fig, ax = plt.subplots(1)
ax.errorbar(count,
period,
yerr=u_period,
marker="x",
linestyle="None",
color="black",
label="experimental data")
ax.set(xlabel='count', ylabel="Period (s)")
fig.suptitle('Pendulum period')
savefig('pendulum_data.png')
# Let us assume that the period is constant
pendulum_model = make_lmfit_model("T", allow_constant_model=True)
pendulum_params = pendulum_model.make_params(T=2.0)
pendulum_fit_results = model_fit(pendulum_model,
pendulum_params,
count,
period,
u_y=u_period)
pendulum_fit = pendulum_fit_results.best_fit
u_pendulum_fit = pendulum_fit_results.eval_uncertainty(sigma=1)
pendulum_fit_parameters = get_fit_parameters(pendulum_fit_results)
pvalue = 1.0 - stats.chi2.cdf(
pendulum_fit_results.chisqr,
pendulum_fit_results.ndata - pendulum_fit_results.nvarys)
print("""
try:
__filepath = os.path.join(os.path.dirname(os.path.realpath(__file__)),
'quiz_data.csv')
data = pandas.read_csv(__filepath).to_numpy()
except:
data = "Warning: failed to import data from csv file. Possibly the monashspa library is not installed correctly."
x = data[:,0]
u_x = data[:,1]
temp = data[:,2]
name = "linear model"
# Create the model and run fit
model = make_lmfit_model("a*x + b")
params = model.make_params(a=1.0, b=0.0)
fit_results = model_fit(model, params, temp, x, u_y=u_x)
# Extract result and print nicely
fit = fit_results.best_fit
u_fit = fit_results.eval_uncertainty(sigma=1)
fit_parameters = get_fit_parameters(fit_results)
pvalue = 1.0 - stats.chi2.cdf(fit_results.chisqr,
fit_results.ndata-fit_results.nvarys)
print("""
[[{name}]]
=================
p-value = {pvalue:.2E}
""".format(name=name, pvalue=pvalue))
# Implement a fit that determines the a, b anc c parameters of a calorimeter resolution model:
# $\frac{\sigma(E)}{E} = \frac{a}{\sqrt{E}} \oplus b \oplus \frac{c}{E}$.
if False:
# Fill these arrays with the relative resolution and the uncertainty on that estimate for each of the
# particle energies
rel_resolutions_withall = []
u_rel_resolutions_withall = []
from lmfit import fit_report
from scipy import stats
from monashspa.common.fitting import linear_fit, get_fit_parameters, make_lmfit_model, model_fit
from monashspa.common.figures import savefig
name = 'Energy resolution'
model = make_lmfit_model("sqrt(a*a/x + b*b + c*c/(x*x))")
params = model.make_params(a=0.09, b=0.03, c=3)
fit_results = model_fit(model,
params,
particle_energies,
rel_resolutions_withall,
u_y=u_rel_resolutions_withall)
# Extract result and print nicely
fit = fit_results.best_fit
u_fit = fit_results.eval_uncertainty(sigma=1)
fit_parameters = get_fit_parameters(fit_results)
pvalue = 1.0 - stats.chi2.cdf(fit_results.chisqr,
fit_results.ndata - fit_results.nvarys)
print("""
fname = os.path.join(folder, datafile)
data = pandas.read_csv(fname, usecols=[0, 1, 2]).to_numpy()
except FileNotFoundError:
print(
"ERROR: Failed to read CSV file {file} with data. It should be in the same folder {folder} as the Python file"
.format(file=datafile, folder=folder))
if type(data) == type(None):
sys.exit(1)
volume = data[:, 0]
u_volume = data[:, 1]
temp = data[:, 2]
# Create the model and run fit
gas_model = make_lmfit_model(model)
gas_params = gas_model.make_params(R=0.01)
gas_fit_results = model_fit(gas_model, gas_params, temp, volume, u_y=u_volume)
# Extract result and print nicely
gas_fit = gas_fit_results.best_fit
u_gas_fit = gas_fit_results.eval_uncertainty(sigma=1)
gas_fit_parameters = get_fit_parameters(gas_fit_results)
pvalue = 1.0 - stats.chi2.cdf(gas_fit_results.chisqr,
gas_fit_results.ndata - gas_fit_results.nvarys)
print("""
[[{name}]]
=================
p-value = {pvalue:.2E}
""".format(name=name, pvalue=pvalue))
from monashspa.common.fitting import linear_fit, get_fit_parameters, make_lmfit_model, model_fit
from monashspa.common.figures import savefig
xval = np.arange(start=-5.0, stop=5.0, step=0.8)
u_yval = 0.8 + xval * xval / 15.
yval = 1.5 * xval + np.random.normal(0.0, u_yval)
#######################################################################
# Name of the model that you fit
name = "linear model"
#######################################################################
# Create the model and run fit
model = make_lmfit_model("a*x + b")
params = model.make_params(a=1.0, b=0.0)
fit_results = model_fit(model, params, xval, yval, u_y=u_yval)
# Extract result and print nicely
fit = fit_results.best_fit
u_fit = fit_results.eval_uncertainty(sigma=1)
fit_parameters = get_fit_parameters(fit_results)
pvalue = 1.0 - stats.chi2.cdf(fit_results.chisqr,
fit_results.ndata - fit_results.nvarys)
print("""
[[{name}]]
=================
p-value = {pvalue:.2E}
""".format(name=name, pvalue=pvalue))