355469}} +2023-06-26 17:07:12,040 INFO HandlerThread:4620 [system_monitor.py:probe():211] Finished collecting system info +2023-06-26 17:07:12,040 INFO HandlerThread:4620 [system_monitor.py:probe():214] Publishing system info +2023-06-26 17:07:12,040 DEBUG HandlerThread:4620 [system_info.py:_save_pip():51] Saving list of pip packages installed into the current environment +2023-06-26 17:07:12,041 DEBUG HandlerThread:4620 [system_info.py:_save_pip():67] Saving pip packages done +2023-06-26 17:07:12,042 DEBUG HandlerThread:4620 [system_info.py:_save_conda():74] Saving list of conda packages installed into the current environment +2023-06-26 17:07:12,045 ERROR HandlerThread:4620 [system_info.py:_save_conda():85] Error saving conda packages: [WinError 2] ϵͳÕÒ²»µ½Ö¸¶¨µÄÎļþ¡£ +Traceback (most recent call last): + File "C:\ProgramData\Anaconda3\envs\pyPower\lib\site-packages\wandb\sdk\internal\system\system_info.py", line 81, in _save_conda + subprocess.call( + File "C:\ProgramData\Anaconda3\envs\pyPower\lib\subprocess.py", line 349, in call + with Popen(*popenargs, **kwargs) as p: + File "C:\ProgramData\Anaconda3\envs\pyPower\lib\subprocess.py", line 951, in __init__ + self._execute_child(args, executable, preexec_fn, close_fds, + File "C:\ProgramData\Anaconda3\envs\pyPower\lib\subprocess.py", line 1420, in _execute_child + hp, ht, pid, tid = _winapi.CreateProcess(executable, args, + File "c:\Users\horiz\.vscode\extensions\ms-python.python-2023.10.1\pythonFiles\lib\python\debugpy\_vendored\pydevd\_pydev_bundle\pydev_monkey.py", line 901, in new_CreateProcess + return getattr(_subprocess, original_name)(app_name, cmd_line, *args) +FileNotFoundError: [WinError 2] ϵͳÕÒ²»µ½Ö¸¶¨µÄÎļþ¡£ +2023-06-26 17:07:12,049 DEBUG HandlerThread:4620 [system_info.py:_save_conda():86] Saving conda packages done +2023-06-26 17:07:12,050 INFO HandlerThread:4620 [system_monitor.py:probe():216] Finished publishing system info +2023-06-26 17:07:16,967 DEBUG 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b/wandb/offline-run-20230626_170711-1ebgx1ch/run-1ebgx1ch.wandb differ diff --git a/wandb/offline-run-20231227_143529-614ttptn/files/conda-environment.yaml b/wandb/offline-run-20231227_143529-614ttptn/files/conda-environment.yaml new file mode 100644 index 0000000..15661b8 --- /dev/null +++ b/wandb/offline-run-20231227_143529-614ttptn/files/conda-environment.yaml @@ -0,0 +1,350 @@ +name: base +channels: + - conda-forge + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main + - defaults + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/fastai/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda/ +dependencies: + - alabaster=0.7.12=pyhd3eb1b0_0 + - appdirs=1.4.4=pyhd3eb1b0_0 + - argh=0.26.2=py39haa95532_0 + - arrow=0.13.1=py39haa95532_0 + - astroid=2.6.6=py39haa95532_0 + - async_generator=1.10=pyhd3eb1b0_0 + - atomicwrites=1.4.0=py_0 + - attrs=21.2.0=pyhd3eb1b0_0 + - autopep8=1.5.7=pyhd3eb1b0_0 + - babel=2.9.1=pyhd3eb1b0_0 + - backcall=0.2.0=pyhd3eb1b0_0 + - bcrypt=3.2.0=py39h196d8e1_0 + - beautifulsoup4=4.12.2=py39haa95532_0 + - binaryornot=0.4.4=pyhd3eb1b0_1 + - black=19.10b0=py_0 + - blas=1.0=mkl + - bleach=4.0.0=pyhd3eb1b0_0 + - boltons=23.0.0=py39haa95532_0 + - bottleneck=1.3.2=py39h7cc1a96_1 + - brotli=1.0.9=ha925a31_2 + - brotlipy=0.7.0=py39h2bbff1b_1003 + - bzip2=1.0.8=he774522_0 + - ca-certificates=2023.05.30=haa95532_0 + - certifi=2023.5.7=py39haa95532_0 + - cffi=1.15.0=py39h2bbff1b_0 + - cftime=1.6.2=py39h080aedc_0 + - chardet=4.0.0=py39haa95532_1003 + - charset-normalizer=2.0.4=pyhd3eb1b0_0 + - 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logical-unification=0.4.6=pyhd8ed1ab_0 + - lz4-c=1.9.3=h2bbff1b_1 + - m2-msys2-runtime=2.5.0.17080.65c939c=3 + - m2-patch=2.7.5=2 + - m2w64-binutils=2.25.1=5 + - m2w64-bzip2=1.0.6=6 + - m2w64-crt-git=5.0.0.4636.2595836=2 + - m2w64-gcc=5.3.0=6 + - m2w64-gcc-ada=5.3.0=6 + - m2w64-gcc-fortran=5.3.0=6 + - m2w64-gcc-libgfortran=5.3.0=6 + - m2w64-gcc-libs=5.3.0=7 + - m2w64-gcc-libs-core=5.3.0=7 + - m2w64-gcc-objc=5.3.0=6 + - m2w64-gmp=6.1.0=2 + - m2w64-headers-git=5.0.0.4636.c0ad18a=2 + - m2w64-isl=0.16.1=2 + - m2w64-libiconv=1.14=6 + - m2w64-libmangle-git=5.0.0.4509.2e5a9a2=2 + - m2w64-libwinpthread-git=5.0.0.4634.697f757=2 + - m2w64-make=4.1.2351.a80a8b8=2 + - m2w64-mpc=1.0.3=3 + - m2w64-mpfr=3.1.4=4 + - m2w64-pkg-config=0.29.1=2 + - m2w64-toolchain=5.3.0=7 + - m2w64-toolchain_win-64=2.4.0=0 + - m2w64-tools-git=5.0.0.4592.90b8472=2 + - m2w64-windows-default-manifest=6.4=3 + - m2w64-winpthreads-git=5.0.0.4634.697f757=2 + - m2w64-zlib=1.2.8=10 + - markupsafe=1.1.1=py39h2bbff1b_0 + - 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python_abi=3.9=2_cp39 + - pytz=2021.3=pyhd3eb1b0_0 + - pywin32-ctypes=0.2.0=py39haa95532_1000 + - pyyaml=6.0=py39h2bbff1b_1 + - qdarkstyle=3.0.2=pyhd3eb1b0_0 + - qstylizer=0.1.10=pyhd3eb1b0_0 + - qt=5.9.7=vc14h73c81de_0 + - qtawesome=1.0.3=pyhd3eb1b0_0 + - qtconsole=5.1.1=pyhd3eb1b0_0 + - qtpy=1.10.0=pyhd3eb1b0_0 + - regex=2021.8.3=py39h2bbff1b_0 + - requests=2.26.0=pyhd3eb1b0_0 + - rope=0.21.1=pyhd3eb1b0_0 + - rtree=0.9.7=py39h2eaa2aa_1 + - ruamel.yaml=0.17.21=py39h2bbff1b_0 + - ruamel.yaml.clib=0.2.6=py39h2bbff1b_1 + - sip=4.19.13=py39hd77b12b_0 + - six=1.16.0=pyhd3eb1b0_0 + - snowballstemmer=2.1.0=pyhd3eb1b0_0 + - sortedcontainers=2.4.0=pyhd3eb1b0_0 + - soupsieve=2.4=py39haa95532_0 + - sphinx=4.2.0=pyhd3eb1b0_1 + - sphinxcontrib-applehelp=1.0.2=pyhd3eb1b0_0 + - sphinxcontrib-devhelp=1.0.2=pyhd3eb1b0_0 + - sphinxcontrib-htmlhelp=2.0.0=pyhd3eb1b0_0 + - sphinxcontrib-jsmath=1.0.1=pyhd3eb1b0_0 + - sphinxcontrib-qthelp=1.0.3=pyhd3eb1b0_0 + - sphinxcontrib-serializinghtml=1.1.5=pyhd3eb1b0_0 + - spyder=5.1.5=py39haa95532_1 + - sqlite=3.36.0=h2bbff1b_0 + - statsmodels=0.13.1=py39h5d4886f_0 + - testpath=0.5.0=pyhd3eb1b0_0 + - text-unidecode=1.3=pyhd3eb1b0_0 + - textdistance=4.2.1=pyhd3eb1b0_0 + - three-merge=0.1.1=pyhd3eb1b0_0 + - tinycss=0.4=pyhd3eb1b0_1002 + - tk=8.6.11=h2bbff1b_0 + - toml=0.10.2=pyhd3eb1b0_0 + - tomli=2.0.1=py39haa95532_0 + - toolz=0.12.0=py39haa95532_0 + - tqdm=4.65.0=py39hd4e2768_0 + - typed-ast=1.4.3=py39h2bbff1b_1 + - ujson=4.0.2=py39hd77b12b_0 + - unidecode=1.2.0=pyhd3eb1b0_0 + - vc=14.2=h21ff451_1 + - vs2015_runtime=14.27.29016=h5e58377_2 + - watchdog=2.1.3=py39haa95532_0 + - wcwidth=0.2.5=pyhd3eb1b0_0 + - webencodings=0.5.1=py39haa95532_1 + - wheel=0.37.0=pyhd3eb1b0_1 + - whichcraft=0.6.1=pyhd3eb1b0_0 + - win_inet_pton=1.1.0=py39haa95532_0 + - wincertstore=0.2=py39haa95532_2 + - wrapt=1.12.1=py39h196d8e1_1 + - xarray-einstats=0.5.1=pyhd8ed1ab_0 + - xz=5.2.5=h62dcd97_0 + - yaml=0.2.5=he774522_0 + - yapf=0.31.0=pyhd3eb1b0_0 + - zipp=3.6.0=pyhd3eb1b0_0 + - zlib=1.2.11=h62dcd97_4 + - zstandard=0.19.0=py39h2bbff1b_0 + - zstd=1.4.9=h19a0ad4_0 + - pip: + - absl-py==1.4.0 + - argparse==1.4.0 + - arviz==0.15.1 + - asttokens==2.2.1 + - cached-property==1.5.2 + - cachetools==5.3.1 + - chex==0.1.7 + - click==7.1.2 + - comm==0.1.3 + - cons==0.4.6 + - contourpy==1.1.0 + - cython==0.29.35 + - debugpy==1.6.7 + - dill==0.3.6 + - dm-tree==0.1.8 + - docker-pycreds==0.4.0 + - etils==1.3.0 + - executing==1.2.0 + - filelock==3.12.2 + - flax==0.6.11 + - frozendict==2.3.8 + - gitdb==4.0.10 + - gitpython==3.1.31 + - h5netcdf==1.2.0 + - h5py==3.9.0 + - hddm-wfpt==0.1.0 + - importlib-metadata==6.8.0 + - importlib-resources==5.12.0 + - ipykernel==6.24.0 + - ipython==8.14.0 + - jax==0.4.13 + - jaxlib==0.4.13 + - joblib==1.2.0 + - jupyter-client==8.3.0 + - jupyter-core==5.3.1 + - kabuki==0.6.5 + - lanfactory==0.3.0.dev0 + - markdown-it-py==3.0.0 + - matplotlib==3.7.2 + - mdurl==0.1.2 + - ml-dtypes==0.2.0 + - mpmath==1.3.0 + - msgpack==1.0.5 + - networkx==3.1 + - numpy==1.25.1 + - onnx==1.14.0 + - openturns==1.21 + - opt-einsum==3.3.0 + - optax==0.1.5 + - orbax-checkpoint==0.2.6 + - pandas==2.0.2 + - pathtools==0.1.2 + - patsy==0.5.3 + - pillow==10.0.0 + - platformdirs==3.8.1 + - prompt-toolkit==3.0.39 + - protobuf==4.23.3 + - pure-eval==0.2.2 + - pygments==2.15.1 + - pymc==5.5.0 + - pyqt5==5.15.4 + - pyqt5-plugins==5.15.4.2.2 + - pyqt5-qt5==5.15.2 + - pyqt5-sip==12.9.0 + - pyqtwebengine==5.15.5 + - pyqtwebengine-qt5==5.15.2 + - pytensor==2.12.3 + - python-dotenv==0.19.2 + - pywin32==306 + - pyzmq==25.1.0 + - qt5-applications==5.15.2.2.2 + - qt5-tools==5.15.2.1.2 + - rich==13.4.2 + - scikit-learn==1.2.2 + - scipy==1.10.1 + - seaborn==0.12.2 + - sentry-sdk==1.26.0 + - setproctitle==1.3.2 + - setuptools==68.1.2 + - shiboken2==5.15.2 + - smmap==5.0.0 + - spyder-kernels==2.4.4 + - ssm-simulators==0.3.1 + - stack-data==0.6.2 + - sympy==1.12 + - tensorstore==0.1.39 + - threadpoolctl==3.1.0 + - torch==2.0.1 + - tornado==6.3.2 + - traitlets==5.9.0 + - typing-extensions==4.6.3 + - tzdata==2023.3 + - urllib3==1.26.16 + - wandb==0.15.4 + - xarray==2023.6.0 +prefix: C:\ProgramData\Anaconda3\envs\pyPower diff --git a/wandb/offline-run-20231227_143529-614ttptn/files/requirements.txt b/wandb/offline-run-20231227_143529-614ttptn/files/requirements.txt new file mode 100644 index 0000000..9f02cef --- /dev/null +++ b/wandb/offline-run-20231227_143529-614ttptn/files/requirements.txt @@ -0,0 +1,260 @@ +absl-py==1.4.0 +alabaster==0.7.12 +appdirs==1.4.4 +argh==0.26.2 +argparse==1.4.0 +arrow==0.13.1 +arviz==0.13.0 +astroid==2.6.6 +asttokens==2.2.1 +async-generator==1.10 +atomicwrites==1.4.0 +attrs==21.2.0 +autopep8==1.5.7 +babel==2.9.1 +backcall==0.2.0 +bcrypt==3.2.0 +beautifulsoup4==4.12.2 +binaryornot==0.4.4 +black==19.10b0 +bleach==4.0.0 +boltons==23.0.0 +bottleneck==1.3.2 +brotlipy==0.7.0 +cached-property==1.5.2 +cachetools==4.2.2 +certifi==2023.5.7 +cffi==1.15.0 +cftime==1.6.2 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MainThread:28644 [wandb_init.py:init():736] communicating run to backend with 60.0 second timeout +2023-12-27 14:35:29,688 INFO MainThread:28644 [wandb_init.py:init():787] starting run threads in backend +2023-12-27 14:35:36,006 INFO MainThread:28644 [wandb_run.py:_console_start():2155] atexit reg +2023-12-27 14:35:36,006 INFO MainThread:28644 [wandb_run.py:_redirect():2010] redirect: SettingsConsole.WRAP_RAW +2023-12-27 14:35:36,006 INFO MainThread:28644 [wandb_run.py:_redirect():2075] Wrapping output streams. +2023-12-27 14:35:36,006 INFO MainThread:28644 [wandb_run.py:_redirect():2100] Redirects installed. +2023-12-27 14:35:36,007 INFO MainThread:28644 [wandb_init.py:init():828] run started, returning control to user process +2023-12-27 14:35:36,008 INFO MainThread:28644 [wandb_watch.py:watch():51] Watching diff --git a/wandb/offline-run-20231227_143529-614ttptn/run-614ttptn.wandb b/wandb/offline-run-20231227_143529-614ttptn/run-614ttptn.wandb new file mode 100644 index 0000000..f15ee82 Binary files /dev/null and b/wandb/offline-run-20231227_143529-614ttptn/run-614ttptn.wandb differ diff --git a/wandb/offline-run-20231227_152929-dyhnxyxb/files/conda-environment.yaml b/wandb/offline-run-20231227_152929-dyhnxyxb/files/conda-environment.yaml new file mode 100644 index 0000000..15661b8 --- /dev/null +++ b/wandb/offline-run-20231227_152929-dyhnxyxb/files/conda-environment.yaml @@ -0,0 +1,350 @@ +name: base +channels: + - conda-forge + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main + - defaults + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/fastai/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda/ +dependencies: + - alabaster=0.7.12=pyhd3eb1b0_0 + - appdirs=1.4.4=pyhd3eb1b0_0 + - argh=0.26.2=py39haa95532_0 + - arrow=0.13.1=py39haa95532_0 + - astroid=2.6.6=py39haa95532_0 + - async_generator=1.10=pyhd3eb1b0_0 + - atomicwrites=1.4.0=py_0 + - attrs=21.2.0=pyhd3eb1b0_0 + - autopep8=1.5.7=pyhd3eb1b0_0 + - babel=2.9.1=pyhd3eb1b0_0 + - backcall=0.2.0=pyhd3eb1b0_0 + - bcrypt=3.2.0=py39h196d8e1_0 + - beautifulsoup4=4.12.2=py39haa95532_0 + - binaryornot=0.4.4=pyhd3eb1b0_1 + - black=19.10b0=py_0 + - blas=1.0=mkl + - bleach=4.0.0=pyhd3eb1b0_0 + - boltons=23.0.0=py39haa95532_0 + - bottleneck=1.3.2=py39h7cc1a96_1 + - brotli=1.0.9=ha925a31_2 + - brotlipy=0.7.0=py39h2bbff1b_1003 + - bzip2=1.0.8=he774522_0 + - ca-certificates=2023.05.30=haa95532_0 + - certifi=2023.5.7=py39haa95532_0 + - cffi=1.15.0=py39h2bbff1b_0 + - cftime=1.6.2=py39h080aedc_0 + - chardet=4.0.0=py39haa95532_1003 + - charset-normalizer=2.0.4=pyhd3eb1b0_0 + - 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yapf=0.31.0=pyhd3eb1b0_0 + - zipp=3.6.0=pyhd3eb1b0_0 + - zlib=1.2.11=h62dcd97_4 + - zstandard=0.19.0=py39h2bbff1b_0 + - zstd=1.4.9=h19a0ad4_0 + - pip: + - absl-py==1.4.0 + - argparse==1.4.0 + - arviz==0.15.1 + - asttokens==2.2.1 + - cached-property==1.5.2 + - cachetools==5.3.1 + - chex==0.1.7 + - click==7.1.2 + - comm==0.1.3 + - cons==0.4.6 + - contourpy==1.1.0 + - cython==0.29.35 + - debugpy==1.6.7 + - dill==0.3.6 + - dm-tree==0.1.8 + - docker-pycreds==0.4.0 + - etils==1.3.0 + - executing==1.2.0 + - filelock==3.12.2 + - flax==0.6.11 + - frozendict==2.3.8 + - gitdb==4.0.10 + - gitpython==3.1.31 + - h5netcdf==1.2.0 + - h5py==3.9.0 + - hddm-wfpt==0.1.0 + - importlib-metadata==6.8.0 + - importlib-resources==5.12.0 + - ipykernel==6.24.0 + - ipython==8.14.0 + - jax==0.4.13 + - jaxlib==0.4.13 + - joblib==1.2.0 + - jupyter-client==8.3.0 + - jupyter-core==5.3.1 + - kabuki==0.6.5 + - lanfactory==0.3.0.dev0 + - markdown-it-py==3.0.0 + - matplotlib==3.7.2 + - mdurl==0.1.2 + - ml-dtypes==0.2.0 + - 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torch==2.0.1 + - tornado==6.3.2 + - traitlets==5.9.0 + - typing-extensions==4.6.3 + - tzdata==2023.3 + - urllib3==1.26.16 + - wandb==0.15.4 + - xarray==2023.6.0 +prefix: C:\ProgramData\Anaconda3\envs\pyPower diff --git a/wandb/offline-run-20231227_152929-dyhnxyxb/files/requirements.txt b/wandb/offline-run-20231227_152929-dyhnxyxb/files/requirements.txt new file mode 100644 index 0000000..9f02cef --- /dev/null +++ b/wandb/offline-run-20231227_152929-dyhnxyxb/files/requirements.txt @@ -0,0 +1,260 @@ +absl-py==1.4.0 +alabaster==0.7.12 +appdirs==1.4.4 +argh==0.26.2 +argparse==1.4.0 +arrow==0.13.1 +arviz==0.13.0 +astroid==2.6.6 +asttokens==2.2.1 +async-generator==1.10 +atomicwrites==1.4.0 +attrs==21.2.0 +autopep8==1.5.7 +babel==2.9.1 +backcall==0.2.0 +bcrypt==3.2.0 +beautifulsoup4==4.12.2 +binaryornot==0.4.4 +black==19.10b0 +bleach==4.0.0 +boltons==23.0.0 +bottleneck==1.3.2 +brotlipy==0.7.0 +cached-property==1.5.2 +cachetools==4.2.2 +certifi==2023.5.7 +cffi==1.15.0 +cftime==1.6.2 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handle_request: status_report +2023-12-27 15:36:33,581 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:36:38,645 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:36:38,645 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:36:43,683 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:36:43,683 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:36:48,706 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:36:48,706 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:36:53,731 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:36:53,731 DEBUG SenderThread:4764 [sender.py:send_request():396] 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[handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:37:55,833 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:37:55,833 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:37:55,833 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:37:57,363 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:37:57,370 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:37:57,371 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:37:57,371 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:37:57,373 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history 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handle_request: status_report +2023-12-27 15:38:39,846 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:38:44,905 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:38:44,906 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:38:49,950 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:38:49,950 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:38:55,010 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:38:55,010 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:39:00,069 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:39:00,069 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:39:05,123 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:39:05,125 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:39:10,183 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:39:10,183 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:39:15,228 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:39:15,228 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:39:20,254 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:39:20,254 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:39:25,290 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:39:25,290 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:39:30,309 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:39:30,309 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:39:35,356 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:39:35,356 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:39:40,394 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:39:40,394 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:39:45,454 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:39:45,454 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:39:50,493 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:39:50,493 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:39:55,532 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:39:55,532 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:39:55,885 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:00,578 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:40:00,578 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:40:03,277 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:03,283 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:03,283 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:03,283 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:03,283 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:03,283 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:04,163 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:04,163 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:04,163 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:04,163 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history 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handle_request: partial_history +2023-12-27 15:40:13,303 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:13,303 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:13,303 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:14,913 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:15,700 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:40:15,700 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:40:20,752 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:40:20,752 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:40:25,007 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:25,013 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:25,015 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:25,015 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:25,015 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:25,015 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:25,780 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:40:25,780 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:40:26,574 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:26,574 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:26,574 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:26,574 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:26,574 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:26,574 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:30,827 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:40:30,827 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:40:35,866 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:40:35,866 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:40:37,323 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:37,323 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:37,323 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:37,323 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:37,323 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:37,323 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:38,606 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:40:40,913 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:40:40,913 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:40:45,980 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:40:45,980 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:40:51,024 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:40:51,024 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:40:56,087 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:40:56,088 DEBUG SenderThread:4764 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:41:00,196 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:41:00,196 DEBUG HandlerThread:4764 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:41:00,196 DEBUG 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D:\horiz\IMPORTANT\0study_graduate\Pro_COMPASS\COMPASS_DDM\wandb\offline-run-20231227_152929-dyhnxyxb\run-dyhnxyxb.wandb +2023-12-27 15:41:04,582 INFO SenderThread:4764 [sender.py:finish():1526] shutting down sender diff --git a/wandb/offline-run-20231227_152929-dyhnxyxb/logs/debug.log b/wandb/offline-run-20231227_152929-dyhnxyxb/logs/debug.log new file mode 100644 index 0000000..75c3059 --- /dev/null +++ b/wandb/offline-run-20231227_152929-dyhnxyxb/logs/debug.log @@ -0,0 +1,32 @@ +2023-12-27 15:29:29,046 INFO MainThread:23348 [wandb_setup.py:_flush():76] Current SDK version is 0.15.4 +2023-12-27 15:29:29,046 INFO MainThread:23348 [wandb_setup.py:_flush():76] Configure stats pid to 23348 +2023-12-27 15:29:29,046 INFO MainThread:23348 [wandb_setup.py:_flush():76] Loading settings from C:\Users\horiz\.config\wandb\settings +2023-12-27 15:29:29,046 INFO MainThread:23348 [wandb_setup.py:_flush():76] Loading settings from 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a/wandb/offline-run-20231227_155322-itcp0yh7/files/conda-environment.yaml b/wandb/offline-run-20231227_155322-itcp0yh7/files/conda-environment.yaml new file mode 100644 index 0000000..15661b8 --- /dev/null +++ b/wandb/offline-run-20231227_155322-itcp0yh7/files/conda-environment.yaml @@ -0,0 +1,350 @@ +name: base +channels: + - conda-forge + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main + - defaults + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/fastai/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda/ +dependencies: + - alabaster=0.7.12=pyhd3eb1b0_0 + - appdirs=1.4.4=pyhd3eb1b0_0 + - argh=0.26.2=py39haa95532_0 + - arrow=0.13.1=py39haa95532_0 + - astroid=2.6.6=py39haa95532_0 + - async_generator=1.10=pyhd3eb1b0_0 + - atomicwrites=1.4.0=py_0 + - attrs=21.2.0=pyhd3eb1b0_0 + - autopep8=1.5.7=pyhd3eb1b0_0 + - babel=2.9.1=pyhd3eb1b0_0 + - backcall=0.2.0=pyhd3eb1b0_0 + - bcrypt=3.2.0=py39h196d8e1_0 + - beautifulsoup4=4.12.2=py39haa95532_0 + - binaryornot=0.4.4=pyhd3eb1b0_1 + - black=19.10b0=py_0 + - blas=1.0=mkl + - bleach=4.0.0=pyhd3eb1b0_0 + - boltons=23.0.0=py39haa95532_0 + - bottleneck=1.3.2=py39h7cc1a96_1 + - brotli=1.0.9=ha925a31_2 + - brotlipy=0.7.0=py39h2bbff1b_1003 + - bzip2=1.0.8=he774522_0 + - ca-certificates=2023.05.30=haa95532_0 + - certifi=2023.5.7=py39haa95532_0 + - cffi=1.15.0=py39h2bbff1b_0 + - cftime=1.6.2=py39h080aedc_0 + - chardet=4.0.0=py39haa95532_1003 + - charset-normalizer=2.0.4=pyhd3eb1b0_0 + - cloudpickle=2.0.0=pyhd3eb1b0_0 + - colorama=0.4.4=pyhd3eb1b0_0 + - conda=23.3.1=py39haa95532_0 + - conda-build=3.25.0=py39haa95532_0 + - conda-index=0.2.3=py39haa95532_0 + - conda-package-handling=2.1.0=py39haa95532_0 + - conda-package-streaming=0.8.0=py39haa95532_0 + - cookiecutter=1.7.2=pyhd3eb1b0_0 + - cryptography=35.0.0=py39h71e12ea_0 + - curl=7.79.1=h789b8ee_1 + - cycler=0.10.0=py39haa95532_0 + - decorator=5.1.0=pyhd3eb1b0_0 + - defusedxml=0.7.1=pyhd3eb1b0_0 + - diff-match-patch=20200713=pyhd3eb1b0_0 + - docutils=0.17.1=py39haa95532_1 + - entrypoints=0.3=py39haa95532_0 + - etuples=0.3.9=pyhd8ed1ab_0 + - fastprogress=1.0.0=pyhb85f177_0 + - flake8=3.9.2=pyhd3eb1b0_0 + - fonttools=4.25.0=pyhd3eb1b0_0 + - freetype=2.10.4=hd328e21_0 + - glob2=0.7=pyhd3eb1b0_0 + - hdf4=4.2.15=h0e5069d_3 + - hdf5=1.12.1=nompi_h2a0e4a3_100 + - icc_rt=2019.0.0=h0cc432a_1 + - icu=58.2=ha925a31_3 + - idna=3.2=pyhd3eb1b0_0 + - imagesize=1.2.0=pyhd3eb1b0_0 + - importlib_metadata=4.8.1=hd3eb1b0_0 + - inflection=0.5.1=py39haa95532_0 + - intel-openmp=2021.4.0=haa95532_3556 + - intervaltree=3.1.0=pyhd3eb1b0_0 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+ - zstd=1.4.9=h19a0ad4_0 + - pip: + - absl-py==1.4.0 + - argparse==1.4.0 + - arviz==0.15.1 + - asttokens==2.2.1 + - cached-property==1.5.2 + - cachetools==5.3.1 + - chex==0.1.7 + - click==7.1.2 + - comm==0.1.3 + - cons==0.4.6 + - contourpy==1.1.0 + - cython==0.29.35 + - debugpy==1.6.7 + - dill==0.3.6 + - dm-tree==0.1.8 + - docker-pycreds==0.4.0 + - etils==1.3.0 + - executing==1.2.0 + - filelock==3.12.2 + - flax==0.6.11 + - frozendict==2.3.8 + - gitdb==4.0.10 + - gitpython==3.1.31 + - h5netcdf==1.2.0 + - h5py==3.9.0 + - hddm-wfpt==0.1.0 + - importlib-metadata==6.8.0 + - importlib-resources==5.12.0 + - ipykernel==6.24.0 + - ipython==8.14.0 + - jax==0.4.13 + - jaxlib==0.4.13 + - joblib==1.2.0 + - jupyter-client==8.3.0 + - jupyter-core==5.3.1 + - kabuki==0.6.5 + - lanfactory==0.3.0.dev0 + - markdown-it-py==3.0.0 + - matplotlib==3.7.2 + - mdurl==0.1.2 + - ml-dtypes==0.2.0 + - mpmath==1.3.0 + - msgpack==1.0.5 + - networkx==3.1 + - numpy==1.25.1 + - onnx==1.14.0 + - openturns==1.21 + - opt-einsum==3.3.0 + - optax==0.1.5 + - orbax-checkpoint==0.2.6 + - pandas==2.0.2 + - pathtools==0.1.2 + - patsy==0.5.3 + - pillow==10.0.0 + - platformdirs==3.8.1 + - prompt-toolkit==3.0.39 + - protobuf==4.23.3 + - pure-eval==0.2.2 + - pygments==2.15.1 + - pymc==5.5.0 + - pyqt5==5.15.4 + - pyqt5-plugins==5.15.4.2.2 + - pyqt5-qt5==5.15.2 + - pyqt5-sip==12.9.0 + - pyqtwebengine==5.15.5 + - pyqtwebengine-qt5==5.15.2 + - pytensor==2.12.3 + - python-dotenv==0.19.2 + - pywin32==306 + - pyzmq==25.1.0 + - qt5-applications==5.15.2.2.2 + - qt5-tools==5.15.2.1.2 + - rich==13.4.2 + - scikit-learn==1.2.2 + - scipy==1.10.1 + - seaborn==0.12.2 + - sentry-sdk==1.26.0 + - setproctitle==1.3.2 + - setuptools==68.1.2 + - shiboken2==5.15.2 + - smmap==5.0.0 + - spyder-kernels==2.4.4 + - ssm-simulators==0.3.1 + - stack-data==0.6.2 + - sympy==1.12 + - tensorstore==0.1.39 + - threadpoolctl==3.1.0 + - torch==2.0.1 + - tornado==6.3.2 + - traitlets==5.9.0 + - typing-extensions==4.6.3 + - tzdata==2023.3 + - urllib3==1.26.16 + - wandb==0.15.4 + - xarray==2023.6.0 +prefix: C:\ProgramData\Anaconda3\envs\pyPower diff --git a/wandb/offline-run-20231227_155322-itcp0yh7/files/requirements.txt b/wandb/offline-run-20231227_155322-itcp0yh7/files/requirements.txt new file mode 100644 index 0000000..9f02cef --- /dev/null +++ b/wandb/offline-run-20231227_155322-itcp0yh7/files/requirements.txt @@ -0,0 +1,260 @@ +absl-py==1.4.0 +alabaster==0.7.12 +appdirs==1.4.4 +argh==0.26.2 +argparse==1.4.0 +arrow==0.13.1 +arviz==0.13.0 +astroid==2.6.6 +asttokens==2.2.1 +async-generator==1.10 +atomicwrites==1.4.0 +attrs==21.2.0 +autopep8==1.5.7 +babel==2.9.1 +backcall==0.2.0 +bcrypt==3.2.0 +beautifulsoup4==4.12.2 +binaryornot==0.4.4 +black==19.10b0 +bleach==4.0.0 +boltons==23.0.0 +bottleneck==1.3.2 +brotlipy==0.7.0 +cached-property==1.5.2 +cachetools==4.2.2 +certifi==2023.5.7 +cffi==1.15.0 +cftime==1.6.2 +chardet==4.0.0 +charset-normalizer==2.0.4 +chex==0.1.7 +click==7.1.2 +cloudpickle==2.0.0 +colorama==0.4.4 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15:55:19,150 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:55:22,014 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:22,014 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:22,014 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:22,014 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:22,014 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:22,014 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:22,154 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:22,154 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:22,154 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:22,154 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:22,154 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:22,154 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:24,170 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:55:24,170 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:55:29,225 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:55:29,225 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:55:32,514 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:32,514 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:32,514 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:32,514 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:32,514 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:32,514 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:34,113 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:34,274 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:55:34,275 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: status_report +2023-12-27 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handle_request: partial_history +2023-12-27 15:55:42,974 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:42,974 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:42,974 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:42,974 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:42,974 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:55:44,392 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:55:44,392 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:55:49,422 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:55:49,422 DEBUG SenderThread:9096 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HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:02,066 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:02,066 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:03,316 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:03,316 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:03,316 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:03,324 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:03,324 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:03,324 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: 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[handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:13,364 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:14,601 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:56:14,601 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:56:15,045 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:19,649 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:56:19,649 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:56:24,124 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:24,124 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:24,134 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:24,134 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:24,134 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:24,134 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:24,684 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:56:24,684 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:56:24,974 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:24,974 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:24,974 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: 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[handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:33,964 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:33,964 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:34,784 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:56:34,784 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:56:35,354 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:39,802 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:56:39,802 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:56:44,834 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:56:44,835 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:56:49,874 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:56:49,874 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:56:53,697 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:53,697 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:53,697 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:53,697 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:53,697 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:53,704 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:54,938 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:56:54,938 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:56:55,355 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 15:56:55,368 DEBUG SenderThread:9096 [sender.py:send():369] send: exit +2023-12-27 15:56:55,368 INFO SenderThread:9096 [sender.py:send_exit():574] handling exit code: 0 +2023-12-27 15:56:55,368 INFO SenderThread:9096 [sender.py:send_exit():576] handling runtime: 212 +2023-12-27 15:56:55,368 INFO SenderThread:9096 [sender.py:_save_file():1354] saving file wandb-summary.json with policy end +2023-12-27 15:56:55,368 INFO SenderThread:9096 [sender.py:send_exit():582] send defer +2023-12-27 15:56:55,372 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: defer +2023-12-27 15:56:55,372 INFO HandlerThread:9096 [handler.py:handle_request_defer():170] handle defer: 0 +2023-12-27 15:56:55,372 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: defer +2023-12-27 15:56:55,372 INFO SenderThread:9096 [sender.py:send_request_defer():598] handle sender defer: 0 +2023-12-27 15:56:55,372 INFO SenderThread:9096 [sender.py:transition_state():602] send defer: 1 +2023-12-27 15:56:55,372 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: defer +2023-12-27 15:56:55,372 INFO HandlerThread:9096 [handler.py:handle_request_defer():170] handle defer: 1 +2023-12-27 15:56:55,372 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: defer +2023-12-27 15:56:55,372 INFO SenderThread:9096 [sender.py:send_request_defer():598] handle sender defer: 1 +2023-12-27 15:56:55,372 INFO SenderThread:9096 [sender.py:transition_state():602] send defer: 2 +2023-12-27 15:56:55,372 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: defer +2023-12-27 15:56:55,372 INFO HandlerThread:9096 [handler.py:handle_request_defer():170] handle defer: 2 +2023-12-27 15:56:55,372 INFO HandlerThread:9096 [system_monitor.py:finish():190] Stopping system monitor +2023-12-27 15:56:55,372 DEBUG SystemMonitor:9096 [system_monitor.py:_start():166] Finished system metrics aggregation loop +2023-12-27 15:56:55,375 DEBUG SystemMonitor:9096 [system_monitor.py:_start():170] Publishing last batch of metrics +2023-12-27 15:56:55,374 INFO HandlerThread:9096 [interfaces.py:finish():202] Joined cpu monitor +2023-12-27 15:56:55,375 INFO HandlerThread:9096 [interfaces.py:finish():202] Joined disk monitor +2023-12-27 15:56:55,375 INFO HandlerThread:9096 [interfaces.py:finish():202] Joined memory monitor +2023-12-27 15:56:55,375 INFO HandlerThread:9096 [interfaces.py:finish():202] Joined network monitor +2023-12-27 15:56:55,375 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: defer +2023-12-27 15:56:55,375 INFO SenderThread:9096 [sender.py:send_request_defer():598] handle sender defer: 2 +2023-12-27 15:56:55,375 INFO SenderThread:9096 [sender.py:transition_state():602] send defer: 3 +2023-12-27 15:56:55,375 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: defer +2023-12-27 15:56:55,375 INFO HandlerThread:9096 [handler.py:handle_request_defer():170] handle defer: 3 +2023-12-27 15:56:55,380 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: defer +2023-12-27 15:56:55,380 INFO SenderThread:9096 [sender.py:send_request_defer():598] handle sender defer: 3 +2023-12-27 15:56:55,380 INFO SenderThread:9096 [sender.py:transition_state():602] send defer: 4 +2023-12-27 15:56:55,380 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: defer +2023-12-27 15:56:55,380 INFO HandlerThread:9096 [handler.py:handle_request_defer():170] handle defer: 4 +2023-12-27 15:56:55,380 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: defer +2023-12-27 15:56:55,380 INFO SenderThread:9096 [sender.py:send_request_defer():598] handle sender defer: 4 +2023-12-27 15:56:55,380 INFO SenderThread:9096 [sender.py:transition_state():602] send defer: 5 +2023-12-27 15:56:55,380 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: defer +2023-12-27 15:56:55,380 INFO HandlerThread:9096 [handler.py:handle_request_defer():170] handle defer: 5 +2023-12-27 15:56:55,384 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: defer +2023-12-27 15:56:55,384 INFO SenderThread:9096 [sender.py:send_request_defer():598] handle sender defer: 5 +2023-12-27 15:56:55,384 INFO SenderThread:9096 [sender.py:transition_state():602] send defer: 6 +2023-12-27 15:56:55,384 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: defer +2023-12-27 15:56:55,384 INFO HandlerThread:9096 [handler.py:handle_request_defer():170] handle defer: 6 +2023-12-27 15:56:55,384 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: defer +2023-12-27 15:56:55,384 INFO SenderThread:9096 [sender.py:send_request_defer():598] handle sender defer: 6 +2023-12-27 15:56:55,384 INFO SenderThread:9096 [sender.py:transition_state():602] send defer: 7 +2023-12-27 15:56:55,384 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 15:56:55,384 DEBUG HandlerThread:9096 [handler.py:handle_request():144] handle_request: defer +2023-12-27 15:56:55,384 INFO HandlerThread:9096 [handler.py:handle_request_defer():170] handle defer: 7 +2023-12-27 15:56:55,384 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: status_report +2023-12-27 15:56:55,384 DEBUG SenderThread:9096 [sender.py:send_request():396] send_request: defer +2023-12-27 15:56:55,384 INFO SenderThread:9096 [sender.py:send_request_defer():598] handle sender defer: 7 +2023-12-27 15:56:55,384 INFO SenderThread:9096 [sender.py:transition_state():602] send defer: 8 +2023-12-27 15:56:55,384 DEBUG 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https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/fastai/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda/ +dependencies: + - alabaster=0.7.12=pyhd3eb1b0_0 + - appdirs=1.4.4=pyhd3eb1b0_0 + - argh=0.26.2=py39haa95532_0 + - arrow=0.13.1=py39haa95532_0 + - astroid=2.6.6=py39haa95532_0 + - async_generator=1.10=pyhd3eb1b0_0 + - atomicwrites=1.4.0=py_0 + - attrs=21.2.0=pyhd3eb1b0_0 + - autopep8=1.5.7=pyhd3eb1b0_0 + - babel=2.9.1=pyhd3eb1b0_0 + - backcall=0.2.0=pyhd3eb1b0_0 + - bcrypt=3.2.0=py39h196d8e1_0 + - beautifulsoup4=4.12.2=py39haa95532_0 + - binaryornot=0.4.4=pyhd3eb1b0_1 + - black=19.10b0=py_0 + - blas=1.0=mkl + - bleach=4.0.0=pyhd3eb1b0_0 + - boltons=23.0.0=py39haa95532_0 + - bottleneck=1.3.2=py39h7cc1a96_1 + - brotli=1.0.9=ha925a31_2 + - brotlipy=0.7.0=py39h2bbff1b_1003 + - bzip2=1.0.8=he774522_0 + - ca-certificates=2023.05.30=haa95532_0 + - certifi=2023.5.7=py39haa95532_0 + - cffi=1.15.0=py39h2bbff1b_0 + - cftime=1.6.2=py39h080aedc_0 + - chardet=4.0.0=py39haa95532_1003 + - charset-normalizer=2.0.4=pyhd3eb1b0_0 + - cloudpickle=2.0.0=pyhd3eb1b0_0 + - colorama=0.4.4=pyhd3eb1b0_0 + - conda=23.3.1=py39haa95532_0 + - conda-build=3.25.0=py39haa95532_0 + - conda-index=0.2.3=py39haa95532_0 + - conda-package-handling=2.1.0=py39haa95532_0 + - conda-package-streaming=0.8.0=py39haa95532_0 + - cookiecutter=1.7.2=pyhd3eb1b0_0 + - cryptography=35.0.0=py39h71e12ea_0 + - curl=7.79.1=h789b8ee_1 + - cycler=0.10.0=py39haa95532_0 + - decorator=5.1.0=pyhd3eb1b0_0 + - defusedxml=0.7.1=pyhd3eb1b0_0 + - diff-match-patch=20200713=pyhd3eb1b0_0 + - docutils=0.17.1=py39haa95532_1 + - entrypoints=0.3=py39haa95532_0 + - etuples=0.3.9=pyhd8ed1ab_0 + - 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partial_history +2023-12-27 19:31:18,585 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:18,585 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:18,585 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:19,154 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:19,154 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:19,154 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:19,154 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:19,154 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:19,154 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:19,710 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:19,710 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:19,710 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:19,710 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:19,710 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:19,710 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:20,855 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:23,018 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 19:31:23,018 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: status_report +2023-12-27 19:31:27,888 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:27,888 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:27,888 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:27,888 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:27,888 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:27,888 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:28,043 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:28,043 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:28,043 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:28,043 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:28,043 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:28,043 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:28,058 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 19:31:28,058 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: status_report +2023-12-27 19:31:29,364 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:29,364 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:29,364 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:29,364 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:29,364 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:29,364 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:29,521 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:29,521 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:29,521 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:29,521 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:29,521 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:29,521 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:30,815 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:30,815 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:30,815 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:30,815 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:30,815 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:30,830 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:31,034 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:31,034 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:31,034 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:31,034 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:31,034 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:31,034 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:33,093 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 19:31:33,093 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: status_report +2023-12-27 19:31:38,129 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 19:31:38,129 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: status_report +2023-12-27 19:31:38,861 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:38,861 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:38,861 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:38,861 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:38,861 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:38,861 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:39,657 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:39,657 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:39,657 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:39,657 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:39,673 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:39,673 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:40,465 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:40,465 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:40,465 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:40,465 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:40,465 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:40,468 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:42,101 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:31:42,105 DEBUG SenderThread:31404 [sender.py:send():369] send: exit +2023-12-27 19:31:42,105 INFO SenderThread:31404 [sender.py:send_exit():574] handling exit code: 0 +2023-12-27 19:31:42,105 INFO SenderThread:31404 [sender.py:send_exit():576] handling runtime: 210 +2023-12-27 19:31:42,105 INFO SenderThread:31404 [sender.py:_save_file():1354] saving file wandb-summary.json with policy end +2023-12-27 19:31:42,105 INFO SenderThread:31404 [sender.py:send_exit():582] send defer +2023-12-27 19:31:42,105 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: defer +2023-12-27 19:31:42,105 INFO HandlerThread:31404 [handler.py:handle_request_defer():170] handle defer: 0 +2023-12-27 19:31:42,105 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: defer +2023-12-27 19:31:42,105 INFO SenderThread:31404 [sender.py:send_request_defer():598] handle sender defer: 0 +2023-12-27 19:31:42,105 INFO SenderThread:31404 [sender.py:transition_state():602] send defer: 1 +2023-12-27 19:31:42,105 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: defer +2023-12-27 19:31:42,105 INFO HandlerThread:31404 [handler.py:handle_request_defer():170] handle defer: 1 +2023-12-27 19:31:42,105 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: defer +2023-12-27 19:31:42,116 INFO SenderThread:31404 [sender.py:send_request_defer():598] handle sender defer: 1 +2023-12-27 19:31:42,116 INFO SenderThread:31404 [sender.py:transition_state():602] send defer: 2 +2023-12-27 19:31:42,116 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: defer +2023-12-27 19:31:42,116 INFO HandlerThread:31404 [handler.py:handle_request_defer():170] handle defer: 2 +2023-12-27 19:31:42,116 INFO HandlerThread:31404 [system_monitor.py:finish():190] Stopping system monitor +2023-12-27 19:31:42,116 DEBUG SystemMonitor:31404 [system_monitor.py:_start():166] Finished system metrics aggregation loop +2023-12-27 19:31:42,117 DEBUG SystemMonitor:31404 [system_monitor.py:_start():170] Publishing last batch of metrics +2023-12-27 19:31:42,118 INFO HandlerThread:31404 [interfaces.py:finish():202] Joined cpu monitor +2023-12-27 19:31:42,118 INFO HandlerThread:31404 [interfaces.py:finish():202] Joined disk monitor +2023-12-27 19:31:42,118 INFO HandlerThread:31404 [interfaces.py:finish():202] Joined memory monitor +2023-12-27 19:31:42,119 INFO HandlerThread:31404 [interfaces.py:finish():202] Joined network monitor +2023-12-27 19:31:42,120 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: defer +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:send_request_defer():598] handle sender defer: 2 +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:transition_state():602] send defer: 3 +2023-12-27 19:31:42,120 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: defer +2023-12-27 19:31:42,120 INFO HandlerThread:31404 [handler.py:handle_request_defer():170] handle defer: 3 +2023-12-27 19:31:42,120 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: defer +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:send_request_defer():598] handle sender defer: 3 +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:transition_state():602] send defer: 4 +2023-12-27 19:31:42,120 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: defer +2023-12-27 19:31:42,120 INFO HandlerThread:31404 [handler.py:handle_request_defer():170] handle defer: 4 +2023-12-27 19:31:42,120 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: defer +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:send_request_defer():598] handle sender defer: 4 +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:transition_state():602] send defer: 5 +2023-12-27 19:31:42,120 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: defer +2023-12-27 19:31:42,120 INFO HandlerThread:31404 [handler.py:handle_request_defer():170] handle defer: 5 +2023-12-27 19:31:42,120 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: defer +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:send_request_defer():598] handle sender defer: 5 +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:transition_state():602] send defer: 6 +2023-12-27 19:31:42,120 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: defer +2023-12-27 19:31:42,120 INFO HandlerThread:31404 [handler.py:handle_request_defer():170] handle defer: 6 +2023-12-27 19:31:42,120 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: defer +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:send_request_defer():598] handle sender defer: 6 +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:transition_state():602] send defer: 7 +2023-12-27 19:31:42,120 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 19:31:42,120 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: defer +2023-12-27 19:31:42,120 INFO HandlerThread:31404 [handler.py:handle_request_defer():170] handle defer: 7 +2023-12-27 19:31:42,120 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: status_report +2023-12-27 19:31:42,120 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: defer +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:send_request_defer():598] handle sender defer: 7 +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:transition_state():602] send defer: 8 +2023-12-27 19:31:42,120 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: defer +2023-12-27 19:31:42,120 INFO HandlerThread:31404 [handler.py:handle_request_defer():170] handle defer: 8 +2023-12-27 19:31:42,120 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: defer +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:send_request_defer():598] handle sender defer: 8 +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:transition_state():602] send defer: 9 +2023-12-27 19:31:42,120 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: defer +2023-12-27 19:31:42,120 INFO HandlerThread:31404 [handler.py:handle_request_defer():170] handle defer: 9 +2023-12-27 19:31:42,120 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: defer +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:send_request_defer():598] handle sender defer: 9 +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:transition_state():602] send defer: 10 +2023-12-27 19:31:42,120 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: defer +2023-12-27 19:31:42,120 INFO HandlerThread:31404 [handler.py:handle_request_defer():170] handle defer: 10 +2023-12-27 19:31:42,120 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: defer +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:send_request_defer():598] handle sender defer: 10 +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:transition_state():602] send defer: 11 +2023-12-27 19:31:42,120 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: defer +2023-12-27 19:31:42,120 INFO HandlerThread:31404 [handler.py:handle_request_defer():170] handle defer: 11 +2023-12-27 19:31:42,120 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: defer +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:send_request_defer():598] handle sender defer: 11 +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:transition_state():602] send defer: 12 +2023-12-27 19:31:42,120 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: defer +2023-12-27 19:31:42,120 INFO HandlerThread:31404 [handler.py:handle_request_defer():170] handle defer: 12 +2023-12-27 19:31:42,120 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: defer +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:send_request_defer():598] handle sender defer: 12 +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:transition_state():602] send defer: 13 +2023-12-27 19:31:42,120 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: defer +2023-12-27 19:31:42,120 INFO HandlerThread:31404 [handler.py:handle_request_defer():170] handle defer: 13 +2023-12-27 19:31:42,120 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: defer +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:send_request_defer():598] handle sender defer: 13 +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:transition_state():602] send defer: 14 +2023-12-27 19:31:42,120 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: defer +2023-12-27 19:31:42,120 DEBUG SenderThread:31404 [sender.py:send():369] send: final +2023-12-27 19:31:42,120 INFO HandlerThread:31404 [handler.py:handle_request_defer():170] handle defer: 14 +2023-12-27 19:31:42,120 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: defer +2023-12-27 19:31:42,120 INFO SenderThread:31404 [sender.py:send_request_defer():598] handle sender defer: 14 +2023-12-27 19:31:42,133 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: poll_exit +2023-12-27 19:31:42,133 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: server_info +2023-12-27 19:31:42,133 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: get_summary +2023-12-27 19:31:42,134 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: sampled_history +2023-12-27 19:31:42,134 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: poll_exit +2023-12-27 19:31:42,136 DEBUG SenderThread:31404 [sender.py:send_request():396] send_request: server_info +2023-12-27 19:31:42,138 DEBUG HandlerThread:31404 [handler.py:handle_request():144] handle_request: shutdown +2023-12-27 19:31:42,138 INFO HandlerThread:31404 [handler.py:finish():854] shutting down handler +2023-12-27 19:31:43,139 INFO SenderThread:31404 [sender.py:finish():1526] shutting down sender +2023-12-27 19:31:43,142 INFO WriterThread:31404 [datastore.py:close():298] close: D:\horiz\IMPORTANT\0study_graduate\Pro_COMPASS\COMPASS_DDM\wandb\offline-run-20231227_192811-0ptgmlv6\run-0ptgmlv6.wandb diff --git a/wandb/offline-run-20231227_192811-0ptgmlv6/logs/debug.log b/wandb/offline-run-20231227_192811-0ptgmlv6/logs/debug.log new file mode 100644 index 0000000..5148885 --- /dev/null +++ b/wandb/offline-run-20231227_192811-0ptgmlv6/logs/debug.log @@ -0,0 +1,32 @@ +2023-12-27 19:28:11,128 INFO MainThread:9952 [wandb_setup.py:_flush():76] Current SDK version is 0.15.4 +2023-12-27 19:28:11,129 INFO MainThread:9952 [wandb_setup.py:_flush():76] Configure stats pid to 9952 +2023-12-27 19:28:11,129 INFO MainThread:9952 [wandb_setup.py:_flush():76] Loading settings from C:\Users\horiz\.config\wandb\settings +2023-12-27 19:28:11,130 INFO MainThread:9952 [wandb_setup.py:_flush():76] Loading settings from D:\horiz\IMPORTANT\0study_graduate\Pro_COMPASS\COMPASS_DDM\wandb\settings +2023-12-27 19:28:11,130 INFO MainThread:9952 [wandb_setup.py:_flush():76] Loading settings from environment variables: {} +2023-12-27 19:28:11,130 INFO MainThread:9952 [wandb_setup.py:_flush():76] Applying setup settings: {'_disable_service': False} +2023-12-27 19:28:11,140 INFO MainThread:9952 [wandb_setup.py:_flush():76] Inferring run settings from compute environment: {'program_relpath': 'LAN\\trainLAN.py', 'program': 'd:\\horiz\\IMPORTANT\\0study_graduate\\Pro_COMPASS\\COMPASS_DDM\\LAN\\trainLAN.py'} +2023-12-27 19:28:11,140 INFO MainThread:9952 [wandb_setup.py:_flush():76] Applying login settings: {'mode': 'offline'} +2023-12-27 19:28:11,140 INFO MainThread:9952 [wandb_init.py:_log_setup():507] Logging user logs to D:\horiz\IMPORTANT\0study_graduate\Pro_COMPASS\COMPASS_DDM\wandb\offline-run-20231227_192811-0ptgmlv6\logs\debug.log +2023-12-27 19:28:11,140 INFO MainThread:9952 [wandb_init.py:_log_setup():508] Logging internal logs to D:\horiz\IMPORTANT\0study_graduate\Pro_COMPASS\COMPASS_DDM\wandb\offline-run-20231227_192811-0ptgmlv6\logs\debug-internal.log +2023-12-27 19:28:11,140 INFO MainThread:9952 [wandb_init.py:init():547] calling init triggers +2023-12-27 19:28:11,140 INFO MainThread:9952 [wandb_init.py:init():554] wandb.init called with sweep_config: {} +config: {'cpu_batch_size': 128, 'gpu_batch_size': 256, 'n_epochs': 10, 'optimizer': 'adam', 'learning_rate': 0.002, 'lr_scheduler': 'reduce_on_plateau', 'lr_scheduler_params': {}, 'weight_decay': 0.0, 'loss': 'huber', 'save_history': True} +2023-12-27 19:28:11,140 INFO MainThread:9952 [wandb_init.py:init():596] starting backend +2023-12-27 19:28:11,140 INFO MainThread:9952 [wandb_init.py:init():600] setting up manager +2023-12-27 19:28:11,145 INFO MainThread:9952 [backend.py:_multiprocessing_setup():106] multiprocessing start_methods=spawn, using: spawn +2023-12-27 19:28:11,145 INFO MainThread:9952 [wandb_init.py:init():606] backend started and connected +2023-12-27 19:28:11,164 INFO MainThread:9952 [wandb_init.py:init():703] updated telemetry +2023-12-27 19:28:11,391 INFO MainThread:9952 [wandb_init.py:init():736] communicating run to backend with 60.0 second timeout +2023-12-27 19:28:11,404 INFO MainThread:9952 [wandb_init.py:init():787] starting run threads in backend +2023-12-27 19:28:17,537 INFO MainThread:9952 [wandb_run.py:_console_start():2155] atexit reg +2023-12-27 19:28:17,537 INFO MainThread:9952 [wandb_run.py:_redirect():2010] redirect: SettingsConsole.WRAP_RAW +2023-12-27 19:28:17,537 INFO MainThread:9952 [wandb_run.py:_redirect():2075] Wrapping output streams. +2023-12-27 19:28:17,537 INFO MainThread:9952 [wandb_run.py:_redirect():2100] Redirects installed. +2023-12-27 19:28:17,537 INFO MainThread:9952 [wandb_init.py:init():828] run started, returning control to user process +2023-12-27 19:28:17,537 INFO MainThread:9952 [wandb_watch.py:watch():51] Watching +2023-12-27 19:31:42,105 INFO MainThread:9952 [wandb_run.py:_finish():1890] finishing run projectid/0ptgmlv6 +2023-12-27 19:31:42,105 INFO MainThread:9952 [wandb_run.py:_atexit_cleanup():2124] got exitcode: 0 +2023-12-27 19:31:42,105 INFO MainThread:9952 [wandb_run.py:_restore():2107] restore +2023-12-27 19:31:42,105 INFO MainThread:9952 [wandb_run.py:_restore():2113] restore done +2023-12-27 19:31:43,146 INFO MainThread:9952 [wandb_run.py:_footer_history_summary_info():3467] rendering history +2023-12-27 19:31:43,146 INFO MainThread:9952 [wandb_run.py:_footer_history_summary_info():3499] rendering summary diff --git a/wandb/offline-run-20231227_192811-0ptgmlv6/run-0ptgmlv6.wandb b/wandb/offline-run-20231227_192811-0ptgmlv6/run-0ptgmlv6.wandb new file mode 100644 index 0000000..3e3492e Binary files /dev/null and b/wandb/offline-run-20231227_192811-0ptgmlv6/run-0ptgmlv6.wandb differ diff --git a/wandb/offline-run-20231227_195707-nzclrd64/files/conda-environment.yaml b/wandb/offline-run-20231227_195707-nzclrd64/files/conda-environment.yaml new file mode 100644 index 0000000..15661b8 --- /dev/null +++ b/wandb/offline-run-20231227_195707-nzclrd64/files/conda-environment.yaml @@ -0,0 +1,350 @@ +name: base +channels: + - conda-forge + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main + - defaults + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/fastai/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda/ +dependencies: + - alabaster=0.7.12=pyhd3eb1b0_0 + - appdirs=1.4.4=pyhd3eb1b0_0 + - argh=0.26.2=py39haa95532_0 + - arrow=0.13.1=py39haa95532_0 + - astroid=2.6.6=py39haa95532_0 + - async_generator=1.10=pyhd3eb1b0_0 + - atomicwrites=1.4.0=py_0 + - attrs=21.2.0=pyhd3eb1b0_0 + - autopep8=1.5.7=pyhd3eb1b0_0 + - babel=2.9.1=pyhd3eb1b0_0 + - backcall=0.2.0=pyhd3eb1b0_0 + - bcrypt=3.2.0=py39h196d8e1_0 + - beautifulsoup4=4.12.2=py39haa95532_0 + - binaryornot=0.4.4=pyhd3eb1b0_1 + - black=19.10b0=py_0 + - blas=1.0=mkl + - bleach=4.0.0=pyhd3eb1b0_0 + - boltons=23.0.0=py39haa95532_0 + - bottleneck=1.3.2=py39h7cc1a96_1 + - brotli=1.0.9=ha925a31_2 + - brotlipy=0.7.0=py39h2bbff1b_1003 + - bzip2=1.0.8=he774522_0 + - ca-certificates=2023.05.30=haa95532_0 + - certifi=2023.5.7=py39haa95532_0 + - cffi=1.15.0=py39h2bbff1b_0 + - cftime=1.6.2=py39h080aedc_0 + - chardet=4.0.0=py39haa95532_1003 + - charset-normalizer=2.0.4=pyhd3eb1b0_0 + - cloudpickle=2.0.0=pyhd3eb1b0_0 + - colorama=0.4.4=pyhd3eb1b0_0 + - conda=23.3.1=py39haa95532_0 + - conda-build=3.25.0=py39haa95532_0 + - conda-index=0.2.3=py39haa95532_0 + - conda-package-handling=2.1.0=py39haa95532_0 + - conda-package-streaming=0.8.0=py39haa95532_0 + - cookiecutter=1.7.2=pyhd3eb1b0_0 + - cryptography=35.0.0=py39h71e12ea_0 + - curl=7.79.1=h789b8ee_1 + - cycler=0.10.0=py39haa95532_0 + - decorator=5.1.0=pyhd3eb1b0_0 + - defusedxml=0.7.1=pyhd3eb1b0_0 + - diff-match-patch=20200713=pyhd3eb1b0_0 + - docutils=0.17.1=py39haa95532_1 + 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vc=14.2=h21ff451_1 + - vs2015_runtime=14.27.29016=h5e58377_2 + - watchdog=2.1.3=py39haa95532_0 + - wcwidth=0.2.5=pyhd3eb1b0_0 + - webencodings=0.5.1=py39haa95532_1 + - wheel=0.37.0=pyhd3eb1b0_1 + - whichcraft=0.6.1=pyhd3eb1b0_0 + - win_inet_pton=1.1.0=py39haa95532_0 + - wincertstore=0.2=py39haa95532_2 + - wrapt=1.12.1=py39h196d8e1_1 + - xarray-einstats=0.5.1=pyhd8ed1ab_0 + - xz=5.2.5=h62dcd97_0 + - yaml=0.2.5=he774522_0 + - yapf=0.31.0=pyhd3eb1b0_0 + - zipp=3.6.0=pyhd3eb1b0_0 + - zlib=1.2.11=h62dcd97_4 + - zstandard=0.19.0=py39h2bbff1b_0 + - zstd=1.4.9=h19a0ad4_0 + - pip: + - absl-py==1.4.0 + - argparse==1.4.0 + - arviz==0.15.1 + - asttokens==2.2.1 + - cached-property==1.5.2 + - cachetools==5.3.1 + - chex==0.1.7 + - click==7.1.2 + - comm==0.1.3 + - cons==0.4.6 + - contourpy==1.1.0 + - cython==0.29.35 + - debugpy==1.6.7 + - dill==0.3.6 + - dm-tree==0.1.8 + - docker-pycreds==0.4.0 + - etils==1.3.0 + - executing==1.2.0 + - filelock==3.12.2 + - flax==0.6.11 + - frozendict==2.3.8 + - 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python-dotenv==0.19.2 + - pywin32==306 + - pyzmq==25.1.0 + - qt5-applications==5.15.2.2.2 + - qt5-tools==5.15.2.1.2 + - rich==13.4.2 + - scikit-learn==1.2.2 + - scipy==1.10.1 + - seaborn==0.12.2 + - sentry-sdk==1.26.0 + - setproctitle==1.3.2 + - setuptools==68.1.2 + - shiboken2==5.15.2 + - smmap==5.0.0 + - spyder-kernels==2.4.4 + - ssm-simulators==0.3.1 + - stack-data==0.6.2 + - sympy==1.12 + - tensorstore==0.1.39 + - threadpoolctl==3.1.0 + - torch==2.0.1 + - tornado==6.3.2 + - traitlets==5.9.0 + - typing-extensions==4.6.3 + - tzdata==2023.3 + - urllib3==1.26.16 + - wandb==0.15.4 + - xarray==2023.6.0 +prefix: C:\ProgramData\Anaconda3\envs\pyPower diff --git a/wandb/offline-run-20231227_195707-nzclrd64/files/requirements.txt b/wandb/offline-run-20231227_195707-nzclrd64/files/requirements.txt new file mode 100644 index 0000000..9f02cef --- /dev/null +++ b/wandb/offline-run-20231227_195707-nzclrd64/files/requirements.txt @@ -0,0 +1,260 @@ +absl-py==1.4.0 +alabaster==0.7.12 +appdirs==1.4.4 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[handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:58:17,791 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:58:17,795 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:58:17,795 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:58:17,795 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:58:18,837 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 19:58:18,837 DEBUG SenderThread:27232 [sender.py:send_request():396] send_request: status_report +2023-12-27 19:58:19,180 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:58:23,862 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: status_report 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DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:58:41,784 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:58:41,785 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:58:41,968 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:58:41,968 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:58:41,968 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:58:41,968 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:58:41,968 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:58:41,968 DEBUG HandlerThread:27232 [handler.py:handle_request():144] 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[handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:58:50,637 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:58:52,015 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:58:54,101 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 19:58:54,101 DEBUG SenderThread:27232 [sender.py:send_request():396] send_request: status_report +2023-12-27 19:58:58,555 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:58:58,555 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:58:58,555 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:58:58,557 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history 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[sender.py:send_request():396] send_request: status_report +2023-12-27 19:59:54,587 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: status_report +2023-12-27 19:59:54,587 DEBUG SenderThread:27232 [sender.py:send_request():396] send_request: status_report +2023-12-27 19:59:55,395 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:59:55,397 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:59:55,397 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:59:55,397 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:59:55,398 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 19:59:55,398 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: partial_history +2023-12-27 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handle_request: sampled_history +2023-12-27 19:59:56,878 DEBUG SenderThread:27232 [sender.py:send_request():396] send_request: server_info +2023-12-27 19:59:56,878 DEBUG HandlerThread:27232 [handler.py:handle_request():144] handle_request: shutdown +2023-12-27 19:59:56,878 INFO HandlerThread:27232 [handler.py:finish():854] shutting down handler +2023-12-27 19:59:57,882 INFO WriterThread:27232 [datastore.py:close():298] close: D:\horiz\IMPORTANT\0study_graduate\Pro_COMPASS\COMPASS_DDM\wandb\offline-run-20231227_195707-nzclrd64\run-nzclrd64.wandb +2023-12-27 19:59:57,882 INFO SenderThread:27232 [sender.py:finish():1526] shutting down sender diff --git a/wandb/offline-run-20231227_195707-nzclrd64/logs/debug.log b/wandb/offline-run-20231227_195707-nzclrd64/logs/debug.log new file mode 100644 index 0000000..bf59814 --- /dev/null +++ b/wandb/offline-run-20231227_195707-nzclrd64/logs/debug.log @@ -0,0 +1,32 @@ +2023-12-27 19:57:07,465 INFO MainThread:13244 [wandb_setup.py:_flush():76] Current 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[wandb_setup.py:_flush():76] Applying login settings: {'mode': 'offline'} +2023-12-27 19:57:07,478 INFO MainThread:13244 [wandb_init.py:_log_setup():507] Logging user logs to D:\horiz\IMPORTANT\0study_graduate\Pro_COMPASS\COMPASS_DDM\wandb\offline-run-20231227_195707-nzclrd64\logs\debug.log +2023-12-27 19:57:07,480 INFO MainThread:13244 [wandb_init.py:_log_setup():508] Logging internal logs to D:\horiz\IMPORTANT\0study_graduate\Pro_COMPASS\COMPASS_DDM\wandb\offline-run-20231227_195707-nzclrd64\logs\debug-internal.log +2023-12-27 19:57:07,480 INFO MainThread:13244 [wandb_init.py:init():547] calling init triggers +2023-12-27 19:57:07,480 INFO MainThread:13244 [wandb_init.py:init():554] wandb.init called with sweep_config: {} +config: {'cpu_batch_size': 128, 'gpu_batch_size': 256, 'n_epochs': 10, 'optimizer': 'adam', 'learning_rate': 0.002, 'lr_scheduler': 'reduce_on_plateau', 'lr_scheduler_params': {}, 'weight_decay': 0.0, 'loss': 'huber', 'save_history': True} +2023-12-27 19:57:07,481 INFO MainThread:13244 [wandb_init.py:init():596] starting backend +2023-12-27 19:57:07,481 INFO MainThread:13244 [wandb_init.py:init():600] setting up manager +2023-12-27 19:57:07,485 INFO MainThread:13244 [backend.py:_multiprocessing_setup():106] multiprocessing start_methods=spawn, using: spawn +2023-12-27 19:57:07,497 INFO MainThread:13244 [wandb_init.py:init():606] backend started and connected +2023-12-27 19:57:07,502 INFO MainThread:13244 [wandb_init.py:init():703] updated telemetry +2023-12-27 19:57:07,549 INFO MainThread:13244 [wandb_init.py:init():736] communicating run to backend with 60.0 second timeout +2023-12-27 19:57:07,563 INFO MainThread:13244 [wandb_init.py:init():787] starting run threads in backend +2023-12-27 19:57:13,386 INFO MainThread:13244 [wandb_run.py:_console_start():2155] atexit reg +2023-12-27 19:57:13,386 INFO MainThread:13244 [wandb_run.py:_redirect():2010] redirect: SettingsConsole.WRAP_RAW +2023-12-27 19:57:13,386 INFO MainThread:13244 [wandb_run.py:_redirect():2075] Wrapping output streams. +2023-12-27 19:57:13,386 INFO MainThread:13244 [wandb_run.py:_redirect():2100] Redirects installed. +2023-12-27 19:57:13,386 INFO MainThread:13244 [wandb_init.py:init():828] run started, returning control to user process +2023-12-27 19:57:13,391 INFO MainThread:13244 [wandb_watch.py:watch():51] Watching +2023-12-27 19:59:56,860 INFO MainThread:13244 [wandb_run.py:_finish():1890] finishing run projectid/nzclrd64 +2023-12-27 19:59:56,860 INFO MainThread:13244 [wandb_run.py:_atexit_cleanup():2124] got exitcode: 0 +2023-12-27 19:59:56,860 INFO MainThread:13244 [wandb_run.py:_restore():2107] restore +2023-12-27 19:59:56,860 INFO MainThread:13244 [wandb_run.py:_restore():2113] restore done +2023-12-27 19:59:57,882 INFO MainThread:13244 [wandb_run.py:_footer_history_summary_info():3467] rendering history +2023-12-27 19:59:57,885 INFO MainThread:13244 [wandb_run.py:_footer_history_summary_info():3499] rendering summary diff --git a/wandb/offline-run-20231227_195707-nzclrd64/run-nzclrd64.wandb b/wandb/offline-run-20231227_195707-nzclrd64/run-nzclrd64.wandb new file mode 100644 index 0000000..4cdcdd4 Binary files /dev/null and b/wandb/offline-run-20231227_195707-nzclrd64/run-nzclrd64.wandb differ diff --git a/wandb/offline-run-20240105_124216-jyj8wvj0/files/conda-environment.yaml b/wandb/offline-run-20240105_124216-jyj8wvj0/files/conda-environment.yaml new file mode 100644 index 0000000..15661b8 --- /dev/null +++ b/wandb/offline-run-20240105_124216-jyj8wvj0/files/conda-environment.yaml @@ -0,0 +1,350 @@ +name: base +channels: + - conda-forge + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main + - defaults + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/fastai/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/ + - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda/ +dependencies: + - alabaster=0.7.12=pyhd3eb1b0_0 + - appdirs=1.4.4=pyhd3eb1b0_0 + - argh=0.26.2=py39haa95532_0 + - arrow=0.13.1=py39haa95532_0 + - astroid=2.6.6=py39haa95532_0 + - async_generator=1.10=pyhd3eb1b0_0 + - atomicwrites=1.4.0=py_0 + - attrs=21.2.0=pyhd3eb1b0_0 + - autopep8=1.5.7=pyhd3eb1b0_0 + - babel=2.9.1=pyhd3eb1b0_0 + - backcall=0.2.0=pyhd3eb1b0_0 + - bcrypt=3.2.0=py39h196d8e1_0 + - beautifulsoup4=4.12.2=py39haa95532_0 + - binaryornot=0.4.4=pyhd3eb1b0_1 + - black=19.10b0=py_0 + - blas=1.0=mkl + - bleach=4.0.0=pyhd3eb1b0_0 + - boltons=23.0.0=py39haa95532_0 + - bottleneck=1.3.2=py39h7cc1a96_1 + - brotli=1.0.9=ha925a31_2 + - brotlipy=0.7.0=py39h2bbff1b_1003 + - bzip2=1.0.8=he774522_0 + - ca-certificates=2023.05.30=haa95532_0 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hdf5=1.12.1=nompi_h2a0e4a3_100 + - icc_rt=2019.0.0=h0cc432a_1 + - icu=58.2=ha925a31_3 + - idna=3.2=pyhd3eb1b0_0 + - imagesize=1.2.0=pyhd3eb1b0_0 + - importlib_metadata=4.8.1=hd3eb1b0_0 + - inflection=0.5.1=py39haa95532_0 + - intel-openmp=2021.4.0=haa95532_3556 + - intervaltree=3.1.0=pyhd3eb1b0_0 + - ipython_genutils=0.2.0=pyhd3eb1b0_1 + - isort=5.9.3=pyhd3eb1b0_0 + - jedi=0.18.0=py39haa95532_1 + - jinja2=2.11.3=pyhd3eb1b0_0 + - jinja2-time=0.2.0=pyhd3eb1b0_2 + - jpeg=9d=h2bbff1b_0 + - jsonpatch=1.32=pyhd3eb1b0_0 + - jsonpointer=2.1=pyhd3eb1b0_0 + - jsonschema=3.2.0=pyhd3eb1b0_2 + - jupyterlab_pygments=0.1.2=py_0 + - keyring=23.1.0=py39haa95532_0 + - kiwisolver=1.3.1=py39hd77b12b_0 + - krb5=1.19.4=h5b6d351_0 + - lazy-object-proxy=1.6.0=py39h2bbff1b_0 + - libarchive=3.4.2=h5e25573_0 + - libcurl=7.79.1=h789b8ee_1 + - libiconv=1.16=h2bbff1b_2 + - liblief=0.12.3=hd77b12b_0 + - libnetcdf=4.8.1=nompi_h1cc8e9d_101 + - libpng=1.6.37=h2a8f88b_0 + - libpython=2.2=py39hcbf5309_2 + - 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whichcraft=0.6.1=pyhd3eb1b0_0 + - win_inet_pton=1.1.0=py39haa95532_0 + - wincertstore=0.2=py39haa95532_2 + - wrapt=1.12.1=py39h196d8e1_1 + - xarray-einstats=0.5.1=pyhd8ed1ab_0 + - xz=5.2.5=h62dcd97_0 + - yaml=0.2.5=he774522_0 + - yapf=0.31.0=pyhd3eb1b0_0 + - zipp=3.6.0=pyhd3eb1b0_0 + - zlib=1.2.11=h62dcd97_4 + - zstandard=0.19.0=py39h2bbff1b_0 + - zstd=1.4.9=h19a0ad4_0 + - pip: + - absl-py==1.4.0 + - argparse==1.4.0 + - arviz==0.15.1 + - asttokens==2.2.1 + - cached-property==1.5.2 + - cachetools==5.3.1 + - chex==0.1.7 + - click==7.1.2 + - comm==0.1.3 + - cons==0.4.6 + - contourpy==1.1.0 + - cython==0.29.35 + - debugpy==1.6.7 + - dill==0.3.6 + - dm-tree==0.1.8 + - docker-pycreds==0.4.0 + - etils==1.3.0 + - executing==1.2.0 + - filelock==3.12.2 + - flax==0.6.11 + - frozendict==2.3.8 + - gitdb==4.0.10 + - gitpython==3.1.31 + - h5netcdf==1.2.0 + - h5py==3.9.0 + - hddm-wfpt==0.1.0 + - importlib-metadata==6.8.0 + - importlib-resources==5.12.0 + - ipykernel==6.24.0 + - ipython==8.14.0 + - jax==0.4.13 + - jaxlib==0.4.13 + - joblib==1.2.0 + - jupyter-client==8.3.0 + - jupyter-core==5.3.1 + - kabuki==0.6.5 + - lanfactory==0.3.0.dev0 + - markdown-it-py==3.0.0 + - matplotlib==3.7.2 + - mdurl==0.1.2 + - ml-dtypes==0.2.0 + - mpmath==1.3.0 + - msgpack==1.0.5 + - networkx==3.1 + - numpy==1.25.1 + - onnx==1.14.0 + - openturns==1.21 + - opt-einsum==3.3.0 + - optax==0.1.5 + - orbax-checkpoint==0.2.6 + - pandas==2.0.2 + - pathtools==0.1.2 + - patsy==0.5.3 + - pillow==10.0.0 + - platformdirs==3.8.1 + - prompt-toolkit==3.0.39 + - protobuf==4.23.3 + - pure-eval==0.2.2 + - pygments==2.15.1 + - pymc==5.5.0 + - pyqt5==5.15.4 + - pyqt5-plugins==5.15.4.2.2 + - pyqt5-qt5==5.15.2 + - pyqt5-sip==12.9.0 + - pyqtwebengine==5.15.5 + - pyqtwebengine-qt5==5.15.2 + - pytensor==2.12.3 + - python-dotenv==0.19.2 + - pywin32==306 + - pyzmq==25.1.0 + - qt5-applications==5.15.2.2.2 + - qt5-tools==5.15.2.1.2 + - rich==13.4.2 + - scikit-learn==1.2.2 + - scipy==1.10.1 + - seaborn==0.12.2 + - sentry-sdk==1.26.0 + - setproctitle==1.3.2 + - setuptools==68.1.2 + - shiboken2==5.15.2 + - smmap==5.0.0 + - spyder-kernels==2.4.4 + - ssm-simulators==0.3.1 + - stack-data==0.6.2 + - sympy==1.12 + - tensorstore==0.1.39 + - threadpoolctl==3.1.0 + - torch==2.0.1 + - tornado==6.3.2 + - traitlets==5.9.0 + - typing-extensions==4.6.3 + - tzdata==2023.3 + - urllib3==1.26.16 + - wandb==0.15.4 + - xarray==2023.6.0 +prefix: C:\ProgramData\Anaconda3\envs\pyPower diff --git a/wandb/offline-run-20240105_124216-jyj8wvj0/files/requirements.txt b/wandb/offline-run-20240105_124216-jyj8wvj0/files/requirements.txt new file mode 100644 index 0000000..9f02cef --- /dev/null +++ b/wandb/offline-run-20240105_124216-jyj8wvj0/files/requirements.txt @@ -0,0 +1,260 @@ +absl-py==1.4.0 +alabaster==0.7.12 +appdirs==1.4.4 +argh==0.26.2 +argparse==1.4.0 +arrow==0.13.1 +arviz==0.13.0 +astroid==2.6.6 +asttokens==2.2.1 +async-generator==1.10 +atomicwrites==1.4.0 +attrs==21.2.0 +autopep8==1.5.7 +babel==2.9.1 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DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:43:54,173 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:43:54,173 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:43:54,173 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:43:54,173 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:43:54,299 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:43:54,299 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:43:54,299 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:43:54,299 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:43:54,299 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:43:54,299 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:43:57,564 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: status_report +2024-01-05 12:43:57,564 