this is for holding javascript data
Edward Brown deleted figures/sample-datasets/measurements.py
about 9 years ago
Commit id: e0c738fbfb21fb1269a8e745a5ef45053dcc86c8
deletions | additions
diff --git a/figures/sample-datasets/measurements.py b/figures/sample-datasets/measurements.py
deleted file mode 100644
index baa495c..0000000
--- a/figures/sample-datasets/measurements.py
+++ /dev/null
...
################################################################################
# Edward Brown
# Michigan State University
#
# a simple class to make a measurement with gaussian uncertainties.
#
################################################################################
import numpy as np
import numpy.random as nr
class measurementWithUncertainty:
"""
Upon initialization, generates a normal (gaussian) distribution with a mean
in the range [10.0,20.0] and a standard deviation in the range [1.0,2.0].
The mean and standard deviation are themselves chosen at random.
Example
-------
>>> from measurements import measurementWithUncertainty
>>> d = measurementWithUncertainty()
made data 12.492 +/- 1.12
>>> x = d.make_measurements(10)
>>> print x
[ 13.85981811 11.70694115 10.28276517 12.41290813 11.7629273
11.5970737 10.06126323 13.05767109 13.45734073 12.21781776]
>>> print d.mean()
12.4922289309
>>> print d.stddev()
1.12196280663
Here the call to make_measurements(N) generates N measurements chosen
randomly from this distribution.
"""
_sample_low = 10.0
_sample_high = 20.0
_stddev_bias = 1.0
def __init__(self):
"""
Sets the mean and standard deviation of our sample.
"""
a = self._sample_low
b = self._sample_high
self._mean = (b-a)*nr.random() + a
self._stddev = nr.random() + self._stddev_bias
print 'made data {0:7.3f} +/- {1:6.3}'.format(self._mean,self._stddev)
def make_measurements(self,n):
"""
Returns a set of measurements drawn from distribution. Called with
argument n, the number of desired measurements.
"""
return nr.normal(loc=self._mean,scale=self._stddev,size=n)
def mean(self):
"""
Returns the mean of the distribution. Note that this returns the
parameter in the distribution. and *not* the average of a set of
numbers drawn from the distribution.
"""
return self._mean
def stddev(self):
"""
Returns the standard deviation of the distribution. Note that this
returns the parameter in the distribution, and *not* the variance of a
set of numbers drawn from the distribution.
"""
return self._stddev