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Monte Carlo errors estimation routine
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$begingroup$
I would value your opinion on the following piece of code. I am rather new to both Python and Monte Carlo analysis, so I was wondering whether the routine makes sense to more experienced and knowledgeable users.
def MC_analysis_a():
x = spin_lock_durations
y_signal_a = (a_norm1, a_norm2, a_norm3, a_norm4, a_norm5, a_norm6, a_norm7, a_norm8)
x = np.array(x, dtype = float)
y_signal_a = np.array(y_signal_a, dtype = float)
def func(x, a, b):
return a * np.exp(-b * x)
initial_guess = [1.0, 1.0]
fitting_parameters, covariance_matrix = optimize.curve_fit(func, x, y_signal_a, initial_guess)
print(round(fitting_parameters[1], 2))
# ---> PRODUCING PARAMETERS ESTIMATES
total_iterations = 5000
MC_pars = np.array([])
for iTrial in range(total_iterations):
xTrial = x
yTrial = y_signal_a + np.random.normal(loc = y_signal_a, scale = e_signal_a, size = np.size(y_signal_a))
try:
iteration_identifiers, covariance_matrix = optimize.curve_fit(func, xTrial, yTrial, initial_guess)
except:
dumdum = 1
continue
# ---> STACKING RESULTS
if np.size(MC_pars) < 1:
MC_pars = np.copy(iteration_identifiers)
else:
MC_pars = np.vstack((MC_pars, iteration_identifiers))
# ---> SLICING THE ARRAY
print(np.shape(MC_pars))
# print(np.median(aFitpyars[:,1]))
print(np.std(MC_pars[:,1]))
The output I get is apparently satisfactory and plausible.
Many thanks in advance to any contributor!
python numpy statistics
asked 1 hour ago
Shawn Marion fanShawn Marion fan
61
New contributor
Shawn Marion fan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I would value your opinion on the following piece of code. I am rather new to both Python and Monte Carlo analysis, so I was wondering whether the routine makes sense to more experienced and knowledgeable users.
def MC_analysis_a():
x = spin_lock_durations
y_signal_a = (a_norm1, a_norm2, a_norm3, a_norm4, a_norm5, a_norm6, a_norm7, a_norm8)
x = np.array(x, dtype = float)
y_signal_a = np.array(y_signal_a, dtype = float)
def func(x, a, b):
return a * np.exp(-b * x)
initial_guess = [1.0, 1.0]
fitting_parameters, covariance_matrix = optimize.curve_fit(func, x, y_signal_a, initial_guess)
print(round(fitting_parameters[1], 2))
# ---> PRODUCING PARAMETERS ESTIMATES
total_iterations = 5000
MC_pars = np.array([])
for iTrial in range(total_iterations):
xTrial = x
yTrial = y_signal_a + np.random.normal(loc = y_signal_a, scale = e_signal_a, size = np.size(y_signal_a))
try:
iteration_identifiers, covariance_matrix = optimize.curve_fit(func, xTrial, yTrial, initial_guess)
except:
dumdum = 1
continue
# ---> STACKING RESULTS
if np.size(MC_pars) < 1:
MC_pars = np.copy(iteration_identifiers)
else:
MC_pars = np.vstack((MC_pars, iteration_identifiers))
# ---> SLICING THE ARRAY
print(np.shape(MC_pars))
# print(np.median(aFitpyars[:,1]))
print(np.std(MC_pars[:,1]))
The output I get is apparently satisfactory and plausible.
Many thanks in advance to any contributor!
python numpy statistics
asked 1 hour ago
Shawn Marion fanShawn Marion fan
61
New contributor
Shawn Marion fan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
Welcome to Code Review! In order to get the best possible outcome from the review please change your code so that is is runnable and complete (How to create a MWE?, minimal is not so important here). This allows users to verify that it is actually on-topic as well as to get a better feeling on what you actually want to accomplish.
$endgroup$
– Alex
47 mins ago
$begingroup$
Does the code need to be generalised in any particular way? Are you wanting to apply it to different datasets, or to fit a different family of curves? Do you want to store the output for further analysis?
$endgroup$
– Russ Hyde
46 mins ago
add a comment |
$begingroup$
I would value your opinion on the following piece of code. I am rather new to both Python and Monte Carlo analysis, so I was wondering whether the routine makes sense to more experienced and knowledgeable users.
def MC_analysis_a():
x = spin_lock_durations
y_signal_a = (a_norm1, a_norm2, a_norm3, a_norm4, a_norm5, a_norm6, a_norm7, a_norm8)
x = np.array(x, dtype = float)
y_signal_a = np.array(y_signal_a, dtype = float)
def func(x, a, b):
return a * np.exp(-b * x)
initial_guess = [1.0, 1.0]
fitting_parameters, covariance_matrix = optimize.curve_fit(func, x, y_signal_a, initial_guess)
print(round(fitting_parameters[1], 2))
# ---> PRODUCING PARAMETERS ESTIMATES
total_iterations = 5000
MC_pars = np.array([])
for iTrial in range(total_iterations):
xTrial = x
yTrial = y_signal_a + np.random.normal(loc = y_signal_a, scale = e_signal_a, size = np.size(y_signal_a))
try:
iteration_identifiers, covariance_matrix = optimize.curve_fit(func, xTrial, yTrial, initial_guess)
except:
dumdum = 1
continue
# ---> STACKING RESULTS
if np.size(MC_pars) < 1:
MC_pars = np.copy(iteration_identifiers)
else:
MC_pars = np.vstack((MC_pars, iteration_identifiers))
# ---> SLICING THE ARRAY
print(np.shape(MC_pars))
# print(np.median(aFitpyars[:,1]))
print(np.std(MC_pars[:,1]))
The output I get is apparently satisfactory and plausible.