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: status_report +2024-01-05 12:44:02,589 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: status_report +2024-01-05 12:44:02,589 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: status_report +2024-01-05 12:44:03,124 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:03,124 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:03,124 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:03,124 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:03,124 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:03,124 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:04,540 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:07,641 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: status_report +2024-01-05 12:44:07,641 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: status_report +2024-01-05 12:44:11,676 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:11,676 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:11,676 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:11,676 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:11,676 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:11,676 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:12,255 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:12,255 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:12,255 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:12,255 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:12,255 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:12,255 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:12,681 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: status_report +2024-01-05 12:44:12,681 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: status_report +2024-01-05 12:44:17,717 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: status_report +2024-01-05 12:44:17,717 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: status_report +2024-01-05 12:44:21,683 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:22,748 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: status_report +2024-01-05 12:44:22,748 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: status_report +2024-01-05 12:44:27,859 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: status_report +2024-01-05 12:44:27,859 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: status_report +2024-01-05 12:44:28,232 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:28,232 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:28,232 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:28,247 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:28,247 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:28,247 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:29,232 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:29,232 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:29,232 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:29,248 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:29,248 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:29,248 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:32,891 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: status_report +2024-01-05 12:44:32,891 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: status_report +2024-01-05 12:44:37,484 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:37,484 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:37,484 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:37,484 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:37,484 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:37,484 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:37,965 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: status_report +2024-01-05 12:44:37,965 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: status_report +2024-01-05 12:44:38,946 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:43,027 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: status_report +2024-01-05 12:44:43,027 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: status_report +2024-01-05 12:44:46,516 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:46,516 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:46,516 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:46,516 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:46,516 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:46,516 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:47,144 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:47,144 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:47,144 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:47,144 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:47,144 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:47,144 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:48,102 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: status_report +2024-01-05 12:44:48,102 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: status_report +2024-01-05 12:44:53,149 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: status_report +2024-01-05 12:44:53,149 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: status_report +2024-01-05 12:44:56,198 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:56,198 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:56,198 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:56,198 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:56,198 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:56,198 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:57,725 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:44:58,180 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: status_report +2024-01-05 12:44:58,180 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: status_report +2024-01-05 12:45:03,250 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: status_report +2024-01-05 12:45:03,250 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: status_report +2024-01-05 12:45:08,299 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: status_report +2024-01-05 12:45:08,300 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: status_report +2024-01-05 12:45:12,798 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:45:12,798 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:45:12,798 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:45:12,798 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:45:12,798 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:45:12,814 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:45:13,335 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: status_report +2024-01-05 12:45:13,335 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: status_report +2024-01-05 12:45:14,995 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: partial_history +2024-01-05 12:45:15,011 DEBUG SenderThread:30312 [sender.py:send():369] send: exit +2024-01-05 12:45:15,011 INFO SenderThread:30312 [sender.py:send_exit():574] handling exit code: 0 +2024-01-05 12:45:15,011 INFO SenderThread:30312 [sender.py:send_exit():576] handling runtime: 178 +2024-01-05 12:45:15,015 INFO SenderThread:30312 [sender.py:_save_file():1354] saving file wandb-summary.json with policy end +2024-01-05 12:45:15,015 INFO SenderThread:30312 [sender.py:send_exit():582] send defer +2024-01-05 12:45:15,015 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: defer +2024-01-05 12:45:15,015 INFO HandlerThread:30312 [handler.py:handle_request_defer():170] handle defer: 0 +2024-01-05 12:45:15,015 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: defer +2024-01-05 12:45:15,015 INFO SenderThread:30312 [sender.py:send_request_defer():598] handle sender defer: 0 +2024-01-05 12:45:15,015 INFO SenderThread:30312 [sender.py:transition_state():602] send defer: 1 +2024-01-05 12:45:15,015 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: defer +2024-01-05 12:45:15,015 INFO HandlerThread:30312 [handler.py:handle_request_defer():170] handle defer: 1 +2024-01-05 12:45:15,015 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: defer +2024-01-05 12:45:15,015 INFO SenderThread:30312 [sender.py:send_request_defer():598] handle sender defer: 1 +2024-01-05 12:45:15,015 INFO SenderThread:30312 [sender.py:transition_state():602] send defer: 2 +2024-01-05 12:45:15,015 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: defer +2024-01-05 12:45:15,015 INFO HandlerThread:30312 [handler.py:handle_request_defer():170] handle defer: 2 +2024-01-05 12:45:15,015 INFO HandlerThread:30312 [system_monitor.py:finish():190] Stopping system monitor +2024-01-05 12:45:15,015 INFO HandlerThread:30312 [interfaces.py:finish():202] Joined cpu monitor +2024-01-05 12:45:15,015 DEBUG SystemMonitor:30312 [system_monitor.py:_start():166] Finished system metrics aggregation loop +2024-01-05 12:45:15,015 DEBUG SystemMonitor:30312 [system_monitor.py:_start():170] Publishing last batch of metrics +2024-01-05 12:45:15,015 INFO HandlerThread:30312 [interfaces.py:finish():202] Joined disk monitor +2024-01-05 12:45:15,015 INFO HandlerThread:30312 [interfaces.py:finish():202] Joined memory monitor +2024-01-05 12:45:15,015 INFO HandlerThread:30312 [interfaces.py:finish():202] Joined network monitor +2024-01-05 12:45:15,015 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: defer +2024-01-05 12:45:15,015 INFO SenderThread:30312 [sender.py:send_request_defer():598] handle sender defer: 2 +2024-01-05 12:45:15,015 INFO SenderThread:30312 [sender.py:transition_state():602] send defer: 3 +2024-01-05 12:45:15,015 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: defer +2024-01-05 12:45:15,015 INFO HandlerThread:30312 [handler.py:handle_request_defer():170] handle defer: 3 +2024-01-05 12:45:15,015 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: defer +2024-01-05 12:45:15,015 INFO SenderThread:30312 [sender.py:send_request_defer():598] handle sender defer: 3 +2024-01-05 12:45:15,015 INFO SenderThread:30312 [sender.py:transition_state():602] send defer: 4 +2024-01-05 12:45:15,015 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: defer +2024-01-05 12:45:15,023 INFO HandlerThread:30312 [handler.py:handle_request_defer():170] handle defer: 4 +2024-01-05 12:45:15,023 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: defer +2024-01-05 12:45:15,023 INFO SenderThread:30312 [sender.py:send_request_defer():598] handle sender defer: 4 +2024-01-05 12:45:15,023 INFO SenderThread:30312 [sender.py:transition_state():602] send defer: 5 +2024-01-05 12:45:15,023 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: defer +2024-01-05 12:45:15,023 INFO HandlerThread:30312 [handler.py:handle_request_defer():170] handle defer: 5 +2024-01-05 12:45:15,023 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: defer +2024-01-05 12:45:15,023 INFO SenderThread:30312 [sender.py:send_request_defer():598] handle sender defer: 5 +2024-01-05 12:45:15,023 INFO SenderThread:30312 [sender.py:transition_state():602] send defer: 6 +2024-01-05 12:45:15,023 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: defer +2024-01-05 12:45:15,023 INFO HandlerThread:30312 [handler.py:handle_request_defer():170] handle defer: 6 +2024-01-05 12:45:15,023 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: defer +2024-01-05 12:45:15,023 INFO SenderThread:30312 [sender.py:send_request_defer():598] handle sender defer: 6 +2024-01-05 12:45:15,023 INFO SenderThread:30312 [sender.py:transition_state():602] send defer: 7 +2024-01-05 12:45:15,023 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: status_report +2024-01-05 12:45:15,023 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: defer +2024-01-05 12:45:15,023 INFO HandlerThread:30312 [handler.py:handle_request_defer():170] handle defer: 7 +2024-01-05 12:45:15,023 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: status_report +2024-01-05 12:45:15,023 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: defer +2024-01-05 12:45:15,023 INFO SenderThread:30312 [sender.py:send_request_defer():598] handle sender defer: 7 +2024-01-05 12:45:15,023 INFO SenderThread:30312 [sender.py:transition_state():602] send defer: 8 +2024-01-05 12:45:15,023 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: defer +2024-01-05 12:45:15,023 INFO HandlerThread:30312 [handler.py:handle_request_defer():170] handle defer: 8 +2024-01-05 12:45:15,023 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: defer +2024-01-05 12:45:15,023 INFO SenderThread:30312 [sender.py:send_request_defer():598] handle sender defer: 8 +2024-01-05 12:45:15,023 INFO SenderThread:30312 [sender.py:transition_state():602] send defer: 9 +2024-01-05 12:45:15,023 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: defer +2024-01-05 12:45:15,023 INFO HandlerThread:30312 [handler.py:handle_request_defer():170] handle defer: 9 +2024-01-05 12:45:15,023 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: defer +2024-01-05 12:45:15,023 INFO SenderThread:30312 [sender.py:send_request_defer():598] handle sender defer: 9 +2024-01-05 12:45:15,023 INFO SenderThread:30312 [sender.py:transition_state():602] send defer: 10 +2024-01-05 12:45:15,023 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: defer +2024-01-05 12:45:15,031 INFO HandlerThread:30312 [handler.py:handle_request_defer():170] handle defer: 10 +2024-01-05 12:45:15,031 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: defer +2024-01-05 12:45:15,031 INFO SenderThread:30312 [sender.py:send_request_defer():598] handle sender defer: 10 +2024-01-05 12:45:15,031 INFO SenderThread:30312 [sender.py:transition_state():602] send defer: 11 +2024-01-05 12:45:15,031 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: defer +2024-01-05 12:45:15,031 INFO HandlerThread:30312 [handler.py:handle_request_defer():170] handle defer: 11 +2024-01-05 12:45:15,031 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: defer +2024-01-05 12:45:15,031 INFO SenderThread:30312 [sender.py:send_request_defer():598] handle sender defer: 11 +2024-01-05 12:45:15,031 INFO SenderThread:30312 [sender.py:transition_state():602] send defer: 12 +2024-01-05 12:45:15,031 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: defer +2024-01-05 12:45:15,031 INFO HandlerThread:30312 [handler.py:handle_request_defer():170] handle defer: 12 +2024-01-05 12:45:15,031 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: defer +2024-01-05 12:45:15,031 INFO SenderThread:30312 [sender.py:send_request_defer():598] handle sender defer: 12 +2024-01-05 12:45:15,031 INFO SenderThread:30312 [sender.py:transition_state():602] send defer: 13 +2024-01-05 12:45:15,031 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: defer +2024-01-05 12:45:15,031 INFO HandlerThread:30312 [handler.py:handle_request_defer():170] handle defer: 13 +2024-01-05 12:45:15,031 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: defer +2024-01-05 12:45:15,031 INFO SenderThread:30312 [sender.py:send_request_defer():598] handle sender defer: 13 +2024-01-05 12:45:15,031 INFO SenderThread:30312 [sender.py:transition_state():602] send defer: 14 +2024-01-05 12:45:15,031 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: defer +2024-01-05 12:45:15,031 DEBUG SenderThread:30312 [sender.py:send():369] send: final +2024-01-05 12:45:15,031 INFO HandlerThread:30312 [handler.py:handle_request_defer():170] handle defer: 14 +2024-01-05 12:45:15,031 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: defer +2024-01-05 12:45:15,031 INFO SenderThread:30312 [sender.py:send_request_defer():598] handle sender defer: 14 +2024-01-05 12:45:15,031 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: poll_exit +2024-01-05 12:45:15,031 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: server_info +2024-01-05 12:45:15,031 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: poll_exit +2024-01-05 12:45:15,031 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: get_summary +2024-01-05 12:45:15,031 DEBUG SenderThread:30312 [sender.py:send_request():396] send_request: server_info +2024-01-05 12:45:15,031 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: sampled_history +2024-01-05 12:45:15,039 DEBUG HandlerThread:30312 [handler.py:handle_request():144] handle_request: shutdown +2024-01-05 12:45:15,039 INFO HandlerThread:30312 [handler.py:finish():854] shutting down handler +2024-01-05 12:45:16,051 INFO SenderThread:30312 [sender.py:finish():1526] shutting down sender +2024-01-05 12:45:16,051 INFO WriterThread:30312 [datastore.py:close():298] close: D:\horiz\IMPORTANT\0study_graduate\Pro_COMPASS\COMPASS_DDM\wandb\offline-run-20240105_124216-jyj8wvj0\run-jyj8wvj0.wandb diff --git a/wandb/offline-run-20240105_124216-jyj8wvj0/logs/debug.log b/wandb/offline-run-20240105_124216-jyj8wvj0/logs/debug.log new file mode 100644 index 0000000..a53a027 --- /dev/null +++ b/wandb/offline-run-20240105_124216-jyj8wvj0/logs/debug.log @@ -0,0 +1,32 @@ +2024-01-05 12:42:16,028 INFO MainThread:8296 [wandb_setup.py:_flush():76] Current SDK version is 0.15.4 +2024-01-05 12:42:16,028 INFO MainThread:8296 [wandb_setup.py:_flush():76] Configure stats pid to 8296 +2024-01-05 12:42:16,028 INFO MainThread:8296 [wandb_setup.py:_flush():76] Loading settings from C:\Users\horiz\.config\wandb\settings +2024-01-05 12:42:16,028 INFO MainThread:8296 [wandb_setup.py:_flush():76] Loading settings from D:\horiz\IMPORTANT\0study_graduate\Pro_COMPASS\COMPASS_DDM\wandb\settings +2024-01-05 12:42:16,028 INFO MainThread:8296 [wandb_setup.py:_flush():76] Loading 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+2024-01-05 12:42:16,037 INFO MainThread:8296 [wandb_init.py:init():547] calling init triggers +2024-01-05 12:42:16,037 INFO MainThread:8296 [wandb_init.py:init():554] wandb.init called with sweep_config: {} +config: {'cpu_batch_size': 128, 'gpu_batch_size': 256, 'n_epochs': 10, 'optimizer': 'adam', 'learning_rate': 0.002, 'lr_scheduler': 'reduce_on_plateau', 'lr_scheduler_params': {}, 'weight_decay': 0.0, 'loss': 'huber', 'save_history': True} +2024-01-05 12:42:16,037 INFO MainThread:8296 [wandb_init.py:init():596] starting backend +2024-01-05 12:42:16,037 INFO MainThread:8296 [wandb_init.py:init():600] setting up manager +2024-01-05 12:42:16,037 INFO MainThread:8296 [backend.py:_multiprocessing_setup():106] multiprocessing start_methods=spawn, using: spawn +2024-01-05 12:42:16,062 INFO MainThread:8296 [wandb_init.py:init():606] backend started and connected +2024-01-05 12:42:16,073 INFO MainThread:8296 [wandb_init.py:init():703] updated telemetry +2024-01-05 12:42:16,151 INFO 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diff --git a/wfpt_n/build/temp.win-amd64-cpython-39/Release/wfpt_n.o b/wfpt_n/build/temp.win-amd64-cpython-39/Release/wfpt_n.o new file mode 100644 index 0000000..1c8978a Binary files /dev/null and b/wfpt_n/build/temp.win-amd64-cpython-39/Release/wfpt_n.o differ diff --git a/wfpt_n/dist/wfpt_n-0.0.0-py3.11-linux-x86_64.egg b/wfpt_n/dist/wfpt_n-0.0.0-py3.11-linux-x86_64.egg new file mode 100644 index 0000000..3a5d2b4 Binary files /dev/null and b/wfpt_n/dist/wfpt_n-0.0.0-py3.11-linux-x86_64.egg differ diff --git a/wfpt_n/integrate.pxi b/wfpt_n/integrate.pxi new file mode 100644 index 0000000..c2505c9 --- /dev/null +++ b/wfpt_n/integrate.pxi @@ -0,0 +1,206 @@ +#cython: embedsignature=True +#cython: cdivision=True +#cython: wraparound=False +#cython: boundscheck=False + +import numpy as np +cimport numpy as np +cimport cython + +include 'pdf.pxi' + +cdef double simpson_1D(double x, double v, double sv, double a, double z, double t, double err, + double lb_z, double ub_z, int n_sz, double lb_t, double ub_t, int n_st) nogil: + #assert ((n_sz&1)==0 and (n_st&1)==0), "n_st and n_sz have to be even" + #assert ((ub_t-lb_t)*(ub_z-lb_z)==0 and (n_sz*n_st)==0), "the function is defined for 1D-integration only" + + cdef double ht, hz + cdef int n = max(n_st, n_sz) + if n_st==0: #integration over z + hz = (ub_z-lb_z)/n + ht = 0 + lb_t = t + ub_t = t + else: #integration over t + hz = 0 + ht = (ub_t-lb_t)/n + lb_z = z + ub_z = z + + cdef double S = pdf_sv(x - lb_t, v, sv, a, lb_z, err) + cdef double z_tag, t_tag, y + cdef int i + + for i from 1 <= i <= n: + z_tag = lb_z + hz * i + t_tag = lb_t + ht * i + y = pdf_sv(x - t_tag, v, sv, a, z_tag, err) + if i&1: #check if i is odd + S += (4 * y) + else: + S += (2 * y) + S = S - y #the last term should be f(b) and not 2*f(b) so we subtract y + S = S / ((ub_t-lb_t)+(ub_z-lb_z)) #the right function if pdf_sv()/sz or pdf_sv()/st + + return ((ht+hz) * S / 3) + +cdef double simpson_2D(double x, double v, double sv, double a, double z, double t, double err, double lb_z, double ub_z, int n_sz, double lb_t, double ub_t, int n_st) nogil: + #assert ((n_sz&1)==0 and (n_st&1)==0), "n_st and n_sz have to be even" + #assert ((ub_t-lb_t)*(ub_z-lb_z)>0 and (n_sz*n_st)>0), "the function is defined for 2D-integration only, lb_t: %f, ub_t %f, lb_z %f, ub_z %f, n_sz: %d, n_st %d" % (lb_t, ub_t, lb_z, ub_z, n_sz, n_st) + + cdef double ht + cdef double S + cdef double t_tag, y + cdef int i_t + + ht = (ub_t-lb_t)/n_st + + S = simpson_1D(x, v, sv, a, z, lb_t, err, lb_z, ub_z, n_sz, 0, 0, 0) + + for i_t from 1 <= i_t <= n_st: + t_tag = lb_t + ht * i_t + y = simpson_1D(x, v, sv, a, z, t_tag, err, lb_z, ub_z, n_sz, 0, 0, 0) + if i_t&1: #check if i is odd + S += (4 * y) + else: + S += (2 * y) + S = S - y #the last term should be f(b) and not 2*f(b) so we subtract y + S = S/ (ub_t-lb_t) + + return (ht * S / 3) + +cdef double adaptiveSimpsonsAux(double x, double v, double sv, double a, double z, double t, double pdf_err, + double lb_z, double ub_z, double lb_t, double ub_t, double ZT, double simps_err, + double S, double f_beg, double f_end, double f_mid, int bottom) nogil: + + cdef double z_c, z_d, z_e, t_c, t_d, t_e, h + cdef double fd, fe + cdef double Sleft, Sright, S2 + #print "in AdaptiveSimpsAux: lb_z: %f, ub_z: %f, lb_t %f, ub_t %f, f_beg: %f, f_end: %f, bottom: %d" % (lb_z, ub_z, lb_t, ub_t, f_beg, f_end, bottom) + + if (ub_t-lb_t) == 0: #integration over sz + h = ub_z - lb_z + z_c = (ub_z + lb_z)/2. + z_d = (lb_z + z_c)/2. + z_e = (z_c + ub_z)/2. + t_c = t + t_d = t + t_e = t + + else: #integration over t + h = ub_t - lb_t + t_c = (ub_t + lb_t)/2. + t_d = (lb_t + t_c)/2. + t_e = (t_c + ub_t)/2. + z_c = z + z_d = z + z_e = z + + fd = pdf_sv(x - t_d, v, sv, a, z_d, pdf_err)/ZT + fe = pdf_sv(x - t_e, v, sv, a, z_e, pdf_err)/ZT + + Sleft = (h/12)*(f_beg + 4*fd + f_mid) + Sright = (h/12)*(f_mid + 4*fe + f_end) + S2 = Sleft + Sright + if (bottom <= 0 or fabs(S2 - S) <= 15*simps_err): + return S2 + (S2 - S)/15 + return adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, + lb_z, z_c, lb_t, t_c, ZT, simps_err/2, + Sleft, f_beg, f_mid, fd, bottom-1) + \ + adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, + z_c, ub_z, t_c, ub_t, ZT, simps_err/2, + Sright, f_mid, f_end, fe, bottom-1) + +cdef double adaptiveSimpsons_1D(double x, double v, double sv, double a, double z, double t, + double pdf_err, double lb_z, double ub_z, double lb_t, double ub_t, + double simps_err, int maxRecursionDepth) nogil: + + cdef double h + + if (ub_t - lb_t) == 0: #integration over z + lb_t = t + ub_t = t + h = ub_z - lb_z + else: #integration over t + h = (ub_t-lb_t) + lb_z = z + ub_z = z + + cdef double ZT = h + cdef double c_t = (lb_t + ub_t)/2. + cdef double c_z = (lb_z + ub_z)/2. + + cdef double f_beg, f_end, f_mid, S + f_beg = pdf_sv(x - lb_t, v, sv, a, lb_z, pdf_err)/ZT + f_end = pdf_sv(x - ub_t, v, sv, a, ub_z, pdf_err)/ZT + f_mid = pdf_sv(x - c_t, v, sv, a, c_z, pdf_err)/ZT + S = (h/6)*(f_beg + 4*f_mid + f_end) + cdef double res = adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, + lb_z, ub_z, lb_t, ub_t, ZT, simps_err, + S, f_beg, f_end, f_mid, maxRecursionDepth) + return res + +cdef double adaptiveSimpsonsAux_2D(double x, double v, double sv, + double a, double z, double t, double + pdf_err, double err_1d, double lb_z, + double ub_z, double lb_t, double + ub_t, double st, double err_2d, double + S, double f_beg, double f_end, double + f_mid, int maxRecursionDepth_sz, int + bottom) nogil: + + cdef double fd, fe + cdef double Sleft, Sright, S2 + #print "in AdaptiveSimpsAux_2D: lb_z: %f, ub_z: %f, lb_t %f, ub_t %f, f_beg: %f, f_end: %f, bottom: %d" % (lb_z, ub_z, lb_t, ub_t, f_beg, f_end, bottom) + + cdef double t_c = (ub_t + lb_t)/2. + cdef double t_d = (lb_t + t_c)/2. + cdef double t_e = (t_c + ub_t)/2. + cdef double h = ub_t - lb_t + + fd = adaptiveSimpsons_1D(x, v, sv, a, z, t_d, pdf_err, lb_z, ub_z, + 0, 0, err_1d, maxRecursionDepth_sz)/st + fe = adaptiveSimpsons_1D(x, v, sv, a, z, t_e, pdf_err, lb_z, ub_z, + 0, 0, err_1d, maxRecursionDepth_sz)/st + + Sleft = (h/12)*(f_beg + 4*fd + f_mid) + Sright = (h/12)*(f_mid + 4*fe + f_end) + S2 = Sleft + Sright + + if (bottom <= 0 or fabs(S2 - S) <= 15*err_2d): + return S2 + (S2 - S)/15; + + return adaptiveSimpsonsAux_2D(x, v, sv, a, z, t, pdf_err, err_1d, + lb_z, ub_z, lb_t, t_c, st, err_2d/2, + Sleft, f_beg, f_mid, fd, maxRecursionDepth_sz, bottom-1) + \ + adaptiveSimpsonsAux_2D(x, v, sv, a, z, t, pdf_err, err_1d, + lb_z, ub_z, t_c, ub_t, st, err_2d/2, + Sright, f_mid, f_end, fe, maxRecursionDepth_sz, bottom-1) + + +cdef double adaptiveSimpsons_2D(double x, double v, double sv, double a, double z, double t, + double pdf_err, double lb_z, double ub_z, double lb_t, double ub_t, + double simps_err, int maxRecursionDepth_sz, int maxRecursionDepth_st) nogil: + + cdef double h = (ub_t-lb_t) + + cdef double st = (ub_t - lb_t) + cdef double c_t = (lb_t + ub_t)/2. + cdef double c_z = (lb_z + ub_z)/2. + + cdef double f_beg, f_end, f_mid, S + cdef double err_1d = simps_err + cdef double err_2d = simps_err + + f_beg = adaptiveSimpsons_1D(x, v, sv, a, z, lb_t, pdf_err, lb_z, ub_z, + 0, 0, err_1d, maxRecursionDepth_sz)/st + + f_end = adaptiveSimpsons_1D(x, v, sv, a, z, ub_t, pdf_err, lb_z, ub_z, + 0, 0, err_1d, maxRecursionDepth_sz)/st + f_mid = adaptiveSimpsons_1D(x, v, sv, a, z, (lb_t+ub_t)/2, pdf_err, lb_z, ub_z, + 0, 0, err_1d, maxRecursionDepth_sz)/st + S = (h/6)*(f_beg + 4*f_mid + f_end) + cdef double res = adaptiveSimpsonsAux_2D(x, v, sv, a, z, t, pdf_err, err_1d, + lb_z, ub_z, lb_t, ub_t, st, err_2d, + S, f_beg, f_end, f_mid, maxRecursionDepth_sz, maxRecursionDepth_st) + return res diff --git a/wfpt_n/pdf.pxi b/wfpt_n/pdf.pxi new file mode 100644 index 0000000..3964f74 --- /dev/null +++ b/wfpt_n/pdf.pxi @@ -0,0 +1,146 @@ +#cython: embedsignature=True +#cython: cdivision=True +#cython: wraparound=False +#cython: boundscheck=False + +cimport cython + +#include "integrate.pxi" + +#from libc.math cimport tan, sin, cos, log, exp, sqrt, fmax, pow, ceil, floor, fabs, M_PI + +cdef extern from "math.h" nogil: + double sin(double) + double cos(double) + double log(double) + double exp(double) + double sqrt(double) +# double fmax(double, double) + double pow(double, double) + double ceil(double) + double floor(double) + double fabs(double) + double M_PI + +cdef extern from "" namespace "std" nogil: + T max[T](T a, T b) + +cdef double ftt_01w(double tt, double w, double err) nogil: + """Compute f(t|0,1,w) for the likelihood of the drift diffusion model using the method + and implementation of Navarro & Fuss, 2009. + """ + cdef double kl, ks, p + cdef int k, K, lower, upper + + # calculate number of terms needed for large t + if M_PI*tt*err<1: # if error threshold is set low enough + kl=sqrt(-2*log(M_PI*tt*err)/(M_PI**2*tt)) # bound + kl=max(kl,1./(M_PI*sqrt(tt))) # ensure boundary conditions met + else: # if error threshold set too high + kl=1./(M_PI*sqrt(tt)) # set to boundary condition + + # calculate number of terms needed for small t + if 2*sqrt(2*M_PI*tt)*err<1: # if error threshold is set low enough + ks=2+sqrt(-2*tt*log(2*sqrt(2*M_PI*tt)*err)) # bound + ks=max(ks,sqrt(tt)+1) # ensure boundary conditions are met + else: # if error threshold was set too high + ks=2 # minimal kappa for that case + + # compute f(tt|0,1,w) + p=0 #initialize density + if ks(ceil(ks)) # round to smallest integer meeting error + lower = (-floor((K-1)/2.)) + upper = (ceil((K-1)/2.)) + for k from lower <= k <= upper: # loop over k + p+=(w+2*k)*exp(-(pow((w+2*k),2))/2/tt) # increment sum + p/=sqrt(2*M_PI*pow(tt,3)) # add con_stant term + + else: # if large t is better... + K=(ceil(kl)) # round to smallest integer meeting error + for k from 1 <= k <= K: + p+=k*exp(-(pow(k,2))*(M_PI**2)*tt/2)*sin(k*M_PI*w) # increment sum + p*=M_PI # add con_stant term + + return p + +cdef inline double prob_ub(double v, double a, double z) nogil: + """Probability of hitting upper boundary.""" + if v == 0: + return z + else: + return (exp(-2 * a * z * v) - 1) / (exp(-2 * a * v) - 1) + +cdef double pdf(double x, double v, double a, double w, double err) nogil: + """Compute the likelihood of the drift diffusion model f(t|v,a,z) using the method + and implementation of Navarro & Fuss, 2009. + """ + if x <= 0: + return 0 + + cdef double tt = x/a**2 # use normalized time + cdef double p = ftt_01w(tt, w, err) #get f(t|0,1,w) + + # convert to f(t|v,a,w) + return p*exp(-v*a*w -(pow(v,2))*x/2.)/(pow(a,2)) + +cdef double pdf_sv(double x, double v, double sv, double a, double z, double err) nogil: + """Compute the likelihood of the drift diffusion model f(t|v,a,z,sv) using the method + and implementation of Navarro & Fuss, 2009. + sv is the std of the drift rate + """ + if x <= 0: + return 0 + + if sv==0: + return pdf(x, v, a, z, err) + + cdef double tt = x/(pow(a,2)) # use normalized time + cdef double p = ftt_01w(tt, z, err) #get f(t|0,1,w) + + # convert to f(t|v,a,w) + return exp(log(p) + ((a*z*sv)**2 - 2*a*v*z - (v**2)*x)/(2*(sv**2)*x+2))/sqrt((sv**2)*x+1)/(a**2) + +cpdef double full_pdf(double x, double v, double sv, double a, double + z, double sz, double t, double st, double err, int + n_st=2, int n_sz=2, bint use_adaptive=1, double + simps_err=1e-3) nogil: + """full pdf""" + + # Check if parpameters are valid + if (z<0) or (z>1) or (a<0) or (t<0) or (st<0) or (sv<0) or (sz<0) or (sz>1) or \ + ((fabs(x)-(t-st/2.))<0) or (z+sz/2.>1) or (z-sz/2.<0) or (t-st/2.<0): + return 0 + + # transform x,v,z if x is upper bound response + if x > 0: + v = -v + z = 1.-z + + x = fabs(x) + + if st<1e-3: + st = 0 + if sz <1e-3: + sz = 0 + + if (sz==0): + if (st==0): #sv=0,sz=0,st=0 + return pdf_sv(x - t, v, sv, a, z, err) + else: #sv=0,sz=0,st=$ + if use_adaptive>0: + return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z, z, t-st/2., t+st/2., simps_err, n_st) + else: + return simpson_1D(x, v, sv, a, z, t, err, z, z, 0, t-st/2., t+st/2., n_st) + + else: #sz=$ + if (st==0): #sv=0,sz=$,st=0 + if use_adaptive: + return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t, t, simps_err, n_sz) + else: + return simpson_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., n_sz, t, t , 0) + else: #sv=0,sz=$,st=$ + if use_adaptive: + return adaptiveSimpsons_2D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t-st/2., t+st/2., simps_err, n_sz, n_st) + else: + return simpson_2D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., n_sz, t-st/2., t+st/2., n_st) diff --git a/wfpt_n/setup.py b/wfpt_n/setup.py new file mode 100644 index 0000000..cf2bfca --- /dev/null +++ b/wfpt_n/setup.py @@ -0,0 +1,9 @@ +from distutils.core import setup +from Cython.Build import cythonize +import numpy as np + +setup ( + name = 'wfpt_n', + ext_modules = cythonize("wfpt_n.pyx"), + include_dirs=[np.get_include()] +) \ No newline at end of file diff --git a/wfpt_n/wfpt_n.cp39-win_amd64.pyd b/wfpt_n/wfpt_n.cp39-win_amd64.pyd new file mode 100644 index 0000000..fe989b6 Binary files /dev/null and b/wfpt_n/wfpt_n.cp39-win_amd64.pyd differ diff --git a/wfpt_n/wfpt_n.cpp b/wfpt_n/wfpt_n.cpp new file mode 100644 index 0000000..ad06308 --- /dev/null +++ b/wfpt_n/wfpt_n.cpp @@ -0,0 +1,51518 @@ +/* Generated by Cython 3.0.6 */ + +/* BEGIN: Cython Metadata +{ + "distutils": { + "depends": [], + "language": "c++", + "name": "wfpt_n", + "sources": [ + "wfpt_n.pyx" + ] + }, + "module_name": "wfpt_n" +} +END: Cython Metadata */ + +#ifndef PY_SSIZE_T_CLEAN +#define PY_SSIZE_T_CLEAN +#endif /* PY_SSIZE_T_CLEAN */ +#if defined(CYTHON_LIMITED_API) && 0 + #ifndef Py_LIMITED_API + #if CYTHON_LIMITED_API+0 > 0x03030000 + #define Py_LIMITED_API CYTHON_LIMITED_API + #else + #define Py_LIMITED_API 0x03030000 + #endif + #endif +#endif + +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#elif PY_VERSION_HEX < 0x02070000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) + #error Cython requires Python 2.7+ or Python 3.3+. +#else +#if defined(CYTHON_LIMITED_API) && CYTHON_LIMITED_API +#define __PYX_EXTRA_ABI_MODULE_NAME "limited" +#else +#define __PYX_EXTRA_ABI_MODULE_NAME "" +#endif +#define CYTHON_ABI "3_0_6" __PYX_EXTRA_ABI_MODULE_NAME +#define __PYX_ABI_MODULE_NAME "_cython_" CYTHON_ABI +#define __PYX_TYPE_MODULE_PREFIX __PYX_ABI_MODULE_NAME "." +#define CYTHON_HEX_VERSION 0x030006F0 +#define CYTHON_FUTURE_DIVISION 1 +#include +#ifndef offsetof + #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(_WIN32) && !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#define __PYX_COMMA , +#ifndef HAVE_LONG_LONG + #define HAVE_LONG_LONG +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#define __PYX_LIMITED_VERSION_HEX PY_VERSION_HEX +#if defined(GRAALVM_PYTHON) + /* For very preliminary testing purposes. Most variables are set the same as PyPy. + The existence of this section does not imply that anything works or is even tested */ + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_LIMITED_API 0 + #define CYTHON_COMPILING_IN_GRAAL 1 + #define CYTHON_COMPILING_IN_NOGIL 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_TYPE_SPECS + #define CYTHON_USE_TYPE_SPECS 0 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_GIL + #define CYTHON_FAST_GIL 0 + #undef CYTHON_METH_FASTCALL + #define CYTHON_METH_FASTCALL 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #ifndef CYTHON_PEP487_INIT_SUBCLASS + #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3) + #endif + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #undef CYTHON_USE_MODULE_STATE + #define CYTHON_USE_MODULE_STATE 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 + #endif +#elif defined(PYPY_VERSION) + #define CYTHON_COMPILING_IN_PYPY 1 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_LIMITED_API 0 + #define CYTHON_COMPILING_IN_GRAAL 0 + #define CYTHON_COMPILING_IN_NOGIL 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #ifndef CYTHON_USE_TYPE_SPECS + #define CYTHON_USE_TYPE_SPECS 0 + #endif + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_GIL + #define CYTHON_FAST_GIL 0 + #undef CYTHON_METH_FASTCALL + #define CYTHON_METH_FASTCALL 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #ifndef CYTHON_PEP487_INIT_SUBCLASS + #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3) + #endif + #if PY_VERSION_HEX < 0x03090000 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT) + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #endif + #undef CYTHON_USE_MODULE_STATE + #define CYTHON_USE_MODULE_STATE 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1 && PYPY_VERSION_NUM >= 0x07030C00) + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 + #endif +#elif defined(CYTHON_LIMITED_API) + #ifdef Py_LIMITED_API + #undef __PYX_LIMITED_VERSION_HEX + #define __PYX_LIMITED_VERSION_HEX Py_LIMITED_API + #endif + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_LIMITED_API 1 + #define CYTHON_COMPILING_IN_GRAAL 0 + #define CYTHON_COMPILING_IN_NOGIL 0 + #undef CYTHON_CLINE_IN_TRACEBACK + #define CYTHON_CLINE_IN_TRACEBACK 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_TYPE_SPECS + #define CYTHON_USE_TYPE_SPECS 1 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #endif + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_GIL + #define CYTHON_FAST_GIL 0 + #undef CYTHON_METH_FASTCALL + #define CYTHON_METH_FASTCALL 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #ifndef CYTHON_PEP487_INIT_SUBCLASS + #define CYTHON_PEP487_INIT_SUBCLASS 1 + #endif + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #undef CYTHON_USE_MODULE_STATE + #define CYTHON_USE_MODULE_STATE 1 + #ifndef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #endif + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 + #endif +#elif defined(Py_GIL_DISABLED) || defined(Py_NOGIL) + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_LIMITED_API 0 + #define CYTHON_COMPILING_IN_GRAAL 0 + #define CYTHON_COMPILING_IN_NOGIL 1 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #ifndef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #ifndef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #endif + #ifndef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 1 + #endif + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 +#else + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_CPYTHON 1 + #define CYTHON_COMPILING_IN_LIMITED_API 0 + #define CYTHON_COMPILING_IN_GRAAL 0 + #define CYTHON_COMPILING_IN_NOGIL 0 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #ifndef CYTHON_USE_TYPE_SPECS + #define CYTHON_USE_TYPE_SPECS 0 + #endif + #ifndef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 1 + #endif + #if PY_MAJOR_VERSION < 3 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #ifndef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 1 + #endif + #ifndef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 1 + #endif + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #if PY_VERSION_HEX < 0x030300F0 || PY_VERSION_HEX >= 0x030B00A2 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #elif !defined(CYTHON_USE_UNICODE_WRITER) + #define CYTHON_USE_UNICODE_WRITER 1 + #endif + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #ifndef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 1 + #endif + #ifndef CYTHON_FAST_GIL + #define CYTHON_FAST_GIL (PY_MAJOR_VERSION < 3 || PY_VERSION_HEX >= 0x03060000 && PY_VERSION_HEX < 0x030C00A6) + #endif + #ifndef CYTHON_METH_FASTCALL + #define CYTHON_METH_FASTCALL (PY_VERSION_HEX >= 0x030700A1) + #endif + #ifndef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 1 + #endif + #ifndef CYTHON_PEP487_INIT_SUBCLASS + #define CYTHON_PEP487_INIT_SUBCLASS 1 + #endif + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT) + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #endif + #ifndef CYTHON_USE_MODULE_STATE + #define CYTHON_USE_MODULE_STATE 0 + #endif + #if PY_VERSION_HEX < 0x030400a1 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #elif !defined(CYTHON_USE_TP_FINALIZE) + #define CYTHON_USE_TP_FINALIZE 1 + #endif + #if PY_VERSION_HEX < 0x030600B1 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #elif !defined(CYTHON_USE_DICT_VERSIONS) + #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX < 0x030C00A5) + #endif + #if PY_VERSION_HEX < 0x030700A3 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 + #elif !defined(CYTHON_USE_EXC_INFO_STACK) + #define CYTHON_USE_EXC_INFO_STACK 1 + #endif + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 1 + #endif +#endif +#if !defined(CYTHON_FAST_PYCCALL) +#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) +#endif +#if !defined(CYTHON_VECTORCALL) +#define CYTHON_VECTORCALL (CYTHON_FAST_PYCCALL && PY_VERSION_HEX >= 0x030800B1) +#endif +#define CYTHON_BACKPORT_VECTORCALL (CYTHON_METH_FASTCALL && PY_VERSION_HEX < 0x030800B1) +#if CYTHON_USE_PYLONG_INTERNALS + #if PY_MAJOR_VERSION < 3 + #include "longintrepr.h" + #endif + #undef SHIFT + #undef BASE + #undef MASK + #ifdef SIZEOF_VOID_P + enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) }; + #endif +#endif +#ifndef __has_attribute + #define __has_attribute(x) 0 +#endif +#ifndef __has_cpp_attribute + #define __has_cpp_attribute(x) 0 +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#ifndef CYTHON_UNUSED + #if defined(__cplusplus) + /* for clang __has_cpp_attribute(maybe_unused) is true even before C++17 + * but leads to warnings with -pedantic, since it is a C++17 feature */ + #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L) + #if __has_cpp_attribute(maybe_unused) + #define CYTHON_UNUSED [[maybe_unused]] + #endif + #endif + #endif +#endif +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_UNUSED_VAR +# if defined(__cplusplus) + template void CYTHON_UNUSED_VAR( const T& ) { } +# else +# define CYTHON_UNUSED_VAR(x) (void)(x) +# endif +#endif +#ifndef CYTHON_MAYBE_UNUSED_VAR + #define CYTHON_MAYBE_UNUSED_VAR(x) CYTHON_UNUSED_VAR(x) +#endif +#ifndef CYTHON_NCP_UNUSED +# if CYTHON_COMPILING_IN_CPYTHON +# define CYTHON_NCP_UNUSED +# else +# define CYTHON_NCP_UNUSED CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_USE_CPP_STD_MOVE + #if defined(__cplusplus) && (\ + __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1600)) + #define CYTHON_USE_CPP_STD_MOVE 1 + #else + #define CYTHON_USE_CPP_STD_MOVE 0 + #endif +#endif +#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) +#ifdef _MSC_VER + #ifndef _MSC_STDINT_H_ + #if _MSC_VER < 1300 + typedef unsigned char uint8_t; + typedef unsigned short uint16_t; + typedef unsigned int uint32_t; + #else + typedef unsigned __int8 uint8_t; + typedef unsigned __int16 uint16_t; + typedef unsigned __int32 uint32_t; + #endif + #endif + #if _MSC_VER < 1300 + #ifdef _WIN64 + typedef unsigned long long __pyx_uintptr_t; + #else + typedef unsigned int __pyx_uintptr_t; + #endif + #else + #ifdef _WIN64 + typedef unsigned __int64 __pyx_uintptr_t; + #else + typedef unsigned __int32 __pyx_uintptr_t; + #endif + #endif +#else + #include + typedef uintptr_t __pyx_uintptr_t; +#endif +#ifndef CYTHON_FALLTHROUGH + #if defined(__cplusplus) + /* for clang __has_cpp_attribute(fallthrough) is true even before C++17 + * but leads to warnings with -pedantic, since it is a C++17 feature */ + #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L) + #if __has_cpp_attribute(fallthrough) + #define CYTHON_FALLTHROUGH [[fallthrough]] + #endif + #endif + #ifndef CYTHON_FALLTHROUGH + #if __has_cpp_attribute(clang::fallthrough) + #define CYTHON_FALLTHROUGH [[clang::fallthrough]] + #elif __has_cpp_attribute(gnu::fallthrough) + #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] + #endif + #endif + #endif + #ifndef CYTHON_FALLTHROUGH + #if __has_attribute(fallthrough) + #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) + #else + #define CYTHON_FALLTHROUGH + #endif + #endif + #if defined(__clang__) && defined(__apple_build_version__) + #if __apple_build_version__ < 7000000 + #undef CYTHON_FALLTHROUGH + #define CYTHON_FALLTHROUGH + #endif + #endif +#endif +#ifdef __cplusplus + template + struct __PYX_IS_UNSIGNED_IMPL {static const bool value = T(0) < T(-1);}; + #define __PYX_IS_UNSIGNED(type) (__PYX_IS_UNSIGNED_IMPL::value) +#else + #define __PYX_IS_UNSIGNED(type) (((type)-1) > 0) +#endif +#if CYTHON_COMPILING_IN_PYPY == 1 + #define __PYX_NEED_TP_PRINT_SLOT (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x030A0000) +#else + #define __PYX_NEED_TP_PRINT_SLOT (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000) +#endif +#define __PYX_REINTERPRET_FUNCION(func_pointer, other_pointer) ((func_pointer)(void(*)(void))(other_pointer)) + +#ifndef __cplusplus + #error "Cython files generated with the C++ option must be compiled with a C++ compiler." +#endif +#ifndef CYTHON_INLINE + #if defined(__clang__) + #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) + #else + #define CYTHON_INLINE inline + #endif +#endif +template +void __Pyx_call_destructor(T& x) { + x.~T(); +} +template +class __Pyx_FakeReference { + public: + __Pyx_FakeReference() : ptr(NULL) { } + __Pyx_FakeReference(const T& ref) : ptr(const_cast(&ref)) { } + T *operator->() { return ptr; } + T *operator&() { return ptr; } + operator T&() { return *ptr; } + template bool operator ==(const U& other) const { return *ptr == other; } + template bool operator !=(const U& other) const { return *ptr != other; } + template bool operator==(const __Pyx_FakeReference& other) const { return *ptr == *other.ptr; } + template bool operator!=(const __Pyx_FakeReference& other) const { return *ptr != *other.ptr; } + private: + T *ptr; +}; + +#define __PYX_BUILD_PY_SSIZE_T "n" +#define CYTHON_FORMAT_SSIZE_T "z" +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_DefaultClassType PyClass_Type + #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" + #define __Pyx_DefaultClassType PyType_Type +#if CYTHON_COMPILING_IN_LIMITED_API + static CYTHON_INLINE PyObject* __Pyx_PyCode_New(int a, int p, int k, int l, int s, int f, + PyObject *code, PyObject *c, PyObject* n, PyObject *v, + PyObject *fv, PyObject *cell, PyObject* fn, + PyObject *name, int fline, PyObject *lnos) { + PyObject *exception_table = NULL; + PyObject *types_module=NULL, *code_type=NULL, *result=NULL; + #if __PYX_LIMITED_VERSION_HEX < 0x030B0000 + PyObject *version_info; // borrowed + PyObject *py_minor_version = NULL; + #endif + long minor_version = 0; + PyObject *type, *value, *traceback; + PyErr_Fetch(&type, &value, &traceback); + #if __PYX_LIMITED_VERSION_HEX >= 0x030B0000 + minor_version = 11; // we don't yet need to distinguish between versions > 11 + #else + if (!(version_info = PySys_GetObject("version_info"))) goto end; + if (!(py_minor_version = PySequence_GetItem(version_info, 1))) goto end; + minor_version = PyLong_AsLong(py_minor_version); + Py_DECREF(py_minor_version); + if (minor_version == -1 && PyErr_Occurred()) goto end; + #endif + if (!(types_module = PyImport_ImportModule("types"))) goto end; + if (!(code_type = PyObject_GetAttrString(types_module, "CodeType"))) goto end; + if (minor_version <= 7) { + (void)p; + result = PyObject_CallFunction(code_type, "iiiiiOOOOOOiOO", a, k, l, s, f, code, + c, n, v, fn, name, fline, lnos, fv, cell); + } else if (minor_version <= 10) { + result = PyObject_CallFunction(code_type, "iiiiiiOOOOOOiOO", a,p, k, l, s, f, code, + c, n, v, fn, name, fline, lnos, fv, cell); + } else { + if (!(exception_table = PyBytes_FromStringAndSize(NULL, 0))) goto end; + result = PyObject_CallFunction(code_type, "iiiiiiOOOOOOOiOO", a,p, k, l, s, f, code, + c, n, v, fn, name, name, fline, lnos, exception_table, fv, cell); + } + end: + Py_XDECREF(code_type); + Py_XDECREF(exception_table); + Py_XDECREF(types_module); + if (type) { + PyErr_Restore(type, value, traceback); + } + return result; + } + #ifndef CO_OPTIMIZED + #define CO_OPTIMIZED 0x0001 + #endif + #ifndef CO_NEWLOCALS + #define CO_NEWLOCALS 0x0002 + #endif + #ifndef CO_VARARGS + #define CO_VARARGS 0x0004 + #endif + #ifndef CO_VARKEYWORDS + #define CO_VARKEYWORDS 0x0008 + #endif + #ifndef CO_ASYNC_GENERATOR + #define CO_ASYNC_GENERATOR 0x0200 + #endif + #ifndef CO_GENERATOR + #define CO_GENERATOR 0x0020 + #endif + #ifndef CO_COROUTINE + #define CO_COROUTINE 0x0080 + #endif +#elif PY_VERSION_HEX >= 0x030B0000 + static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int p, int k, int l, int s, int f, + PyObject *code, PyObject *c, PyObject* n, PyObject *v, + PyObject *fv, PyObject *cell, PyObject* fn, + PyObject *name, int fline, PyObject *lnos) { + PyCodeObject *result; + PyObject *empty_bytes = PyBytes_FromStringAndSize("", 0); // we don't have access to __pyx_empty_bytes here + if (!empty_bytes) return NULL; + result = + #if PY_VERSION_HEX >= 0x030C0000 + PyUnstable_Code_NewWithPosOnlyArgs + #else + PyCode_NewWithPosOnlyArgs + #endif + (a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, name, fline, lnos, empty_bytes); + Py_DECREF(empty_bytes); + return result; + } +#elif PY_VERSION_HEX >= 0x030800B2 && !CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_NewWithPosOnlyArgs(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) +#else + #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) +#endif +#endif +#if PY_VERSION_HEX >= 0x030900A4 || defined(Py_IS_TYPE) + #define __Pyx_IS_TYPE(ob, type) Py_IS_TYPE(ob, type) +#else + #define __Pyx_IS_TYPE(ob, type) (((const PyObject*)ob)->ob_type == (type)) +#endif +#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_Is) + #define __Pyx_Py_Is(x, y) Py_Is(x, y) +#else + #define __Pyx_Py_Is(x, y) ((x) == (y)) +#endif +#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsNone) + #define __Pyx_Py_IsNone(ob) Py_IsNone(ob) +#else + #define __Pyx_Py_IsNone(ob) __Pyx_Py_Is((ob), Py_None) +#endif +#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsTrue) + #define __Pyx_Py_IsTrue(ob) Py_IsTrue(ob) +#else + #define __Pyx_Py_IsTrue(ob) __Pyx_Py_Is((ob), Py_True) +#endif +#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsFalse) + #define __Pyx_Py_IsFalse(ob) Py_IsFalse(ob) +#else + #define __Pyx_Py_IsFalse(ob) __Pyx_Py_Is((ob), Py_False) +#endif +#define __Pyx_NoneAsNull(obj) (__Pyx_Py_IsNone(obj) ? NULL : (obj)) +#if PY_VERSION_HEX >= 0x030900F0 && !CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyObject_GC_IsFinalized(o) PyObject_GC_IsFinalized(o) +#else + #define __Pyx_PyObject_GC_IsFinalized(o) _PyGC_FINALIZED(o) +#endif +#ifndef CO_COROUTINE + #define CO_COROUTINE 0x80 +#endif +#ifndef CO_ASYNC_GENERATOR + #define CO_ASYNC_GENERATOR 0x200 +#endif +#ifndef Py_TPFLAGS_CHECKTYPES + #define Py_TPFLAGS_CHECKTYPES 0 +#endif +#ifndef Py_TPFLAGS_HAVE_INDEX + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#ifndef Py_TPFLAGS_HAVE_FINALIZE + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#ifndef Py_TPFLAGS_SEQUENCE + #define Py_TPFLAGS_SEQUENCE 0 +#endif +#ifndef Py_TPFLAGS_MAPPING + #define Py_TPFLAGS_MAPPING 0 +#endif +#ifndef METH_STACKLESS + #define METH_STACKLESS 0 +#endif +#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL) + #ifndef METH_FASTCALL + #define METH_FASTCALL 0x80 + #endif + typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs); + typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args, + Py_ssize_t nargs, PyObject *kwnames); +#else + #define __Pyx_PyCFunctionFast _PyCFunctionFast + #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords +#endif +#if CYTHON_METH_FASTCALL + #define __Pyx_METH_FASTCALL METH_FASTCALL + #define __Pyx_PyCFunction_FastCall __Pyx_PyCFunctionFast + #define __Pyx_PyCFunction_FastCallWithKeywords __Pyx_PyCFunctionFastWithKeywords +#else + #define __Pyx_METH_FASTCALL METH_VARARGS + #define __Pyx_PyCFunction_FastCall PyCFunction + #define __Pyx_PyCFunction_FastCallWithKeywords PyCFunctionWithKeywords +#endif +#if CYTHON_VECTORCALL + #define __pyx_vectorcallfunc vectorcallfunc + #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET PY_VECTORCALL_ARGUMENTS_OFFSET + #define __Pyx_PyVectorcall_NARGS(n) PyVectorcall_NARGS((size_t)(n)) +#elif CYTHON_BACKPORT_VECTORCALL + typedef PyObject *(*__pyx_vectorcallfunc)(PyObject *callable, PyObject *const *args, + size_t nargsf, PyObject *kwnames); + #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET ((size_t)1 << (8 * sizeof(size_t) - 1)) + #define __Pyx_PyVectorcall_NARGS(n) ((Py_ssize_t)(((size_t)(n)) & ~__Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET)) +#else + #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET 0 + #define __Pyx_PyVectorcall_NARGS(n) ((Py_ssize_t)(n)) +#endif +#if PY_MAJOR_VERSION >= 0x030900B1 +#define __Pyx_PyCFunction_CheckExact(func) PyCFunction_CheckExact(func) +#else +#define __Pyx_PyCFunction_CheckExact(func) PyCFunction_Check(func) +#endif +#define __Pyx_CyOrPyCFunction_Check(func) PyCFunction_Check(func) +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_CyOrPyCFunction_GET_FUNCTION(func) (((PyCFunctionObject*)(func))->m_ml->ml_meth) +#elif !CYTHON_COMPILING_IN_LIMITED_API +#define __Pyx_CyOrPyCFunction_GET_FUNCTION(func) PyCFunction_GET_FUNCTION(func) +#endif +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_CyOrPyCFunction_GET_FLAGS(func) (((PyCFunctionObject*)(func))->m_ml->ml_flags) +static CYTHON_INLINE PyObject* __Pyx_CyOrPyCFunction_GET_SELF(PyObject *func) { + return (__Pyx_CyOrPyCFunction_GET_FLAGS(func) & METH_STATIC) ? NULL : ((PyCFunctionObject*)func)->m_self; +} +#endif +static CYTHON_INLINE int __Pyx__IsSameCFunction(PyObject *func, void *cfunc) { +#if CYTHON_COMPILING_IN_LIMITED_API + return PyCFunction_Check(func) && PyCFunction_GetFunction(func) == (PyCFunction) cfunc; +#else + return PyCFunction_Check(func) && PyCFunction_GET_FUNCTION(func) == (PyCFunction) cfunc; +#endif +} +#define __Pyx_IsSameCFunction(func, cfunc) __Pyx__IsSameCFunction(func, cfunc) +#if __PYX_LIMITED_VERSION_HEX < 0x030900B1 + #define __Pyx_PyType_FromModuleAndSpec(m, s, b) ((void)m, PyType_FromSpecWithBases(s, b)) + typedef PyObject *(*__Pyx_PyCMethod)(PyObject *, PyTypeObject *, PyObject *const *, size_t, PyObject *); +#else + #define __Pyx_PyType_FromModuleAndSpec(m, s, b) PyType_FromModuleAndSpec(m, s, b) + #define __Pyx_PyCMethod PyCMethod +#endif +#ifndef METH_METHOD + #define METH_METHOD 0x200 +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) + #define PyObject_Malloc(s) PyMem_Malloc(s) + #define PyObject_Free(p) PyMem_Free(p) + #define PyObject_Realloc(p) PyMem_Realloc(p) +#endif +#if CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) +#else + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) +#endif +#if CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_PyThreadState_Current PyThreadState_Get() +#elif !CYTHON_FAST_THREAD_STATE + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#elif PY_VERSION_HEX >= 0x030d00A1 + #define __Pyx_PyThreadState_Current PyThreadState_GetUnchecked() +#elif PY_VERSION_HEX >= 0x03060000 + #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() +#elif PY_VERSION_HEX >= 0x03000000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#else + #define __Pyx_PyThreadState_Current _PyThreadState_Current +#endif +#if CYTHON_COMPILING_IN_LIMITED_API +static CYTHON_INLINE void *__Pyx_PyModule_GetState(PyObject *op) +{ + void *result; + result = PyModule_GetState(op); + if (!result) + Py_FatalError("Couldn't find the module state"); + return result; +} +#endif +#define __Pyx_PyObject_GetSlot(obj, name, func_ctype) __Pyx_PyType_GetSlot(Py_TYPE(obj), name, func_ctype) +#if CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_PyType_GetSlot(type, name, func_ctype) ((func_ctype) PyType_GetSlot((type), Py_##name)) +#else + #define __Pyx_PyType_GetSlot(type, name, func_ctype) ((type)->name) +#endif +#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT) +#include "pythread.h" +#define Py_tss_NEEDS_INIT 0 +typedef int Py_tss_t; +static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) { + *key = PyThread_create_key(); + return 0; +} +static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) { + Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t)); + *key = Py_tss_NEEDS_INIT; + return key; +} +static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) { + PyObject_Free(key); +} +static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) { + return *key != Py_tss_NEEDS_INIT; +} +static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) { + PyThread_delete_key(*key); + *key = Py_tss_NEEDS_INIT; +} +static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) { + return PyThread_set_key_value(*key, value); +} +static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { + return PyThread_get_key_value(*key); +} +#endif +#if PY_MAJOR_VERSION < 3 + #if CYTHON_COMPILING_IN_PYPY + #if PYPY_VERSION_NUM < 0x07030600 + #if defined(__cplusplus) && __cplusplus >= 201402L + [[deprecated("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6")]] + #elif defined(__GNUC__) || defined(__clang__) + __attribute__ ((__deprecated__("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6"))) + #elif defined(_MSC_VER) + __declspec(deprecated("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6")) + #endif + static CYTHON_INLINE int PyGILState_Check(void) { + return 0; + } + #else // PYPY_VERSION_NUM < 0x07030600 + #endif // PYPY_VERSION_NUM < 0x07030600 + #else + static CYTHON_INLINE int PyGILState_Check(void) { + PyThreadState * tstate = _PyThreadState_Current; + return tstate && (tstate == PyGILState_GetThisThreadState()); + } + #endif +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030d0000 || defined(_PyDict_NewPresized) +#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) +#else +#define __Pyx_PyDict_NewPresized(n) PyDict_New() +#endif +#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX > 0x030600B4 && PY_VERSION_HEX < 0x030d0000 && CYTHON_USE_UNICODE_INTERNALS +#define __Pyx_PyDict_GetItemStrWithError(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) +static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStr(PyObject *dict, PyObject *name) { + PyObject *res = __Pyx_PyDict_GetItemStrWithError(dict, name); + if (res == NULL) PyErr_Clear(); + return res; +} +#elif PY_MAJOR_VERSION >= 3 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07020000) +#define __Pyx_PyDict_GetItemStrWithError PyDict_GetItemWithError +#define __Pyx_PyDict_GetItemStr PyDict_GetItem +#else +static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStrWithError(PyObject *dict, PyObject *name) { +#if CYTHON_COMPILING_IN_PYPY + return PyDict_GetItem(dict, name); +#else + PyDictEntry *ep; + PyDictObject *mp = (PyDictObject*) dict; + long hash = ((PyStringObject *) name)->ob_shash; + assert(hash != -1); + ep = (mp->ma_lookup)(mp, name, hash); + if (ep == NULL) { + return NULL; + } + return ep->me_value; +#endif +} +#define __Pyx_PyDict_GetItemStr PyDict_GetItem +#endif +#if CYTHON_USE_TYPE_SLOTS + #define __Pyx_PyType_GetFlags(tp) (((PyTypeObject *)tp)->tp_flags) + #define __Pyx_PyType_HasFeature(type, feature) ((__Pyx_PyType_GetFlags(type) & (feature)) != 0) + #define __Pyx_PyObject_GetIterNextFunc(obj) (Py_TYPE(obj)->tp_iternext) +#else + #define __Pyx_PyType_GetFlags(tp) (PyType_GetFlags((PyTypeObject *)tp)) + #define __Pyx_PyType_HasFeature(type, feature) PyType_HasFeature(type, feature) + #define __Pyx_PyObject_GetIterNextFunc(obj) PyIter_Next +#endif +#if CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_SetItemOnTypeDict(tp, k, v) PyObject_GenericSetAttr((PyObject*)tp, k, v) +#else + #define __Pyx_SetItemOnTypeDict(tp, k, v) PyDict_SetItem(tp->tp_dict, k, v) +#endif +#if CYTHON_USE_TYPE_SPECS && PY_VERSION_HEX >= 0x03080000 +#define __Pyx_PyHeapTypeObject_GC_Del(obj) {\ + PyTypeObject *type = Py_TYPE((PyObject*)obj);\ + assert(__Pyx_PyType_HasFeature(type, Py_TPFLAGS_HEAPTYPE));\ + PyObject_GC_Del(obj);\ + Py_DECREF(type);\ +} +#else +#define __Pyx_PyHeapTypeObject_GC_Del(obj) PyObject_GC_Del(obj) +#endif +#if CYTHON_COMPILING_IN_LIMITED_API + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GetLength(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_ReadChar(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((void)u, 1114111U) + #define __Pyx_PyUnicode_KIND(u) ((void)u, (0)) + #define __Pyx_PyUnicode_DATA(u) ((void*)u) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)k, PyUnicode_ReadChar((PyObject*)(d), i)) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GetLength(u)) +#elif PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #if PY_VERSION_HEX >= 0x030C0000 + #define __Pyx_PyUnicode_READY(op) (0) + #else + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #endif + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) + #define __Pyx_PyUnicode_KIND(u) ((int)PyUnicode_KIND(u)) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, (Py_UCS4) ch) + #if PY_VERSION_HEX >= 0x030C0000 + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_LENGTH(u)) + #else + #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03090000 + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : ((PyCompactUnicodeObject *)(u))->wstr_length)) + #else + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) + #endif + #endif +#else + #define CYTHON_PEP393_ENABLED 0 + #define PyUnicode_1BYTE_KIND 1 + #define PyUnicode_2BYTE_KIND 2 + #define PyUnicode_4BYTE_KIND 4 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535U : 1114111U) + #define __Pyx_PyUnicode_KIND(u) ((int)sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = (Py_UNICODE) ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #if !defined(PyUnicode_DecodeUnicodeEscape) + #define PyUnicode_DecodeUnicodeEscape(s, size, errors) PyUnicode_Decode(s, size, "unicode_escape", errors) + #endif + #if !defined(PyUnicode_Contains) || (PY_MAJOR_VERSION == 2 && PYPY_VERSION_NUM < 0x07030500) + #undef PyUnicode_Contains + #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) + #endif + #if !defined(PyByteArray_Check) + #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) + #endif + #if !defined(PyObject_Format) + #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) + #endif +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) + #define PyObject_ASCII(o) PyObject_Repr(o) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact +#ifndef PyObject_Unicode + #define PyObject_Unicode PyObject_Str +#endif +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#if CYTHON_COMPILING_IN_CPYTHON + #define __Pyx_PySequence_ListKeepNew(obj)\ + (likely(PyList_CheckExact(obj) && Py_REFCNT(obj) == 1) ? __Pyx_NewRef(obj) : PySequence_List(obj)) +#else + #define __Pyx_PySequence_ListKeepNew(obj) PySequence_List(obj) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) __Pyx_IS_TYPE(obj, &PySet_Type) +#endif +#if PY_VERSION_HEX >= 0x030900A4 + #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt) + #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size) +#else + #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt) + #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size) +#endif +#if CYTHON_ASSUME_SAFE_MACROS + #define __Pyx_PySequence_ITEM(o, i) PySequence_ITEM(o, i) + #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) + #define __Pyx_PyTuple_SET_ITEM(o, i, v) (PyTuple_SET_ITEM(o, i, v), (0)) + #define __Pyx_PyList_SET_ITEM(o, i, v) (PyList_SET_ITEM(o, i, v), (0)) + #define __Pyx_PyTuple_GET_SIZE(o) PyTuple_GET_SIZE(o) + #define __Pyx_PyList_GET_SIZE(o) PyList_GET_SIZE(o) + #define __Pyx_PySet_GET_SIZE(o) PySet_GET_SIZE(o) + #define __Pyx_PyBytes_GET_SIZE(o) PyBytes_GET_SIZE(o) + #define __Pyx_PyByteArray_GET_SIZE(o) PyByteArray_GET_SIZE(o) +#else + #define __Pyx_PySequence_ITEM(o, i) PySequence_GetItem(o, i) + #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq) + #define __Pyx_PyTuple_SET_ITEM(o, i, v) PyTuple_SetItem(o, i, v) + #define __Pyx_PyList_SET_ITEM(o, i, v) PyList_SetItem(o, i, v) + #define __Pyx_PyTuple_GET_SIZE(o) PyTuple_Size(o) + #define __Pyx_PyList_GET_SIZE(o) PyList_Size(o) + #define __Pyx_PySet_GET_SIZE(o) PySet_Size(o) + #define __Pyx_PyBytes_GET_SIZE(o) PyBytes_Size(o) + #define __Pyx_PyByteArray_GET_SIZE(o) PyByteArray_Size(o) +#endif +#if PY_VERSION_HEX >= 0x030d00A1 + #define __Pyx_PyImport_AddModuleRef(name) PyImport_AddModuleRef(name) +#else + static CYTHON_INLINE PyObject *__Pyx_PyImport_AddModuleRef(const char *name) { + PyObject *module = PyImport_AddModule(name); + Py_XINCREF(module); + return module; + } +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define __Pyx_Py3Int_Check(op) PyLong_Check(op) + #define __Pyx_Py3Int_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#else + #define __Pyx_Py3Int_Check(op) (PyLong_Check(op) || PyInt_Check(op)) + #define __Pyx_Py3Int_CheckExact(op) (PyLong_CheckExact(op) || PyInt_CheckExact(op)) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t __Pyx_PyIndex_AsHash_t +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t __Pyx_PyIndex_AsSsize_t +#endif +#if CYTHON_USE_ASYNC_SLOTS + #if PY_VERSION_HEX >= 0x030500B1 + #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods + #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) + #else + #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) + #endif +#else + #define __Pyx_PyType_AsAsync(obj) NULL +#endif +#ifndef __Pyx_PyAsyncMethodsStruct + typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; + } __Pyx_PyAsyncMethodsStruct; +#endif + +#if defined(_WIN32) || defined(WIN32) || defined(MS_WINDOWS) + #if !defined(_USE_MATH_DEFINES) + #define _USE_MATH_DEFINES + #endif +#endif +#include +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif +#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) +#define __Pyx_truncl trunc +#else +#define __Pyx_truncl truncl +#endif + +#define __PYX_MARK_ERR_POS(f_index, lineno) \ + { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; } +#define __PYX_ERR(f_index, lineno, Ln_error) \ + { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; } + +#ifdef CYTHON_EXTERN_C + #undef __PYX_EXTERN_C + #define __PYX_EXTERN_C CYTHON_EXTERN_C +#elif defined(__PYX_EXTERN_C) + #ifdef _MSC_VER + #pragma message ("Please do not define the '__PYX_EXTERN_C' macro externally. Use 'CYTHON_EXTERN_C' instead.") + #else + #warning Please do not define the '__PYX_EXTERN_C' macro externally. Use 'CYTHON_EXTERN_C' instead. + #endif +#else + #define __PYX_EXTERN_C extern "C++" +#endif + +#define __PYX_HAVE__wfpt_n +#define __PYX_HAVE_API__wfpt_n +/* Early includes */ +#include +#include + + /* Using NumPy API declarations from "numpy/__init__.cython-30.pxd" */ + +#include "numpy/arrayobject.h" +#include "numpy/ndarrayobject.h" +#include "numpy/ndarraytypes.h" +#include "numpy/arrayscalars.h" +#include "numpy/ufuncobject.h" +#include "math.h" +#include +#include "pythread.h" +#include +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8) +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_uchar_cast(c) ((unsigned char)c) +#define __Pyx_long_cast(x) ((long)x) +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ + (sizeof(type) < sizeof(Py_ssize_t)) ||\ + (sizeof(type) > sizeof(Py_ssize_t) &&\ + likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX) &&\ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ + v == (type)PY_SSIZE_T_MIN))) ||\ + (sizeof(type) == sizeof(Py_ssize_t) &&\ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX))) ) +static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) { + return (size_t) i < (size_t) limit; +} +#if defined (__cplusplus) && __cplusplus >= 201103L + #include + #define __Pyx_sst_abs(value) std::abs(value) +#elif SIZEOF_INT >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) abs(value) +#elif SIZEOF_LONG >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) labs(value) +#elif defined (_MSC_VER) + #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define __Pyx_sst_abs(value) llabs(value) +#elif defined (__GNUC__) + #define __Pyx_sst_abs(value) __builtin_llabs(value) +#else + #define __Pyx_sst_abs(value) ((value<0) ? -value : value) +#endif +static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s); +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +static CYTHON_INLINE PyObject* __Pyx_PyByteArray_FromString(const char*); +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyObject_AsWritableString(s) ((char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableSString(s) ((signed char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) +#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) +#if CYTHON_COMPILING_IN_LIMITED_API +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const wchar_t *u) +{ + const wchar_t *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#else +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) +{ + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#endif +#define __Pyx_PyUnicode_FromOrdinal(o) PyUnicode_FromOrdinal((int)o) +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) +#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); +#define __Pyx_PySequence_Tuple(obj)\ + (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*); +#if CYTHON_ASSUME_SAFE_MACROS +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) +#else +#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) +#endif +#if CYTHON_USE_PYLONG_INTERNALS + #if PY_VERSION_HEX >= 0x030C00A7 + #ifndef _PyLong_SIGN_MASK + #define _PyLong_SIGN_MASK 3 + #endif + #ifndef _PyLong_NON_SIZE_BITS + #define _PyLong_NON_SIZE_BITS 3 + #endif + #define __Pyx_PyLong_Sign(x) (((PyLongObject*)x)->long_value.lv_tag & _PyLong_SIGN_MASK) + #define __Pyx_PyLong_IsNeg(x) ((__Pyx_PyLong_Sign(x) & 2) != 0) + #define __Pyx_PyLong_IsNonNeg(x) (!__Pyx_PyLong_IsNeg(x)) + #define __Pyx_PyLong_IsZero(x) (__Pyx_PyLong_Sign(x) & 1) + #define __Pyx_PyLong_IsPos(x) (__Pyx_PyLong_Sign(x) == 0) + #define __Pyx_PyLong_CompactValueUnsigned(x) (__Pyx_PyLong_Digits(x)[0]) + #define __Pyx_PyLong_DigitCount(x) ((Py_ssize_t) (((PyLongObject*)x)->long_value.lv_tag >> _PyLong_NON_SIZE_BITS)) + #define __Pyx_PyLong_SignedDigitCount(x)\ + ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * __Pyx_PyLong_DigitCount(x)) + #if defined(PyUnstable_Long_IsCompact) && defined(PyUnstable_Long_CompactValue) + #define __Pyx_PyLong_IsCompact(x) PyUnstable_Long_IsCompact((PyLongObject*) x) + #define __Pyx_PyLong_CompactValue(x) PyUnstable_Long_CompactValue((PyLongObject*) x) + #else + #define __Pyx_PyLong_IsCompact(x) (((PyLongObject*)x)->long_value.lv_tag < (2 << _PyLong_NON_SIZE_BITS)) + #define __Pyx_PyLong_CompactValue(x) ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * (Py_ssize_t) __Pyx_PyLong_Digits(x)[0]) + #endif + typedef Py_ssize_t __Pyx_compact_pylong; + typedef size_t __Pyx_compact_upylong; + #else // Py < 3.12 + #define __Pyx_PyLong_IsNeg(x) (Py_SIZE(x) < 0) + #define __Pyx_PyLong_IsNonNeg(x) (Py_SIZE(x) >= 0) + #define __Pyx_PyLong_IsZero(x) (Py_SIZE(x) == 0) + #define __Pyx_PyLong_IsPos(x) (Py_SIZE(x) > 0) + #define __Pyx_PyLong_CompactValueUnsigned(x) ((Py_SIZE(x) == 0) ? 