def confidence_interval(data, confidence=0.90):
s = np.array(data)
n, min_max, mean, var, skew, kurt = scipy.stats.describe(s)
normalized_var = var / math.sqrt(0.5)
std = math.sqrt(normalized_var)
interval = scipy.stats.norm.interval(0.90, loc=mean, scale=std)
return interval
#here starts the main script to brute force try many constant joining values and evaluate the error with expt
#*abdul sees 3e6 per molar per second in his work*
#the kjoins predicted by bernie/hill are ~20 and ~2, so fairly close to 1, makes sense that roughly the same range of param would work
jode_constant = joining_ode_class.ODE_joining('constant')
jode_hill = joining_ode_class.ODE_joining('hill')
jode_bernie = joining_ode_class.ODE_joining('bernie')
plot_dir_name = "bin10/optimal_parameter/"
n_bootstrap = 1
n_bins = 10
max_tube_length = 10.0
def optimal_parameter_hill(hill_model):
param_list = np.linspace(1e10, 1.5e11, 400)
kjoin_list = [param_list[i] for i in range(len(param_list))]
#original: kjoin_list = [1e10+(i*1e9) for i in range(200)]
squared_error_list = []
#kjoin_list = [1e1, 1e2, 1e3, 1e4, 1e5, 1e6, 1e7, 1e8, 1e9, 1e10, 1e11, 1e12, 1e13, 1e14]
#kjoin_list = [10000*(i*10) for i in range(10)]
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
#script to generate data files needed for plotting
import numpy as np
import joining_ode_class
import matplotlib.pyplot as plt
import scipy.stats
from scipy.stats import gaussian_kde
from scipy.interpolate import UnivariateSpline
jode_constant = joining_ode_class.ODE_joining('constant')
jode_hill = joining_ode_class.ODE_joining('hill')
jode_bernie = joining_ode_class.ODE_joining('bernie')
plot_dir_name = "bin10/optimal_parameter/"
params = [3.86e6, 2.98e10, 7.175e7] #optimized parameters
models = [jode_constant, jode_hill, jode_bernie]
########################################################################################################
#plotting simulated distributions with time for each variable of interest here
for i in range(len(models)):
model = models[i]
param = params[i]
line_types = ['red', 'green', 'blue', 'black']
t, w = model.perform_integration(param)
t, A_conc_data, B_conc_data, C_conc_data, B_joined_conc_data = model.unpack_concentrations_from_ode_format(
w, t)
#simulated C tube distributions with time
#xfmt = matplotlib.ticker.ScalarFormatter()
#xfmt.set_powerlimits((-3,3))
f_cost_fxns, axarr_cost_fxns = plt.subplots(3,3, sharex = False, sharey = False, figsize = (9,9))
#here starts the main script to brute force try many constant joining values and evaluate the error with expt
#*abdul sees 3e6 per molar per second in his work*
#the kjoins predicted by bernie/hill are ~20 and ~2, so fairly close to 1, makes sense that roughly the same range of param would work
#this script will perform the same brute force search as the original brute_force_parameter_search but it will use three different cost functions
#and will output a 3x3 grid of subplots for inclusion in our paper
jode_constant = joining_ode_class.ODE_joining('constant')
jode_hill = joining_ode_class.ODE_joining('hill')
jode_bernie = joining_ode_class.ODE_joining('bernie')
plot_dir_name = "bin10/optimal_parameter/"
n_bootstrap = 1
n_bins = 10
max_tube_length = 10.0
models = ['constant','hill','bernie']
cost_fxns = [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]
for model in models:
for cost_fxn in cost_fxns:
jode = joining_ode_class.ODE_joining(joining_model = model, weight_joining_percentage = cost_fxn[0], weight_a_cdf = cost_fxn[1], weight_c_cdf = cost_fxn[2])
if model == 'hill':
param_list = np.linspace(1e10, 1.5e11, 100)
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
#script to generate data files needed for plotting
import numpy as np
import joining_ode_class
import matplotlib.pyplot as plt
jode_constant = joining_ode_class.ODE_joining('constant')
jode_hill = joining_ode_class.ODE_joining('hill')
jode_bernie = joining_ode_class.ODE_joining('bernie')
plot_dir_name = "bin10/bootstrapping/"
params = [.4e7, .6e11, 1e8] #optimized parameters
models = [jode_constant, jode_hill, jode_bernie]
########################################################################################################
#plotting simulated distributions with time for each variable of interest here
for i in range(len(models)):
model = models[i]
param = params[i]
line_types = ['red', 'green', 'blue', 'black']
t, w = model.perform_integration(param)
t, A_conc_data, B_conc_data, C_conc_data, B_joined_conc_data = model.unpack_concentrations_from_ode_format(
w, t)
#simulated C tube distributions with time
for timepoint in range(len(t)):
C_conc_timepoint = C_conc_data[timepoint]
lengths = [
float((bin + 1)) * model.bin_length