Many thanks in advance to any contributor!
python numpy statistics
asked 1 hour ago
Shawn Marion fanShawn Marion fan
61
New contributor
Shawn Marion fan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I would value your opinion on the following piece of code. I am rather new to both Python and Monte Carlo analysis, so I was wondering whether the routine makes sense to more experienced and knowledgeable users.
def MC_analysis_a():
x = spin_lock_durations
y_signal_a = (a_norm1, a_norm2, a_norm3, a_norm4, a_norm5, a_norm6, a_norm7, a_norm8)
x = np.array(x, dtype = float)
y_signal_a = np.array(y_signal_a, dtype = float)
def func(x, a, b):
return a * np.exp(-b * x)
initial_guess = [1.0, 1.0]
fitting_parameters, covariance_matrix = optimize.curve_fit(func, x, y_signal_a, initial_guess)
print(round(fitting_parameters[1], 2))
# ---> PRODUCING PARAMETERS ESTIMATES
total_iterations = 5000
MC_pars = np.array([])
for iTrial in range(total_iterations):
xTrial = x
yTrial = y_signal_a + np.random.normal(loc = y_signal_a, scale = e_signal_a, size = np.size(y_signal_a))
try:
iteration_identifiers, covariance_matrix = optimize.curve_fit(func, xTrial, yTrial, initial_guess)
except:
dumdum = 1
continue
# ---> STACKING RESULTS
if np.size(MC_pars) < 1:
MC_pars = np.copy(iteration_identifiers)
else:
MC_pars = np.vstack((MC_pars, iteration_identifiers))
# ---> SLICING THE ARRAY
print(np.shape(MC_pars))
# print(np.median(aFitpyars[:,1]))
print(np.std(MC_pars[:,1]))
The output I get is apparently satisfactory and plausible.
Many thanks in advance to any contributor!
python numpy statistics
python numpy statistics
asked 1 hour ago
Shawn Marion fanShawn Marion fan
61
New contributor
Shawn Marion fan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 hour ago
Shawn Marion fanShawn Marion fan
61
New contributor
Shawn Marion fan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 hour ago
Shawn Marion fanShawn Marion fan
61
New contributor
Shawn Marion fan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 hour ago
Shawn Marion fanShawn Marion fan
61
asked 1 hour ago
Shawn Marion fanShawn Marion fan
61
61
New contributor
Shawn Marion fan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Shawn Marion fan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Shawn Marion fan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
Welcome to Code Review! In order to get the best possible outcome from the review please change your code so that is is runnable and complete (How to create a MWE?, minimal is not so important here). This allows users to verify that it is actually on-topic as well as to get a better feeling on what you actually want to accomplish.
$endgroup$
– Alex
47 mins ago
$begingroup$
Does the code need to be generalised in any particular way? Are you wanting to apply it to different datasets, or to fit a different family of curves? Do you want to store the output for further analysis?
$endgroup$
– Russ Hyde
46 mins ago
add a comment |
$begingroup$
Welcome to Code Review! In order to get the best possible outcome from the review please change your code so that is is runnable and complete (How to create a MWE?, minimal is not so important here). This allows users to verify that it is actually on-topic as well as to get a better feeling on what you actually want to accomplish.
$endgroup$
– Alex
47 mins ago
$begingroup$
Does the code need to be generalised in any particular way? Are you wanting to apply it to different datasets, or to fit a different family of curves? Do you want to store the output for further analysis?
$endgroup$
– Russ Hyde
46 mins ago
$begingroup$
Welcome to Code Review! In order to get the best possible outcome from the review please change your code so that is is runnable and complete (How to create a MWE?, minimal is not so important here). This allows users to verify that it is actually on-topic as well as to get a better feeling on what you actually want to accomplish.
$endgroup$
– Alex
47 mins ago
$begingroup$
Welcome to Code Review! In order to get the best possible outcome from the review please change your code so that is is runnable and complete (How to create a MWE?, minimal is not so important here). This allows users to verify that it is actually on-topic as well as to get a better feeling on what you actually want to accomplish.
$endgroup$
– Alex
47 mins ago
$begingroup$
Does the code need to be generalised in any particular way? Are you wanting to apply it to different datasets, or to fit a different family of curves? Do you want to store the output for further analysis?
$endgroup$
– Russ Hyde
46 mins ago
$begingroup$
Does the code need to be generalised in any particular way? Are you wanting to apply it to different datasets, or to fit a different family of curves? Do you want to store the output for further analysis?
$endgroup$
– Russ Hyde
46 mins ago
add a comment |
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Shawn Marion fan is a new contributor. Be nice, and check out our Code of Conduct.
Shawn Marion fan is a new contributor. Be nice, and check out our Code of Conduct.
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$begingroup$
Welcome to Code Review! In order to get the best possible outcome from the review please change your code so that is is runnable and complete (How to create a MWE?, minimal is not so important here). This allows users to verify that it is actually on-topic as well as to get a better feeling on what you actually want to accomplish.
$endgroup$
– Alex
47 mins ago
$begingroup$
Does the code need to be generalised in any particular way? Are you wanting to apply it to different datasets, or to fit a different family of curves? Do you want to store the output for further analysis?
$endgroup$
– Russ Hyde
46 mins ago