0 : __Pyx_PyLong_Digits(x)[0]) + #define __Pyx_PyLong_DigitCount(x) __Pyx_sst_abs(Py_SIZE(x)) + #define __Pyx_PyLong_SignedDigitCount(x) Py_SIZE(x) + #define __Pyx_PyLong_IsCompact(x) (Py_SIZE(x) == 0 || Py_SIZE(x) == 1 || Py_SIZE(x) == -1) + #define __Pyx_PyLong_CompactValue(x)\ + ((Py_SIZE(x) == 0) ? (sdigit) 0 : ((Py_SIZE(x) < 0) ? -(sdigit)__Pyx_PyLong_Digits(x)[0] : (sdigit)__Pyx_PyLong_Digits(x)[0])) + typedef sdigit __Pyx_compact_pylong; + typedef digit __Pyx_compact_upylong; + #endif + #if PY_VERSION_HEX >= 0x030C00A5 + #define __Pyx_PyLong_Digits(x) (((PyLongObject*)x)->long_value.ob_digit) + #else + #define __Pyx_PyLong_Digits(x) (((PyLongObject*)x)->ob_digit) + #endif +#endif +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +#include +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + const char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + if (strcmp(default_encoding_c, "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = (char) c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (!ascii_chars_u) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + Py_DECREF(ascii_chars_u); + Py_DECREF(ascii_chars_b); + } + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +#include +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1); + if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ +static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } + +#if !CYTHON_USE_MODULE_STATE +static PyObject *__pyx_m = NULL; +#endif +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm = __FILE__; +static const char *__pyx_filename; + +/* Header.proto */ +#if !defined(CYTHON_CCOMPLEX) + #if defined(__cplusplus) + #define CYTHON_CCOMPLEX 1 + #elif (defined(_Complex_I) && !defined(_MSC_VER)) || ((defined (__STDC_VERSION__) && __STDC_VERSION__ >= 201112L) && !defined(__STDC_NO_COMPLEX__) && !defined(_MSC_VER)) + #define CYTHON_CCOMPLEX 1 + #else + #define CYTHON_CCOMPLEX 0 + #endif +#endif +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #include + #else + #include + #endif +#endif +#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) + #undef _Complex_I + #define _Complex_I 1.0fj +#endif + +/* #### Code section: filename_table ### */ + +static const char *__pyx_f[] = { + "wfpt_n.pyx", + "", + "__init__.cython-30.pxd", + "pdf.pxi", + "integrate.pxi", + "type.pxd", +}; +/* #### Code section: utility_code_proto_before_types ### */ +/* ForceInitThreads.proto */ +#ifndef __PYX_FORCE_INIT_THREADS + #define __PYX_FORCE_INIT_THREADS 0 +#endif + +/* NoFastGil.proto */ +#define __Pyx_PyGILState_Ensure PyGILState_Ensure +#define __Pyx_PyGILState_Release PyGILState_Release +#define __Pyx_FastGIL_Remember() +#define __Pyx_FastGIL_Forget() +#define __Pyx_FastGilFuncInit() + +/* BufferFormatStructs.proto */ +struct __Pyx_StructField_; +#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) +typedef struct { + const char* name; + struct __Pyx_StructField_* fields; + size_t size; + size_t arraysize[8]; + int ndim; + char typegroup; + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + +/* Atomics.proto */ +#include +#ifndef CYTHON_ATOMICS + #define CYTHON_ATOMICS 1 +#endif +#define __PYX_CYTHON_ATOMICS_ENABLED() CYTHON_ATOMICS +#define __pyx_atomic_int_type int +#define __pyx_nonatomic_int_type int +#if CYTHON_ATOMICS && (defined(__STDC_VERSION__) &&\ + (__STDC_VERSION__ >= 201112L) &&\ + !defined(__STDC_NO_ATOMICS__)) + #include +#elif CYTHON_ATOMICS && (defined(__cplusplus) && (\ + (__cplusplus >= 201103L) ||\ + (defined(_MSC_VER) && _MSC_VER >= 1700))) + #include +#endif +#if CYTHON_ATOMICS && (defined(__STDC_VERSION__) &&\ + (__STDC_VERSION__ >= 201112L) &&\ + !defined(__STDC_NO_ATOMICS__) &&\ + ATOMIC_INT_LOCK_FREE == 2) + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type atomic_int + #define __pyx_atomic_incr_aligned(value) atomic_fetch_add_explicit(value, 1, memory_order_relaxed) + #define __pyx_atomic_decr_aligned(value) atomic_fetch_sub_explicit(value, 1, memory_order_acq_rel) + #if defined(__PYX_DEBUG_ATOMICS) && defined(_MSC_VER) + #pragma message ("Using standard C atomics") + #elif defined(__PYX_DEBUG_ATOMICS) + #warning "Using standard C atomics" + #endif +#elif CYTHON_ATOMICS && (defined(__cplusplus) && (\ + (__cplusplus >= 201103L) ||\ +\ + (defined(_MSC_VER) && _MSC_VER >= 1700)) &&\ + ATOMIC_INT_LOCK_FREE == 2) + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type std::atomic_int + #define __pyx_atomic_incr_aligned(value) std::atomic_fetch_add_explicit(value, 1, std::memory_order_relaxed) + #define __pyx_atomic_decr_aligned(value) std::atomic_fetch_sub_explicit(value, 1, std::memory_order_acq_rel) + #if defined(__PYX_DEBUG_ATOMICS) && defined(_MSC_VER) + #pragma message ("Using standard C++ atomics") + #elif defined(__PYX_DEBUG_ATOMICS) + #warning "Using standard C++ atomics" + #endif +#elif CYTHON_ATOMICS && (__GNUC__ >= 5 || (__GNUC__ == 4 &&\ + (__GNUC_MINOR__ > 1 ||\ + (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL__ >= 2)))) + #define __pyx_atomic_incr_aligned(value) __sync_fetch_and_add(value, 1) + #define __pyx_atomic_decr_aligned(value) __sync_fetch_and_sub(value, 1) + #ifdef __PYX_DEBUG_ATOMICS + #warning "Using GNU atomics" + #endif +#elif CYTHON_ATOMICS && defined(_MSC_VER) + #include + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type long + #undef __pyx_nonatomic_int_type + #define __pyx_nonatomic_int_type long + #pragma intrinsic (_InterlockedExchangeAdd) + #define __pyx_atomic_incr_aligned(value) _InterlockedExchangeAdd(value, 1) + #define __pyx_atomic_decr_aligned(value) _InterlockedExchangeAdd(value, -1) + #ifdef __PYX_DEBUG_ATOMICS + #pragma message ("Using MSVC atomics") + #endif +#else + #undef CYTHON_ATOMICS + #define CYTHON_ATOMICS 0 + #ifdef __PYX_DEBUG_ATOMICS + #warning "Not using atomics" + #endif +#endif +#if CYTHON_ATOMICS + #define __pyx_add_acquisition_count(memview)\ + __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview)) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview)) +#else + #define __pyx_add_acquisition_count(memview)\ + __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) +#endif + +/* MemviewSliceStruct.proto */ +struct __pyx_memoryview_obj; +typedef struct { + struct __pyx_memoryview_obj *memview; + char *data; + Py_ssize_t shape[8]; + Py_ssize_t strides[8]; + Py_ssize_t suboffsets[8]; +} __Pyx_memviewslice; +#define __Pyx_MemoryView_Len(m) (m.shape[0]) + +/* #### Code section: numeric_typedefs ### */ + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":730 + * # in Cython to enable them only on the right systems. + * + * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + */ +typedef npy_int8 __pyx_t_5numpy_int8_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":731 + * + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t + */ +typedef npy_int16 __pyx_t_5numpy_int16_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":732 + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< + * ctypedef npy_int64 int64_t + * #ctypedef npy_int96 int96_t + */ +typedef npy_int32 __pyx_t_5numpy_int32_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":733 + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< + * #ctypedef npy_int96 int96_t + * #ctypedef npy_int128 int128_t + */ +typedef npy_int64 __pyx_t_5numpy_int64_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":737 + * #ctypedef npy_int128 int128_t + * + * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + */ +typedef npy_uint8 __pyx_t_5numpy_uint8_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":738 + * + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t + */ +typedef npy_uint16 __pyx_t_5numpy_uint16_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":739 + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< + * ctypedef npy_uint64 uint64_t + * #ctypedef npy_uint96 uint96_t + */ +typedef npy_uint32 __pyx_t_5numpy_uint32_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":740 + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< + * #ctypedef npy_uint96 uint96_t + * #ctypedef npy_uint128 uint128_t + */ +typedef npy_uint64 __pyx_t_5numpy_uint64_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":744 + * #ctypedef npy_uint128 uint128_t + * + * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< + * ctypedef npy_float64 float64_t + * #ctypedef npy_float80 float80_t + */ +typedef npy_float32 __pyx_t_5numpy_float32_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":745 + * + * ctypedef npy_float32 float32_t + * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< + * #ctypedef npy_float80 float80_t + * #ctypedef npy_float128 float128_t + */ +typedef npy_float64 __pyx_t_5numpy_float64_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":754 + * # The int types are mapped a bit surprising -- + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong longlong_t + * + */ +typedef npy_long __pyx_t_5numpy_int_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":755 + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t + * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_ulong uint_t + */ +typedef npy_longlong __pyx_t_5numpy_longlong_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":757 + * ctypedef npy_longlong longlong_t + * + * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulonglong_t + * + */ +typedef npy_ulong __pyx_t_5numpy_uint_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":758 + * + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_intp intp_t + */ +typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":760 + * ctypedef npy_ulonglong ulonglong_t + * + * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< + * ctypedef npy_uintp uintp_t + * + */ +typedef npy_intp __pyx_t_5numpy_intp_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":761 + * + * ctypedef npy_intp intp_t + * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< + * + * ctypedef npy_double float_t + */ +typedef npy_uintp __pyx_t_5numpy_uintp_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":763 + * ctypedef npy_uintp uintp_t + * + * ctypedef npy_double float_t # <<<<<<<<<<<<<< + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t + */ +typedef npy_double __pyx_t_5numpy_float_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":764 + * + * ctypedef npy_double float_t + * ctypedef npy_double double_t # <<<<<<<<<<<<<< + * ctypedef npy_longdouble longdouble_t + * + */ +typedef npy_double __pyx_t_5numpy_double_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":765 + * ctypedef npy_double float_t + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< + * + * ctypedef npy_cfloat cfloat_t + */ +typedef npy_longdouble __pyx_t_5numpy_longdouble_t; +/* #### Code section: complex_type_declarations ### */ +/* Declarations.proto */ +#if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus) + #ifdef __cplusplus + typedef ::std::complex< float > __pyx_t_float_complex; + #else + typedef float _Complex __pyx_t_float_complex; + #endif +#else + typedef struct { float real, imag; } __pyx_t_float_complex; +#endif +static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); + +/* Declarations.proto */ +#if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus) + #ifdef __cplusplus + typedef ::std::complex< double > __pyx_t_double_complex; + #else + typedef double _Complex __pyx_t_double_complex; + #endif +#else + typedef struct { double real, imag; } __pyx_t_double_complex; +#endif +static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); + +/* #### Code section: type_declarations ### */ + +/*--- Type declarations ---*/ +struct __pyx_array_obj; +struct __pyx_MemviewEnum_obj; +struct __pyx_memoryview_obj; +struct __pyx_memoryviewslice_obj; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":767 + * ctypedef npy_longdouble longdouble_t + * + * ctypedef npy_cfloat cfloat_t # <<<<<<<<<<<<<< + * ctypedef npy_cdouble cdouble_t + * ctypedef npy_clongdouble clongdouble_t + */ +typedef npy_cfloat __pyx_t_5numpy_cfloat_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":768 + * + * ctypedef npy_cfloat cfloat_t + * ctypedef npy_cdouble cdouble_t # <<<<<<<<<<<<<< + * ctypedef npy_clongdouble clongdouble_t + * + */ +typedef npy_cdouble __pyx_t_5numpy_cdouble_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":769 + * ctypedef npy_cfloat cfloat_t + * ctypedef npy_cdouble cdouble_t + * ctypedef npy_clongdouble clongdouble_t # <<<<<<<<<<<<<< + * + * ctypedef npy_cdouble complex_t + */ +typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; + +/* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":771 + * ctypedef npy_clongdouble clongdouble_t + * + * ctypedef npy_cdouble complex_t # <<<<<<<<<<<<<< + * + * cdef inline object PyArray_MultiIterNew1(a): + */ +typedef npy_cdouble __pyx_t_5numpy_complex_t; +struct __pyx_opt_args_6wfpt_n_full_pdf; + +/* "pdf.pxi":104 + * return exp(log(p) + ((a*z*sv)**2 - 2*a*v*z - (v**2)*x)/(2*(sv**2)*x+2))/sqrt((sv**2)*x+1)/(a**2) + * + * cpdef double full_pdf(double x, double v, double sv, double a, double # <<<<<<<<<<<<<< + * z, double sz, double t, double st, double err, int + * n_st=2, int n_sz=2, bint use_adaptive=1, double + */ +struct __pyx_opt_args_6wfpt_n_full_pdf { + int __pyx_n; + int n_st; + int n_sz; + int use_adaptive; + double simps_err; +}; + +/* "View.MemoryView":114 + * @cython.collection_type("sequence") + * @cname("__pyx_array") + * cdef class array: # <<<<<<<<<<<<<< + * + * cdef: + */ +struct __pyx_array_obj { + PyObject_HEAD + struct __pyx_vtabstruct_array *__pyx_vtab; + char *data; + Py_ssize_t len; + char *format; + int ndim; + Py_ssize_t *_shape; + Py_ssize_t *_strides; + Py_ssize_t itemsize; + PyObject *mode; + PyObject *_format; + void (*callback_free_data)(void *); + int free_data; + int dtype_is_object; +}; + + +/* "View.MemoryView":302 + * + * @cname('__pyx_MemviewEnum') + * cdef class Enum(object): # <<<<<<<<<<<<<< + * cdef object name + * def __init__(self, name): + */ +struct __pyx_MemviewEnum_obj { + PyObject_HEAD + PyObject *name; +}; + + +/* "View.MemoryView":337 + * + * @cname('__pyx_memoryview') + * cdef class memoryview: # <<<<<<<<<<<<<< + * + * cdef object obj + */ +struct __pyx_memoryview_obj { + PyObject_HEAD + struct __pyx_vtabstruct_memoryview *__pyx_vtab; + PyObject *obj; + PyObject *_size; + PyObject *_array_interface; + PyThread_type_lock lock; + __pyx_atomic_int_type acquisition_count; + Py_buffer view; + int flags; + int dtype_is_object; + __Pyx_TypeInfo *typeinfo; +}; + + +/* "View.MemoryView":952 + * @cython.collection_type("sequence") + * @cname('__pyx_memoryviewslice') + * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< + * "Internal class for passing memoryview slices to Python" + * + */ +struct __pyx_memoryviewslice_obj { + struct __pyx_memoryview_obj __pyx_base; + __Pyx_memviewslice from_slice; + PyObject *from_object; + PyObject *(*to_object_func)(char *); + int (*to_dtype_func)(char *, PyObject *); +}; + + + +/* "View.MemoryView":114 + * @cython.collection_type("sequence") + * @cname("__pyx_array") + * cdef class array: # <<<<<<<<<<<<<< + * + * cdef: + */ + +struct __pyx_vtabstruct_array { + PyObject *(*get_memview)(struct __pyx_array_obj *); +}; +static struct __pyx_vtabstruct_array *__pyx_vtabptr_array; + + +/* "View.MemoryView":337 + * + * @cname('__pyx_memoryview') + * cdef class memoryview: # <<<<<<<<<<<<<< + * + * cdef object obj + */ + +struct __pyx_vtabstruct_memoryview { + char *(*get_item_pointer)(struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); + PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); + PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *); + PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *); + PyObject *(*_get_base)(struct __pyx_memoryview_obj *); +}; +static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview; + + +/* "View.MemoryView":952 + * @cython.collection_type("sequence") + * @cname('__pyx_memoryviewslice') + * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< + * "Internal class for passing memoryview slices to Python" + * + */ + +struct __pyx_vtabstruct__memoryviewslice { + struct __pyx_vtabstruct_memoryview __pyx_base; +}; +static struct __pyx_vtabstruct__memoryviewslice *__pyx_vtabptr__memoryviewslice; +/* #### Code section: utility_code_proto ### */ + +/* --- Runtime support code (head) --- */ +/* Refnanny.proto */ +#ifndef CYTHON_REFNANNY + #define CYTHON_REFNANNY 0 +#endif +#if CYTHON_REFNANNY + typedef struct { + void (*INCREF)(void*, PyObject*, Py_ssize_t); + void (*DECREF)(void*, PyObject*, Py_ssize_t); + void (*GOTREF)(void*, PyObject*, Py_ssize_t); + void (*GIVEREF)(void*, PyObject*, Py_ssize_t); + void* (*SetupContext)(const char*, Py_ssize_t, const char*); + void (*FinishContext)(void**); + } __Pyx_RefNannyAPIStruct; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); + #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL; +#ifdef WITH_THREAD + #define __Pyx_RefNannySetupContext(name, acquire_gil)\ + if (acquire_gil) {\ + PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), (__LINE__), (__FILE__));\ + PyGILState_Release(__pyx_gilstate_save);\ + } else {\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), (__LINE__), (__FILE__));\ + } + #define __Pyx_RefNannyFinishContextNogil() {\ + PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ + __Pyx_RefNannyFinishContext();\ + PyGILState_Release(__pyx_gilstate_save);\ + } +#else + #define __Pyx_RefNannySetupContext(name, acquire_gil)\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), (__LINE__), (__FILE__)) + #define __Pyx_RefNannyFinishContextNogil() __Pyx_RefNannyFinishContext() +#endif + #define __Pyx_RefNannyFinishContextNogil() {\ + PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ + __Pyx_RefNannyFinishContext();\ + PyGILState_Release(__pyx_gilstate_save);\ + } + #define __Pyx_RefNannyFinishContext()\ + __Pyx_RefNanny->FinishContext(&__pyx_refnanny) + #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), (__LINE__)) + #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), (__LINE__)) + #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), (__LINE__)) + #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), (__LINE__)) + #define __Pyx_XINCREF(r) do { if((r) == NULL); else {__Pyx_INCREF(r); }} while(0) + #define __Pyx_XDECREF(r) do { if((r) == NULL); else {__Pyx_DECREF(r); }} while(0) + #define __Pyx_XGOTREF(r) do { if((r) == NULL); else {__Pyx_GOTREF(r); }} while(0) + #define __Pyx_XGIVEREF(r) do { if((r) == NULL); else {__Pyx_GIVEREF(r);}} while(0) +#else + #define __Pyx_RefNannyDeclarations + #define __Pyx_RefNannySetupContext(name, acquire_gil) + #define __Pyx_RefNannyFinishContextNogil() + #define __Pyx_RefNannyFinishContext() + #define __Pyx_INCREF(r) Py_INCREF(r) + #define __Pyx_DECREF(r) Py_DECREF(r) + #define __Pyx_GOTREF(r) + #define __Pyx_GIVEREF(r) + #define __Pyx_XINCREF(r) Py_XINCREF(r) + #define __Pyx_XDECREF(r) Py_XDECREF(r) + #define __Pyx_XGOTREF(r) + #define __Pyx_XGIVEREF(r) +#endif +#define __Pyx_Py_XDECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; Py_XDECREF(tmp);\ + } while (0) +#define __Pyx_XDECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_XDECREF(tmp);\ + } while (0) +#define __Pyx_DECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_DECREF(tmp);\ + } while (0) +#define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) +#define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) + +/* PyErrExceptionMatches.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) +static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); +#else +#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) +#endif + +/* PyThreadStateGet.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; +#define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current; +#if PY_VERSION_HEX >= 0x030C00A6 +#define __Pyx_PyErr_Occurred() (__pyx_tstate->current_exception != NULL) +#define __Pyx_PyErr_CurrentExceptionType() (__pyx_tstate->current_exception ? (PyObject*) Py_TYPE(__pyx_tstate->current_exception) : (PyObject*) NULL) +#else +#define __Pyx_PyErr_Occurred() (__pyx_tstate->curexc_type != NULL) +#define __Pyx_PyErr_CurrentExceptionType() (__pyx_tstate->curexc_type) +#endif +#else +#define __Pyx_PyThreadState_declare +#define __Pyx_PyThreadState_assign +#define __Pyx_PyErr_Occurred() (PyErr_Occurred() != NULL) +#define __Pyx_PyErr_CurrentExceptionType() PyErr_Occurred() +#endif + +/* PyErrFetchRestore.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL) +#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A6 +#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL)) +#else +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#endif +#else +#define __Pyx_PyErr_Clear() PyErr_Clear() +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) +#endif + +/* PyObjectGetAttrStr.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +/* PyObjectGetAttrStrNoError.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name); + +/* GetBuiltinName.proto */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +/* TupleAndListFromArray.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyList_FromArray(PyObject *const *src, Py_ssize_t n); +static CYTHON_INLINE PyObject* __Pyx_PyTuple_FromArray(PyObject *const *src, Py_ssize_t n); +#endif + +/* IncludeStringH.proto */ +#include + +/* BytesEquals.proto */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); + +/* UnicodeEquals.proto */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); + +/* fastcall.proto */ +#if CYTHON_AVOID_BORROWED_REFS + #define __Pyx_Arg_VARARGS(args, i) PySequence_GetItem(args, i) +#elif CYTHON_ASSUME_SAFE_MACROS + #define __Pyx_Arg_VARARGS(args, i) PyTuple_GET_ITEM(args, i) +#else + #define __Pyx_Arg_VARARGS(args, i) PyTuple_GetItem(args, i) +#endif +#if CYTHON_AVOID_BORROWED_REFS + #define __Pyx_Arg_NewRef_VARARGS(arg) __Pyx_NewRef(arg) + #define __Pyx_Arg_XDECREF_VARARGS(arg) Py_XDECREF(arg) +#else + #define __Pyx_Arg_NewRef_VARARGS(arg) arg // no-op + #define __Pyx_Arg_XDECREF_VARARGS(arg) // no-op - arg is borrowed +#endif +#define __Pyx_NumKwargs_VARARGS(kwds) PyDict_Size(kwds) +#define __Pyx_KwValues_VARARGS(args, nargs) NULL +#define __Pyx_GetKwValue_VARARGS(kw, kwvalues, s) __Pyx_PyDict_GetItemStrWithError(kw, s) +#define __Pyx_KwargsAsDict_VARARGS(kw, kwvalues) PyDict_Copy(kw) +#if CYTHON_METH_FASTCALL + #define __Pyx_Arg_FASTCALL(args, i) args[i] + #define __Pyx_NumKwargs_FASTCALL(kwds) PyTuple_GET_SIZE(kwds) + #define __Pyx_KwValues_FASTCALL(args, nargs) ((args) + (nargs)) + static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues, PyObject *s); +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030d0000 + CYTHON_UNUSED static PyObject *__Pyx_KwargsAsDict_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues); + #else + #define __Pyx_KwargsAsDict_FASTCALL(kw, kwvalues) _PyStack_AsDict(kwvalues, kw) + #endif + #define __Pyx_Arg_NewRef_FASTCALL(arg) arg // no-op, __Pyx_Arg_FASTCALL is direct and this needs + #define __Pyx_Arg_XDECREF_FASTCALL(arg) // no-op - arg was returned from array +#else + #define __Pyx_Arg_FASTCALL __Pyx_Arg_VARARGS + #define __Pyx_NumKwargs_FASTCALL __Pyx_NumKwargs_VARARGS + #define __Pyx_KwValues_FASTCALL __Pyx_KwValues_VARARGS + #define __Pyx_GetKwValue_FASTCALL __Pyx_GetKwValue_VARARGS + #define __Pyx_KwargsAsDict_FASTCALL __Pyx_KwargsAsDict_VARARGS + #define __Pyx_Arg_NewRef_FASTCALL(arg) __Pyx_Arg_NewRef_VARARGS(arg) + #define __Pyx_Arg_XDECREF_FASTCALL(arg) __Pyx_Arg_XDECREF_VARARGS(arg) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS +#define __Pyx_ArgsSlice_VARARGS(args, start, stop) __Pyx_PyTuple_FromArray(&__Pyx_Arg_VARARGS(args, start), stop - start) +#define __Pyx_ArgsSlice_FASTCALL(args, start, stop) __Pyx_PyTuple_FromArray(&__Pyx_Arg_FASTCALL(args, start), stop - start) +#else +#define __Pyx_ArgsSlice_VARARGS(args, start, stop) PyTuple_GetSlice(args, start, stop) +#define __Pyx_ArgsSlice_FASTCALL(args, start, stop) PyTuple_GetSlice(args, start, stop) +#endif + +/* RaiseArgTupleInvalid.proto */ +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); + +/* RaiseDoubleKeywords.proto */ +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +/* ParseKeywords.proto */ +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject *const *kwvalues, + PyObject **argnames[], + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, + const char* function_name); + +/* ArgTypeTest.proto */ +#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ + ((likely(__Pyx_IS_TYPE(obj, type) | (none_allowed && (obj == Py_None)))) ? 1 :\ + __Pyx__ArgTypeTest(obj, type, name, exact)) +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); + +/* RaiseException.proto */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + +/* PyFunctionFastCall.proto */ +#if CYTHON_FAST_PYCALL +#if !CYTHON_VECTORCALL +#define __Pyx_PyFunction_FastCall(func, args, nargs)\ + __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs); +#endif +#define __Pyx_BUILD_ASSERT_EXPR(cond)\ + (sizeof(char [1 - 2*!(cond)]) - 1) +#ifndef Py_MEMBER_SIZE +#define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member) +#endif +#if !CYTHON_VECTORCALL +#if PY_VERSION_HEX >= 0x03080000 + #include "frameobject.h" +#if PY_VERSION_HEX >= 0x030b00a6 && !CYTHON_COMPILING_IN_LIMITED_API + #ifndef Py_BUILD_CORE + #define Py_BUILD_CORE 1 + #endif + #include "internal/pycore_frame.h" +#endif + #define __Pxy_PyFrame_Initialize_Offsets() + #define __Pyx_PyFrame_GetLocalsplus(frame) ((frame)->f_localsplus) +#else + static size_t __pyx_pyframe_localsplus_offset = 0; + #include "frameobject.h" + #define __Pxy_PyFrame_Initialize_Offsets()\ + ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\ + (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus))) + #define __Pyx_PyFrame_GetLocalsplus(frame)\ + (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset)) +#endif +#endif +#endif + +/* PyObjectCall.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +/* PyObjectCallMethO.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); +#endif + +/* PyObjectFastCall.proto */ +#define __Pyx_PyObject_FastCall(func, args, nargs) __Pyx_PyObject_FastCallDict(func, args, (size_t)(nargs), NULL) +static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs); + +/* RaiseUnexpectedTypeError.proto */ +static int __Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj); + +/* GCCDiagnostics.proto */ +#if !defined(__INTEL_COMPILER) && defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)) +#define __Pyx_HAS_GCC_DIAGNOSTIC +#endif + +/* BuildPyUnicode.proto */ +static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, char* chars, int clength, + int prepend_sign, char padding_char); + +/* CIntToPyUnicode.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_int(int value, Py_ssize_t width, char padding_char, char format_char); + +/* CIntToPyUnicode.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_Py_ssize_t(Py_ssize_t value, Py_ssize_t width, char padding_char, char format_char); + +/* JoinPyUnicode.proto */ +static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength, + Py_UCS4 max_char); + +/* StrEquals.proto */ +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals +#else +#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals +#endif + +/* PyObjectFormatSimple.proto */ +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + PyObject_Format(s, f)) +#elif PY_MAJOR_VERSION < 3 + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + likely(PyString_CheckExact(s)) ? PyUnicode_FromEncodedObject(s, NULL, "strict") :\ + PyObject_Format(s, f)) +#elif CYTHON_USE_TYPE_SLOTS + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + likely(PyLong_CheckExact(s)) ? PyLong_Type.tp_repr(s) :\ + likely(PyFloat_CheckExact(s)) ? PyFloat_Type.tp_repr(s) :\ + PyObject_Format(s, f)) +#else + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + PyObject_Format(s, f)) +#endif + +CYTHON_UNUSED static int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/ +/* GetAttr.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); + +/* GetItemInt.proto */ +#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ + (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ + __Pyx_GetItemInt_Generic(o, to_py_func(i)))) +#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, + int is_list, int wraparound, int boundscheck); + +/* PyObjectCallOneArg.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); + +/* ObjectGetItem.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key); +#else +#define __Pyx_PyObject_GetItem(obj, key) PyObject_GetItem(obj, key) +#endif + +/* KeywordStringCheck.proto */ +static int __Pyx_CheckKeywordStrings(PyObject *kw, const char* function_name, int kw_allowed); + +/* DivInt[Py_ssize_t].proto */ +static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t); + +/* UnaryNegOverflows.proto */ +#define __Pyx_UNARY_NEG_WOULD_OVERFLOW(x)\ + (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x))) + +/* GetAttr3.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *); + +/* PyDictVersioning.proto */ +#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS +#define __PYX_DICT_VERSION_INIT ((PY_UINT64_T) -1) +#define __PYX_GET_DICT_VERSION(dict) (((PyDictObject*)(dict))->ma_version_tag) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\ + (version_var) = __PYX_GET_DICT_VERSION(dict);\ + (cache_var) = (value); +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\ + (VAR) = __pyx_dict_cached_value;\ + } else {\ + (VAR) = __pyx_dict_cached_value = (LOOKUP);\ + __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\ + }\ +} +static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj); +static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj); +static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version); +#else +#define __PYX_GET_DICT_VERSION(dict) (0) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var) +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP); +#endif + +/* GetModuleGlobalName.proto */ +#if CYTHON_USE_DICT_VERSIONS +#define __Pyx_GetModuleGlobalName(var, name) do {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\ + (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\ + __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} while(0) +#define __Pyx_GetModuleGlobalNameUncached(var, name) do {\ + PY_UINT64_T __pyx_dict_version;\ + PyObject *__pyx_dict_cached_value;\ + (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} while(0) +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value); +#else +#define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name) +#define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name) +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name); +#endif + +/* AssertionsEnabled.proto */ +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) + #define __Pyx_init_assertions_enabled() (0) + #define __pyx_assertions_enabled() (1) +#elif CYTHON_COMPILING_IN_LIMITED_API || (CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030C0000) + static int __pyx_assertions_enabled_flag; + #define __pyx_assertions_enabled() (__pyx_assertions_enabled_flag) + static int __Pyx_init_assertions_enabled(void) { + PyObject *builtins, *debug, *debug_str; + int flag; + builtins = PyEval_GetBuiltins(); + if (!builtins) goto bad; + debug_str = PyUnicode_FromStringAndSize("__debug__", 9); + if (!debug_str) goto bad; + debug = PyObject_GetItem(builtins, debug_str); + Py_DECREF(debug_str); + if (!debug) goto bad; + flag = PyObject_IsTrue(debug); + Py_DECREF(debug); + if (flag == -1) goto bad; + __pyx_assertions_enabled_flag = flag; + return 0; + bad: + __pyx_assertions_enabled_flag = 1; + return -1; + } +#else + #define __Pyx_init_assertions_enabled() (0) + #define __pyx_assertions_enabled() (!Py_OptimizeFlag) +#endif + +/* RaiseTooManyValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +/* RaiseNeedMoreValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +/* RaiseNoneIterError.proto */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +/* ExtTypeTest.proto */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); + +/* GetTopmostException.proto */ +#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE +static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate); +#endif + +/* SaveResetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +#else +#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) +#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) +#endif + +/* GetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* SwapException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* Import.proto */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +/* ImportDottedModule.proto */ +static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple); +#if PY_MAJOR_VERSION >= 3 +static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple); +#endif + +/* FastTypeChecks.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) +#define __Pyx_TypeCheck2(obj, type1, type2) __Pyx_IsAnySubtype2(Py_TYPE(obj), (PyTypeObject *)type1, (PyTypeObject *)type2) +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); +static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); +#else +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#define __Pyx_TypeCheck2(obj, type1, type2) (PyObject_TypeCheck(obj, (PyTypeObject *)type1) || PyObject_TypeCheck(obj, (PyTypeObject *)type2)) +#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) +#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) +#endif +#define __Pyx_PyErr_ExceptionMatches2(err1, err2) __Pyx_PyErr_GivenExceptionMatches2(__Pyx_PyErr_CurrentExceptionType(), err1, err2) +#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) + +CYTHON_UNUSED static int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +/* ListCompAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len)) { + Py_INCREF(x); + #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030d0000 + L->ob_item[len] = x; + #else + PyList_SET_ITEM(list, len, x); + #endif + __Pyx_SET_SIZE(list, len + 1); + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) +#endif + +/* PySequenceMultiply.proto */ +#define __Pyx_PySequence_Multiply_Left(mul, seq) __Pyx_PySequence_Multiply(seq, mul) +static CYTHON_INLINE PyObject* __Pyx_PySequence_Multiply(PyObject *seq, Py_ssize_t mul); + +/* SetItemInt.proto */ +#define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_SetItemInt_Fast(o, (Py_ssize_t)i, v, is_list, wraparound, boundscheck) :\ + (is_list ? (PyErr_SetString(PyExc_IndexError, "list assignment index out of range"), -1) :\ + __Pyx_SetItemInt_Generic(o, to_py_func(i), v))) +static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v); +static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, + int is_list, int wraparound, int boundscheck); + +/* RaiseUnboundLocalError.proto */ +static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); + +/* DivInt[long].proto */ +static CYTHON_INLINE long __Pyx_div_long(long, long); + +/* PySequenceContains.proto */ +static CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* seq, int eq) { + int result = PySequence_Contains(seq, item); + return unlikely(result < 0) ? result : (result == (eq == Py_EQ)); +} + +/* ImportFrom.proto */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); + +/* HasAttr.proto */ +#if __PYX_LIMITED_VERSION_HEX >= 0x030d00A1 +#define __Pyx_HasAttr(o, n) PyObject_HasAttrWithError(o, n) +#else +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *); +#endif + +/* ErrOccurredWithGIL.proto */ +static CYTHON_INLINE int __Pyx_ErrOccurredWithGIL(void); + +/* IsLittleEndian.proto */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void); + +/* BufferFormatCheck.proto */ +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type); + +/* BufferGetAndValidate.proto */ +#define __Pyx_GetBufferAndValidate(buf, obj, dtype, flags, nd, cast, stack)\ + ((obj == Py_None || obj == NULL) ?\ + (__Pyx_ZeroBuffer(buf), 0) :\ + __Pyx__GetBufferAndValidate(buf, obj, dtype, flags, nd, cast, stack)) +static int __Pyx__GetBufferAndValidate(Py_buffer* buf, PyObject* obj, + __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack); +static void __Pyx_ZeroBuffer(Py_buffer* buf); +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info); +static Py_ssize_t __Pyx_minusones[] = { -1, -1, -1, -1, -1, -1, -1, -1 }; +static Py_ssize_t __Pyx_zeros[] = { 0, 0, 0, 0, 0, 0, 0, 0 }; + +#define __Pyx_BufPtrStrided1d(type, buf, i0, s0) (type)((char*)buf + i0 * s0) +/* BufferFallbackError.proto */ +static void __Pyx_RaiseBufferFallbackError(void); + +#define __Pyx_BufPtrStrided2d(type, buf, i0, s0, i1, s1) (type)((char*)buf + i0 * s0 + i1 * s1) +/* DictGetItem.proto */ +#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY +static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key); +#define __Pyx_PyObject_Dict_GetItem(obj, name)\ + (likely(PyDict_CheckExact(obj)) ?\ + __Pyx_PyDict_GetItem(obj, name) : PyObject_GetItem(obj, name)) +#else +#define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key) +#define __Pyx_PyObject_Dict_GetItem(obj, name) PyObject_GetItem(obj, name) +#endif + +/* PyIntCompare.proto */ +static CYTHON_INLINE int __Pyx_PyInt_BoolNeObjC(PyObject *op1, PyObject *op2, long intval, long inplace); + +/* PyIntBinop.proto */ +#if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_SubtractObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check); +#else +#define __Pyx_PyInt_SubtractObjC(op1, op2, intval, inplace, zerodivision_check)\ + (inplace ? PyNumber_InPlaceSubtract(op1, op2) : PyNumber_Subtract(op1, op2)) +#endif + +/* PyIntBinop.proto */ +#if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_AddCObj(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check); +#else +#define __Pyx_PyInt_AddCObj(op1, op2, intval, inplace, zerodivision_check)\ + (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2)) +#endif + +/* PyObject_GenericGetAttrNoDict.proto */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr +#endif + +/* PyObject_GenericGetAttr.proto */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr +#endif + +/* IncludeStructmemberH.proto */ +#include + +/* FixUpExtensionType.proto */ +#if CYTHON_USE_TYPE_SPECS +static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type); +#endif + +/* PyObjectCallNoArg.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func); + +/* PyObjectGetMethod.proto */ +static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method); + +/* PyObjectCallMethod0.proto */ +static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name); + +/* ValidateBasesTuple.proto */ +#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS +static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases); +#endif + +/* PyType_Ready.proto */ +CYTHON_UNUSED static int __Pyx_PyType_Ready(PyTypeObject *t); + +/* SetVTable.proto */ +static int __Pyx_SetVtable(PyTypeObject* typeptr , void* vtable); + +/* GetVTable.proto */ +static void* __Pyx_GetVtable(PyTypeObject *type); + +/* MergeVTables.proto */ +#if !CYTHON_COMPILING_IN_LIMITED_API +static int __Pyx_MergeVtables(PyTypeObject *type); +#endif + +/* SetupReduce.proto */ +#if !CYTHON_COMPILING_IN_LIMITED_API +static int __Pyx_setup_reduce(PyObject* type_obj); +#endif + +/* TypeImport.proto */ +#ifndef __PYX_HAVE_RT_ImportType_proto_3_0_6 +#define __PYX_HAVE_RT_ImportType_proto_3_0_6 +#if defined (__STDC_VERSION__) && __STDC_VERSION__ >= 201112L +#include +#endif +#if (defined (__STDC_VERSION__) && __STDC_VERSION__ >= 201112L) || __cplusplus >= 201103L +#define __PYX_GET_STRUCT_ALIGNMENT_3_0_6(s) alignof(s) +#else +#define __PYX_GET_STRUCT_ALIGNMENT_3_0_6(s) sizeof(void*) +#endif +enum __Pyx_ImportType_CheckSize_3_0_6 { + __Pyx_ImportType_CheckSize_Error_3_0_6 = 0, + __Pyx_ImportType_CheckSize_Warn_3_0_6 = 1, + __Pyx_ImportType_CheckSize_Ignore_3_0_6 = 2 +}; +static PyTypeObject *__Pyx_ImportType_3_0_6(PyObject* module, const char *module_name, const char *class_name, size_t size, size_t alignment, enum __Pyx_ImportType_CheckSize_3_0_6 check_size); +#endif + +/* FetchSharedCythonModule.proto */ +static PyObject *__Pyx_FetchSharedCythonABIModule(void); + +/* FetchCommonType.proto */ +#if !CYTHON_USE_TYPE_SPECS +static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type); +#else +static PyTypeObject* __Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases); +#endif + +/* PyMethodNew.proto */ +#if CYTHON_COMPILING_IN_LIMITED_API +static PyObject *__Pyx_PyMethod_New(PyObject *func, PyObject *self, PyObject *typ) { + PyObject *typesModule=NULL, *methodType=NULL, *result=NULL; + CYTHON_UNUSED_VAR(typ); + if (!self) + return __Pyx_NewRef(func); + typesModule = PyImport_ImportModule("types"); + if (!typesModule) return NULL; + methodType = PyObject_GetAttrString(typesModule, "MethodType"); + Py_DECREF(typesModule); + if (!methodType) return NULL; + result = PyObject_CallFunctionObjArgs(methodType, func, self, NULL); + Py_DECREF(methodType); + return result; +} +#elif PY_MAJOR_VERSION >= 3 +static PyObject *__Pyx_PyMethod_New(PyObject *func, PyObject *self, PyObject *typ) { + CYTHON_UNUSED_VAR(typ); + if (!self) + return __Pyx_NewRef(func); + return PyMethod_New(func, self); +} +#else + #define __Pyx_PyMethod_New PyMethod_New +#endif + +/* PyVectorcallFastCallDict.proto */ +#if CYTHON_METH_FASTCALL +static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw); +#endif + +/* CythonFunctionShared.proto */ +#define __Pyx_CyFunction_USED +#define __Pyx_CYFUNCTION_STATICMETHOD 0x01 +#define __Pyx_CYFUNCTION_CLASSMETHOD 0x02 +#define __Pyx_CYFUNCTION_CCLASS 0x04 +#define __Pyx_CYFUNCTION_COROUTINE 0x08 +#define __Pyx_CyFunction_GetClosure(f)\ + (((__pyx_CyFunctionObject *) (f))->func_closure) +#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_CyFunction_GetClassObj(f)\ + (((__pyx_CyFunctionObject *) (f))->func_classobj) +#else + #define __Pyx_CyFunction_GetClassObj(f)\ + ((PyObject*) ((PyCMethodObject *) (f))->mm_class) +#endif +#define __Pyx_CyFunction_SetClassObj(f, classobj)\ + __Pyx__CyFunction_SetClassObj((__pyx_CyFunctionObject *) (f), (classobj)) +#define __Pyx_CyFunction_Defaults(type, f)\ + ((type *)(((__pyx_CyFunctionObject *) (f))->defaults)) +#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\ + ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g) +typedef struct { +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject_HEAD + PyObject *func; +#elif PY_VERSION_HEX < 0x030900B1 + PyCFunctionObject func; +#else + PyCMethodObject func; +#endif +#if CYTHON_BACKPORT_VECTORCALL + __pyx_vectorcallfunc func_vectorcall; +#endif +#if PY_VERSION_HEX < 0x030500A0 || CYTHON_COMPILING_IN_LIMITED_API + PyObject *func_weakreflist; +#endif + PyObject *func_dict; + PyObject *func_name; + PyObject *func_qualname; + PyObject *func_doc; + PyObject *func_globals; + PyObject *func_code; + PyObject *func_closure; +#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API + PyObject *func_classobj; +#endif + void *defaults; + int defaults_pyobjects; + size_t defaults_size; // used by FusedFunction for copying defaults + int flags; + PyObject *defaults_tuple; + PyObject *defaults_kwdict; + PyObject *(*defaults_getter)(PyObject *); + PyObject *func_annotations; + PyObject *func_is_coroutine; +} __pyx_CyFunctionObject; +#undef __Pyx_CyOrPyCFunction_Check +#define __Pyx_CyFunction_Check(obj) __Pyx_TypeCheck(obj, __pyx_CyFunctionType) +#define __Pyx_CyOrPyCFunction_Check(obj) __Pyx_TypeCheck2(obj, __pyx_CyFunctionType, &PyCFunction_Type) +#define __Pyx_CyFunction_CheckExact(obj) __Pyx_IS_TYPE(obj, __pyx_CyFunctionType) +static CYTHON_INLINE int __Pyx__IsSameCyOrCFunction(PyObject *func, void *cfunc); +#undef __Pyx_IsSameCFunction +#define __Pyx_IsSameCFunction(func, cfunc) __Pyx__IsSameCyOrCFunction(func, cfunc) +static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject* op, PyMethodDef *ml, + int flags, PyObject* qualname, + PyObject *closure, + PyObject *module, PyObject *globals, + PyObject* code); +static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj); +static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m, + size_t size, + int pyobjects); +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m, + PyObject *tuple); +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m, + PyObject *dict); +static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m, + PyObject *dict); +static int __pyx_CyFunction_init(PyObject *module); +#if CYTHON_METH_FASTCALL +static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); +static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); +static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); +static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); +#if CYTHON_BACKPORT_VECTORCALL +#define __Pyx_CyFunction_func_vectorcall(f) (((__pyx_CyFunctionObject*)f)->func_vectorcall) +#else +#define __Pyx_CyFunction_func_vectorcall(f) (((PyCFunctionObject*)f)->vectorcall) +#endif +#endif + +/* CythonFunction.proto */ +static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, + int flags, PyObject* qualname, + PyObject *closure, + PyObject *module, PyObject *globals, + PyObject* code); + +/* CLineInTraceback.proto */ +#ifdef CYTHON_CLINE_IN_TRACEBACK +#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) +#else +static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); +#endif + +/* CodeObjectCache.proto */ +#if !CYTHON_COMPILING_IN_LIMITED_API +typedef struct { + PyCodeObject* code_object; + int code_line; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); +#endif + +/* AddTraceback.proto */ +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); + +#if PY_MAJOR_VERSION < 3 + static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); + static void __Pyx_ReleaseBuffer(Py_buffer *view); +#else + #define __Pyx_GetBuffer PyObject_GetBuffer + #define __Pyx_ReleaseBuffer PyBuffer_Release +#endif + + +/* BufferStructDeclare.proto */ +typedef struct { + Py_ssize_t shape, strides, suboffsets; +} __Pyx_Buf_DimInfo; +typedef struct { + size_t refcount; + Py_buffer pybuffer; +} __Pyx_Buffer; +typedef struct { + __Pyx_Buffer *rcbuffer; + char *data; + __Pyx_Buf_DimInfo diminfo[8]; +} __Pyx_LocalBuf_ND; + +/* MemviewSliceIsContig.proto */ +static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim); + +/* OverlappingSlices.proto */ +static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize); + +/* TypeInfoCompare.proto */ +static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); + +/* MemviewSliceValidateAndInit.proto */ +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_float(PyObject *, int writable_flag); + +/* RealImag.proto */ +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #define __Pyx_CREAL(z) ((z).real()) + #define __Pyx_CIMAG(z) ((z).imag()) + #else + #define __Pyx_CREAL(z) (__real__(z)) + #define __Pyx_CIMAG(z) (__imag__(z)) + #endif +#else + #define __Pyx_CREAL(z) ((z).real) + #define __Pyx_CIMAG(z) ((z).imag) +#endif +#if defined(__cplusplus) && CYTHON_CCOMPLEX\ + && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103) + #define __Pyx_SET_CREAL(z,x) ((z).real(x)) + #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) +#else + #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) + #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) +#endif + +/* Arithmetic.proto */ +#if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus) + #define __Pyx_c_eq_float(a, b) ((a)==(b)) + #define __Pyx_c_sum_float(a, b) ((a)+(b)) + #define __Pyx_c_diff_float(a, b) ((a)-(b)) + #define __Pyx_c_prod_float(a, b) ((a)*(b)) + #define __Pyx_c_quot_float(a, b) ((a)/(b)) + #define __Pyx_c_neg_float(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero_float(z) ((z)==(float)0) + #define __Pyx_c_conj_float(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs_float(z) (::std::abs(z)) + #define __Pyx_c_pow_float(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero_float(z) ((z)==0) + #define __Pyx_c_conj_float(z) (conjf(z)) + #if 1 + #define __Pyx_c_abs_float(z) (cabsf(z)) + #define __Pyx_c_pow_float(a, b) (cpowf(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex); + static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex); + #if 1 + static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex); + #endif +#endif + +/* Arithmetic.proto */ +#if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus) + #define __Pyx_c_eq_double(a, b) ((a)==(b)) + #define __Pyx_c_sum_double(a, b) ((a)+(b)) + #define __Pyx_c_diff_double(a, b) ((a)-(b)) + #define __Pyx_c_prod_double(a, b) ((a)*(b)) + #define __Pyx_c_quot_double(a, b) ((a)/(b)) + #define __Pyx_c_neg_double(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero_double(z) ((z)==(double)0) + #define __Pyx_c_conj_double(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs_double(z) (::std::abs(z)) + #define __Pyx_c_pow_double(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero_double(z) ((z)==0) + #define __Pyx_c_conj_double(z) (conj(z)) + #if 1 + #define __Pyx_c_abs_double(z) (cabs(z)) + #define __Pyx_c_pow_double(a, b) (cpow(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex); + static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex); + #if 1 + static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex); + #endif +#endif + +/* MemviewSliceCopyTemplate.proto */ +static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object); + +/* MemviewSliceInit.proto */ +#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d +#define __Pyx_MEMVIEW_DIRECT 1 +#define __Pyx_MEMVIEW_PTR 2 +#define __Pyx_MEMVIEW_FULL 4 +#define __Pyx_MEMVIEW_CONTIG 8 +#define __Pyx_MEMVIEW_STRIDED 16 +#define __Pyx_MEMVIEW_FOLLOW 32 +#define __Pyx_IS_C_CONTIG 1 +#define __Pyx_IS_F_CONTIG 2 +static int __Pyx_init_memviewslice( + struct __pyx_memoryview_obj *memview, + int ndim, + __Pyx_memviewslice *memviewslice, + int memview_is_new_reference); +static CYTHON_INLINE int __pyx_add_acquisition_count_locked( + __pyx_atomic_int_type *acquisition_count, PyThread_type_lock lock); +static CYTHON_INLINE int __pyx_sub_acquisition_count_locked( + __pyx_atomic_int_type *acquisition_count, PyThread_type_lock lock); +#define __pyx_get_slice_count_pointer(memview) (&memview->acquisition_count) +#define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__) +#define __PYX_XCLEAR_MEMVIEW(slice, have_gil) __Pyx_XCLEAR_MEMVIEW(slice, have_gil, __LINE__) +static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int); +static CYTHON_INLINE void __Pyx_XCLEAR_MEMVIEW(__Pyx_memviewslice *, int, int); + +/* CIntFromPy.proto */ +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_Py_intptr_t(Py_intptr_t value); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +/* None.proto */ +#include + +/* CIntFromPy.proto */ +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); + +/* FormatTypeName.proto */ +#if CYTHON_COMPILING_IN_LIMITED_API +typedef PyObject *__Pyx_TypeName; +#define __Pyx_FMT_TYPENAME "%U" +static __Pyx_TypeName __Pyx_PyType_GetName(PyTypeObject* tp); +#define __Pyx_DECREF_TypeName(obj) Py_XDECREF(obj) +#else +typedef const char *__Pyx_TypeName; +#define __Pyx_FMT_TYPENAME "%.200s" +#define __Pyx_PyType_GetName(tp) ((tp)->tp_name) +#define __Pyx_DECREF_TypeName(obj) +#endif + +/* CheckBinaryVersion.proto */ +static unsigned long __Pyx_get_runtime_version(void); +static int __Pyx_check_binary_version(unsigned long ct_version, unsigned long rt_version, int allow_newer); + +/* InitStrings.proto */ +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + +/* #### Code section: module_declarations ### */ +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ +static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ +static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview__get_base(struct __pyx_memoryview_obj *__pyx_v_self); /* proto*/ +static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryviewslice__get_base(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto*/ +static CYTHON_INLINE PyObject *__pyx_f_5numpy_7ndarray_4base_base(PyArrayObject *__pyx_v_self); /* proto*/ +static CYTHON_INLINE PyArray_Descr *__pyx_f_5numpy_7ndarray_5descr_descr(PyArrayObject *__pyx_v_self); /* proto*/ +static CYTHON_INLINE int __pyx_f_5numpy_7ndarray_4ndim_ndim(PyArrayObject *__pyx_v_self); /* proto*/ +static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_5shape_shape(PyArrayObject *__pyx_v_self); /* proto*/ +static CYTHON_INLINE npy_intp *__pyx_f_5numpy_7ndarray_7strides_strides(PyArrayObject *__pyx_v_self); /* proto*/ +static CYTHON_INLINE npy_intp __pyx_f_5numpy_7ndarray_4size_size(PyArrayObject *__pyx_v_self); /* proto*/ +static CYTHON_INLINE char *__pyx_f_5numpy_7ndarray_4data_data(PyArrayObject *__pyx_v_self); /* proto*/ + +/* Module declarations from "libc.string" */ + +/* Module declarations from "libc.stdio" */ + +/* Module declarations from "__builtin__" */ + +/* Module declarations from "cpython.type" */ + +/* Module declarations from "cpython" */ + +/* Module declarations from "cpython.object" */ + +/* Module declarations from "cpython.ref" */ + +/* Module declarations from "numpy" */ + +/* Module declarations from "numpy" */ + +/* Module declarations from "cython.view" */ + +/* Module declarations from "cython.dataclasses" */ + +/* Module declarations from "cython" */ + +/* Module declarations from "wfpt_n" */ +static PyObject *__pyx_collections_abc_Sequence = 0; +static PyObject *generic = 0; +static PyObject *strided = 0; +static PyObject *indirect = 0; +static PyObject *contiguous = 0; +static PyObject *indirect_contiguous = 0; +static int __pyx_memoryview_thread_locks_used; +static PyThread_type_lock __pyx_memoryview_thread_locks[8]; +static double __pyx_f_6wfpt_n_ftt_01w(double, double, double); /*proto*/ +static CYTHON_INLINE double __pyx_f_6wfpt_n_prob_ub(double, double, double); /*proto*/ +static double __pyx_f_6wfpt_n_pdf(double, double, double, double, double); /*proto*/ +static double __pyx_f_6wfpt_n_pdf_sv(double, double, double, double, double, double); /*proto*/ +static double __pyx_f_6wfpt_n_full_pdf(double, double, double, double, double, double, double, double, double, int __pyx_skip_dispatch, struct __pyx_opt_args_6wfpt_n_full_pdf *__pyx_optional_args); /*proto*/ +static double __pyx_f_6wfpt_n_simpson_1D(double, double, double, double, double, double, double, double, double, int, double, double, int); /*proto*/ +static double __pyx_f_6wfpt_n_simpson_2D(double, double, double, double, double, double, double, double, double, int, double, double, int); /*proto*/ +static double __pyx_f_6wfpt_n_adaptiveSimpsonsAux(double, double, double, double, double, double, double, double, double, double, double, double, double, double, double, double, double, int); /*proto*/ +static double __pyx_f_6wfpt_n_adaptiveSimpsons_1D(double, double, double, double, double, double, double, double, double, double, double, double, int); /*proto*/ +static double __pyx_f_6wfpt_n_adaptiveSimpsonsAux_2D(double, double, double, double, double, double, double, double, double, double, double, double, double, double, double, double, double, double, int, int); /*proto*/ +static double __pyx_f_6wfpt_n_adaptiveSimpsons_2D(double, double, double, double, double, double, double, double, double, double, double, double, int, int); /*proto*/ +static CYTHON_INLINE int __pyx_f_6wfpt_n_p_outlier_in_range(double); /*proto*/ +static int __pyx_array_allocate_buffer(struct __pyx_array_obj *); /*proto*/ +static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ +static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ +static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ +static PyObject *_unellipsify(PyObject *, int); /*proto*/ +static int assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ +static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ +static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ +static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ +static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ +static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ +static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ +static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ +static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ +static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ +static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ +static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memoryview_err_dim(PyObject *, PyObject *, int); /*proto*/ +static int __pyx_memoryview_err(PyObject *, PyObject *); /*proto*/ +static int __pyx_memoryview_err_no_memory(void); /*proto*/ +static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ +static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ +static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ +static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ +/* #### Code section: typeinfo ### */ +static __Pyx_TypeInfo __Pyx_TypeInfo_double = { "double", NULL, sizeof(double), { 0 }, 0, 'R', 0, 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_long = { "long", NULL, sizeof(long), { 0 }, 0, __PYX_IS_UNSIGNED(long) ? 'U' : 'I', __PYX_IS_UNSIGNED(long), 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_float = { "float", NULL, sizeof(float), { 0 }, 0, 'R', 0, 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, __PYX_IS_UNSIGNED(int) ? 'U' : 'I', __PYX_IS_UNSIGNED(int), 0 }; +/* #### Code section: before_global_var ### */ +#define __Pyx_MODULE_NAME "wfpt_n" +extern int __pyx_module_is_main_wfpt_n; +int __pyx_module_is_main_wfpt_n = 0; + +/* Implementation of "wfpt_n" */ +/* #### Code section: global_var ### */ +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin___import__; +static PyObject *__pyx_builtin_MemoryError; +static PyObject *__pyx_builtin_enumerate; +static PyObject *__pyx_builtin_TypeError; +static PyObject *__pyx_builtin_AssertionError; +static PyObject *__pyx_builtin_Ellipsis; +static PyObject *__pyx_builtin_id; +static PyObject *__pyx_builtin_IndexError; +static PyObject *__pyx_builtin_ImportError; +/* #### Code section: string_decls ### */ +static const char __pyx_k_[] = ": "; +static const char __pyx_k_N[] = "N"; +static const char __pyx_k_O[] = "O"; +static const char __pyx_k_a[] = "a"; +static const char __pyx_k_c[] = "c"; +static const char __pyx_k_f[] = "f"; +static const char __pyx_k_i[] = "i"; +static const char __pyx_k_j[] = "j"; +static const char __pyx_k_p[] = "p"; +static const char __pyx_k_q[] = "q"; +static const char __pyx_k_s[] = "s"; +static const char __pyx_k_t[] = "t"; +static const char __pyx_k_v[] = "v"; +static const char __pyx_k_x[] = "x"; +static const char __pyx_k_y[] = "y"; +static const char __pyx_k_z[] = "z"; +static const char __pyx_k__2[] = "."; +static const char __pyx_k__3[] = "*"; +static const char __pyx_k__6[] = "'"; +static const char __pyx_k__7[] = ")"; +static const char __pyx_k_dt[] = "dt"; +static const char __pyx_k_gc[] = "gc"; +static const char __pyx_k_id[] = "id"; +static const char __pyx_k_ij[] = "ij"; +static const char __pyx_k_lb[] = "lb"; +static const char __pyx_k_np[] = "np"; +static const char __pyx_k_qs[] = "qs"; +static const char __pyx_k_rt[] = "rt"; +static const char __pyx_k_st[] = "st"; +static const char __pyx_k_sv[] = "sv"; +static const char __pyx_k_sz[] = "sz"; +static const char __pyx_k_ub[] = "ub"; +static const char __pyx_k_xs[] = "xs"; +static const char __pyx_k__62[] = "?"; +static const char __pyx_k_abc[] = "abc"; +static const char __pyx_k_and[] = " and "; +static const char __pyx_k_err[] = "err"; +static const char __pyx_k_exp[] = "exp"; +static const char __pyx_k_got[] = " (got "; +static const char __pyx_k_i_p[] = "i_p"; +static const char __pyx_k_idx[] = "idx"; +static const char __pyx_k_inf[] = "inf"; +static const char __pyx_k_log[] = "log"; +static const char __pyx_k_max[] = "max"; +static const char __pyx_k_min[] = "min"; +static const char __pyx_k_new[] = "__new__"; +static const char __pyx_k_obj[] = "obj"; +static const char __pyx_k_pdf[] = "pdf"; +static const char __pyx_k_rts[] = "rts"; +static const char __pyx_k_sum[] = "sum"; +static const char __pyx_k_sys[] = "sys"; +static const char __pyx_k_alfa[] = "alfa"; +static const char __pyx_k_axis[] = "axis"; +static const char __pyx_k_base[] = "base"; +static const char __pyx_k_copy[] = "copy"; +static const char __pyx_k_core[] = "core"; +static const char __pyx_k_data[] = "data"; +static const char __pyx_k_dict[] = "__dict__"; +static const char __pyx_k_diff[] = "diff"; +static const char __pyx_k_logp[] = "logp"; +static const char __pyx_k_main[] = "__main__"; +static const char __pyx_k_mode[] = "mode"; +static const char __pyx_k_n_st[] = "n_st"; +static const char __pyx_k_n_sz[] = "n_sz"; +static const char __pyx_k_name[] = "name"; +static const char __pyx_k_ndim[] = "ndim"; +static const char __pyx_k_pack[] = "pack"; +static const char __pyx_k_rand[] = "rand"; +static const char __pyx_k_sign[] = "sign"; +static const char __pyx_k_size[] = "size"; +static const char __pyx_k_spec[] = "__spec__"; +static const char __pyx_k_step[] = "step"; +static const char __pyx_k_stop[] = "stop"; +static const char __pyx_k_test[] = "__test__"; +static const char __pyx_k_tile[] = "tile"; +static const char __pyx_k_time[] = "time"; +static const char __pyx_k_x_lb[] = "x_lb"; +static const char __pyx_k_x_ub[] = "x_ub"; +static const char __pyx_k_ASCII[] = "ASCII"; +static const char __pyx_k_alpha[] = "alpha"; +static const char __pyx_k_array[] = "array"; +static const char __pyx_k_class[] = "__class__"; +static const char __pyx_k_count[] = "count"; +static const char __pyx_k_delay[] = "delay"; +static const char __pyx_k_drift[] = "drift"; +static const char __pyx_k_dtype[] = "dtype"; +static const char __pyx_k_empty[] = "empty"; +static const char __pyx_k_error[] = "error"; +static const char __pyx_k_flags[] = "flags"; +static const char __pyx_k_index[] = "index"; +static const char __pyx_k_l_cdf[] = "l_cdf"; +static const char __pyx_k_log_p[] = "log_p"; +static const char __pyx_k_model[] = "model"; +static const char __pyx_k_multi[] = "multi"; +static const char __pyx_k_numpy[] = "numpy"; +static const char __pyx_k_param[] = "param"; +static const char __pyx_k_range[] = "range"; +static const char __pyx_k_scipy[] = "scipy"; +static const char __pyx_k_shape[] = "shape"; +static const char __pyx_k_stack[] = "stack"; +static const char __pyx_k_start[] = "start"; +static const char __pyx_k_t_max[] = "t_max"; +static const char __pyx_k_t_min[] = "t_min"; +static const char __pyx_k_umath[] = "umath"; +static const char __pyx_k_zeros[] = "zeros"; +static const char __pyx_k_arange[] = "arange"; +static const char __pyx_k_astype[] = "astype"; +static const char __pyx_k_cdf_lb[] = "cdf_lb"; +static const char __pyx_k_cdf_ub[] = "cdf_ub"; +static const char __pyx_k_cont_x[] = "cont_x"; +static const char __pyx_k_cumsum[] = "cumsum"; +static const char __pyx_k_double[] = "double"; +static const char __pyx_k_enable[] = "enable"; +static const char __pyx_k_encode[] = "encode"; +static const char __pyx_k_format[] = "format"; +static const char __pyx_k_import[] = "__import__"; +static const char __pyx_k_ll_min[] = "ll_min"; +static const char __pyx_k_n_cont[] = "n_cont"; +static const char __pyx_k_name_2[] = "__name__"; +static const char __pyx_k_params[] = "params"; +static const char __pyx_k_pickle[] = "pickle"; +static const char __pyx_k_random[] = "random"; +static const char __pyx_k_reduce[] = "__reduce__"; +static const char __pyx_k_rl_arr[] = "rl_arr"; +static const char __pyx_k_s_size[] = "s_size"; +static const char __pyx_k_struct[] = "struct"; +static const char __pyx_k_unique[] = "unique"; +static const char __pyx_k_unpack[] = "unpack"; +static const char __pyx_k_update[] = "update"; +static const char __pyx_k_wfpt_n[] = "wfpt_n"; +static const char __pyx_k_disable[] = "disable"; +static const char __pyx_k_float32[] = "float32"; +static const char __pyx_k_fortran[] = "fortran"; +static const char __pyx_k_maximum[] = "maximum"; +static const char __pyx_k_memview[] = "memview"; +static const char __pyx_k_network[] = "network"; +static const char __pyx_k_pdf_pxi[] = "pdf.pxi"; +static const char __pyx_k_samples[] = "samples"; +static const char __pyx_k_squeeze[] = "squeeze"; +static const char __pyx_k_Ellipsis[] = "Ellipsis"; +static const char __pyx_k_Sequence[] = "Sequence"; +static const char __pyx_k_cumtrapz[] = "cumtrapz"; +static const char __pyx_k_feedback[] = "feedback"; +static const char __pyx_k_full_pdf[] = "full_pdf"; +static const char __pyx_k_getstate[] = "__getstate__"; +static const char __pyx_k_itemsize[] = "itemsize"; +static const char __pyx_k_linspace[] = "linspace"; +static const char __pyx_k_n_params[] = "n_params"; +static const char __pyx_k_pos_alfa[] = "pos_alfa"; +static const char __pyx_k_pos_cont[] = "pos_cont"; +static const char __pyx_k_pyx_type[] = "__pyx_type"; +static const char __pyx_k_register[] = "register"; +static const char __pyx_k_response[] = "response"; +static const char __pyx_k_rl_alpha[] = "rl_alpha"; +static const char __pyx_k_setstate[] = "__setstate__"; +static const char __pyx_k_split_by[] = "split_by"; +static const char __pyx_k_sum_logp[] = "sum_logp"; +static const char __pyx_k_tp_scale[] = "tp_scale"; +static const char __pyx_k_TypeError[] = "TypeError"; +static const char __pyx_k_cdf_array[] = "cdf_array"; +static const char __pyx_k_data_copy[] = "data_copy"; +static const char __pyx_k_enumerate[] = "enumerate"; +static const char __pyx_k_feedbacks[] = "feedbacks"; +static const char __pyx_k_integrate[] = "integrate"; +static const char __pyx_k_isenabled[] = "isenabled"; +static const char __pyx_k_lower_bnd[] = "lower_bnd"; +static const char __pyx_k_p_outlier[] = "p_outlier"; +static const char __pyx_k_params_rl[] = "params_rl"; +static const char __pyx_k_pdf_array[] = "pdf_array"; +static const char __pyx_k_pos_alpha[] = "pos_alpha"; +static const char __pyx_k_pyx_state[] = "__pyx_state"; +static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; +static const char __pyx_k_responses[] = "responses"; +static const char __pyx_k_simps_err[] = "simps_err"; +static const char __pyx_k_split_cdf[] = "split_cdf"; +static const char __pyx_k_upper_bnd[] = "upper_bnd"; +static const char __pyx_k_w_outlier[] = "w_outlier"; +static const char __pyx_k_IndexError[] = "IndexError"; +static const char __pyx_k_ValueError[] = "ValueError"; +static const char __pyx_k_params_ssm[] = "params_ssm"; +static const char __pyx_k_pyx_result[] = "__pyx_result"; +static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; +static const char __pyx_k_wfpt_n_pyx[] = "wfpt_n.pyx"; +static const char __pyx_k_wp_outlier[] = "wp_outlier"; +static const char __pyx_k_ImportError[] = "ImportError"; +static const char __pyx_k_MemoryError[] = "MemoryError"; +static const char __pyx_k_PickleError[] = "PickleError"; +static const char __pyx_k_collections[] = "collections"; +static const char __pyx_k_concatenate[] = "concatenate"; +static const char __pyx_k_cumm_s_size[] = "cumm_s_size"; +static const char __pyx_k_params_bnds[] = "params_bnds"; +static const char __pyx_k_params_iter[] = "params_iter"; +static const char __pyx_k_params_view[] = "params_view"; +static const char __pyx_k_initializing[] = "_initializing"; +static const char __pyx_k_is_coroutine[] = "_is_coroutine"; +static const char __pyx_k_lower_bounds[] = "lower_bounds"; +static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; +static const char __pyx_k_responses_qs[] = "responses_qs"; +static const char __pyx_k_searchsorted[] = "searchsorted"; +static const char __pyx_k_stringsource[] = ""; +static const char __pyx_k_upper_bounds[] = "upper_bounds"; +static const char __pyx_k_use_adaptive[] = "use_adaptive"; +static const char __pyx_k_version_info[] = "version_info"; +static const char __pyx_k_class_getitem[] = "__class_getitem__"; +static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; +static const char __pyx_k_wiener_like_n[] = "wiener_like_n"; +static const char __pyx_k_AssertionError[] = "AssertionError"; +static const char __pyx_k_wiener_like_rl[] = "wiener_like_rl"; +static const char __pyx_k_View_MemoryView[] = "View.MemoryView"; +static const char __pyx_k_allocate_buffer[] = "allocate_buffer"; +static const char __pyx_k_collections_abc[] = "collections.abc"; +static const char __pyx_k_dtype_is_object[] = "dtype_is_object"; +static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError"; +static const char __pyx_k_scipy_integrate[] = "scipy.integrate"; +static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; +static const char __pyx_k_gen_rts_from_cdf[] = "gen_rts_from_cdf"; +static const char __pyx_k_predict_on_batch[] = "predict_on_batch"; +static const char __pyx_k_gen_cdf_using_pdf[] = "gen_cdf_using_pdf"; +static const char __pyx_k_lower_bounds_view[] = "lower_bounds_view"; +static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum"; +static const char __pyx_k_upper_bounds_view[] = "upper_bounds_view"; +static const char __pyx_k_wiener_like_multi[] = "wiener_like_multi"; +static const char __pyx_k_wiener_like_rlddm[] = "wiener_like_rlddm"; +static const char __pyx_k_asyncio_coroutines[] = "asyncio.coroutines"; +static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; +static const char __pyx_k_strided_and_direct[] = ""; +static const char __pyx_k_wiener_like_nn_mlp[] = "wiener_like_nn_mlp"; +static const char __pyx_k_strided_and_indirect[] = ""; +static const char __pyx_k_wiener_like_rlssm_nn[] = "wiener_like_rlssm_nn"; +static const char __pyx_k_Invalid_shape_in_axis[] = "Invalid shape in axis "; +static const char __pyx_k_contiguous_and_direct[] = ""; +static const char __pyx_k_Cannot_index_with_type[] = "Cannot index with type '"; +static const char __pyx_k_MemoryView_of_r_object[] = ""; +static const char __pyx_k_wiener_like_nn_mlp_pdf[] = "wiener_like_nn_mlp_pdf"; +static const char __pyx_k_MemoryView_of_r_at_0x_x[] = ""; +static const char __pyx_k_contiguous_and_indirect[] = ""; +static const char __pyx_k_wiener_like_contaminant[] = "wiener_like_contaminant"; +static const char __pyx_k_wiener_like_multi_rlddm[] = "wiener_like_multi_rlddm"; +static const char __pyx_k_wiener_like_nn_mlp_info[] = "wiener_like_nn_mlp_info"; +static const char __pyx_k_wiener_like_multi_nn_mlp[] = "wiener_like_multi_nn_mlp"; +static const char __pyx_k_wiener_like_rlssm_nn_reg[] = "wiener_like_rlssm_nn_reg"; +static const char __pyx_k_Dimension_d_is_not_direct[] = "Dimension %d is not direct"; +static const char __pyx_k_Index_out_of_bounds_axis_d[] = "Index out of bounds (axis %d)"; +static const char __pyx_k_Step_may_not_be_zero_axis_d[] = "Step may not be zero (axis %d)"; +static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; +static const char __pyx_k_wiener_like_multi_nn_mlp_pdf[] = "wiener_like_multi_nn_mlp_pdf"; +static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; +static const char __pyx_k_strided_and_direct_or_indirect[] = ""; +static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import"; +static const char __pyx_k_All_dimensions_preceding_dimensi[] = "All dimensions preceding dimension %d must be indexed and not sliced"; +static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; +static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; +static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview"; +static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview"; +static const char __pyx_k_Cannot_transpose_memoryview_with[] = "Cannot transpose memoryview with indirect dimensions"; +static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; +static const char __pyx_k_Incompatible_checksums_0x_x_vs_0[] = "Incompatible checksums (0x%x vs (0x82a3537, 0x6ae9995, 0xb068931) = (name))"; +static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; +static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got "; +static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis "; +static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; +static const char __pyx_k_at_least_one_of_the_parameters_i[] = "at least one of the parameters is out of the support"; +static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension "; +static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; +static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import"; +static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; +/* #### Code section: decls ### */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ +static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ +static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ +static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ +static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); /* proto */ +static PyObject *__pyx_pf_6wfpt_n_full_pdf(CYTHON_UNUSED PyObject *__pyx_self, double __pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_sz, double __pyx_v_t, double __pyx_v_st, double __pyx_v_err, int __pyx_v_n_st, int __pyx_v_n_sz, int __pyx_v_use_adaptive, double __pyx_v_simps_err); /* proto */ +static PyObject *__pyx_pf_6wfpt_n_2pdf_array(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_sz, double __pyx_v_t, double __pyx_v_st, double __pyx_v_err, int __pyx_v_logp, int __pyx_v_n_st, int __pyx_v_n_sz, int __pyx_v_use_adaptive, double __pyx_v_simps_err, double __pyx_v_p_outlier, double __pyx_v_w_outlier); /* proto */ +static PyObject *__pyx_pf_6wfpt_n_4wiener_like_n(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_sz, double __pyx_v_t, double __pyx_v_st, double __pyx_v_err, int __pyx_v_n_st, int __pyx_v_n_sz, int __pyx_v_use_adaptive, double __pyx_v_simps_err, double __pyx_v_p_outlier, double __pyx_v_w_outlier); /* proto */ +static PyObject *__pyx_pf_6wfpt_n_6wiener_like_rlddm(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_x, PyArrayObject *__pyx_v_response, PyArrayObject *__pyx_v_feedback, PyArrayObject *__pyx_v_split_by, double __pyx_v_q, double __pyx_v_alpha, double __pyx_v_pos_alpha, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_sz, double __pyx_v_t, double __pyx_v_st, double __pyx_v_err, int __pyx_v_n_st, int __pyx_v_n_sz, int __pyx_v_use_adaptive, double __pyx_v_simps_err, double __pyx_v_p_outlier, double __pyx_v_w_outlier); /* proto */ +static PyObject *__pyx_pf_6wfpt_n_8wiener_like_rlssm_nn(CYTHON_UNUSED PyObject *__pyx_self, CYTHON_UNUSED PyObject *__pyx_v_model, PyArrayObject *__pyx_v_x, PyArrayObject *__pyx_v_response, PyArrayObject *__pyx_v_feedback, PyArrayObject *__pyx_v_split_by, double __pyx_v_q, PyArrayObject *__pyx_v_params_ssm, PyArrayObject *__pyx_v_params_rl, PyArrayObject *__pyx_v_params_bnds, double __pyx_v_p_outlier, double __pyx_v_w_outlier, PyObject *__pyx_v_network); /* proto */ +static PyObject *__pyx_pf_6wfpt_n_10wiener_like_rl(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_response, PyArrayObject *__pyx_v_feedback, PyArrayObject *__pyx_v_split_by, double __pyx_v_q, double __pyx_v_alpha, double __pyx_v_pos_alpha, double __pyx_v_v, double __pyx_v_z, CYTHON_UNUSED double __pyx_v_err, CYTHON_UNUSED int __pyx_v_n_st, CYTHON_UNUSED int __pyx_v_n_sz, CYTHON_UNUSED int __pyx_v_use_adaptive, CYTHON_UNUSED double __pyx_v_simps_err, double __pyx_v_p_outlier, double __pyx_v_w_outlier); /* proto */ +static PyObject *__pyx_pf_6wfpt_n_12wiener_like_multi(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_x, PyObject *__pyx_v_v, PyObject *__pyx_v_sv, PyObject *__pyx_v_a, PyObject *__pyx_v_z, PyObject *__pyx_v_sz, PyObject *__pyx_v_t, PyObject *__pyx_v_st, double __pyx_v_err, PyObject *__pyx_v_multi, int __pyx_v_n_st, int __pyx_v_n_sz, int __pyx_v_use_adaptive, double __pyx_v_simps_err, double __pyx_v_p_outlier, double __pyx_v_w_outlier); /* proto */ +static PyObject *__pyx_pf_6wfpt_n_14wiener_like_multi_rlddm(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_x, PyArrayObject *__pyx_v_response, PyArrayObject *__pyx_v_feedback, PyArrayObject *__pyx_v_split_by, double __pyx_v_q, PyObject *__pyx_v_v, PyObject *__pyx_v_sv, PyObject *__pyx_v_a, PyObject *__pyx_v_z, PyObject *__pyx_v_sz, PyObject *__pyx_v_t, PyObject *__pyx_v_st, PyObject *__pyx_v_alpha, double __pyx_v_err, PyObject *__pyx_v_multi, int __pyx_v_n_st, int __pyx_v_n_sz, int __pyx_v_use_adaptive, double __pyx_v_simps_err, double __pyx_v_p_outlier, double __pyx_v_w_outlier); /* proto */ +static PyObject *__pyx_pf_6wfpt_n_16wiener_like_rlssm_nn_reg(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_data, PyArrayObject *__pyx_v_rl_arr, PyArrayObject *__pyx_v_x, PyArrayObject *__pyx_v_response, PyArrayObject *__pyx_v_feedback, PyArrayObject *__pyx_v_split_by, double __pyx_v_q, PyArrayObject *__pyx_v_params_bnds, double __pyx_v_p_outlier, double __pyx_v_w_outlier, PyObject *__pyx_v_network); /* proto */ +static PyObject *__pyx_pf_6wfpt_n_18gen_rts_from_cdf(CYTHON_UNUSED PyObject *__pyx_self, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_sz, double __pyx_v_t, double __pyx_v_st, int __pyx_v_samples, double __pyx_v_cdf_lb, double __pyx_v_cdf_ub, double __pyx_v_dt); /* proto */ +static PyObject *__pyx_pf_6wfpt_n_20wiener_like_contaminant(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_x, PyArrayObject *__pyx_v_cont_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_sz, double __pyx_v_t, double __pyx_v_st, double __pyx_v_t_min, double __pyx_v_t_max, double __pyx_v_err, int __pyx_v_n_st, int __pyx_v_n_sz, int __pyx_v_use_adaptive, double __pyx_v_simps_err); /* proto */ +static PyObject *__pyx_pf_6wfpt_n_22gen_cdf_using_pdf(CYTHON_UNUSED PyObject *__pyx_self, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_sz, double __pyx_v_t, double __pyx_v_st, double __pyx_v_err, int __pyx_v_N, double __pyx_v_time, int __pyx_v_n_st, int __pyx_v_n_sz, int __pyx_v_use_adaptive, double __pyx_v_simps_err, double __pyx_v_p_outlier, double __pyx_v_w_outlier); /* proto */ +static PyObject *__pyx_pf_6wfpt_n_24split_cdf(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_x, PyArrayObject *__pyx_v_data); /* proto */ +static PyObject *__pyx_pf_6wfpt_n_26wiener_like_nn_mlp(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_rt, PyArrayObject *__pyx_v_response, PyArrayObject *__pyx_v_params, double __pyx_v_p_outlier, double __pyx_v_w_outlier, PyObject *__pyx_v_network); /* proto */ +static PyObject *__pyx_pf_6wfpt_n_28wiener_like_nn_mlp_info(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_rt, PyArrayObject *__pyx_v_response, PyArrayObject *__pyx_v_params, PyArrayObject *__pyx_v_upper_bounds, PyArrayObject *__pyx_v_lower_bounds, double __pyx_v_p_outlier, double __pyx_v_w_outlier, PyObject *__pyx_v_network); /* proto */ +static PyObject *__pyx_pf_6wfpt_n_30wiener_like_nn_mlp_pdf(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_rt, PyArrayObject *__pyx_v_response, PyArrayObject *__pyx_v_params, double __pyx_v_p_outlier, double __pyx_v_w_outlier, int __pyx_v_logp, PyObject *__pyx_v_network); /* proto */ +static PyObject *__pyx_pf_6wfpt_n_32wiener_like_multi_nn_mlp(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_data, double __pyx_v_p_outlier, double __pyx_v_w_outlier, PyObject *__pyx_v_network); /* proto */ +static PyObject *__pyx_pf_6wfpt_n_34wiener_like_multi_nn_mlp_pdf(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_data, double __pyx_v_p_outlier, double __pyx_v_w_outlier, PyObject *__pyx_v_network); /* proto */ +static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +/* #### Code section: late_includes ### */ +/* #### Code section: module_state ### */ +typedef struct { + PyObject *__pyx_d; + PyObject *__pyx_b; + PyObject *__pyx_cython_runtime; + PyObject *__pyx_empty_tuple; + PyObject *__pyx_empty_bytes; + PyObject *__pyx_empty_unicode; + #ifdef __Pyx_CyFunction_USED + PyTypeObject *__pyx_CyFunctionType; + #endif + #ifdef __Pyx_FusedFunction_USED + PyTypeObject *__pyx_FusedFunctionType; + #endif + #ifdef __Pyx_Generator_USED + PyTypeObject *__pyx_GeneratorType; + #endif + #ifdef __Pyx_IterableCoroutine_USED + PyTypeObject *__pyx_IterableCoroutineType; + #endif + #ifdef __Pyx_Coroutine_USED + PyTypeObject *__pyx_CoroutineAwaitType; + #endif + #ifdef __Pyx_Coroutine_USED + PyTypeObject *__pyx_CoroutineType; + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + PyTypeObject *__pyx_ptype_7cpython_4type_type; + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + PyTypeObject *__pyx_ptype_5numpy_dtype; + PyTypeObject *__pyx_ptype_5numpy_flatiter; + PyTypeObject *__pyx_ptype_5numpy_broadcast; + PyTypeObject *__pyx_ptype_5numpy_ndarray; + PyTypeObject *__pyx_ptype_5numpy_generic; + PyTypeObject *__pyx_ptype_5numpy_number; + PyTypeObject *__pyx_ptype_5numpy_integer; + PyTypeObject *__pyx_ptype_5numpy_signedinteger; + PyTypeObject *__pyx_ptype_5numpy_unsignedinteger; + PyTypeObject *__pyx_ptype_5numpy_inexact; + PyTypeObject *__pyx_ptype_5numpy_floating; + PyTypeObject *__pyx_ptype_5numpy_complexfloating; + PyTypeObject *__pyx_ptype_5numpy_flexible; + PyTypeObject *__pyx_ptype_5numpy_character; + PyTypeObject *__pyx_ptype_5numpy_ufunc; + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + #endif + #if CYTHON_USE_MODULE_STATE + PyObject *__pyx_type___pyx_array; + PyObject *__pyx_type___pyx_MemviewEnum; + PyObject *__pyx_type___pyx_memoryview; + PyObject *__pyx_type___pyx_memoryviewslice; + #endif + PyTypeObject *__pyx_array_type; + PyTypeObject *__pyx_MemviewEnum_type; + PyTypeObject *__pyx_memoryview_type; + PyTypeObject *__pyx_memoryviewslice_type; + PyObject *__pyx_kp_u_; + PyObject *__pyx_n_s_ASCII; + PyObject *__pyx_kp_s_All_dimensions_preceding_dimensi; + PyObject *__pyx_n_s_AssertionError; + PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri; + PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is; + PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor; + PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi; + PyObject *__pyx_kp_u_Cannot_index_with_type; + PyObject *__pyx_kp_s_Cannot_transpose_memoryview_with; + PyObject *__pyx_kp_s_Dimension_d_is_not_direct; + PyObject *__pyx_n_s_Ellipsis; + PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr; + PyObject *__pyx_n_s_ImportError; + PyObject *__pyx_kp_s_Incompatible_checksums_0x_x_vs_0; + PyObject *__pyx_n_s_IndexError; + PyObject *__pyx_kp_s_Index_out_of_bounds_axis_d; + PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte; + PyObject *__pyx_kp_u_Invalid_mode_expected_c_or_fortr; + PyObject *__pyx_kp_u_Invalid_shape_in_axis; + PyObject *__pyx_n_s_MemoryError; + PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x; + PyObject *__pyx_kp_s_MemoryView_of_r_object; + PyObject *__pyx_n_s_N; + PyObject *__pyx_n_b_O; + PyObject *__pyx_kp_u_Out_of_bounds_on_buffer_access_a; + PyObject *__pyx_n_s_PickleError; + PyObject *__pyx_n_s_Sequence; + PyObject *__pyx_kp_s_Step_may_not_be_zero_axis_d; + PyObject *__pyx_n_s_TypeError; + PyObject *__pyx_kp_s_Unable_to_convert_item_to_object; + PyObject *__pyx_n_s_ValueError; + PyObject *__pyx_n_s_View_MemoryView; + PyObject *__pyx_kp_u__2; + PyObject *__pyx_n_s__3; + PyObject *__pyx_kp_u__6; + PyObject *__pyx_n_s__62; + PyObject *__pyx_kp_u__7; + PyObject *__pyx_n_s_a; + PyObject *__pyx_n_s_abc; + PyObject *__pyx_n_s_alfa; + PyObject *__pyx_n_s_allocate_buffer; + PyObject *__pyx_n_s_alpha; + PyObject *__pyx_kp_u_and; + PyObject *__pyx_n_s_arange; + PyObject *__pyx_n_s_array; + PyObject *__pyx_n_s_astype; + PyObject *__pyx_n_s_asyncio_coroutines; + PyObject *__pyx_kp_s_at_least_one_of_the_parameters_i; + PyObject *__pyx_n_s_axis; + PyObject *__pyx_n_s_base; + PyObject *__pyx_n_s_c; + PyObject *__pyx_n_u_c; + PyObject *__pyx_n_s_cdf_array; + PyObject *__pyx_n_s_cdf_lb; + PyObject *__pyx_n_s_cdf_ub; + PyObject *__pyx_n_s_class; + PyObject *__pyx_n_s_class_getitem; + PyObject *__pyx_n_s_cline_in_traceback; + PyObject *__pyx_n_s_collections; + PyObject *__pyx_kp_s_collections_abc; + PyObject *__pyx_n_s_concatenate; + PyObject *__pyx_n_s_cont_x; + PyObject *__pyx_kp_s_contiguous_and_direct; + PyObject *__pyx_kp_s_contiguous_and_indirect; + PyObject *__pyx_n_s_copy; + PyObject *__pyx_n_s_core; + PyObject *__pyx_n_s_count; + PyObject *__pyx_n_s_cumm_s_size; + PyObject *__pyx_n_s_cumsum; + PyObject *__pyx_n_s_cumtrapz; + PyObject *__pyx_n_s_data; + PyObject *__pyx_n_s_data_copy; + PyObject *__pyx_n_s_delay; + PyObject *__pyx_n_s_dict; + PyObject *__pyx_n_s_diff; + PyObject *__pyx_kp_u_disable; + PyObject *__pyx_n_s_double; + PyObject *__pyx_n_s_drift; + PyObject *__pyx_n_s_dt; + PyObject *__pyx_n_s_dtype; + PyObject *__pyx_n_s_dtype_is_object; + PyObject *__pyx_n_s_empty; + PyObject *__pyx_kp_u_enable; + PyObject *__pyx_n_s_encode; + PyObject *__pyx_n_s_enumerate; + PyObject *__pyx_n_s_err; + PyObject *__pyx_n_s_error; + PyObject *__pyx_n_s_exp; + PyObject *__pyx_n_s_f; + PyObject *__pyx_n_s_feedback; + PyObject *__pyx_n_s_feedbacks; + PyObject *__pyx_n_s_flags; + PyObject *__pyx_n_s_float32; + PyObject *__pyx_n_s_format; + PyObject *__pyx_n_s_fortran; + PyObject *__pyx_n_u_fortran; + PyObject *__pyx_n_s_full_pdf; + PyObject *__pyx_kp_u_gc; + PyObject *__pyx_n_s_gen_cdf_using_pdf; + PyObject *__pyx_n_s_gen_rts_from_cdf; + PyObject *__pyx_n_s_getstate; + PyObject *__pyx_kp_u_got; + PyObject *__pyx_kp_u_got_differing_extents_in_dimensi; + PyObject *__pyx_n_s_i; + PyObject *__pyx_n_s_i_p; + PyObject *__pyx_n_s_id; + PyObject *__pyx_n_s_idx; + PyObject *__pyx_n_s_ij; + PyObject *__pyx_n_s_import; + PyObject *__pyx_n_s_index; + PyObject *__pyx_n_s_inf; + PyObject *__pyx_n_s_initializing; + PyObject *__pyx_n_s_integrate; + PyObject *__pyx_n_s_is_coroutine; + PyObject *__pyx_kp_u_isenabled; + PyObject *__pyx_n_s_itemsize; + PyObject *__pyx_kp_s_itemsize_0_for_cython_array; + PyObject *__pyx_n_s_j; + PyObject *__pyx_n_s_l_cdf; + PyObject *__pyx_n_s_lb; + PyObject *__pyx_n_s_linspace; + PyObject *__pyx_n_s_ll_min; + PyObject *__pyx_n_s_log; + PyObject *__pyx_n_s_log_p; + PyObject *__pyx_n_s_logp; + PyObject *__pyx_n_s_lower_bnd; + PyObject *__pyx_n_s_lower_bounds; + PyObject *__pyx_n_s_lower_bounds_view; + PyObject *__pyx_n_s_main; + PyObject *__pyx_n_s_max; + PyObject *__pyx_n_s_maximum; + PyObject *__pyx_n_s_memview; + PyObject *__pyx_n_s_min; + PyObject *__pyx_n_s_mode; + PyObject *__pyx_n_s_model; + PyObject *__pyx_n_s_multi; + PyObject *__pyx_n_s_n_cont; + PyObject *__pyx_n_s_n_params; + PyObject *__pyx_n_s_n_st; + PyObject *__pyx_n_s_n_sz; + PyObject *__pyx_n_s_name; + PyObject *__pyx_n_s_name_2; + PyObject *__pyx_n_s_ndim; + PyObject *__pyx_n_s_network; + PyObject *__pyx_n_s_new; + PyObject *__pyx_kp_s_no_default___reduce___due_to_non; + PyObject *__pyx_n_s_np; + PyObject *__pyx_n_s_numpy; + PyObject *__pyx_kp_s_numpy_core_multiarray_failed_to; + PyObject *__pyx_kp_s_numpy_core_umath_failed_to_impor; + PyObject *__pyx_n_s_obj; + PyObject *__pyx_n_s_p; + PyObject *__pyx_n_s_p_outlier; + PyObject *__pyx_n_s_pack; + PyObject *__pyx_n_s_param; + PyObject *__pyx_n_s_params; + PyObject *__pyx_n_s_params_bnds; + PyObject *__pyx_n_s_params_iter; + PyObject *__pyx_n_s_params_rl; + PyObject *__pyx_n_s_params_ssm; + PyObject *__pyx_n_s_params_view; + PyObject *__pyx_n_s_pdf; + PyObject *__pyx_n_s_pdf_array; + PyObject *__pyx_kp_s_pdf_pxi; + PyObject *__pyx_n_s_pickle; + PyObject *__pyx_n_s_pos_alfa; + PyObject *__pyx_n_s_pos_alpha; + PyObject *__pyx_n_s_pos_cont; + PyObject *__pyx_n_s_predict_on_batch; + PyObject *__pyx_n_s_pyx_PickleError; + PyObject *__pyx_n_s_pyx_checksum; + PyObject *__pyx_n_s_pyx_result; + PyObject *__pyx_n_s_pyx_state; + PyObject *__pyx_n_s_pyx_type; + PyObject *__pyx_n_s_pyx_unpickle_Enum; + PyObject *__pyx_n_s_pyx_vtable; + PyObject *__pyx_n_s_q; + PyObject *__pyx_n_s_qs; + PyObject *__pyx_n_s_rand; + PyObject *__pyx_n_s_random; + PyObject *__pyx_n_s_range; + PyObject *__pyx_n_s_reduce; + PyObject *__pyx_n_s_reduce_cython; + PyObject *__pyx_n_s_reduce_ex; + PyObject *__pyx_n_s_register; + PyObject *__pyx_n_s_response; + PyObject *__pyx_n_s_responses; + PyObject *__pyx_n_s_responses_qs; + PyObject *__pyx_n_s_rl_alpha; + PyObject *__pyx_n_s_rl_arr; + PyObject *__pyx_n_s_rt; + PyObject *__pyx_n_s_rts; + PyObject *__pyx_n_s_s; + PyObject *__pyx_n_s_s_size; + PyObject *__pyx_n_s_samples; + PyObject *__pyx_n_s_scipy; + PyObject *__pyx_n_s_scipy_integrate; + PyObject *__pyx_n_s_searchsorted; + PyObject *__pyx_n_s_setstate; + PyObject *__pyx_n_s_setstate_cython; + PyObject *__pyx_n_s_shape; + PyObject 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PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str; + PyObject *__pyx_n_s_unique; + PyObject *__pyx_n_s_unpack; + PyObject *__pyx_n_s_update; + PyObject *__pyx_n_s_upper_bnd; + PyObject *__pyx_n_s_upper_bounds; + PyObject *__pyx_n_s_upper_bounds_view; + PyObject *__pyx_n_s_use_adaptive; + PyObject *__pyx_n_s_v; + PyObject *__pyx_n_s_version_info; + PyObject *__pyx_n_s_w_outlier; + PyObject *__pyx_n_s_wfpt_n; + PyObject *__pyx_kp_s_wfpt_n_pyx; + PyObject *__pyx_n_s_wiener_like_contaminant; + PyObject *__pyx_n_s_wiener_like_multi; + PyObject *__pyx_n_s_wiener_like_multi_nn_mlp; + PyObject *__pyx_n_s_wiener_like_multi_nn_mlp_pdf; + PyObject *__pyx_n_s_wiener_like_multi_rlddm; + PyObject *__pyx_n_s_wiener_like_n; + PyObject *__pyx_n_s_wiener_like_nn_mlp; + PyObject *__pyx_n_s_wiener_like_nn_mlp_info; + PyObject *__pyx_n_s_wiener_like_nn_mlp_pdf; + PyObject *__pyx_n_s_wiener_like_rl; + PyObject *__pyx_n_s_wiener_like_rlddm; + PyObject *__pyx_n_s_wiener_like_rlssm_nn; + PyObject *__pyx_n_s_wiener_like_rlssm_nn_reg; + PyObject *__pyx_n_s_wp_outlier; + PyObject *__pyx_n_s_x; + PyObject *__pyx_n_s_x_lb; + PyObject *__pyx_n_s_x_ub; + PyObject *__pyx_n_s_xs; + PyObject *__pyx_n_s_y; + PyObject *__pyx_n_s_z; + PyObject *__pyx_n_s_zeros; + PyObject *__pyx_float_1eneg_3; + PyObject *__pyx_float_2_718281828459; + PyObject *__pyx_int_0; + PyObject *__pyx_int_1; + PyObject *__pyx_int_2; + PyObject *__pyx_int_3; + PyObject *__pyx_int_112105877; + PyObject *__pyx_int_136983863; + PyObject *__pyx_int_184977713; + PyObject *__pyx_int_neg_1; + PyObject *__pyx_slice__5; + PyObject *__pyx_tuple__4; + PyObject *__pyx_tuple__8; + PyObject *__pyx_tuple__9; + PyObject *__pyx_slice__11; + PyObject *__pyx_slice__13; + PyObject *__pyx_tuple__10; + PyObject *__pyx_tuple__12; + PyObject *__pyx_tuple__14; + PyObject *__pyx_tuple__15; + PyObject *__pyx_tuple__16; + PyObject *__pyx_tuple__17; + PyObject *__pyx_tuple__18; + PyObject *__pyx_tuple__19; + PyObject *__pyx_tuple__20; + PyObject *__pyx_tuple__21; + PyObject *__pyx_tuple__22; + PyObject *__pyx_tuple__23; + PyObject *__pyx_tuple__25; + PyObject *__pyx_tuple__26; + PyObject *__pyx_tuple__28; + PyObject *__pyx_tuple__29; + PyObject *__pyx_tuple__31; + PyObject *__pyx_tuple__33; + PyObject *__pyx_tuple__35; + PyObject *__pyx_tuple__37; + PyObject *__pyx_tuple__39; + PyObject *__pyx_tuple__41; + PyObject *__pyx_tuple__43; + PyObject *__pyx_tuple__45; + PyObject *__pyx_tuple__47; + PyObject *__pyx_tuple__49; + PyObject *__pyx_tuple__51; + PyObject *__pyx_tuple__53; + PyObject *__pyx_tuple__55; + PyObject *__pyx_tuple__57; + PyObject *__pyx_tuple__59; + PyObject *__pyx_codeobj__24; + PyObject *__pyx_codeobj__27; + PyObject *__pyx_codeobj__30; + PyObject *__pyx_codeobj__32; + PyObject *__pyx_codeobj__34; + PyObject *__pyx_codeobj__36; + PyObject *__pyx_codeobj__38; + PyObject *__pyx_codeobj__40; + PyObject *__pyx_codeobj__42; + PyObject *__pyx_codeobj__44; + PyObject *__pyx_codeobj__46; + PyObject *__pyx_codeobj__48; + PyObject *__pyx_codeobj__50; + PyObject *__pyx_codeobj__52; + PyObject *__pyx_codeobj__54; + PyObject *__pyx_codeobj__56; + PyObject *__pyx_codeobj__58; + PyObject *__pyx_codeobj__60; + PyObject *__pyx_codeobj__61; +} __pyx_mstate; + +#if CYTHON_USE_MODULE_STATE +#ifdef __cplusplus +namespace { + extern struct PyModuleDef __pyx_moduledef; +} /* anonymous namespace */ +#else +static struct PyModuleDef __pyx_moduledef; +#endif + +#define __pyx_mstate(o) ((__pyx_mstate *)__Pyx_PyModule_GetState(o)) + +#define __pyx_mstate_global (__pyx_mstate(PyState_FindModule(&__pyx_moduledef))) + +#define __pyx_m (PyState_FindModule(&__pyx_moduledef)) +#else +static __pyx_mstate __pyx_mstate_global_static = +#ifdef __cplusplus + {}; +#else + {0}; +#endif +static __pyx_mstate *__pyx_mstate_global = &__pyx_mstate_global_static; +#endif +/* #### Code section: module_state_clear ### */ +#if CYTHON_USE_MODULE_STATE +static int __pyx_m_clear(PyObject *m) { + __pyx_mstate *clear_module_state = __pyx_mstate(m); + if (!clear_module_state) return 0; + Py_CLEAR(clear_module_state->__pyx_d); + Py_CLEAR(clear_module_state->__pyx_b); + Py_CLEAR(clear_module_state->__pyx_cython_runtime); + Py_CLEAR(clear_module_state->__pyx_empty_tuple); + Py_CLEAR(clear_module_state->__pyx_empty_bytes); + Py_CLEAR(clear_module_state->__pyx_empty_unicode); + #ifdef __Pyx_CyFunction_USED + Py_CLEAR(clear_module_state->__pyx_CyFunctionType); + #endif + #ifdef __Pyx_FusedFunction_USED + Py_CLEAR(clear_module_state->__pyx_FusedFunctionType); + #endif + Py_CLEAR(clear_module_state->__pyx_ptype_7cpython_4type_type); + Py_CLEAR(clear_module_state->__pyx_ptype_5numpy_dtype); + Py_CLEAR(clear_module_state->__pyx_ptype_5numpy_flatiter); + Py_CLEAR(clear_module_state->__pyx_ptype_5numpy_broadcast); + Py_CLEAR(clear_module_state->__pyx_ptype_5numpy_ndarray); + Py_CLEAR(clear_module_state->__pyx_ptype_5numpy_generic); + Py_CLEAR(clear_module_state->__pyx_ptype_5numpy_number); + Py_CLEAR(clear_module_state->__pyx_ptype_5numpy_integer); + Py_CLEAR(clear_module_state->__pyx_ptype_5numpy_signedinteger); + Py_CLEAR(clear_module_state->__pyx_ptype_5numpy_unsignedinteger); + Py_CLEAR(clear_module_state->__pyx_ptype_5numpy_inexact); + Py_CLEAR(clear_module_state->__pyx_ptype_5numpy_floating); + Py_CLEAR(clear_module_state->__pyx_ptype_5numpy_complexfloating); + Py_CLEAR(clear_module_state->__pyx_ptype_5numpy_flexible); + Py_CLEAR(clear_module_state->__pyx_ptype_5numpy_character); + Py_CLEAR(clear_module_state->__pyx_ptype_5numpy_ufunc); + Py_CLEAR(clear_module_state->__pyx_array_type); + Py_CLEAR(clear_module_state->__pyx_type___pyx_array); + Py_CLEAR(clear_module_state->__pyx_MemviewEnum_type); + Py_CLEAR(clear_module_state->__pyx_type___pyx_MemviewEnum); + Py_CLEAR(clear_module_state->__pyx_memoryview_type); + Py_CLEAR(clear_module_state->__pyx_type___pyx_memoryview); + Py_CLEAR(clear_module_state->__pyx_memoryviewslice_type); + Py_CLEAR(clear_module_state->__pyx_type___pyx_memoryviewslice); + Py_CLEAR(clear_module_state->__pyx_kp_u_); + Py_CLEAR(clear_module_state->__pyx_n_s_ASCII); + Py_CLEAR(clear_module_state->__pyx_kp_s_All_dimensions_preceding_dimensi); + Py_CLEAR(clear_module_state->__pyx_n_s_AssertionError); + Py_CLEAR(clear_module_state->__pyx_kp_s_Buffer_view_does_not_expose_stri); + Py_CLEAR(clear_module_state->__pyx_kp_s_Can_only_create_a_buffer_that_is); + Py_CLEAR(clear_module_state->__pyx_kp_s_Cannot_assign_to_read_only_memor); + Py_CLEAR(clear_module_state->__pyx_kp_s_Cannot_create_writable_memory_vi); + Py_CLEAR(clear_module_state->__pyx_kp_u_Cannot_index_with_type); + Py_CLEAR(clear_module_state->__pyx_kp_s_Cannot_transpose_memoryview_with); + Py_CLEAR(clear_module_state->__pyx_kp_s_Dimension_d_is_not_direct); + Py_CLEAR(clear_module_state->__pyx_n_s_Ellipsis); + Py_CLEAR(clear_module_state->__pyx_kp_s_Empty_shape_tuple_for_cython_arr); + Py_CLEAR(clear_module_state->__pyx_n_s_ImportError); + Py_CLEAR(clear_module_state->__pyx_kp_s_Incompatible_checksums_0x_x_vs_0); + Py_CLEAR(clear_module_state->__pyx_n_s_IndexError); + Py_CLEAR(clear_module_state->__pyx_kp_s_Index_out_of_bounds_axis_d); + Py_CLEAR(clear_module_state->__pyx_kp_s_Indirect_dimensions_not_supporte); + Py_CLEAR(clear_module_state->__pyx_kp_u_Invalid_mode_expected_c_or_fortr); + Py_CLEAR(clear_module_state->__pyx_kp_u_Invalid_shape_in_axis); + Py_CLEAR(clear_module_state->__pyx_n_s_MemoryError); + Py_CLEAR(clear_module_state->__pyx_kp_s_MemoryView_of_r_at_0x_x); + Py_CLEAR(clear_module_state->__pyx_kp_s_MemoryView_of_r_object); + Py_CLEAR(clear_module_state->__pyx_n_s_N); + Py_CLEAR(clear_module_state->__pyx_n_b_O); + Py_CLEAR(clear_module_state->__pyx_kp_u_Out_of_bounds_on_buffer_access_a); + Py_CLEAR(clear_module_state->__pyx_n_s_PickleError); + Py_CLEAR(clear_module_state->__pyx_n_s_Sequence); + Py_CLEAR(clear_module_state->__pyx_kp_s_Step_may_not_be_zero_axis_d); + Py_CLEAR(clear_module_state->__pyx_n_s_TypeError); + Py_CLEAR(clear_module_state->__pyx_kp_s_Unable_to_convert_item_to_object); + Py_CLEAR(clear_module_state->__pyx_n_s_ValueError); + Py_CLEAR(clear_module_state->__pyx_n_s_View_MemoryView); + Py_CLEAR(clear_module_state->__pyx_kp_u__2); + Py_CLEAR(clear_module_state->__pyx_n_s__3); + Py_CLEAR(clear_module_state->__pyx_kp_u__6); + Py_CLEAR(clear_module_state->__pyx_n_s__62); + Py_CLEAR(clear_module_state->__pyx_kp_u__7); + Py_CLEAR(clear_module_state->__pyx_n_s_a); + Py_CLEAR(clear_module_state->__pyx_n_s_abc); + Py_CLEAR(clear_module_state->__pyx_n_s_alfa); + Py_CLEAR(clear_module_state->__pyx_n_s_allocate_buffer); + Py_CLEAR(clear_module_state->__pyx_n_s_alpha); + Py_CLEAR(clear_module_state->__pyx_kp_u_and); + Py_CLEAR(clear_module_state->__pyx_n_s_arange); + Py_CLEAR(clear_module_state->__pyx_n_s_array); + Py_CLEAR(clear_module_state->__pyx_n_s_astype); + Py_CLEAR(clear_module_state->__pyx_n_s_asyncio_coroutines); + Py_CLEAR(clear_module_state->__pyx_kp_s_at_least_one_of_the_parameters_i); + Py_CLEAR(clear_module_state->__pyx_n_s_axis); + Py_CLEAR(clear_module_state->__pyx_n_s_base); + Py_CLEAR(clear_module_state->__pyx_n_s_c); + Py_CLEAR(clear_module_state->__pyx_n_u_c); + Py_CLEAR(clear_module_state->__pyx_n_s_cdf_array); + Py_CLEAR(clear_module_state->__pyx_n_s_cdf_lb); + Py_CLEAR(clear_module_state->__pyx_n_s_cdf_ub); + Py_CLEAR(clear_module_state->__pyx_n_s_class); + Py_CLEAR(clear_module_state->__pyx_n_s_class_getitem); + Py_CLEAR(clear_module_state->__pyx_n_s_cline_in_traceback); + Py_CLEAR(clear_module_state->__pyx_n_s_collections); + Py_CLEAR(clear_module_state->__pyx_kp_s_collections_abc); + Py_CLEAR(clear_module_state->__pyx_n_s_concatenate); + Py_CLEAR(clear_module_state->__pyx_n_s_cont_x); + Py_CLEAR(clear_module_state->__pyx_kp_s_contiguous_and_direct); + Py_CLEAR(clear_module_state->__pyx_kp_s_contiguous_and_indirect); + Py_CLEAR(clear_module_state->__pyx_n_s_copy); + Py_CLEAR(clear_module_state->__pyx_n_s_core); + Py_CLEAR(clear_module_state->__pyx_n_s_count); + Py_CLEAR(clear_module_state->__pyx_n_s_cumm_s_size); + Py_CLEAR(clear_module_state->__pyx_n_s_cumsum); + Py_CLEAR(clear_module_state->__pyx_n_s_cumtrapz); + Py_CLEAR(clear_module_state->__pyx_n_s_data); + Py_CLEAR(clear_module_state->__pyx_n_s_data_copy); + Py_CLEAR(clear_module_state->__pyx_n_s_delay); + Py_CLEAR(clear_module_state->__pyx_n_s_dict); + Py_CLEAR(clear_module_state->__pyx_n_s_diff); + Py_CLEAR(clear_module_state->__pyx_kp_u_disable); + Py_CLEAR(clear_module_state->__pyx_n_s_double); + Py_CLEAR(clear_module_state->__pyx_n_s_drift); + Py_CLEAR(clear_module_state->__pyx_n_s_dt); + Py_CLEAR(clear_module_state->__pyx_n_s_dtype); + Py_CLEAR(clear_module_state->__pyx_n_s_dtype_is_object); + Py_CLEAR(clear_module_state->__pyx_n_s_empty); + Py_CLEAR(clear_module_state->__pyx_kp_u_enable); + Py_CLEAR(clear_module_state->__pyx_n_s_encode); + Py_CLEAR(clear_module_state->__pyx_n_s_enumerate); + Py_CLEAR(clear_module_state->__pyx_n_s_err); + Py_CLEAR(clear_module_state->__pyx_n_s_error); + Py_CLEAR(clear_module_state->__pyx_n_s_exp); + Py_CLEAR(clear_module_state->__pyx_n_s_f); + Py_CLEAR(clear_module_state->__pyx_n_s_feedback); + Py_CLEAR(clear_module_state->__pyx_n_s_feedbacks); + Py_CLEAR(clear_module_state->__pyx_n_s_flags); + Py_CLEAR(clear_module_state->__pyx_n_s_float32); + Py_CLEAR(clear_module_state->__pyx_n_s_format); + Py_CLEAR(clear_module_state->__pyx_n_s_fortran); + Py_CLEAR(clear_module_state->__pyx_n_u_fortran); + Py_CLEAR(clear_module_state->__pyx_n_s_full_pdf); + Py_CLEAR(clear_module_state->__pyx_kp_u_gc); + Py_CLEAR(clear_module_state->__pyx_n_s_gen_cdf_using_pdf); + Py_CLEAR(clear_module_state->__pyx_n_s_gen_rts_from_cdf); + Py_CLEAR(clear_module_state->__pyx_n_s_getstate); + Py_CLEAR(clear_module_state->__pyx_kp_u_got); + Py_CLEAR(clear_module_state->__pyx_kp_u_got_differing_extents_in_dimensi); + Py_CLEAR(clear_module_state->__pyx_n_s_i); + Py_CLEAR(clear_module_state->__pyx_n_s_i_p); + Py_CLEAR(clear_module_state->__pyx_n_s_id); + Py_CLEAR(clear_module_state->__pyx_n_s_idx); + Py_CLEAR(clear_module_state->__pyx_n_s_ij); + Py_CLEAR(clear_module_state->__pyx_n_s_import); + Py_CLEAR(clear_module_state->__pyx_n_s_index); + Py_CLEAR(clear_module_state->__pyx_n_s_inf); + Py_CLEAR(clear_module_state->__pyx_n_s_initializing); + Py_CLEAR(clear_module_state->__pyx_n_s_integrate); + Py_CLEAR(clear_module_state->__pyx_n_s_is_coroutine); + Py_CLEAR(clear_module_state->__pyx_kp_u_isenabled); + Py_CLEAR(clear_module_state->__pyx_n_s_itemsize); + Py_CLEAR(clear_module_state->__pyx_kp_s_itemsize_0_for_cython_array); + Py_CLEAR(clear_module_state->__pyx_n_s_j); + Py_CLEAR(clear_module_state->__pyx_n_s_l_cdf); + Py_CLEAR(clear_module_state->__pyx_n_s_lb); + Py_CLEAR(clear_module_state->__pyx_n_s_linspace); + Py_CLEAR(clear_module_state->__pyx_n_s_ll_min); + Py_CLEAR(clear_module_state->__pyx_n_s_log); + Py_CLEAR(clear_module_state->__pyx_n_s_log_p); + Py_CLEAR(clear_module_state->__pyx_n_s_logp); + Py_CLEAR(clear_module_state->__pyx_n_s_lower_bnd); + Py_CLEAR(clear_module_state->__pyx_n_s_lower_bounds); + Py_CLEAR(clear_module_state->__pyx_n_s_lower_bounds_view); + Py_CLEAR(clear_module_state->__pyx_n_s_main); + Py_CLEAR(clear_module_state->__pyx_n_s_max); + Py_CLEAR(clear_module_state->__pyx_n_s_maximum); + Py_CLEAR(clear_module_state->__pyx_n_s_memview); + Py_CLEAR(clear_module_state->__pyx_n_s_min); + Py_CLEAR(clear_module_state->__pyx_n_s_mode); + Py_CLEAR(clear_module_state->__pyx_n_s_model); + Py_CLEAR(clear_module_state->__pyx_n_s_multi); + Py_CLEAR(clear_module_state->__pyx_n_s_n_cont); + Py_CLEAR(clear_module_state->__pyx_n_s_n_params); + Py_CLEAR(clear_module_state->__pyx_n_s_n_st); + Py_CLEAR(clear_module_state->__pyx_n_s_n_sz); + Py_CLEAR(clear_module_state->__pyx_n_s_name); + Py_CLEAR(clear_module_state->__pyx_n_s_name_2); + Py_CLEAR(clear_module_state->__pyx_n_s_ndim); + Py_CLEAR(clear_module_state->__pyx_n_s_network); + Py_CLEAR(clear_module_state->__pyx_n_s_new); + Py_CLEAR(clear_module_state->__pyx_kp_s_no_default___reduce___due_to_non); + Py_CLEAR(clear_module_state->__pyx_n_s_np); + Py_CLEAR(clear_module_state->__pyx_n_s_numpy); + Py_CLEAR(clear_module_state->__pyx_kp_s_numpy_core_multiarray_failed_to); + Py_CLEAR(clear_module_state->__pyx_kp_s_numpy_core_umath_failed_to_impor); + Py_CLEAR(clear_module_state->__pyx_n_s_obj); + Py_CLEAR(clear_module_state->__pyx_n_s_p); + Py_CLEAR(clear_module_state->__pyx_n_s_p_outlier); + Py_CLEAR(clear_module_state->__pyx_n_s_pack); + Py_CLEAR(clear_module_state->__pyx_n_s_param); + Py_CLEAR(clear_module_state->__pyx_n_s_params); + Py_CLEAR(clear_module_state->__pyx_n_s_params_bnds); + Py_CLEAR(clear_module_state->__pyx_n_s_params_iter); + Py_CLEAR(clear_module_state->__pyx_n_s_params_rl); + Py_CLEAR(clear_module_state->__pyx_n_s_params_ssm); + Py_CLEAR(clear_module_state->__pyx_n_s_params_view); + Py_CLEAR(clear_module_state->__pyx_n_s_pdf); + Py_CLEAR(clear_module_state->__pyx_n_s_pdf_array); + Py_CLEAR(clear_module_state->__pyx_kp_s_pdf_pxi); + Py_CLEAR(clear_module_state->__pyx_n_s_pickle); + Py_CLEAR(clear_module_state->__pyx_n_s_pos_alfa); + Py_CLEAR(clear_module_state->__pyx_n_s_pos_alpha); + Py_CLEAR(clear_module_state->__pyx_n_s_pos_cont); + Py_CLEAR(clear_module_state->__pyx_n_s_predict_on_batch); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_PickleError); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_checksum); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_result); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_state); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_type); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_unpickle_Enum); + Py_CLEAR(clear_module_state->__pyx_n_s_pyx_vtable); + Py_CLEAR(clear_module_state->__pyx_n_s_q); + Py_CLEAR(clear_module_state->__pyx_n_s_qs); + Py_CLEAR(clear_module_state->__pyx_n_s_rand); + Py_CLEAR(clear_module_state->__pyx_n_s_random); + Py_CLEAR(clear_module_state->__pyx_n_s_range); + Py_CLEAR(clear_module_state->__pyx_n_s_reduce); + Py_CLEAR(clear_module_state->__pyx_n_s_reduce_cython); + Py_CLEAR(clear_module_state->__pyx_n_s_reduce_ex); + Py_CLEAR(clear_module_state->__pyx_n_s_register); + Py_CLEAR(clear_module_state->__pyx_n_s_response); + Py_CLEAR(clear_module_state->__pyx_n_s_responses); + Py_CLEAR(clear_module_state->__pyx_n_s_responses_qs); + Py_CLEAR(clear_module_state->__pyx_n_s_rl_alpha); + Py_CLEAR(clear_module_state->__pyx_n_s_rl_arr); + Py_CLEAR(clear_module_state->__pyx_n_s_rt); + Py_CLEAR(clear_module_state->__pyx_n_s_rts); + Py_CLEAR(clear_module_state->__pyx_n_s_s); + Py_CLEAR(clear_module_state->__pyx_n_s_s_size); + Py_CLEAR(clear_module_state->__pyx_n_s_samples); + Py_CLEAR(clear_module_state->__pyx_n_s_scipy); + Py_CLEAR(clear_module_state->__pyx_n_s_scipy_integrate); + Py_CLEAR(clear_module_state->__pyx_n_s_searchsorted); + Py_CLEAR(clear_module_state->__pyx_n_s_setstate); + Py_CLEAR(clear_module_state->__pyx_n_s_setstate_cython); + Py_CLEAR(clear_module_state->__pyx_n_s_shape); + Py_CLEAR(clear_module_state->__pyx_n_s_sign); + Py_CLEAR(clear_module_state->__pyx_n_s_simps_err); + Py_CLEAR(clear_module_state->__pyx_n_s_size); + Py_CLEAR(clear_module_state->__pyx_n_s_spec); + Py_CLEAR(clear_module_state->__pyx_n_s_split_by); + Py_CLEAR(clear_module_state->__pyx_n_s_split_cdf); + Py_CLEAR(clear_module_state->__pyx_n_s_squeeze); + Py_CLEAR(clear_module_state->__pyx_n_s_st); + Py_CLEAR(clear_module_state->__pyx_n_s_stack); + Py_CLEAR(clear_module_state->__pyx_n_s_start); + Py_CLEAR(clear_module_state->__pyx_n_s_step); + Py_CLEAR(clear_module_state->__pyx_n_s_stop); + Py_CLEAR(clear_module_state->__pyx_kp_s_strided_and_direct); + Py_CLEAR(clear_module_state->__pyx_kp_s_strided_and_direct_or_indirect); + Py_CLEAR(clear_module_state->__pyx_kp_s_strided_and_indirect); + Py_CLEAR(clear_module_state->__pyx_kp_s_stringsource); + Py_CLEAR(clear_module_state->__pyx_n_s_struct); + Py_CLEAR(clear_module_state->__pyx_n_s_sum); + Py_CLEAR(clear_module_state->__pyx_n_s_sum_logp); + Py_CLEAR(clear_module_state->__pyx_n_s_sv); + Py_CLEAR(clear_module_state->__pyx_n_s_sys); + Py_CLEAR(clear_module_state->__pyx_n_s_sz); + Py_CLEAR(clear_module_state->__pyx_n_s_t); + Py_CLEAR(clear_module_state->__pyx_n_s_t_max); + Py_CLEAR(clear_module_state->__pyx_n_s_t_min); + Py_CLEAR(clear_module_state->__pyx_n_s_test); + Py_CLEAR(clear_module_state->__pyx_n_s_tile); + Py_CLEAR(clear_module_state->__pyx_n_s_time); + Py_CLEAR(clear_module_state->__pyx_n_s_tp_scale); + Py_CLEAR(clear_module_state->__pyx_n_s_ub); + Py_CLEAR(clear_module_state->__pyx_n_s_umath); + Py_CLEAR(clear_module_state->__pyx_kp_s_unable_to_allocate_array_data); + Py_CLEAR(clear_module_state->__pyx_kp_s_unable_to_allocate_shape_and_str); + Py_CLEAR(clear_module_state->__pyx_n_s_unique); + Py_CLEAR(clear_module_state->__pyx_n_s_unpack); + Py_CLEAR(clear_module_state->__pyx_n_s_update); + Py_CLEAR(clear_module_state->__pyx_n_s_upper_bnd); + Py_CLEAR(clear_module_state->__pyx_n_s_upper_bounds); + Py_CLEAR(clear_module_state->__pyx_n_s_upper_bounds_view); + Py_CLEAR(clear_module_state->__pyx_n_s_use_adaptive); + Py_CLEAR(clear_module_state->__pyx_n_s_v); + Py_CLEAR(clear_module_state->__pyx_n_s_version_info); + Py_CLEAR(clear_module_state->__pyx_n_s_w_outlier); + Py_CLEAR(clear_module_state->__pyx_n_s_wfpt_n); + Py_CLEAR(clear_module_state->__pyx_kp_s_wfpt_n_pyx); + Py_CLEAR(clear_module_state->__pyx_n_s_wiener_like_contaminant); + Py_CLEAR(clear_module_state->__pyx_n_s_wiener_like_multi); + Py_CLEAR(clear_module_state->__pyx_n_s_wiener_like_multi_nn_mlp); + Py_CLEAR(clear_module_state->__pyx_n_s_wiener_like_multi_nn_mlp_pdf); + Py_CLEAR(clear_module_state->__pyx_n_s_wiener_like_multi_rlddm); + Py_CLEAR(clear_module_state->__pyx_n_s_wiener_like_n); + Py_CLEAR(clear_module_state->__pyx_n_s_wiener_like_nn_mlp); + Py_CLEAR(clear_module_state->__pyx_n_s_wiener_like_nn_mlp_info); + Py_CLEAR(clear_module_state->__pyx_n_s_wiener_like_nn_mlp_pdf); + Py_CLEAR(clear_module_state->__pyx_n_s_wiener_like_rl); + Py_CLEAR(clear_module_state->__pyx_n_s_wiener_like_rlddm); + Py_CLEAR(clear_module_state->__pyx_n_s_wiener_like_rlssm_nn); + Py_CLEAR(clear_module_state->__pyx_n_s_wiener_like_rlssm_nn_reg); + Py_CLEAR(clear_module_state->__pyx_n_s_wp_outlier); + Py_CLEAR(clear_module_state->__pyx_n_s_x); + Py_CLEAR(clear_module_state->__pyx_n_s_x_lb); + Py_CLEAR(clear_module_state->__pyx_n_s_x_ub); + Py_CLEAR(clear_module_state->__pyx_n_s_xs); + Py_CLEAR(clear_module_state->__pyx_n_s_y); + Py_CLEAR(clear_module_state->__pyx_n_s_z); + Py_CLEAR(clear_module_state->__pyx_n_s_zeros); + Py_CLEAR(clear_module_state->__pyx_float_1eneg_3); + Py_CLEAR(clear_module_state->__pyx_float_2_718281828459); + Py_CLEAR(clear_module_state->__pyx_int_0); + Py_CLEAR(clear_module_state->__pyx_int_1); + Py_CLEAR(clear_module_state->__pyx_int_2); + Py_CLEAR(clear_module_state->__pyx_int_3); + Py_CLEAR(clear_module_state->__pyx_int_112105877); + Py_CLEAR(clear_module_state->__pyx_int_136983863); + Py_CLEAR(clear_module_state->__pyx_int_184977713); + Py_CLEAR(clear_module_state->__pyx_int_neg_1); + Py_CLEAR(clear_module_state->__pyx_slice__5); + Py_CLEAR(clear_module_state->__pyx_tuple__4); + Py_CLEAR(clear_module_state->__pyx_tuple__8); + Py_CLEAR(clear_module_state->__pyx_tuple__9); + Py_CLEAR(clear_module_state->__pyx_slice__11); + Py_CLEAR(clear_module_state->__pyx_slice__13); + Py_CLEAR(clear_module_state->__pyx_tuple__10); + Py_CLEAR(clear_module_state->__pyx_tuple__12); + Py_CLEAR(clear_module_state->__pyx_tuple__14); + Py_CLEAR(clear_module_state->__pyx_tuple__15); + Py_CLEAR(clear_module_state->__pyx_tuple__16); + Py_CLEAR(clear_module_state->__pyx_tuple__17); + Py_CLEAR(clear_module_state->__pyx_tuple__18); + Py_CLEAR(clear_module_state->__pyx_tuple__19); + Py_CLEAR(clear_module_state->__pyx_tuple__20); + Py_CLEAR(clear_module_state->__pyx_tuple__21); + Py_CLEAR(clear_module_state->__pyx_tuple__22); + Py_CLEAR(clear_module_state->__pyx_tuple__23); + Py_CLEAR(clear_module_state->__pyx_tuple__25); + Py_CLEAR(clear_module_state->__pyx_tuple__26); + Py_CLEAR(clear_module_state->__pyx_tuple__28); + Py_CLEAR(clear_module_state->__pyx_tuple__29); + Py_CLEAR(clear_module_state->__pyx_tuple__31); + Py_CLEAR(clear_module_state->__pyx_tuple__33); + Py_CLEAR(clear_module_state->__pyx_tuple__35); + Py_CLEAR(clear_module_state->__pyx_tuple__37); + Py_CLEAR(clear_module_state->__pyx_tuple__39); + Py_CLEAR(clear_module_state->__pyx_tuple__41); + Py_CLEAR(clear_module_state->__pyx_tuple__43); + Py_CLEAR(clear_module_state->__pyx_tuple__45); + Py_CLEAR(clear_module_state->__pyx_tuple__47); + Py_CLEAR(clear_module_state->__pyx_tuple__49); + Py_CLEAR(clear_module_state->__pyx_tuple__51); + Py_CLEAR(clear_module_state->__pyx_tuple__53); + Py_CLEAR(clear_module_state->__pyx_tuple__55); + Py_CLEAR(clear_module_state->__pyx_tuple__57); + Py_CLEAR(clear_module_state->__pyx_tuple__59); + Py_CLEAR(clear_module_state->__pyx_codeobj__24); + Py_CLEAR(clear_module_state->__pyx_codeobj__27); + Py_CLEAR(clear_module_state->__pyx_codeobj__30); + Py_CLEAR(clear_module_state->__pyx_codeobj__32); + Py_CLEAR(clear_module_state->__pyx_codeobj__34); + Py_CLEAR(clear_module_state->__pyx_codeobj__36); + Py_CLEAR(clear_module_state->__pyx_codeobj__38); + Py_CLEAR(clear_module_state->__pyx_codeobj__40); + Py_CLEAR(clear_module_state->__pyx_codeobj__42); + Py_CLEAR(clear_module_state->__pyx_codeobj__44); + Py_CLEAR(clear_module_state->__pyx_codeobj__46); + Py_CLEAR(clear_module_state->__pyx_codeobj__48); + Py_CLEAR(clear_module_state->__pyx_codeobj__50); + Py_CLEAR(clear_module_state->__pyx_codeobj__52); + Py_CLEAR(clear_module_state->__pyx_codeobj__54); + Py_CLEAR(clear_module_state->__pyx_codeobj__56); + Py_CLEAR(clear_module_state->__pyx_codeobj__58); + Py_CLEAR(clear_module_state->__pyx_codeobj__60); + Py_CLEAR(clear_module_state->__pyx_codeobj__61); + return 0; +} +#endif +/* #### Code section: module_state_traverse ### */ +#if CYTHON_USE_MODULE_STATE +static int __pyx_m_traverse(PyObject *m, visitproc visit, void *arg) { + __pyx_mstate *traverse_module_state = __pyx_mstate(m); + if (!traverse_module_state) return 0; + Py_VISIT(traverse_module_state->__pyx_d); + Py_VISIT(traverse_module_state->__pyx_b); + Py_VISIT(traverse_module_state->__pyx_cython_runtime); + Py_VISIT(traverse_module_state->__pyx_empty_tuple); + Py_VISIT(traverse_module_state->__pyx_empty_bytes); + Py_VISIT(traverse_module_state->__pyx_empty_unicode); + #ifdef __Pyx_CyFunction_USED + Py_VISIT(traverse_module_state->__pyx_CyFunctionType); + #endif + #ifdef __Pyx_FusedFunction_USED + Py_VISIT(traverse_module_state->__pyx_FusedFunctionType); + #endif + Py_VISIT(traverse_module_state->__pyx_ptype_7cpython_4type_type); + Py_VISIT(traverse_module_state->__pyx_ptype_5numpy_dtype); + Py_VISIT(traverse_module_state->__pyx_ptype_5numpy_flatiter); + Py_VISIT(traverse_module_state->__pyx_ptype_5numpy_broadcast); + Py_VISIT(traverse_module_state->__pyx_ptype_5numpy_ndarray); + Py_VISIT(traverse_module_state->__pyx_ptype_5numpy_generic); + Py_VISIT(traverse_module_state->__pyx_ptype_5numpy_number); + Py_VISIT(traverse_module_state->__pyx_ptype_5numpy_integer); + Py_VISIT(traverse_module_state->__pyx_ptype_5numpy_signedinteger); + Py_VISIT(traverse_module_state->__pyx_ptype_5numpy_unsignedinteger); + Py_VISIT(traverse_module_state->__pyx_ptype_5numpy_inexact); + Py_VISIT(traverse_module_state->__pyx_ptype_5numpy_floating); + Py_VISIT(traverse_module_state->__pyx_ptype_5numpy_complexfloating); + Py_VISIT(traverse_module_state->__pyx_ptype_5numpy_flexible); + Py_VISIT(traverse_module_state->__pyx_ptype_5numpy_character); + Py_VISIT(traverse_module_state->__pyx_ptype_5numpy_ufunc); + Py_VISIT(traverse_module_state->__pyx_array_type); + Py_VISIT(traverse_module_state->__pyx_type___pyx_array); + Py_VISIT(traverse_module_state->__pyx_MemviewEnum_type); + Py_VISIT(traverse_module_state->__pyx_type___pyx_MemviewEnum); + Py_VISIT(traverse_module_state->__pyx_memoryview_type); + Py_VISIT(traverse_module_state->__pyx_type___pyx_memoryview); + Py_VISIT(traverse_module_state->__pyx_memoryviewslice_type); + Py_VISIT(traverse_module_state->__pyx_type___pyx_memoryviewslice); + Py_VISIT(traverse_module_state->__pyx_kp_u_); + Py_VISIT(traverse_module_state->__pyx_n_s_ASCII); + Py_VISIT(traverse_module_state->__pyx_kp_s_All_dimensions_preceding_dimensi); + Py_VISIT(traverse_module_state->__pyx_n_s_AssertionError); + Py_VISIT(traverse_module_state->__pyx_kp_s_Buffer_view_does_not_expose_stri); + Py_VISIT(traverse_module_state->__pyx_kp_s_Can_only_create_a_buffer_that_is); + 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Py_VISIT(traverse_module_state->__pyx_codeobj__34); + Py_VISIT(traverse_module_state->__pyx_codeobj__36); + Py_VISIT(traverse_module_state->__pyx_codeobj__38); + Py_VISIT(traverse_module_state->__pyx_codeobj__40); + Py_VISIT(traverse_module_state->__pyx_codeobj__42); + Py_VISIT(traverse_module_state->__pyx_codeobj__44); + Py_VISIT(traverse_module_state->__pyx_codeobj__46); + Py_VISIT(traverse_module_state->__pyx_codeobj__48); + Py_VISIT(traverse_module_state->__pyx_codeobj__50); + Py_VISIT(traverse_module_state->__pyx_codeobj__52); + Py_VISIT(traverse_module_state->__pyx_codeobj__54); + Py_VISIT(traverse_module_state->__pyx_codeobj__56); + Py_VISIT(traverse_module_state->__pyx_codeobj__58); + Py_VISIT(traverse_module_state->__pyx_codeobj__60); + Py_VISIT(traverse_module_state->__pyx_codeobj__61); + return 0; +} +#endif +/* #### Code section: module_state_defines ### */ +#define __pyx_d __pyx_mstate_global->__pyx_d +#define __pyx_b __pyx_mstate_global->__pyx_b +#define __pyx_cython_runtime __pyx_mstate_global->__pyx_cython_runtime +#define __pyx_empty_tuple __pyx_mstate_global->__pyx_empty_tuple +#define __pyx_empty_bytes __pyx_mstate_global->__pyx_empty_bytes +#define __pyx_empty_unicode __pyx_mstate_global->__pyx_empty_unicode +#ifdef __Pyx_CyFunction_USED +#define __pyx_CyFunctionType __pyx_mstate_global->__pyx_CyFunctionType +#endif +#ifdef __Pyx_FusedFunction_USED +#define __pyx_FusedFunctionType __pyx_mstate_global->__pyx_FusedFunctionType +#endif +#ifdef __Pyx_Generator_USED +#define __pyx_GeneratorType __pyx_mstate_global->__pyx_GeneratorType +#endif +#ifdef __Pyx_IterableCoroutine_USED +#define __pyx_IterableCoroutineType __pyx_mstate_global->__pyx_IterableCoroutineType +#endif +#ifdef __Pyx_Coroutine_USED +#define __pyx_CoroutineAwaitType __pyx_mstate_global->__pyx_CoroutineAwaitType +#endif +#ifdef __Pyx_Coroutine_USED +#define __pyx_CoroutineType __pyx_mstate_global->__pyx_CoroutineType +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#define __pyx_ptype_7cpython_4type_type __pyx_mstate_global->__pyx_ptype_7cpython_4type_type +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#define __pyx_ptype_5numpy_dtype __pyx_mstate_global->__pyx_ptype_5numpy_dtype +#define __pyx_ptype_5numpy_flatiter __pyx_mstate_global->__pyx_ptype_5numpy_flatiter +#define __pyx_ptype_5numpy_broadcast __pyx_mstate_global->__pyx_ptype_5numpy_broadcast +#define __pyx_ptype_5numpy_ndarray __pyx_mstate_global->__pyx_ptype_5numpy_ndarray +#define __pyx_ptype_5numpy_generic __pyx_mstate_global->__pyx_ptype_5numpy_generic +#define __pyx_ptype_5numpy_number __pyx_mstate_global->__pyx_ptype_5numpy_number +#define __pyx_ptype_5numpy_integer __pyx_mstate_global->__pyx_ptype_5numpy_integer +#define __pyx_ptype_5numpy_signedinteger __pyx_mstate_global->__pyx_ptype_5numpy_signedinteger +#define __pyx_ptype_5numpy_unsignedinteger __pyx_mstate_global->__pyx_ptype_5numpy_unsignedinteger +#define __pyx_ptype_5numpy_inexact __pyx_mstate_global->__pyx_ptype_5numpy_inexact +#define __pyx_ptype_5numpy_floating __pyx_mstate_global->__pyx_ptype_5numpy_floating +#define __pyx_ptype_5numpy_complexfloating __pyx_mstate_global->__pyx_ptype_5numpy_complexfloating +#define __pyx_ptype_5numpy_flexible __pyx_mstate_global->__pyx_ptype_5numpy_flexible +#define __pyx_ptype_5numpy_character __pyx_mstate_global->__pyx_ptype_5numpy_character +#define __pyx_ptype_5numpy_ufunc __pyx_mstate_global->__pyx_ptype_5numpy_ufunc +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#endif +#if CYTHON_USE_MODULE_STATE +#define __pyx_type___pyx_array __pyx_mstate_global->__pyx_type___pyx_array +#define __pyx_type___pyx_MemviewEnum __pyx_mstate_global->__pyx_type___pyx_MemviewEnum +#define __pyx_type___pyx_memoryview __pyx_mstate_global->__pyx_type___pyx_memoryview +#define __pyx_type___pyx_memoryviewslice __pyx_mstate_global->__pyx_type___pyx_memoryviewslice +#endif +#define __pyx_array_type __pyx_mstate_global->__pyx_array_type +#define __pyx_MemviewEnum_type __pyx_mstate_global->__pyx_MemviewEnum_type +#define __pyx_memoryview_type __pyx_mstate_global->__pyx_memoryview_type +#define __pyx_memoryviewslice_type __pyx_mstate_global->__pyx_memoryviewslice_type +#define __pyx_kp_u_ __pyx_mstate_global->__pyx_kp_u_ +#define __pyx_n_s_ASCII __pyx_mstate_global->__pyx_n_s_ASCII +#define __pyx_kp_s_All_dimensions_preceding_dimensi __pyx_mstate_global->__pyx_kp_s_All_dimensions_preceding_dimensi +#define __pyx_n_s_AssertionError __pyx_mstate_global->__pyx_n_s_AssertionError +#define __pyx_kp_s_Buffer_view_does_not_expose_stri __pyx_mstate_global->__pyx_kp_s_Buffer_view_does_not_expose_stri +#define __pyx_kp_s_Can_only_create_a_buffer_that_is __pyx_mstate_global->__pyx_kp_s_Can_only_create_a_buffer_that_is +#define __pyx_kp_s_Cannot_assign_to_read_only_memor __pyx_mstate_global->__pyx_kp_s_Cannot_assign_to_read_only_memor +#define __pyx_kp_s_Cannot_create_writable_memory_vi __pyx_mstate_global->__pyx_kp_s_Cannot_create_writable_memory_vi +#define __pyx_kp_u_Cannot_index_with_type __pyx_mstate_global->__pyx_kp_u_Cannot_index_with_type +#define __pyx_kp_s_Cannot_transpose_memoryview_with __pyx_mstate_global->__pyx_kp_s_Cannot_transpose_memoryview_with +#define __pyx_kp_s_Dimension_d_is_not_direct __pyx_mstate_global->__pyx_kp_s_Dimension_d_is_not_direct +#define __pyx_n_s_Ellipsis __pyx_mstate_global->__pyx_n_s_Ellipsis +#define __pyx_kp_s_Empty_shape_tuple_for_cython_arr __pyx_mstate_global->__pyx_kp_s_Empty_shape_tuple_for_cython_arr +#define __pyx_n_s_ImportError __pyx_mstate_global->__pyx_n_s_ImportError +#define __pyx_kp_s_Incompatible_checksums_0x_x_vs_0 __pyx_mstate_global->__pyx_kp_s_Incompatible_checksums_0x_x_vs_0 +#define __pyx_n_s_IndexError __pyx_mstate_global->__pyx_n_s_IndexError +#define __pyx_kp_s_Index_out_of_bounds_axis_d __pyx_mstate_global->__pyx_kp_s_Index_out_of_bounds_axis_d +#define __pyx_kp_s_Indirect_dimensions_not_supporte __pyx_mstate_global->__pyx_kp_s_Indirect_dimensions_not_supporte +#define __pyx_kp_u_Invalid_mode_expected_c_or_fortr __pyx_mstate_global->__pyx_kp_u_Invalid_mode_expected_c_or_fortr +#define __pyx_kp_u_Invalid_shape_in_axis __pyx_mstate_global->__pyx_kp_u_Invalid_shape_in_axis +#define __pyx_n_s_MemoryError __pyx_mstate_global->__pyx_n_s_MemoryError +#define __pyx_kp_s_MemoryView_of_r_at_0x_x __pyx_mstate_global->__pyx_kp_s_MemoryView_of_r_at_0x_x +#define __pyx_kp_s_MemoryView_of_r_object __pyx_mstate_global->__pyx_kp_s_MemoryView_of_r_object +#define __pyx_n_s_N __pyx_mstate_global->__pyx_n_s_N +#define __pyx_n_b_O __pyx_mstate_global->__pyx_n_b_O +#define __pyx_kp_u_Out_of_bounds_on_buffer_access_a __pyx_mstate_global->__pyx_kp_u_Out_of_bounds_on_buffer_access_a +#define __pyx_n_s_PickleError __pyx_mstate_global->__pyx_n_s_PickleError +#define __pyx_n_s_Sequence __pyx_mstate_global->__pyx_n_s_Sequence +#define __pyx_kp_s_Step_may_not_be_zero_axis_d __pyx_mstate_global->__pyx_kp_s_Step_may_not_be_zero_axis_d +#define __pyx_n_s_TypeError __pyx_mstate_global->__pyx_n_s_TypeError +#define __pyx_kp_s_Unable_to_convert_item_to_object __pyx_mstate_global->__pyx_kp_s_Unable_to_convert_item_to_object +#define __pyx_n_s_ValueError __pyx_mstate_global->__pyx_n_s_ValueError +#define __pyx_n_s_View_MemoryView __pyx_mstate_global->__pyx_n_s_View_MemoryView +#define __pyx_kp_u__2 __pyx_mstate_global->__pyx_kp_u__2 +#define __pyx_n_s__3 __pyx_mstate_global->__pyx_n_s__3 +#define __pyx_kp_u__6 __pyx_mstate_global->__pyx_kp_u__6 +#define __pyx_n_s__62 __pyx_mstate_global->__pyx_n_s__62 +#define __pyx_kp_u__7 __pyx_mstate_global->__pyx_kp_u__7 +#define __pyx_n_s_a __pyx_mstate_global->__pyx_n_s_a +#define __pyx_n_s_abc __pyx_mstate_global->__pyx_n_s_abc +#define __pyx_n_s_alfa __pyx_mstate_global->__pyx_n_s_alfa +#define __pyx_n_s_allocate_buffer __pyx_mstate_global->__pyx_n_s_allocate_buffer +#define __pyx_n_s_alpha __pyx_mstate_global->__pyx_n_s_alpha +#define __pyx_kp_u_and __pyx_mstate_global->__pyx_kp_u_and +#define __pyx_n_s_arange __pyx_mstate_global->__pyx_n_s_arange +#define __pyx_n_s_array __pyx_mstate_global->__pyx_n_s_array +#define __pyx_n_s_astype __pyx_mstate_global->__pyx_n_s_astype +#define __pyx_n_s_asyncio_coroutines __pyx_mstate_global->__pyx_n_s_asyncio_coroutines +#define __pyx_kp_s_at_least_one_of_the_parameters_i __pyx_mstate_global->__pyx_kp_s_at_least_one_of_the_parameters_i +#define __pyx_n_s_axis __pyx_mstate_global->__pyx_n_s_axis +#define __pyx_n_s_base __pyx_mstate_global->__pyx_n_s_base +#define __pyx_n_s_c __pyx_mstate_global->__pyx_n_s_c +#define __pyx_n_u_c __pyx_mstate_global->__pyx_n_u_c +#define __pyx_n_s_cdf_array __pyx_mstate_global->__pyx_n_s_cdf_array +#define __pyx_n_s_cdf_lb __pyx_mstate_global->__pyx_n_s_cdf_lb +#define __pyx_n_s_cdf_ub __pyx_mstate_global->__pyx_n_s_cdf_ub +#define __pyx_n_s_class __pyx_mstate_global->__pyx_n_s_class +#define __pyx_n_s_class_getitem __pyx_mstate_global->__pyx_n_s_class_getitem +#define __pyx_n_s_cline_in_traceback __pyx_mstate_global->__pyx_n_s_cline_in_traceback +#define __pyx_n_s_collections __pyx_mstate_global->__pyx_n_s_collections +#define __pyx_kp_s_collections_abc __pyx_mstate_global->__pyx_kp_s_collections_abc +#define __pyx_n_s_concatenate __pyx_mstate_global->__pyx_n_s_concatenate +#define __pyx_n_s_cont_x __pyx_mstate_global->__pyx_n_s_cont_x +#define __pyx_kp_s_contiguous_and_direct __pyx_mstate_global->__pyx_kp_s_contiguous_and_direct +#define __pyx_kp_s_contiguous_and_indirect __pyx_mstate_global->__pyx_kp_s_contiguous_and_indirect +#define __pyx_n_s_copy __pyx_mstate_global->__pyx_n_s_copy +#define __pyx_n_s_core __pyx_mstate_global->__pyx_n_s_core +#define __pyx_n_s_count __pyx_mstate_global->__pyx_n_s_count +#define __pyx_n_s_cumm_s_size __pyx_mstate_global->__pyx_n_s_cumm_s_size +#define __pyx_n_s_cumsum __pyx_mstate_global->__pyx_n_s_cumsum +#define __pyx_n_s_cumtrapz __pyx_mstate_global->__pyx_n_s_cumtrapz +#define __pyx_n_s_data __pyx_mstate_global->__pyx_n_s_data +#define __pyx_n_s_data_copy __pyx_mstate_global->__pyx_n_s_data_copy +#define __pyx_n_s_delay __pyx_mstate_global->__pyx_n_s_delay +#define __pyx_n_s_dict __pyx_mstate_global->__pyx_n_s_dict +#define __pyx_n_s_diff __pyx_mstate_global->__pyx_n_s_diff +#define __pyx_kp_u_disable __pyx_mstate_global->__pyx_kp_u_disable +#define __pyx_n_s_double __pyx_mstate_global->__pyx_n_s_double +#define __pyx_n_s_drift __pyx_mstate_global->__pyx_n_s_drift +#define __pyx_n_s_dt __pyx_mstate_global->__pyx_n_s_dt +#define __pyx_n_s_dtype __pyx_mstate_global->__pyx_n_s_dtype +#define __pyx_n_s_dtype_is_object __pyx_mstate_global->__pyx_n_s_dtype_is_object +#define __pyx_n_s_empty __pyx_mstate_global->__pyx_n_s_empty +#define __pyx_kp_u_enable __pyx_mstate_global->__pyx_kp_u_enable +#define __pyx_n_s_encode __pyx_mstate_global->__pyx_n_s_encode +#define __pyx_n_s_enumerate __pyx_mstate_global->__pyx_n_s_enumerate +#define __pyx_n_s_err __pyx_mstate_global->__pyx_n_s_err +#define __pyx_n_s_error __pyx_mstate_global->__pyx_n_s_error +#define __pyx_n_s_exp __pyx_mstate_global->__pyx_n_s_exp +#define __pyx_n_s_f __pyx_mstate_global->__pyx_n_s_f +#define __pyx_n_s_feedback __pyx_mstate_global->__pyx_n_s_feedback +#define __pyx_n_s_feedbacks __pyx_mstate_global->__pyx_n_s_feedbacks +#define __pyx_n_s_flags __pyx_mstate_global->__pyx_n_s_flags +#define __pyx_n_s_float32 __pyx_mstate_global->__pyx_n_s_float32 +#define __pyx_n_s_format __pyx_mstate_global->__pyx_n_s_format +#define __pyx_n_s_fortran __pyx_mstate_global->__pyx_n_s_fortran +#define __pyx_n_u_fortran __pyx_mstate_global->__pyx_n_u_fortran +#define __pyx_n_s_full_pdf __pyx_mstate_global->__pyx_n_s_full_pdf +#define __pyx_kp_u_gc __pyx_mstate_global->__pyx_kp_u_gc +#define __pyx_n_s_gen_cdf_using_pdf __pyx_mstate_global->__pyx_n_s_gen_cdf_using_pdf +#define __pyx_n_s_gen_rts_from_cdf __pyx_mstate_global->__pyx_n_s_gen_rts_from_cdf +#define __pyx_n_s_getstate __pyx_mstate_global->__pyx_n_s_getstate +#define __pyx_kp_u_got __pyx_mstate_global->__pyx_kp_u_got +#define __pyx_kp_u_got_differing_extents_in_dimensi __pyx_mstate_global->__pyx_kp_u_got_differing_extents_in_dimensi +#define __pyx_n_s_i __pyx_mstate_global->__pyx_n_s_i +#define __pyx_n_s_i_p __pyx_mstate_global->__pyx_n_s_i_p +#define __pyx_n_s_id __pyx_mstate_global->__pyx_n_s_id +#define __pyx_n_s_idx __pyx_mstate_global->__pyx_n_s_idx +#define __pyx_n_s_ij __pyx_mstate_global->__pyx_n_s_ij +#define __pyx_n_s_import __pyx_mstate_global->__pyx_n_s_import +#define __pyx_n_s_index __pyx_mstate_global->__pyx_n_s_index +#define __pyx_n_s_inf __pyx_mstate_global->__pyx_n_s_inf +#define __pyx_n_s_initializing __pyx_mstate_global->__pyx_n_s_initializing +#define __pyx_n_s_integrate __pyx_mstate_global->__pyx_n_s_integrate +#define __pyx_n_s_is_coroutine __pyx_mstate_global->__pyx_n_s_is_coroutine +#define __pyx_kp_u_isenabled __pyx_mstate_global->__pyx_kp_u_isenabled +#define __pyx_n_s_itemsize __pyx_mstate_global->__pyx_n_s_itemsize +#define __pyx_kp_s_itemsize_0_for_cython_array __pyx_mstate_global->__pyx_kp_s_itemsize_0_for_cython_array +#define __pyx_n_s_j __pyx_mstate_global->__pyx_n_s_j +#define __pyx_n_s_l_cdf __pyx_mstate_global->__pyx_n_s_l_cdf +#define __pyx_n_s_lb __pyx_mstate_global->__pyx_n_s_lb +#define __pyx_n_s_linspace __pyx_mstate_global->__pyx_n_s_linspace +#define __pyx_n_s_ll_min __pyx_mstate_global->__pyx_n_s_ll_min +#define __pyx_n_s_log __pyx_mstate_global->__pyx_n_s_log +#define __pyx_n_s_log_p __pyx_mstate_global->__pyx_n_s_log_p +#define __pyx_n_s_logp __pyx_mstate_global->__pyx_n_s_logp +#define __pyx_n_s_lower_bnd __pyx_mstate_global->__pyx_n_s_lower_bnd +#define __pyx_n_s_lower_bounds __pyx_mstate_global->__pyx_n_s_lower_bounds +#define __pyx_n_s_lower_bounds_view __pyx_mstate_global->__pyx_n_s_lower_bounds_view +#define __pyx_n_s_main __pyx_mstate_global->__pyx_n_s_main +#define __pyx_n_s_max __pyx_mstate_global->__pyx_n_s_max +#define __pyx_n_s_maximum __pyx_mstate_global->__pyx_n_s_maximum +#define __pyx_n_s_memview __pyx_mstate_global->__pyx_n_s_memview +#define __pyx_n_s_min __pyx_mstate_global->__pyx_n_s_min +#define __pyx_n_s_mode __pyx_mstate_global->__pyx_n_s_mode +#define __pyx_n_s_model __pyx_mstate_global->__pyx_n_s_model +#define __pyx_n_s_multi __pyx_mstate_global->__pyx_n_s_multi +#define __pyx_n_s_n_cont __pyx_mstate_global->__pyx_n_s_n_cont +#define __pyx_n_s_n_params __pyx_mstate_global->__pyx_n_s_n_params +#define __pyx_n_s_n_st __pyx_mstate_global->__pyx_n_s_n_st +#define __pyx_n_s_n_sz __pyx_mstate_global->__pyx_n_s_n_sz +#define __pyx_n_s_name __pyx_mstate_global->__pyx_n_s_name +#define __pyx_n_s_name_2 __pyx_mstate_global->__pyx_n_s_name_2 +#define __pyx_n_s_ndim __pyx_mstate_global->__pyx_n_s_ndim +#define __pyx_n_s_network __pyx_mstate_global->__pyx_n_s_network +#define __pyx_n_s_new __pyx_mstate_global->__pyx_n_s_new +#define __pyx_kp_s_no_default___reduce___due_to_non __pyx_mstate_global->__pyx_kp_s_no_default___reduce___due_to_non +#define __pyx_n_s_np __pyx_mstate_global->__pyx_n_s_np +#define __pyx_n_s_numpy __pyx_mstate_global->__pyx_n_s_numpy +#define __pyx_kp_s_numpy_core_multiarray_failed_to __pyx_mstate_global->__pyx_kp_s_numpy_core_multiarray_failed_to +#define __pyx_kp_s_numpy_core_umath_failed_to_impor __pyx_mstate_global->__pyx_kp_s_numpy_core_umath_failed_to_impor +#define __pyx_n_s_obj __pyx_mstate_global->__pyx_n_s_obj +#define __pyx_n_s_p __pyx_mstate_global->__pyx_n_s_p +#define __pyx_n_s_p_outlier __pyx_mstate_global->__pyx_n_s_p_outlier +#define __pyx_n_s_pack __pyx_mstate_global->__pyx_n_s_pack +#define __pyx_n_s_param __pyx_mstate_global->__pyx_n_s_param +#define __pyx_n_s_params __pyx_mstate_global->__pyx_n_s_params +#define __pyx_n_s_params_bnds __pyx_mstate_global->__pyx_n_s_params_bnds +#define __pyx_n_s_params_iter __pyx_mstate_global->__pyx_n_s_params_iter +#define __pyx_n_s_params_rl __pyx_mstate_global->__pyx_n_s_params_rl +#define __pyx_n_s_params_ssm __pyx_mstate_global->__pyx_n_s_params_ssm +#define __pyx_n_s_params_view __pyx_mstate_global->__pyx_n_s_params_view +#define __pyx_n_s_pdf __pyx_mstate_global->__pyx_n_s_pdf +#define __pyx_n_s_pdf_array __pyx_mstate_global->__pyx_n_s_pdf_array +#define __pyx_kp_s_pdf_pxi __pyx_mstate_global->__pyx_kp_s_pdf_pxi +#define __pyx_n_s_pickle __pyx_mstate_global->__pyx_n_s_pickle +#define __pyx_n_s_pos_alfa __pyx_mstate_global->__pyx_n_s_pos_alfa +#define __pyx_n_s_pos_alpha __pyx_mstate_global->__pyx_n_s_pos_alpha +#define __pyx_n_s_pos_cont __pyx_mstate_global->__pyx_n_s_pos_cont +#define __pyx_n_s_predict_on_batch __pyx_mstate_global->__pyx_n_s_predict_on_batch +#define __pyx_n_s_pyx_PickleError __pyx_mstate_global->__pyx_n_s_pyx_PickleError +#define __pyx_n_s_pyx_checksum __pyx_mstate_global->__pyx_n_s_pyx_checksum +#define __pyx_n_s_pyx_result __pyx_mstate_global->__pyx_n_s_pyx_result +#define __pyx_n_s_pyx_state __pyx_mstate_global->__pyx_n_s_pyx_state +#define __pyx_n_s_pyx_type __pyx_mstate_global->__pyx_n_s_pyx_type +#define __pyx_n_s_pyx_unpickle_Enum __pyx_mstate_global->__pyx_n_s_pyx_unpickle_Enum +#define __pyx_n_s_pyx_vtable __pyx_mstate_global->__pyx_n_s_pyx_vtable +#define __pyx_n_s_q __pyx_mstate_global->__pyx_n_s_q +#define __pyx_n_s_qs __pyx_mstate_global->__pyx_n_s_qs +#define __pyx_n_s_rand __pyx_mstate_global->__pyx_n_s_rand +#define __pyx_n_s_random __pyx_mstate_global->__pyx_n_s_random +#define __pyx_n_s_range __pyx_mstate_global->__pyx_n_s_range +#define __pyx_n_s_reduce __pyx_mstate_global->__pyx_n_s_reduce +#define __pyx_n_s_reduce_cython __pyx_mstate_global->__pyx_n_s_reduce_cython +#define __pyx_n_s_reduce_ex __pyx_mstate_global->__pyx_n_s_reduce_ex +#define __pyx_n_s_register __pyx_mstate_global->__pyx_n_s_register +#define __pyx_n_s_response __pyx_mstate_global->__pyx_n_s_response +#define __pyx_n_s_responses __pyx_mstate_global->__pyx_n_s_responses +#define __pyx_n_s_responses_qs __pyx_mstate_global->__pyx_n_s_responses_qs +#define __pyx_n_s_rl_alpha __pyx_mstate_global->__pyx_n_s_rl_alpha +#define __pyx_n_s_rl_arr __pyx_mstate_global->__pyx_n_s_rl_arr +#define __pyx_n_s_rt __pyx_mstate_global->__pyx_n_s_rt +#define __pyx_n_s_rts __pyx_mstate_global->__pyx_n_s_rts +#define __pyx_n_s_s __pyx_mstate_global->__pyx_n_s_s +#define __pyx_n_s_s_size __pyx_mstate_global->__pyx_n_s_s_size +#define __pyx_n_s_samples __pyx_mstate_global->__pyx_n_s_samples +#define __pyx_n_s_scipy __pyx_mstate_global->__pyx_n_s_scipy +#define __pyx_n_s_scipy_integrate __pyx_mstate_global->__pyx_n_s_scipy_integrate +#define __pyx_n_s_searchsorted __pyx_mstate_global->__pyx_n_s_searchsorted +#define __pyx_n_s_setstate __pyx_mstate_global->__pyx_n_s_setstate +#define __pyx_n_s_setstate_cython __pyx_mstate_global->__pyx_n_s_setstate_cython +#define __pyx_n_s_shape __pyx_mstate_global->__pyx_n_s_shape +#define __pyx_n_s_sign __pyx_mstate_global->__pyx_n_s_sign +#define __pyx_n_s_simps_err __pyx_mstate_global->__pyx_n_s_simps_err +#define __pyx_n_s_size __pyx_mstate_global->__pyx_n_s_size +#define __pyx_n_s_spec __pyx_mstate_global->__pyx_n_s_spec +#define __pyx_n_s_split_by __pyx_mstate_global->__pyx_n_s_split_by +#define __pyx_n_s_split_cdf __pyx_mstate_global->__pyx_n_s_split_cdf +#define __pyx_n_s_squeeze __pyx_mstate_global->__pyx_n_s_squeeze +#define __pyx_n_s_st __pyx_mstate_global->__pyx_n_s_st +#define __pyx_n_s_stack __pyx_mstate_global->__pyx_n_s_stack +#define __pyx_n_s_start __pyx_mstate_global->__pyx_n_s_start +#define __pyx_n_s_step __pyx_mstate_global->__pyx_n_s_step +#define __pyx_n_s_stop __pyx_mstate_global->__pyx_n_s_stop +#define __pyx_kp_s_strided_and_direct __pyx_mstate_global->__pyx_kp_s_strided_and_direct +#define __pyx_kp_s_strided_and_direct_or_indirect __pyx_mstate_global->__pyx_kp_s_strided_and_direct_or_indirect +#define __pyx_kp_s_strided_and_indirect __pyx_mstate_global->__pyx_kp_s_strided_and_indirect +#define __pyx_kp_s_stringsource __pyx_mstate_global->__pyx_kp_s_stringsource +#define __pyx_n_s_struct __pyx_mstate_global->__pyx_n_s_struct +#define __pyx_n_s_sum __pyx_mstate_global->__pyx_n_s_sum +#define __pyx_n_s_sum_logp __pyx_mstate_global->__pyx_n_s_sum_logp +#define __pyx_n_s_sv __pyx_mstate_global->__pyx_n_s_sv +#define __pyx_n_s_sys __pyx_mstate_global->__pyx_n_s_sys +#define __pyx_n_s_sz __pyx_mstate_global->__pyx_n_s_sz +#define __pyx_n_s_t __pyx_mstate_global->__pyx_n_s_t +#define __pyx_n_s_t_max __pyx_mstate_global->__pyx_n_s_t_max +#define __pyx_n_s_t_min __pyx_mstate_global->__pyx_n_s_t_min +#define __pyx_n_s_test __pyx_mstate_global->__pyx_n_s_test +#define __pyx_n_s_tile __pyx_mstate_global->__pyx_n_s_tile +#define __pyx_n_s_time __pyx_mstate_global->__pyx_n_s_time +#define __pyx_n_s_tp_scale __pyx_mstate_global->__pyx_n_s_tp_scale +#define __pyx_n_s_ub __pyx_mstate_global->__pyx_n_s_ub +#define __pyx_n_s_umath __pyx_mstate_global->__pyx_n_s_umath +#define __pyx_kp_s_unable_to_allocate_array_data __pyx_mstate_global->__pyx_kp_s_unable_to_allocate_array_data +#define __pyx_kp_s_unable_to_allocate_shape_and_str __pyx_mstate_global->__pyx_kp_s_unable_to_allocate_shape_and_str +#define __pyx_n_s_unique __pyx_mstate_global->__pyx_n_s_unique +#define __pyx_n_s_unpack __pyx_mstate_global->__pyx_n_s_unpack +#define __pyx_n_s_update __pyx_mstate_global->__pyx_n_s_update +#define __pyx_n_s_upper_bnd __pyx_mstate_global->__pyx_n_s_upper_bnd +#define __pyx_n_s_upper_bounds __pyx_mstate_global->__pyx_n_s_upper_bounds +#define __pyx_n_s_upper_bounds_view __pyx_mstate_global->__pyx_n_s_upper_bounds_view +#define __pyx_n_s_use_adaptive __pyx_mstate_global->__pyx_n_s_use_adaptive +#define __pyx_n_s_v __pyx_mstate_global->__pyx_n_s_v +#define __pyx_n_s_version_info __pyx_mstate_global->__pyx_n_s_version_info +#define __pyx_n_s_w_outlier __pyx_mstate_global->__pyx_n_s_w_outlier +#define __pyx_n_s_wfpt_n __pyx_mstate_global->__pyx_n_s_wfpt_n +#define __pyx_kp_s_wfpt_n_pyx __pyx_mstate_global->__pyx_kp_s_wfpt_n_pyx +#define __pyx_n_s_wiener_like_contaminant __pyx_mstate_global->__pyx_n_s_wiener_like_contaminant +#define __pyx_n_s_wiener_like_multi __pyx_mstate_global->__pyx_n_s_wiener_like_multi +#define __pyx_n_s_wiener_like_multi_nn_mlp __pyx_mstate_global->__pyx_n_s_wiener_like_multi_nn_mlp +#define __pyx_n_s_wiener_like_multi_nn_mlp_pdf __pyx_mstate_global->__pyx_n_s_wiener_like_multi_nn_mlp_pdf +#define __pyx_n_s_wiener_like_multi_rlddm __pyx_mstate_global->__pyx_n_s_wiener_like_multi_rlddm +#define __pyx_n_s_wiener_like_n __pyx_mstate_global->__pyx_n_s_wiener_like_n +#define __pyx_n_s_wiener_like_nn_mlp __pyx_mstate_global->__pyx_n_s_wiener_like_nn_mlp +#define __pyx_n_s_wiener_like_nn_mlp_info __pyx_mstate_global->__pyx_n_s_wiener_like_nn_mlp_info +#define __pyx_n_s_wiener_like_nn_mlp_pdf __pyx_mstate_global->__pyx_n_s_wiener_like_nn_mlp_pdf +#define __pyx_n_s_wiener_like_rl __pyx_mstate_global->__pyx_n_s_wiener_like_rl +#define __pyx_n_s_wiener_like_rlddm __pyx_mstate_global->__pyx_n_s_wiener_like_rlddm +#define __pyx_n_s_wiener_like_rlssm_nn __pyx_mstate_global->__pyx_n_s_wiener_like_rlssm_nn +#define __pyx_n_s_wiener_like_rlssm_nn_reg __pyx_mstate_global->__pyx_n_s_wiener_like_rlssm_nn_reg +#define __pyx_n_s_wp_outlier __pyx_mstate_global->__pyx_n_s_wp_outlier +#define __pyx_n_s_x __pyx_mstate_global->__pyx_n_s_x +#define __pyx_n_s_x_lb __pyx_mstate_global->__pyx_n_s_x_lb +#define __pyx_n_s_x_ub __pyx_mstate_global->__pyx_n_s_x_ub +#define __pyx_n_s_xs __pyx_mstate_global->__pyx_n_s_xs +#define __pyx_n_s_y __pyx_mstate_global->__pyx_n_s_y +#define __pyx_n_s_z __pyx_mstate_global->__pyx_n_s_z +#define __pyx_n_s_zeros __pyx_mstate_global->__pyx_n_s_zeros +#define __pyx_float_1eneg_3 __pyx_mstate_global->__pyx_float_1eneg_3 +#define __pyx_float_2_718281828459 __pyx_mstate_global->__pyx_float_2_718281828459 +#define __pyx_int_0 __pyx_mstate_global->__pyx_int_0 +#define __pyx_int_1 __pyx_mstate_global->__pyx_int_1 +#define __pyx_int_2 __pyx_mstate_global->__pyx_int_2 +#define __pyx_int_3 __pyx_mstate_global->__pyx_int_3 +#define __pyx_int_112105877 __pyx_mstate_global->__pyx_int_112105877 +#define __pyx_int_136983863 __pyx_mstate_global->__pyx_int_136983863 +#define __pyx_int_184977713 __pyx_mstate_global->__pyx_int_184977713 +#define __pyx_int_neg_1 __pyx_mstate_global->__pyx_int_neg_1 +#define __pyx_slice__5 __pyx_mstate_global->__pyx_slice__5 +#define __pyx_tuple__4 __pyx_mstate_global->__pyx_tuple__4 +#define __pyx_tuple__8 __pyx_mstate_global->__pyx_tuple__8 +#define __pyx_tuple__9 __pyx_mstate_global->__pyx_tuple__9 +#define __pyx_slice__11 __pyx_mstate_global->__pyx_slice__11 +#define __pyx_slice__13 __pyx_mstate_global->__pyx_slice__13 +#define __pyx_tuple__10 __pyx_mstate_global->__pyx_tuple__10 +#define __pyx_tuple__12 __pyx_mstate_global->__pyx_tuple__12 +#define __pyx_tuple__14 __pyx_mstate_global->__pyx_tuple__14 +#define __pyx_tuple__15 __pyx_mstate_global->__pyx_tuple__15 +#define __pyx_tuple__16 __pyx_mstate_global->__pyx_tuple__16 +#define __pyx_tuple__17 __pyx_mstate_global->__pyx_tuple__17 +#define __pyx_tuple__18 __pyx_mstate_global->__pyx_tuple__18 +#define __pyx_tuple__19 __pyx_mstate_global->__pyx_tuple__19 +#define __pyx_tuple__20 __pyx_mstate_global->__pyx_tuple__20 +#define __pyx_tuple__21 __pyx_mstate_global->__pyx_tuple__21 +#define __pyx_tuple__22 __pyx_mstate_global->__pyx_tuple__22 +#define __pyx_tuple__23 __pyx_mstate_global->__pyx_tuple__23 +#define __pyx_tuple__25 __pyx_mstate_global->__pyx_tuple__25 +#define __pyx_tuple__26 __pyx_mstate_global->__pyx_tuple__26 +#define __pyx_tuple__28 __pyx_mstate_global->__pyx_tuple__28 +#define __pyx_tuple__29 __pyx_mstate_global->__pyx_tuple__29 +#define __pyx_tuple__31 __pyx_mstate_global->__pyx_tuple__31 +#define __pyx_tuple__33 __pyx_mstate_global->__pyx_tuple__33 +#define __pyx_tuple__35 __pyx_mstate_global->__pyx_tuple__35 +#define __pyx_tuple__37 __pyx_mstate_global->__pyx_tuple__37 +#define __pyx_tuple__39 __pyx_mstate_global->__pyx_tuple__39 +#define __pyx_tuple__41 __pyx_mstate_global->__pyx_tuple__41 +#define __pyx_tuple__43 __pyx_mstate_global->__pyx_tuple__43 +#define __pyx_tuple__45 __pyx_mstate_global->__pyx_tuple__45 +#define __pyx_tuple__47 __pyx_mstate_global->__pyx_tuple__47 +#define __pyx_tuple__49 __pyx_mstate_global->__pyx_tuple__49 +#define __pyx_tuple__51 __pyx_mstate_global->__pyx_tuple__51 +#define __pyx_tuple__53 __pyx_mstate_global->__pyx_tuple__53 +#define __pyx_tuple__55 __pyx_mstate_global->__pyx_tuple__55 +#define __pyx_tuple__57 __pyx_mstate_global->__pyx_tuple__57 +#define __pyx_tuple__59 __pyx_mstate_global->__pyx_tuple__59 +#define __pyx_codeobj__24 __pyx_mstate_global->__pyx_codeobj__24 +#define __pyx_codeobj__27 __pyx_mstate_global->__pyx_codeobj__27 +#define __pyx_codeobj__30 __pyx_mstate_global->__pyx_codeobj__30 +#define __pyx_codeobj__32 __pyx_mstate_global->__pyx_codeobj__32 +#define __pyx_codeobj__34 __pyx_mstate_global->__pyx_codeobj__34 +#define __pyx_codeobj__36 __pyx_mstate_global->__pyx_codeobj__36 +#define __pyx_codeobj__38 __pyx_mstate_global->__pyx_codeobj__38 +#define __pyx_codeobj__40 __pyx_mstate_global->__pyx_codeobj__40 +#define __pyx_codeobj__42 __pyx_mstate_global->__pyx_codeobj__42 +#define __pyx_codeobj__44 __pyx_mstate_global->__pyx_codeobj__44 +#define __pyx_codeobj__46 __pyx_mstate_global->__pyx_codeobj__46 +#define __pyx_codeobj__48 __pyx_mstate_global->__pyx_codeobj__48 +#define __pyx_codeobj__50 __pyx_mstate_global->__pyx_codeobj__50 +#define __pyx_codeobj__52 __pyx_mstate_global->__pyx_codeobj__52 +#define __pyx_codeobj__54 __pyx_mstate_global->__pyx_codeobj__54 +#define __pyx_codeobj__56 __pyx_mstate_global->__pyx_codeobj__56 +#define __pyx_codeobj__58 __pyx_mstate_global->__pyx_codeobj__58 +#define __pyx_codeobj__60 __pyx_mstate_global->__pyx_codeobj__60 +#define __pyx_codeobj__61 __pyx_mstate_global->__pyx_codeobj__61 +/* #### Code section: module_code ### */ + +/* "View.MemoryView":131 + * cdef bint dtype_is_object + * + * def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None, # <<<<<<<<<<<<<< + * mode="c", bint allocate_buffer=True): + * + */ + +/* Python wrapper */ +static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ +static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { + PyObject *__pyx_v_shape = 0; + Py_ssize_t __pyx_v_itemsize; + 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start >= shape: + */ + __pyx_t_2 = (__pyx_v_start < 0); + if (__pyx_t_2) { + + /* "View.MemoryView":834 + * start += shape + * if start < 0: + * start = 0 # <<<<<<<<<<<<<< + * elif start >= shape: + * if negative_step: + */ + __pyx_v_start = 0; + + /* "View.MemoryView":833 + * if start < 0: + * start += shape + * if start < 0: # <<<<<<<<<<<<<< + * start = 0 + * elif start >= shape: + */ + } + + /* "View.MemoryView":831 + * + * if have_start: + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if start < 0: + */ + goto __pyx_L9; + } + + /* "View.MemoryView":835 + * if start < 0: + * start = 0 + * elif start >= shape: # <<<<<<<<<<<<<< + * if negative_step: + * start = shape - 1 + */ + __pyx_t_2 = (__pyx_v_start >= __pyx_v_shape); + if (__pyx_t_2) { + + /* "View.MemoryView":836 + * start = 0 + * elif start >= shape: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + if (__pyx_v_negative_step) { + + /* "View.MemoryView":837 + * elif start >= shape: + * if 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<= abs_py_ssize_t(f_stride): + * return 'C' # <<<<<<<<<<<<<< + * else: + * return 'F' + */ + __pyx_r = 'C'; + goto __pyx_L0; + + /* "View.MemoryView":1131 + * break + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< + * return 'C' + * else: + */ + } + + /* "View.MemoryView":1134 + * return 'C' + * else: + * return 'F' # <<<<<<<<<<<<<< + * + * @cython.cdivision(True) + */ + /*else*/ { + __pyx_r = 'F'; + goto __pyx_L0; + } + + /* "View.MemoryView":1113 + * + * @cname('__pyx_get_best_slice_order') + * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) noexcept nogil: # <<<<<<<<<<<<<< + * """ + * Figure out the best memory access order for a given slice. + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1137 + * + * @cython.cdivision(True) + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< + * char *dst_data, Py_ssize_t *dst_strides, + * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, + */ + +static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) { + CYTHON_UNUSED Py_ssize_t __pyx_v_i; + CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent; + Py_ssize_t __pyx_v_dst_extent; + Py_ssize_t __pyx_v_src_stride; + Py_ssize_t __pyx_v_dst_stride; + int __pyx_t_1; + int __pyx_t_2; + Py_ssize_t __pyx_t_3; + Py_ssize_t __pyx_t_4; + Py_ssize_t __pyx_t_5; + + /* "View.MemoryView":1144 + * + * cdef Py_ssize_t i + * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] + */ + __pyx_v_src_extent = (__pyx_v_src_shape[0]); + + /* "View.MemoryView":1145 + * cdef Py_ssize_t i + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] + */ + __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); + + /* "View.MemoryView":1146 + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + */ + __pyx_v_src_stride = (__pyx_v_src_strides[0]); + + /* "View.MemoryView":1147 + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< + * + * if ndim == 1: + */ + __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); + + /* "View.MemoryView":1149 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + */ + __pyx_t_1 = (__pyx_v_ndim == 1); + if (__pyx_t_1) { + + /* "View.MemoryView":1150 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + __pyx_t_2 = (__pyx_v_src_stride > 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + __pyx_t_2 = (__pyx_v_dst_stride > 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + + /* "View.MemoryView":1151 + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + */ + __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); + if (__pyx_t_2) { + __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); + } + __pyx_t_1 = __pyx_t_2; + __pyx_L5_bool_binop_done:; + + /* "View.MemoryView":1150 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + if (__pyx_t_1) { + + /* "View.MemoryView":1152 + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): + */ + (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent))); + + /* "View.MemoryView":1150 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + goto __pyx_L4; + } + + /* "View.MemoryView":1154 + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + */ + /*else*/ { + __pyx_t_3 = __pyx_v_dst_extent; + __pyx_t_4 = __pyx_t_3; + for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { + __pyx_v_i = __pyx_t_5; + + /* "View.MemoryView":1155 + * else: + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< + * src_data += src_stride + * dst_data += dst_stride + */ + (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize)); + + /* "View.MemoryView":1156 + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * else: + */ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1157 + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): + */ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L4:; + + /* "View.MemoryView":1149 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + */ + goto __pyx_L3; + } + + /* "View.MemoryView":1159 + * dst_data += dst_stride + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * _copy_strided_to_strided(src_data, src_strides + 1, + * dst_data, dst_strides + 1, + */ + /*else*/ { + __pyx_t_3 = __pyx_v_dst_extent; + __pyx_t_4 = __pyx_t_3; + for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { + __pyx_v_i = __pyx_t_5; + + /* "View.MemoryView":1160 + * else: + * for i in range(dst_extent): + * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< + * dst_data, dst_strides + 1, + * src_shape + 1, dst_shape + 1, + */ + _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); + + /* "View.MemoryView":1164 + * src_shape + 1, dst_shape + 1, + * ndim - 1, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * + */ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1165 + * ndim - 1, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, + */ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L3:; + + /* "View.MemoryView":1137 + * + * @cython.cdivision(True) + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< + * char *dst_data, Py_ssize_t *dst_strides, + * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, + */ + + /* function exit code */ +} + +/* "View.MemoryView":1167 + * dst_data += dst_stride + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) noexcept nogil: + */ + +static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) { + + /* "View.MemoryView":1170 + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) noexcept nogil: + * _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides, # <<<<<<<<<<<<<< + * src.shape, dst.shape, ndim, itemsize) + * + */ + _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize); + + /* "View.MemoryView":1167 + * dst_data += dst_stride + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) noexcept nogil: + */ + + /* function exit code */ +} + +/* "View.MemoryView":1174 + * + * @cname('__pyx_memoryview_slice_get_size') + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil: # <<<<<<<<<<<<<< + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef Py_ssize_t shape, size = src.memview.view.itemsize + */ + +static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) { + Py_ssize_t __pyx_v_shape; + Py_ssize_t __pyx_v_size; + Py_ssize_t __pyx_r; + Py_ssize_t __pyx_t_1; + Py_ssize_t *__pyx_t_2; + Py_ssize_t *__pyx_t_3; + Py_ssize_t *__pyx_t_4; + + /* "View.MemoryView":1176 + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil: + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef Py_ssize_t shape, size = src.memview.view.itemsize # <<<<<<<<<<<<<< + * + * for shape in src.shape[:ndim]: + */ + __pyx_t_1 = __pyx_v_src->memview->view.itemsize; + __pyx_v_size = __pyx_t_1; + + /* "View.MemoryView":1178 + * cdef Py_ssize_t shape, size = src.memview.view.itemsize + * + * for shape in src.shape[:ndim]: # <<<<<<<<<<<<<< + * size *= shape + * + */ + __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim); + for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) { + __pyx_t_2 = __pyx_t_4; + __pyx_v_shape = (__pyx_t_2[0]); + + /* "View.MemoryView":1179 + * + * for shape in src.shape[:ndim]: + * size *= shape # <<<<<<<<<<<<<< + * + * return size + */ + __pyx_v_size = (__pyx_v_size * __pyx_v_shape); + } + + /* "View.MemoryView":1181 + * size *= shape + * + * return size # <<<<<<<<<<<<<< + * + * @cname('__pyx_fill_contig_strides_array') + */ + __pyx_r = __pyx_v_size; + goto __pyx_L0; + + /* "View.MemoryView":1174 + * + * @cname('__pyx_memoryview_slice_get_size') + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil: # <<<<<<<<<<<<<< + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef Py_ssize_t shape, size = src.memview.view.itemsize + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1184 + * + * @cname('__pyx_fill_contig_strides_array') + * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< + * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, + * int ndim, char order) noexcept nogil: + */ + +static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) { + int __pyx_v_idx; + Py_ssize_t __pyx_r; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_t_4; + + /* "View.MemoryView":1193 + * cdef int idx + * + * if order == 'F': # <<<<<<<<<<<<<< + * for idx in range(ndim): + * strides[idx] = stride + */ + __pyx_t_1 = (__pyx_v_order == 'F'); + if (__pyx_t_1) { + + /* "View.MemoryView":1194 + * + * if order == 'F': + * for idx in range(ndim): # <<<<<<<<<<<<<< + * strides[idx] = stride + * stride *= shape[idx] + */ + __pyx_t_2 = __pyx_v_ndim; + __pyx_t_3 = __pyx_t_2; + for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { + __pyx_v_idx = __pyx_t_4; + + /* "View.MemoryView":1195 + * if order == 'F': + * for idx in range(ndim): + * strides[idx] = stride # <<<<<<<<<<<<<< + * stride *= shape[idx] + * else: + */ + (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; + + /* "View.MemoryView":1196 + * for idx in range(ndim): + * strides[idx] = stride + * stride *= shape[idx] # <<<<<<<<<<<<<< + * else: + * for idx in range(ndim - 1, -1, -1): + */ + __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); + } + + /* "View.MemoryView":1193 + * cdef int idx + * + * if order == 'F': # <<<<<<<<<<<<<< + * for idx in range(ndim): + * strides[idx] = stride + */ + goto __pyx_L3; + } + + /* "View.MemoryView":1198 + * stride *= shape[idx] + * else: + * for idx in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< + * strides[idx] = stride + * stride *= shape[idx] + */ + /*else*/ { + for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) { + __pyx_v_idx = __pyx_t_2; + + /* "View.MemoryView":1199 + * else: + * for idx in range(ndim - 1, -1, -1): + * strides[idx] = stride # <<<<<<<<<<<<<< + * stride *= shape[idx] + * + */ + (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; + + /* "View.MemoryView":1200 + * for idx in range(ndim - 1, -1, -1): + * strides[idx] = stride + * stride *= shape[idx] # <<<<<<<<<<<<<< + * + * return stride + */ + __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); + } + } + __pyx_L3:; + + /* "View.MemoryView":1202 + * stride *= shape[idx] + * + * return stride # <<<<<<<<<<<<<< + * + * @cname('__pyx_memoryview_copy_data_to_temp') + */ + __pyx_r = __pyx_v_stride; + goto __pyx_L0; + + /* "View.MemoryView":1184 + * + * @cname('__pyx_fill_contig_strides_array') + * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< + * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, + * int ndim, char order) noexcept nogil: + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1205 + * + * @cname('__pyx_memoryview_copy_data_to_temp') + * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice *tmpslice, + * char order, + */ + +static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) { + int __pyx_v_i; + void *__pyx_v_result; + size_t __pyx_v_itemsize; + size_t __pyx_v_size; + void *__pyx_r; + Py_ssize_t __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + struct __pyx_memoryview_obj *__pyx_t_4; + int __pyx_t_5; + int __pyx_t_6; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + #ifdef WITH_THREAD + PyGILState_STATE __pyx_gilstate_save; + #endif + + /* "View.MemoryView":1216 + * cdef void *result + * + * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< + * cdef size_t size = slice_get_size(src, ndim) + * + */ + __pyx_t_1 = __pyx_v_src->memview->view.itemsize; + __pyx_v_itemsize = __pyx_t_1; + + /* "View.MemoryView":1217 + * + * cdef size_t itemsize = src.memview.view.itemsize + * cdef size_t size = slice_get_size(src, ndim) # <<<<<<<<<<<<<< + * + * result = malloc(size) + */ + __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim); + + /* "View.MemoryView":1219 + * cdef size_t size = slice_get_size(src, ndim) + * + * result = malloc(size) # <<<<<<<<<<<<<< + * if not result: + * _err_no_memory() + */ + __pyx_v_result = malloc(__pyx_v_size); + + /* "View.MemoryView":1220 + * + * result = malloc(size) + * if not result: # <<<<<<<<<<<<<< + * _err_no_memory() + * + */ + __pyx_t_2 = (!(__pyx_v_result != 0)); + if (__pyx_t_2) { + + /* "View.MemoryView":1221 + * result = malloc(size) + * if not result: + * _err_no_memory() # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_3 = __pyx_memoryview_err_no_memory(); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 1221, __pyx_L1_error) + + /* "View.MemoryView":1220 + * + * result = malloc(size) + * if not result: # <<<<<<<<<<<<<< + * _err_no_memory() + * + */ + } + + /* "View.MemoryView":1224 + * + * + * tmpslice.data = result # <<<<<<<<<<<<<< + * tmpslice.memview = src.memview + * for i in range(ndim): + */ + __pyx_v_tmpslice->data = ((char *)__pyx_v_result); + + /* "View.MemoryView":1225 + * + * tmpslice.data = result + * tmpslice.memview = src.memview # <<<<<<<<<<<<<< + * for i in range(ndim): + * tmpslice.shape[i] = src.shape[i] + */ + __pyx_t_4 = __pyx_v_src->memview; + __pyx_v_tmpslice->memview = __pyx_t_4; + + /* "View.MemoryView":1226 + * tmpslice.data = result + * tmpslice.memview = src.memview + * for i in range(ndim): # <<<<<<<<<<<<<< + * tmpslice.shape[i] = src.shape[i] + * tmpslice.suboffsets[i] = -1 + */ + __pyx_t_3 = __pyx_v_ndim; + __pyx_t_5 = __pyx_t_3; + for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { + __pyx_v_i = __pyx_t_6; + + /* "View.MemoryView":1227 + * tmpslice.memview = src.memview + * for i in range(ndim): + * tmpslice.shape[i] = src.shape[i] # <<<<<<<<<<<<<< + * tmpslice.suboffsets[i] = -1 + * + */ + (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]); + + /* "View.MemoryView":1228 + * for i in range(ndim): + * tmpslice.shape[i] = src.shape[i] + * tmpslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< + * + * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, ndim, order) + */ + (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1L; + } + + /* "View.MemoryView":1230 + * tmpslice.suboffsets[i] = -1 + * + * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, ndim, order) # <<<<<<<<<<<<<< + * + * + */ + (void)(__pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order)); + + /* "View.MemoryView":1233 + * + * + * for i in range(ndim): # <<<<<<<<<<<<<< + * if tmpslice.shape[i] == 1: + * tmpslice.strides[i] = 0 + */ + __pyx_t_3 = __pyx_v_ndim; + __pyx_t_5 = __pyx_t_3; + for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { + __pyx_v_i = __pyx_t_6; + + /* "View.MemoryView":1234 + * + * for i in range(ndim): + * if 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src_ndim: + * broadcast_leading(&dst, dst_ndim, src_ndim) # <<<<<<<<<<<<<< + * + * cdef int ndim = max(src_ndim, dst_ndim) + */ + __pyx_memoryview_broadcast_leading((&__pyx_v_dst), __pyx_v_dst_ndim, __pyx_v_src_ndim); + + /* "View.MemoryView":1283 + * if src_ndim < dst_ndim: + * broadcast_leading(&src, src_ndim, dst_ndim) + * elif dst_ndim < src_ndim: # <<<<<<<<<<<<<< + * broadcast_leading(&dst, dst_ndim, src_ndim) + * + */ + } + __pyx_L3:; + + /* "View.MemoryView":1286 + * broadcast_leading(&dst, dst_ndim, src_ndim) + * + * cdef int ndim = max(src_ndim, dst_ndim) # <<<<<<<<<<<<<< + * + * for i in range(ndim): + */ + __pyx_t_3 = __pyx_v_dst_ndim; + __pyx_t_4 = __pyx_v_src_ndim; + __pyx_t_2 = (__pyx_t_3 > __pyx_t_4); + if (__pyx_t_2) { + __pyx_t_5 = __pyx_t_3; + } else { + __pyx_t_5 = __pyx_t_4; + } + __pyx_v_ndim = __pyx_t_5; + + /* "View.MemoryView":1288 + * cdef int ndim = max(src_ndim, dst_ndim) + * + * for i in range(ndim): # <<<<<<<<<<<<<< + * if src.shape[i] != dst.shape[i]: + * if src.shape[i] == 1: + */ + __pyx_t_5 = __pyx_v_ndim; + __pyx_t_3 = __pyx_t_5; + for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { + __pyx_v_i = __pyx_t_4; + + /* "View.MemoryView":1289 + * + * for i in range(ndim): + * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< + * if src.shape[i] == 1: + * broadcasting = True + */ + __pyx_t_2 = ((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i])); + if (__pyx_t_2) { + + /* "View.MemoryView":1290 + * for i in range(ndim): + * if src.shape[i] != dst.shape[i]: + * if src.shape[i] == 1: # <<<<<<<<<<<<<< + * broadcasting = True + * src.strides[i] = 0 + */ + __pyx_t_2 = ((__pyx_v_src.shape[__pyx_v_i]) == 1); + if (__pyx_t_2) { + + /* "View.MemoryView":1291 + * if src.shape[i] != dst.shape[i]: + * if src.shape[i] == 1: + * broadcasting = True # <<<<<<<<<<<<<< + * src.strides[i] = 0 + * else: + */ + __pyx_v_broadcasting = 1; + + /* "View.MemoryView":1292 + * if src.shape[i] == 1: + * broadcasting = True + * src.strides[i] = 0 # <<<<<<<<<<<<<< + * else: + * _err_extents(i, dst.shape[i], src.shape[i]) + */ + (__pyx_v_src.strides[__pyx_v_i]) = 0; + + /* "View.MemoryView":1290 + * for i in range(ndim): + * if src.shape[i] != dst.shape[i]: + * if src.shape[i] == 1: # <<<<<<<<<<<<<< + * broadcasting = True + * src.strides[i] = 0 + */ + goto __pyx_L7; + } + + /* "View.MemoryView":1294 + * src.strides[i] = 0 + * else: + * _err_extents(i, dst.shape[i], src.shape[i]) # <<<<<<<<<<<<<< + * + * if src.suboffsets[i] >= 0: + */ + /*else*/ { + __pyx_t_6 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1294, __pyx_L1_error) + } + __pyx_L7:; + + /* "View.MemoryView":1289 + * + * for i in range(ndim): + * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< + * if src.shape[i] == 1: + * broadcasting = True + */ + } + + /* "View.MemoryView":1296 + * _err_extents(i, dst.shape[i], src.shape[i]) + * + * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< + * _err_dim(PyExc_ValueError, "Dimension %d is not direct", i) + * + */ + __pyx_t_2 = ((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1297 + * + * if src.suboffsets[i] >= 0: + * _err_dim(PyExc_ValueError, "Dimension %d is not direct", i) # <<<<<<<<<<<<<< + * + * if slices_overlap(&src, &dst, ndim, itemsize): + */ + __pyx_t_6 = __pyx_memoryview_err_dim(PyExc_ValueError, __pyx_kp_s_Dimension_d_is_not_direct, __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1297, __pyx_L1_error) + + /* "View.MemoryView":1296 + * _err_extents(i, dst.shape[i], src.shape[i]) + * + * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< + * _err_dim(PyExc_ValueError, "Dimension %d is not direct", i) + * + */ + } + } + + /* "View.MemoryView":1299 + * _err_dim(PyExc_ValueError, "Dimension %d is not direct", i) + * + * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< + * + * if not slice_is_contig(src, order, ndim): + */ + __pyx_t_2 = __pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize); + if (__pyx_t_2) { + + /* "View.MemoryView":1301 + * if slices_overlap(&src, &dst, ndim, itemsize): + * + * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< + * order = get_best_order(&dst, ndim) + * + */ + __pyx_t_2 = (!