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# astroML.density_estimation.bayesian_blocks

All Samples(22)  |  Call(16)  |  Derive(0)  |  Import(6)

```from matplotlib import pyplot as plt

from astroML.density_estimation import\
scotts_bin_width, freedman_bin_width,\
knuth_bin_width, bayesian_blocks
```
```
if bins in ['blocks']:
bins = bayesian_blocks(x)
elif bins in ['knuth', 'knuths']:
dx, bins = knuth_bin_width(x, True)
```

```from matplotlib import pyplot as plt

from astroML.density_estimation import\
scotts_bin_width, freedman_bin_width,\
knuth_bin_width, bayesian_blocks
```
```
if bins in ['blocks']:
bins = bayesian_blocks(x)
elif bins in ['knuth', 'knuths']:
dx, bins = knuth_bin_width(x, True)
```

```"""
Tools for working with distributions
"""
import numpy as np
from astroML.density_estimation import bayesian_blocks
```
```
if bins == 'blocks':
bins = bayesian_blocks(a)
elif bins == 'knuth':
da, bins = knuth_bin_width(a, True)
```

```"""
Tools for working with distributions
"""
import numpy as np
from astroML.density_estimation import bayesian_blocks
```
```
if bins == 'blocks':
bins = bayesian_blocks(a)
elif bins == 'knuth':
da, bins = knuth_bin_width(a, True)
```

```import numpy as np
from  numpy.testing import assert_allclose, assert_
from astroML.density_estimation import bayesian_blocks

```
```                        1 + np.random.random(200)])

bins = bayesian_blocks(x)

assert_(len(bins) == 3)
```
```    x[:20] += 1

bins1 = bayesian_blocks(t)
bins2 = bayesian_blocks(t[:80], x[:80])

```
```    x = np.random.normal(x, sigma)

bins = bayesian_blocks(t, x, sigma, fitness='measures')

assert_allclose(bins, [0, 0.45, 0.55, 1])
```

```import numpy as np
from  numpy.testing import assert_allclose, assert_
from astroML.density_estimation import bayesian_blocks

```
```                        1 + np.random.random(200)])

bins = bayesian_blocks(x)

assert_(len(bins) == 3)
```
```    x[:20] += 1

bins1 = bayesian_blocks(t)
bins2 = bayesian_blocks(t[:80], x[:80])

```
```    x = np.random.normal(x, sigma)

bins = bayesian_blocks(t, x, sigma, fitness='measures')

assert_allclose(bins, [0, 0.45, 0.55, 1])
```