def input6():
'''
Get input for calculating correlation coefficient of x/y data, carrying
out hypothesis testing on the correlation coefficient, or performing a linear
regression on the x/y data.
Return the following:
x_values: numpy array of the x values
y_values: numpy array of the y values, where the i^th y value
corresponds to the i^th x value.
y_errors: if specified, a numpy array of error bars (e.g., standard
deviations) for the y values. If not specified, return None.
'''
msg1 = "Input the x values."
x_values = UserDataSet(msg1).data
msg2 = (
"Input the y values. The number of y values should be the same as the "
"number of x values, and the i^th y value should correspond to the "
"i^th x value.")
y_values = UserDataSet(msg2, requiredsize=x_values.shape[0]).data
# Query the user regarding the inclusion of error bars
includeerrorbars = query.binary_query(
"Would you like to include error bars?")
if includeerrorbars:
msg3 = "Input the error bars associated with y values."
y_errors = UserDataSet(msg3, requiredsize=x_values.shape[0]).data
else:
y_errors = None
return x_values, y_values, y_errors
def input3():
'''
Get input for computing confidence limits for the difference between two samples
'''
msg1 = "Please enter the first sample."
dataset1 = UserDataSet(msg1).data
msg2 = "Please enter the second sample."
dataset2 = UserDataSet(msg2).data
return dataset1, dataset2
def input5b():
'''
Get input for testing whether there is a difference between two means.
'''
msg1 = "Please enter the values of the first sample."
dataset1 = UserDataSet(msg1).data
msg2 = "Please enter the values of the second sample."
dataset2 = UserDataSet(msg2).data
msg = "1 or 2-tailed test? (Enter 1 or 2, then press return)"
print(msg)
tails = input('>>> ')
tails = int(tails)
return dataset1, dataset2, tails
def input1():
'''
Get input for a Q-Q plot of x/y data
'''
msg = "Please enter the first sample."
dataset = UserDataSet(msg).data
return dataset
def input4():
'''
Get input for computing confidence limits of a sample using simulation
'''
msg = "Please enter the values of the sample."
dataset = UserDataSet(msg).data
return dataset
def input2():
'''
Get input computing mean confidence limits.
'''
msg = "Please enter the values of the sample."
dataset = UserDataSet(msg).data
return dataset
def input7():
'''
Get input for performing propagation of error.
Return the following:
variable_values: The value of variables a, b, c, ...
variable_errors: The value of the errors associated with
variables a, b, c, ...
calculation_str: A string specifying the calculation to be performed.
'''
msg = '''
--------------------------------------------------------------------------------
Propagation of error
How to:
In the first data set, input the value of each variable. In the second data
set, input the errors associated with each variable (in the corresponding
order). Variables are labelled with letters a-z in the order in which they
are input; a maximum of 26 variables are allowed. Once the variables and
associated uncertainties are entered, specify the calculation using the
following syntax:
+ : addition
- : subtraction
* : multiplication
/ : division
** : exponentiation
ln(value) : natural logarithm of value
For example, you could enter:
a+b: find the sum of the first two numbers entered, as well as the
associated error.
a-b*c: find the difference between a and (b times c), as well as the
associated error.
a**b: calculate a to the b power, and the associated error
--------------------------------------------------------------------------------
'''
msg1 = "Input the variables' values."
values = UserDataSet(msg1).data
msg2 = ("Input the uncertainties associated with each variable, in the same \n" +\
"order as which the values were specified")
uncertainties = UserDataSet(msg2, requiredsize=values.shape[0]).data
msg3 = "Input the expression for the desired calculation."
expression = query.string_query(msg3)
return values, uncertainties, expression
def input5c():
'''
Get input for a chi-squared test.
'''
datasets = []
msg1 = "Input the number of values in each category, for the first group."
datasets.append(UserDataSet(msg1).data)
msg2 = "Input the number of values in each category, for the second group."
datasets.append(UserDataSet(msg2, requiredsize=datasets[0].shape[0]).data)
dataset_idx = 3
add_dataset = query.binary_query("Enter another sample?")
while add_dataset:
msg = "Input the number of values in ech category, for group #{:d}"\
.format(dataset_idx)
datasets.append(
UserDataSet(msg, requiredsize=datasets[0].shape[0]).data)
dataset_idx += 1
add_dataset = query.binary_query("Enter another sample?")
return datasets
def input5a():
'''
Get input for testing the hypothesis that the mean of a specified sample is
equal to a specified value.
'''
msg = "Please enter the values of the sample."
dataset = UserDataSet(msg).data
proposedmean = float(
input("Please enter the proposed mean and press then "
"press the enter key\n"))
msg = "1 or 2-tailed test? (Enter 1 or 2, then press return)"
print(msg)
tails = input('>>> ')
tails = int(tails)
return dataset, proposedmean, tails