__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim)); + if (__pyx_t_2) { + + /* "View.MemoryView":1302 + * + * if not slice_is_contig(src, order, ndim): + * order = get_best_order(&dst, ndim) # <<<<<<<<<<<<<< + * + * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) + */ + __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim); + + /* "View.MemoryView":1301 + * if slices_overlap(&src, &dst, ndim, itemsize): + * + * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< + * order = get_best_order(&dst, ndim) + * + */ + } + + /* "View.MemoryView":1304 + * order = get_best_order(&dst, ndim) + * + * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) # <<<<<<<<<<<<<< + * src = tmp + * + */ + __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(1, 1304, __pyx_L1_error) + __pyx_v_tmpdata = __pyx_t_7; + + /* "View.MemoryView":1305 + * + * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) + * src = tmp # <<<<<<<<<<<<<< + * + * if not broadcasting: + */ + __pyx_v_src = __pyx_v_tmp; + + /* "View.MemoryView":1299 + * _err_dim(PyExc_ValueError, "Dimension %d is not direct", i) + * + * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< + * + * if not slice_is_contig(src, order, ndim): + */ + } + + /* "View.MemoryView":1307 + * src = tmp + * + * if not broadcasting: # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_2 = (!__pyx_v_broadcasting); + if (__pyx_t_2) { + + /* "View.MemoryView":1310 + * + * + * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + */ + __pyx_t_2 = __pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim); + if (__pyx_t_2) { + + /* "View.MemoryView":1311 + * + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) # <<<<<<<<<<<<<< + * elif slice_is_contig(src, 'F', ndim): + * direct_copy = slice_is_contig(dst, 'F', ndim) + */ + __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim); + + /* "View.MemoryView":1310 + * + * + * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + */ + goto __pyx_L12; + } + + /* "View.MemoryView":1312 + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + */ + __pyx_t_2 = __pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim); + if (__pyx_t_2) { + + /* "View.MemoryView":1313 + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + * direct_copy = slice_is_contig(dst, 'F', ndim) # <<<<<<<<<<<<<< + * + * if direct_copy: + */ + __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim); + + /* "View.MemoryView":1312 + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + */ + } + __pyx_L12:; + + /* "View.MemoryView":1315 + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + * if direct_copy: # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + */ + if (__pyx_v_direct_copy) { + + /* "View.MemoryView":1317 + * if direct_copy: + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) # <<<<<<<<<<<<<< + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); + + /* "View.MemoryView":1318 + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<< + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * free(tmpdata) + */ + (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim))); + + /* "View.MemoryView":1319 + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) # <<<<<<<<<<<<<< + * free(tmpdata) + * return 0 + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); + + /* "View.MemoryView":1320 + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * free(tmpdata) # <<<<<<<<<<<<<< + * return 0 + * + */ + free(__pyx_v_tmpdata); + + /* "View.MemoryView":1321 + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * free(tmpdata) + * return 0 # <<<<<<<<<<<<<< + * + * if order == 'F' == get_best_order(&dst, ndim): + */ + __pyx_r = 0; + goto __pyx_L0; + + /* "View.MemoryView":1315 + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + * if direct_copy: # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + */ + } + + /* "View.MemoryView":1307 + * src = tmp + * + * if not broadcasting: # <<<<<<<<<<<<<< + * + * + */ + } + + /* "View.MemoryView":1323 + * return 0 + * + * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_2 = (__pyx_v_order == 'F'); + if (__pyx_t_2) { + __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim)); + } + if (__pyx_t_2) { + + /* "View.MemoryView":1326 + * + * + * transpose_memslice(&src) # <<<<<<<<<<<<<< + * transpose_memslice(&dst) + * + */ + __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)-1))) __PYX_ERR(1, 1326, __pyx_L1_error) + + /* "View.MemoryView":1327 + * + * transpose_memslice(&src) + * transpose_memslice(&dst) # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + */ + __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)-1))) __PYX_ERR(1, 1327, __pyx_L1_error) + + /* "View.MemoryView":1323 + * return 0 + * + * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< + * + * + */ + } + + /* "View.MemoryView":1329 + * transpose_memslice(&dst) + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) # <<<<<<<<<<<<<< + * copy_strided_to_strided(&src, &dst, ndim, itemsize) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); + + /* "View.MemoryView":1330 + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + * copy_strided_to_strided(&src, &dst, ndim, itemsize) # <<<<<<<<<<<<<< + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * + */ + copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize); + + /* "View.MemoryView":1331 + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + * copy_strided_to_strided(&src, &dst, ndim, itemsize) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) # <<<<<<<<<<<<<< + * + * free(tmpdata) + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); + + /* "View.MemoryView":1333 + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * + * free(tmpdata) # <<<<<<<<<<<<<< + * return 0 + * + */ + free(__pyx_v_tmpdata); + + /* "View.MemoryView":1334 + * + * free(tmpdata) + * return 0 # <<<<<<<<<<<<<< + * + * @cname('__pyx_memoryview_broadcast_leading') + */ + __pyx_r = 0; + goto __pyx_L0; + + /* "View.MemoryView":1265 + * + * @cname('__pyx_memoryview_copy_contents') + * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice dst, + * int src_ndim, int dst_ndim, + */ + + /* function exit code */ + __pyx_L1_error:; + #ifdef WITH_THREAD + __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); + #endif + __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = -1; + #ifdef WITH_THREAD + __Pyx_PyGILState_Release(__pyx_gilstate_save); + #endif + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1337 + * + * @cname('__pyx_memoryview_broadcast_leading') + * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< + * int ndim, + * int ndim_other) noexcept nogil: + */ + +static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim, int __pyx_v_ndim_other) { + int __pyx_v_i; + int __pyx_v_offset; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + + /* "View.MemoryView":1341 + * int ndim_other) noexcept nogil: + * cdef int i + * cdef int offset = ndim_other - ndim # <<<<<<<<<<<<<< + * + * for i in range(ndim - 1, -1, -1): + */ + __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim); + + /* "View.MemoryView":1343 + * cdef int offset = ndim_other - ndim + * + * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< + * mslice.shape[i + offset] = mslice.shape[i] + * mslice.strides[i + offset] = mslice.strides[i] + */ + for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { + __pyx_v_i = __pyx_t_1; + + /* "View.MemoryView":1344 + * + * for i in range(ndim - 1, -1, -1): + * mslice.shape[i + offset] = mslice.shape[i] # <<<<<<<<<<<<<< + * mslice.strides[i + offset] = mslice.strides[i] + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] + */ + (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]); + + /* "View.MemoryView":1345 + * for i in range(ndim - 1, -1, -1): + * mslice.shape[i + offset] = mslice.shape[i] + * mslice.strides[i + offset] = mslice.strides[i] # <<<<<<<<<<<<<< + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] + * + */ + (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1346 + * mslice.shape[i + offset] = mslice.shape[i] + * mslice.strides[i + offset] = mslice.strides[i] + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] # <<<<<<<<<<<<<< + * + * for i in range(offset): + */ + (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]); + } + + /* "View.MemoryView":1348 + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] + * + * for i in range(offset): # <<<<<<<<<<<<<< + * mslice.shape[i] = 1 + * mslice.strides[i] = mslice.strides[0] + */ + __pyx_t_1 = __pyx_v_offset; + __pyx_t_2 = __pyx_t_1; + for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { + __pyx_v_i = __pyx_t_3; + + /* "View.MemoryView":1349 + * + * for i in range(offset): + * mslice.shape[i] = 1 # <<<<<<<<<<<<<< + * mslice.strides[i] = mslice.strides[0] + * mslice.suboffsets[i] = -1 + */ + (__pyx_v_mslice->shape[__pyx_v_i]) = 1; + + /* "View.MemoryView":1350 + * for i in range(offset): + * mslice.shape[i] = 1 + * mslice.strides[i] = mslice.strides[0] # <<<<<<<<<<<<<< + * mslice.suboffsets[i] = -1 + * + */ + (__pyx_v_mslice->strides[__pyx_v_i]) = (__pyx_v_mslice->strides[0]); + + /* "View.MemoryView":1351 + * mslice.shape[i] = 1 + * mslice.strides[i] = mslice.strides[0] + * mslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< + * + * + */ + (__pyx_v_mslice->suboffsets[__pyx_v_i]) = -1L; + } + + /* "View.MemoryView":1337 + * + * @cname('__pyx_memoryview_broadcast_leading') + * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< + * int ndim, + * int ndim_other) noexcept nogil: + */ + + /* function exit code */ +} + +/* "View.MemoryView":1359 + * + * @cname('__pyx_memoryview_refcount_copying') + * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, int ndim, bint inc) noexcept nogil: # <<<<<<<<<<<<<< + * + * if dtype_is_object: + */ + +static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) { + + /* "View.MemoryView":1361 + * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, int ndim, bint inc) noexcept nogil: + * + * if dtype_is_object: # <<<<<<<<<<<<<< + * refcount_objects_in_slice_with_gil(dst.data, dst.shape, dst.strides, ndim, inc) + * + */ + if (__pyx_v_dtype_is_object) { + + /* "View.MemoryView":1362 + * + * if dtype_is_object: + * refcount_objects_in_slice_with_gil(dst.data, dst.shape, dst.strides, ndim, inc) # <<<<<<<<<<<<<< + * + * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') + */ + __pyx_memoryview_refcount_objects_in_slice_with_gil(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_inc); + + /* "View.MemoryView":1361 + * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, int ndim, bint inc) noexcept nogil: + * + * if dtype_is_object: # <<<<<<<<<<<<<< + * refcount_objects_in_slice_with_gil(dst.data, dst.shape, dst.strides, ndim, inc) + * + */ + } + + /* "View.MemoryView":1359 + * + * @cname('__pyx_memoryview_refcount_copying') + * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, int ndim, bint inc) noexcept nogil: # <<<<<<<<<<<<<< + * + * if dtype_is_object: + */ + + /* function exit code */ +} + +/* "View.MemoryView":1365 + * + * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') + * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape, # <<<<<<<<<<<<<< + * Py_ssize_t *strides, int ndim, + * bint inc) noexcept with gil: + */ + +static void 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+ + /* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":1050 + * returns the unit part of the dtype for a numpy datetime64 object. + * """ + * return (obj).obmeta.base # <<<<<<<<<<<<<< + */ + __pyx_r = ((NPY_DATETIMEUNIT)((PyDatetimeScalarObject *)__pyx_v_obj)->obmeta.base); + goto __pyx_L0; + + /* "../../../../../../../users/afengler/data/software/miniconda3/envs/compass_ddm/lib/python3.11/site-packages/numpy/__init__.cython-30.pxd":1046 + * + * + * cdef inline NPY_DATETIMEUNIT get_datetime64_unit(object obj) nogil: # <<<<<<<<<<<<<< + * """ + * returns the unit part of the dtype for a numpy datetime64 object. + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "pdf.pxi":28 + * T max[T](T a, T b) + * + * cdef double ftt_01w(double tt, double w, double err) nogil: # <<<<<<<<<<<<<< + * """Compute f(t|0,1,w) for the likelihood of the drift diffusion model using the method + * and implementation of Navarro & Fuss, 2009. + */ + +static double __pyx_f_6wfpt_n_ftt_01w(double __pyx_v_tt, double __pyx_v_w, double __pyx_v_err) { + double __pyx_v_kl; 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+ + /* "pdf.pxi":36 + * + * # calculate number of terms needed for large t + * if M_PI*tt*err<1: # if error threshold is set low enough # <<<<<<<<<<<<<< + * kl=sqrt(-2*log(M_PI*tt*err)/(M_PI**2*tt)) # bound + * kl=max(kl,1./(M_PI*sqrt(tt))) # ensure boundary conditions met + */ + goto __pyx_L3; + } + + /* "pdf.pxi":40 + * kl=max(kl,1./(M_PI*sqrt(tt))) # ensure boundary conditions met + * else: # if error threshold set too high + * kl=1./(M_PI*sqrt(tt)) # set to boundary condition # <<<<<<<<<<<<<< + * + * # calculate number of terms needed for small t + */ + /*else*/ { + __pyx_v_kl = (1. / (M_PI * sqrt(__pyx_v_tt))); + } + __pyx_L3:; + + /* "pdf.pxi":43 + * + * # calculate number of terms needed for small t + * if 2*sqrt(2*M_PI*tt)*err<1: # if error threshold is set low enough # <<<<<<<<<<<<<< + * ks=2+sqrt(-2*tt*log(2*sqrt(2*M_PI*tt)*err)) # bound + * ks=max(ks,sqrt(tt)+1) # ensure boundary conditions are met + */ + __pyx_t_1 = (((2.0 * sqrt(((2.0 * M_PI) * __pyx_v_tt))) * __pyx_v_err) < 1.0); + if (__pyx_t_1) { + + /* "pdf.pxi":44 + * # calculate number of terms needed for small t + * if 2*sqrt(2*M_PI*tt)*err<1: # if error threshold is set low enough + * ks=2+sqrt(-2*tt*log(2*sqrt(2*M_PI*tt)*err)) # bound # <<<<<<<<<<<<<< + * ks=max(ks,sqrt(tt)+1) # ensure boundary conditions are met + * else: # if error threshold was set too high + */ + __pyx_v_ks = (2.0 + sqrt(((-2.0 * __pyx_v_tt) * log(((2.0 * sqrt(((2.0 * M_PI) * __pyx_v_tt))) * __pyx_v_err))))); + + /* "pdf.pxi":45 + * if 2*sqrt(2*M_PI*tt)*err<1: # if error threshold is set low enough + * ks=2+sqrt(-2*tt*log(2*sqrt(2*M_PI*tt)*err)) # bound + * ks=max(ks,sqrt(tt)+1) # ensure boundary conditions are met # <<<<<<<<<<<<<< + * else: # if error threshold was set too high + * ks=2 # minimal kappa for that case + */ + __pyx_v_ks = std::max(__pyx_v_ks, (sqrt(__pyx_v_tt) + 1.0)); + + /* "pdf.pxi":43 + * + * # calculate number of terms needed for small t + * if 2*sqrt(2*M_PI*tt)*err<1: # if error threshold is set low enough # <<<<<<<<<<<<<< + * ks=2+sqrt(-2*tt*log(2*sqrt(2*M_PI*tt)*err)) # bound + * ks=max(ks,sqrt(tt)+1) # ensure boundary conditions are met + */ + goto __pyx_L4; + } + + /* "pdf.pxi":47 + * ks=max(ks,sqrt(tt)+1) # ensure boundary conditions are met + * else: # if error threshold was set too high + * ks=2 # minimal kappa for that case # <<<<<<<<<<<<<< + * + * # compute f(tt|0,1,w) + */ + /*else*/ { + __pyx_v_ks = 2.0; + } + __pyx_L4:; + + /* "pdf.pxi":50 + * + * # compute f(tt|0,1,w) + * p=0 #initialize density # <<<<<<<<<<<<<< + * if ks(ceil(ks)) # round to smallest integer meeting error + */ + __pyx_v_p = 0.0; + + /* "pdf.pxi":51 + * # compute f(tt|0,1,w) + * p=0 #initialize density + * if ks(ceil(ks)) # round to smallest integer meeting error + * lower = (-floor((K-1)/2.)) + */ + __pyx_t_1 = (__pyx_v_ks < __pyx_v_kl); + if (__pyx_t_1) { + + /* "pdf.pxi":52 + * p=0 #initialize density + * if ks(ceil(ks)) # round to smallest integer meeting error # <<<<<<<<<<<<<< + * lower = (-floor((K-1)/2.)) + * upper = (ceil((K-1)/2.)) + */ + __pyx_v_K = ((int)ceil(__pyx_v_ks)); + + /* "pdf.pxi":53 + * if ks(ceil(ks)) # round to smallest integer meeting error + * lower = (-floor((K-1)/2.)) # <<<<<<<<<<<<<< + * upper = (ceil((K-1)/2.)) + * for k from lower <= k <= upper: # loop over k + */ + __pyx_v_lower = ((int)(-floor((((double)(__pyx_v_K - 1)) / 2.)))); + + /* "pdf.pxi":54 + * K=(ceil(ks)) # round to smallest integer meeting error + * lower = (-floor((K-1)/2.)) + * upper = (ceil((K-1)/2.)) # <<<<<<<<<<<<<< + * for k from lower <= k <= upper: # loop over k + * p+=(w+2*k)*exp(-(pow((w+2*k),2))/2/tt) # increment sum + */ + __pyx_v_upper = ((int)ceil((((double)(__pyx_v_K - 1)) / 2.))); + + /* "pdf.pxi":55 + * lower = (-floor((K-1)/2.)) + * upper = (ceil((K-1)/2.)) + * for k from lower <= k <= upper: # loop over k # <<<<<<<<<<<<<< + * p+=(w+2*k)*exp(-(pow((w+2*k),2))/2/tt) # increment sum + * p/=sqrt(2*M_PI*pow(tt,3)) # add con_stant term + */ + __pyx_t_2 = __pyx_v_upper; + for (__pyx_v_k = __pyx_v_lower; __pyx_v_k <= __pyx_t_2; __pyx_v_k++) { + + /* "pdf.pxi":56 + * upper = (ceil((K-1)/2.)) + * for k from lower <= k <= upper: # loop over k + * p+=(w+2*k)*exp(-(pow((w+2*k),2))/2/tt) # increment sum # <<<<<<<<<<<<<< + * p/=sqrt(2*M_PI*pow(tt,3)) # add con_stant term + * + */ + __pyx_v_p = (__pyx_v_p + ((__pyx_v_w + (2 * __pyx_v_k)) * exp((((-pow((__pyx_v_w + (2 * __pyx_v_k)), 2.0)) / 2.0) / __pyx_v_tt)))); + } + + /* "pdf.pxi":57 + * for k from lower <= k <= upper: # loop over k + * p+=(w+2*k)*exp(-(pow((w+2*k),2))/2/tt) # increment sum + * p/=sqrt(2*M_PI*pow(tt,3)) # add con_stant term # <<<<<<<<<<<<<< + * + * else: # if large t is better... + */ + __pyx_v_p = (__pyx_v_p / sqrt(((2.0 * M_PI) * pow(__pyx_v_tt, 3.0)))); + + /* "pdf.pxi":51 + * # compute f(tt|0,1,w) + * p=0 #initialize density + * if ks(ceil(ks)) # round to smallest integer meeting error + * lower = (-floor((K-1)/2.)) + */ + goto __pyx_L5; + } + + /* "pdf.pxi":60 + * + * else: # if large t is better... + * K=(ceil(kl)) # round to smallest integer meeting error # <<<<<<<<<<<<<< + * for k from 1 <= k <= K: + * p+=k*exp(-(pow(k,2))*(M_PI**2)*tt/2)*sin(k*M_PI*w) # increment sum + */ + /*else*/ { + __pyx_v_K = ((int)ceil(__pyx_v_kl)); + + /* "pdf.pxi":61 + * else: # if large t is better... + * K=(ceil(kl)) # round to smallest integer meeting error + * for k from 1 <= k <= K: # <<<<<<<<<<<<<< + * p+=k*exp(-(pow(k,2))*(M_PI**2)*tt/2)*sin(k*M_PI*w) # increment sum + * p*=M_PI # add con_stant term + */ + __pyx_t_2 = __pyx_v_K; + for (__pyx_v_k = 1; __pyx_v_k <= __pyx_t_2; __pyx_v_k++) { + + /* "pdf.pxi":62 + * K=(ceil(kl)) # round to smallest integer meeting error + * for k from 1 <= k <= K: + * p+=k*exp(-(pow(k,2))*(M_PI**2)*tt/2)*sin(k*M_PI*w) # increment sum # <<<<<<<<<<<<<< + * p*=M_PI # add con_stant term + * + */ + __pyx_v_p = (__pyx_v_p + ((__pyx_v_k * exp(((((-pow(__pyx_v_k, 2.0)) * pow(M_PI, 2.0)) * __pyx_v_tt) / 2.0))) * sin(((__pyx_v_k * M_PI) * __pyx_v_w)))); + } + + /* "pdf.pxi":63 + * for k from 1 <= k <= K: + * p+=k*exp(-(pow(k,2))*(M_PI**2)*tt/2)*sin(k*M_PI*w) # increment sum + * p*=M_PI # add con_stant term # <<<<<<<<<<<<<< + * + * return p + */ + __pyx_v_p = (__pyx_v_p * M_PI); + } + __pyx_L5:; + + /* "pdf.pxi":65 + * p*=M_PI # add con_stant term + * + * return p # <<<<<<<<<<<<<< + * + * cdef inline double prob_ub(double v, double a, double z) nogil: + */ + __pyx_r = __pyx_v_p; + goto __pyx_L0; + + /* "pdf.pxi":28 + * T max[T](T a, T b) + * + * cdef double ftt_01w(double tt, double w, double err) nogil: # <<<<<<<<<<<<<< + * """Compute f(t|0,1,w) for the likelihood of the drift diffusion model using the method + * and implementation of Navarro & Fuss, 2009. + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "pdf.pxi":67 + * return p + * + * cdef inline double prob_ub(double v, double a, double z) nogil: # <<<<<<<<<<<<<< + * """Probability of hitting upper boundary.""" + * if v == 0: + */ + +static CYTHON_INLINE double __pyx_f_6wfpt_n_prob_ub(double __pyx_v_v, double __pyx_v_a, double __pyx_v_z) { + double __pyx_r; + int __pyx_t_1; + + /* "pdf.pxi":69 + * cdef inline double prob_ub(double v, double a, double z) nogil: + * """Probability of hitting upper boundary.""" + * if v == 0: # <<<<<<<<<<<<<< + * return z + * else: + */ + __pyx_t_1 = (__pyx_v_v == 0.0); + if (__pyx_t_1) { + + /* "pdf.pxi":70 + * """Probability of hitting upper boundary.""" + * if v == 0: + * return z # <<<<<<<<<<<<<< + * else: + * return (exp(-2 * a * z * v) - 1) / (exp(-2 * a * v) - 1) + */ + __pyx_r = __pyx_v_z; + goto __pyx_L0; + + /* "pdf.pxi":69 + * cdef inline double prob_ub(double v, double a, double z) nogil: + * """Probability of hitting upper boundary.""" + * if v == 0: # <<<<<<<<<<<<<< + * return z + * else: + */ + } + + /* "pdf.pxi":72 + * return z + * else: + * return (exp(-2 * a * z * v) - 1) / (exp(-2 * a * v) - 1) # <<<<<<<<<<<<<< + * + * cdef double pdf(double x, double v, double a, double w, double err) nogil: + */ + /*else*/ { + __pyx_r = ((exp((((-2.0 * __pyx_v_a) * __pyx_v_z) * __pyx_v_v)) - 1.0) / (exp(((-2.0 * __pyx_v_a) * __pyx_v_v)) - 1.0)); + goto __pyx_L0; + } + + /* "pdf.pxi":67 + * return p + * + * cdef inline double prob_ub(double v, double a, double z) nogil: # <<<<<<<<<<<<<< + * """Probability of hitting upper boundary.""" + * if v == 0: + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "pdf.pxi":74 + * return (exp(-2 * a * z * v) - 1) / (exp(-2 * a * v) - 1) + * + * cdef double pdf(double x, double v, double a, double w, double err) nogil: # <<<<<<<<<<<<<< + * """Compute the likelihood of the drift diffusion model f(t|v,a,z) using the method + * and implementation of Navarro & Fuss, 2009. + */ + +static double __pyx_f_6wfpt_n_pdf(double __pyx_v_x, double __pyx_v_v, double __pyx_v_a, double __pyx_v_w, double __pyx_v_err) { + double __pyx_v_tt; + double __pyx_v_p; + double __pyx_r; + int __pyx_t_1; + double __pyx_t_2; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + #ifdef WITH_THREAD + PyGILState_STATE __pyx_gilstate_save; + #endif + + /* "pdf.pxi":78 + * and implementation of Navarro & Fuss, 2009. + * """ + * if x <= 0: # <<<<<<<<<<<<<< + * return 0 + * + */ + __pyx_t_1 = (__pyx_v_x <= 0.0); + if (__pyx_t_1) { + + /* "pdf.pxi":79 + * """ + * if x <= 0: + * return 0 # <<<<<<<<<<<<<< + * + * cdef double tt = x/a**2 # use normalized time + */ + __pyx_r = 0.0; + goto __pyx_L0; + + /* "pdf.pxi":78 + * and implementation of Navarro & Fuss, 2009. + * """ + * if x <= 0: # <<<<<<<<<<<<<< + * return 0 + * + */ + } + + /* "pdf.pxi":81 + * return 0 + * + * cdef double tt = x/a**2 # use normalized time # <<<<<<<<<<<<<< + * cdef double p = ftt_01w(tt, w, err) #get f(t|0,1,w) + * + */ + __pyx_v_tt = (__pyx_v_x / pow(__pyx_v_a, 2.0)); + + /* "pdf.pxi":82 + * + * cdef double tt = x/a**2 # use normalized time + * cdef double p = ftt_01w(tt, w, err) #get f(t|0,1,w) # <<<<<<<<<<<<<< + * + * # convert to f(t|v,a,w) + */ + __pyx_t_2 = __pyx_f_6wfpt_n_ftt_01w(__pyx_v_tt, __pyx_v_w, __pyx_v_err); if (unlikely(__pyx_t_2 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(3, 82, __pyx_L1_error) + __pyx_v_p = __pyx_t_2; + + /* "pdf.pxi":85 + * + * # convert to f(t|v,a,w) + * return p*exp(-v*a*w -(pow(v,2))*x/2.)/(pow(a,2)) # <<<<<<<<<<<<<< + * + * cdef double pdf_sv(double x, double v, double sv, double a, double z, double err) nogil: + */ + __pyx_r = ((__pyx_v_p * exp(((((-__pyx_v_v) * __pyx_v_a) * __pyx_v_w) - ((pow(__pyx_v_v, 2.0) * __pyx_v_x) / 2.)))) / pow(__pyx_v_a, 2.0)); + goto __pyx_L0; + + /* "pdf.pxi":74 + * return (exp(-2 * a * z * v) - 1) / (exp(-2 * a * v) - 1) + * + * cdef double pdf(double x, double v, double a, double w, double err) nogil: # <<<<<<<<<<<<<< + * """Compute the likelihood of the drift diffusion model f(t|v,a,z) using the method + * and implementation of Navarro & Fuss, 2009. + */ + + /* function exit code */ + __pyx_L1_error:; + #ifdef WITH_THREAD + __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); + #endif + __Pyx_AddTraceback("wfpt_n.pdf", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = -1; + #ifdef WITH_THREAD + __Pyx_PyGILState_Release(__pyx_gilstate_save); + #endif + __pyx_L0:; + return __pyx_r; +} + +/* "pdf.pxi":87 + * return p*exp(-v*a*w -(pow(v,2))*x/2.)/(pow(a,2)) + * + * cdef double pdf_sv(double x, double v, double sv, double a, double z, double err) nogil: # <<<<<<<<<<<<<< + * """Compute the likelihood of the drift diffusion model f(t|v,a,z,sv) using the method + * and implementation of Navarro & Fuss, 2009. + */ + +static double __pyx_f_6wfpt_n_pdf_sv(double __pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_err) { + double __pyx_v_tt; + double __pyx_v_p; + double __pyx_r; + int __pyx_t_1; + double __pyx_t_2; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + #ifdef WITH_THREAD + PyGILState_STATE __pyx_gilstate_save; + #endif + + /* "pdf.pxi":92 + * sv is the std of the drift rate + * """ + * if x <= 0: # <<<<<<<<<<<<<< + * return 0 + * + */ + __pyx_t_1 = (__pyx_v_x <= 0.0); + if (__pyx_t_1) { + + /* "pdf.pxi":93 + * """ + * if x <= 0: + * return 0 # <<<<<<<<<<<<<< + * + * if sv==0: + */ + __pyx_r = 0.0; + goto __pyx_L0; + + /* "pdf.pxi":92 + * sv is the std of the drift rate + * """ + * if x <= 0: # <<<<<<<<<<<<<< + * return 0 + * + */ + } + + /* "pdf.pxi":95 + * return 0 + * + * if sv==0: # <<<<<<<<<<<<<< + * return pdf(x, v, a, z, err) + * + */ + __pyx_t_1 = (__pyx_v_sv == 0.0); + if (__pyx_t_1) { + + /* "pdf.pxi":96 + * + * if sv==0: + * return pdf(x, v, a, z, err) # <<<<<<<<<<<<<< + * + * cdef double tt = x/(pow(a,2)) # use normalized time + */ + __pyx_t_2 = __pyx_f_6wfpt_n_pdf(__pyx_v_x, __pyx_v_v, __pyx_v_a, __pyx_v_z, __pyx_v_err); if (unlikely(__pyx_t_2 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(3, 96, __pyx_L1_error) + __pyx_r = __pyx_t_2; + goto __pyx_L0; + + /* "pdf.pxi":95 + * return 0 + * + * if sv==0: # <<<<<<<<<<<<<< + * return pdf(x, v, a, z, err) + * + */ + } + + /* "pdf.pxi":98 + * return pdf(x, v, a, z, err) + * + * cdef double tt = x/(pow(a,2)) # use normalized time # <<<<<<<<<<<<<< + * cdef double p = ftt_01w(tt, z, err) #get f(t|0,1,w) + * + */ + __pyx_v_tt = (__pyx_v_x / pow(__pyx_v_a, 2.0)); + + /* "pdf.pxi":99 + * + * cdef double tt = x/(pow(a,2)) # use normalized time + * cdef double p = ftt_01w(tt, z, err) #get f(t|0,1,w) # <<<<<<<<<<<<<< + * + * # convert to f(t|v,a,w) + */ + __pyx_t_2 = __pyx_f_6wfpt_n_ftt_01w(__pyx_v_tt, __pyx_v_z, __pyx_v_err); if (unlikely(__pyx_t_2 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(3, 99, __pyx_L1_error) + __pyx_v_p = __pyx_t_2; + + /* "pdf.pxi":102 + * + * # convert to f(t|v,a,w) + * return exp(log(p) + ((a*z*sv)**2 - 2*a*v*z - (v**2)*x)/(2*(sv**2)*x+2))/sqrt((sv**2)*x+1)/(a**2) # <<<<<<<<<<<<<< + * + * cpdef double full_pdf(double x, double v, double sv, double a, double + */ + __pyx_r = ((exp((log(__pyx_v_p) + (((pow(((__pyx_v_a * __pyx_v_z) * __pyx_v_sv), 2.0) - (((2.0 * __pyx_v_a) * __pyx_v_v) * __pyx_v_z)) - (pow(__pyx_v_v, 2.0) * __pyx_v_x)) / (((2.0 * pow(__pyx_v_sv, 2.0)) * __pyx_v_x) + 2.0)))) / sqrt(((pow(__pyx_v_sv, 2.0) * __pyx_v_x) + 1.0))) / pow(__pyx_v_a, 2.0)); + goto __pyx_L0; + + /* "pdf.pxi":87 + * return p*exp(-v*a*w -(pow(v,2))*x/2.)/(pow(a,2)) + * + * cdef double pdf_sv(double x, double v, double sv, double a, double z, double err) nogil: # <<<<<<<<<<<<<< + * """Compute the likelihood of the drift diffusion model f(t|v,a,z,sv) using the method + * and implementation of Navarro & Fuss, 2009. + */ + + /* function exit code */ + __pyx_L1_error:; + #ifdef WITH_THREAD + __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); + #endif + __Pyx_AddTraceback("wfpt_n.pdf_sv", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = -1; + #ifdef WITH_THREAD + __Pyx_PyGILState_Release(__pyx_gilstate_save); + #endif + __pyx_L0:; + return __pyx_r; +} + +/* "pdf.pxi":104 + * return exp(log(p) + ((a*z*sv)**2 - 2*a*v*z - (v**2)*x)/(2*(sv**2)*x+2))/sqrt((sv**2)*x+1)/(a**2) + * + * cpdef double full_pdf(double x, double v, double sv, double a, double # <<<<<<<<<<<<<< + * z, double sz, double t, double st, double err, int + * n_st=2, int n_sz=2, bint use_adaptive=1, double + */ + +static PyObject *__pyx_pw_6wfpt_n_1full_pdf(PyObject *__pyx_self, +#if CYTHON_METH_FASTCALL +PyObject *const *__pyx_args, Py_ssize_t __pyx_nargs, PyObject *__pyx_kwds +#else +PyObject *__pyx_args, PyObject *__pyx_kwds +#endif +); /*proto*/ +static double __pyx_f_6wfpt_n_full_pdf(double __pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_sz, double __pyx_v_t, double __pyx_v_st, double __pyx_v_err, CYTHON_UNUSED int __pyx_skip_dispatch, struct __pyx_opt_args_6wfpt_n_full_pdf *__pyx_optional_args) { + int __pyx_v_n_st = ((int)2); + int __pyx_v_n_sz = ((int)2); + int __pyx_v_use_adaptive = ((int)1); + double __pyx_v_simps_err = ((double)1e-3); + double __pyx_r; + int __pyx_t_1; + int __pyx_t_2; + double __pyx_t_3; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + #ifdef WITH_THREAD + PyGILState_STATE __pyx_gilstate_save; + #endif + if (__pyx_optional_args) { + if (__pyx_optional_args->__pyx_n > 0) { + __pyx_v_n_st = __pyx_optional_args->n_st; + if (__pyx_optional_args->__pyx_n > 1) { + __pyx_v_n_sz = __pyx_optional_args->n_sz; + if (__pyx_optional_args->__pyx_n > 2) { + __pyx_v_use_adaptive = __pyx_optional_args->use_adaptive; + if (__pyx_optional_args->__pyx_n > 3) { + __pyx_v_simps_err = __pyx_optional_args->simps_err; + } + } + } + } + } + + /* "pdf.pxi":111 + * + * # Check if parpameters are valid + * if (z<0) or (z>1) or (a<0) or (t<0) or (st<0) or (sv<0) or (sz<0) or (sz>1) or \ # <<<<<<<<<<<<<< + * ((fabs(x)-(t-st/2.))<0) or (z+sz/2.>1) or (z-sz/2.<0) or (t-st/2.<0): + * return 0 + */ + __pyx_t_2 = (__pyx_v_z < 0.0); + if (!__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L4_bool_binop_done; + } + __pyx_t_2 = (__pyx_v_z > 1.0); + if (!__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L4_bool_binop_done; + } + __pyx_t_2 = (__pyx_v_a < 0.0); + if (!__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L4_bool_binop_done; + } + __pyx_t_2 = (__pyx_v_t < 0.0); + if (!__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L4_bool_binop_done; + } + __pyx_t_2 = (__pyx_v_st < 0.0); + if (!__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L4_bool_binop_done; + } + __pyx_t_2 = (__pyx_v_sv < 0.0); + if (!__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L4_bool_binop_done; + } + __pyx_t_2 = (__pyx_v_sz < 0.0); + if (!__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L4_bool_binop_done; + } + __pyx_t_2 = (__pyx_v_sz > 1.0); + if (!__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L4_bool_binop_done; + } + + /* "pdf.pxi":112 + * # Check if parpameters are valid + * if (z<0) or (z>1) or (a<0) or (t<0) or (st<0) or (sv<0) or (sz<0) or (sz>1) or \ + * ((fabs(x)-(t-st/2.))<0) or (z+sz/2.>1) or (z-sz/2.<0) or (t-st/2.<0): # <<<<<<<<<<<<<< + * return 0 + * + */ + __pyx_t_2 = ((fabs(__pyx_v_x) - (__pyx_v_t - (__pyx_v_st / 2.))) < 0.0); + if (!__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L4_bool_binop_done; + } + __pyx_t_2 = ((__pyx_v_z + (__pyx_v_sz / 2.)) > 1.0); + if (!__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L4_bool_binop_done; + } + __pyx_t_2 = ((__pyx_v_z - (__pyx_v_sz / 2.)) < 0.0); + if (!__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L4_bool_binop_done; + } + __pyx_t_2 = ((__pyx_v_t - (__pyx_v_st / 2.)) < 0.0); + __pyx_t_1 = __pyx_t_2; + __pyx_L4_bool_binop_done:; + + /* "pdf.pxi":111 + * + * # Check if parpameters are valid + * if (z<0) or (z>1) or (a<0) or (t<0) or (st<0) or (sv<0) or (sz<0) or (sz>1) or \ # <<<<<<<<<<<<<< + * ((fabs(x)-(t-st/2.))<0) or (z+sz/2.>1) or (z-sz/2.<0) or (t-st/2.<0): + * return 0 + */ + if (__pyx_t_1) { + + /* "pdf.pxi":113 + * if (z<0) or (z>1) or (a<0) or (t<0) or (st<0) or (sv<0) or (sz<0) or (sz>1) or \ + * ((fabs(x)-(t-st/2.))<0) or (z+sz/2.>1) or (z-sz/2.<0) or (t-st/2.<0): + * return 0 # <<<<<<<<<<<<<< + * + * # transform x,v,z if x is upper bound response + */ + __pyx_r = 0.0; + goto __pyx_L0; + + /* "pdf.pxi":111 + * + * # Check if parpameters are valid + * if (z<0) or (z>1) or (a<0) or (t<0) or (st<0) or (sv<0) or (sz<0) or (sz>1) or \ # <<<<<<<<<<<<<< + * ((fabs(x)-(t-st/2.))<0) or (z+sz/2.>1) or (z-sz/2.<0) or (t-st/2.<0): + * return 0 + */ + } + + /* "pdf.pxi":116 + * + * # transform x,v,z if x is upper bound response + * if x > 0: # <<<<<<<<<<<<<< + * v = -v + * z = 1.-z + */ + __pyx_t_1 = (__pyx_v_x > 0.0); + if (__pyx_t_1) { + + /* "pdf.pxi":117 + * # transform x,v,z if x is upper bound response + * if x > 0: + * v = -v # <<<<<<<<<<<<<< + * z = 1.-z + * + */ + __pyx_v_v = (-__pyx_v_v); + + /* "pdf.pxi":118 + * if x > 0: + * v = -v + * z = 1.-z # <<<<<<<<<<<<<< + * + * x = fabs(x) + */ + __pyx_v_z = (1. - __pyx_v_z); + + /* "pdf.pxi":116 + * + * # transform x,v,z if x is upper bound response + * if x > 0: # <<<<<<<<<<<<<< + * v = -v + * z = 1.-z + */ + } + + /* "pdf.pxi":120 + * z = 1.-z + * + * x = fabs(x) # <<<<<<<<<<<<<< + * + * if st<1e-3: + */ + __pyx_v_x = fabs(__pyx_v_x); + + /* "pdf.pxi":122 + * x = fabs(x) + * + * if st<1e-3: # <<<<<<<<<<<<<< + * st = 0 + * if sz <1e-3: + */ + __pyx_t_1 = (__pyx_v_st < 1e-3); + if (__pyx_t_1) { + + /* "pdf.pxi":123 + * + * if st<1e-3: + * st = 0 # <<<<<<<<<<<<<< + * if sz <1e-3: + * sz = 0 + */ + __pyx_v_st = 0.0; + + /* "pdf.pxi":122 + * x = fabs(x) + * + * if st<1e-3: # <<<<<<<<<<<<<< + * st = 0 + * if sz <1e-3: + */ + } + + /* "pdf.pxi":124 + * if st<1e-3: + * st = 0 + * if sz <1e-3: # <<<<<<<<<<<<<< + * sz = 0 + * + */ + __pyx_t_1 = (__pyx_v_sz < 1e-3); + if (__pyx_t_1) { + + /* "pdf.pxi":125 + * st = 0 + * if sz <1e-3: + * sz = 0 # <<<<<<<<<<<<<< + * + * if (sz==0): + */ + __pyx_v_sz = 0.0; + + /* "pdf.pxi":124 + * if st<1e-3: + * st = 0 + * if sz <1e-3: # <<<<<<<<<<<<<< + * sz = 0 + * + */ + } + + /* "pdf.pxi":127 + * sz = 0 + * + * if (sz==0): # <<<<<<<<<<<<<< + * if (st==0): #sv=0,sz=0,st=0 + * return pdf_sv(x - t, v, sv, a, z, err) + */ + __pyx_t_1 = (__pyx_v_sz == 0.0); + if (__pyx_t_1) { + + /* "pdf.pxi":128 + * + * if (sz==0): + * if (st==0): #sv=0,sz=0,st=0 # <<<<<<<<<<<<<< + * return pdf_sv(x - t, v, sv, a, z, err) + * else: #sv=0,sz=0,st=$ + */ + __pyx_t_1 = (__pyx_v_st == 0.0); + if (__pyx_t_1) { + + /* "pdf.pxi":129 + * if (sz==0): + * if (st==0): #sv=0,sz=0,st=0 + * return pdf_sv(x - t, v, sv, a, z, err) # <<<<<<<<<<<<<< + * else: #sv=0,sz=0,st=$ + * if use_adaptive>0: + */ + __pyx_t_3 = __pyx_f_6wfpt_n_pdf_sv((__pyx_v_x - __pyx_v_t), __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_err); if (unlikely(__pyx_t_3 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(3, 129, __pyx_L1_error) + __pyx_r = __pyx_t_3; + goto __pyx_L0; + + /* "pdf.pxi":128 + * + * if (sz==0): + * if (st==0): #sv=0,sz=0,st=0 # <<<<<<<<<<<<<< + * return pdf_sv(x - t, v, sv, a, z, err) + * else: #sv=0,sz=0,st=$ + */ + } + + /* "pdf.pxi":131 + * return pdf_sv(x - t, v, sv, a, z, err) + * else: #sv=0,sz=0,st=$ + * if use_adaptive>0: # <<<<<<<<<<<<<< + * return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z, z, t-st/2., t+st/2., simps_err, n_st) + * else: + */ + /*else*/ { + __pyx_t_1 = (__pyx_v_use_adaptive > 0); + if (__pyx_t_1) { + + /* "pdf.pxi":132 + * else: #sv=0,sz=0,st=$ + * if use_adaptive>0: + * return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z, z, t-st/2., t+st/2., simps_err, n_st) # <<<<<<<<<<<<<< + * else: + * return simpson_1D(x, v, sv, a, z, t, err, z, z, 0, t-st/2., t+st/2., n_st) + */ + __pyx_t_3 = __pyx_f_6wfpt_n_adaptiveSimpsons_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_err, __pyx_v_z, __pyx_v_z, (__pyx_v_t - (__pyx_v_st / 2.)), (__pyx_v_t + (__pyx_v_st / 2.)), __pyx_v_simps_err, __pyx_v_n_st); if (unlikely(__pyx_t_3 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(3, 132, __pyx_L1_error) + __pyx_r = __pyx_t_3; + goto __pyx_L0; + + /* "pdf.pxi":131 + * return pdf_sv(x - t, v, sv, a, z, err) + * else: #sv=0,sz=0,st=$ + * if use_adaptive>0: # <<<<<<<<<<<<<< + * return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z, z, t-st/2., t+st/2., simps_err, n_st) + * else: + */ + } + + /* "pdf.pxi":134 + * return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z, z, t-st/2., t+st/2., simps_err, n_st) + * else: + * return simpson_1D(x, v, sv, a, z, t, err, z, z, 0, t-st/2., t+st/2., n_st) # <<<<<<<<<<<<<< + * + * else: #sz=$ + */ + /*else*/ { + __pyx_t_3 = __pyx_f_6wfpt_n_simpson_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_err, __pyx_v_z, __pyx_v_z, 0, (__pyx_v_t - (__pyx_v_st / 2.)), (__pyx_v_t + (__pyx_v_st / 2.)), __pyx_v_n_st); if (unlikely(__pyx_t_3 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(3, 134, __pyx_L1_error) + __pyx_r = __pyx_t_3; + goto __pyx_L0; + } + } + + /* "pdf.pxi":127 + * sz = 0 + * + * if (sz==0): # <<<<<<<<<<<<<< + * if (st==0): #sv=0,sz=0,st=0 + * return pdf_sv(x - t, v, sv, a, z, err) + */ + } + + /* "pdf.pxi":137 + * + * else: #sz=$ + * if (st==0): #sv=0,sz=$,st=0 # <<<<<<<<<<<<<< + * if use_adaptive: + * return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t, t, simps_err, n_sz) + */ + /*else*/ { + __pyx_t_1 = (__pyx_v_st == 0.0); + if (__pyx_t_1) { + + /* "pdf.pxi":138 + * else: #sz=$ + * if (st==0): #sv=0,sz=$,st=0 + * if use_adaptive: # <<<<<<<<<<<<<< + * return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t, t, simps_err, n_sz) + * else: + */ + if (__pyx_v_use_adaptive) { + + /* "pdf.pxi":139 + * if (st==0): #sv=0,sz=$,st=0 + * if use_adaptive: + * return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t, t, simps_err, n_sz) # <<<<<<<<<<<<<< + * else: + * return simpson_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., n_sz, t, t , 0) + */ + __pyx_t_3 = __pyx_f_6wfpt_n_adaptiveSimpsons_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_err, (__pyx_v_z - (__pyx_v_sz / 2.)), (__pyx_v_z + (__pyx_v_sz / 2.)), __pyx_v_t, __pyx_v_t, __pyx_v_simps_err, __pyx_v_n_sz); if (unlikely(__pyx_t_3 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(3, 139, __pyx_L1_error) + __pyx_r = __pyx_t_3; + goto __pyx_L0; + + /* "pdf.pxi":138 + * else: #sz=$ + * if (st==0): #sv=0,sz=$,st=0 + * if use_adaptive: # <<<<<<<<<<<<<< + * return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t, t, simps_err, n_sz) + * else: + */ + } + + /* "pdf.pxi":141 + * return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t, t, simps_err, n_sz) + * else: + * return simpson_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., n_sz, t, t , 0) # <<<<<<<<<<<<<< + * else: #sv=0,sz=$,st=$ + * if use_adaptive: + */ + /*else*/ { + __pyx_t_3 = __pyx_f_6wfpt_n_simpson_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_err, (__pyx_v_z - (__pyx_v_sz / 2.)), (__pyx_v_z + (__pyx_v_sz / 2.)), __pyx_v_n_sz, __pyx_v_t, __pyx_v_t, 0); if (unlikely(__pyx_t_3 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(3, 141, __pyx_L1_error) + __pyx_r = __pyx_t_3; + goto __pyx_L0; + } + + /* "pdf.pxi":137 + * + * else: #sz=$ + * if (st==0): #sv=0,sz=$,st=0 # <<<<<<<<<<<<<< + * if use_adaptive: + * return adaptiveSimpsons_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t, t, simps_err, n_sz) + */ + } + + /* "pdf.pxi":143 + * return simpson_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., n_sz, t, t , 0) + * else: #sv=0,sz=$,st=$ + * if use_adaptive: # <<<<<<<<<<<<<< + * return adaptiveSimpsons_2D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t-st/2., t+st/2., simps_err, n_sz, n_st) + * else: + */ + /*else*/ { + if (__pyx_v_use_adaptive) { + + /* "pdf.pxi":144 + * else: #sv=0,sz=$,st=$ + * if use_adaptive: + * return adaptiveSimpsons_2D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t-st/2., t+st/2., simps_err, n_sz, n_st) # <<<<<<<<<<<<<< + * else: + * return simpson_2D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., n_sz, t-st/2., t+st/2., n_st) + */ + __pyx_t_3 = __pyx_f_6wfpt_n_adaptiveSimpsons_2D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_err, (__pyx_v_z - (__pyx_v_sz / 2.)), (__pyx_v_z + (__pyx_v_sz / 2.)), (__pyx_v_t - (__pyx_v_st / 2.)), (__pyx_v_t + (__pyx_v_st / 2.)), __pyx_v_simps_err, __pyx_v_n_sz, __pyx_v_n_st); if (unlikely(__pyx_t_3 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(3, 144, __pyx_L1_error) + __pyx_r = __pyx_t_3; + goto __pyx_L0; + + /* "pdf.pxi":143 + * return simpson_1D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., n_sz, t, t , 0) + * else: #sv=0,sz=$,st=$ + * if use_adaptive: # <<<<<<<<<<<<<< + * return adaptiveSimpsons_2D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t-st/2., t+st/2., simps_err, n_sz, n_st) + * else: + */ + } + + /* "pdf.pxi":146 + * return adaptiveSimpsons_2D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., t-st/2., t+st/2., simps_err, n_sz, n_st) + * else: + * return simpson_2D(x, v, sv, a, z, t, err, z-sz/2., z+sz/2., n_sz, t-st/2., t+st/2., n_st) # <<<<<<<<<<<<<< + */ + /*else*/ { + __pyx_t_3 = __pyx_f_6wfpt_n_simpson_2D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_err, (__pyx_v_z - (__pyx_v_sz / 2.)), (__pyx_v_z + (__pyx_v_sz / 2.)), __pyx_v_n_sz, (__pyx_v_t - (__pyx_v_st / 2.)), (__pyx_v_t + (__pyx_v_st / 2.)), __pyx_v_n_st); if (unlikely(__pyx_t_3 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(3, 146, __pyx_L1_error) + __pyx_r = __pyx_t_3; + goto __pyx_L0; + } + } + } + + /* "pdf.pxi":104 + * return exp(log(p) + ((a*z*sv)**2 - 2*a*v*z - (v**2)*x)/(2*(sv**2)*x+2))/sqrt((sv**2)*x+1)/(a**2) + * + * cpdef double full_pdf(double x, double v, double sv, double a, double # <<<<<<<<<<<<<< + * z, double sz, double t, double st, double err, int + * n_st=2, int n_sz=2, bint use_adaptive=1, double + */ + + /* function exit code */ + __pyx_L1_error:; + #ifdef WITH_THREAD + __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); + #endif + __Pyx_AddTraceback("wfpt_n.full_pdf", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = -1; + #ifdef WITH_THREAD + __Pyx_PyGILState_Release(__pyx_gilstate_save); + #endif + __pyx_L0:; + return __pyx_r; +} + +/* Python wrapper */ +static PyObject *__pyx_pw_6wfpt_n_1full_pdf(PyObject *__pyx_self, +#if CYTHON_METH_FASTCALL +PyObject *const *__pyx_args, Py_ssize_t __pyx_nargs, PyObject *__pyx_kwds +#else +PyObject *__pyx_args, PyObject *__pyx_kwds +#endif +); /*proto*/ +PyDoc_STRVAR(__pyx_doc_6wfpt_n_full_pdf, "full_pdf(double x, double v, double sv, double a, double z, double sz, double t, double st, double err, int n_st=2, int n_sz=2, bool use_adaptive=1, double simps_err=1e-3) -> double\nfull pdf"); +static PyMethodDef __pyx_mdef_6wfpt_n_1full_pdf = {"full_pdf", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw_6wfpt_n_1full_pdf, __Pyx_METH_FASTCALL|METH_KEYWORDS, __pyx_doc_6wfpt_n_full_pdf}; +static PyObject *__pyx_pw_6wfpt_n_1full_pdf(PyObject *__pyx_self, +#if CYTHON_METH_FASTCALL +PyObject *const *__pyx_args, Py_ssize_t __pyx_nargs, PyObject *__pyx_kwds +#else +PyObject *__pyx_args, PyObject *__pyx_kwds +#endif +) { + double __pyx_v_x; + double __pyx_v_v; + double __pyx_v_sv; + double __pyx_v_a; + double __pyx_v_z; + double __pyx_v_sz; + double __pyx_v_t; + double __pyx_v_st; + double __pyx_v_err; + int __pyx_v_n_st; + int __pyx_v_n_sz; + int __pyx_v_use_adaptive; + double __pyx_v_simps_err; + #if !CYTHON_METH_FASTCALL + CYTHON_UNUSED Py_ssize_t __pyx_nargs; + #endif + CYTHON_UNUSED PyObject *const *__pyx_kwvalues; + PyObject* values[13] = {0,0,0,0,0,0,0,0,0,0,0,0,0}; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + PyObject *__pyx_r = 0; + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("full_pdf (wrapper)", 0); + #if !CYTHON_METH_FASTCALL + #if CYTHON_ASSUME_SAFE_MACROS + __pyx_nargs = PyTuple_GET_SIZE(__pyx_args); + #else + __pyx_nargs = PyTuple_Size(__pyx_args); if (unlikely(__pyx_nargs < 0)) return NULL; + #endif + #endif + __pyx_kwvalues = __Pyx_KwValues_FASTCALL(__pyx_args, __pyx_nargs); + { + PyObject **__pyx_pyargnames[] = {&__pyx_n_s_x,&__pyx_n_s_v,&__pyx_n_s_sv,&__pyx_n_s_a,&__pyx_n_s_z,&__pyx_n_s_sz,&__pyx_n_s_t,&__pyx_n_s_st,&__pyx_n_s_err,&__pyx_n_s_n_st,&__pyx_n_s_n_sz,&__pyx_n_s_use_adaptive,&__pyx_n_s_simps_err,0}; + if (__pyx_kwds) { + Py_ssize_t kw_args; + switch (__pyx_nargs) { + case 13: values[12] = __Pyx_Arg_FASTCALL(__pyx_args, 12); + CYTHON_FALLTHROUGH; + case 12: values[11] = __Pyx_Arg_FASTCALL(__pyx_args, 11); + CYTHON_FALLTHROUGH; + case 11: values[10] = __Pyx_Arg_FASTCALL(__pyx_args, 10); + CYTHON_FALLTHROUGH; + case 10: values[9] = __Pyx_Arg_FASTCALL(__pyx_args, 9); + CYTHON_FALLTHROUGH; + case 9: values[8] = __Pyx_Arg_FASTCALL(__pyx_args, 8); + CYTHON_FALLTHROUGH; + case 8: values[7] = __Pyx_Arg_FASTCALL(__pyx_args, 7); + CYTHON_FALLTHROUGH; + case 7: values[6] = __Pyx_Arg_FASTCALL(__pyx_args, 6); + CYTHON_FALLTHROUGH; + case 6: values[5] = __Pyx_Arg_FASTCALL(__pyx_args, 5); + CYTHON_FALLTHROUGH; + case 5: values[4] = __Pyx_Arg_FASTCALL(__pyx_args, 4); + CYTHON_FALLTHROUGH; + case 4: values[3] = __Pyx_Arg_FASTCALL(__pyx_args, 3); + CYTHON_FALLTHROUGH; + case 3: values[2] = __Pyx_Arg_FASTCALL(__pyx_args, 2); + CYTHON_FALLTHROUGH; + case 2: values[1] = __Pyx_Arg_FASTCALL(__pyx_args, 1); + CYTHON_FALLTHROUGH; + case 1: values[0] = __Pyx_Arg_FASTCALL(__pyx_args, 0); + CYTHON_FALLTHROUGH; + case 0: break; + default: goto __pyx_L5_argtuple_error; + } + kw_args = __Pyx_NumKwargs_FASTCALL(__pyx_kwds); + switch (__pyx_nargs) { + case 0: + if (likely((values[0] = __Pyx_GetKwValue_FASTCALL(__pyx_kwds, __pyx_kwvalues, __pyx_n_s_x)) != 0)) { + (void)__Pyx_Arg_NewRef_FASTCALL(values[0]); 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if (unlikely(__pyx_t_1 == ((double)-1) && PyErr_Occurred())) __PYX_ERR(3, 104, __pyx_L1_error) + __pyx_t_3 = PyFloat_FromDouble(__pyx_t_1); if (unlikely(!__pyx_t_3)) __PYX_ERR(3, 104, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_3); + __pyx_r = __pyx_t_3; + __pyx_t_3 = 0; + goto __pyx_L0; + + /* function exit code */ + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_3); + __Pyx_AddTraceback("wfpt_n.full_pdf", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = NULL; + __pyx_L0:; + __Pyx_XGIVEREF(__pyx_r); + __Pyx_RefNannyFinishContext(); + return __pyx_r; +} + +/* "integrate.pxi":12 + * include 'pdf.pxi' + * + * cdef double simpson_1D(double x, double v, double sv, double a, double z, double t, double err, # <<<<<<<<<<<<<< + * double lb_z, double ub_z, int n_sz, double lb_t, double ub_t, int n_st) nogil: + * #assert ((n_sz&1)==0 and (n_st&1)==0), "n_st and n_sz have to be even" + */ + +static double __pyx_f_6wfpt_n_simpson_1D(double __pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_t, double __pyx_v_err, double __pyx_v_lb_z, double __pyx_v_ub_z, int __pyx_v_n_sz, double __pyx_v_lb_t, double __pyx_v_ub_t, int __pyx_v_n_st) { + double __pyx_v_ht; 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+ + /* "integrate.pxi":36 + * for i from 1 <= i <= n: + * z_tag = lb_z + hz * i + * t_tag = lb_t + ht * i # <<<<<<<<<<<<<< + * y = pdf_sv(x - t_tag, v, sv, a, z_tag, err) + * if i&1: #check if i is odd + */ + __pyx_v_t_tag = (__pyx_v_lb_t + (__pyx_v_ht * __pyx_v_i)); + + /* "integrate.pxi":37 + * z_tag = lb_z + hz * i + * t_tag = lb_t + ht * i + * y = pdf_sv(x - t_tag, v, sv, a, z_tag, err) # <<<<<<<<<<<<<< + * if i&1: #check if i is odd + * S += (4 * y) + */ + __pyx_t_2 = __pyx_f_6wfpt_n_pdf_sv((__pyx_v_x - __pyx_v_t_tag), __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z_tag, __pyx_v_err); if (unlikely(__pyx_t_2 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(4, 37, __pyx_L1_error) + __pyx_v_y = __pyx_t_2; + + /* "integrate.pxi":38 + * t_tag = lb_t + ht * i + * y = pdf_sv(x - t_tag, v, sv, a, z_tag, err) + * if i&1: #check if i is odd # <<<<<<<<<<<<<< + * S += (4 * y) + * else: + */ + __pyx_t_1 = ((__pyx_v_i & 1) != 0); + if (__pyx_t_1) { + + /* "integrate.pxi":39 + * y = pdf_sv(x - t_tag, v, sv, a, z_tag, err) + * if i&1: #check if i is odd + * S += (4 * y) # <<<<<<<<<<<<<< + * else: + * S += (2 * y) + */ + __pyx_v_S = (__pyx_v_S + (4.0 * __pyx_v_y)); + + /* "integrate.pxi":38 + * t_tag = lb_t + ht * i + * y = pdf_sv(x - t_tag, v, sv, a, z_tag, err) + * if i&1: #check if i is odd # <<<<<<<<<<<<<< + * S += (4 * y) + * else: + */ + goto __pyx_L6; + } + + /* "integrate.pxi":41 + * S += (4 * y) + * else: + * S += (2 * y) # <<<<<<<<<<<<<< + * S = S - y #the last term should be f(b) and not 2*f(b) so we subtract y + * S = S / ((ub_t-lb_t)+(ub_z-lb_z)) #the right function if pdf_sv()/sz or pdf_sv()/st + */ + /*else*/ { + __pyx_v_S = (__pyx_v_S + (2.0 * __pyx_v_y)); + } + __pyx_L6:; + } + + /* "integrate.pxi":42 + * else: + * S += (2 * y) + * S = S - y #the last term should be f(b) and not 2*f(b) so we subtract y # <<<<<<<<<<<<<< + * S = S / ((ub_t-lb_t)+(ub_z-lb_z)) #the right function if pdf_sv()/sz or pdf_sv()/st + * + */ + __pyx_v_S = (__pyx_v_S - __pyx_v_y); + + /* "integrate.pxi":43 + * S += (2 * y) + * S = S - y #the last term should be f(b) and not 2*f(b) so we subtract y + * S = S / ((ub_t-lb_t)+(ub_z-lb_z)) #the right function if pdf_sv()/sz or pdf_sv()/st # <<<<<<<<<<<<<< + * + * return ((ht+hz) * S / 3) + */ + __pyx_v_S = (__pyx_v_S / ((__pyx_v_ub_t - __pyx_v_lb_t) + (__pyx_v_ub_z - __pyx_v_lb_z))); + + /* "integrate.pxi":45 + * S = S / ((ub_t-lb_t)+(ub_z-lb_z)) #the right function if pdf_sv()/sz or pdf_sv()/st + * + * return ((ht+hz) * S / 3) # <<<<<<<<<<<<<< + * + * cdef double simpson_2D(double x, double v, double sv, double a, double z, double t, double err, double lb_z, double ub_z, int n_sz, double lb_t, double ub_t, int n_st) nogil: + */ + __pyx_r = (((__pyx_v_ht + __pyx_v_hz) * __pyx_v_S) / 3.0); + goto __pyx_L0; + + /* "integrate.pxi":12 + * include 'pdf.pxi' + * + * cdef double simpson_1D(double x, double v, double sv, double a, double z, double t, double err, # <<<<<<<<<<<<<< + * double lb_z, double ub_z, int n_sz, double lb_t, double ub_t, int n_st) nogil: + * #assert ((n_sz&1)==0 and (n_st&1)==0), "n_st and n_sz have to be even" + */ + + /* function exit code */ + __pyx_L1_error:; + #ifdef WITH_THREAD + __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); + #endif + __Pyx_AddTraceback("wfpt_n.simpson_1D", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = -1; + #ifdef WITH_THREAD + __Pyx_PyGILState_Release(__pyx_gilstate_save); + #endif + __pyx_L0:; + return __pyx_r; +} + +/* "integrate.pxi":47 + * return ((ht+hz) * S / 3) + * + * cdef double simpson_2D(double x, double v, double sv, double a, double z, double t, double err, double lb_z, double ub_z, int n_sz, double lb_t, double ub_t, int n_st) nogil: # <<<<<<<<<<<<<< + * #assert ((n_sz&1)==0 and (n_st&1)==0), "n_st and n_sz have to be even" + * #assert ((ub_t-lb_t)*(ub_z-lb_z)>0 and (n_sz*n_st)>0), "the function is defined for 2D-integration only, lb_t: %f, ub_t %f, lb_z %f, ub_z %f, n_sz: %d, n_st %d" % (lb_t, ub_t, lb_z, ub_z, n_sz, n_st) + */ + +static double __pyx_f_6wfpt_n_simpson_2D(double __pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, CYTHON_UNUSED double __pyx_v_t, double __pyx_v_err, double __pyx_v_lb_z, double __pyx_v_ub_z, int __pyx_v_n_sz, double __pyx_v_lb_t, double __pyx_v_ub_t, int __pyx_v_n_st) { + double __pyx_v_ht; + double __pyx_v_S; + double __pyx_v_t_tag; + double __pyx_v_y; + int __pyx_v_i_t; + double __pyx_r; + double __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + #ifdef WITH_THREAD + PyGILState_STATE __pyx_gilstate_save; + #endif + + /* "integrate.pxi":56 + * cdef int i_t + * + * ht = (ub_t-lb_t)/n_st # <<<<<<<<<<<<<< + * + * S = simpson_1D(x, v, sv, a, z, lb_t, err, lb_z, ub_z, n_sz, 0, 0, 0) + */ + __pyx_v_ht = ((__pyx_v_ub_t - __pyx_v_lb_t) / ((double)__pyx_v_n_st)); + + /* "integrate.pxi":58 + * ht = (ub_t-lb_t)/n_st + * + * S = simpson_1D(x, v, sv, a, z, lb_t, err, lb_z, ub_z, n_sz, 0, 0, 0) # <<<<<<<<<<<<<< + * + * for i_t from 1 <= i_t <= n_st: + */ + __pyx_t_1 = __pyx_f_6wfpt_n_simpson_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_lb_t, __pyx_v_err, __pyx_v_lb_z, __pyx_v_ub_z, __pyx_v_n_sz, 0.0, 0.0, 0); if (unlikely(__pyx_t_1 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(4, 58, __pyx_L1_error) + __pyx_v_S = __pyx_t_1; + + /* "integrate.pxi":60 + * S = simpson_1D(x, v, sv, a, z, lb_t, err, lb_z, ub_z, n_sz, 0, 0, 0) + * + * for i_t from 1 <= i_t <= n_st: # <<<<<<<<<<<<<< + * t_tag = lb_t + ht * i_t + * y = simpson_1D(x, v, sv, a, z, t_tag, err, lb_z, ub_z, n_sz, 0, 0, 0) + */ + __pyx_t_2 = __pyx_v_n_st; + for (__pyx_v_i_t = 1; __pyx_v_i_t <= __pyx_t_2; __pyx_v_i_t++) { + + /* "integrate.pxi":61 + * + * for i_t from 1 <= i_t <= n_st: + * t_tag = lb_t + ht * i_t # <<<<<<<<<<<<<< + * y = simpson_1D(x, v, sv, a, z, t_tag, err, lb_z, ub_z, n_sz, 0, 0, 0) + * if i_t&1: #check if i is odd + */ + __pyx_v_t_tag = (__pyx_v_lb_t + (__pyx_v_ht * __pyx_v_i_t)); + + /* "integrate.pxi":62 + * for i_t from 1 <= i_t <= n_st: + * t_tag = lb_t + ht * i_t + * y = simpson_1D(x, v, sv, a, z, t_tag, err, lb_z, ub_z, n_sz, 0, 0, 0) # <<<<<<<<<<<<<< + * if i_t&1: #check if i is odd + * S += (4 * y) + */ + __pyx_t_1 = __pyx_f_6wfpt_n_simpson_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t_tag, __pyx_v_err, __pyx_v_lb_z, __pyx_v_ub_z, __pyx_v_n_sz, 0.0, 0.0, 0); if (unlikely(__pyx_t_1 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(4, 62, __pyx_L1_error) + __pyx_v_y = __pyx_t_1; + + /* "integrate.pxi":63 + * t_tag = lb_t + ht * i_t + * y = simpson_1D(x, v, sv, a, z, t_tag, err, lb_z, ub_z, n_sz, 0, 0, 0) + * if i_t&1: #check if i is odd # <<<<<<<<<<<<<< + * S += (4 * y) + * else: + */ + __pyx_t_3 = ((__pyx_v_i_t & 1) != 0); + if (__pyx_t_3) { + + /* "integrate.pxi":64 + * y = simpson_1D(x, v, sv, a, z, t_tag, err, lb_z, ub_z, n_sz, 0, 0, 0) + * if i_t&1: #check if i is odd + * S += (4 * y) # <<<<<<<<<<<<<< + * else: + * S += (2 * y) + */ + __pyx_v_S = (__pyx_v_S + (4.0 * __pyx_v_y)); + + /* "integrate.pxi":63 + * t_tag = lb_t + ht * i_t + * y = simpson_1D(x, v, sv, a, z, t_tag, err, lb_z, ub_z, n_sz, 0, 0, 0) + * if i_t&1: #check if i is odd # <<<<<<<<<<<<<< + * S += (4 * y) + * else: + */ + goto __pyx_L5; + } + + /* "integrate.pxi":66 + * S += (4 * y) + * else: + * S += (2 * y) # <<<<<<<<<<<<<< + * S = S - y #the last term should be f(b) and not 2*f(b) so we subtract y + * S = S/ (ub_t-lb_t) + */ + /*else*/ { + __pyx_v_S = (__pyx_v_S + (2.0 * __pyx_v_y)); + } + __pyx_L5:; + } + + /* "integrate.pxi":67 + * else: + * S += (2 * y) + * S = S - y #the last term should be f(b) and not 2*f(b) so we subtract y # <<<<<<<<<<<<<< + * S = S/ (ub_t-lb_t) + * + */ + __pyx_v_S = (__pyx_v_S - __pyx_v_y); + + /* "integrate.pxi":68 + * S += (2 * y) + * S = S - y #the last term should be f(b) and not 2*f(b) so we subtract y + * S = S/ (ub_t-lb_t) # <<<<<<<<<<<<<< + * + * return (ht * S / 3) + */ + __pyx_v_S = (__pyx_v_S / (__pyx_v_ub_t - __pyx_v_lb_t)); + + /* "integrate.pxi":70 + * S = S/ (ub_t-lb_t) + * + * return (ht * S / 3) # <<<<<<<<<<<<<< + * + * cdef double adaptiveSimpsonsAux(double x, double v, double sv, double a, double z, double t, double pdf_err, + */ + __pyx_r = ((__pyx_v_ht * __pyx_v_S) / 3.0); + goto __pyx_L0; + + /* "integrate.pxi":47 + * return ((ht+hz) * S / 3) + * + * cdef double simpson_2D(double x, double v, double sv, double a, double z, double t, double err, double lb_z, double ub_z, int n_sz, double lb_t, double ub_t, int n_st) nogil: # <<<<<<<<<<<<<< + * #assert ((n_sz&1)==0 and (n_st&1)==0), "n_st and n_sz have to be even" + * #assert ((ub_t-lb_t)*(ub_z-lb_z)>0 and (n_sz*n_st)>0), "the function is defined for 2D-integration only, lb_t: %f, ub_t %f, lb_z %f, ub_z %f, n_sz: %d, n_st %d" % (lb_t, ub_t, lb_z, ub_z, n_sz, n_st) + */ + + /* function exit code */ + __pyx_L1_error:; + #ifdef WITH_THREAD + __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); + #endif + __Pyx_AddTraceback("wfpt_n.simpson_2D", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = -1; + #ifdef WITH_THREAD + __Pyx_PyGILState_Release(__pyx_gilstate_save); + #endif + __pyx_L0:; + return __pyx_r; +} + +/* "integrate.pxi":72 + * return (ht * S / 3) + * + * cdef double adaptiveSimpsonsAux(double x, double v, double sv, double a, double z, double t, double pdf_err, # <<<<<<<<<<<<<< + * double lb_z, double ub_z, double lb_t, double ub_t, double ZT, double simps_err, + * double S, double f_beg, double f_end, double f_mid, int bottom) nogil: + */ + +static double __pyx_f_6wfpt_n_adaptiveSimpsonsAux(double __pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_t, double __pyx_v_pdf_err, double __pyx_v_lb_z, double __pyx_v_ub_z, double __pyx_v_lb_t, double __pyx_v_ub_t, double __pyx_v_ZT, double __pyx_v_simps_err, double __pyx_v_S, double __pyx_v_f_beg, double __pyx_v_f_end, double __pyx_v_f_mid, int __pyx_v_bottom) { + double __pyx_v_z_c; + double __pyx_v_z_d; + double __pyx_v_z_e; + double __pyx_v_t_c; + double __pyx_v_t_d; + double __pyx_v_t_e; + double __pyx_v_h; + double __pyx_v_fd; + double __pyx_v_fe; + double __pyx_v_Sleft; + double __pyx_v_Sright; + double __pyx_v_S2; + double __pyx_r; + int __pyx_t_1; + double __pyx_t_2; + int __pyx_t_3; + double __pyx_t_4; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + #ifdef WITH_THREAD + PyGILState_STATE __pyx_gilstate_save; + #endif + + /* "integrate.pxi":81 + * #print "in AdaptiveSimpsAux: lb_z: %f, ub_z: %f, lb_t %f, ub_t %f, f_beg: %f, f_end: %f, bottom: %d" % (lb_z, ub_z, lb_t, ub_t, f_beg, f_end, bottom) + * + * if (ub_t-lb_t) == 0: #integration over sz # <<<<<<<<<<<<<< + * h = ub_z - lb_z + * z_c = (ub_z + lb_z)/2. + */ + __pyx_t_1 = ((__pyx_v_ub_t - __pyx_v_lb_t) == 0.0); + if (__pyx_t_1) { + + /* "integrate.pxi":82 + * + * if (ub_t-lb_t) == 0: #integration over sz + * h = ub_z - lb_z # <<<<<<<<<<<<<< + * z_c = (ub_z + lb_z)/2. + * z_d = (lb_z + z_c)/2. + */ + __pyx_v_h = (__pyx_v_ub_z - __pyx_v_lb_z); + + /* "integrate.pxi":83 + * if (ub_t-lb_t) == 0: #integration over sz + * h = ub_z - lb_z + * z_c = (ub_z + lb_z)/2. # <<<<<<<<<<<<<< + * z_d = (lb_z + z_c)/2. + * z_e = (z_c + ub_z)/2. + */ + __pyx_v_z_c = ((__pyx_v_ub_z + __pyx_v_lb_z) / 2.); + + /* "integrate.pxi":84 + * h = ub_z - lb_z + * z_c = (ub_z + lb_z)/2. + * z_d = (lb_z + z_c)/2. # <<<<<<<<<<<<<< + * z_e = (z_c + ub_z)/2. + * t_c = t + */ + __pyx_v_z_d = ((__pyx_v_lb_z + __pyx_v_z_c) / 2.); + + /* "integrate.pxi":85 + * z_c = (ub_z + lb_z)/2. + * z_d = (lb_z + z_c)/2. + * z_e = (z_c + ub_z)/2. # <<<<<<<<<<<<<< + * t_c = t + * t_d = t + */ + __pyx_v_z_e = ((__pyx_v_z_c + __pyx_v_ub_z) / 2.); + + /* "integrate.pxi":86 + * z_d = (lb_z + z_c)/2. + * z_e = (z_c + ub_z)/2. + * t_c = t # <<<<<<<<<<<<<< + * t_d = t + * t_e = t + */ + __pyx_v_t_c = __pyx_v_t; + + /* "integrate.pxi":87 + * z_e = (z_c + ub_z)/2. + * t_c = t + * t_d = t # <<<<<<<<<<<<<< + * t_e = t + * + */ + __pyx_v_t_d = __pyx_v_t; + + /* "integrate.pxi":88 + * t_c = t + * t_d = t + * t_e = t # <<<<<<<<<<<<<< + * + * else: #integration over t + */ + __pyx_v_t_e = __pyx_v_t; + + /* "integrate.pxi":81 + * #print "in AdaptiveSimpsAux: lb_z: %f, ub_z: %f, lb_t %f, ub_t %f, f_beg: %f, f_end: %f, bottom: %d" % (lb_z, ub_z, lb_t, ub_t, f_beg, f_end, bottom) + * + * if (ub_t-lb_t) == 0: #integration over sz # <<<<<<<<<<<<<< + * h = ub_z - lb_z + * z_c = (ub_z + lb_z)/2. + */ + goto __pyx_L3; + } + + /* "integrate.pxi":91 + * + * else: #integration over t + * h = ub_t - lb_t # <<<<<<<<<<<<<< + * t_c = (ub_t + lb_t)/2. + * t_d = (lb_t + t_c)/2. + */ + /*else*/ { + __pyx_v_h = (__pyx_v_ub_t - __pyx_v_lb_t); + + /* "integrate.pxi":92 + * else: #integration over t + * h = ub_t - lb_t + * t_c = (ub_t + lb_t)/2. # <<<<<<<<<<<<<< + * t_d = (lb_t + t_c)/2. + * t_e = (t_c + ub_t)/2. + */ + __pyx_v_t_c = ((__pyx_v_ub_t + __pyx_v_lb_t) / 2.); + + /* "integrate.pxi":93 + * h = ub_t - lb_t + * t_c = (ub_t + lb_t)/2. + * t_d = (lb_t + t_c)/2. # <<<<<<<<<<<<<< + * t_e = (t_c + ub_t)/2. + * z_c = z + */ + __pyx_v_t_d = ((__pyx_v_lb_t + __pyx_v_t_c) / 2.); + + /* "integrate.pxi":94 + * t_c = (ub_t + lb_t)/2. + * t_d = (lb_t + t_c)/2. + * t_e = (t_c + ub_t)/2. # <<<<<<<<<<<<<< + * z_c = z + * z_d = z + */ + __pyx_v_t_e = ((__pyx_v_t_c + __pyx_v_ub_t) / 2.); + + /* "integrate.pxi":95 + * t_d = (lb_t + t_c)/2. + * t_e = (t_c + ub_t)/2. + * z_c = z # <<<<<<<<<<<<<< + * z_d = z + * z_e = z + */ + __pyx_v_z_c = __pyx_v_z; + + /* "integrate.pxi":96 + * t_e = (t_c + ub_t)/2. + * z_c = z + * z_d = z # <<<<<<<<<<<<<< + * z_e = z + * + */ + __pyx_v_z_d = __pyx_v_z; + + /* "integrate.pxi":97 + * z_c = z + * z_d = z + * z_e = z # <<<<<<<<<<<<<< + * + * fd = pdf_sv(x - t_d, v, sv, a, z_d, pdf_err)/ZT + */ + __pyx_v_z_e = __pyx_v_z; + } + __pyx_L3:; + + /* "integrate.pxi":99 + * z_e = z + * + * fd = pdf_sv(x - t_d, v, sv, a, z_d, pdf_err)/ZT # <<<<<<<<<<<<<< + * fe = pdf_sv(x - t_e, v, sv, a, z_e, pdf_err)/ZT + * + */ + __pyx_t_2 = __pyx_f_6wfpt_n_pdf_sv((__pyx_v_x - __pyx_v_t_d), __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z_d, __pyx_v_pdf_err); if (unlikely(__pyx_t_2 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(4, 99, __pyx_L1_error) + __pyx_v_fd = (__pyx_t_2 / __pyx_v_ZT); + + /* "integrate.pxi":100 + * + * fd = pdf_sv(x - t_d, v, sv, a, z_d, pdf_err)/ZT + * fe = pdf_sv(x - t_e, v, sv, a, z_e, pdf_err)/ZT # <<<<<<<<<<<<<< + * + * Sleft = (h/12)*(f_beg + 4*fd + f_mid) + */ + __pyx_t_2 = __pyx_f_6wfpt_n_pdf_sv((__pyx_v_x - __pyx_v_t_e), __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z_e, __pyx_v_pdf_err); if (unlikely(__pyx_t_2 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(4, 100, __pyx_L1_error) + __pyx_v_fe = (__pyx_t_2 / __pyx_v_ZT); + + /* "integrate.pxi":102 + * fe = pdf_sv(x - t_e, v, sv, a, z_e, pdf_err)/ZT + * + * Sleft = (h/12)*(f_beg + 4*fd + f_mid) # <<<<<<<<<<<<<< + * Sright = (h/12)*(f_mid + 4*fe + f_end) + * S2 = Sleft + Sright + */ + __pyx_v_Sleft = ((__pyx_v_h / 12.0) * ((__pyx_v_f_beg + (4.0 * __pyx_v_fd)) + __pyx_v_f_mid)); + + /* "integrate.pxi":103 + * + * Sleft = (h/12)*(f_beg + 4*fd + f_mid) + * Sright = (h/12)*(f_mid + 4*fe + f_end) # <<<<<<<<<<<<<< + * S2 = Sleft + Sright + * if (bottom <= 0 or fabs(S2 - S) <= 15*simps_err): + */ + __pyx_v_Sright = ((__pyx_v_h / 12.0) * ((__pyx_v_f_mid + (4.0 * __pyx_v_fe)) + __pyx_v_f_end)); + + /* "integrate.pxi":104 + * Sleft = (h/12)*(f_beg + 4*fd + f_mid) + * Sright = (h/12)*(f_mid + 4*fe + f_end) + * S2 = Sleft + Sright # <<<<<<<<<<<<<< + * if (bottom <= 0 or fabs(S2 - S) <= 15*simps_err): + * return S2 + (S2 - S)/15 + */ + __pyx_v_S2 = (__pyx_v_Sleft + __pyx_v_Sright); + + /* "integrate.pxi":105 + * Sright = (h/12)*(f_mid + 4*fe + f_end) + * S2 = Sleft + Sright + * if (bottom <= 0 or fabs(S2 - S) <= 15*simps_err): # <<<<<<<<<<<<<< + * return S2 + (S2 - S)/15 + * return adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, + */ + __pyx_t_3 = (__pyx_v_bottom <= 0); + if (!__pyx_t_3) { + } else { + __pyx_t_1 = __pyx_t_3; + goto __pyx_L5_bool_binop_done; + } + __pyx_t_3 = (fabs((__pyx_v_S2 - __pyx_v_S)) <= (15.0 * __pyx_v_simps_err)); + __pyx_t_1 = __pyx_t_3; + __pyx_L5_bool_binop_done:; + if (__pyx_t_1) { + + /* "integrate.pxi":106 + * S2 = Sleft + Sright + * if (bottom <= 0 or fabs(S2 - S) <= 15*simps_err): + * return S2 + (S2 - S)/15 # <<<<<<<<<<<<<< + * return adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, + * lb_z, z_c, lb_t, t_c, ZT, simps_err/2, + */ + __pyx_r = (__pyx_v_S2 + ((__pyx_v_S2 - __pyx_v_S) / 15.0)); + goto __pyx_L0; + + /* "integrate.pxi":105 + * Sright = (h/12)*(f_mid + 4*fe + f_end) + * S2 = Sleft + Sright + * if (bottom <= 0 or fabs(S2 - S) <= 15*simps_err): # <<<<<<<<<<<<<< + * return S2 + (S2 - S)/15 + * return adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, + */ + } + + /* "integrate.pxi":107 + * if (bottom <= 0 or fabs(S2 - S) <= 15*simps_err): + * return S2 + (S2 - S)/15 + * return adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, # <<<<<<<<<<<<<< + * lb_z, z_c, lb_t, t_c, ZT, simps_err/2, + * Sleft, f_beg, f_mid, fd, bottom-1) + \ + */ + __pyx_t_2 = __pyx_f_6wfpt_n_adaptiveSimpsonsAux(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_pdf_err, __pyx_v_lb_z, __pyx_v_z_c, __pyx_v_lb_t, __pyx_v_t_c, __pyx_v_ZT, (__pyx_v_simps_err / 2.0), __pyx_v_Sleft, __pyx_v_f_beg, __pyx_v_f_mid, __pyx_v_fd, (__pyx_v_bottom - 1)); if (unlikely(__pyx_t_2 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(4, 107, __pyx_L1_error) + + /* "integrate.pxi":110 + * lb_z, z_c, lb_t, t_c, ZT, simps_err/2, + * Sleft, f_beg, f_mid, fd, bottom-1) + \ + * adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, # <<<<<<<<<<<<<< + * z_c, ub_z, t_c, ub_t, ZT, simps_err/2, + * Sright, f_mid, f_end, fe, bottom-1) + */ + __pyx_t_4 = __pyx_f_6wfpt_n_adaptiveSimpsonsAux(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_pdf_err, __pyx_v_z_c, __pyx_v_ub_z, __pyx_v_t_c, __pyx_v_ub_t, __pyx_v_ZT, (__pyx_v_simps_err / 2.0), __pyx_v_Sright, __pyx_v_f_mid, __pyx_v_f_end, __pyx_v_fe, (__pyx_v_bottom - 1)); if (unlikely(__pyx_t_4 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(4, 110, __pyx_L1_error) + + /* "integrate.pxi":109 + * return adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, + * lb_z, z_c, lb_t, t_c, ZT, simps_err/2, + * Sleft, f_beg, f_mid, fd, bottom-1) + \ # <<<<<<<<<<<<<< + * adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, + * z_c, ub_z, t_c, ub_t, ZT, simps_err/2, + */ + __pyx_r = (__pyx_t_2 + __pyx_t_4); + goto __pyx_L0; + + /* "integrate.pxi":72 + * return (ht * S / 3) + * + * cdef double adaptiveSimpsonsAux(double x, double v, double sv, double a, double z, double t, double pdf_err, # <<<<<<<<<<<<<< + * double lb_z, double ub_z, double lb_t, double ub_t, double ZT, double simps_err, + * double S, double f_beg, double f_end, double f_mid, int bottom) nogil: + */ + + /* function exit code */ + __pyx_L1_error:; + #ifdef WITH_THREAD + __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); + #endif + __Pyx_AddTraceback("wfpt_n.adaptiveSimpsonsAux", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = -1; + #ifdef WITH_THREAD + __Pyx_PyGILState_Release(__pyx_gilstate_save); + #endif + __pyx_L0:; + return __pyx_r; +} + +/* "integrate.pxi":114 + * Sright, f_mid, f_end, fe, bottom-1) + * + * cdef double adaptiveSimpsons_1D(double x, double v, double sv, double a, double z, double t, # <<<<<<<<<<<<<< + * double pdf_err, double lb_z, double ub_z, double lb_t, double ub_t, + * double simps_err, int maxRecursionDepth) nogil: + */ + +static double __pyx_f_6wfpt_n_adaptiveSimpsons_1D(double __pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_t, double __pyx_v_pdf_err, double __pyx_v_lb_z, double __pyx_v_ub_z, double __pyx_v_lb_t, double __pyx_v_ub_t, double __pyx_v_simps_err, int __pyx_v_maxRecursionDepth) { + double __pyx_v_h; + double __pyx_v_ZT; + double __pyx_v_c_t; + double __pyx_v_c_z; + double __pyx_v_f_beg; + double __pyx_v_f_end; + double __pyx_v_f_mid; + double __pyx_v_S; + double __pyx_v_res; + double __pyx_r; + int __pyx_t_1; + double __pyx_t_2; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + #ifdef WITH_THREAD + PyGILState_STATE __pyx_gilstate_save; + #endif + + /* "integrate.pxi":120 + * cdef double h + * + * if (ub_t - lb_t) == 0: #integration over z # <<<<<<<<<<<<<< + * lb_t = t + * ub_t = t + */ + __pyx_t_1 = ((__pyx_v_ub_t - __pyx_v_lb_t) == 0.0); + if (__pyx_t_1) { + + /* "integrate.pxi":121 + * + * if (ub_t - lb_t) == 0: #integration over z + * lb_t = t # <<<<<<<<<<<<<< + * ub_t = t + * h = ub_z - lb_z + */ + __pyx_v_lb_t = __pyx_v_t; + + /* "integrate.pxi":122 + * if (ub_t - lb_t) == 0: #integration over z + * lb_t = t + * ub_t = t # <<<<<<<<<<<<<< + * h = ub_z - lb_z + * else: #integration over t + */ + __pyx_v_ub_t = __pyx_v_t; + + /* "integrate.pxi":123 + * lb_t = t + * ub_t = t + * h = ub_z - lb_z # <<<<<<<<<<<<<< + * else: #integration over t + * h = (ub_t-lb_t) + */ + __pyx_v_h = (__pyx_v_ub_z - __pyx_v_lb_z); + + /* "integrate.pxi":120 + * cdef double h + * + * if (ub_t - lb_t) == 0: #integration over z # <<<<<<<<<<<<<< + * lb_t = t + * ub_t = t + */ + goto __pyx_L3; + } + + /* "integrate.pxi":125 + * h = ub_z - lb_z + * else: #integration over t + * h = (ub_t-lb_t) # <<<<<<<<<<<<<< + * lb_z = z + * ub_z = z + */ + /*else*/ { + __pyx_v_h = (__pyx_v_ub_t - __pyx_v_lb_t); + + /* "integrate.pxi":126 + * else: #integration over t + * h = (ub_t-lb_t) + * lb_z = z # <<<<<<<<<<<<<< + * ub_z = z + * + */ + __pyx_v_lb_z = __pyx_v_z; + + /* "integrate.pxi":127 + * h = (ub_t-lb_t) + * lb_z = z + * ub_z = z # <<<<<<<<<<<<<< + * + * cdef double ZT = h + */ + __pyx_v_ub_z = __pyx_v_z; + } + __pyx_L3:; + + /* "integrate.pxi":129 + * ub_z = z + * + * cdef double ZT = h # <<<<<<<<<<<<<< + * cdef double c_t = (lb_t + ub_t)/2. + * cdef double c_z = (lb_z + ub_z)/2. + */ + __pyx_v_ZT = __pyx_v_h; + + /* "integrate.pxi":130 + * + * cdef double ZT = h + * cdef double c_t = (lb_t + ub_t)/2. # <<<<<<<<<<<<<< + * cdef double c_z = (lb_z + ub_z)/2. + * + */ + __pyx_v_c_t = ((__pyx_v_lb_t + __pyx_v_ub_t) / 2.); + + /* "integrate.pxi":131 + * cdef double ZT = h + * cdef double c_t = (lb_t + ub_t)/2. + * cdef double c_z = (lb_z + ub_z)/2. # <<<<<<<<<<<<<< + * + * cdef double f_beg, f_end, f_mid, S + */ + __pyx_v_c_z = ((__pyx_v_lb_z + __pyx_v_ub_z) / 2.); + + /* "integrate.pxi":134 + * + * cdef double f_beg, f_end, f_mid, S + * f_beg = pdf_sv(x - lb_t, v, sv, a, lb_z, pdf_err)/ZT # <<<<<<<<<<<<<< + * f_end = pdf_sv(x - ub_t, v, sv, a, ub_z, pdf_err)/ZT + * f_mid = pdf_sv(x - c_t, v, sv, a, c_z, pdf_err)/ZT + */ + __pyx_t_2 = __pyx_f_6wfpt_n_pdf_sv((__pyx_v_x - __pyx_v_lb_t), __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_lb_z, __pyx_v_pdf_err); if (unlikely(__pyx_t_2 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(4, 134, __pyx_L1_error) + __pyx_v_f_beg = (__pyx_t_2 / __pyx_v_ZT); + + /* "integrate.pxi":135 + * cdef double f_beg, f_end, f_mid, S + * f_beg = pdf_sv(x - lb_t, v, sv, a, lb_z, pdf_err)/ZT + * f_end = pdf_sv(x - ub_t, v, sv, a, ub_z, pdf_err)/ZT # <<<<<<<<<<<<<< + * f_mid = pdf_sv(x - c_t, v, sv, a, c_z, pdf_err)/ZT + * S = (h/6)*(f_beg + 4*f_mid + f_end) + */ + __pyx_t_2 = __pyx_f_6wfpt_n_pdf_sv((__pyx_v_x - __pyx_v_ub_t), __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_ub_z, __pyx_v_pdf_err); if (unlikely(__pyx_t_2 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(4, 135, __pyx_L1_error) + __pyx_v_f_end = (__pyx_t_2 / __pyx_v_ZT); + + /* "integrate.pxi":136 + * f_beg = pdf_sv(x - lb_t, v, sv, a, lb_z, pdf_err)/ZT + * f_end = pdf_sv(x - ub_t, v, sv, a, ub_z, pdf_err)/ZT + * f_mid = pdf_sv(x - c_t, v, sv, a, c_z, pdf_err)/ZT # <<<<<<<<<<<<<< + * S = (h/6)*(f_beg + 4*f_mid + f_end) + * cdef double res = adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, + */ + __pyx_t_2 = __pyx_f_6wfpt_n_pdf_sv((__pyx_v_x - __pyx_v_c_t), __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_c_z, __pyx_v_pdf_err); if (unlikely(__pyx_t_2 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(4, 136, __pyx_L1_error) + __pyx_v_f_mid = (__pyx_t_2 / __pyx_v_ZT); + + /* "integrate.pxi":137 + * f_end = pdf_sv(x - ub_t, v, sv, a, ub_z, pdf_err)/ZT + * f_mid = pdf_sv(x - c_t, v, sv, a, c_z, pdf_err)/ZT + * S = (h/6)*(f_beg + 4*f_mid + f_end) # <<<<<<<<<<<<<< + * cdef double res = adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, + * lb_z, ub_z, lb_t, ub_t, ZT, simps_err, + */ + __pyx_v_S = ((__pyx_v_h / 6.0) * ((__pyx_v_f_beg + (4.0 * __pyx_v_f_mid)) + __pyx_v_f_end)); + + /* "integrate.pxi":138 + * f_mid = pdf_sv(x - c_t, v, sv, a, c_z, pdf_err)/ZT + * S = (h/6)*(f_beg + 4*f_mid + f_end) + * cdef double res = adaptiveSimpsonsAux(x, v, sv, a, z, t, pdf_err, # <<<<<<<<<<<<<< + * lb_z, ub_z, lb_t, ub_t, ZT, simps_err, + * S, f_beg, f_end, f_mid, maxRecursionDepth) + */ + __pyx_t_2 = __pyx_f_6wfpt_n_adaptiveSimpsonsAux(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_pdf_err, __pyx_v_lb_z, __pyx_v_ub_z, __pyx_v_lb_t, __pyx_v_ub_t, __pyx_v_ZT, __pyx_v_simps_err, __pyx_v_S, __pyx_v_f_beg, __pyx_v_f_end, __pyx_v_f_mid, __pyx_v_maxRecursionDepth); if (unlikely(__pyx_t_2 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(4, 138, __pyx_L1_error) + __pyx_v_res = __pyx_t_2; + + /* "integrate.pxi":141 + * lb_z, ub_z, lb_t, ub_t, ZT, simps_err, + * S, f_beg, f_end, f_mid, maxRecursionDepth) + * return res # <<<<<<<<<<<<<< + * + * cdef double adaptiveSimpsonsAux_2D(double x, double v, double sv, + */ + __pyx_r = __pyx_v_res; + goto __pyx_L0; + + /* "integrate.pxi":114 + * Sright, f_mid, f_end, fe, bottom-1) + * + * cdef double adaptiveSimpsons_1D(double x, double v, double sv, double a, double z, double t, # <<<<<<<<<<<<<< + * double pdf_err, double lb_z, double ub_z, double lb_t, double ub_t, + * double simps_err, int maxRecursionDepth) nogil: + */ + + /* function exit code */ + __pyx_L1_error:; + #ifdef WITH_THREAD + __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); + #endif + __Pyx_AddTraceback("wfpt_n.adaptiveSimpsons_1D", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = -1; + #ifdef WITH_THREAD + __Pyx_PyGILState_Release(__pyx_gilstate_save); + #endif + __pyx_L0:; + return __pyx_r; +} + +/* "integrate.pxi":143 + * return res + * + * cdef double adaptiveSimpsonsAux_2D(double x, double v, double sv, # <<<<<<<<<<<<<< + * double a, double z, double t, double + * pdf_err, double err_1d, double lb_z, + */ + +static double __pyx_f_6wfpt_n_adaptiveSimpsonsAux_2D(double __pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_t, double __pyx_v_pdf_err, double __pyx_v_err_1d, double __pyx_v_lb_z, double __pyx_v_ub_z, double __pyx_v_lb_t, double __pyx_v_ub_t, double __pyx_v_st, double __pyx_v_err_2d, double __pyx_v_S, double __pyx_v_f_beg, double __pyx_v_f_end, double __pyx_v_f_mid, int __pyx_v_maxRecursionDepth_sz, int __pyx_v_bottom) { + double __pyx_v_fd; + double __pyx_v_fe; + double __pyx_v_Sleft; + double __pyx_v_Sright; + double __pyx_v_S2; + double __pyx_v_t_c; + double __pyx_v_t_d; + double __pyx_v_t_e; + double __pyx_v_h; + double __pyx_r; + double __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + double __pyx_t_4; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + #ifdef WITH_THREAD + PyGILState_STATE __pyx_gilstate_save; + #endif + + /* "integrate.pxi":156 + * #print "in AdaptiveSimpsAux_2D: lb_z: %f, ub_z: %f, lb_t %f, ub_t %f, f_beg: %f, f_end: %f, bottom: %d" % (lb_z, ub_z, lb_t, ub_t, f_beg, f_end, bottom) + * + * cdef double t_c = (ub_t + lb_t)/2. # <<<<<<<<<<<<<< + * cdef double t_d = (lb_t + t_c)/2. + * cdef double t_e = (t_c + ub_t)/2. + */ + __pyx_v_t_c = ((__pyx_v_ub_t + __pyx_v_lb_t) / 2.); + + /* "integrate.pxi":157 + * + * cdef double t_c = (ub_t + lb_t)/2. + * cdef double t_d = (lb_t + t_c)/2. # <<<<<<<<<<<<<< + * cdef double t_e = (t_c + ub_t)/2. + * cdef double h = ub_t - lb_t + */ + __pyx_v_t_d = ((__pyx_v_lb_t + __pyx_v_t_c) / 2.); + + /* "integrate.pxi":158 + * cdef double t_c = (ub_t + lb_t)/2. + * cdef double t_d = (lb_t + t_c)/2. + * cdef double t_e = (t_c + ub_t)/2. # <<<<<<<<<<<<<< + * cdef double h = ub_t - lb_t + * + */ + __pyx_v_t_e = ((__pyx_v_t_c + __pyx_v_ub_t) / 2.); + + /* "integrate.pxi":159 + * cdef double t_d = (lb_t + t_c)/2. + * cdef double t_e = (t_c + ub_t)/2. + * cdef double h = ub_t - lb_t # <<<<<<<<<<<<<< + * + * fd = adaptiveSimpsons_1D(x, v, sv, a, z, t_d, pdf_err, lb_z, ub_z, + */ + __pyx_v_h = (__pyx_v_ub_t - __pyx_v_lb_t); + + /* "integrate.pxi":161 + * cdef double h = ub_t - lb_t + * + * fd = adaptiveSimpsons_1D(x, v, sv, a, z, t_d, pdf_err, lb_z, ub_z, # <<<<<<<<<<<<<< + * 0, 0, err_1d, maxRecursionDepth_sz)/st + * fe = adaptiveSimpsons_1D(x, v, sv, a, z, t_e, pdf_err, lb_z, ub_z, + */ + __pyx_t_1 = __pyx_f_6wfpt_n_adaptiveSimpsons_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t_d, __pyx_v_pdf_err, __pyx_v_lb_z, __pyx_v_ub_z, 0.0, 0.0, __pyx_v_err_1d, __pyx_v_maxRecursionDepth_sz); if (unlikely(__pyx_t_1 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(4, 161, __pyx_L1_error) + + /* "integrate.pxi":162 + * + * fd = adaptiveSimpsons_1D(x, v, sv, a, z, t_d, pdf_err, lb_z, ub_z, + * 0, 0, err_1d, maxRecursionDepth_sz)/st # <<<<<<<<<<<<<< + * fe = adaptiveSimpsons_1D(x, v, sv, a, z, t_e, pdf_err, lb_z, ub_z, + * 0, 0, err_1d, maxRecursionDepth_sz)/st + */ + __pyx_v_fd = (__pyx_t_1 / __pyx_v_st); + + /* "integrate.pxi":163 + * fd = adaptiveSimpsons_1D(x, v, sv, a, z, t_d, pdf_err, lb_z, ub_z, + * 0, 0, err_1d, maxRecursionDepth_sz)/st + * fe = adaptiveSimpsons_1D(x, v, sv, a, z, t_e, pdf_err, lb_z, ub_z, # <<<<<<<<<<<<<< + * 0, 0, err_1d, maxRecursionDepth_sz)/st + * + */ + __pyx_t_1 = __pyx_f_6wfpt_n_adaptiveSimpsons_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t_e, __pyx_v_pdf_err, __pyx_v_lb_z, __pyx_v_ub_z, 0.0, 0.0, __pyx_v_err_1d, __pyx_v_maxRecursionDepth_sz); if (unlikely(__pyx_t_1 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(4, 163, __pyx_L1_error) + + /* "integrate.pxi":164 + * 0, 0, err_1d, maxRecursionDepth_sz)/st + * fe = adaptiveSimpsons_1D(x, v, sv, a, z, t_e, pdf_err, lb_z, ub_z, + * 0, 0, err_1d, maxRecursionDepth_sz)/st # <<<<<<<<<<<<<< + * + * Sleft = (h/12)*(f_beg + 4*fd + f_mid) + */ + __pyx_v_fe = (__pyx_t_1 / __pyx_v_st); + + /* "integrate.pxi":166 + * 0, 0, err_1d, maxRecursionDepth_sz)/st + * + * Sleft = (h/12)*(f_beg + 4*fd + f_mid) # <<<<<<<<<<<<<< + * Sright = (h/12)*(f_mid + 4*fe + f_end) + * S2 = Sleft + Sright + */ + __pyx_v_Sleft = ((__pyx_v_h / 12.0) * ((__pyx_v_f_beg + (4.0 * __pyx_v_fd)) + __pyx_v_f_mid)); + + /* "integrate.pxi":167 + * + * Sleft = (h/12)*(f_beg + 4*fd + f_mid) + * Sright = (h/12)*(f_mid + 4*fe + f_end) # <<<<<<<<<<<<<< + * S2 = Sleft + Sright + * + */ + __pyx_v_Sright = ((__pyx_v_h / 12.0) * ((__pyx_v_f_mid + (4.0 * __pyx_v_fe)) + __pyx_v_f_end)); + + /* "integrate.pxi":168 + * Sleft = (h/12)*(f_beg + 4*fd + f_mid) + * Sright = (h/12)*(f_mid + 4*fe + f_end) + * S2 = Sleft + Sright # <<<<<<<<<<<<<< + * + * if (bottom <= 0 or fabs(S2 - S) <= 15*err_2d): + */ + __pyx_v_S2 = (__pyx_v_Sleft + __pyx_v_Sright); + + /* "integrate.pxi":170 + * S2 = Sleft + Sright + * + * if (bottom <= 0 or fabs(S2 - S) <= 15*err_2d): # <<<<<<<<<<<<<< + * return S2 + (S2 - S)/15; + * + */ + __pyx_t_3 = (__pyx_v_bottom <= 0); + if (!