Python bottleneck.nanmean() Examples
The following are 10
code examples of bottleneck.nanmean().
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Example #1
| Source File: accessors.py From vectorbt with GNU General Public License v3.0 | 6 votes |
|
def apply_and_reduce(self, apply_func_nb, reduce_func_nb, *args, **kwargs):
"""See `vectorbt.tseries.nb.apply_and_reduce_nb`.
`**kwargs` will be passed to `vectorbt.tseries.common.TSArrayWrapper.wrap_reduced`.
Example:
```python-repl
>>> greater_nb = njit(lambda col, a: a[a > 2])
>>> mean_nb = njit(lambda col, a: np.nanmean(a))
>>> print(df.vbt.tseries.apply_and_reduce(greater_nb, mean_nb))
a 4.0
b 4.0
c 3.0
dtype: float64
```"""
checks.assert_numba_func(apply_func_nb)
checks.assert_numba_func(reduce_func_nb)
result = nb.apply_and_reduce_nb(self.to_2d_array(), apply_func_nb, reduce_func_nb, *args)
return self.wrap_reduced(result, **kwargs)
Example #2
| Source File: oPCA.py From sima with GNU General Public License v2.0 | 6 votes |
|
def _method_1(data, num_pcs=None):
"""Compute OPCA when num_observations > num_dimensions."""
data = np.nan_to_num(data - nanmean(data, axis=0))
T = data.shape[0]
corr_offset = np.dot(data[1:].T, data[:-1])
corr_offset += corr_offset.T
if num_pcs is None:
eivals, eivects = eigh(corr_offset)
else:
eivals, eivects = eigsh(corr_offset, num_pcs, which='LA')
eivals = np.real(eivals)
eivects = np.real(eivects)
idx = np.argsort(-eivals) # sort the eigenvectors and eigenvalues
eivals = old_div(eivals[idx], (2. * (T - 1)))
eivects = eivects[:, idx]
return eivals, eivects, np.dot(data, eivects)
Example #3
| Source File: oPCA.py From sima with GNU General Public License v2.0 | 6 votes |
|
def _method_2(data, num_pcs=None):
"""Compute OPCA when num_observations <= num_dimensions."""
data = np.nan_to_num(data - nanmean(data, axis=0))
T = data.shape[0]
tmp = np.dot(data, data.T)
corr_offset = np.zeros(tmp.shape)
corr_offset[1:] = tmp[:-1]
corr_offset[:-1] += tmp[1:]
if num_pcs is None:
eivals, eivects = eig(corr_offset)
else:
eivals, eivects = eigs(corr_offset, num_pcs, which='LR')
eivals = np.real(eivals)
eivects = np.real(eivects)
idx = np.argsort(-eivals) # sort the eigenvectors and eigenvalues
eivals = old_div(eivals[idx], (2. * (T - 1)))
eivects = eivects[:, idx]
transformed_eivects = np.dot(data.T, eivects)
for i in range(transformed_eivects.shape[1]): # normalize the eigenvectors
transformed_eivects[:, i] /= np.linalg.norm(transformed_eivects[:, i])
return eivals, transformed_eivects, np.dot(data, transformed_eivects)
Example #4
| Source File: accessors.py From vectorbt with GNU General Public License v3.0 | 5 votes |
|
def rolling_apply(self, window, apply_func_nb, *args, on_matrix=False):
"""See `vectorbt.tseries.nb.rolling_apply_nb` and
`vectorbt.tseries.nb.rolling_apply_matrix_nb` for `on_matrix=True`.
