def __init__(self, csv_file_name, wtp_range,
csv_file_name_proj_thresholds='ProjectedOptimalThreshold.csv'):
self.cols = io.read_csv_cols(
file_name=csv_file_name, n_cols=3, if_ignore_first_row=True, if_convert_float=True)
self.wtps = self.cols[0]
self.OnTs = self.cols[1]
self.OffTs = self.cols[2]
self.RegToOn = Reg.ExpRegression(x=self.wtps,
y=self.OnTs,
if_c0_zero=True)
print(self.RegToOn.get_coeffs())
self.RegToOff = Reg.ExpRegression(x=self.wtps,
y=self.OffTs,
if_c0_zero=True)
print(self.RegToOff.get_coeffs())
wtps = np.linspace(wtp_range[0], wtp_range[1], 13)
to_on_ts = self.RegToOn.get_predicted_y(wtps)
to_off_ts = self.RegToOff.get_predicted_y(wtps)
rows = []
for i in range(len(wtps)):
row = [wtps[i], to_on_ts[i], to_off_ts[i]]
rows.append(row)
io.write_csv(rows=rows, file_name=csv_file_name_proj_thresholds)
def __init__(self, csv_file_name):
""" extracts seeds, mortality probabilities and the associated likelihood from
the csv file where the calibration results are stored
:param csv_file_name: name of the csv file where the calibrated results are stored
:param drug_effectiveness_ratio: effectiveness of the drug
"""
# read the columns of the csv files containing the calibration results
cols = InOutSupport.read_csv_cols(file_name=csv_file_name,
n_cols=3,
if_ignore_first_row=True,
if_convert_float=True)
# store likelihood weights, cohort IDs and sampled mortality probabilities
self.cohortIDs = cols[CalibrationColIndex.ID.value].astype(int)
self.weights = cols[CalibrationColIndex.W.value]
self.mortalityProbs = cols[CalibrationColIndex.MORT_PROB.value]
self.multiCohorts = None # multi-cohort
import SimPy.InOutFunctions as IO
import SimPy.Plots.Histogram as Hist
import SimPy.Plots.ProbDist as Plot
import SimPy.RandomVariateGenerators as RVGs
import SimPy.Statistics as Stat
# read weekly number of drinks
cols = IO.read_csv_cols(file_name='dataNumOfDrinks.csv',
n_cols=1,
if_ignore_first_row=True,
if_convert_float=True)
# make a histogram
Hist.plot_histogram(data=cols[0], title='Weekly Number of Drinks', bin_width=1)
# mean and standard deviation
stat = Stat.SummaryStat(name='Weekly number of drinks', data=cols[0])
print('Mean = ', stat.get_mean())
print('StDev = ', stat.get_stdev())
# fit a Poisson distribution
fit_results = RVGs.Poisson.fit_ml(data=cols[0])
print('Fitting a Poisson distribution:', fit_results)
# plot the fitted Poisson distribution
Plot.plot_poisson_fit(data=cols[0],
fit_results=fit_results,
x_label='Weekly number of drinks',
x_range=(0, 40),
bin_width=1)
from SimPy import InOutFunctions as InOutSupport
# test reading by rows
rows = InOutSupport.read_csv_rows('myCSV',
if_del_first_row=True,
if_convert_float=True)
print('Testing reading by rows:')
for row in rows:
print(sum(row))
# test reading by columns
cols = InOutSupport.read_csv_cols('myCSV',
n_cols=3,
if_ignore_first_row=True,
if_convert_float=True)
print('Testing reading by columns:')
for j in range(0, 3):
print(sum(cols[j]))
# rest reading by columns into a dictionary
dict_cols = InOutSupport.read_csv_cols_to_dictionary('myCSV',
if_convert_float=True)
print('Testing reading by columns into a dictionary:')
print(dict_cols)
from SimPy import InOutFunctions as io
# ---------------
# first run the TestCSVWritter.py to produce the csv file
# ---------------
# test reading by rows
rows = io.read_csv_rows('CSVFolder/myCSV.csv',
if_ignore_first_row=True,
if_convert_float=True)
print('Testing reading by rows:')
for row in rows:
print(sum(row[1:]))
# test reading by columns
cols = io.read_csv_cols('CSVFolder/myCSV.csv',
n_cols=4,
if_ignore_first_row=True,
if_convert_float=True)
print('Testing reading by columns:')
for j in range(1, 4):
print(sum(cols[j]))
# rest reading by columns into a dictionary
dict_cols = io.read_csv_cols_to_dictionary('CSVFolder/myCSV.csv',
if_convert_float=True)
print('Testing reading by columns into a dictionary:')
print(dict_cols)
import SimPy.Plots.EffectiveSampleSize as P
import SimPy.InOutFunctions as IO
# read likelihoods
data = IO.read_csv_cols(file_name='csv_files/TBLikelihoods.csv',
n_cols=1,
if_ignore_first_row=True,
if_convert_float=True)
P.plot_eff_sample_size(likelihood_weights=data[0],
if_randomize=True,
y_range=(0, 10),
file_name='results/EffSampleSize.png')