__pyx_t_3) { + } else { + __pyx_t_2 = __pyx_t_3; + goto __pyx_L4_bool_binop_done; + } + __pyx_t_3 = (fabs((__pyx_v_S2 - __pyx_v_S)) <= (15.0 * __pyx_v_err_2d)); + __pyx_t_2 = __pyx_t_3; + __pyx_L4_bool_binop_done:; + if (__pyx_t_2) { + + /* "integrate.pxi":171 + * + * if (bottom <= 0 or fabs(S2 - S) <= 15*err_2d): + * return S2 + (S2 - S)/15; # <<<<<<<<<<<<<< + * + * return adaptiveSimpsonsAux_2D(x, v, sv, a, z, t, pdf_err, err_1d, + */ + __pyx_r = (__pyx_v_S2 + ((__pyx_v_S2 - __pyx_v_S) / 15.0)); + goto __pyx_L0; + + /* "integrate.pxi":170 + * S2 = Sleft + Sright + * + * if (bottom <= 0 or fabs(S2 - S) <= 15*err_2d): # <<<<<<<<<<<<<< + * return S2 + (S2 - S)/15; + * + */ + } + + /* "integrate.pxi":173 + * return S2 + (S2 - S)/15; + * + * return adaptiveSimpsonsAux_2D(x, v, sv, a, z, t, pdf_err, err_1d, # <<<<<<<<<<<<<< + * lb_z, ub_z, lb_t, t_c, st, err_2d/2, + * Sleft, f_beg, f_mid, fd, maxRecursionDepth_sz, bottom-1) + \ + */ + __pyx_t_1 = __pyx_f_6wfpt_n_adaptiveSimpsonsAux_2D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_pdf_err, __pyx_v_err_1d, __pyx_v_lb_z, __pyx_v_ub_z, __pyx_v_lb_t, __pyx_v_t_c, __pyx_v_st, (__pyx_v_err_2d / 2.0), __pyx_v_Sleft, __pyx_v_f_beg, __pyx_v_f_mid, __pyx_v_fd, __pyx_v_maxRecursionDepth_sz, (__pyx_v_bottom - 1)); if (unlikely(__pyx_t_1 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(4, 173, __pyx_L1_error) + + /* "integrate.pxi":176 + * lb_z, ub_z, lb_t, t_c, st, err_2d/2, + * Sleft, f_beg, f_mid, fd, maxRecursionDepth_sz, bottom-1) + \ + * adaptiveSimpsonsAux_2D(x, v, sv, a, z, t, pdf_err, err_1d, # <<<<<<<<<<<<<< + * lb_z, ub_z, t_c, ub_t, st, err_2d/2, + * Sright, f_mid, f_end, fe, maxRecursionDepth_sz, bottom-1) + */ + __pyx_t_4 = __pyx_f_6wfpt_n_adaptiveSimpsonsAux_2D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_pdf_err, __pyx_v_err_1d, __pyx_v_lb_z, __pyx_v_ub_z, __pyx_v_t_c, __pyx_v_ub_t, __pyx_v_st, (__pyx_v_err_2d / 2.0), __pyx_v_Sright, __pyx_v_f_mid, __pyx_v_f_end, __pyx_v_fe, __pyx_v_maxRecursionDepth_sz, (__pyx_v_bottom - 1)); if (unlikely(__pyx_t_4 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(4, 176, __pyx_L1_error) + + /* "integrate.pxi":175 + * return adaptiveSimpsonsAux_2D(x, v, sv, a, z, t, pdf_err, err_1d, + * lb_z, ub_z, lb_t, t_c, st, err_2d/2, + * Sleft, f_beg, f_mid, fd, maxRecursionDepth_sz, bottom-1) + \ # <<<<<<<<<<<<<< + * adaptiveSimpsonsAux_2D(x, v, sv, a, z, t, pdf_err, err_1d, + * lb_z, ub_z, t_c, ub_t, st, err_2d/2, + */ + __pyx_r = (__pyx_t_1 + __pyx_t_4); + goto __pyx_L0; + + /* "integrate.pxi":143 + * return res + * + * cdef double adaptiveSimpsonsAux_2D(double x, double v, double sv, # <<<<<<<<<<<<<< + * double a, double z, double t, double + * pdf_err, double err_1d, double lb_z, + */ + + /* function exit code */ + __pyx_L1_error:; + #ifdef WITH_THREAD + __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); + #endif + __Pyx_AddTraceback("wfpt_n.adaptiveSimpsonsAux_2D", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = -1; + #ifdef WITH_THREAD + __Pyx_PyGILState_Release(__pyx_gilstate_save); + #endif + __pyx_L0:; + return __pyx_r; +} + +/* "integrate.pxi":181 + * + * + * cdef double adaptiveSimpsons_2D(double x, double v, double sv, double a, double z, double t, # <<<<<<<<<<<<<< + * double pdf_err, double lb_z, double ub_z, double lb_t, double ub_t, + * double simps_err, int maxRecursionDepth_sz, int maxRecursionDepth_st) nogil: + */ + +static double __pyx_f_6wfpt_n_adaptiveSimpsons_2D(double __pyx_v_x, double __pyx_v_v, double __pyx_v_sv, double __pyx_v_a, double __pyx_v_z, double __pyx_v_t, double __pyx_v_pdf_err, double __pyx_v_lb_z, double __pyx_v_ub_z, double __pyx_v_lb_t, double __pyx_v_ub_t, double __pyx_v_simps_err, int __pyx_v_maxRecursionDepth_sz, int __pyx_v_maxRecursionDepth_st) { + double __pyx_v_h; + double __pyx_v_st; + CYTHON_UNUSED double __pyx_v_c_t; + CYTHON_UNUSED double __pyx_v_c_z; + double __pyx_v_f_beg; + double __pyx_v_f_end; + double __pyx_v_f_mid; + double __pyx_v_S; + double __pyx_v_err_1d; + double __pyx_v_err_2d; + double __pyx_v_res; + double __pyx_r; + double __pyx_t_1; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + #ifdef WITH_THREAD + PyGILState_STATE __pyx_gilstate_save; + #endif + + /* "integrate.pxi":185 + * double simps_err, int maxRecursionDepth_sz, int maxRecursionDepth_st) nogil: + * + * cdef double h = (ub_t-lb_t) # <<<<<<<<<<<<<< + * + * cdef double st = (ub_t - lb_t) + */ + __pyx_v_h = (__pyx_v_ub_t - __pyx_v_lb_t); + + /* "integrate.pxi":187 + * cdef double h = (ub_t-lb_t) + * + * cdef double st = (ub_t - lb_t) # <<<<<<<<<<<<<< + * cdef double c_t = (lb_t + ub_t)/2. + * cdef double c_z = (lb_z + ub_z)/2. + */ + __pyx_v_st = (__pyx_v_ub_t - __pyx_v_lb_t); + + /* "integrate.pxi":188 + * + * cdef double st = (ub_t - lb_t) + * cdef double c_t = (lb_t + ub_t)/2. # <<<<<<<<<<<<<< + * cdef double c_z = (lb_z + ub_z)/2. + * + */ + __pyx_v_c_t = ((__pyx_v_lb_t + __pyx_v_ub_t) / 2.); + + /* "integrate.pxi":189 + * cdef double st = (ub_t - lb_t) + * cdef double c_t = (lb_t + ub_t)/2. + * cdef double c_z = (lb_z + ub_z)/2. # <<<<<<<<<<<<<< + * + * cdef double f_beg, f_end, f_mid, S + */ + __pyx_v_c_z = ((__pyx_v_lb_z + __pyx_v_ub_z) / 2.); + + /* "integrate.pxi":192 + * + * cdef double f_beg, f_end, f_mid, S + * cdef double err_1d = simps_err # <<<<<<<<<<<<<< + * cdef double err_2d = simps_err + * + */ + __pyx_v_err_1d = __pyx_v_simps_err; + + /* "integrate.pxi":193 + * cdef double f_beg, f_end, f_mid, S + * cdef double err_1d = simps_err + * cdef double err_2d = simps_err # <<<<<<<<<<<<<< + * + * f_beg = adaptiveSimpsons_1D(x, v, sv, a, z, lb_t, pdf_err, lb_z, ub_z, + */ + __pyx_v_err_2d = __pyx_v_simps_err; + + /* "integrate.pxi":195 + * cdef double err_2d = simps_err + * + * f_beg = adaptiveSimpsons_1D(x, v, sv, a, z, lb_t, pdf_err, lb_z, ub_z, # <<<<<<<<<<<<<< + * 0, 0, err_1d, maxRecursionDepth_sz)/st + * + */ + __pyx_t_1 = __pyx_f_6wfpt_n_adaptiveSimpsons_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_lb_t, __pyx_v_pdf_err, __pyx_v_lb_z, __pyx_v_ub_z, 0.0, 0.0, __pyx_v_err_1d, __pyx_v_maxRecursionDepth_sz); if (unlikely(__pyx_t_1 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(4, 195, __pyx_L1_error) + + /* "integrate.pxi":196 + * + * f_beg = adaptiveSimpsons_1D(x, v, sv, a, z, lb_t, pdf_err, lb_z, ub_z, + * 0, 0, err_1d, maxRecursionDepth_sz)/st # <<<<<<<<<<<<<< + * + * f_end = adaptiveSimpsons_1D(x, v, sv, a, z, ub_t, pdf_err, lb_z, ub_z, + */ + __pyx_v_f_beg = (__pyx_t_1 / __pyx_v_st); + + /* "integrate.pxi":198 + * 0, 0, err_1d, maxRecursionDepth_sz)/st + * + * f_end = adaptiveSimpsons_1D(x, v, sv, a, z, ub_t, pdf_err, lb_z, ub_z, # <<<<<<<<<<<<<< + * 0, 0, err_1d, maxRecursionDepth_sz)/st + * f_mid = adaptiveSimpsons_1D(x, v, sv, a, z, (lb_t+ub_t)/2, pdf_err, lb_z, ub_z, + */ + __pyx_t_1 = __pyx_f_6wfpt_n_adaptiveSimpsons_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_ub_t, __pyx_v_pdf_err, __pyx_v_lb_z, __pyx_v_ub_z, 0.0, 0.0, __pyx_v_err_1d, __pyx_v_maxRecursionDepth_sz); if (unlikely(__pyx_t_1 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(4, 198, __pyx_L1_error) + + /* "integrate.pxi":199 + * + * f_end = adaptiveSimpsons_1D(x, v, sv, a, z, ub_t, pdf_err, lb_z, ub_z, + * 0, 0, err_1d, maxRecursionDepth_sz)/st # <<<<<<<<<<<<<< + * f_mid = adaptiveSimpsons_1D(x, v, sv, a, z, (lb_t+ub_t)/2, pdf_err, lb_z, ub_z, + * 0, 0, err_1d, maxRecursionDepth_sz)/st + */ + __pyx_v_f_end = (__pyx_t_1 / __pyx_v_st); + + /* "integrate.pxi":200 + * f_end = adaptiveSimpsons_1D(x, v, sv, a, z, ub_t, pdf_err, lb_z, ub_z, + * 0, 0, err_1d, maxRecursionDepth_sz)/st + * f_mid = adaptiveSimpsons_1D(x, v, sv, a, z, (lb_t+ub_t)/2, pdf_err, lb_z, ub_z, # <<<<<<<<<<<<<< + * 0, 0, err_1d, maxRecursionDepth_sz)/st + * S = (h/6)*(f_beg + 4*f_mid + f_end) + */ + __pyx_t_1 = __pyx_f_6wfpt_n_adaptiveSimpsons_1D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, ((__pyx_v_lb_t + __pyx_v_ub_t) / 2.0), __pyx_v_pdf_err, __pyx_v_lb_z, __pyx_v_ub_z, 0.0, 0.0, __pyx_v_err_1d, __pyx_v_maxRecursionDepth_sz); if (unlikely(__pyx_t_1 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(4, 200, __pyx_L1_error) + + /* "integrate.pxi":201 + * 0, 0, err_1d, maxRecursionDepth_sz)/st + * f_mid = adaptiveSimpsons_1D(x, v, sv, a, z, (lb_t+ub_t)/2, pdf_err, lb_z, ub_z, + * 0, 0, err_1d, maxRecursionDepth_sz)/st # <<<<<<<<<<<<<< + * S = (h/6)*(f_beg + 4*f_mid + f_end) + * cdef double res = adaptiveSimpsonsAux_2D(x, v, sv, a, z, t, pdf_err, err_1d, + */ + __pyx_v_f_mid = (__pyx_t_1 / __pyx_v_st); + + /* "integrate.pxi":202 + * f_mid = adaptiveSimpsons_1D(x, v, sv, a, z, (lb_t+ub_t)/2, pdf_err, lb_z, ub_z, + * 0, 0, err_1d, maxRecursionDepth_sz)/st + * S = (h/6)*(f_beg + 4*f_mid + f_end) # <<<<<<<<<<<<<< + * cdef double res = adaptiveSimpsonsAux_2D(x, v, sv, a, z, t, pdf_err, err_1d, + * lb_z, ub_z, lb_t, ub_t, st, err_2d, + */ + __pyx_v_S = ((__pyx_v_h / 6.0) * ((__pyx_v_f_beg + (4.0 * __pyx_v_f_mid)) + __pyx_v_f_end)); + + /* "integrate.pxi":203 + * 0, 0, err_1d, maxRecursionDepth_sz)/st + * S = (h/6)*(f_beg + 4*f_mid + f_end) + * cdef double res = adaptiveSimpsonsAux_2D(x, v, sv, a, z, t, pdf_err, err_1d, # <<<<<<<<<<<<<< + * lb_z, ub_z, lb_t, ub_t, st, err_2d, + * S, f_beg, f_end, f_mid, maxRecursionDepth_sz, maxRecursionDepth_st) + */ + __pyx_t_1 = __pyx_f_6wfpt_n_adaptiveSimpsonsAux_2D(__pyx_v_x, __pyx_v_v, __pyx_v_sv, __pyx_v_a, __pyx_v_z, __pyx_v_t, __pyx_v_pdf_err, __pyx_v_err_1d, __pyx_v_lb_z, __pyx_v_ub_z, __pyx_v_lb_t, __pyx_v_ub_t, __pyx_v_st, __pyx_v_err_2d, __pyx_v_S, __pyx_v_f_beg, __pyx_v_f_end, __pyx_v_f_mid, __pyx_v_maxRecursionDepth_sz, __pyx_v_maxRecursionDepth_st); if (unlikely(__pyx_t_1 == ((double)-1) && __Pyx_ErrOccurredWithGIL())) __PYX_ERR(4, 203, __pyx_L1_error) + __pyx_v_res = __pyx_t_1; + + /* "integrate.pxi":206 + * lb_z, ub_z, lb_t, ub_t, st, err_2d, + * S, f_beg, f_end, f_mid, maxRecursionDepth_sz, maxRecursionDepth_st) + * return res # <<<<<<<<<<<<<< + */ + __pyx_r = __pyx_v_res; + goto __pyx_L0; + + /* "integrate.pxi":181 + * + * + * cdef double adaptiveSimpsons_2D(double x, double v, double sv, double a, double z, double t, # <<<<<<<<<<<<<< + * double pdf_err, double lb_z, double ub_z, double lb_t, double ub_t, + * double simps_err, int maxRecursionDepth_sz, int maxRecursionDepth_st) nogil: + */ + + /* function exit code */ + __pyx_L1_error:; + #ifdef WITH_THREAD + __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); + #endif + __Pyx_AddTraceback("wfpt_n.adaptiveSimpsons_2D", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = -1; + #ifdef WITH_THREAD + __Pyx_PyGILState_Release(__pyx_gilstate_save); + #endif + __pyx_L0:; + return __pyx_r; 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+ case 13: + if (kw_args > 0) { + PyObject* value = __Pyx_GetKwValue_FASTCALL(__pyx_kwds, __pyx_kwvalues, __pyx_n_s_simps_err); + if (value) { values[13] = __Pyx_Arg_NewRef_FASTCALL(value); kw_args--; } + else if (unlikely(PyErr_Occurred())) __PYX_ERR(0, 32, __pyx_L3_error) + } + CYTHON_FALLTHROUGH; + case 14: + if (kw_args > 0) { + PyObject* value = __Pyx_GetKwValue_FASTCALL(__pyx_kwds, __pyx_kwvalues, __pyx_n_s_p_outlier); + if (value) { values[14] = __Pyx_Arg_NewRef_FASTCALL(value); kw_args--; } + else if (unlikely(PyErr_Occurred())) __PYX_ERR(0, 32, __pyx_L3_error) + } + CYTHON_FALLTHROUGH; + case 15: + if (kw_args > 0) { + PyObject* value = __Pyx_GetKwValue_FASTCALL(__pyx_kwds, __pyx_kwvalues, __pyx_n_s_w_outlier); + if (value) { values[15] = __Pyx_Arg_NewRef_FASTCALL(value); kw_args--; } + else if (unlikely(PyErr_Occurred())) __PYX_ERR(0, 32, __pyx_L3_error) + } + } + if (unlikely(kw_args > 0)) { + const Py_ssize_t kwd_pos_args = __pyx_nargs; + if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_kwvalues, __pyx_pyargnames, 0, values + 0, kwd_pos_args, "pdf_array") < 0)) __PYX_ERR(0, 32, __pyx_L3_error) + } + } else { + switch (__pyx_nargs) { + case 16: values[15] = __Pyx_Arg_FASTCALL(__pyx_args, 15); 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if (unlikely((__pyx_v_st == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 33, __pyx_L3_error) + if (values[8]) { + __pyx_v_err = __pyx_PyFloat_AsDouble(values[8]); if (unlikely((__pyx_v_err == (double)-1) && PyErr_Occurred())) __PYX_ERR(0, 33, __pyx_L3_error) + } else { + __pyx_v_err = ((double)((double)1e-4)); + } + if (values[9]) { + __pyx_v_logp = __Pyx_PyObject_IsTrue(values[9]); if (unlikely((__pyx_v_logp == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 33, __pyx_L3_error) + } else { + __pyx_v_logp = ((int)((int)0)); + } + if (values[10]) { + __pyx_v_n_st = __Pyx_PyInt_As_int(values[10]); if (unlikely((__pyx_v_n_st == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 33, __pyx_L3_error) + } else { + __pyx_v_n_st = ((int)((int)2)); + } + if (values[11]) { + __pyx_v_n_sz = __Pyx_PyInt_As_int(values[11]); if (unlikely((__pyx_v_n_sz == (int)-1) && PyErr_Occurred())) __PYX_ERR(0, 33, __pyx_L3_error) + } else { + __pyx_v_n_sz = ((int)((int)2)); + } + if (values[12]) { + __pyx_v_use_adaptive = __Pyx_PyObject_IsTrue(values[12]); 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__PYX_ERR(0, 392, __pyx_L3_error) + } + CYTHON_FALLTHROUGH; + case 9: + if (likely((values[9] = __Pyx_GetKwValue_FASTCALL(__pyx_kwds, __pyx_kwvalues, __pyx_n_s_sz)) != 0)) { + (void)__Pyx_Arg_NewRef_FASTCALL(values[9]); + kw_args--; + } + else if (unlikely(PyErr_Occurred())) __PYX_ERR(0, 392, __pyx_L3_error) + else { + __Pyx_RaiseArgtupleInvalid("wiener_like_multi_rlddm", 0, 14, 21, 9); __PYX_ERR(0, 392, __pyx_L3_error) + } + CYTHON_FALLTHROUGH; + case 10: + if (likely((values[10] = __Pyx_GetKwValue_FASTCALL(__pyx_kwds, __pyx_kwvalues, __pyx_n_s_t)) != 0)) { + (void)__Pyx_Arg_NewRef_FASTCALL(values[10]); + kw_args--; + } + else if (unlikely(PyErr_Occurred())) __PYX_ERR(0, 392, __pyx_L3_error) + else { + __Pyx_RaiseArgtupleInvalid("wiener_like_multi_rlddm", 0, 14, 21, 10); __PYX_ERR(0, 392, __pyx_L3_error) + } + CYTHON_FALLTHROUGH; + case 11: + if (likely((values[11] = __Pyx_GetKwValue_FASTCALL(__pyx_kwds, __pyx_kwvalues, __pyx_n_s_st)) != 0)) { + (void)__Pyx_Arg_NewRef_FASTCALL(values[11]); 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/*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_array, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + 0, /*tp_repr*/ + 0, /*tp_as_number*/ + &__pyx_tp_as_sequence_array, /*tp_as_sequence*/ + &__pyx_tp_as_mapping_array, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + 0, /*tp_str*/ + __pyx_tp_getattro_array, /*tp_getattro*/ + 0, /*tp_setattro*/ + &__pyx_tp_as_buffer_array, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_SEQUENCE, /*tp_flags*/ + 0, /*tp_doc*/ + 0, /*tp_traverse*/ + 0, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_array, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_array, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + #if !CYTHON_USE_TYPE_SPECS + 0, /*tp_dictoffset*/ + #endif + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_array, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + #if CYTHON_USE_TP_FINALIZE + 0, /*tp_finalize*/ + #else + NULL, /*tp_finalize*/ + #endif + #endif + #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, /*tp_vectorcall*/ + #endif + #if __PYX_NEED_TP_PRINT_SLOT == 1 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030C0000 + 0, /*tp_watched*/ + #endif + #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, /*tp_pypy_flags*/ + #endif +}; +#endif + +static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { + struct __pyx_MemviewEnum_obj *p; + PyObject *o; + #if CYTHON_COMPILING_IN_LIMITED_API + allocfunc alloc_func = (allocfunc)PyType_GetSlot(t, Py_tp_alloc); + o = alloc_func(t, 0); + #else + if (likely(!__Pyx_PyType_HasFeature(t, Py_TPFLAGS_IS_ABSTRACT))) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + #endif + p = ((struct __pyx_MemviewEnum_obj *)o); + p->name = Py_None; Py_INCREF(Py_None); + return o; +} + +static void __pyx_tp_dealloc_Enum(PyObject *o) { + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) { + if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc_Enum) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + } + #endif + PyObject_GC_UnTrack(o); + Py_CLEAR(p->name); + #if CYTHON_USE_TYPE_SLOTS || CYTHON_COMPILING_IN_PYPY + (*Py_TYPE(o)->tp_free)(o); + #else + { + freefunc tp_free = (freefunc)PyType_GetSlot(Py_TYPE(o), Py_tp_free); + if (tp_free) tp_free(o); + } + #endif +} + +static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + if (p->name) { + e = (*v)(p->name, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_Enum(PyObject *o) { + PyObject* tmp; + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + tmp = ((PyObject*)p->name); + p->name = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + return 0; +} + +static PyObject *__pyx_specialmethod___pyx_MemviewEnum___repr__(PyObject *self, CYTHON_UNUSED PyObject *arg) { + return __pyx_MemviewEnum___repr__(self); +} + +static PyMethodDef __pyx_methods_Enum[] = { + {"__repr__", (PyCFunction)__pyx_specialmethod___pyx_MemviewEnum___repr__, METH_NOARGS|METH_COEXIST, 0}, + {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {0, 0, 0, 0} +}; +#if CYTHON_USE_TYPE_SPECS +static PyType_Slot __pyx_type___pyx_MemviewEnum_slots[] = { + {Py_tp_dealloc, (void *)__pyx_tp_dealloc_Enum}, + {Py_tp_repr, (void *)__pyx_MemviewEnum___repr__}, + {Py_tp_traverse, (void *)__pyx_tp_traverse_Enum}, + {Py_tp_clear, (void *)__pyx_tp_clear_Enum}, + {Py_tp_methods, (void *)__pyx_methods_Enum}, + {Py_tp_init, (void *)__pyx_MemviewEnum___init__}, + {Py_tp_new, (void *)__pyx_tp_new_Enum}, + {0, 0}, +}; +static PyType_Spec __pyx_type___pyx_MemviewEnum_spec = { + "wfpt_n.Enum", + sizeof(struct __pyx_MemviewEnum_obj), + 0, + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, + __pyx_type___pyx_MemviewEnum_slots, +}; +#else + +static PyTypeObject __pyx_type___pyx_MemviewEnum = { + PyVarObject_HEAD_INIT(0, 0) + "wfpt_n.""Enum", /*tp_name*/ + sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_Enum, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + __pyx_MemviewEnum___repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + 0, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + 0, /*tp_doc*/ + __pyx_tp_traverse_Enum, /*tp_traverse*/ + __pyx_tp_clear_Enum, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_Enum, /*tp_methods*/ + 0, /*tp_members*/ + 0, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + #if !CYTHON_USE_TYPE_SPECS + 0, /*tp_dictoffset*/ + #endif + __pyx_MemviewEnum___init__, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_Enum, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + #if CYTHON_USE_TP_FINALIZE + 0, /*tp_finalize*/ + #else + NULL, /*tp_finalize*/ + #endif + #endif + #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, /*tp_vectorcall*/ + #endif + #if __PYX_NEED_TP_PRINT_SLOT == 1 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030C0000 + 0, /*tp_watched*/ + #endif + #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, /*tp_pypy_flags*/ + #endif +}; +#endif +static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview; + +static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_memoryview_obj *p; + PyObject *o; + #if CYTHON_COMPILING_IN_LIMITED_API + allocfunc alloc_func = (allocfunc)PyType_GetSlot(t, Py_tp_alloc); + o = alloc_func(t, 0); + #else + if (likely(!__Pyx_PyType_HasFeature(t, Py_TPFLAGS_IS_ABSTRACT))) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + #endif + p = ((struct __pyx_memoryview_obj *)o); + p->__pyx_vtab = __pyx_vtabptr_memoryview; + p->obj = Py_None; Py_INCREF(Py_None); + p->_size = Py_None; Py_INCREF(Py_None); + p->_array_interface = Py_None; Py_INCREF(Py_None); + p->view.obj = NULL; + if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad; + return o; + bad: + Py_DECREF(o); o = 0; + return NULL; +} + +static void __pyx_tp_dealloc_memoryview(PyObject *o) { + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) { + if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc_memoryview) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); + __pyx_memoryview___dealloc__(o); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->obj); + Py_CLEAR(p->_size); + Py_CLEAR(p->_array_interface); + #if CYTHON_USE_TYPE_SLOTS || CYTHON_COMPILING_IN_PYPY + (*Py_TYPE(o)->tp_free)(o); + #else + { + freefunc tp_free = (freefunc)PyType_GetSlot(Py_TYPE(o), Py_tp_free); + if (tp_free) tp_free(o); + } + #endif +} + +static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + if (p->obj) { + e = (*v)(p->obj, a); if (e) return e; + } + if (p->_size) { + e = (*v)(p->_size, a); if (e) return e; + } + if (p->_array_interface) { + e = (*v)(p->_array_interface, a); if (e) return e; + } + if (p->view.obj) { + e = (*v)(p->view.obj, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_memoryview(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + tmp = ((PyObject*)p->obj); + p->obj = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_size); + p->_size = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_array_interface); + p->_array_interface = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + Py_CLEAR(p->view.obj); + return 0; +} +static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) { + PyObject *r; + PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; + r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); + Py_DECREF(x); + return r; +} + +static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) { + if (v) { + return __pyx_memoryview___setitem__(o, i, v); + } + else { + __Pyx_TypeName o_type_name; + o_type_name = __Pyx_PyType_GetName(Py_TYPE(o)); + PyErr_Format(PyExc_NotImplementedError, + "Subscript deletion not supported by " __Pyx_FMT_TYPENAME, o_type_name); + __Pyx_DECREF_TypeName(o_type_name); + return -1; + } +} + +static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o); +} + +static PyObject *__pyx_specialmethod___pyx_memoryview___repr__(PyObject *self, CYTHON_UNUSED PyObject *arg) { + return __pyx_memoryview___repr__(self); +} + +static PyMethodDef __pyx_methods_memoryview[] = { + {"__repr__", (PyCFunction)__pyx_specialmethod___pyx_memoryview___repr__, METH_NOARGS|METH_COEXIST, 0}, + {"is_c_contig", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_is_c_contig, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"is_f_contig", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_is_f_contig, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"copy", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_copy, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"copy_fortran", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_copy_fortran, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryview_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryview_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets_memoryview[] = { + {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0}, + {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0}, + {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0}, + {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0}, + {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0}, + {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0}, + {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0}, + {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0}, + {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; +#if CYTHON_USE_TYPE_SPECS +#if !CYTHON_COMPILING_IN_LIMITED_API + +static PyBufferProcs __pyx_tp_as_buffer_memoryview = { + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getreadbuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getwritebuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getsegcount*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getcharbuffer*/ + #endif + __pyx_memoryview_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; +#endif +static PyType_Slot __pyx_type___pyx_memoryview_slots[] = { + {Py_tp_dealloc, (void *)__pyx_tp_dealloc_memoryview}, + {Py_tp_repr, (void *)__pyx_memoryview___repr__}, + {Py_sq_length, (void *)__pyx_memoryview___len__}, + {Py_sq_item, (void *)__pyx_sq_item_memoryview}, + {Py_mp_length, (void *)__pyx_memoryview___len__}, + {Py_mp_subscript, (void *)__pyx_memoryview___getitem__}, + {Py_mp_ass_subscript, (void *)__pyx_mp_ass_subscript_memoryview}, + {Py_tp_str, (void *)__pyx_memoryview___str__}, + #if defined(Py_bf_getbuffer) + {Py_bf_getbuffer, (void *)__pyx_memoryview_getbuffer}, + #endif + {Py_tp_traverse, (void *)__pyx_tp_traverse_memoryview}, + {Py_tp_clear, (void *)__pyx_tp_clear_memoryview}, + {Py_tp_methods, (void *)__pyx_methods_memoryview}, + {Py_tp_getset, (void *)__pyx_getsets_memoryview}, + {Py_tp_new, (void *)__pyx_tp_new_memoryview}, + {0, 0}, +}; +static PyType_Spec __pyx_type___pyx_memoryview_spec = { + "wfpt_n.memoryview", + sizeof(struct __pyx_memoryview_obj), + 0, + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, + __pyx_type___pyx_memoryview_slots, +}; +#else + +static PySequenceMethods __pyx_tp_as_sequence_memoryview = { + __pyx_memoryview___len__, /*sq_length*/ + 0, /*sq_concat*/ + 0, /*sq_repeat*/ + __pyx_sq_item_memoryview, /*sq_item*/ + 0, /*sq_slice*/ + 0, /*sq_ass_item*/ + 0, /*sq_ass_slice*/ + 0, /*sq_contains*/ + 0, /*sq_inplace_concat*/ + 0, /*sq_inplace_repeat*/ +}; + +static PyMappingMethods __pyx_tp_as_mapping_memoryview = { + __pyx_memoryview___len__, /*mp_length*/ + __pyx_memoryview___getitem__, /*mp_subscript*/ + __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/ +}; + +static PyBufferProcs __pyx_tp_as_buffer_memoryview = { + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getreadbuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getwritebuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getsegcount*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getcharbuffer*/ + #endif + __pyx_memoryview_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; + +static PyTypeObject __pyx_type___pyx_memoryview = { + PyVarObject_HEAD_INIT(0, 0) + "wfpt_n.""memoryview", /*tp_name*/ + sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_memoryview, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + __pyx_memoryview___repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/ + &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + __pyx_memoryview___str__, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + 0, /*tp_doc*/ + __pyx_tp_traverse_memoryview, /*tp_traverse*/ + __pyx_tp_clear_memoryview, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_memoryview, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_memoryview, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + #if !CYTHON_USE_TYPE_SPECS + 0, /*tp_dictoffset*/ + #endif + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_memoryview, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + #if CYTHON_USE_TP_FINALIZE + 0, /*tp_finalize*/ + #else + NULL, /*tp_finalize*/ + #endif + #endif + #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, /*tp_vectorcall*/ + #endif + #if __PYX_NEED_TP_PRINT_SLOT == 1 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030C0000 + 0, /*tp_watched*/ + #endif + #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, /*tp_pypy_flags*/ + #endif +}; +#endif +static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice; + +static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_memoryviewslice_obj *p; + PyObject *o = __pyx_tp_new_memoryview(t, a, k); + if (unlikely(!o)) return 0; + p = ((struct __pyx_memoryviewslice_obj *)o); + p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice; + new((void*)&(p->from_slice)) __Pyx_memviewslice(); + p->from_object = Py_None; Py_INCREF(Py_None); + p->from_slice.memview = NULL; + return o; +} + +static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) { + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) { + if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc__memoryviewslice) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); + __pyx_memoryviewslice___dealloc__(o); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); + PyErr_Restore(etype, eval, etb); + } + __Pyx_call_destructor(p->from_slice); + Py_CLEAR(p->from_object); + PyObject_GC_Track(o); + __pyx_tp_dealloc_memoryview(o); +} + +static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e; + if (p->from_object) { + e = (*v)(p->from_object, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear__memoryviewslice(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + __pyx_tp_clear_memoryview(o); + tmp = ((PyObject*)p->from_object); + p->from_object = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + __PYX_XCLEAR_MEMVIEW(&p->from_slice, 1); + return 0; +} + +static PyMethodDef __pyx_methods__memoryviewslice[] = { + {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {0, 0, 0, 0} +}; +#if CYTHON_USE_TYPE_SPECS +static PyType_Slot __pyx_type___pyx_memoryviewslice_slots[] = { + {Py_tp_dealloc, (void *)__pyx_tp_dealloc__memoryviewslice}, + {Py_tp_doc, (void *)PyDoc_STR("Internal class for passing memoryview slices to Python")}, + {Py_tp_traverse, (void *)__pyx_tp_traverse__memoryviewslice}, + {Py_tp_clear, (void *)__pyx_tp_clear__memoryviewslice}, + {Py_tp_methods, (void *)__pyx_methods__memoryviewslice}, + {Py_tp_new, (void *)__pyx_tp_new__memoryviewslice}, + {0, 0}, +}; +static PyType_Spec __pyx_type___pyx_memoryviewslice_spec = { + "wfpt_n._memoryviewslice", + sizeof(struct __pyx_memoryviewslice_obj), + 0, + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC|Py_TPFLAGS_SEQUENCE, + __pyx_type___pyx_memoryviewslice_slots, +}; +#else + +static PyTypeObject __pyx_type___pyx_memoryviewslice = { + PyVarObject_HEAD_INIT(0, 0) + "wfpt_n.""_memoryviewslice", /*tp_name*/ + sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + #if CYTHON_COMPILING_IN_PYPY || 0 + __pyx_memoryview___repr__, /*tp_repr*/ + #else + 0, /*tp_repr*/ + #endif + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + #if CYTHON_COMPILING_IN_PYPY || 0 + __pyx_memoryview___str__, /*tp_str*/ + #else + 0, /*tp_str*/ + #endif + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC|Py_TPFLAGS_SEQUENCE, /*tp_flags*/ + PyDoc_STR("Internal class for passing memoryview slices to Python"), /*tp_doc*/ + __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/ + __pyx_tp_clear__memoryviewslice, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods__memoryviewslice, /*tp_methods*/ + 0, /*tp_members*/ + 0, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + #if !CYTHON_USE_TYPE_SPECS + 0, /*tp_dictoffset*/ + #endif + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new__memoryviewslice, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + #if CYTHON_USE_TP_FINALIZE + 0, /*tp_finalize*/ + #else + NULL, /*tp_finalize*/ + #endif + #endif + #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, /*tp_vectorcall*/ + #endif + #if __PYX_NEED_TP_PRINT_SLOT == 1 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030C0000 + 0, /*tp_watched*/ + #endif + #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, /*tp_pypy_flags*/ + #endif +}; +#endif + +static PyMethodDef __pyx_methods[] = { + {0, 0, 0, 0} +}; +#ifndef CYTHON_SMALL_CODE +#if defined(__clang__) + #define CYTHON_SMALL_CODE +#elif defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 3)) + #define CYTHON_SMALL_CODE __attribute__((cold)) +#else + #define CYTHON_SMALL_CODE +#endif +#endif +/* #### Code section: pystring_table ### */ + +static int __Pyx_CreateStringTabAndInitStrings(void) { + __Pyx_StringTabEntry __pyx_string_tab[] = { + {&__pyx_kp_u_, __pyx_k_, sizeof(__pyx_k_), 0, 1, 0, 0}, + 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+ if (value) { + #if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(((PyBaseExceptionObject*) value)->traceback != tb)) + #endif + PyException_SetTraceback(value, tb); + } + tmp_value = tstate->current_exception; + tstate->current_exception = value; + Py_XDECREF(tmp_value); + Py_XDECREF(type); + Py_XDECREF(tb); +#else + PyObject *tmp_type, *tmp_value, *tmp_tb; + tmp_type = tstate->curexc_type; + tmp_value = tstate->curexc_value; + tmp_tb = tstate->curexc_traceback; + tstate->curexc_type = type; + tstate->curexc_value = value; + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#endif +} +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { +#if PY_VERSION_HEX >= 0x030C00A6 + PyObject* exc_value; + exc_value = tstate->current_exception; + tstate->current_exception = 0; + *value = exc_value; + *type = NULL; + *tb = NULL; + if (exc_value) { + *type = (PyObject*) Py_TYPE(exc_value); + Py_INCREF(*type); + #if CYTHON_COMPILING_IN_CPYTHON + *tb = ((PyBaseExceptionObject*) exc_value)->traceback; + Py_XINCREF(*tb); + #else + *tb = PyException_GetTraceback(exc_value); + #endif + } +#else + *type = tstate->curexc_type; + *value = tstate->curexc_value; + *tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +#endif +} +#endif + +/* PyObjectGetAttrStr */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro)) + return tp->tp_getattro(obj, attr_name); +#if PY_MAJOR_VERSION < 3 + if (likely(tp->tp_getattr)) + return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); +#endif + return PyObject_GetAttr(obj, attr_name); +} +#endif + +/* PyObjectGetAttrStrNoError */ +#if __PYX_LIMITED_VERSION_HEX < 0x030d00A1 +static void __Pyx_PyObject_GetAttrStr_ClearAttributeError(void) { + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + if (likely(__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) + __Pyx_PyErr_Clear(); +} +#endif +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name) { + PyObject *result; +#if __PYX_LIMITED_VERSION_HEX >= 0x030d00A1 + (void) PyObject_GetOptionalAttr(obj, attr_name, &result); + return result; +#else +#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_TYPE_SLOTS && PY_VERSION_HEX >= 0x030700B1 + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro == PyObject_GenericGetAttr)) { + return _PyObject_GenericGetAttrWithDict(obj, attr_name, NULL, 1); + } +#endif + result = __Pyx_PyObject_GetAttrStr(obj, attr_name); + if (unlikely(!result)) { + __Pyx_PyObject_GetAttrStr_ClearAttributeError(); + } + return result; +#endif +} + +/* GetBuiltinName */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name) { + PyObject* result = __Pyx_PyObject_GetAttrStrNoError(__pyx_b, name); + if (unlikely(!result) && !PyErr_Occurred()) { + PyErr_Format(PyExc_NameError, +#if PY_MAJOR_VERSION >= 3 + "name '%U' is not defined", name); +#else + "name '%.200s' is not defined", PyString_AS_STRING(name)); +#endif + } + return result; +} + +/* TupleAndListFromArray */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE void __Pyx_copy_object_array(PyObject *const *CYTHON_RESTRICT src, PyObject** CYTHON_RESTRICT dest, Py_ssize_t length) { + PyObject *v; + Py_ssize_t i; + for (i = 0; i < length; i++) { + v = dest[i] = src[i]; + Py_INCREF(v); + } +} +static CYTHON_INLINE PyObject * +__Pyx_PyTuple_FromArray(PyObject *const *src, Py_ssize_t n) +{ + PyObject *res; + if (n <= 0) { + Py_INCREF(__pyx_empty_tuple); + return __pyx_empty_tuple; + } + res = PyTuple_New(n); + if (unlikely(res == NULL)) return NULL; + __Pyx_copy_object_array(src, ((PyTupleObject*)res)->ob_item, n); + return res; +} +static CYTHON_INLINE PyObject * +__Pyx_PyList_FromArray(PyObject *const *src, Py_ssize_t n) +{ + PyObject *res; + if (n <= 0) { + return PyList_New(0); + } + res = PyList_New(n); + if (unlikely(res == NULL)) return NULL; + __Pyx_copy_object_array(src, ((PyListObject*)res)->ob_item, n); + return res; +} +#endif + +/* BytesEquals */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API + return PyObject_RichCompareBool(s1, s2, equals); +#else + if (s1 == s2) { + return (equals == Py_EQ); + } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { + const char *ps1, *ps2; + Py_ssize_t length = PyBytes_GET_SIZE(s1); + if (length != PyBytes_GET_SIZE(s2)) + return (equals == Py_NE); + ps1 = PyBytes_AS_STRING(s1); + ps2 = PyBytes_AS_STRING(s2); + if (ps1[0] != ps2[0]) { + return (equals == Py_NE); + } else if (length == 1) { + return (equals == Py_EQ); + } else { + int result; +#if CYTHON_USE_UNICODE_INTERNALS && (PY_VERSION_HEX < 0x030B0000) + Py_hash_t hash1, hash2; + hash1 = ((PyBytesObject*)s1)->ob_shash; + hash2 = ((PyBytesObject*)s2)->ob_shash; + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + return (equals == Py_NE); + } +#endif + result = memcmp(ps1, ps2, (size_t)length); + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { + return (equals == Py_NE); + } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { + return (equals == Py_NE); + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +#endif +} + +/* UnicodeEquals */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API + return PyObject_RichCompareBool(s1, s2, equals); +#else +#if PY_MAJOR_VERSION < 3 + PyObject* owned_ref = NULL; +#endif + int s1_is_unicode, s2_is_unicode; + if (s1 == s2) { + goto return_eq; + } + s1_is_unicode = PyUnicode_CheckExact(s1); + s2_is_unicode = PyUnicode_CheckExact(s2); +#if PY_MAJOR_VERSION < 3 + if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { + owned_ref = PyUnicode_FromObject(s2); + if (unlikely(!owned_ref)) + return -1; + s2 = owned_ref; + s2_is_unicode = 1; + } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { + owned_ref = PyUnicode_FromObject(s1); + if (unlikely(!owned_ref)) + return -1; + s1 = owned_ref; + s1_is_unicode = 1; + } else if (((!s2_is_unicode) & (!s1_is_unicode))) { + return __Pyx_PyBytes_Equals(s1, s2, equals); + } +#endif + if (s1_is_unicode & s2_is_unicode) { + Py_ssize_t length; + int kind; + void *data1, *data2; + if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) + return -1; + length = __Pyx_PyUnicode_GET_LENGTH(s1); + if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { + goto return_ne; + } +#if CYTHON_USE_UNICODE_INTERNALS + { + Py_hash_t hash1, hash2; + #if CYTHON_PEP393_ENABLED + hash1 = ((PyASCIIObject*)s1)->hash; + hash2 = ((PyASCIIObject*)s2)->hash; + #else + hash1 = ((PyUnicodeObject*)s1)->hash; + hash2 = ((PyUnicodeObject*)s2)->hash; + #endif + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + goto return_ne; + } + } +#endif + kind = __Pyx_PyUnicode_KIND(s1); + if (kind != __Pyx_PyUnicode_KIND(s2)) { + goto return_ne; + } + data1 = __Pyx_PyUnicode_DATA(s1); + data2 = __Pyx_PyUnicode_DATA(s2); + if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { + goto return_ne; + } else if (length == 1) { + goto return_eq; + } else { + int result = memcmp(data1, data2, (size_t)(length * kind)); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & s2_is_unicode) { + goto return_ne; + } else if ((s2 == Py_None) & s1_is_unicode) { + goto return_ne; + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +return_eq: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ); +return_ne: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_NE); +#endif +} + +/* fastcall */ +#if CYTHON_METH_FASTCALL +static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues, PyObject *s) +{ + Py_ssize_t i, n = PyTuple_GET_SIZE(kwnames); + for (i = 0; i < n; i++) + { + if (s == PyTuple_GET_ITEM(kwnames, i)) return kwvalues[i]; + } + for (i = 0; i < n; i++) + { + int eq = __Pyx_PyUnicode_Equals(s, PyTuple_GET_ITEM(kwnames, i), Py_EQ); + if (unlikely(eq != 0)) { + if (unlikely(eq < 0)) return NULL; // error + return kwvalues[i]; + } + } + return NULL; // not found (no exception set) +} +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030d0000 +CYTHON_UNUSED static PyObject *__Pyx_KwargsAsDict_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues) { + Py_ssize_t i, nkwargs = PyTuple_GET_SIZE(kwnames); + PyObject *dict; + dict = PyDict_New(); + if (unlikely(!dict)) + return NULL; + for (i=0; i= 3 + "%s() got multiple values for keyword argument '%U'", func_name, kw_name); + #else + "%s() got multiple values for keyword argument '%s'", func_name, + PyString_AsString(kw_name)); + #endif +} + +/* ParseKeywords */ +static int __Pyx_ParseOptionalKeywords( + PyObject *kwds, + PyObject *const *kwvalues, + PyObject **argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject *key = 0, *value = 0; + Py_ssize_t pos = 0; + PyObject*** name; + PyObject*** first_kw_arg = argnames + num_pos_args; + int kwds_is_tuple = CYTHON_METH_FASTCALL && likely(PyTuple_Check(kwds)); + while (1) { + Py_XDECREF(key); key = NULL; + Py_XDECREF(value); value = NULL; + if (kwds_is_tuple) { + Py_ssize_t size; +#if CYTHON_ASSUME_SAFE_MACROS + size = PyTuple_GET_SIZE(kwds); +#else + size = PyTuple_Size(kwds); + if (size < 0) goto bad; +#endif + if (pos >= size) break; +#if CYTHON_AVOID_BORROWED_REFS + key = __Pyx_PySequence_ITEM(kwds, pos); + if (!key) goto bad; +#elif CYTHON_ASSUME_SAFE_MACROS + key = PyTuple_GET_ITEM(kwds, pos); +#else + key = PyTuple_GetItem(kwds, pos); + if (!key) goto bad; +#endif + value = kwvalues[pos]; + pos++; + } + else + { + if (!PyDict_Next(kwds, &pos, &key, &value)) break; +#if CYTHON_AVOID_BORROWED_REFS + Py_INCREF(key); +#endif + } + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; +#if CYTHON_AVOID_BORROWED_REFS + Py_INCREF(value); // transfer ownership of value to values + Py_DECREF(key); +#endif + key = NULL; + value = NULL; + continue; + } +#if !CYTHON_AVOID_BORROWED_REFS + Py_INCREF(key); +#endif + Py_INCREF(value); + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { + values[name-argnames] = value; +#if CYTHON_AVOID_BORROWED_REFS + value = NULL; // ownership transferred to values +#endif + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + if ((**argname == key) || ( + (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) + && _PyString_Eq(**argname, key))) { + goto arg_passed_twice; + } + argname++; + } + } + } else + #endif + if (likely(PyUnicode_Check(key))) { + while (*name) { + int cmp = ( + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key) + ); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; +#if CYTHON_AVOID_BORROWED_REFS + value = NULL; // ownership transferred to values +#endif + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + Py_XDECREF(key); + Py_XDECREF(value); + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + #if PY_MAJOR_VERSION < 3 + PyErr_Format(PyExc_TypeError, + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + PyErr_Format(PyExc_TypeError, + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + Py_XDECREF(key); + Py_XDECREF(value); + return -1; +} + +/* ArgTypeTest */ +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) +{ + __Pyx_TypeName type_name; + __Pyx_TypeName obj_type_name; + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + else if (exact) { + #if PY_MAJOR_VERSION == 2 + if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(__Pyx_TypeCheck(obj, type))) return 1; + } + type_name = __Pyx_PyType_GetName(type); + obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected " __Pyx_FMT_TYPENAME + ", got " __Pyx_FMT_TYPENAME ")", name, type_name, obj_type_name); + __Pyx_DECREF_TypeName(type_name); + __Pyx_DECREF_TypeName(obj_type_name); + return 0; +} + +/* RaiseException */ +#if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + __Pyx_PyThreadState_declare + CYTHON_UNUSED_VAR(cause); + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + if (PyType_Check(type)) { +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + } + __Pyx_PyThreadState_assign + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + int is_subclass = PyObject_IsSubclass(instance_class, type); + if (!is_subclass) { + instance_class = NULL; + } else if (unlikely(is_subclass == -1)) { + goto bad; + } else { + type = instance_class; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } + if (cause) { + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { + #if PY_VERSION_HEX >= 0x030C00A6 + PyException_SetTraceback(value, tb); + #elif CYTHON_FAST_THREAD_STATE + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#else + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +/* PyFunctionFastCall */ +#if CYTHON_FAST_PYCALL && !CYTHON_VECTORCALL +static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, + PyObject *globals) { + PyFrameObject *f; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject **fastlocals; + Py_ssize_t i; + PyObject *result; + assert(globals != NULL); + /* XXX Perhaps we should create a specialized + PyFrame_New() that doesn't take locals, but does + take builtins without sanity checking them. + */ + assert(tstate != NULL); + f = PyFrame_New(tstate, co, globals, NULL); + if (f == NULL) { + return NULL; + } + fastlocals = __Pyx_PyFrame_GetLocalsplus(f); + for (i = 0; i < na; i++) { + Py_INCREF(*args); + fastlocals[i] = *args++; + } + result = PyEval_EvalFrameEx(f,0); + ++tstate->recursion_depth; + Py_DECREF(f); + --tstate->recursion_depth; + return result; +} +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) { + PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); + PyObject *globals = PyFunction_GET_GLOBALS(func); + PyObject *argdefs = PyFunction_GET_DEFAULTS(func); + PyObject *closure; +#if PY_MAJOR_VERSION >= 3 + PyObject *kwdefs; +#endif + PyObject *kwtuple, **k; + PyObject **d; + Py_ssize_t nd; + Py_ssize_t nk; + PyObject *result; + assert(kwargs == NULL || PyDict_Check(kwargs)); + nk = kwargs ? PyDict_Size(kwargs) : 0; + #if PY_MAJOR_VERSION < 3 + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) { + return NULL; + } + #else + if (unlikely(Py_EnterRecursiveCall(" while calling a Python object"))) { + return NULL; + } + #endif + if ( +#if PY_MAJOR_VERSION >= 3 + co->co_kwonlyargcount == 0 && +#endif + likely(kwargs == NULL || nk == 0) && + co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { + if (argdefs == NULL && co->co_argcount == nargs) { + result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); + goto done; + } + else if (nargs == 0 && argdefs != NULL + && co->co_argcount == Py_SIZE(argdefs)) { + /* function called with no arguments, but all parameters have + a default value: use default values as arguments .*/ + args = &PyTuple_GET_ITEM(argdefs, 0); + result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); + goto done; + } + } + if (kwargs != NULL) { + Py_ssize_t pos, i; + kwtuple = PyTuple_New(2 * nk); + if (kwtuple == NULL) { + result = NULL; + goto done; + } + k = &PyTuple_GET_ITEM(kwtuple, 0); + pos = i = 0; + while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { + Py_INCREF(k[i]); + Py_INCREF(k[i+1]); + i += 2; + } + nk = i / 2; + } + else { + kwtuple = NULL; + k = NULL; + } + closure = PyFunction_GET_CLOSURE(func); +#if PY_MAJOR_VERSION >= 3 + kwdefs = PyFunction_GET_KW_DEFAULTS(func); +#endif + if (argdefs != NULL) { + d = &PyTuple_GET_ITEM(argdefs, 0); + nd = Py_SIZE(argdefs); + } + else { + d = NULL; + nd = 0; + } +#if PY_MAJOR_VERSION >= 3 + result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, + args, (int)nargs, + k, (int)nk, + d, (int)nd, kwdefs, closure); +#else + result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, + args, (int)nargs, + k, (int)nk, + d, (int)nd, closure); +#endif + Py_XDECREF(kwtuple); +done: + Py_LeaveRecursiveCall(); + return result; +} +#endif + +/* PyObjectCall */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *result; + ternaryfunc call = Py_TYPE(func)->tp_call; + if (unlikely(!call)) + return PyObject_Call(func, arg, kw); + #if PY_MAJOR_VERSION < 3 + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + #else + if (unlikely(Py_EnterRecursiveCall(" while calling a Python object"))) + return NULL; + #endif + result = (*call)(func, arg, kw); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectCallMethO */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { + PyObject *self, *result; + PyCFunction cfunc; + cfunc = __Pyx_CyOrPyCFunction_GET_FUNCTION(func); + self = __Pyx_CyOrPyCFunction_GET_SELF(func); + #if PY_MAJOR_VERSION < 3 + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + #else + if (unlikely(Py_EnterRecursiveCall(" while calling a Python object"))) + return NULL; + #endif + result = cfunc(self, arg); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectFastCall */ +#if PY_VERSION_HEX < 0x03090000 || CYTHON_COMPILING_IN_LIMITED_API +static PyObject* __Pyx_PyObject_FastCall_fallback(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs) { + PyObject *argstuple; + PyObject *result = 0; + size_t i; + argstuple = PyTuple_New((Py_ssize_t)nargs); + if (unlikely(!argstuple)) return NULL; + for (i = 0; i < nargs; i++) { + Py_INCREF(args[i]); + if (__Pyx_PyTuple_SET_ITEM(argstuple, (Py_ssize_t)i, args[i]) < 0) goto bad; + } + result = __Pyx_PyObject_Call(func, argstuple, kwargs); + bad: + Py_DECREF(argstuple); + return result; +} +#endif +static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t _nargs, PyObject *kwargs) { + Py_ssize_t nargs = __Pyx_PyVectorcall_NARGS(_nargs); +#if CYTHON_COMPILING_IN_CPYTHON + if (nargs == 0 && kwargs == NULL) { + if (__Pyx_CyOrPyCFunction_Check(func) && likely( __Pyx_CyOrPyCFunction_GET_FLAGS(func) & METH_NOARGS)) + return __Pyx_PyObject_CallMethO(func, NULL); + } + else if (nargs == 1 && kwargs == NULL) { + if (__Pyx_CyOrPyCFunction_Check(func) && likely( __Pyx_CyOrPyCFunction_GET_FLAGS(func) & METH_O)) + return __Pyx_PyObject_CallMethO(func, args[0]); + } +#endif + #if PY_VERSION_HEX < 0x030800B1 + #if CYTHON_FAST_PYCCALL + if (PyCFunction_Check(func)) { + if (kwargs) { + return _PyCFunction_FastCallDict(func, args, nargs, kwargs); + } else { + return _PyCFunction_FastCallKeywords(func, args, nargs, NULL); + } + } + #if PY_VERSION_HEX >= 0x030700A1 + if (!kwargs && __Pyx_IS_TYPE(func, &PyMethodDescr_Type)) { + return _PyMethodDescr_FastCallKeywords(func, args, nargs, NULL); + } + #endif + #endif + #if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs); + } + #endif + #endif + if (kwargs == NULL) { + #if CYTHON_VECTORCALL + #if PY_VERSION_HEX < 0x03090000 + vectorcallfunc f = _PyVectorcall_Function(func); + #else + vectorcallfunc f = PyVectorcall_Function(func); + #endif + if (f) { + return f(func, args, (size_t)nargs, NULL); + } + #elif defined(__Pyx_CyFunction_USED) && CYTHON_BACKPORT_VECTORCALL + if (__Pyx_CyFunction_CheckExact(func)) { + __pyx_vectorcallfunc f = __Pyx_CyFunction_func_vectorcall(func); + if (f) return f(func, args, (size_t)nargs, NULL); + } + #endif + } + if (nargs == 0) { + return __Pyx_PyObject_Call(func, __pyx_empty_tuple, kwargs); + } + #if PY_VERSION_HEX >= 0x03090000 && !CYTHON_COMPILING_IN_LIMITED_API + return PyObject_VectorcallDict(func, args, (size_t)nargs, kwargs); + #else + return __Pyx_PyObject_FastCall_fallback(func, args, (size_t)nargs, kwargs); + #endif +} + +/* RaiseUnexpectedTypeError */ +static int +__Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj) +{ + __Pyx_TypeName obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); + PyErr_Format(PyExc_TypeError, "Expected %s, got " __Pyx_FMT_TYPENAME, + expected, obj_type_name); + __Pyx_DECREF_TypeName(obj_type_name); + return 0; +} + +/* CIntToDigits */ +static const char DIGIT_PAIRS_10[2*10*10+1] = { + "00010203040506070809" + "10111213141516171819" + "20212223242526272829" + "30313233343536373839" + "40414243444546474849" + "50515253545556575859" + "60616263646566676869" + "70717273747576777879" + "80818283848586878889" + "90919293949596979899" +}; +static const char DIGIT_PAIRS_8[2*8*8+1] = { + "0001020304050607" + "1011121314151617" + "2021222324252627" + "3031323334353637" + "4041424344454647" + "5051525354555657" + "6061626364656667" + "7071727374757677" +}; +static const char DIGITS_HEX[2*16+1] = { + "0123456789abcdef" + "0123456789ABCDEF" +}; + +/* BuildPyUnicode */ +static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, char* chars, int clength, + int prepend_sign, char padding_char) { + PyObject *uval; + Py_ssize_t uoffset = ulength - clength; +#if CYTHON_USE_UNICODE_INTERNALS + Py_ssize_t i; +#if CYTHON_PEP393_ENABLED + void *udata; + uval = PyUnicode_New(ulength, 127); + if (unlikely(!uval)) return NULL; + udata = PyUnicode_DATA(uval); +#else + Py_UNICODE *udata; + uval = PyUnicode_FromUnicode(NULL, ulength); + if (unlikely(!uval)) return NULL; + udata = PyUnicode_AS_UNICODE(uval); +#endif + if (uoffset > 0) { + i = 0; + if (prepend_sign) { + __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, 0, '-'); + i++; + } + for (; i < uoffset; i++) { + __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, i, padding_char); + } + } + for (i=0; i < clength; i++) { + __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, uoffset+i, chars[i]); + } +#else + { + PyObject *sign = NULL, *padding = NULL; + uval = NULL; + if (uoffset > 0) { + prepend_sign = !!prepend_sign; + if (uoffset > prepend_sign) { + padding = PyUnicode_FromOrdinal(padding_char); + if (likely(padding) && uoffset > prepend_sign + 1) { + PyObject *tmp; + PyObject *repeat = PyInt_FromSsize_t(uoffset - prepend_sign); + if (unlikely(!repeat)) goto done_or_error; + tmp = PyNumber_Multiply(padding, repeat); + Py_DECREF(repeat); + Py_DECREF(padding); + padding = tmp; + } + if (unlikely(!padding)) goto done_or_error; + } + if (prepend_sign) { + sign = PyUnicode_FromOrdinal('-'); + if (unlikely(!sign)) goto done_or_error; + } + } + uval = PyUnicode_DecodeASCII(chars, clength, NULL); + if (likely(uval) && padding) { + PyObject *tmp = PyNumber_Add(padding, uval); + Py_DECREF(uval); + uval = tmp; + } + if (likely(uval) && sign) { + PyObject *tmp = PyNumber_Add(sign, uval); + Py_DECREF(uval); + uval = tmp; + } +done_or_error: + Py_XDECREF(padding); + Py_XDECREF(sign); + } +#endif + return uval; +} + +/* CIntToPyUnicode */ +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_int(int value, Py_ssize_t width, char padding_char, char format_char) { + char digits[sizeof(int)*3+2]; + char *dpos, *end = digits + sizeof(int)*3+2; + const char *hex_digits = DIGITS_HEX; + Py_ssize_t length, ulength; + int prepend_sign, last_one_off; + int remaining; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const int neg_one = (int) -1, const_zero = (int) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (format_char == 'X') { + hex_digits += 16; + format_char = 'x'; + } + remaining = value; + last_one_off = 0; + dpos = end; + do { + int digit_pos; + switch (format_char) { + case 'o': + digit_pos = abs((int)(remaining % (8*8))); + remaining = (int) (remaining / (8*8)); + dpos -= 2; + memcpy(dpos, DIGIT_PAIRS_8 + digit_pos * 2, 2); + last_one_off = (digit_pos < 8); + break; + case 'd': + digit_pos = abs((int)(remaining % (10*10))); + remaining = (int) (remaining / (10*10)); + dpos -= 2; + memcpy(dpos, DIGIT_PAIRS_10 + digit_pos * 2, 2); + last_one_off = (digit_pos < 10); + break; + case 'x': + *(--dpos) = hex_digits[abs((int)(remaining % 16))]; + remaining = (int) (remaining / 16); + break; + default: + assert(0); + break; + } + } while (unlikely(remaining != 0)); + assert(!last_one_off || *dpos == '0'); + dpos += last_one_off; + length = end - dpos; + ulength = length; + prepend_sign = 0; + if (!is_unsigned && value <= neg_one) { + if (padding_char == ' ' || width <= length + 1) { + *(--dpos) = '-'; + ++length; + } else { + prepend_sign = 1; + } + ++ulength; + } + if (width > ulength) { + ulength = width; + } + if (ulength == 1) { + return PyUnicode_FromOrdinal(*dpos); + } + return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char); +} + +/* CIntToPyUnicode */ +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_Py_ssize_t(Py_ssize_t value, Py_ssize_t width, char padding_char, char format_char) { + char digits[sizeof(Py_ssize_t)*3+2]; + char *dpos, *end = digits + sizeof(Py_ssize_t)*3+2; + const char *hex_digits = DIGITS_HEX; + Py_ssize_t length, ulength; + int prepend_sign, last_one_off; + Py_ssize_t remaining; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const Py_ssize_t neg_one = (Py_ssize_t) -1, const_zero = (Py_ssize_t) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (format_char == 'X') { + hex_digits += 16; + format_char = 'x'; + } + remaining = value; + last_one_off = 0; + dpos = end; + do { + int digit_pos; + switch (format_char) { + case 'o': + digit_pos = abs((int)(remaining % (8*8))); + remaining = (Py_ssize_t) (remaining / (8*8)); + dpos -= 2; + memcpy(dpos, DIGIT_PAIRS_8 + digit_pos * 2, 2); + last_one_off = (digit_pos < 8); + break; + case 'd': + digit_pos = abs((int)(remaining % (10*10))); + remaining = (Py_ssize_t) (remaining / (10*10)); + dpos -= 2; + memcpy(dpos, DIGIT_PAIRS_10 + digit_pos * 2, 2); + last_one_off = (digit_pos < 10); + break; + case 'x': + *(--dpos) = hex_digits[abs((int)(remaining % 16))]; + remaining = (Py_ssize_t) (remaining / 16); + break; + default: + assert(0); + break; + } + } while (unlikely(remaining != 0)); + assert(!last_one_off || *dpos == '0'); + dpos += last_one_off; + length = end - dpos; + ulength = length; + prepend_sign = 0; + if (!is_unsigned && value <= neg_one) { + if (padding_char == ' ' || width <= length + 1) { + *(--dpos) = '-'; + ++length; + } else { + prepend_sign = 1; + } + ++ulength; + } + if (width > ulength) { + ulength = width; + } + if (ulength == 1) { + return PyUnicode_FromOrdinal(*dpos); + } + return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char); +} + +/* JoinPyUnicode */ +static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength, + Py_UCS4 max_char) { +#if CYTHON_USE_UNICODE_INTERNALS && CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + PyObject *result_uval; + int result_ukind, kind_shift; + Py_ssize_t i, char_pos; + void *result_udata; + CYTHON_MAYBE_UNUSED_VAR(max_char); +#if CYTHON_PEP393_ENABLED + result_uval = PyUnicode_New(result_ulength, max_char); + if (unlikely(!result_uval)) return NULL; + result_ukind = (max_char <= 255) ? PyUnicode_1BYTE_KIND : (max_char <= 65535) ? PyUnicode_2BYTE_KIND : PyUnicode_4BYTE_KIND; + kind_shift = (result_ukind == PyUnicode_4BYTE_KIND) ? 2 : result_ukind - 1; + result_udata = PyUnicode_DATA(result_uval); +#else + result_uval = PyUnicode_FromUnicode(NULL, result_ulength); + if (unlikely(!result_uval)) return NULL; + result_ukind = sizeof(Py_UNICODE); + kind_shift = (result_ukind == 4) ? 2 : result_ukind - 1; + result_udata = PyUnicode_AS_UNICODE(result_uval); +#endif + assert(kind_shift == 2 || kind_shift == 1 || kind_shift == 0); + char_pos = 0; + for (i=0; i < value_count; i++) { + int ukind; + Py_ssize_t ulength; + void *udata; + PyObject *uval = PyTuple_GET_ITEM(value_tuple, i); + if (unlikely(__Pyx_PyUnicode_READY(uval))) + goto bad; + ulength = __Pyx_PyUnicode_GET_LENGTH(uval); + if (unlikely(!ulength)) + continue; + if (unlikely((PY_SSIZE_T_MAX >> kind_shift) - ulength < char_pos)) + goto overflow; + ukind = __Pyx_PyUnicode_KIND(uval); + udata = __Pyx_PyUnicode_DATA(uval); + if (!CYTHON_PEP393_ENABLED || ukind == result_ukind) { + memcpy((char *)result_udata + (char_pos << kind_shift), udata, (size_t) (ulength << kind_shift)); + } else { + #if PY_VERSION_HEX >= 0x030d0000 + if (unlikely(PyUnicode_CopyCharacters(result_uval, char_pos, uval, 0, ulength) < 0)) goto bad; + #elif CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030300F0 || defined(_PyUnicode_FastCopyCharacters) + _PyUnicode_FastCopyCharacters(result_uval, char_pos, uval, 0, ulength); + #else + Py_ssize_t j; + for (j=0; j < ulength; j++) { + Py_UCS4 uchar = __Pyx_PyUnicode_READ(ukind, udata, j); + __Pyx_PyUnicode_WRITE(result_ukind, result_udata, char_pos+j, uchar); + } + #endif + } + char_pos += ulength; + } + return result_uval; +overflow: + PyErr_SetString(PyExc_OverflowError, "join() result is too long for a Python string"); +bad: + Py_DECREF(result_uval); + return NULL; +#else + CYTHON_UNUSED_VAR(max_char); + CYTHON_UNUSED_VAR(result_ulength); + CYTHON_UNUSED_VAR(value_count); + return PyUnicode_Join(__pyx_empty_unicode, value_tuple); +#endif +} + +/* GetAttr */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { +#if CYTHON_USE_TYPE_SLOTS +#if PY_MAJOR_VERSION >= 3 + if (likely(PyUnicode_Check(n))) +#else + if (likely(PyString_Check(n))) +#endif + return __Pyx_PyObject_GetAttrStr(o, n); +#endif + return PyObject_GetAttr(o, n); +} + +/* GetItemInt */ +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { + PyObject *r; + if (unlikely(!j)) return NULL; + r = PyObject_GetItem(o, j); + Py_DECREF(j); + return r; +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyList_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { + PyObject *r = PyList_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyTuple_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS + if (is_list || PyList_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); + if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { + PyObject *r = PyList_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } + else if (PyTuple_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); + if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } else { + PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping; + PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence; + if (mm && mm->mp_subscript) { + PyObject *r, *key = PyInt_FromSsize_t(i); + if (unlikely(!key)) return NULL; + r = mm->mp_subscript(o, key); + Py_DECREF(key); + return r; + } + if (likely(sm && sm->sq_item)) { + if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) { + Py_ssize_t l = sm->sq_length(o); + if (likely(l >= 0)) { + i += l; + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return NULL; + PyErr_Clear(); + } + } + return sm->sq_item(o, i); + } + } +#else + if (is_list || !PyMapping_Check(o)) { + return PySequence_GetItem(o, i); + } +#endif + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +} + +/* PyObjectCallOneArg */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *args[2] = {NULL, arg}; + return __Pyx_PyObject_FastCall(func, args+1, 1 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET); +} + +/* ObjectGetItem */ +#if CYTHON_USE_TYPE_SLOTS +static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject *index) { + PyObject *runerr = NULL; + Py_ssize_t key_value; + key_value = __Pyx_PyIndex_AsSsize_t(index); + if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) { + return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1); + } + if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) { + __Pyx_TypeName index_type_name = __Pyx_PyType_GetName(Py_TYPE(index)); + PyErr_Clear(); + PyErr_Format(PyExc_IndexError, + "cannot fit '" __Pyx_FMT_TYPENAME "' into an index-sized integer", index_type_name); + __Pyx_DECREF_TypeName(index_type_name); + } + return NULL; +} +static PyObject *__Pyx_PyObject_GetItem_Slow(PyObject *obj, PyObject *key) { + __Pyx_TypeName obj_type_name; + if (likely(PyType_Check(obj))) { + PyObject *meth = __Pyx_PyObject_GetAttrStrNoError(obj, __pyx_n_s_class_getitem); + if (!meth) { + PyErr_Clear(); + } else { + PyObject *result = __Pyx_PyObject_CallOneArg(meth, key); + Py_DECREF(meth); + return result; + } + } + obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); + PyErr_Format(PyExc_TypeError, + "'" __Pyx_FMT_TYPENAME "' object is not subscriptable", obj_type_name); + __Pyx_DECREF_TypeName(obj_type_name); + return NULL; +} +static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key) { + PyTypeObject *tp = Py_TYPE(obj); + PyMappingMethods *mm = tp->tp_as_mapping; + PySequenceMethods *sm = tp->tp_as_sequence; + if (likely(mm && mm->mp_subscript)) { + return mm->mp_subscript(obj, key); + } + if (likely(sm && sm->sq_item)) { + return __Pyx_PyObject_GetIndex(obj, key); + } + return __Pyx_PyObject_GetItem_Slow(obj, key); +} +#endif + +/* KeywordStringCheck */ +static int __Pyx_CheckKeywordStrings( + PyObject *kw, + const char* function_name, + int kw_allowed) +{ + PyObject* key = 0; + Py_ssize_t pos = 0; +#if CYTHON_COMPILING_IN_PYPY + if (!kw_allowed && PyDict_Next(kw, &pos, &key, 0)) + goto invalid_keyword; + return 1; +#else + if (CYTHON_METH_FASTCALL && likely(PyTuple_Check(kw))) { + Py_ssize_t kwsize; +#if CYTHON_ASSUME_SAFE_MACROS + kwsize = PyTuple_GET_SIZE(kw); +#else + kwsize = PyTuple_Size(kw); + if (kwsize < 0) return 0; +#endif + if (unlikely(kwsize == 0)) + return 1; + if (!kw_allowed) { +#if CYTHON_ASSUME_SAFE_MACROS + key = PyTuple_GET_ITEM(kw, 0); +#else + key = PyTuple_GetItem(kw, pos); + if (!key) return 0; +#endif + goto invalid_keyword; + } +#if PY_VERSION_HEX < 0x03090000 + for (pos = 0; pos < kwsize; pos++) { +#if CYTHON_ASSUME_SAFE_MACROS + key = PyTuple_GET_ITEM(kw, pos); +#else + key = PyTuple_GetItem(kw, pos); + if (!key) return 0; +#endif + if (unlikely(!PyUnicode_Check(key))) + goto invalid_keyword_type; + } +#endif + return 1; + } + while (PyDict_Next(kw, &pos, &key, 0)) { + #if PY_MAJOR_VERSION < 3 + if (unlikely(!PyString_Check(key))) + #endif + if (unlikely(!PyUnicode_Check(key))) + goto invalid_keyword_type; + } + if (!kw_allowed && unlikely(key)) + goto invalid_keyword; + return 1; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + return 0; +#endif +invalid_keyword: + #if PY_MAJOR_VERSION < 3 + PyErr_Format(PyExc_TypeError, + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + PyErr_Format(PyExc_TypeError, + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif + return 0; +} + +/* DivInt[Py_ssize_t] */ +static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) { + Py_ssize_t q = a / b; + Py_ssize_t r = a - q*b; + q -= ((r != 0) & ((r ^ b) < 0)); + return q; +} + +/* GetAttr3 */ +#if __PYX_LIMITED_VERSION_HEX < 0x030d00A1 +static PyObject *__Pyx_GetAttr3Default(PyObject *d) { + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) + return NULL; + __Pyx_PyErr_Clear(); + Py_INCREF(d); + return d; +} +#endif +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { + PyObject *r; +#if __PYX_LIMITED_VERSION_HEX >= 0x030d00A1 + int res = PyObject_GetOptionalAttr(o, n, &r); + return (res != 0) ? r : __Pyx_NewRef(d); +#else + #if CYTHON_USE_TYPE_SLOTS + if (likely(PyString_Check(n))) { + r = __Pyx_PyObject_GetAttrStrNoError(o, n); + if (unlikely(!r) && likely(!PyErr_Occurred())) { + r = __Pyx_NewRef(d); + } + return r; + } + #endif + r = PyObject_GetAttr(o, n); + return (likely(r)) ? r : __Pyx_GetAttr3Default(d); +#endif +} + +/* PyDictVersioning */ +#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) { + PyObject *dict = Py_TYPE(obj)->tp_dict; + return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0; +} +static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) { + PyObject **dictptr = NULL; + Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset; + if (offset) { +#if CYTHON_COMPILING_IN_CPYTHON + dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj); +#else + dictptr = _PyObject_GetDictPtr(obj); +#endif + } + return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0; +} +static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) { + PyObject *dict = Py_TYPE(obj)->tp_dict; + if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict))) + return 0; + return obj_dict_version == __Pyx_get_object_dict_version(obj); +} +#endif + +/* GetModuleGlobalName */ +#if CYTHON_USE_DICT_VERSIONS +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) +#else +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) +#endif +{ + PyObject *result; +#if !CYTHON_AVOID_BORROWED_REFS +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && PY_VERSION_HEX < 0x030d0000 + result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } else if (unlikely(PyErr_Occurred())) { + return NULL; + } +#elif CYTHON_COMPILING_IN_LIMITED_API + if (unlikely(!__pyx_m)) { + return NULL; + } + result = PyObject_GetAttr(__pyx_m, name); + if (likely(result)) { + return result; + } +#else + result = PyDict_GetItem(__pyx_d, name); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } +#endif +#else + result = PyObject_GetItem(__pyx_d, name); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } + PyErr_Clear(); +#endif + return __Pyx_GetBuiltinName(name); +} + +/* RaiseTooManyValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +/* RaiseNeedMoreValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +/* RaiseNoneIterError */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +/* ExtTypeTest */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + __Pyx_TypeName obj_type_name; + __Pyx_TypeName type_name; + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(__Pyx_TypeCheck(obj, type))) + return 1; + obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); + type_name = __Pyx_PyType_GetName(type); + PyErr_Format(PyExc_TypeError, + "Cannot convert " __Pyx_FMT_TYPENAME " to " __Pyx_FMT_TYPENAME, + obj_type_name, type_name); + __Pyx_DECREF_TypeName(obj_type_name); + __Pyx_DECREF_TypeName(type_name); + return 0; +} + +/* GetTopmostException */ +#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE +static _PyErr_StackItem * +__Pyx_PyErr_GetTopmostException(PyThreadState *tstate) +{ + _PyErr_StackItem *exc_info = tstate->exc_info; + while ((exc_info->exc_value == NULL || exc_info->exc_value == Py_None) && + exc_info->previous_item != NULL) + { + exc_info = exc_info->previous_item; + } + return exc_info; +} +#endif + +/* SaveResetException */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 + _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); + PyObject *exc_value = exc_info->exc_value; + if (exc_value == NULL || exc_value == Py_None) { + *value = NULL; + *type = NULL; + *tb = NULL; + } else { + *value = exc_value; + Py_INCREF(*value); + *type = (PyObject*) Py_TYPE(exc_value); + Py_INCREF(*type); + *tb = PyException_GetTraceback(exc_value); + } + #elif CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); + *type = exc_info->exc_type; + *value = exc_info->exc_value; + *tb = exc_info->exc_traceback; + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); + #else + *type = tstate->exc_type; + *value = tstate->exc_value; + *tb = tstate->exc_traceback; + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); + #endif +} +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 + _PyErr_StackItem *exc_info = tstate->exc_info; + PyObject *tmp_value = exc_info->exc_value; + exc_info->exc_value = value; + Py_XDECREF(tmp_value); + Py_XDECREF(type); + Py_XDECREF(tb); + #else + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = type; + exc_info->exc_value = value; + exc_info->exc_traceback = tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = type; + tstate->exc_value = value; + tstate->exc_traceback = tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); + #endif +} +#endif + +/* GetException */ +#if CYTHON_FAST_THREAD_STATE +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) +#endif +{ + PyObject *local_type = NULL, *local_value, *local_tb = NULL; +#if CYTHON_FAST_THREAD_STATE + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if PY_VERSION_HEX >= 0x030C00A6 + local_value = tstate->current_exception; + tstate->current_exception = 0; + if (likely(local_value)) { + local_type = (PyObject*) Py_TYPE(local_value); + Py_INCREF(local_type); + local_tb = PyException_GetTraceback(local_value); + } + #else + local_type = tstate->curexc_type; + local_value = tstate->curexc_value; + local_tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; + #endif +#else + PyErr_Fetch(&local_type, &local_value, &local_tb); +#endif + PyErr_NormalizeException(&local_type, &local_value, &local_tb); +#if CYTHON_FAST_THREAD_STATE && PY_VERSION_HEX >= 0x030C00A6 + if (unlikely(tstate->current_exception)) +#elif CYTHON_FAST_THREAD_STATE + if (unlikely(tstate->curexc_type)) +#else + if (unlikely(PyErr_Occurred())) +#endif + goto bad; + #if PY_MAJOR_VERSION >= 3 + if (local_tb) { + if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) + goto bad; + } + #endif + Py_XINCREF(local_tb); + Py_XINCREF(local_type); + Py_XINCREF(local_value); + *type = local_type; + *value = local_value; + *tb = local_tb; +#if CYTHON_FAST_THREAD_STATE + #if CYTHON_USE_EXC_INFO_STACK + { + _PyErr_StackItem *exc_info = tstate->exc_info; + #if PY_VERSION_HEX >= 0x030B00a4 + tmp_value = exc_info->exc_value; + exc_info->exc_value = local_value; + tmp_type = NULL; + tmp_tb = NULL; + Py_XDECREF(local_type); + Py_XDECREF(local_tb); + #else + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = local_type; + exc_info->exc_value = local_value; + exc_info->exc_traceback = local_tb; + #endif + } + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = local_type; + tstate->exc_value = local_value; + tstate->exc_traceback = local_tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_SetExcInfo(local_type, local_value, local_tb); +#endif + return 0; +bad: + *type = 0; + *value = 0; + *tb = 0; + Py_XDECREF(local_type); + Py_XDECREF(local_value); + Py_XDECREF(local_tb); + return -1; +} + +/* SwapException */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_value = exc_info->exc_value; + exc_info->exc_value = *value; + if (tmp_value == NULL || tmp_value == Py_None) { + Py_XDECREF(tmp_value); + tmp_value = NULL; + tmp_type = NULL; + tmp_tb = NULL; + } else { + tmp_type = (PyObject*) Py_TYPE(tmp_value); + Py_INCREF(tmp_type); + #if CYTHON_COMPILING_IN_CPYTHON + tmp_tb = ((PyBaseExceptionObject*) tmp_value)->traceback; + Py_XINCREF(tmp_tb); + #else + tmp_tb = PyException_GetTraceback(tmp_value); + #endif + } + #elif CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = *type; + exc_info->exc_value = *value; + exc_info->exc_traceback = *tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = *type; + tstate->exc_value = *value; + tstate->exc_traceback = *tb; + #endif + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); + PyErr_SetExcInfo(*type, *value, *tb); + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#endif + +/* Import */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *module = 0; + PyObject *empty_dict = 0; + PyObject *empty_list = 0; + #if PY_MAJOR_VERSION < 3 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (unlikely(!py_import)) + goto bad; + if (!from_list) { + empty_list = PyList_New(0); + if (unlikely(!empty_list)) + goto bad; + from_list = empty_list; + } + #endif + empty_dict = PyDict_New(); + if (unlikely(!empty_dict)) + goto bad; + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if (strchr(__Pyx_MODULE_NAME, '.') != NULL) { + module = PyImport_ImportModuleLevelObject( + name, __pyx_d, empty_dict, from_list, 1); + if (unlikely(!module)) { + if (unlikely(!PyErr_ExceptionMatches(PyExc_ImportError))) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + #endif + if (!module) { + #if PY_MAJOR_VERSION < 3 + PyObject *py_level = PyInt_FromLong(level); + if (unlikely(!py_level)) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, __pyx_d, empty_dict, from_list, py_level, (PyObject *)NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, __pyx_d, empty_dict, from_list, level); + #endif + } + } +bad: + Py_XDECREF(empty_dict); + Py_XDECREF(empty_list); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(py_import); + #endif + return module; +} + +/* ImportDottedModule */ +#if PY_MAJOR_VERSION >= 3 +static PyObject *__Pyx__ImportDottedModule_Error(PyObject *name, PyObject *parts_tuple, Py_ssize_t count) { + PyObject *partial_name = NULL, *slice = NULL, *sep = NULL; + if (unlikely(PyErr_Occurred())) { + PyErr_Clear(); + } + if (likely(PyTuple_GET_SIZE(parts_tuple) == count)) { + partial_name = name; + } else { + slice = PySequence_GetSlice(parts_tuple, 0, count); + if (unlikely(!slice)) + goto bad; + sep = PyUnicode_FromStringAndSize(".", 1); + if (unlikely(!sep)) + goto bad; + partial_name = PyUnicode_Join(sep, slice); + } + PyErr_Format( +#if PY_MAJOR_VERSION < 3 + PyExc_ImportError, + "No module named '%s'", PyString_AS_STRING(partial_name)); +#else +#if PY_VERSION_HEX >= 0x030600B1 + PyExc_ModuleNotFoundError, +#else + PyExc_ImportError, +#endif + "No module named '%U'", partial_name); +#endif +bad: + Py_XDECREF(sep); + Py_XDECREF(slice); + Py_XDECREF(partial_name); + return NULL; +} +#endif +#if PY_MAJOR_VERSION >= 3 +static PyObject *__Pyx__ImportDottedModule_Lookup(PyObject *name) { + PyObject *imported_module; +#if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM < 0x07030400) + PyObject *modules = PyImport_GetModuleDict(); + if (unlikely(!modules)) + return NULL; + imported_module = __Pyx_PyDict_GetItemStr(modules, name); + Py_XINCREF(imported_module); +#else + imported_module = PyImport_GetModule(name); +#endif + return imported_module; +} +#endif +#if PY_MAJOR_VERSION >= 3 +static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple) { + Py_ssize_t i, nparts; + nparts = PyTuple_GET_SIZE(parts_tuple); + for (i=1; i < nparts && module; i++) { + PyObject *part, *submodule; +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + part = PyTuple_GET_ITEM(parts_tuple, i); +#else + part = PySequence_ITEM(parts_tuple, i); +#endif + submodule = __Pyx_PyObject_GetAttrStrNoError(module, part); +#if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) + Py_DECREF(part); +#endif + Py_DECREF(module); + module = submodule; + } + if (unlikely(!module)) { + return __Pyx__ImportDottedModule_Error(name, parts_tuple, i); + } + return module; +} +#endif +static PyObject *__Pyx__ImportDottedModule(PyObject *name, PyObject *parts_tuple) { +#if PY_MAJOR_VERSION < 3 + PyObject *module, *from_list, *star = __pyx_n_s__3; + CYTHON_UNUSED_VAR(parts_tuple); + from_list = PyList_New(1); + if (unlikely(!from_list)) + return NULL; + Py_INCREF(star); + PyList_SET_ITEM(from_list, 0, star); + module = __Pyx_Import(name, from_list, 0); + Py_DECREF(from_list); + return module; +#else + PyObject *imported_module; + PyObject *module = __Pyx_Import(name, NULL, 0); + if (!parts_tuple || unlikely(!module)) + return module; + imported_module = __Pyx__ImportDottedModule_Lookup(name); + if (likely(imported_module)) { + Py_DECREF(module); + return imported_module; + } + PyErr_Clear(); + return __Pyx_ImportDottedModule_WalkParts(module, name, parts_tuple); +#endif +} +static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple) { +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030400B1 + PyObject *module = __Pyx__ImportDottedModule_Lookup(name); + if (likely(module)) { + PyObject *spec = __Pyx_PyObject_GetAttrStrNoError(module, __pyx_n_s_spec); + if (likely(spec)) { + PyObject *unsafe = __Pyx_PyObject_GetAttrStrNoError(spec, __pyx_n_s_initializing); + if (likely(!unsafe || !