Change continuous distributions to the standard

[alkino]

This PR is here only for continuous:

• Erlang
• LogNorm
• NegExp
• Normal
• Uniform
• Weibull

And unused RndInt is removed.

Here are the data fitted from generated values:

Computation for erlang
KS test 2 samples between old and new
D+=0.004010000000000069, pvalue=0.39631978335488516
KS test 1 sample from old compare to erlang
D+=0.0031418021567620746, pvalue=0.276430366443688
KS test 1 sample from new compare to erlang
D-=0.002751544140646378, pvalue=0.4345156017759174
Fitted old parameters: scale=0.19863874531376705, loc=0, shape=25.14951030822438
Fitted new parameters: scale=0.19932706758506916, loc=0, shape=25.084590186091052
Generating graph...
Over

Computation for lognormal
KS test 2 samples between old and new
D-=0.003009999999999957, pvalue=0.7542898827264349
KS test 1 sample from old compare to lognormal
D+=0.002427843285461251, pvalue=0.5965032909797967
KS test 1 sample from new compare to lognormal
D+=0.0020800761643524623, pvalue=0.7790396649659992
Fitted old parameters: scale=4.8107488739015505, loc=0, shape=0.27763652287355006
Fitted new parameters: scale=4.8093393378099965, loc=0, shape=0.27719507894268125
Generating graph...
Over

Computation for negexp
KS test 2 samples between old and new
D+=0.0036800000000000166, pvalue=0.5062384732899003
KS test 1 sample from old compare to negexp
D+=0.0020296113974088703, pvalue=0.8037645864920905
KS test 1 sample from new compare to negexp
D-=0.002424694125399307, pvalue=0.59816710647604
Fitted old parameters: scale=0.49909674561988776 loc=0.0
Fitted new parameters: scale=0.5008447172512056 loc=0.0
Generating graph...
Over

Computation for normal
KS test 2 samples between old and new
D-=0.0029100000000000237, pvalue=0.7897805451846037
KS test 1 sample from old compare to normal
D-=0.00281200953901356, pvalue=0.40702052300307534
KS test 1 sample from new compare to normal
D-=0.0017727679702720378, pvalue=0.9112110607462915
Fitted old parameters: scale=1.2221744797869214 loc=0
Fitted new parameters: scale=1.2239901661534476 loc=0
Generating graph...
Over

Computation for uniform
KS test 2 samples between old and new
D-=0.0031800000000000717, pvalue=0.6914901624020462
KS test 1 sample from old compare to uniform
D-=0.0027674800981284386, pvalue=0.4271693341284927
KS test 1 sample from new compare to uniform
D-=0.0019578226969142187, pvalue=0.837253161799323
Fitted old parameters: scale=1.9999930942431108 loc=0.0
Fitted new parameters: scale=1.9999915751979327 loc=0.0
Generating graph...
Over

Computation for weibull
KS test 2 samples between old and new
D+=0.0033000000000000806, pvalue=0.6463026318994153
KS test 1 sample from old compare to weibull
D+=0.002500678223098629, pvalue=0.5583433920723122
KS test 1 sample from new compare to weibull
D+=0.0023796913000540543, pvalue=0.6220334366616607
Fitted old parameters: scale=1.084233483717604, loc=0, shape=5.014049611015011
Fitted new parameters: scale=1.084682178649993, loc=0, shape=4.9812025045890795
Generating graph...
Over

Scripts to draw graph and do statistics:

import pickle
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import os.path
from math import pow, sqrt, log, exp

stats_name = {
        'erlang': {'fun': stats.erlang, 'args': [25, 0, 1/5]},
        'lognormal': {'fun': stats.lognorm, 'args': [sqrt(log(2/(5*5) + 1)), 0, 5*5/sqrt(2+5*5)]},
        'negexp': {'fun': stats.expon, 'args': [0, 0.5]},
        'normal': {'fun': stats.norm, 'args': [-1, sqrt(.5)]},
        'uniform': {'fun': stats.uniform, 'args': [0, 2]},
        'weibull': {'fun': stats.weibull_min, 'args': [5, 0, pow(1.5, 1/5)]},
        }
def plot(name):
    print(f"Computation for {name}")
    with open(f'old/{name}.data', 'rb') as f:
        old_data = pickle.load(f)
    with open(f'new/{name}.data', 'rb') as f:
        new_data = pickle.load(f)