__Pyx_PyObject_IsTrue(unsafe))) { + Py_DECREF(spec); + spec = NULL; + } + Py_XDECREF(unsafe); + } + if (likely(!spec)) { + PyErr_Clear(); + return module; + } + Py_DECREF(spec); + Py_DECREF(module); + } else if (PyErr_Occurred()) { + PyErr_Clear(); + } +#endif + return __Pyx__ImportDottedModule(name, parts_tuple); +} + +/* FastTypeChecks */ +#if CYTHON_COMPILING_IN_CPYTHON +static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { + while (a) { + a = __Pyx_PyType_GetSlot(a, tp_base, PyTypeObject*); + if (a == b) + return 1; + } + return b == &PyBaseObject_Type; +} +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { + PyObject *mro; + if (a == b) return 1; + mro = a->tp_mro; + if (likely(mro)) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(mro); + for (i = 0; i < n; i++) { + if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) + return 1; + } + return 0; + } + return __Pyx_InBases(a, b); +} +static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b) { + PyObject *mro; + if (cls == a || cls == b) return 1; + mro = cls->tp_mro; + if (likely(mro)) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(mro); + for (i = 0; i < n; i++) { + PyObject *base = PyTuple_GET_ITEM(mro, i); + if (base == (PyObject *)a || base == (PyObject *)b) + return 1; + } + return 0; + } + return __Pyx_InBases(cls, a) || __Pyx_InBases(cls, b); +} +#if PY_MAJOR_VERSION == 2 +static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { + PyObject *exception, *value, *tb; + int res; + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + __Pyx_ErrFetch(&exception, &value, &tb); + res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + if (!res) { + res = PyObject_IsSubclass(err, exc_type2); + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + } + __Pyx_ErrRestore(exception, value, tb); + return res; +} +#else +static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { + if (exc_type1) { + return __Pyx_IsAnySubtype2((PyTypeObject*)err, (PyTypeObject*)exc_type1, (PyTypeObject*)exc_type2); + } else { + return __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); + } +} +#endif +static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { + Py_ssize_t i, n; + assert(PyExceptionClass_Check(exc_type)); + n = PyTuple_GET_SIZE(tuple); +#if PY_MAJOR_VERSION >= 3 + for (i=0; itp_as_sequence && type->tp_as_sequence->sq_repeat)) { + return type->tp_as_sequence->sq_repeat(seq, mul); + } else +#endif + { + return __Pyx_PySequence_Multiply_Generic(seq, mul); + } +} + +/* SetItemInt */ +static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) { + int r; + if (unlikely(!j)) return -1; + r = PyObject_SetItem(o, j, v); + Py_DECREF(j); + return r; +} +static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, int is_list, + CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS + if (is_list || PyList_CheckExact(o)) { + Py_ssize_t n = (!wraparound) ? i : ((likely(i >= 0)) ? i : i + PyList_GET_SIZE(o)); + if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o)))) { + PyObject* old = PyList_GET_ITEM(o, n); + Py_INCREF(v); + PyList_SET_ITEM(o, n, v); + Py_DECREF(old); + return 1; + } + } else { + PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping; + PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence; + if (mm && mm->mp_ass_subscript) { + int r; + PyObject *key = PyInt_FromSsize_t(i); + if (unlikely(!key)) return -1; + r = mm->mp_ass_subscript(o, key, v); + Py_DECREF(key); + return r; + } + if (likely(sm && sm->sq_ass_item)) { + if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) { + Py_ssize_t l = sm->sq_length(o); + if (likely(l >= 0)) { + i += l; + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return -1; + PyErr_Clear(); + } + } + return sm->sq_ass_item(o, i, v); + } + } +#else + if (is_list || !PyMapping_Check(o)) + { + return PySequence_SetItem(o, i, v); + } +#endif + return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v); +} + +/* RaiseUnboundLocalError */ +static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { + PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); +} + +/* DivInt[long] */ +static CYTHON_INLINE long __Pyx_div_long(long a, long b) { + long q = a / b; + long r = a - q*b; + q -= ((r != 0) & ((r ^ b) < 0)); + return q; +} + +/* ImportFrom */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { + PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); + if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { + const char* module_name_str = 0; + PyObject* module_name = 0; + PyObject* module_dot = 0; + PyObject* full_name = 0; + PyErr_Clear(); + module_name_str = PyModule_GetName(module); + if (unlikely(!module_name_str)) { goto modbad; } + module_name = PyUnicode_FromString(module_name_str); + if (unlikely(!module_name)) { goto modbad; } + module_dot = PyUnicode_Concat(module_name, __pyx_kp_u__2); + if (unlikely(!module_dot)) { goto modbad; } + full_name = PyUnicode_Concat(module_dot, name); + if (unlikely(!full_name)) { goto modbad; } + #if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM < 0x07030400) + { + PyObject *modules = PyImport_GetModuleDict(); + if (unlikely(!modules)) + goto modbad; + value = PyObject_GetItem(modules, full_name); + } + #else + value = PyImport_GetModule(full_name); + #endif + modbad: + Py_XDECREF(full_name); + Py_XDECREF(module_dot); + Py_XDECREF(module_name); + } + if (unlikely(!value)) { + PyErr_Format(PyExc_ImportError, + #if PY_MAJOR_VERSION < 3 + "cannot import name %.230s", PyString_AS_STRING(name)); + #else + "cannot import name %S", name); + #endif + } + return value; +} + +/* HasAttr */ +#if __PYX_LIMITED_VERSION_HEX < 0x030d00A1 +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { + PyObject *r; + if (unlikely(!__Pyx_PyBaseString_Check(n))) { + PyErr_SetString(PyExc_TypeError, + "hasattr(): attribute name must be string"); + return -1; + } + r = __Pyx_GetAttr(o, n); + if (!r) { + PyErr_Clear(); + return 0; + } else { + Py_DECREF(r); + return 1; + } +} +#endif + +/* ErrOccurredWithGIL */ +static CYTHON_INLINE int __Pyx_ErrOccurredWithGIL(void) { + int err; + #ifdef WITH_THREAD + PyGILState_STATE _save = PyGILState_Ensure(); + #endif + err = !!PyErr_Occurred(); + #ifdef WITH_THREAD + PyGILState_Release(_save); + #endif + return err; +} + +/* IsLittleEndian */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) +{ + union { + uint32_t u32; + uint8_t u8[4]; + } S; + S.u32 = 0x01020304; + return S.u8[0] == 4; +} + +/* BufferFormatCheck */ +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->new_packmode = '@'; + ctx->enc_packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + ctx->is_valid_array = 0; + ctx->struct_alignment = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; + } +} +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t <= '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; +} +static int __Pyx_BufFmt_ExpectNumber(const char **ts) { + int number = __Pyx_BufFmt_ParseNumber(ts); + if (number == -1) + PyErr_Format(PyExc_ValueError,\ + "Does not understand character buffer dtype format string ('%c')", **ts); + return number; +} +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + PyErr_Format(PyExc_ValueError, + "Unexpected format string character: '%c'", ch); +} +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case '?': return "'bool'"; + case 'c': return "'char'"; + case 'b': return "'signed char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); + case 'T': return "a struct"; + case 'O': return "Python object"; + case 'P': return "a pointer"; + case 's': case 'p': return "a string"; + case 0: return "end"; + default: return "unparsable format string"; + } +} +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; + } + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, int is_complex) { + CYTHON_UNUSED_VAR(is_complex); + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +/* These are for computing the padding at the end of the struct to align + on the first member of the struct. This will probably the same as above, + but we don't have any guarantees. + */ +typedef struct { short x; char c; } __Pyx_pad_short; +typedef struct { int x; char c; } __Pyx_pad_int; +typedef struct { long x; char c; } __Pyx_pad_long; +typedef struct { float x; char c; } __Pyx_pad_float; +typedef struct { double x; char c; } __Pyx_pad_double; +typedef struct { long double x; char c; } __Pyx_pad_longdouble; +typedef struct { void *x; char c; } __Pyx_pad_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, int is_complex) { + CYTHON_UNUSED_VAR(is_complex); + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); + case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); + case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': + return 'H'; + case 'b': case 'h': case 'i': + case 'l': case 'q': case 's': case 'p': + return 'I'; + case '?': case 'B': case 'H': case 'I': case 'L': case 'Q': + return 'U'; + case 'f': case 'd': case 'g': + return (is_complex ? 'C' : 'R'); + case 'O': + return 'O'; + case 'P': + return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset, arraysize = 1; + if (ctx->enc_type == 0) return 0; + if (ctx->head->field->type->arraysize[0]) { + int i, ndim = 0; + if (ctx->enc_type == 's' || ctx->enc_type == 'p') { + ctx->is_valid_array = ctx->head->field->type->ndim == 1; + ndim = 1; + if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %zu", + ctx->head->field->type->arraysize[0], ctx->enc_count); + return -1; + } + } + if (!ctx->is_valid_array) { + PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", + ctx->head->field->type->ndim, ndim); + return -1; + } + for (i = 0; i < ctx->head->field->type->ndim; i++) { + arraysize *= ctx->head->field->type->arraysize[i]; + } + ctx->is_valid_array = 0; + ctx->enc_count = 1; + } + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; + if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->enc_packmode == '@') { + size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + size_t align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + if (ctx->struct_alignment == 0) + ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, + ctx->is_complex); + } + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + if ((type->typegroup == 'H' || group == 'H') && type->size == size) { + } else { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + } + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", + (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); + return -1; + } + ctx->fmt_offset += size; + if (arraysize) + ctx->fmt_offset += (arraysize - 1) * size; + --ctx->enc_count; + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} +static int +__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) +{ + const char *ts = *tsp; + int i = 0, number, ndim; + ++ts; + if (ctx->new_count != 1) { + PyErr_SetString(PyExc_ValueError, + "Cannot handle repeated arrays in format string"); + return -1; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return -1; + ndim = ctx->head->field->type->ndim; + while (*ts && *ts != ')') { + switch (*ts) { + case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; + default: break; + } + number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return -1; + if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %d", + ctx->head->field->type->arraysize[i], number); + return -1; + } + if (*ts != ',' && *ts != ')') { + PyErr_Format(PyExc_ValueError, + "Expected a comma in format string, got '%c'", *ts); + return -1; + } + if (*ts == ',') ts++; + i++; + } + if (i != ndim) { + PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", + ctx->head->field->type->ndim, i); + return -1; + } + if (!*ts) { + PyErr_SetString(PyExc_ValueError, + "Unexpected end of format string, expected ')'"); + return -1; + } + ctx->is_valid_array = 1; + ctx->new_count = 1; + *tsp = ++ts; + return 0; +} +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case '\r': + case '\n': + ++ts; + break; + case '<': + if (!__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + ctx->new_packmode = *ts++; + break; + case 'T': + { + const char* ts_after_sub; + size_t i, struct_count = ctx->new_count; + size_t struct_alignment = ctx->struct_alignment; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + ctx->enc_count = 0; + ctx->struct_alignment = 0; + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + if (struct_alignment) ctx->struct_alignment = struct_alignment; + } + break; + case '}': + { + size_t alignment = ctx->struct_alignment; + ++ts; + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + if (alignment && ctx->fmt_offset % alignment) { + ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); + } + } + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->enc_packmode = ctx->new_packmode; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } + CYTHON_FALLTHROUGH; + case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': case 'p': + if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) && + (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) { + ctx->enc_count += ctx->new_count; + ctx->new_count = 1; + got_Z = 0; + ++ts; + break; + } + CYTHON_FALLTHROUGH; + case 's': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_packmode = ctx->new_packmode; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + case ':': + ++ts; + while(*ts != ':') ++ts; + ++ts; + break; + case '(': + if (__pyx_buffmt_parse_array(ctx, &ts) < 0) return NULL; + break; + default: + { + int number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + ctx->new_count = (size_t)number; + } + } + } +} + +/* BufferGetAndValidate */ + static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { + if (unlikely(info->buf == NULL)) return; + if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; + __Pyx_ReleaseBuffer(info); +} +static void __Pyx_ZeroBuffer(Py_buffer* buf) { + buf->buf = NULL; + buf->obj = NULL; + buf->strides = __Pyx_zeros; + buf->shape = __Pyx_zeros; + buf->suboffsets = __Pyx_minusones; +} +static int __Pyx__GetBufferAndValidate( + Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, + int nd, int cast, __Pyx_BufFmt_StackElem* stack) +{ + buf->buf = NULL; + if (unlikely(__Pyx_GetBuffer(obj, buf, flags) == -1)) { + __Pyx_ZeroBuffer(buf); + return -1; + } + if (unlikely(buf->ndim != nd)) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + nd, buf->ndim); + goto fail; + } + if (!cast) { + __Pyx_BufFmt_Context ctx; + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; + } + if (unlikely((size_t)buf->itemsize != dtype->size)) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", + buf->itemsize, (buf->itemsize > 1) ? "s" : "", + dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); + goto fail; + } + if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; + return 0; +fail:; + __Pyx_SafeReleaseBuffer(buf); + return -1; +} + +/* BufferFallbackError */ + static void __Pyx_RaiseBufferFallbackError(void) { + PyErr_SetString(PyExc_ValueError, + "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); +} + +/* DictGetItem */ + #if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY +static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) { + PyObject *value; + value = PyDict_GetItemWithError(d, key); + if (unlikely(!value)) { + if (!PyErr_Occurred()) { + if (unlikely(PyTuple_Check(key))) { + PyObject* args = PyTuple_Pack(1, key); + if (likely(args)) { + PyErr_SetObject(PyExc_KeyError, args); + Py_DECREF(args); + } + } else { + PyErr_SetObject(PyExc_KeyError, key); + } + } + return NULL; + } + Py_INCREF(value); + return value; +} +#endif + +/* PyIntCompare */ + static CYTHON_INLINE int __Pyx_PyInt_BoolNeObjC(PyObject *op1, PyObject *op2, long intval, long inplace) { + CYTHON_MAYBE_UNUSED_VAR(intval); + CYTHON_UNUSED_VAR(inplace); + if (op1 == op2) { + return 0; + } + #if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(op1))) { + const long b = intval; + long a = PyInt_AS_LONG(op1); + return (a != b); + } + #endif + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(PyLong_CheckExact(op1))) { + int unequal; + unsigned long uintval; + Py_ssize_t size = __Pyx_PyLong_DigitCount(op1); + const digit* digits = __Pyx_PyLong_Digits(op1); + if (intval == 0) { + return (__Pyx_PyLong_IsZero(op1) != 1); + } else if (intval < 0) { + if (__Pyx_PyLong_IsNonNeg(op1)) + return 1; + intval = -intval; + } else { + if (__Pyx_PyLong_IsNeg(op1)) + return 1; + } + uintval = (unsigned long) intval; +#if PyLong_SHIFT * 4 < SIZEOF_LONG*8 + if (uintval >> (PyLong_SHIFT * 4)) { + unequal = (size != 5) || (digits[0] != (uintval & (unsigned long) PyLong_MASK)) + | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[3] != ((uintval >> (3 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[4] != ((uintval >> (4 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)); + } else +#endif +#if PyLong_SHIFT * 3 < SIZEOF_LONG*8 + if (uintval >> (PyLong_SHIFT * 3)) { + unequal = (size != 4) || (digits[0] != (uintval & (unsigned long) PyLong_MASK)) + | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[3] != ((uintval >> (3 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)); + } else +#endif +#if PyLong_SHIFT * 2 < SIZEOF_LONG*8 + if (uintval >> (PyLong_SHIFT * 2)) { + unequal = (size != 3) || (digits[0] != (uintval & (unsigned long) PyLong_MASK)) + | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)) | (digits[2] != ((uintval >> (2 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)); + } else +#endif +#if PyLong_SHIFT * 1 < SIZEOF_LONG*8 + if (uintval >> (PyLong_SHIFT * 1)) { + unequal = (size != 2) || (digits[0] != (uintval & (unsigned long) PyLong_MASK)) + | (digits[1] != ((uintval >> (1 * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)); + } else +#endif + unequal = (size != 1) || (((unsigned long) digits[0]) != (uintval & (unsigned long) PyLong_MASK)); + return (unequal != 0); + } + #endif + if (PyFloat_CheckExact(op1)) { + const long b = intval; +#if CYTHON_COMPILING_IN_LIMITED_API + double a = __pyx_PyFloat_AsDouble(op1); +#else + double a = PyFloat_AS_DOUBLE(op1); +#endif + return ((double)a != (double)b); + } + return __Pyx_PyObject_IsTrueAndDecref( + PyObject_RichCompare(op1, op2, Py_NE)); +} + +/* PyIntBinop */ + #if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_SubtractObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check) { + CYTHON_MAYBE_UNUSED_VAR(intval); + CYTHON_MAYBE_UNUSED_VAR(inplace); + CYTHON_UNUSED_VAR(zerodivision_check); + #if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(op1))) { + const long b = intval; + long x; + long a = PyInt_AS_LONG(op1); + + x = (long)((unsigned long)a - (unsigned long)b); + if (likely((x^a) >= 0 || (x^~b) >= 0)) + return PyInt_FromLong(x); + return PyLong_Type.tp_as_number->nb_subtract(op1, op2); + } + #endif + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(PyLong_CheckExact(op1))) { + const long b = intval; + long a, x; +#ifdef HAVE_LONG_LONG + const PY_LONG_LONG llb = intval; + PY_LONG_LONG lla, llx; +#endif + if (unlikely(__Pyx_PyLong_IsZero(op1))) { + return PyLong_FromLong(-intval); + } + if (likely(__Pyx_PyLong_IsCompact(op1))) { + a = __Pyx_PyLong_CompactValue(op1); + } else { + const digit* digits = __Pyx_PyLong_Digits(op1); + const Py_ssize_t size = __Pyx_PyLong_SignedDigitCount(op1); + switch (size) { + case -2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + #ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; + #endif + } + CYTHON_FALLTHROUGH; + case 2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + #ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; + #endif + } + CYTHON_FALLTHROUGH; + case -3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + #ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; + #endif + } + CYTHON_FALLTHROUGH; + case 3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + #ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; + #endif + } + CYTHON_FALLTHROUGH; + case -4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + #ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; + #endif + } + CYTHON_FALLTHROUGH; + case 4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + #ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; + #endif + } + CYTHON_FALLTHROUGH; + default: return PyLong_Type.tp_as_number->nb_subtract(op1, op2); + } + } + x = a - b; + return PyLong_FromLong(x); +#ifdef HAVE_LONG_LONG + long_long: + llx = lla - llb; + return PyLong_FromLongLong(llx); +#endif + + + } + #endif + if (PyFloat_CheckExact(op1)) { + const long b = intval; +#if CYTHON_COMPILING_IN_LIMITED_API + double a = __pyx_PyFloat_AsDouble(op1); +#else + double a = PyFloat_AS_DOUBLE(op1); +#endif + double result; + + PyFPE_START_PROTECT("subtract", return NULL) + result = ((double)a) - (double)b; + PyFPE_END_PROTECT(result) + return PyFloat_FromDouble(result); + } + return (inplace ? PyNumber_InPlaceSubtract : PyNumber_Subtract)(op1, op2); +} +#endif + +/* PyIntBinop */ + #if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_AddCObj(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check) { + CYTHON_MAYBE_UNUSED_VAR(intval); + CYTHON_MAYBE_UNUSED_VAR(inplace); + CYTHON_UNUSED_VAR(zerodivision_check); + #if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(op2))) { + const long a = intval; + long x; + long b = PyInt_AS_LONG(op2); + + x = (long)((unsigned long)a + (unsigned long)b); + if (likely((x^a) >= 0 || (x^b) >= 0)) + return PyInt_FromLong(x); + return PyLong_Type.tp_as_number->nb_add(op1, op2); + } + #endif + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(PyLong_CheckExact(op2))) { + const long a = intval; + long b, x; +#ifdef HAVE_LONG_LONG + const PY_LONG_LONG lla = intval; + PY_LONG_LONG llb, llx; +#endif + if (unlikely(__Pyx_PyLong_IsZero(op2))) { + return __Pyx_NewRef(op1); + } + if (likely(__Pyx_PyLong_IsCompact(op2))) { + b = __Pyx_PyLong_CompactValue(op2); + } else { + const digit* digits = __Pyx_PyLong_Digits(op2); + const Py_ssize_t size = __Pyx_PyLong_SignedDigitCount(op2); + switch (size) { + case -2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + b = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + #ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + llb = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; + #endif + } + CYTHON_FALLTHROUGH; + case 2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + b = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + #ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + llb = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; + #endif + } + CYTHON_FALLTHROUGH; + case -3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + b = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + #ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + llb = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; + #endif + } + CYTHON_FALLTHROUGH; + case 3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + b = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + #ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + llb = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; + #endif + } + CYTHON_FALLTHROUGH; + case -4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + b = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + #ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + llb = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; + #endif + } + CYTHON_FALLTHROUGH; + case 4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + b = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + #ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + llb = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; + #endif + } + CYTHON_FALLTHROUGH; + default: return PyLong_Type.tp_as_number->nb_add(op1, op2); + } + } + x = a + b; + return PyLong_FromLong(x); +#ifdef HAVE_LONG_LONG + long_long: + llx = lla + llb; + return PyLong_FromLongLong(llx); +#endif + + + } + #endif + if (PyFloat_CheckExact(op2)) { + const long a = intval; +#if CYTHON_COMPILING_IN_LIMITED_API + double b = __pyx_PyFloat_AsDouble(op2); +#else + double b = PyFloat_AS_DOUBLE(op2); +#endif + double result; + + PyFPE_START_PROTECT("add", return NULL) + result = ((double)a) + (double)b; + PyFPE_END_PROTECT(result) + return PyFloat_FromDouble(result); + } + return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); +} +#endif + +/* PyObject_GenericGetAttrNoDict */ + #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) { + __Pyx_TypeName type_name = __Pyx_PyType_GetName(tp); + PyErr_Format(PyExc_AttributeError, +#if PY_MAJOR_VERSION >= 3 + "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'", + type_name, attr_name); +#else + "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'", + type_name, PyString_AS_STRING(attr_name)); +#endif + __Pyx_DECREF_TypeName(type_name); + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) { + PyObject *descr; + PyTypeObject *tp = Py_TYPE(obj); + if (unlikely(!PyString_Check(attr_name))) { + return PyObject_GenericGetAttr(obj, attr_name); + } + assert(!tp->tp_dictoffset); + descr = _PyType_Lookup(tp, attr_name); + if (unlikely(!descr)) { + return __Pyx_RaiseGenericGetAttributeError(tp, attr_name); + } + Py_INCREF(descr); + #if PY_MAJOR_VERSION < 3 + if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS))) + #endif + { + descrgetfunc f = Py_TYPE(descr)->tp_descr_get; + if (unlikely(f)) { + PyObject *res = f(descr, obj, (PyObject *)tp); + Py_DECREF(descr); + return res; + } + } + return descr; +} +#endif + +/* PyObject_GenericGetAttr */ + #if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) { + if (unlikely(Py_TYPE(obj)->tp_dictoffset)) { + return PyObject_GenericGetAttr(obj, attr_name); + } + return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name); +} +#endif + +/* FixUpExtensionType */ + #if CYTHON_USE_TYPE_SPECS +static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type) { +#if PY_VERSION_HEX > 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API + CYTHON_UNUSED_VAR(spec); + CYTHON_UNUSED_VAR(type); +#else + const PyType_Slot *slot = spec->slots; + while (slot && slot->slot && slot->slot != Py_tp_members) + slot++; + if (slot && slot->slot == Py_tp_members) { + int changed = 0; +#if !(PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON) + const +#endif + PyMemberDef *memb = (PyMemberDef*) slot->pfunc; + while (memb && memb->name) { + if (memb->name[0] == '_' && memb->name[1] == '_') { +#if PY_VERSION_HEX < 0x030900b1 + if (strcmp(memb->name, "__weaklistoffset__") == 0) { + assert(memb->type == T_PYSSIZET); + assert(memb->flags == READONLY); + type->tp_weaklistoffset = memb->offset; + changed = 1; + } + else if (strcmp(memb->name, "__dictoffset__") == 0) { + assert(memb->type == T_PYSSIZET); + assert(memb->flags == READONLY); + type->tp_dictoffset = memb->offset; + changed = 1; + } +#if CYTHON_METH_FASTCALL + else if (strcmp(memb->name, "__vectorcalloffset__") == 0) { + assert(memb->type == T_PYSSIZET); + assert(memb->flags == READONLY); +#if PY_VERSION_HEX >= 0x030800b4 + type->tp_vectorcall_offset = memb->offset; +#else + type->tp_print = (printfunc) memb->offset; +#endif + changed = 1; + } +#endif +#else + if ((0)); +#endif +#if PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON + else if (strcmp(memb->name, "__module__") == 0) { + PyObject *descr; + assert(memb->type == T_OBJECT); + assert(memb->flags == 0 || memb->flags == READONLY); + descr = PyDescr_NewMember(type, memb); + if (unlikely(!descr)) + return -1; + if (unlikely(PyDict_SetItem(type->tp_dict, PyDescr_NAME(descr), descr) < 0)) { + Py_DECREF(descr); + return -1; + } + Py_DECREF(descr); + changed = 1; + } +#endif + } + memb++; + } + if (changed) + PyType_Modified(type); + } +#endif + return 0; +} +#endif + +/* PyObjectCallNoArg */ + static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { + PyObject *arg[2] = {NULL, NULL}; + return __Pyx_PyObject_FastCall(func, arg + 1, 0 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET); +} + +/* PyObjectGetMethod */ + static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method) { + PyObject *attr; +#if CYTHON_UNPACK_METHODS && CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_PYTYPE_LOOKUP + __Pyx_TypeName type_name; + PyTypeObject *tp = Py_TYPE(obj); + PyObject *descr; + descrgetfunc f = NULL; + PyObject **dictptr, *dict; + int meth_found = 0; + assert (*method == NULL); + if (unlikely(tp->tp_getattro != PyObject_GenericGetAttr)) { + attr = __Pyx_PyObject_GetAttrStr(obj, name); + goto try_unpack; + } + if (unlikely(tp->tp_dict == NULL) && unlikely(PyType_Ready(tp) < 0)) { + return 0; + } + descr = _PyType_Lookup(tp, name); + if (likely(descr != NULL)) { + Py_INCREF(descr); +#if defined(Py_TPFLAGS_METHOD_DESCRIPTOR) && Py_TPFLAGS_METHOD_DESCRIPTOR + if (__Pyx_PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_METHOD_DESCRIPTOR)) +#elif PY_MAJOR_VERSION >= 3 + #ifdef __Pyx_CyFunction_USED + if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type) || __Pyx_CyFunction_Check(descr))) + #else + if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type))) + #endif +#else + #ifdef __Pyx_CyFunction_USED + if (likely(PyFunction_Check(descr) || __Pyx_CyFunction_Check(descr))) + #else + if (likely(PyFunction_Check(descr))) + #endif +#endif + { + meth_found = 1; + } else { + f = Py_TYPE(descr)->tp_descr_get; + if (f != NULL && PyDescr_IsData(descr)) { + attr = f(descr, obj, (PyObject *)Py_TYPE(obj)); + Py_DECREF(descr); + goto try_unpack; + } + } + } + dictptr = _PyObject_GetDictPtr(obj); + if (dictptr != NULL && (dict = *dictptr) != NULL) { + Py_INCREF(dict); + attr = __Pyx_PyDict_GetItemStr(dict, name); + if (attr != NULL) { + Py_INCREF(attr); + Py_DECREF(dict); + Py_XDECREF(descr); + goto try_unpack; + } + Py_DECREF(dict); + } + if (meth_found) { + *method = descr; + return 1; + } + if (f != NULL) { + attr = f(descr, obj, (PyObject *)Py_TYPE(obj)); + Py_DECREF(descr); + goto try_unpack; + } + if (likely(descr != NULL)) { + *method = descr; + return 0; + } + type_name = __Pyx_PyType_GetName(tp); + PyErr_Format(PyExc_AttributeError, +#if PY_MAJOR_VERSION >= 3 + "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'", + type_name, name); +#else + "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'", + type_name, PyString_AS_STRING(name)); +#endif + __Pyx_DECREF_TypeName(type_name); + return 0; +#else + attr = __Pyx_PyObject_GetAttrStr(obj, name); + goto try_unpack; +#endif +try_unpack: +#if CYTHON_UNPACK_METHODS + if (likely(attr) && PyMethod_Check(attr) && likely(PyMethod_GET_SELF(attr) == obj)) { + PyObject *function = PyMethod_GET_FUNCTION(attr); + Py_INCREF(function); + Py_DECREF(attr); + *method = function; + return 1; + } +#endif + *method = attr; + return 0; +} + +/* PyObjectCallMethod0 */ + static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name) { + PyObject *method = NULL, *result = NULL; + int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method); + if (likely(is_method)) { + result = __Pyx_PyObject_CallOneArg(method, obj); + Py_DECREF(method); + return result; + } + if (unlikely(!method)) goto bad; + result = __Pyx_PyObject_CallNoArg(method); + Py_DECREF(method); +bad: + return result; +} + +/* ValidateBasesTuple */ + #if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS +static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases) { + Py_ssize_t i, n; +#if CYTHON_ASSUME_SAFE_MACROS + n = PyTuple_GET_SIZE(bases); +#else + n = PyTuple_Size(bases); + if (n < 0) return -1; +#endif + for (i = 1; i < n; i++) + { +#if CYTHON_AVOID_BORROWED_REFS + PyObject *b0 = PySequence_GetItem(bases, i); + if (!b0) return -1; +#elif CYTHON_ASSUME_SAFE_MACROS + PyObject *b0 = PyTuple_GET_ITEM(bases, i); +#else + PyObject *b0 = PyTuple_GetItem(bases, i); + if (!b0) return -1; +#endif + PyTypeObject *b; +#if PY_MAJOR_VERSION < 3 + if (PyClass_Check(b0)) + { + PyErr_Format(PyExc_TypeError, "base class '%.200s' is an old-style class", + PyString_AS_STRING(((PyClassObject*)b0)->cl_name)); +#if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(b0); +#endif + return -1; + } +#endif + b = (PyTypeObject*) b0; + if (!__Pyx_PyType_HasFeature(b, Py_TPFLAGS_HEAPTYPE)) + { + __Pyx_TypeName b_name = __Pyx_PyType_GetName(b); + PyErr_Format(PyExc_TypeError, + "base class '" __Pyx_FMT_TYPENAME "' is not a heap type", b_name); + __Pyx_DECREF_TypeName(b_name); +#if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(b0); +#endif + return -1; + } + if (dictoffset == 0) + { + Py_ssize_t b_dictoffset = 0; +#if CYTHON_USE_TYPE_SLOTS || CYTHON_COMPILING_IN_PYPY + b_dictoffset = b->tp_dictoffset; +#else + PyObject *py_b_dictoffset = PyObject_GetAttrString((PyObject*)b, "__dictoffset__"); + if (!py_b_dictoffset) goto dictoffset_return; + b_dictoffset = PyLong_AsSsize_t(py_b_dictoffset); + Py_DECREF(py_b_dictoffset); + if (b_dictoffset == -1 && PyErr_Occurred()) goto dictoffset_return; +#endif + if (b_dictoffset) { + { + __Pyx_TypeName b_name = __Pyx_PyType_GetName(b); + PyErr_Format(PyExc_TypeError, + "extension type '%.200s' has no __dict__ slot, " + "but base type '" __Pyx_FMT_TYPENAME "' has: " + "either add 'cdef dict __dict__' to the extension type " + "or add '__slots__ = [...]' to the base type", + type_name, b_name); + __Pyx_DECREF_TypeName(b_name); + } +#if !(CYTHON_USE_TYPE_SLOTS || CYTHON_COMPILING_IN_PYPY) + dictoffset_return: +#endif +#if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(b0); +#endif + return -1; + } + } +#if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(b0); +#endif + } + return 0; +} +#endif + +/* PyType_Ready */ + static int __Pyx_PyType_Ready(PyTypeObject *t) { +#if CYTHON_USE_TYPE_SPECS || !(CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API) || defined(PYSTON_MAJOR_VERSION) + (void)__Pyx_PyObject_CallMethod0; +#if CYTHON_USE_TYPE_SPECS + (void)__Pyx_validate_bases_tuple; +#endif + return PyType_Ready(t); +#else + int r; + PyObject *bases = __Pyx_PyType_GetSlot(t, tp_bases, PyObject*); + if (bases && unlikely(__Pyx_validate_bases_tuple(t->tp_name, t->tp_dictoffset, bases) == -1)) + return -1; +#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION) + { + int gc_was_enabled; + #if PY_VERSION_HEX >= 0x030A00b1 + gc_was_enabled = PyGC_Disable(); + (void)__Pyx_PyObject_CallMethod0; + #else + PyObject *ret, *py_status; + PyObject *gc = NULL; + #if PY_VERSION_HEX >= 0x030700a1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM+0 >= 0x07030400) + gc = PyImport_GetModule(__pyx_kp_u_gc); + #endif + if (unlikely(!gc)) gc = PyImport_Import(__pyx_kp_u_gc); + if (unlikely(!gc)) return -1; + py_status = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_isenabled); + if (unlikely(!py_status)) { + Py_DECREF(gc); + return -1; + } + gc_was_enabled = __Pyx_PyObject_IsTrue(py_status); + Py_DECREF(py_status); + if (gc_was_enabled > 0) { + ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_disable); + if (unlikely(!ret)) { + Py_DECREF(gc); + return -1; + } + Py_DECREF(ret); + } else if (unlikely(gc_was_enabled == -1)) { + Py_DECREF(gc); + return -1; + } + #endif + t->tp_flags |= Py_TPFLAGS_HEAPTYPE; +#if PY_VERSION_HEX >= 0x030A0000 + t->tp_flags |= Py_TPFLAGS_IMMUTABLETYPE; +#endif +#else + (void)__Pyx_PyObject_CallMethod0; +#endif + r = PyType_Ready(t); +#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION) + t->tp_flags &= ~Py_TPFLAGS_HEAPTYPE; + #if PY_VERSION_HEX >= 0x030A00b1 + if (gc_was_enabled) + PyGC_Enable(); + #else + if (gc_was_enabled) { + PyObject *tp, *v, *tb; + PyErr_Fetch(&tp, &v, &tb); + ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_enable); + if (likely(ret || r == -1)) { + Py_XDECREF(ret); + PyErr_Restore(tp, v, tb); + } else { + Py_XDECREF(tp); + Py_XDECREF(v); + Py_XDECREF(tb); + r = -1; + } + } + Py_DECREF(gc); + #endif + } +#endif + return r; +#endif +} + +/* SetVTable */ + static int __Pyx_SetVtable(PyTypeObject *type, void *vtable) { + PyObject *ob = PyCapsule_New(vtable, 0, 0); + if (unlikely(!ob)) + goto bad; +#if CYTHON_COMPILING_IN_LIMITED_API + if (unlikely(PyObject_SetAttr((PyObject *) type, __pyx_n_s_pyx_vtable, ob) < 0)) +#else + if (unlikely(PyDict_SetItem(type->tp_dict, __pyx_n_s_pyx_vtable, ob) < 0)) +#endif + goto bad; + Py_DECREF(ob); + return 0; +bad: + Py_XDECREF(ob); + return -1; +} + +/* GetVTable */ + static void* __Pyx_GetVtable(PyTypeObject *type) { + void* ptr; +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject *ob = PyObject_GetAttr((PyObject *)type, __pyx_n_s_pyx_vtable); +#else + PyObject *ob = PyObject_GetItem(type->tp_dict, __pyx_n_s_pyx_vtable); +#endif + if (!ob) + goto bad; + ptr = PyCapsule_GetPointer(ob, 0); + if (!ptr && !PyErr_Occurred()) + PyErr_SetString(PyExc_RuntimeError, "invalid vtable found for imported type"); + Py_DECREF(ob); + return ptr; +bad: + Py_XDECREF(ob); + return NULL; +} + +/* MergeVTables */ + #if !CYTHON_COMPILING_IN_LIMITED_API +static int __Pyx_MergeVtables(PyTypeObject *type) { + int i; + void** base_vtables; + __Pyx_TypeName tp_base_name; + __Pyx_TypeName base_name; + void* unknown = (void*)-1; + PyObject* bases = type->tp_bases; + int base_depth = 0; + { + PyTypeObject* base = type->tp_base; + while (base) { + base_depth += 1; + base = base->tp_base; + } + } + base_vtables = (void**) malloc(sizeof(void*) * (size_t)(base_depth + 1)); + base_vtables[0] = unknown; + for (i = 1; i < PyTuple_GET_SIZE(bases); i++) { + void* base_vtable = __Pyx_GetVtable(((PyTypeObject*)PyTuple_GET_ITEM(bases, i))); + if (base_vtable != NULL) { + int j; + PyTypeObject* base = type->tp_base; + for (j = 0; j < base_depth; j++) { + if (base_vtables[j] == unknown) { + base_vtables[j] = __Pyx_GetVtable(base); + base_vtables[j + 1] = unknown; + } + if (base_vtables[j] == base_vtable) { + break; + } else if (base_vtables[j] == NULL) { + goto bad; + } + base = base->tp_base; + } + } + } + PyErr_Clear(); + free(base_vtables); + return 0; +bad: + tp_base_name = __Pyx_PyType_GetName(type->tp_base); + base_name = __Pyx_PyType_GetName((PyTypeObject*)PyTuple_GET_ITEM(bases, i)); + PyErr_Format(PyExc_TypeError, + "multiple bases have vtable conflict: '" __Pyx_FMT_TYPENAME "' and '" __Pyx_FMT_TYPENAME "'", tp_base_name, base_name); + __Pyx_DECREF_TypeName(tp_base_name); + __Pyx_DECREF_TypeName(base_name); + free(base_vtables); + return -1; +} +#endif + +/* SetupReduce */ + #if !CYTHON_COMPILING_IN_LIMITED_API +static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { + int ret; + PyObject *name_attr; + name_attr = __Pyx_PyObject_GetAttrStrNoError(meth, __pyx_n_s_name_2); + if (likely(name_attr)) { + ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); + } else { + ret = -1; + } + if (unlikely(ret < 0)) { + PyErr_Clear(); + ret = 0; + } + Py_XDECREF(name_attr); + return ret; +} +static int __Pyx_setup_reduce(PyObject* type_obj) { + int ret = 0; + PyObject *object_reduce = NULL; + PyObject *object_getstate = NULL; + PyObject *object_reduce_ex = NULL; + PyObject *reduce = NULL; + PyObject *reduce_ex = NULL; + PyObject *reduce_cython = NULL; + PyObject *setstate = NULL; + PyObject *setstate_cython = NULL; + PyObject *getstate = NULL; +#if CYTHON_USE_PYTYPE_LOOKUP + getstate = _PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate); +#else + getstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_getstate); + if (!getstate && PyErr_Occurred()) { + goto __PYX_BAD; + } +#endif + if (getstate) { +#if CYTHON_USE_PYTYPE_LOOKUP + object_getstate = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_getstate); +#else + object_getstate = __Pyx_PyObject_GetAttrStrNoError((PyObject*)&PyBaseObject_Type, __pyx_n_s_getstate); + if (!object_getstate && PyErr_Occurred()) { + goto __PYX_BAD; + } +#endif + if (object_getstate != getstate) { + goto __PYX_GOOD; + } + } +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; +#else + object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; +#endif + reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD; + if (reduce_ex == object_reduce_ex) { +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; +#else + object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; +#endif + reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD; + if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { + reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython); + if (likely(reduce_cython)) { + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + } else if (reduce == object_reduce || PyErr_Occurred()) { + goto __PYX_BAD; + } + setstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate); + if (!setstate) PyErr_Clear(); + if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { + setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython); + if (likely(setstate_cython)) { + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + } else if (!setstate || PyErr_Occurred()) { + goto __PYX_BAD; + } + } + PyType_Modified((PyTypeObject*)type_obj); + } + } + goto __PYX_GOOD; +__PYX_BAD: + if (!PyErr_Occurred()) { + __Pyx_TypeName type_obj_name = + __Pyx_PyType_GetName((PyTypeObject*)type_obj); + PyErr_Format(PyExc_RuntimeError, + "Unable to initialize pickling for " __Pyx_FMT_TYPENAME, type_obj_name); + __Pyx_DECREF_TypeName(type_obj_name); + } + ret = -1; +__PYX_GOOD: +#if !CYTHON_USE_PYTYPE_LOOKUP + Py_XDECREF(object_reduce); + Py_XDECREF(object_reduce_ex); + Py_XDECREF(object_getstate); + Py_XDECREF(getstate); +#endif + Py_XDECREF(reduce); + Py_XDECREF(reduce_ex); + Py_XDECREF(reduce_cython); + Py_XDECREF(setstate); + Py_XDECREF(setstate_cython); + return ret; +} +#endif + +/* TypeImport */ + #ifndef __PYX_HAVE_RT_ImportType_3_0_6 +#define __PYX_HAVE_RT_ImportType_3_0_6 +static PyTypeObject *__Pyx_ImportType_3_0_6(PyObject *module, const char *module_name, const char *class_name, + size_t size, size_t alignment, enum __Pyx_ImportType_CheckSize_3_0_6 check_size) +{ + PyObject *result = 0; + char warning[200]; + Py_ssize_t basicsize; + Py_ssize_t itemsize; +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject *py_basicsize; + PyObject *py_itemsize; +#endif + result = PyObject_GetAttrString(module, class_name); + if (!result) + goto bad; + if (!PyType_Check(result)) { + PyErr_Format(PyExc_TypeError, + "%.200s.%.200s is not a type object", + module_name, class_name); + goto bad; + } +#if !CYTHON_COMPILING_IN_LIMITED_API + basicsize = ((PyTypeObject *)result)->tp_basicsize; + itemsize = ((PyTypeObject *)result)->tp_itemsize; +#else + py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); + if (!py_basicsize) + goto bad; + basicsize = PyLong_AsSsize_t(py_basicsize); + Py_DECREF(py_basicsize); + py_basicsize = 0; + if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) + goto bad; + py_itemsize = PyObject_GetAttrString(result, "__itemsize__"); + if (!py_itemsize) + goto bad; + itemsize = PyLong_AsSsize_t(py_itemsize); + Py_DECREF(py_itemsize); + py_itemsize = 0; + if (itemsize == (Py_ssize_t)-1 && PyErr_Occurred()) + goto bad; +#endif + if (itemsize) { + if (size % alignment) { + alignment = size % alignment; + } + if (itemsize < (Py_ssize_t)alignment) + itemsize = (Py_ssize_t)alignment; + } + if ((size_t)(basicsize + itemsize) < size) { + PyErr_Format(PyExc_ValueError, + "%.200s.%.200s size changed, may indicate binary incompatibility. " + "Expected %zd from C header, got %zd from PyObject", + module_name, class_name, size, basicsize+itemsize); + goto bad; + } + if (check_size == __Pyx_ImportType_CheckSize_Error_3_0_6 && + ((size_t)basicsize > size || (size_t)(basicsize + itemsize) < size)) { + PyErr_Format(PyExc_ValueError, + "%.200s.%.200s size changed, may indicate binary incompatibility. " + "Expected %zd from C header, got %zd-%zd from PyObject", + module_name, class_name, size, basicsize, basicsize+itemsize); + goto bad; + } + else if (check_size == __Pyx_ImportType_CheckSize_Warn_3_0_6 && (size_t)basicsize > size) { + PyOS_snprintf(warning, sizeof(warning), + "%s.%s size changed, may indicate binary incompatibility. " + "Expected %zd from C header, got %zd from PyObject", + module_name, class_name, size, basicsize); + if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; + } + return (PyTypeObject *)result; +bad: + Py_XDECREF(result); + return NULL; +} +#endif + +/* FetchSharedCythonModule */ + static PyObject *__Pyx_FetchSharedCythonABIModule(void) { + return __Pyx_PyImport_AddModuleRef((char*) __PYX_ABI_MODULE_NAME); +} + +/* FetchCommonType */ + static int __Pyx_VerifyCachedType(PyObject *cached_type, + const char *name, + Py_ssize_t basicsize, + Py_ssize_t expected_basicsize) { + if (!PyType_Check(cached_type)) { + PyErr_Format(PyExc_TypeError, + "Shared Cython type %.200s is not a type object", name); + return -1; + } + if (basicsize != expected_basicsize) { + PyErr_Format(PyExc_TypeError, + "Shared Cython type %.200s has the wrong size, try recompiling", + name); + return -1; + } + return 0; +} +#if !CYTHON_USE_TYPE_SPECS +static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) { + PyObject* abi_module; + const char* object_name; + PyTypeObject *cached_type = NULL; + abi_module = __Pyx_FetchSharedCythonABIModule(); + if (!abi_module) return NULL; + object_name = strrchr(type->tp_name, '.'); + object_name = object_name ? object_name+1 : type->tp_name; + cached_type = (PyTypeObject*) PyObject_GetAttrString(abi_module, object_name); + if (cached_type) { + if (__Pyx_VerifyCachedType( + (PyObject *)cached_type, + object_name, + cached_type->tp_basicsize, + type->tp_basicsize) < 0) { + goto bad; + } + goto done; + } + if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; + PyErr_Clear(); + if (PyType_Ready(type) < 0) goto bad; + if (PyObject_SetAttrString(abi_module, object_name, (PyObject *)type) < 0) + goto bad; + Py_INCREF(type); + cached_type = type; +done: + Py_DECREF(abi_module); + return cached_type; +bad: + Py_XDECREF(cached_type); + cached_type = NULL; + goto done; +} +#else +static PyTypeObject *__Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases) { + PyObject *abi_module, *cached_type = NULL; + const char* object_name = strrchr(spec->name, '.'); + object_name = object_name ? object_name+1 : spec->name; + abi_module = __Pyx_FetchSharedCythonABIModule(); + if (!abi_module) return NULL; + cached_type = PyObject_GetAttrString(abi_module, object_name); + if (cached_type) { + Py_ssize_t basicsize; +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject *py_basicsize; + py_basicsize = PyObject_GetAttrString(cached_type, "__basicsize__"); + if (unlikely(!py_basicsize)) goto bad; + basicsize = PyLong_AsSsize_t(py_basicsize); + Py_DECREF(py_basicsize); + py_basicsize = 0; + if (unlikely(basicsize == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; +#else + basicsize = likely(PyType_Check(cached_type)) ? ((PyTypeObject*) cached_type)->tp_basicsize : -1; +#endif + if (__Pyx_VerifyCachedType( + cached_type, + object_name, + basicsize, + spec->basicsize) < 0) { + goto bad; + } + goto done; + } + if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; + PyErr_Clear(); + CYTHON_UNUSED_VAR(module); + cached_type = __Pyx_PyType_FromModuleAndSpec(abi_module, spec, bases); + if (unlikely(!cached_type)) goto bad; + if (unlikely(__Pyx_fix_up_extension_type_from_spec(spec, (PyTypeObject *) cached_type) < 0)) goto bad; + if (PyObject_SetAttrString(abi_module, object_name, cached_type) < 0) goto bad; +done: + Py_DECREF(abi_module); + assert(cached_type == NULL || PyType_Check(cached_type)); + return (PyTypeObject *) cached_type; +bad: + Py_XDECREF(cached_type); + cached_type = NULL; + goto done; +} +#endif + +/* PyVectorcallFastCallDict */ + #if CYTHON_METH_FASTCALL +static PyObject *__Pyx_PyVectorcall_FastCallDict_kw(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw) +{ + PyObject *res = NULL; + PyObject *kwnames; + PyObject **newargs; + PyObject **kwvalues; + Py_ssize_t i, pos; + size_t j; + PyObject *key, *value; + unsigned long keys_are_strings; + Py_ssize_t nkw = PyDict_GET_SIZE(kw); + newargs = (PyObject **)PyMem_Malloc((nargs + (size_t)nkw) * sizeof(args[0])); + if (unlikely(newargs == NULL)) { + PyErr_NoMemory(); + return NULL; + } + for (j = 0; j < nargs; j++) newargs[j] = args[j]; + kwnames = PyTuple_New(nkw); + if (unlikely(kwnames == NULL)) { + PyMem_Free(newargs); + return NULL; + } + kwvalues = newargs + nargs; + pos = i = 0; + keys_are_strings = Py_TPFLAGS_UNICODE_SUBCLASS; + while (PyDict_Next(kw, &pos, &key, &value)) { + keys_are_strings &= Py_TYPE(key)->tp_flags; + Py_INCREF(key); + Py_INCREF(value); + PyTuple_SET_ITEM(kwnames, i, key); + kwvalues[i] = value; + i++; + } + if (unlikely(!keys_are_strings)) { + PyErr_SetString(PyExc_TypeError, "keywords must be strings"); + goto cleanup; + } + res = vc(func, newargs, nargs, kwnames); +cleanup: + Py_DECREF(kwnames); + for (i = 0; i < nkw; i++) + Py_DECREF(kwvalues[i]); + PyMem_Free(newargs); + return res; +} +static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw) +{ + if (likely(kw == NULL) || PyDict_GET_SIZE(kw) == 0) { + return vc(func, args, nargs, NULL); + } + return __Pyx_PyVectorcall_FastCallDict_kw(func, vc, args, nargs, kw); +} +#endif + +/* CythonFunctionShared */ + #if CYTHON_COMPILING_IN_LIMITED_API +static CYTHON_INLINE int __Pyx__IsSameCyOrCFunction(PyObject *func, void *cfunc) { + if (__Pyx_CyFunction_Check(func)) { + return PyCFunction_GetFunction(((__pyx_CyFunctionObject*)func)->func) == (PyCFunction) cfunc; + } else if (PyCFunction_Check(func)) { + return PyCFunction_GetFunction(func) == (PyCFunction) cfunc; + } + return 0; +} +#else +static CYTHON_INLINE int __Pyx__IsSameCyOrCFunction(PyObject *func, void *cfunc) { + return __Pyx_CyOrPyCFunction_Check(func) && __Pyx_CyOrPyCFunction_GET_FUNCTION(func) == (PyCFunction) cfunc; +} +#endif +static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj) { +#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API + __Pyx_Py_XDECREF_SET( + __Pyx_CyFunction_GetClassObj(f), + ((classobj) ? __Pyx_NewRef(classobj) : NULL)); +#else + __Pyx_Py_XDECREF_SET( + ((PyCMethodObject *) (f))->mm_class, + (PyTypeObject*)((classobj) ? __Pyx_NewRef(classobj) : NULL)); +#endif +} +static PyObject * +__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, void *closure) +{ + CYTHON_UNUSED_VAR(closure); + if (unlikely(op->func_doc == NULL)) { +#if CYTHON_COMPILING_IN_LIMITED_API + op->func_doc = PyObject_GetAttrString(op->func, "__doc__"); + if (unlikely(!op->func_doc)) return NULL; +#else + if (((PyCFunctionObject*)op)->m_ml->ml_doc) { +#if PY_MAJOR_VERSION >= 3 + op->func_doc = PyUnicode_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc); +#else + op->func_doc = PyString_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc); +#endif + if (unlikely(op->func_doc == NULL)) + return NULL; + } else { + Py_INCREF(Py_None); + return Py_None; + } +#endif + } + Py_INCREF(op->func_doc); + return op->func_doc; +} +static int +__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value, void *context) +{ + CYTHON_UNUSED_VAR(context); + if (value == NULL) { + value = Py_None; + } + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->func_doc, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(context); + if (unlikely(op->func_name == NULL)) { +#if CYTHON_COMPILING_IN_LIMITED_API + op->func_name = PyObject_GetAttrString(op->func, "__name__"); +#elif PY_MAJOR_VERSION >= 3 + op->func_name = PyUnicode_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name); +#else + op->func_name = PyString_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name); +#endif + if (unlikely(op->func_name == NULL)) + return NULL; + } + Py_INCREF(op->func_name); + return op->func_name; +} +static int +__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value, void *context) +{ + CYTHON_UNUSED_VAR(context); +#if PY_MAJOR_VERSION >= 3 + if (unlikely(value == NULL || !PyUnicode_Check(value))) +#else + if (unlikely(value == NULL || !PyString_Check(value))) +#endif + { + PyErr_SetString(PyExc_TypeError, + "__name__ must be set to a string object"); + return -1; + } + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->func_name, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(context); + Py_INCREF(op->func_qualname); + return op->func_qualname; +} +static int +__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value, void *context) +{ + CYTHON_UNUSED_VAR(context); +#if PY_MAJOR_VERSION >= 3 + if (unlikely(value == NULL || !PyUnicode_Check(value))) +#else + if (unlikely(value == NULL || !PyString_Check(value))) +#endif + { + PyErr_SetString(PyExc_TypeError, + "__qualname__ must be set to a string object"); + return -1; + } + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->func_qualname, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(context); + if (unlikely(op->func_dict == NULL)) { + op->func_dict = PyDict_New(); + if (unlikely(op->func_dict == NULL)) + return NULL; + } + Py_INCREF(op->func_dict); + return op->func_dict; +} +static int +__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value, void *context) +{ + CYTHON_UNUSED_VAR(context); + if (unlikely(value == NULL)) { + PyErr_SetString(PyExc_TypeError, + "function's dictionary may not be deleted"); + return -1; + } + if (unlikely(!PyDict_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "setting function's dictionary to a non-dict"); + return -1; + } + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->func_dict, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(context); + Py_INCREF(op->func_globals); + return op->func_globals; +} +static PyObject * +__Pyx_CyFunction_get_closure(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(op); + CYTHON_UNUSED_VAR(context); + Py_INCREF(Py_None); + return Py_None; +} +static PyObject * +__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op, void *context) +{ + PyObject* result = (op->func_code) ? op->func_code : Py_None; + CYTHON_UNUSED_VAR(context); + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) { + int result = 0; + PyObject *res = op->defaults_getter((PyObject *) op); + if (unlikely(!res)) + return -1; + #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + op->defaults_tuple = PyTuple_GET_ITEM(res, 0); + Py_INCREF(op->defaults_tuple); + op->defaults_kwdict = PyTuple_GET_ITEM(res, 1); + Py_INCREF(op->defaults_kwdict); + #else + op->defaults_tuple = __Pyx_PySequence_ITEM(res, 0); + if (unlikely(!op->defaults_tuple)) result = -1; + else { + op->defaults_kwdict = __Pyx_PySequence_ITEM(res, 1); + if (unlikely(!op->defaults_kwdict)) result = -1; + } + #endif + Py_DECREF(res); + return result; +} +static int +__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) { + CYTHON_UNUSED_VAR(context); + if (!value) { + value = Py_None; + } else if (unlikely(value != Py_None && !PyTuple_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "__defaults__ must be set to a tuple object"); + return -1; + } + PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__defaults__ will not " + "currently affect the values used in function calls", 1); + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->defaults_tuple, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op, void *context) { + PyObject* result = op->defaults_tuple; + CYTHON_UNUSED_VAR(context); + if (unlikely(!result)) { + if (op->defaults_getter) { + if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL; + result = op->defaults_tuple; + } else { + result = Py_None; + } + } + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) { + CYTHON_UNUSED_VAR(context); + if (!value) { + value = Py_None; + } else if (unlikely(value != Py_None && !PyDict_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "__kwdefaults__ must be set to a dict object"); + return -1; + } + PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__kwdefaults__ will not " + "currently affect the values used in function calls", 1); + Py_INCREF(value); + __Pyx_Py_XDECREF_SET(op->defaults_kwdict, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op, void *context) { + PyObject* result = op->defaults_kwdict; + CYTHON_UNUSED_VAR(context); + if (unlikely(!result)) { + if (op->defaults_getter) { + if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL; + result = op->defaults_kwdict; + } else { + result = Py_None; + } + } + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value, void *context) { + CYTHON_UNUSED_VAR(context); + if (!value || value == Py_None) { + value = NULL; + } else if (unlikely(!PyDict_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "__annotations__ must be set to a dict object"); + return -1; + } + Py_XINCREF(value); + __Pyx_Py_XDECREF_SET(op->func_annotations, value); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op, void *context) { + PyObject* result = op->func_annotations; + CYTHON_UNUSED_VAR(context); + if (unlikely(!result)) { + result = PyDict_New(); + if (unlikely(!result)) return NULL; + op->func_annotations = result; + } + Py_INCREF(result); + return result; +} +static PyObject * +__Pyx_CyFunction_get_is_coroutine(__pyx_CyFunctionObject *op, void *context) { + int is_coroutine; + CYTHON_UNUSED_VAR(context); + if (op->func_is_coroutine) { + return __Pyx_NewRef(op->func_is_coroutine); + } + is_coroutine = op->flags & __Pyx_CYFUNCTION_COROUTINE; +#if PY_VERSION_HEX >= 0x03050000 + if (is_coroutine) { + PyObject *module, *fromlist, *marker = __pyx_n_s_is_coroutine; + fromlist = PyList_New(1); + if (unlikely(!fromlist)) return NULL; + Py_INCREF(marker); +#if CYTHON_ASSUME_SAFE_MACROS + PyList_SET_ITEM(fromlist, 0, marker); +#else + if (unlikely(PyList_SetItem(fromlist, 0, marker) < 0)) { + Py_DECREF(marker); + Py_DECREF(fromlist); + return NULL; + } +#endif + module = PyImport_ImportModuleLevelObject(__pyx_n_s_asyncio_coroutines, NULL, NULL, fromlist, 0); + Py_DECREF(fromlist); + if (unlikely(!module)) goto ignore; + op->func_is_coroutine = __Pyx_PyObject_GetAttrStr(module, marker); + Py_DECREF(module); + if (likely(op->func_is_coroutine)) { + return __Pyx_NewRef(op->func_is_coroutine); + } +ignore: + PyErr_Clear(); + } +#endif + op->func_is_coroutine = __Pyx_PyBool_FromLong(is_coroutine); + return __Pyx_NewRef(op->func_is_coroutine); +} +#if CYTHON_COMPILING_IN_LIMITED_API +static PyObject * +__Pyx_CyFunction_get_module(__pyx_CyFunctionObject *op, void *context) { + CYTHON_UNUSED_VAR(context); + return PyObject_GetAttrString(op->func, "__module__"); +} +static int +__Pyx_CyFunction_set_module(__pyx_CyFunctionObject *op, PyObject* value, void *context) { + CYTHON_UNUSED_VAR(context); + return PyObject_SetAttrString(op->func, "__module__", value); +} +#endif +static PyGetSetDef __pyx_CyFunction_getsets[] = { + {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, + {(char *) "__doc__", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, + {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, + {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, + {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0}, + {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, + {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, + {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, + {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, + {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, + {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, + {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, + {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, + {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, + {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, + {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0}, + {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0}, + {(char *) "_is_coroutine", (getter)__Pyx_CyFunction_get_is_coroutine, 0, 0, 0}, +#if CYTHON_COMPILING_IN_LIMITED_API + {"__module__", (getter)__Pyx_CyFunction_get_module, (setter)__Pyx_CyFunction_set_module, 0, 0}, +#endif + {0, 0, 0, 0, 0} +}; +static PyMemberDef __pyx_CyFunction_members[] = { +#if !CYTHON_COMPILING_IN_LIMITED_API + {(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), 0, 0}, +#endif +#if CYTHON_USE_TYPE_SPECS + {(char *) "__dictoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_dict), READONLY, 0}, +#if CYTHON_METH_FASTCALL +#if CYTHON_BACKPORT_VECTORCALL + {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_vectorcall), READONLY, 0}, +#else +#if !CYTHON_COMPILING_IN_LIMITED_API + {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(PyCFunctionObject, vectorcall), READONLY, 0}, +#endif +#endif +#endif +#if PY_VERSION_HEX < 0x030500A0 || CYTHON_COMPILING_IN_LIMITED_API + {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_weakreflist), READONLY, 0}, +#else + {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(PyCFunctionObject, m_weakreflist), READONLY, 0}, +#endif +#endif + {0, 0, 0, 0, 0} +}; +static PyObject * +__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, PyObject *args) +{ + CYTHON_UNUSED_VAR(args); +#if PY_MAJOR_VERSION >= 3 + Py_INCREF(m->func_qualname); + return m->func_qualname; +#else + return PyString_FromString(((PyCFunctionObject*)m)->m_ml->ml_name); +#endif +} +static PyMethodDef __pyx_CyFunction_methods[] = { + {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0}, + {0, 0, 0, 0} +}; +#if PY_VERSION_HEX < 0x030500A0 || CYTHON_COMPILING_IN_LIMITED_API +#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist) +#else +#define __Pyx_CyFunction_weakreflist(cyfunc) (((PyCFunctionObject*)cyfunc)->m_weakreflist) +#endif +static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject *op, PyMethodDef *ml, int flags, PyObject* qualname, + PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { +#if !CYTHON_COMPILING_IN_LIMITED_API + PyCFunctionObject *cf = (PyCFunctionObject*) op; +#endif + if (unlikely(op == NULL)) + return NULL; +#if CYTHON_COMPILING_IN_LIMITED_API + op->func = PyCFunction_NewEx(ml, (PyObject*)op, module); + if (unlikely(!op->func)) return NULL; +#endif + op->flags = flags; + __Pyx_CyFunction_weakreflist(op) = NULL; +#if !CYTHON_COMPILING_IN_LIMITED_API + cf->m_ml = ml; + cf->m_self = (PyObject *) op; +#endif + Py_XINCREF(closure); + op->func_closure = closure; +#if !CYTHON_COMPILING_IN_LIMITED_API + Py_XINCREF(module); + cf->m_module = module; +#endif + op->func_dict = NULL; + op->func_name = NULL; + Py_INCREF(qualname); + op->func_qualname = qualname; + op->func_doc = NULL; +#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API + op->func_classobj = NULL; +#else + ((PyCMethodObject*)op)->mm_class = NULL; +#endif + op->func_globals = globals; + Py_INCREF(op->func_globals); + Py_XINCREF(code); + op->func_code = code; + op->defaults_pyobjects = 0; + op->defaults_size = 0; + op->defaults = NULL; + op->defaults_tuple = NULL; + op->defaults_kwdict = NULL; + op->defaults_getter = NULL; + op->func_annotations = NULL; + op->func_is_coroutine = NULL; +#if CYTHON_METH_FASTCALL + switch (ml->ml_flags & (METH_VARARGS | METH_FASTCALL | METH_NOARGS | METH_O | METH_KEYWORDS | METH_METHOD)) { + case METH_NOARGS: + __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_NOARGS; + break; + case METH_O: + __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_O; + break; + case METH_METHOD | METH_FASTCALL | METH_KEYWORDS: + __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD; + break; + case METH_FASTCALL | METH_KEYWORDS: + __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS; + break; + case METH_VARARGS | METH_KEYWORDS: + __Pyx_CyFunction_func_vectorcall(op) = NULL; + break; + default: + PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction"); + Py_DECREF(op); + return NULL; + } +#endif + return (PyObject *) op; +} +static int +__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m) +{ + Py_CLEAR(m->func_closure); +#if CYTHON_COMPILING_IN_LIMITED_API + Py_CLEAR(m->func); +#else + Py_CLEAR(((PyCFunctionObject*)m)->m_module); +#endif + Py_CLEAR(m->func_dict); + Py_CLEAR(m->func_name); + Py_CLEAR(m->func_qualname); + Py_CLEAR(m->func_doc); + Py_CLEAR(m->func_globals); + Py_CLEAR(m->func_code); +#if !CYTHON_COMPILING_IN_LIMITED_API +#if PY_VERSION_HEX < 0x030900B1 + Py_CLEAR(__Pyx_CyFunction_GetClassObj(m)); +#else + { + PyObject *cls = (PyObject*) ((PyCMethodObject *) (m))->mm_class; + ((PyCMethodObject *) (m))->mm_class = NULL; + Py_XDECREF(cls); + } +#endif +#endif + Py_CLEAR(m->defaults_tuple); + Py_CLEAR(m->defaults_kwdict); + Py_CLEAR(m->func_annotations); + Py_CLEAR(m->func_is_coroutine); + if (m->defaults) { + PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); + int i; + for (i = 0; i < m->defaults_pyobjects; i++) + Py_XDECREF(pydefaults[i]); + PyObject_Free(m->defaults); + m->defaults = NULL; + } + return 0; +} +static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m) +{ + if (__Pyx_CyFunction_weakreflist(m) != NULL) + PyObject_ClearWeakRefs((PyObject *) m); + __Pyx_CyFunction_clear(m); + __Pyx_PyHeapTypeObject_GC_Del(m); +} +static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m) +{ + PyObject_GC_UnTrack(m); + __Pyx__CyFunction_dealloc(m); +} +static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg) +{ + Py_VISIT(m->func_closure); +#if CYTHON_COMPILING_IN_LIMITED_API + Py_VISIT(m->func); +#else + Py_VISIT(((PyCFunctionObject*)m)->m_module); +#endif + Py_VISIT(m->func_dict); + Py_VISIT(m->func_name); + Py_VISIT(m->func_qualname); + Py_VISIT(m->func_doc); + Py_VISIT(m->func_globals); + Py_VISIT(m->func_code); +#if !CYTHON_COMPILING_IN_LIMITED_API + Py_VISIT(__Pyx_CyFunction_GetClassObj(m)); +#endif + Py_VISIT(m->defaults_tuple); + Py_VISIT(m->defaults_kwdict); + Py_VISIT(m->func_is_coroutine); + if (m->defaults) { + PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); + int i; + for (i = 0; i < m->defaults_pyobjects; i++) + Py_VISIT(pydefaults[i]); + } + return 0; +} +static PyObject* +__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op) +{ +#if PY_MAJOR_VERSION >= 3 + return PyUnicode_FromFormat("", + op->func_qualname, (void *)op); +#else + return PyString_FromFormat("", + PyString_AsString(op->func_qualname), (void *)op); +#endif +} +static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) { +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject *f = ((__pyx_CyFunctionObject*)func)->func; + PyObject *py_name = NULL; + PyCFunction meth; + int flags; + meth = PyCFunction_GetFunction(f); + if (unlikely(!meth)) return NULL; + flags = PyCFunction_GetFlags(f); + if (unlikely(flags < 0)) return NULL; +#else + PyCFunctionObject* f = (PyCFunctionObject*)func; + PyCFunction meth = f->m_ml->ml_meth; + int flags = f->m_ml->ml_flags; +#endif + Py_ssize_t size; + switch (flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) { + case METH_VARARGS: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) + return (*meth)(self, arg); + break; + case METH_VARARGS | METH_KEYWORDS: + return (*(PyCFunctionWithKeywords)(void*)meth)(self, arg, kw); + case METH_NOARGS: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) { +#if CYTHON_ASSUME_SAFE_MACROS + size = PyTuple_GET_SIZE(arg); +#else + size = PyTuple_Size(arg); + if (unlikely(size < 0)) return NULL; +#endif + if (likely(size == 0)) + return (*meth)(self, NULL); +#if CYTHON_COMPILING_IN_LIMITED_API + py_name = __Pyx_CyFunction_get_name((__pyx_CyFunctionObject*)func, NULL); + if (!py_name) return NULL; + PyErr_Format(PyExc_TypeError, + "%.200S() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)", + py_name, size); + Py_DECREF(py_name); +#else + PyErr_Format(PyExc_TypeError, + "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)", + f->m_ml->ml_name, size); +#endif + return NULL; + } + break; + case METH_O: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) { +#if CYTHON_ASSUME_SAFE_MACROS + size = PyTuple_GET_SIZE(arg); +#else + size = PyTuple_Size(arg); + if (unlikely(size < 0)) return NULL; +#endif + if (likely(size == 1)) { + PyObject *result, *arg0; + #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + arg0 = PyTuple_GET_ITEM(arg, 0); + #else + arg0 = __Pyx_PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL; + #endif + result = (*meth)(self, arg0); + #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) + Py_DECREF(arg0); + #endif + return result; + } +#if CYTHON_COMPILING_IN_LIMITED_API + py_name = __Pyx_CyFunction_get_name((__pyx_CyFunctionObject*)func, NULL); + if (!py_name) return NULL; + PyErr_Format(PyExc_TypeError, + "%.200S() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)", + py_name, size); + Py_DECREF(py_name); +#else + PyErr_Format(PyExc_TypeError, + "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)", + f->m_ml->ml_name, size); +#endif + return NULL; + } + break; + default: + PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction"); + return NULL; + } +#if CYTHON_COMPILING_IN_LIMITED_API + py_name = __Pyx_CyFunction_get_name((__pyx_CyFunctionObject*)func, NULL); + if (!py_name) return NULL; + PyErr_Format(PyExc_TypeError, "%.200S() takes no keyword arguments", + py_name); + Py_DECREF(py_name); +#else + PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments", + f->m_ml->ml_name); +#endif + return NULL; +} +static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *self, *result; +#if CYTHON_COMPILING_IN_LIMITED_API + self = PyCFunction_GetSelf(((__pyx_CyFunctionObject*)func)->func); + if (unlikely(!self) && PyErr_Occurred()) return NULL; +#else + self = ((PyCFunctionObject*)func)->m_self; +#endif + result = __Pyx_CyFunction_CallMethod(func, self, arg, kw); + return result; +} +static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) { + PyObject *result; + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func; +#if CYTHON_METH_FASTCALL + __pyx_vectorcallfunc vc = __Pyx_CyFunction_func_vectorcall(cyfunc); + if (vc) { +#if CYTHON_ASSUME_SAFE_MACROS + return __Pyx_PyVectorcall_FastCallDict(func, vc, &PyTuple_GET_ITEM(args, 0), (size_t)PyTuple_GET_SIZE(args), kw); +#else + (void) &__Pyx_PyVectorcall_FastCallDict; + return PyVectorcall_Call(func, args, kw); +#endif + } +#endif + if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) { + Py_ssize_t argc; + PyObject *new_args; + PyObject *self; +#if CYTHON_ASSUME_SAFE_MACROS + argc = PyTuple_GET_SIZE(args); +#else + argc = PyTuple_Size(args); + if (unlikely(!argc) < 0) return NULL; +#endif + new_args = PyTuple_GetSlice(args, 1, argc); + if (unlikely(!new_args)) + return NULL; + self = PyTuple_GetItem(args, 0); + if (unlikely(!self)) { + Py_DECREF(new_args); +#if PY_MAJOR_VERSION > 2 + PyErr_Format(PyExc_TypeError, + "unbound method %.200S() needs an argument", + cyfunc->func_qualname); +#else + PyErr_SetString(PyExc_TypeError, + "unbound method needs an argument"); +#endif + return NULL; + } + result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw); + Py_DECREF(new_args); + } else { + result = __Pyx_CyFunction_Call(func, args, kw); + } + return result; +} +#if CYTHON_METH_FASTCALL +static CYTHON_INLINE int __Pyx_CyFunction_Vectorcall_CheckArgs(__pyx_CyFunctionObject *cyfunc, Py_ssize_t nargs, PyObject *kwnames) +{ + int ret = 0; + if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) { + if (unlikely(nargs < 1)) { + PyErr_Format(PyExc_TypeError, "%.200s() needs an argument", + ((PyCFunctionObject*)cyfunc)->m_ml->ml_name); + return -1; + } + ret = 1; + } + if (unlikely(kwnames) && unlikely(PyTuple_GET_SIZE(kwnames))) { + PyErr_Format(PyExc_TypeError, + "%.200s() takes no keyword arguments", ((PyCFunctionObject*)cyfunc)->m_ml->ml_name); + return -1; + } + return ret; +} +static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; + PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml; +#if CYTHON_BACKPORT_VECTORCALL + Py_ssize_t nargs = (Py_ssize_t)nargsf; +#else + Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); +#endif + PyObject *self; + switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) { + case 1: + self = args[0]; + args += 1; + nargs -= 1; + break; + case 0: + self = ((PyCFunctionObject*)cyfunc)->m_self; + break; + default: + return NULL; + } + if (unlikely(nargs != 0)) { + PyErr_Format(PyExc_TypeError, + "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)", + def->ml_name, nargs); + return NULL; + } + return def->ml_meth(self, NULL); +} +static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; + PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml; +#if CYTHON_BACKPORT_VECTORCALL + Py_ssize_t nargs = (Py_ssize_t)nargsf; +#else + Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); +#endif + PyObject *self; + switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) { + case 1: + self = args[0]; + args += 1; + nargs -= 1; + break; + case 0: + self = ((PyCFunctionObject*)cyfunc)->m_self; + break; + default: + return NULL; + } + if (unlikely(nargs != 1)) { + PyErr_Format(PyExc_TypeError, + "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)", + def->ml_name, nargs); + return NULL; + } + return def->ml_meth(self, args[0]); +} +static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; + PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml; +#if CYTHON_BACKPORT_VECTORCALL + Py_ssize_t nargs = (Py_ssize_t)nargsf; +#else + Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); +#endif + PyObject *self; + switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) { + case 1: + self = args[0]; + args += 1; + nargs -= 1; + break; + case 0: + self = ((PyCFunctionObject*)cyfunc)->m_self; + break; + default: + return NULL; + } + return ((_PyCFunctionFastWithKeywords)(void(*)(void))def->ml_meth)(self, args, nargs, kwnames); +} +static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; + PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml; + PyTypeObject *cls = (PyTypeObject *) __Pyx_CyFunction_GetClassObj(cyfunc); +#if CYTHON_BACKPORT_VECTORCALL + Py_ssize_t nargs = (Py_ssize_t)nargsf; +#else + Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); +#endif + PyObject *self; + switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) { + case 1: + self = args[0]; + args += 1; + nargs -= 1; + break; + case 0: + self = ((PyCFunctionObject*)cyfunc)->m_self; + break; + default: + return NULL; + } + return ((__Pyx_PyCMethod)(void(*)(void))def->ml_meth)(self, cls, args, (size_t)nargs, kwnames); +} +#endif +#if CYTHON_USE_TYPE_SPECS +static PyType_Slot __pyx_CyFunctionType_slots[] = { + {Py_tp_dealloc, (void *)__Pyx_CyFunction_dealloc}, + {Py_tp_repr, (void *)__Pyx_CyFunction_repr}, + {Py_tp_call, (void *)__Pyx_CyFunction_CallAsMethod}, + {Py_tp_traverse, (void *)__Pyx_CyFunction_traverse}, + {Py_tp_clear, (void *)__Pyx_CyFunction_clear}, + {Py_tp_methods, (void *)__pyx_CyFunction_methods}, + {Py_tp_members, (void *)__pyx_CyFunction_members}, + {Py_tp_getset, (void *)__pyx_CyFunction_getsets}, + {Py_tp_descr_get, (void *)__Pyx_PyMethod_New}, + {0, 0}, +}; +static PyType_Spec __pyx_CyFunctionType_spec = { + __PYX_TYPE_MODULE_PREFIX "cython_function_or_method", + sizeof(__pyx_CyFunctionObject), + 0, +#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR + Py_TPFLAGS_METHOD_DESCRIPTOR | +#endif +#if (defined(_Py_TPFLAGS_HAVE_VECTORCALL) && CYTHON_METH_FASTCALL) + _Py_TPFLAGS_HAVE_VECTORCALL | +#endif + Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE, + __pyx_CyFunctionType_slots +}; +#else +static PyTypeObject __pyx_CyFunctionType_type = { + PyVarObject_HEAD_INIT(0, 0) + __PYX_TYPE_MODULE_PREFIX "cython_function_or_method", + sizeof(__pyx_CyFunctionObject), + 0, + (destructor) __Pyx_CyFunction_dealloc, +#if !CYTHON_METH_FASTCALL + 0, +#elif CYTHON_BACKPORT_VECTORCALL + (printfunc)offsetof(__pyx_CyFunctionObject, func_vectorcall), +#else + offsetof(PyCFunctionObject, vectorcall), +#endif + 0, + 0, +#if PY_MAJOR_VERSION < 3 + 0, +#else + 0, +#endif + (reprfunc) __Pyx_CyFunction_repr, + 0, + 0, + 0, + 0, + __Pyx_CyFunction_CallAsMethod, + 0, + 0, + 0, + 0, +#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR + Py_TPFLAGS_METHOD_DESCRIPTOR | +#endif +#if defined(_Py_TPFLAGS_HAVE_VECTORCALL) && CYTHON_METH_FASTCALL + _Py_TPFLAGS_HAVE_VECTORCALL | +#endif + Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE, + 0, + (traverseproc) __Pyx_CyFunction_traverse, + (inquiry) __Pyx_CyFunction_clear, + 0, +#if PY_VERSION_HEX < 0x030500A0 + offsetof(__pyx_CyFunctionObject, func_weakreflist), +#else + offsetof(PyCFunctionObject, m_weakreflist), +#endif + 0, + 0, + __pyx_CyFunction_methods, + __pyx_CyFunction_members, + __pyx_CyFunction_getsets, + 0, + 0, + __Pyx_PyMethod_New, + 0, + offsetof(__pyx_CyFunctionObject, func_dict), + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, +#if PY_VERSION_HEX >= 0x030400a1 + 0, +#endif +#if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, +#endif +#if __PYX_NEED_TP_PRINT_SLOT + 0, +#endif +#if PY_VERSION_HEX >= 0x030C0000 + 0, +#endif +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, +#endif +}; +#endif +static int __pyx_CyFunction_init(PyObject *module) { +#if CYTHON_USE_TYPE_SPECS + __pyx_CyFunctionType = __Pyx_FetchCommonTypeFromSpec(module, &__pyx_CyFunctionType_spec, NULL); +#else + CYTHON_UNUSED_VAR(module); + __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type); +#endif + if (unlikely(__pyx_CyFunctionType == NULL)) { + return -1; + } + return 0; +} +static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults = PyObject_Malloc(size); + if (unlikely(!m->defaults)) + return PyErr_NoMemory(); + memset(m->defaults, 0, size); + m->defaults_pyobjects = pyobjects; + m->defaults_size = size; + return m->defaults; +} +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults_tuple = tuple; + Py_INCREF(tuple); +} +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults_kwdict = dict; + Py_INCREF(dict); +} +static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->func_annotations = dict; + Py_INCREF(dict); +} + +/* CythonFunction */ + static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, int flags, PyObject* qualname, + PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { + PyObject *op = __Pyx_CyFunction_Init( + PyObject_GC_New(__pyx_CyFunctionObject, __pyx_CyFunctionType), + ml, flags, qualname, closure, module, globals, code + ); + if (likely(op)) { + PyObject_GC_Track(op); + } + return op; +} + +/* CLineInTraceback */ + #ifndef CYTHON_CLINE_IN_TRACEBACK +static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line) { + PyObject *use_cline; + PyObject *ptype, *pvalue, *ptraceback; +#if CYTHON_COMPILING_IN_CPYTHON + PyObject **cython_runtime_dict; +#endif + CYTHON_MAYBE_UNUSED_VAR(tstate); + if (unlikely(!__pyx_cython_runtime)) { + return c_line; + } + __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); +#if CYTHON_COMPILING_IN_CPYTHON + cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); + if (likely(cython_runtime_dict)) { + __PYX_PY_DICT_LOOKUP_IF_MODIFIED( + use_cline, *cython_runtime_dict, + __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback)) + } else +#endif + { + PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStrNoError(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); + if (use_cline_obj) { + use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; + Py_DECREF(use_cline_obj); + } else { + PyErr_Clear(); + use_cline = NULL; + } + } + if (!use_cline) { + c_line = 0; + (void) PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); + } + else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) { + c_line = 0; + } + __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); + return c_line; +} +#endif + +/* CodeObjectCache */ + #if !CYTHON_COMPILING_IN_LIMITED_API +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = start + (end - start) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} +#endif + +/* AddTraceback */ + #include "compile.h" +#include "frameobject.h" +#include "traceback.h" +#if PY_VERSION_HEX >= 0x030b00a6 && !CYTHON_COMPILING_IN_LIMITED_API + #ifndef Py_BUILD_CORE + #define Py_BUILD_CORE 1 + #endif + #include "internal/pycore_frame.h" +#endif +#if CYTHON_COMPILING_IN_LIMITED_API +static PyObject *__Pyx_PyCode_Replace_For_AddTraceback(PyObject *code, PyObject *scratch_dict, + PyObject *firstlineno, PyObject *name) { + PyObject *replace = NULL; + if (unlikely(PyDict_SetItemString(scratch_dict, "co_firstlineno", firstlineno))) return NULL; + if (unlikely(PyDict_SetItemString(scratch_dict, "co_name", name))) return NULL; + replace = PyObject_GetAttrString(code, "replace"); + if (likely(replace)) { + PyObject *result; + result = PyObject_Call(replace, __pyx_empty_tuple, scratch_dict); + Py_DECREF(replace); + return result; + } + PyErr_Clear(); + #if __PYX_LIMITED_VERSION_HEX < 0x030780000 + { + PyObject *compiled = NULL, *result = NULL; + if (unlikely(PyDict_SetItemString(scratch_dict, "code", code))) return NULL; + if (unlikely(PyDict_SetItemString(scratch_dict, "type", (PyObject*)(&PyType_Type)))) return NULL; + compiled = Py_CompileString( + "out = type(code)(\n" + " code.co_argcount, code.co_kwonlyargcount, code.co_nlocals, code.co_stacksize,\n" + " code.co_flags, code.co_code, code.co_consts, code.co_names,\n" + " code.co_varnames, code.co_filename, co_name, co_firstlineno,\n" + " code.co_lnotab)\n", "", Py_file_input); + if (!compiled) return NULL; + result = PyEval_EvalCode(compiled, scratch_dict, scratch_dict); + Py_DECREF(compiled); + if (!result) PyErr_Print(); + Py_DECREF(result); + result = PyDict_GetItemString(scratch_dict, "out"); + if (result) Py_INCREF(result); + return result; + } + #else + return NULL; + #endif +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyObject *code_object = NULL, *py_py_line = NULL, *py_funcname = NULL, *dict = NULL; + PyObject *replace = NULL, *getframe = NULL, *frame = NULL; + PyObject *exc_type, *exc_value, *exc_traceback; + int success = 0; + if (c_line) { + (void) __pyx_cfilenm; + (void) __Pyx_CLineForTraceback(__Pyx_PyThreadState_Current, c_line); + } + PyErr_Fetch(&exc_type, &exc_value, &exc_traceback); + code_object = Py_CompileString("_getframe()", filename, Py_eval_input); + if (unlikely(!code_object)) goto bad; + py_py_line = PyLong_FromLong(py_line); + if (unlikely(!py_py_line)) goto bad; + py_funcname = PyUnicode_FromString(funcname); + if (unlikely(!py_funcname)) goto bad; + dict = PyDict_New(); + if (unlikely(!dict)) goto bad; + { + PyObject *old_code_object = code_object; + code_object = __Pyx_PyCode_Replace_For_AddTraceback(code_object, dict, py_py_line, py_funcname); + Py_DECREF(old_code_object); + } + if (unlikely(!code_object)) goto bad; + getframe = PySys_GetObject("_getframe"); + if (unlikely(!getframe)) goto bad; + if (unlikely(PyDict_SetItemString(dict, "_getframe", getframe))) goto bad; + frame = PyEval_EvalCode(code_object, dict, dict); + if (unlikely(!frame) || frame == Py_None) goto bad; + success = 1; + bad: + PyErr_Restore(exc_type, exc_value, exc_traceback); + Py_XDECREF(code_object); + Py_XDECREF(py_py_line); + Py_XDECREF(py_funcname); + Py_XDECREF(dict); + Py_XDECREF(replace); + if (success) { + PyTraceBack_Here( + (struct _frame*)frame); + } + Py_XDECREF(frame); +} +#else +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = NULL; + PyObject *py_funcname = NULL; + #if PY_MAJOR_VERSION < 3 + PyObject *py_srcfile = NULL; + py_srcfile = PyString_FromString(filename); + if (!py_srcfile) goto bad; + #endif + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + if (!py_funcname) goto bad; + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + if (!py_funcname) goto bad; + funcname = PyUnicode_AsUTF8(py_funcname); + if (!funcname) goto bad; + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + if (!py_funcname) goto bad; + #endif + } + #if PY_MAJOR_VERSION < 3 + py_code = __Pyx_PyCode_New( + 0, + 0, + 0, + 0, + 0, + 0, + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + #else + py_code = PyCode_NewEmpty(filename, funcname, py_line); + #endif + Py_XDECREF(py_funcname); // XDECREF since it's only set on Py3 if cline + return py_code; +bad: + Py_XDECREF(py_funcname); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(py_srcfile); + #endif + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject *ptype, *pvalue, *ptraceback; + if (c_line) { + c_line = __Pyx_CLineForTraceback(tstate, c_line); + } + py_code = __pyx_find_code_object(c_line ? -c_line : py_line); + if (!py_code) { + __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) { + /* If the code object creation fails, then we should clear the + fetched exception references and propagate the new exception */ + Py_XDECREF(ptype); + Py_XDECREF(pvalue); + Py_XDECREF(ptraceback); + goto bad; + } + __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); + __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); + } + py_frame = PyFrame_New( + tstate, /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + __Pyx_PyFrame_SetLineNumber(py_frame, py_line); + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} +#endif + +#if PY_MAJOR_VERSION < 3 +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + __Pyx_TypeName obj_type_name; + if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); + if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); + if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags); + obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); + PyErr_Format(PyExc_TypeError, + "'" __Pyx_FMT_TYPENAME "' does not have the buffer interface", + obj_type_name); + __Pyx_DECREF_TypeName(obj_type_name); + return -1; +} +static void __Pyx_ReleaseBuffer(Py_buffer *view) { + PyObject *obj = view->obj; + if (!obj) return; + if (PyObject_CheckBuffer(obj)) { + PyBuffer_Release(view); + return; + } + if ((0)) {} + view->obj = NULL; + Py_DECREF(obj); +} +#endif + + + /* MemviewSliceIsContig */ + static int +__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim) +{ + int i, index, step, start; + Py_ssize_t itemsize = mvs.memview->view.itemsize; + if (order == 'F') { + step = 1; + start = 0; + } else { + step = -1; + start = ndim - 1; + } + for (i = 0; i < ndim; i++) { + index = start + step * i; + if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize) + return 0; + itemsize *= mvs.shape[index]; + } + return 1; +} + +/* OverlappingSlices */ + static void +__pyx_get_array_memory_extents(__Pyx_memviewslice *slice, + void **out_start, void **out_end, + int ndim, size_t itemsize) +{ + char *start, *end; + int i; + start = end = slice->data; + for (i = 0; i < ndim; i++) { + Py_ssize_t stride = slice->strides[i]; + Py_ssize_t extent = slice->shape[i]; + if (extent == 0) { + *out_start = *out_end = start; + return; + } else { + if (stride > 0) + end += stride * (extent - 1); + else + start += stride * (extent - 1); + } + } + *out_start = start; + *out_end = end + itemsize; +} +static int +__pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize) +{ + void *start1, *end1, *start2, *end2; + __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); + __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); + return (start1 < end2) && (start2 < end1); +} + +/* CIntFromPyVerify */ + #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +/* TypeInfoCompare */ + static int +__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) +{ + int i; + if (!a || !b) + return 0; + if (a == b) + return 1; + if (a->size != b->size || a->typegroup != b->typegroup || + a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { + if (a->typegroup == 'H' || b->typegroup == 'H') { + return a->size == b->size; + } else { + return 0; + } + } + if (a->ndim) { + for (i = 0; i < a->ndim; i++) + if (a->arraysize[i] != b->arraysize[i]) + return 0; + } + if (a->typegroup == 'S') { + if (a->flags != b->flags) + return 0; + if (a->fields || b->fields) { + if (!(a->fields && b->fields)) + return 0; + for (i = 0; a->fields[i].type && b->fields[i].type; i++) { + __Pyx_StructField *field_a = a->fields + i; + __Pyx_StructField *field_b = b->fields + i; + if (field_a->offset != field_b->offset || + !__pyx_typeinfo_cmp(field_a->type, field_b->type)) + return 0; + } + return !a->fields[i].type && !b->fields[i].type; + } + } + return 1; +} + +/* MemviewSliceValidateAndInit */ + static int +__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) +{ + if (buf->shape[dim] <= 1) + return 1; + if (buf->strides) { + if (spec & __Pyx_MEMVIEW_CONTIG) { + if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { + if (unlikely(buf->strides[dim] != sizeof(void *))) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly contiguous " + "in dimension %d.", dim); + goto fail; + } + } else if (unlikely(buf->strides[dim] != buf->itemsize)) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_FOLLOW) { + Py_ssize_t stride = buf->strides[dim]; + if (stride < 0) + stride = -stride; + if (unlikely(stride < buf->itemsize)) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + } else { + if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not contiguous in " + "dimension %d", dim); + goto fail; + } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not indirect in " + "dimension %d", dim); + goto fail; + } else if (unlikely(buf->suboffsets)) { + PyErr_SetString(PyExc_ValueError, + "Buffer exposes suboffsets but no strides"); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_check_suboffsets(Py_buffer *buf, int dim, int ndim, int spec) +{ + CYTHON_UNUSED_VAR(ndim); + if (spec & __Pyx_MEMVIEW_DIRECT) { + if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) { + PyErr_Format(PyExc_ValueError, + "Buffer not compatible with direct access " + "in dimension %d.", dim); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_PTR) { + if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly accessible " + "in dimension %d.", dim); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) +{ + int i; + if (c_or_f_flag & __Pyx_IS_F_CONTIG) { + Py_ssize_t stride = 1; + for (i = 0; i < ndim; i++) { + if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { + PyErr_SetString(PyExc_ValueError, + "Buffer not fortran contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { + Py_ssize_t stride = 1; + for (i = ndim - 1; i >- 1; i--) { + if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { + PyErr_SetString(PyExc_ValueError, + "Buffer not C contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } + return 1; +fail: + return 0; +} +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj) +{ + struct __pyx_memoryview_obj *memview, *new_memview; + __Pyx_RefNannyDeclarations + Py_buffer *buf; + int i, spec = 0, retval = -1; + __Pyx_BufFmt_Context ctx; + int from_memoryview = __pyx_memoryview_check(original_obj); + __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); + if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) + original_obj)->typeinfo)) { + memview = (struct __pyx_memoryview_obj *) original_obj; + new_memview = NULL; + } else { + memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + original_obj, buf_flags, 0, dtype); + new_memview = memview; + if (unlikely(!memview)) + goto fail; + } + buf = &memview->view; + if (unlikely(buf->ndim != ndim)) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + ndim, buf->ndim); + goto fail; + } + if (new_memview) { + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail; + } + if (unlikely((unsigned) buf->itemsize != dtype->size)) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " + "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", + buf->itemsize, + (buf->itemsize > 1) ? "s" : "", + dtype->name, + dtype->size, + (dtype->size > 1) ? "s" : ""); + goto fail; + } + if (buf->len > 0) { + for (i = 0; i < ndim; i++) { + spec = axes_specs[i]; + if (unlikely(!__pyx_check_strides(buf, i, ndim, spec))) + goto fail; + if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec))) + goto fail; + } + if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag))) + goto fail; + } + if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, + new_memview != NULL) == -1)) { + goto fail; + } + retval = 0; + goto no_fail; +fail: + Py_XDECREF(new_memview); + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_float(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS_RO | writable_flag, 1, + &__Pyx_TypeInfo_float, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* Declarations */ + #if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus) + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return ::std::complex< float >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return x + y*(__pyx_t_float_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + __pyx_t_float_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +/* Arithmetic */ + #if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus) +#else + static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + #if 1 + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + if (b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); + } else if (fabsf(b.real) >= fabsf(b.imag)) { + if (b.real == 0 && b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); + } else { + float r = b.imag / b.real; + float s = (float)(1.0) / (b.real + b.imag * r); + return __pyx_t_float_complex_from_parts( + (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); + } + } else { + float r = b.real / b.imag; + float s = (float)(1.0) / (b.imag + b.real * r); + return __pyx_t_float_complex_from_parts( + (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); + } + } + #else + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + if (b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); + } else { + float denom = b.real * b.real + b.imag * b.imag; + return __pyx_t_float_complex_from_parts( + (a.real * b.real + a.imag * b.imag) / denom, + (a.imag * b.real - a.real * b.imag) / denom); + } + } + #endif + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrtf(z.real*z.real + z.imag*z.imag); + #else + return hypotf(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + float denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + return __Pyx_c_prod_float(a, a); + case 3: + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(z, a); + case 4: + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } else if ((b.imag == 0) && (a.real >= 0)) { + z.real = powf(a.real, b.real); + z.imag = 0; + return z; + } else if (a.real > 0) { + r = a.real; + theta = 0; + } else { + r = -a.real; + theta = atan2f(0.0, -1.0); + } + } else { + r = __Pyx_c_abs_float(a); + theta = atan2f(a.imag, a.real); + } + lnr = logf(r); + z_r = expf(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cosf(z_theta); + z.imag = z_r * sinf(z_theta); + return z; + } + #endif +#endif + +/* Declarations */ + #if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus) + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return ::std::complex< double >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return x + y*(__pyx_t_double_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + __pyx_t_double_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +/* Arithmetic */ + #if CYTHON_CCOMPLEX && (1) && (!0 || __cplusplus) +#else + static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + #if 1 + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + if (b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); + } else if (fabs(b.real) >= fabs(b.imag)) { + if (b.real == 0 && b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); + } else { + double r = b.imag / b.real; + double s = (double)(1.0) / (b.real + b.imag * r); + return __pyx_t_double_complex_from_parts( + (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); + } + } else { + double r = b.real / b.imag; + double s = (double)(1.0) / (b.imag + b.real * r); + return __pyx_t_double_complex_from_parts( + (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); + } + } + #else + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + if (b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); + } else { + double denom = b.real * b.real + b.imag * b.imag; + return __pyx_t_double_complex_from_parts( + (a.real * b.real + a.imag * b.imag) / denom, + (a.imag * b.real - a.real * b.imag) / denom); + } + } + #endif + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrt(z.real*z.real + z.imag*z.imag); + #else + return hypot(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + double denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + return __Pyx_c_prod_double(a, a); + case 3: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(z, a); + case 4: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } else if ((b.imag == 0) && (a.real >= 0)) { + z.real = pow(a.real, b.real); + z.imag = 0; + return z; + } else if (a.real > 0) { + r = a.real; + theta = 0; + } else { + r = -a.real; + theta = atan2(0.0, -1.0); + } + } else { + r = __Pyx_c_abs_double(a); + theta = atan2(a.imag, a.real); + } + lnr = log(r); + z_r = exp(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cos(z_theta); + z.imag = z_r * sin(z_theta); + return z; + } + #endif +#endif + +/* MemviewSliceCopyTemplate */ + static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object) +{ + __Pyx_RefNannyDeclarations + int i; + __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; + struct __pyx_memoryview_obj *from_memview = from_mvs->memview; + Py_buffer *buf = &from_memview->view; + PyObject *shape_tuple = NULL; + PyObject *temp_int = NULL; + struct __pyx_array_obj *array_obj = NULL; + struct __pyx_memoryview_obj *memview_obj = NULL; + __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); + for (i = 0; i < ndim; i++) { + if (unlikely(from_mvs->suboffsets[i] >= 0)) { + PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " + "indirect dimensions (axis %d)", i); + goto fail; + } + } + shape_tuple = PyTuple_New(ndim); + if (unlikely(!shape_tuple)) { + goto fail; + } + __Pyx_GOTREF(shape_tuple); + for(i = 0; i < ndim; i++) { + temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); + if(unlikely(!temp_int)) { + goto fail; + } else { + PyTuple_SET_ITEM(shape_tuple, i, temp_int); + temp_int = NULL; + } + } + array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); + if (unlikely(!array_obj)) { + goto fail; + } + __Pyx_GOTREF(array_obj); + memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + (PyObject *) array_obj, contig_flag, + dtype_is_object, + from_mvs->memview->typeinfo); + if (unlikely(!memview_obj)) + goto fail; + if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) + goto fail; + if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, + dtype_is_object) < 0)) + goto fail; + goto no_fail; +fail: + __Pyx_XDECREF(new_mvs.memview); + new_mvs.memview = NULL; + new_mvs.data = NULL; +no_fail: + __Pyx_XDECREF(shape_tuple); + __Pyx_XDECREF(temp_int); + __Pyx_XDECREF(array_obj); + __Pyx_RefNannyFinishContext(); + return new_mvs; +} + +/* MemviewSliceInit */ + static int +__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, + int ndim, + __Pyx_memviewslice *memviewslice, + int memview_is_new_reference) +{ + __Pyx_RefNannyDeclarations + int i, retval=-1; + Py_buffer *buf = &memview->view; + __Pyx_RefNannySetupContext("init_memviewslice", 0); + if (unlikely(memviewslice->memview || memviewslice->data)) { + PyErr_SetString(PyExc_ValueError, + "memviewslice is already initialized!"); + goto fail; + } + if (buf->strides) { + for (i = 0; i < ndim; i++) { + memviewslice->strides[i] = buf->strides[i]; + } + } else { + Py_ssize_t stride = buf->itemsize; + for (i = ndim - 1; i >= 0; i--) { + memviewslice->strides[i] = stride; + stride *= buf->shape[i]; + } + } + for (i = 0; i < ndim; i++) { + memviewslice->shape[i] = buf->shape[i]; + if (buf->suboffsets) { + memviewslice->suboffsets[i] = buf->suboffsets[i]; + } else { + memviewslice->suboffsets[i] = -1; + } + } + memviewslice->memview = memview; + memviewslice->data = (char *)buf->buf; + if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { + Py_INCREF(memview); + } + retval = 0; + goto no_fail; +fail: + memviewslice->memview = 0; + memviewslice->data = 0; + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} +#ifndef Py_NO_RETURN +#define Py_NO_RETURN +#endif +static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN { + va_list vargs; + char msg[200]; +#if PY_VERSION_HEX >= 0x030A0000 || defined(HAVE_STDARG_PROTOTYPES) + va_start(vargs, fmt); +#else + va_start(vargs); +#endif + vsnprintf(msg, 200, fmt, vargs); + va_end(vargs); + Py_FatalError(msg); +} +static CYTHON_INLINE int +__pyx_add_acquisition_count_locked(__pyx_atomic_int_type *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)++; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE int +__pyx_sub_acquisition_count_locked(__pyx_atomic_int_type *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)--; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE void +__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) +{ + __pyx_nonatomic_int_type old_acquisition_count; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (unlikely(!memview || (PyObject *) memview == Py_None)) { + return; + } + old_acquisition_count = __pyx_add_acquisition_count(memview); + if (unlikely(old_acquisition_count <= 0)) { + if (likely(old_acquisition_count == 0)) { + if (have_gil) { + Py_INCREF((PyObject *) memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_INCREF((PyObject *) memview); + PyGILState_Release(_gilstate); + } + } else { + __pyx_fatalerror("Acquisition count is %d (line %d)", + old_acquisition_count+1, lineno); + } + } +} +static CYTHON_INLINE void __Pyx_XCLEAR_MEMVIEW(__Pyx_memviewslice *memslice, + int have_gil, int lineno) { + __pyx_nonatomic_int_type old_acquisition_count; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (unlikely(!memview || (PyObject *) memview == Py_None)) { + memslice->memview = NULL; + return; + } + old_acquisition_count = __pyx_sub_acquisition_count(memview); + memslice->data = NULL; + if (likely(old_acquisition_count > 1)) { + memslice->memview = NULL; + } else if (likely(old_acquisition_count == 1)) { + if (have_gil) { + Py_CLEAR(memslice->memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_CLEAR(memslice->memview); + PyGILState_Release(_gilstate); + } + } else { + __pyx_fatalerror("Acquisition count is %d (line %d)", + old_acquisition_count-1, lineno); + } +} + +/* CIntFromPy */ + static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const int neg_one = (int) -1, const_zero = (int) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if ((sizeof(int) < sizeof(long))) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + if (unlikely(__Pyx_PyLong_IsNeg(x))) { + goto raise_neg_overflow; + } else if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_DigitCount(x)) { + case 2: + if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) >= 2 * PyLong_SHIFT)) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) >= 3 * PyLong_SHIFT)) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) >= 4 * PyLong_SHIFT)) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if ((sizeof(int) <= sizeof(unsigned long))) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(int) <= sizeof(unsigned PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_SignedDigitCount(x)) { + case -2: + if ((8 * sizeof(int) - 1 > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } + } +#endif + if ((sizeof(int) <= sizeof(long))) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(int) <= sizeof(PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { + int val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); +#if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } +#endif + if (likely(v)) { + int ret = -1; +#if PY_VERSION_HEX < 0x030d0000 && !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); +#else + PyObject *stepval = NULL, *mask = NULL, *shift = NULL; + int bits, remaining_bits, is_negative = 0; + long idigit; + int chunk_size = (sizeof(long) < 8) ? 30 : 62; + if (unlikely(!PyLong_CheckExact(v))) { + PyObject *tmp = v; + v = PyNumber_Long(v); + assert(PyLong_CheckExact(v)); + Py_DECREF(tmp); + if (unlikely(!v)) return (int) -1; + } +#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(x) == 0) + return (int) 0; + is_negative = Py_SIZE(x) < 0; +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + is_negative = result == 1; + } +#endif + if (is_unsigned && unlikely(is_negative)) { + goto raise_neg_overflow; + } else if (is_negative) { + stepval = PyNumber_Invert(v); + if (unlikely(!stepval)) + return (int) -1; + } else { + stepval = __Pyx_NewRef(v); + } + val = (int) 0; + mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; + shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; + for (bits = 0; bits < (int) sizeof(int) * 8 - chunk_size; bits += chunk_size) { + PyObject *tmp, *digit; + digit = PyNumber_And(stepval, mask); + if (unlikely(!digit)) goto done; + idigit = PyLong_AsLong(digit); + Py_DECREF(digit); + if (unlikely(idigit < 0)) goto done; + tmp = PyNumber_Rshift(stepval, shift); + if (unlikely(!tmp)) goto done; + Py_DECREF(stepval); stepval = tmp; + val |= ((int) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(stepval) == 0) + goto unpacking_done; + #endif + } + idigit = PyLong_AsLong(stepval); + if (unlikely(idigit < 0)) goto done; + remaining_bits = ((int) sizeof(int) * 8) - bits - (is_unsigned ? 0 : 1); + if (unlikely(idigit >= (1L << remaining_bits))) + goto raise_overflow; + val |= ((int) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + unpacking_done: + #endif + if (!is_unsigned) { + if (unlikely(val & (((int) 1) << (sizeof(int) * 8 - 1)))) + goto raise_overflow; + if (is_negative) + val = ~val; + } + ret = 0; + done: + Py_XDECREF(shift); + Py_XDECREF(mask); + Py_XDECREF(stepval); +#endif + Py_DECREF(v); + if (likely(!ret)) + return val; + } + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const int neg_one = (int) -1, const_zero = (int) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; +#if !CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030d0000 + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); +#else + PyObject *from_bytes, *result = NULL; + PyObject *py_bytes = NULL, *arg_tuple = NULL, *kwds = NULL, *order_str = NULL; + from_bytes = PyObject_GetAttrString((PyObject*)&PyLong_Type, "from_bytes"); + if (!from_bytes) return NULL; + py_bytes = PyBytes_FromStringAndSize((char*)bytes, sizeof(int)); + if (!py_bytes) goto limited_bad; + order_str = PyUnicode_FromString(little ? "little" : "big"); + if (!order_str) goto limited_bad; + arg_tuple = PyTuple_Pack(2, py_bytes, order_str); + if (!arg_tuple) goto limited_bad; + if (!is_unsigned) { + kwds = PyDict_New(); + if (!kwds) goto limited_bad; + if (PyDict_SetItemString(kwds, "signed", __Pyx_NewRef(Py_True))) goto limited_bad; + } + result = PyObject_Call(from_bytes, arg_tuple, kwds); + limited_bad: + Py_XDECREF(kwds); + Py_XDECREF(arg_tuple); + Py_XDECREF(order_str); + Py_XDECREF(py_bytes); + Py_XDECREF(from_bytes); + return result; +#endif + } +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_Py_intptr_t(Py_intptr_t value) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const Py_intptr_t neg_one = (Py_intptr_t) -1, const_zero = (Py_intptr_t) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(Py_intptr_t) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(Py_intptr_t) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(Py_intptr_t) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(Py_intptr_t) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(Py_intptr_t) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; +#if !CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030d0000 + return _PyLong_FromByteArray(bytes, sizeof(Py_intptr_t), + little, !is_unsigned); +#else + PyObject *from_bytes, *result = NULL; + PyObject *py_bytes = NULL, *arg_tuple = NULL, *kwds = NULL, *order_str = NULL; + from_bytes = PyObject_GetAttrString((PyObject*)&PyLong_Type, "from_bytes"); + if (!from_bytes) return NULL; + py_bytes = PyBytes_FromStringAndSize((char*)bytes, sizeof(Py_intptr_t)); + if (!py_bytes) goto limited_bad; + order_str = PyUnicode_FromString(little ? "little" : "big"); + if (!order_str) goto limited_bad; + arg_tuple = PyTuple_Pack(2, py_bytes, order_str); + if (!arg_tuple) goto limited_bad; + if (!is_unsigned) { + kwds = PyDict_New(); + if (!kwds) goto limited_bad; + if (PyDict_SetItemString(kwds, "signed", __Pyx_NewRef(Py_True))) goto limited_bad; + } + result = PyObject_Call(from_bytes, arg_tuple, kwds); + limited_bad: + Py_XDECREF(kwds); + Py_XDECREF(arg_tuple); + Py_XDECREF(order_str); + Py_XDECREF(py_bytes); + Py_XDECREF(from_bytes); + return result; +#endif + } +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const long neg_one = (long) -1, const_zero = (long) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; +#if !CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030d0000 + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); +#else + PyObject *from_bytes, *result = NULL; + PyObject *py_bytes = NULL, *arg_tuple = NULL, *kwds = NULL, *order_str = NULL; + from_bytes = PyObject_GetAttrString((PyObject*)&PyLong_Type, "from_bytes"); + if (!from_bytes) return NULL; + py_bytes = PyBytes_FromStringAndSize((char*)bytes, sizeof(long)); + if (!py_bytes) goto limited_bad; + order_str = PyUnicode_FromString(little ? "little" : "big"); + if (!order_str) goto limited_bad; + arg_tuple = PyTuple_Pack(2, py_bytes, order_str); + if (!arg_tuple) goto limited_bad; + if (!is_unsigned) { + kwds = PyDict_New(); + if (!kwds) goto limited_bad; + if (PyDict_SetItemString(kwds, "signed", __Pyx_NewRef(Py_True))) goto limited_bad; + } + result = PyObject_Call(from_bytes, arg_tuple, kwds); + limited_bad: + Py_XDECREF(kwds); + Py_XDECREF(arg_tuple); + Py_XDECREF(order_str); + Py_XDECREF(py_bytes); + Py_XDECREF(from_bytes); + return result; +#endif + } +} + +/* CIntFromPy */ + static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const long neg_one = (long) -1, const_zero = (long) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if ((sizeof(long) < sizeof(long))) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + if (unlikely(__Pyx_PyLong_IsNeg(x))) { + goto raise_neg_overflow; + } else if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_DigitCount(x)) { + case 2: + if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) >= 2 * PyLong_SHIFT)) { + return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 3: + if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) >= 3 * PyLong_SHIFT)) { + return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 4: + if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) >= 4 * PyLong_SHIFT)) { + return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + } + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if ((sizeof(long) <= sizeof(unsigned long))) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(long) <= sizeof(unsigned PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_SignedDigitCount(x)) { + case -2: + if ((8 * sizeof(long) - 1 > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { + return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 2: + if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { + return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -3: + if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { + return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 3: + if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { + return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -4: + if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) { + return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 4: + if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) { + return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + } + } +#endif + if ((sizeof(long) <= sizeof(long))) { + __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(long) <= sizeof(PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { + long val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); +#if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } +#endif + if (likely(v)) { + int ret = -1; +#if PY_VERSION_HEX < 0x030d0000 && !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); +#else + PyObject *stepval = NULL, *mask = NULL, *shift = NULL; + int bits, remaining_bits, is_negative = 0; + long idigit; + int chunk_size = (sizeof(long) < 8) ? 30 : 62; + if (unlikely(!PyLong_CheckExact(v))) { + PyObject *tmp = v; + v = PyNumber_Long(v); + assert(PyLong_CheckExact(v)); + Py_DECREF(tmp); + if (unlikely(!v)) return (long) -1; + } +#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(x) == 0) + return (long) 0; + is_negative = Py_SIZE(x) < 0; +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + is_negative = result == 1; + } +#endif + if (is_unsigned && unlikely(is_negative)) { + goto raise_neg_overflow; + } else if (is_negative) { + stepval = PyNumber_Invert(v); + if (unlikely(!stepval)) + return (long) -1; + } else { + stepval = __Pyx_NewRef(v); + } + val = (long) 0; + mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; + shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; + for (bits = 0; bits < (int) sizeof(long) * 8 - chunk_size; bits += chunk_size) { + PyObject *tmp, *digit; + digit = PyNumber_And(stepval, mask); + if (unlikely(!digit)) goto done; + idigit = PyLong_AsLong(digit); + Py_DECREF(digit); + if (unlikely(idigit < 0)) goto done; + tmp = PyNumber_Rshift(stepval, shift); + if (unlikely(!tmp)) goto done; + Py_DECREF(stepval); stepval = tmp; + val |= ((long) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(stepval) == 0) + goto unpacking_done; + #endif + } + idigit = PyLong_AsLong(stepval); + if (unlikely(idigit < 0)) goto done; + remaining_bits = ((int) sizeof(long) * 8) - bits - (is_unsigned ? 0 : 1); + if (unlikely(idigit >= (1L << remaining_bits))) + goto raise_overflow; + val |= ((long) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + unpacking_done: + #endif + if (!is_unsigned) { + if (unlikely(val & (((long) 1) << (sizeof(long) * 8 - 1)))) + goto raise_overflow; + if (is_negative) + val = ~val; + } + ret = 0; + done: + Py_XDECREF(shift); + Py_XDECREF(mask); + Py_XDECREF(stepval); +#endif + Py_DECREF(v); + if (likely(!ret)) + return val; + } + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +/* CIntFromPy */ + static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const char neg_one = (char) -1, const_zero = (char) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if ((sizeof(char) < sizeof(long))) { + __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (char) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + if (unlikely(__Pyx_PyLong_IsNeg(x))) { + goto raise_neg_overflow; + } else if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(char, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_DigitCount(x)) { + case 2: + if ((8 * sizeof(char) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) >= 2 * PyLong_SHIFT)) { + return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 3: + if ((8 * sizeof(char) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) >= 3 * PyLong_SHIFT)) { + return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 4: + if ((8 * sizeof(char) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) >= 4 * PyLong_SHIFT)) { + return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + } + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (char) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if ((sizeof(char) <= sizeof(unsigned long))) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(char) <= sizeof(unsigned PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(char, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_SignedDigitCount(x)) { + case -2: + if ((8 * sizeof(char) - 1 > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { + return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 2: + if ((8 * sizeof(char) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { + return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -3: + if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { + return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 3: + if ((8 * sizeof(char) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { + return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -4: + if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 4 * PyLong_SHIFT)) { + return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 4: + if ((8 * sizeof(char) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 4 * PyLong_SHIFT)) { + return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + } + } +#endif + if ((sizeof(char) <= sizeof(long))) { + __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(char) <= sizeof(PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { + char val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); +#if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } +#endif + if (likely(v)) { + int ret = -1; +#if PY_VERSION_HEX < 0x030d0000 && !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); +#else + PyObject *stepval = NULL, *mask = NULL, *shift = NULL; + int bits, remaining_bits, is_negative = 0; + long idigit; + int chunk_size = (sizeof(long) < 8) ? 30 : 62; + if (unlikely(!PyLong_CheckExact(v))) { + PyObject *tmp = v; + v = PyNumber_Long(v); + assert(PyLong_CheckExact(v)); + Py_DECREF(tmp); + if (unlikely(!v)) return (char) -1; + } +#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(x) == 0) + return (char) 0; + is_negative = Py_SIZE(x) < 0; +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (char) -1; + is_negative = result == 1; + } +#endif + if (is_unsigned && unlikely(is_negative)) { + goto raise_neg_overflow; + } else if (is_negative) { + stepval = PyNumber_Invert(v); + if (unlikely(!stepval)) + return (char) -1; + } else { + stepval = __Pyx_NewRef(v); + } + val = (char) 0; + mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; + shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; + for (bits = 0; bits < (int) sizeof(char) * 8 - chunk_size; bits += chunk_size) { + PyObject *tmp, *digit; + digit = PyNumber_And(stepval, mask); + if (unlikely(!digit)) goto done; + idigit = PyLong_AsLong(digit); + Py_DECREF(digit); + if (unlikely(idigit < 0)) goto done; + tmp = PyNumber_Rshift(stepval, shift); + if (unlikely(!tmp)) goto done; + Py_DECREF(stepval); stepval = tmp; + val |= ((char) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + if (Py_SIZE(stepval) == 0) + goto unpacking_done; + #endif + } + idigit = PyLong_AsLong(stepval); + if (unlikely(idigit < 0)) goto done; + remaining_bits = ((int) sizeof(char) * 8) - bits - (is_unsigned ? 0 : 1); + if (unlikely(idigit >= (1L << remaining_bits))) + goto raise_overflow; + val |= ((char) idigit) << bits; + #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 + unpacking_done: + #endif + if (!is_unsigned) { + if (unlikely(val & (((char) 1) << (sizeof(char) * 8 - 1)))) + goto raise_overflow; + if (is_negative) + val = ~val; + } + ret = 0; + done: + Py_XDECREF(shift); + Py_XDECREF(mask); + Py_XDECREF(stepval); +#endif + Py_DECREF(v); + if (likely(!ret)) + return val; + } + return (char) -1; + } + } else { + char val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (char) -1; + val = __Pyx_PyInt_As_char(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to char"); + return (char) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to char"); + return (char) -1; +} + +/* FormatTypeName */ + #if CYTHON_COMPILING_IN_LIMITED_API +static __Pyx_TypeName +__Pyx_PyType_GetName(PyTypeObject* tp) +{ + PyObject *name = __Pyx_PyObject_GetAttrStr((PyObject *)tp, + __pyx_n_s_name_2); + if (unlikely(name == NULL) || unlikely(!PyUnicode_Check(name))) { + PyErr_Clear(); + Py_XDECREF(name); + name = __Pyx_NewRef(__pyx_n_s__62); + } + return name; +} +#endif + +/* CheckBinaryVersion */ + static unsigned long __Pyx_get_runtime_version(void) { +#if __PYX_LIMITED_VERSION_HEX >= 0x030B00A4 + return Py_Version & ~0xFFUL; +#else + const char* rt_version = Py_GetVersion(); + unsigned long version = 0; + unsigned long factor = 0x01000000UL; + unsigned int digit = 0; + int i = 0; + while (factor) { + while ('0' <= rt_version[i] && rt_version[i] <= '9') { + digit = digit * 10 + (unsigned int) (rt_version[i] - '0'); + ++i; + } + version += factor * digit; + if (rt_version[i] != '.') + break; + digit = 0; + factor >>= 8; + ++i; + } + return version; +#endif +} +static int __Pyx_check_binary_version(unsigned long ct_version, unsigned long rt_version, int allow_newer) { + const unsigned long MAJOR_MINOR = 0xFFFF0000UL; + if ((rt_version & MAJOR_MINOR) == (ct_version & MAJOR_MINOR)) + return 0; + if (likely(allow_newer && (rt_version & MAJOR_MINOR) > (ct_version & MAJOR_MINOR))) + return 1; + { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compile time Python version %d.%d " + "of module '%.100s' " + "%s " + "runtime version %d.%d", + (int) (ct_version >> 24), (int) ((ct_version >> 16) & 0xFF), + __Pyx_MODULE_NAME, + (allow_newer) ? "was newer than" : "does not match", + (int) (rt_version >> 24), (int) ((rt_version >> 16) & 0xFF) + ); + return PyErr_WarnEx(NULL, message, 1); + } +} + +/* InitStrings */ + #if PY_MAJOR_VERSION >= 3 +static int __Pyx_InitString(__Pyx_StringTabEntry t, PyObject **str) { + if (t.is_unicode | t.is_str) { + if (t.intern) { + *str = PyUnicode_InternFromString(t.s); + } else if (t.encoding) { + *str = PyUnicode_Decode(t.s, t.n - 1, t.encoding, NULL); + } else { + *str = PyUnicode_FromStringAndSize(t.s, t.n - 1); + } + } else { + *str = PyBytes_FromStringAndSize(t.s, t.n - 1); + } + if (!*str) + return -1; + if (PyObject_Hash(*str) == -1) + return -1; + return 0; +} +#endif +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION >= 3 + __Pyx_InitString(*t, t->p); + #else + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + if (!*t->p) + return -1; + if (PyObject_Hash(*t->p) == -1) + return -1; + #endif + ++t; + } + return 0; +} + +#include +static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s) { + size_t len = strlen(s); + if (unlikely(len > (size_t) PY_SSIZE_T_MAX)) { + PyErr_SetString(PyExc_OverflowError, "byte string is too long"); + return -1; + } + return (Py_ssize_t) len; +} +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + Py_ssize_t len = __Pyx_ssize_strlen(c_str); + if (unlikely(len < 0)) return NULL; + return __Pyx_PyUnicode_FromStringAndSize(c_str, len); +} +static CYTHON_INLINE PyObject* __Pyx_PyByteArray_FromString(const char* c_str) { + Py_ssize_t len = __Pyx_ssize_strlen(c_str); + if (unlikely(len < 0)) return NULL; + return PyByteArray_FromStringAndSize(c_str, len); +} +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +#if !CYTHON_PEP393_ENABLED +static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +} +#else +static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (likely(PyUnicode_IS_ASCII(o))) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +} +#endif +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { + return __Pyx_PyUnicode_AsStringAndSize(o, length); + } else +#endif +#if (!CYTHON_COMPILING_IN_PYPY && !CYTHON_COMPILING_IN_LIMITED_API) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { + int retval; + if (unlikely(!x)) return -1; + retval = __Pyx_PyObject_IsTrue(x); + Py_DECREF(x); + return retval; +} +static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { + __Pyx_TypeName result_type_name = __Pyx_PyType_GetName(Py_TYPE(result)); +#if PY_MAJOR_VERSION >= 3 + if (PyLong_Check(result)) { + if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, + "__int__ returned non-int (type " __Pyx_FMT_TYPENAME "). " + "The ability to return an instance of a strict subclass of int is deprecated, " + "and may be removed in a future version of Python.", + result_type_name)) { + __Pyx_DECREF_TypeName(result_type_name); + Py_DECREF(result); + return NULL; + } + __Pyx_DECREF_TypeName(result_type_name); + return result; + } +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type " __Pyx_FMT_TYPENAME ")", + type_name, type_name, result_type_name); + __Pyx_DECREF_TypeName(result_type_name); + Py_DECREF(result); + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { +#if CYTHON_USE_TYPE_SLOTS + PyNumberMethods *m; +#endif + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x) || PyLong_Check(x))) +#else + if (likely(PyLong_Check(x))) +#endif + return __Pyx_NewRef(x); +#if CYTHON_USE_TYPE_SLOTS + m = Py_TYPE(x)->tp_as_number; + #if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = m->nb_int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = m->nb_long(x); + } + #else + if (likely(m && m->nb_int)) { + name = "int"; + res = m->nb_int(x); + } + #endif +#else + if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { + res = PyNumber_Int(x); + } +#endif + if (likely(res)) { +#if PY_MAJOR_VERSION < 3 + if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { +#else + if (unlikely(!PyLong_CheckExact(res))) { +#endif + return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) { + if (sizeof(Py_ssize_t) >= sizeof(long)) + return PyInt_AS_LONG(b); + else + return PyInt_AsSsize_t(b); + } +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(__Pyx_PyLong_IsCompact(b))) { + return __Pyx_PyLong_CompactValue(b); + } else { + const digit* digits = __Pyx_PyLong_Digits(b); + const Py_ssize_t size = __Pyx_PyLong_SignedDigitCount(b); + switch (size) { + case 2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + } + } + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject* o) { + if (sizeof(Py_hash_t) == sizeof(Py_ssize_t)) { + return (Py_hash_t) __Pyx_PyIndex_AsSsize_t(o); +#if PY_MAJOR_VERSION < 3 + } else if (likely(PyInt_CheckExact(o))) { + return PyInt_AS_LONG(o); +#endif + } else { + Py_ssize_t ival; + PyObject *x; + x = PyNumber_Index(o); + if (!x) return -1; + ival = PyInt_AsLong(x); + Py_DECREF(x); + return ival; + } +} +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { + return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { + return PyInt_FromSize_t(ival); +} + + +/* #### Code section: utility_code_pragmas_end ### */ +#ifdef _MSC_VER +#pragma warning( pop ) +#endif + + + +/* #### Code section: end ### */ +#endif /* Py_PYTHON_H */ diff --git a/wfpt_n/wfpt_n.egg-info/PKG-INFO b/wfpt_n/wfpt_n.egg-info/PKG-INFO new file mode 100644 index 0000000..adbf21d --- /dev/null +++ b/wfpt_n/wfpt_n.egg-info/PKG-INFO @@ -0,0 +1,3 @@ +Metadata-Version: 2.1 +Name: wfpt-n +Version: 0.0.0 diff --git a/wfpt_n/wfpt_n.egg-info/SOURCES.txt b/wfpt_n/wfpt_n.egg-info/SOURCES.txt new file mode 100644 index 0000000..2ab6b6f --- /dev/null +++ b/wfpt_n/wfpt_n.egg-info/SOURCES.txt @@ -0,0 +1,6 @@ +setup.py +wfpt_n.cpp +wfpt_n.egg-info/PKG-INFO +wfpt_n.egg-info/SOURCES.txt +wfpt_n.egg-info/dependency_links.txt +wfpt_n.egg-info/top_level.txt \ No newline at end of file diff --git a/wfpt_n/wfpt_n.egg-info/dependency_links.txt b/wfpt_n/wfpt_n.egg-info/dependency_links.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/wfpt_n/wfpt_n.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/wfpt_n/wfpt_n.egg-info/top_level.txt b/wfpt_n/wfpt_n.egg-info/top_level.txt new file mode 100644 index 0000000..f33a6cb --- /dev/null +++ b/wfpt_n/wfpt_n.egg-info/top_level.txt @@ -0,0 +1 @@ +wfpt_n diff --git a/wfpt_n/wfpt_n.pyx b/wfpt_n/wfpt_n.pyx new file mode 100644 index 0000000..dc7ff7d --- /dev/null +++ b/wfpt_n/wfpt_n.pyx @@ -0,0 +1,832 @@ +# cython: embedsignature=True +# cython: cdivision=True +# cython: wraparound=False +# cython: boundscheck=False +# distutils: language = c++ +# +# Cython version of the Navarro & Fuss, 2009 DDM PDF. Based on the following code by Navarro & Fuss: +# http://www.psychocmath.logy.adelaide.edu.au/personalpages/staff/danielnavarro/resources/wfpt.m +# +# This implementation is about 170 times faster than the matlab +# reference version. +# +# Copyleft Thomas Wiecki (thomas_wiecki[at]brown.edu) & Imri Sofer, 2011 +# GPLv3 + + +#from hddm.model_config import model_config + +import scipy.integrate as integrate +from copy import copy +import numpy as np + +cimport numpy as np +cimport cython + +from cython.parallel import * +# cimport openmp + +# include "pdf.pxi" +include 'integrate.pxi' + +def pdf_array(np.ndarray[double, ndim=1] x, double v, double sv, double a, double z, double sz, + double t, double st, double err=1e-4, bint logp=0, int n_st=2, int n_sz=2, bint use_adaptive=1, + double simps_err=1e-3, double p_outlier=0, double w_outlier=0): + + cdef Py_ssize_t size = x.shape[0] + cdef Py_ssize_t i + cdef np.ndarray[double, ndim = 1] y = np.empty(size, dtype=np.double) + + for i in prange(size, nogil=True): + y[i] = full_pdf(x[i], v, sv, a, z, sz, t, st, err, + n_st, n_sz, use_adaptive, simps_err) + + y = y * (1 - p_outlier) + (w_outlier * p_outlier) + if logp == 1: + return np.log(y) + else: + return y + +cdef inline bint p_outlier_in_range(double p_outlier): + return (p_outlier >= 0) & (p_outlier <= 1) + + +def wiener_like_n(np.ndarray[double, ndim=1] x, double v, double sv, double a, double z, double sz, double t, + double st, double err, int n_st=10, int n_sz=10, bint use_adaptive=1, double simps_err=1e-8, + double p_outlier=0, double w_outlier=0.1): + cdef Py_ssize_t size = x.shape[0] + cdef Py_ssize_t i + cdef double p + cdef double sum_logp = 0 + cdef double wp_outlier = w_outlier * p_outlier + + if not p_outlier_in_range(p_outlier): + p = 10 ** (-20) + return log(p) + + for i in range(size): + p = full_pdf(x[i], v, sv, a, z, sz, t, st, err, + n_st, n_sz, use_adaptive, simps_err) + # If one probability = 0, the log sum will be -Inf + p = p * (1 - p_outlier) + wp_outlier + if p == 0: + p = 10 ** (-20) + # return -np.inf + + sum_logp += log(p) + + return sum_logp + +def wiener_like_rlddm(np.ndarray[double, ndim=1] x, + np.ndarray[long, ndim=1] response, + np.ndarray[double, ndim=1] feedback, + np.ndarray[long, ndim=1] split_by, + double q, double alpha, double pos_alpha, double v, + double sv, double a, double z, double sz, double t, + double st, double err, int n_st=10, int n_sz=10, bint use_adaptive=1, double simps_err=1e-8, + double p_outlier=0, double w_outlier=0): + cdef Py_ssize_t size = x.shape[0] + cdef Py_ssize_t i, j + cdef Py_ssize_t s_size + cdef int s + cdef double p + cdef double sum_logp = 0 + cdef double wp_outlier = w_outlier * p_outlier + cdef double alfa + cdef double pos_alfa + cdef np.ndarray[double, ndim=1] qs = np.array([q, q]) + cdef np.ndarray[double, ndim=1] xs + cdef np.ndarray[double, ndim=1] feedbacks + cdef np.ndarray[long, ndim=1] responses + cdef np.ndarray[long, ndim=1] unique = np.unique(split_by) + + if not p_outlier_in_range(p_outlier): + return -np.inf + + if pos_alpha==100.00: + pos_alfa = alpha + else: + pos_alfa = pos_alpha + + # unique represent # of conditions + for j in range(unique.shape[0]): + s = unique[j] + # select trials for current condition, identified by the split_by-array + feedbacks = feedback[split_by == s] + responses = response[split_by == s] + xs = x[split_by == s] + s_size = xs.shape[0] + qs[0] = q + qs[1] = q + + # don't calculate pdf for first trial but still update q + if feedbacks[0] > qs[responses[0]]: + alfa = (2.718281828459**pos_alfa) / (1 + 2.718281828459**pos_alfa) + else: + alfa = (2.718281828459**alpha) / (1 + 2.718281828459**alpha) + + # qs[1] is upper bound, qs[0] is lower bound. feedbacks is reward + # received on current trial. + qs[responses[0]] = qs[responses[0]] + \ + alfa * (feedbacks[0] - qs[responses[0]]) + + # loop through all trials in current condition + for i in range(1, s_size): + p = full_pdf(xs[i], ((qs[1] - qs[0]) * v), sv, a, z, + sz, t, st, err, n_st, n_sz, use_adaptive, simps_err) + # If one probability = 0, the log sum will be -Inf + p = p * (1 - p_outlier) + wp_outlier + if p == 0: + return -np.inf + sum_logp += log(p) + + # get learning rate for current trial. if pos_alpha is not in + # include it will be same as alpha so can still use this + # calculation: + if feedbacks[i] > qs[responses[i]]: + alfa = (2.718281828459**pos_alfa) / (1 + 2.718281828459**pos_alfa) + else: + alfa = (2.718281828459**alpha) / (1 + 2.718281828459**alpha) + + # qs[1] is upper bound, qs[0] is lower bound. feedbacks is reward + # received on current trial. + qs[responses[i]] = qs[responses[i]] + \ + alfa * (feedbacks[i] - qs[responses[i]]) + return sum_logp + + +def wiener_like_rlssm_nn(str model, + np.ndarray[double, ndim=1] x, + np.ndarray[long, ndim=1] response, + np.ndarray[double, ndim=1] feedback, + np.ndarray[long, ndim=1] split_by, + double q, + np.ndarray[double, ndim=1] params_ssm, + np.ndarray[double, ndim=1] params_rl, + np.ndarray[double, ndim=2] params_bnds, + double p_outlier=0, double w_outlier=0, network = None): + + cdef double v = params_ssm[0] + cdef double rl_alpha = params_rl[0] + + cdef Py_ssize_t size = x.shape[0] + cdef Py_ssize_t i, j, i_p + cdef Py_ssize_t s_size + cdef int s + cdef double log_p = 0 + cdef double sum_logp = 0 + cdef double wp_outlier = w_outlier * p_outlier + cdef double alfa + cdef double pos_alfa + cdef np.ndarray[double, ndim=1] qs = np.array([q, q]) + cdef np.ndarray[double, ndim=1] xs + cdef np.ndarray[double, ndim=1] feedbacks + cdef np.ndarray[long, ndim=1] responses + cdef np.ndarray[long, ndim=1] responses_qs + cdef np.ndarray[long, ndim=1] unique = np.unique(split_by) + cdef Py_ssize_t n_params = params_ssm.shape[0] #+ params_rl.shape[0] + cdef np.ndarray[float, ndim=2] data = np.zeros((size, n_params + 2), dtype = np.float32) + cdef float ll_min = -16.11809 + cdef int cumm_s_size = 0 + + if not p_outlier_in_range(p_outlier): + return -np.inf + + # Check for boundary violations -- if true, return -np.inf + for i_p in np.arange(1, len(params_ssm)): + lower_bnd = params_bnds[0][i_p] + upper_bnd = params_bnds[1][i_p] + + if params_ssm[i_p] < lower_bnd or params_ssm[i_p] > upper_bnd: + return -np.inf + + + if len(params_rl) == 2: + pos_alfa = params_rl[1] + else: + pos_alfa = params_rl[0] + + + # unique represent # of conditions + for j in range(unique.shape[0]): + s = unique[j] + # select trials for current condition, identified by the split_by-array + feedbacks = feedback[split_by == s] + responses = response[split_by == s] + xs = x[split_by == s] + s_size = xs.shape[0] + qs[0] = q + qs[1] = q + + responses_qs = responses + responses_qs[responses_qs == -1] = 0 + + # don't calculate pdf for first trial but still update q + if feedbacks[0] > qs[responses_qs[0]]: + alfa = (2.718281828459**pos_alfa) / (1 + 2.718281828459**pos_alfa) + else: + alfa = (2.718281828459**rl_alpha) / (1 + 2.718281828459**rl_alpha) + + + # qs[1] is upper bound, qs[0] is lower bound. feedbacks is reward + # received on current trial. + qs[responses_qs[0]] = qs[responses_qs[0]] + \ + alfa * (feedbacks[0] - qs[responses_qs[0]]) + + + data[0, 0] = 0.0 + # loop through all trials in current condition + for i in range(1, s_size): + data[cumm_s_size + i, 0] = (qs[1] - qs[0]) * v + # Check for boundary violations -- if true, return -np.inf + if data[cumm_s_size + i, 0] < params_bnds[0][0] or data[cumm_s_size + i, 0] > params_bnds[1][0]: + return -np.inf + + # get learning rate for current trial. if pos_alpha is not in + # include it will be same as alpha so can still use this + # calculation: + if feedbacks[i] > qs[responses_qs[i]]: + alfa = (2.718281828459**pos_alfa) / (1 + 2.718281828459**pos_alfa) + else: + alfa = (2.718281828459**rl_alpha) / (1 + 2.718281828459**rl_alpha) + + # qs[1] is upper bound, qs[0] is lower bound. feedbacks is reward + # received on current trial. + qs[responses_qs[i]] = qs[responses_qs[i]] + \ + alfa * (feedbacks[i] - qs[responses_qs[i]]) + cumm_s_size += s_size + + + data[:, 1:n_params] = np.tile(params_ssm[1:], (size, 1)).astype(np.float32) + data[:, n_params:] = np.stack([x, response], axis = 1) + + # Call to network: + if p_outlier == 0: + sum_logp = np.sum(np.core.umath.maximum(network.predict_on_batch(data), ll_min)) + else: + sum_logp = np.sum(np.log(np.exp(np.core.umath.maximum(network.predict_on_batch(data), ll_min)) * (1.0 - p_outlier) + (w_outlier * p_outlier))) + + return sum_logp + + +def wiener_like_rl(np.ndarray[long, ndim=1] response, + np.ndarray[double, ndim=1] feedback, + np.ndarray[long, ndim=1] split_by, + double q, double alpha, double pos_alpha, double v, double z, + double err=1e-4, int n_st=10, int n_sz=10, bint use_adaptive=1, double simps_err=1e-8, + double p_outlier=0, double w_outlier=0): + cdef Py_ssize_t size = response.shape[0] + cdef Py_ssize_t i, j + cdef Py_ssize_t s_size + cdef int s + cdef double drift + cdef double p + cdef double sum_logp = 0 + cdef double wp_outlier = w_outlier * p_outlier + cdef double alfa + cdef double pos_alfa + cdef np.ndarray[double, ndim=1] qs = np.array([q, q]) + cdef np.ndarray[double, ndim=1] feedbacks + cdef np.ndarray[long, ndim=1] responses + cdef np.ndarray[long, ndim=1] unique = np.unique(split_by) + + if not p_outlier_in_range(p_outlier): + return -np.inf + + if pos_alpha==100.00: + pos_alfa = alpha + else: + pos_alfa = pos_alpha + + # unique represent # of conditions + for j in range(unique.shape[0]): + s = unique[j] + # select trials for current condition, identified by the split_by-array + feedbacks = feedback[split_by == s] + responses = response[split_by == s] + s_size = responses.shape[0] + qs[0] = q + qs[1] = q + + # don't calculate pdf for first trial but still update q + if feedbacks[0] > qs[responses[0]]: + alfa = (2.718281828459**pos_alfa) / (1 + 2.718281828459**pos_alfa) + else: + alfa = (2.718281828459**alpha) / (1 + 2.718281828459**alpha) + + # qs[1] is upper bound, qs[0] is lower bound. feedbacks is reward + # received on current trial. + qs[responses[0]] = qs[responses[0]] + \ + alfa * (feedbacks[0] - qs[responses[0]]) + + # loop through all trials in current condition + for i in range(1, s_size): + + drift = (qs[1] - qs[0]) * v + + if drift == 0: + p = 0.5 + else: + if responses[i] == 1: + p = (2.718281828459**(-2 * z * drift) - 1) / \ + (2.718281828459**(-2 * drift) - 1) + else: + p = 1 - (2.718281828459**(-2 * z * drift) - 1) / \ + (2.718281828459**(-2 * drift) - 1) + + # If one probability = 0, the log sum will be -Inf + p = p * (1 - p_outlier) + wp_outlier + if p == 0: + return -np.inf + + sum_logp += log(p) + + # get learning rate for current trial. if pos_alpha is not in + # include it will be same as alpha so can still use this + # calculation: + if feedbacks[i] > qs[responses[i]]: + alfa = (2.718281828459**pos_alfa) / (1 + 2.718281828459**pos_alfa) + else: + alfa = (2.718281828459**alpha) / (1 + 2.718281828459**alpha) + + # qs[1] is upper bound, qs[0] is lower bound. feedbacks is reward + # received on current trial. + qs[responses[i]] = qs[responses[i]] + \ + alfa * (feedbacks[i] - qs[responses[i]]) + return sum_logp + + +def wiener_like_multi(np.ndarray[double, ndim=1] x, v, sv, a, z, sz, t, st, double err, multi=None, + int n_st=10, int n_sz=10, bint use_adaptive=1, double simps_err=1e-3, + double p_outlier=0, double w_outlier=0): + cdef Py_ssize_t size = x.shape[0] + cdef Py_ssize_t i + cdef double p = 0 + cdef double sum_logp = 0 + cdef double wp_outlier = w_outlier * p_outlier + + if multi is None: + return full_pdf(x, v, sv, a, z, sz, t, st, err) + else: + params = {'v': v, 'z': z, 't': t, 'a': a, 'sv': sv, 'sz': sz, 'st': st} + params_iter = copy(params) + for i in range(size): + for param in multi: + params_iter[param] = params[param][i] + if abs(x[i]) != 999.: + p = full_pdf(x[i], params_iter['v'], + params_iter['sv'], params_iter['a'], params_iter['z'], + params_iter['sz'], params_iter['t'], params_iter['st'], + err, n_st, n_sz, use_adaptive, simps_err) + p = p * (1 - p_outlier) + wp_outlier + elif x[i] == 999.: + p = prob_ub(params_iter['v'], params_iter['a'], params_iter['z']) + else: # x[i] == -999. + p = 1 - prob_ub(params_iter['v'], params_iter['a'], params_iter['z']) + + sum_logp += log(p) + + return sum_logp + + +def wiener_like_multi_rlddm(np.ndarray[double, ndim=1] x, + np.ndarray[long, ndim=1] response, + np.ndarray[double, ndim=1] feedback, + np.ndarray[long, ndim=1] split_by, + double q, v, sv, a, z, sz, t, st, alpha, double err, multi=None, + int n_st=10, int n_sz=10, bint use_adaptive=1, double simps_err=1e-3, + double p_outlier=0, double w_outlier=0): + cdef Py_ssize_t size = x.shape[0] + cdef Py_ssize_t ij + cdef Py_ssize_t s_size + cdef double p = 0 + cdef double sum_logp = 0 + cdef double wp_outlier = w_outlier * p_outlier + cdef int s + cdef np.ndarray[double, ndim=1] qs = np.array([q, q]) + + if multi is None: + return full_pdf(x, v, sv, a, z, sz, t, st, err) + else: + params = {'v': v, 'z': z, 't': t, 'a': a, 'sv': sv, 'sz': sz, 'st': st, 'alpha':alpha} + params_iter = copy(params) + qs[0] = q + qs[1] = q + for i in range(size): + for param in multi: + params_iter[param] = params[param][i] + + if (i != 0): + if (split_by[i] != split_by[i-1]): + qs[0] = q + qs[1] = q + + p = full_pdf(x[i], params_iter['v'] * (qs[1] - qs[0]), + params_iter['sv'], params_iter['a'], params_iter['z'], + params_iter['sz'], params_iter[ + 't'], params_iter['st'], + err, n_st, n_sz, use_adaptive, simps_err) + p = p * (1 - p_outlier) + wp_outlier + sum_logp += log(p) + + alfa = (2.718281828459**params_iter['alpha']) / (1 + 2.718281828459**params_iter['alpha']) + qs[response[i]] = qs[response[i]] + alfa * (feedback[i] - qs[response[i]]) + + return sum_logp + + +def wiener_like_rlssm_nn_reg(np.ndarray[float, ndim=2] data, + np.ndarray[float, ndim=2] rl_arr, + np.ndarray[double, ndim=1] x, + np.ndarray[long, ndim=1] response, + np.ndarray[double, ndim=1] feedback, + np.ndarray[long, ndim=1] split_by, + double q, + np.ndarray[double, ndim=2] params_bnds, + double p_outlier=0, double w_outlier=0, network = None): + cdef double rl_alpha + cdef Py_ssize_t size = x.shape[0] + cdef Py_ssize_t i, j, i_p + cdef Py_ssize_t s_size + cdef int s + cdef double log_p = 0 + cdef double sum_logp = 0 + cdef double wp_outlier = w_outlier * p_outlier + cdef double alfa + cdef np.ndarray[double, ndim=1] qs = np.array([q, q]) + cdef np.ndarray[double, ndim=1] xs + cdef np.ndarray[double, ndim=1] feedbacks + cdef np.ndarray[long, ndim=1] responses + cdef np.ndarray[long, ndim=1] responses_qs + cdef np.ndarray[long, ndim=1] unique = np.unique(split_by) + cdef np.ndarray[float, ndim=2] data_copy = data + cdef float ll_min = -16.11809 + cdef int cumm_s_size = 0 + + if not p_outlier_in_range(p_outlier): + return -np.inf + + # Check for boundary violations -- if true, return -np.inf + for i_p in np.arange(1, data.shape[1]-2): + lower_bnd = params_bnds[0][i_p] + upper_bnd = params_bnds[1][i_p] + + if data[:,i_p].min() < lower_bnd or data[:,i_p].max() > upper_bnd: + return -np.inf + + # unique represent # of conditions + for j in range(unique.shape[0]): + s = unique[j] + # select trials for current condition, identified by the split_by-array + feedbacks = feedback[split_by == s] + responses = response[split_by == s] + xs = x[split_by == s] + s_size = xs.shape[0] + qs[0] = q + qs[1] = q + + responses_qs = responses + responses_qs[responses_qs == -1] = 0 + + # loop through all trials in current condition + for i in range(0, s_size): + tp_scale = data[cumm_s_size + i, 0] + if tp_scale < 0: + return -np.inf + + data_copy[cumm_s_size + i, 0] = (qs[1] - qs[0]) * tp_scale + + # Check for boundary violations -- if true, return -np.inf + if data_copy[cumm_s_size + i, 0] < params_bnds[0][0] or data_copy[cumm_s_size + i, 0] > params_bnds[1][0]: + return -np.inf + + rl_alpha = rl_arr[cumm_s_size + i, 0] + alfa = (2.718281828459**rl_alpha) / (1 + 2.718281828459**rl_alpha) + + qs[responses_qs[i]] = qs[responses_qs[i]] + \ + alfa * (feedbacks[i] - qs[responses_qs[i]]) + cumm_s_size += s_size + + # Call to network: + if p_outlier == 0: + sum_logp = np.sum(np.core.umath.maximum(network.predict_on_batch(data_copy), ll_min)) + else: + sum_logp = np.sum(np.log(np.exp(np.core.umath.maximum(network.predict_on_batch(data_copy), ll_min)) * (1.0 - p_outlier) + (w_outlier * p_outlier))) + + return sum_logp + + +def gen_rts_from_cdf(double v, double sv, double a, double z, double sz, double t, + double st, int samples=1000, double cdf_lb=-6, double cdf_ub=6, double dt=1e-2): + + cdef np.ndarray[double, ndim = 1] x = np.arange(cdf_lb, cdf_ub, dt) + cdef np.ndarray[double, ndim = 1] l_cdf = np.empty(x.shape[0], dtype=np.double) + cdef double pdf, rt + cdef Py_ssize_t size = x.shape[0] + cdef Py_ssize_t i, j + cdef int idx + + l_cdf[0] = 0 + for i from 1 <= i < size: + pdf = full_pdf(x[i], v, sv, a, z, sz, 0, 0, 1e-4) + l_cdf[i] = l_cdf[i - 1] + pdf + + l_cdf /= l_cdf[x.shape[0] - 1] + + cdef np.ndarray[double, ndim = 1] rts = np.empty(samples, dtype=np.double) + cdef np.ndarray[double, ndim = 1] f = np.random.rand(samples) + cdef np.ndarray[double, ndim = 1] delay + + if st != 0: + delay = (np.random.rand(samples) * st + (t - st / 2.)) + for i from 0 <= i < samples: + idx = np.searchsorted(l_cdf, f[i]) + rt = x[idx] + if st == 0: + rt = rt + np.sign(rt) * t + else: + rt = rt + np.sign(rt) * delay[i] + rts[i] = rt + return rts + + +def wiener_like_contaminant(np.ndarray[double, ndim=1] x, np.ndarray[int, ndim=1] cont_x, double v, + double sv, double a, double z, double sz, double t, double st, double t_min, + double t_max, double err, int n_st=10, int n_sz=10, bint use_adaptive=1, + double simps_err=1e-8): + """Wiener likelihood function where RTs could come from a + separate, uniform contaminant distribution. + + Reference: Lee, Vandekerckhove, Navarro, & Tuernlinckx (2007) + """ + cdef Py_ssize_t size = x.shape[0] + cdef Py_ssize_t i + cdef double p + cdef double sum_logp = 0 + cdef int n_cont = np.sum(cont_x) + cdef int pos_cont = 0 + + for i in prange(size, nogil=True): + if cont_x[i] == 0: + p = full_pdf(x[i], v, sv, a, z, sz, t, st, err, + n_st, n_sz, use_adaptive, simps_err) + if p == 0: + with gil: + return -np.inf + sum_logp += log(p) + # If one probability = 0, the log sum will be -Inf + + # add the log likelihood of the contaminations + sum_logp += n_cont * log(0.5 * 1. / (t_max - t_min)) + + return sum_logp + +def gen_cdf_using_pdf(double v, double sv, double a, double z, double sz, double t, double st, double err, + int N=500, double time=5., int n_st=2, int n_sz=2, bint use_adaptive=1, double simps_err=1e-3, + double p_outlier=0, double w_outlier=0): + """ + generate cdf vector using the pdf + """ + if (sv < 0) or (a <= 0 ) or (z < 0) or (z > 1) or (sz < 0) or (sz > 1) or (z + sz / 2. > 1) or \ + (z - sz / 2. < 0) or (t - st / 2. < 0) or (t < 0) or (st < 0) or not p_outlier_in_range(p_outlier): + raise ValueError( + "at least one of the parameters is out of the support") + + cdef np.ndarray[double, ndim = 1] x = np.linspace(-time, time, 2 * N + 1) + cdef np.ndarray[double, ndim = 1] cdf_array = np.empty(x.shape[0], dtype=np.double) + cdef int idx + + # compute pdf on the real line + cdf_array = pdf_array(x, v, sv, a, z, sz, t, st, err, 0, + n_st, n_sz, use_adaptive, simps_err, p_outlier, w_outlier) + + # integrate + cdf_array[1:] = integrate.cumtrapz(cdf_array) + + # normalize + cdf_array /= cdf_array[x.shape[0] - 1] + + return x, cdf_array + + +def split_cdf(np.ndarray[double, ndim=1] x, np.ndarray[double, ndim=1] data): + + # get length of data + cdef int N = (len(data) - 1) / 2 + + # lower bound is reversed + cdef np.ndarray[double, ndim = 1] x_lb = -x[:N][::-1] + cdef np.ndarray[double, ndim = 1] lb = data[:N][::-1] + # lower bound is cumulative in the wrong direction + lb = np.cumsum(np.concatenate([np.array([0]), -np.diff(lb)])) + + cdef np.ndarray[double, ndim = 1] x_ub = x[N + 1:] + cdef np.ndarray[double, ndim = 1] ub = data[N + 1:] + # ub does not start at 0 + ub -= ub[0] + + return (x_lb, lb, x_ub, ub) + + +# NEW WITH NN-EXTENSION + +############# +# Basic MLP Likelihoods +def wiener_like_nn_mlp(np.ndarray[float, ndim = 1] rt, + np.ndarray[float, ndim = 1] response, + np.ndarray[float, ndim = 1] params, + double p_outlier = 0, + double w_outlier = 0, + network = None): + + cdef Py_ssize_t size = rt.shape[0] + cdef Py_ssize_t n_params = params.shape[0] + cdef float log_p = 0 + cdef float ll_min = -16.11809 + + cdef np.ndarray[float, ndim = 2] data = np.zeros((size, n_params + 2), dtype = np.float32) + data[:, :n_params] = np.tile(params, (size, 1)).astype(np.float32) + data[:, n_params:] = np.stack([rt, response], axis = 1) + + # Call to network: + if p_outlier == 0: + log_p = np.sum(np.core.umath.maximum(network.predict_on_batch(data), ll_min)) + else: + log_p = np.sum(np.log(np.exp(np.core.umath.maximum(network.predict_on_batch(data), ll_min)) * (1.0 - p_outlier) + (w_outlier * p_outlier))) + + return log_p + +# Basic MLP Likelihoods +def wiener_like_nn_mlp_info(np.ndarray[float, ndim = 1] rt, + np.ndarray[float, ndim = 1] response, + np.ndarray[float, ndim = 1] params, + np.ndarray[float, ndim = 1] upper_bounds, + np.ndarray[float, ndim = 1] lower_bounds, + double p_outlier = 0, + double w_outlier = 0, + network = None): + + cdef Py_ssize_t size = rt.shape[0] + cdef Py_ssize_t n_params = params.shape[0] + cdef float log_p = 0 + cdef float ll_min = -16.11809 + cdef float[:] upper_bounds_view = upper_bounds + cdef float[:] lower_bounds_view = lower_bounds + cdef float[:] params_view = params + + cdef np.ndarray[float, ndim = 2] data = np.zeros((size, n_params + 2), dtype = np.float32) + data[:, :n_params] = np.tile(params, (size, 1)).astype(np.float32) + data[:, n_params:] = np.stack([rt, response], axis = 1) + + for i in range(n_params): + if params_view[i] > upper_bounds_view[i]: + return -np.inf + elif params_view[i] < lower_bounds_view[i]: + return -np.inf + + # Call to network: + if p_outlier == 0: + log_p = np.sum(np.core.umath.maximum(network.predict_on_batch(data), ll_min)) + else: + log_p = np.sum(np.log(np.exp(np.core.umath.maximum(network.predict_on_batch(data), ll_min)) * (1.0 - p_outlier) + (w_outlier * p_outlier))) + + return log_p + +def wiener_like_nn_mlp_pdf(np.ndarray[float, ndim = 1] rt, + np.ndarray[float, ndim = 1] response, + np.ndarray[float, ndim = 1] params, + double p_outlier = 0, + double w_outlier = 0, + bint logp = 0, + network = None): + + cdef Py_ssize_t size = rt.shape[0] + cdef Py_ssize_t n_params = params.shape[0] + + cdef np.ndarray[float, ndim = 1] log_p = np.zeros(size, dtype = np.float32) + cdef float ll_min = -16.11809 + + cdef np.ndarray[float, ndim = 2] data = np.zeros((size, n_params + 2), dtype = np.float32) + data[:, :n_params] = np.tile(params, (size, 1)).astype(np.float32) + data[:, n_params:] = np.stack([rt, response], axis = 1) + + # Call to network: + if p_outlier == 0: # ddm_model + log_p = np.squeeze(np.core.umath.maximum(network.predict_on_batch(data), ll_min)) + else: # ddm_model + log_p = np.squeeze(np.log(np.exp(np.core.umath.maximum(network.predict_on_batch(data), ll_min)) * (1.0 - p_outlier) + (w_outlier * p_outlier))) + if logp == 0: + log_p = np.exp(log_p) # shouldn't be called log_p anymore but no need for an extra array here + return log_p + +################ +# Regression style likelihoods: (Can prob simplify and make all mlp likelihoods of this form) + +def wiener_like_multi_nn_mlp(np.ndarray[float, ndim = 2] data, + double p_outlier = 0, + double w_outlier = 0, + network = None): + #**kwargs): + + cdef float ll_min = -16.11809 + cdef float log_p + + # Call to network: + if p_outlier == 0: # previous ddm_model + log_p = np.sum(np.core.umath.maximum(network.predict_on_batch(data), ll_min)) + else: + log_p = np.sum(np.log(np.exp(np.core.umath.maximum(network.predict_on_batch(data), ll_min)) * (1.0 - p_outlier) + (w_outlier * p_outlier))) + return log_p + +def wiener_like_multi_nn_mlp_pdf(np.ndarray[float, ndim = 2] data, + double p_outlier = 0, + double w_outlier = 0, + network = None): + #**kwargs): + + cdef float ll_min = -16.11809 + cdef float log_p + + # Call to network: + if p_outlier == 0: # previous ddm_model + log_p = np.squeeze(np.core.umath.maximum(network.predict_on_batch(data), ll_min)) + else: + log_p = np.squeeze(np.log(np.exp(np.core.umath.maximum(network.predict_on_batch(data), ll_min)) * (1.0 - p_outlier) + (w_outlier * p_outlier))) + return log_p + +########### +# Basic CNN likelihoods + +#def wiener_like_cnn_2(np.ndarray[long, ndim = 1] x, +# np.ndarray[long, ndim = 1] response, +# np.ndarray[float, ndim = 1] parameters, +# double p_outlier = 0, +# double w_outlier = 0, +# **kwargs): +# +# cdef Py_ssize_t size = x.shape[0] +# cdef Py_ssize_t i +# cdef float log_p = 0 +# cdef np.ndarray[float, ndim = 2] pred = kwargs['network'](parameters) +# #log_p = 0 +# +# for i in range(size): +# if response[i] == 0: +# log_p += np.log(pred[0, 2 * x[i]] * (1 - p_outlier) + w_outlier * p_outlier) +# else: +# log_p += np.log(pred[0, 2 * x[i] + 1] * (1 - p_outlier) + w_outlier * p_outlier) +# +# # Call to network: +# return log_p +# +#def wiener_pdf_cnn_2(np.ndarray[long, ndim = 1] x, +# np.ndarray[long, ndim = 1] response, +# np.ndarray[float, ndim = 1] parameters, +# double p_outlier = 0, +# double w_outlier = 0, +# bint logp = 0, +# **kwargs): +# +# cdef Py_ssize_t size = x.shape[0] +# cdef Py_ssize_t i +# cdef np.ndarray[float, ndim = 1] log_p = np.zeros(size, dtype = np.float32) +# cdef np.ndarray[float, ndim = 2] pred = kwargs['network'](parameters) +# #print(pred.shape) +# #log_p = 0 +# for i in range(size): +# if response[i] == 0: +# log_p[i] += np.log(pred[0, 2 * x[i]] * (1 - p_outlier) + w_outlier * p_outlier) +# else: +# log_p[i] += np.log(pred[0, 2 * x[i] + 1] * (1 - p_outlier) + w_outlier * p_outlier) +# +# if logp == 0: +# log_p = np.exp(log_p) +# +# # Call to network: +# return log_p +# +#def wiener_like_reg_cnn_2(np.ndarray[long, ndim = 1] x, +# np.ndarray[long, ndim = 1] response, +# np.ndarray[float, ndim = 2] parameters, +# double p_outlier = 0, +# double w_outlier = 0, +# bint logp = 0, +# **kwargs): +# +# cdef Py_ssize_t size = x.shape[0] +# cdef Py_ssize_t i +# cdef float log_p = 0 +# cdef np.ndarray[float, ndim = 2] pred = kwargs['network'](parameters) +# #log_p = 0 +# #print(pred.shape) +# #print(pred) +# for i in range(size): +# if response[i] == 0: +# log_p += np.log(pred[i, 2 * x[i]] * (1 - p_outlier) + w_outlier * p_outlier) +# else: +# log_p += np.log(pred[i, 2 * x[i] + 1] * (1 - p_outlier) + w_outlier * p_outlier) +# +# # Call to network: +# return log_p +# +# \ No newline at end of file