    if name in stats_name:
        fun = stats_name[name]["fun"]
        args = stats_name[name]["args"]
        print("KS test 2 samples from old to new")
        res = stats.ks_2samp(old_data, new_data)
        print(f"D{'-' if res.statistic_sign == -1 else '+'}={res.statistic}, pvalue={res.pvalue}")
        print(f"KS test 1 sample from old compare to {name}")
        res = stats.ks_1samp(x=old_data, cdf=fun.cdf, args=args)
        print(f"D{'-' if res.statistic_sign == -1 else '+'}={res.statistic}, pvalue={res.pvalue}")
        print(f"KS test 1 sample from new compare to {name}")
        res = stats.ks_1samp(x=new_data, cdf=fun.cdf, args=args)
        print(f"D{'-' if res.statistic_sign == -1 else '+'}={res.statistic}, pvalue={res.pvalue}")
        if name == 'negexp' or name == 'normal' or name == 'uniform':
            old_loc, old_scale = fun.fit(old_data, floc=0)  # floc=0 fixes the location at 0, as lognormal is defined on (0, inf)
            new_loc, new_scale = fun.fit(new_data, floc=0)  # floc=0 fixes the location at 0, as lognormal is defined on (0, inf)

            print(f"Fitted old parameters: scale={old_scale} loc={old_loc}")
            print(f"Fitted new parameters: scale={new_scale} loc={new_loc}")
        else:
            old_shape, old_loc, old_scale = fun.fit(old_data, floc=0)  # floc=0 fixes the location at 0, as lognormal is defined on (0, inf)
            new_shape, new_loc, new_scale = fun.fit(new_data, floc=0)  # floc=0 fixes the location at 0, as lognormal is defined on (0, inf)

            print(f"Fitted old parameters: scale={old_scale}, loc={old_loc}, shape={old_shape}")
            print(f"Fitted new parameters: scale={new_scale}, loc={new_loc}, shape={new_shape}")

        NUM_BINS = 2000  # Increase this number for smaller bins
    else:
        NUM_BINS = 'auto'

    print("Generating graph...")
    plt.hist(old_data, bins=NUM_BINS, density=True, alpha=0.6, color='b')
    plt.hist(new_data, bins=NUM_BINS, density=True, alpha=0.6, color='g')
    plt.title(f"Histogram of Data and Fitted {name} Distribution")
    plt.savefig(f"{name}_comparison.png")
    plt.clf()
    print("Over")

if __name__ == "__main__":
    import sys
    if len(sys.argv) > 1:
        plot(sys.argv[1])
    else:
        for n in stats_name.keys():
            if os.path.isfile(os.path.join("old", f"{n}.data")) and os.path.isfile(os.path.join("new", f"{n}.data")):
                plot(n)

from neuron import h
import pickle

r = h.Random()

nrun = int(1e6)

def generate_data(name, *args):
    fun = getattr(r, name)
    fun(*args)
    hist = []
    for i in range(nrun):
        j = r.repick()
        hist.append(j)
    with open(f"{name}.data", "wb") as h:
        pickle.dump(hist, h)

# Discrete
# generate_data("binomial", 20, .5)
# generate_data("discunif", 0, 10)
# generate_data("poisson", 3)

# Continuous
generate_data("negexp", 0.5)
generate_data("normal", -1, .5)
generate_data("lognormal", 5, 2) # mean = 5, variance = 2
generate_data("uniform", 0, 2)
generate_data("erlang", 5, 1)
generate_data("weibull", 5, 1.5)

# Not implemented
# generate_data("geometric", .8)
# generate_data("hypergeo", 10, 150)

[sonarcloud]

Quality Gate passed

Issues
0 New issues
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code

See analysis details on SonarQube Cloud

[alkino]

Need the review of @nrnhines on that

[azure-pipelines]

✔️ f9e61d7 -> Azure artifacts URL

[azure-pipelines]

✔️ c361243 -> Azure artifacts URL

[bbpbuildbot]

Logfiles from GitLab pipeline #238556 (:no_entry:) have been uploaded here!

Status and direct links:

• ⛔ mac_m1_cmake_build: [cmake, ON, OFF, OFF, address]
• ✅ spack_setup
• ✅ build:nmodl
• ✅ build:neuron:nmodl:intel:legacy
• ✅ build:neuron:nmodl:intel:shared
• ✅ build:neuron:nmodl:nvhpc:acc:legacy
• ✅ build:neuron:nmodl:nvhpc:acc:shared
• ✅ build:neuron:nmodl:nvhpc:omp:legacy
• ✅ build:neuron:nmodl:nvhpc:omp
• ⛔ test:neuron:nmodl:intel:legacy
• ⛔ test:neuron:nmodl:intel:shared
• ⛔ test:neuron:nmodl:nvhpc:acc:legacy
• ⛔ test:neuron:nmodl:nvhpc:acc:shared
• ⛔ test:neuron:nmodl:nvhpc:omp:legacy
• ⛔ test:neuron:nmodl:nvhpc:omp