Example:
```python-repl
>>> mean_nb = njit(lambda col, i, a: np.nanmean(a))
>>> print(df.vbt.tseries.rolling_apply(3, mean_nb))
a b c
2020-01-01 1.0 5.0 1.000000
2020-01-02 1.5 4.5 1.500000
2020-01-03 2.0 4.0 2.000000
2020-01-04 3.0 3.0 2.333333
2020-01-05 4.0 2.0 2.000000
>>> mean_matrix_nb = njit(lambda i, a: np.nanmean(a))
>>> print(df.vbt.tseries.rolling_apply(3,
... mean_matrix_nb, on_matrix=True))
a b c
2020-01-01 2.333333 2.333333 2.333333
2020-01-02 2.500000 2.500000 2.500000
2020-01-03 2.666667 2.666667 2.666667
2020-01-04 2.777778 2.777778 2.777778
2020-01-05 2.666667 2.666667 2.666667
```"""
checks.assert_numba_func(apply_func_nb)
if on_matrix:
result = nb.rolling_apply_matrix_nb(self.to_2d_array(), window, apply_func_nb, *args)
else:
result = nb.rolling_apply_nb(self.to_2d_array(), window, apply_func_nb, *args)
return self.wrap(result)
Example #5
| Source File: accessors.py From vectorbt with GNU General Public License v3.0 | 5 votes |
|
def expanding_apply(self, apply_func_nb, *args, on_matrix=False):
"""See `vectorbt.tseries.nb.expanding_apply_nb` and
`vectorbt.tseries.nb.expanding_apply_matrix_nb` for `on_matrix=True`.
Example:
```python-repl
>>> mean_nb = njit(lambda col, i, a: np.nanmean(a))
>>> print(df.vbt.tseries.expanding_apply(mean_nb))
a b c
2020-01-01 1.0 5.0 1.0
2020-01-02 1.5 4.5 1.5
2020-01-03 2.0 4.0 2.0
2020-01-04 2.5 3.5 2.0
2020-01-05 3.0 3.0 1.8
>>> mean_matrix_nb = njit(lambda i, a: np.nanmean(a))
>>> print(df.vbt.tseries.expanding_apply(
... mean_matrix_nb, on_matrix=True))
a b c
2020-01-01 2.333333 2.333333 2.333333
2020-01-02 2.500000 2.500000 2.500000
2020-01-03 2.666667 2.666667 2.666667
2020-01-04 2.666667 2.666667 2.666667
2020-01-05 2.600000 2.600000 2.600000
```"""
checks.assert_numba_func(apply_func_nb)
if on_matrix:
result = nb.expanding_apply_matrix_nb(self.to_2d_array(), apply_func_nb, *args)
else:
result = nb.expanding_apply_nb(self.to_2d_array(), apply_func_nb, *args)
return self.wrap(result)
Example #6
| Source File: accessors.py From vectorbt with GNU General Public License v3.0 | 5 votes |
|
def groupby_apply(self, by, apply_func_nb, *args, on_matrix=False, **kwargs):
"""See `vectorbt.tseries.nb.groupby_apply_nb` and
`vectorbt.tseries.nb.groupby_apply_matrix_nb` for `on_matrix=True`.
For `by`, see `pandas.DataFrame.groupby`.
Example:
```python-repl
>>> mean_nb = njit(lambda col, i, a: np.nanmean(a))
>>> print(df.vbt.tseries.groupby_apply([1, 1, 2, 2, 3], mean_nb))
a b c
1 1.5 4.5 1.5
2 3.5 2.5 2.5
3 5.0 1.0 1.0
>>> mean_matrix_nb = njit(lambda i, a: np.nanmean(a))
>>> print(df.vbt.tseries.groupby_apply([1, 1, 2, 2, 3],
... mean_matrix_nb, on_matrix=True))
a b c
1 2.500000 2.500000 2.500000
2 2.833333 2.833333 2.833333
3 2.333333 2.333333 2.333333
```"""
checks.assert_numba_func(apply_func_nb)
regrouped = self._obj.groupby(by, axis=0, **kwargs)
groups = Dict()
for i, (k, v) in enumerate(regrouped.indices.items()):
groups[i] = np.asarray(v)
if on_matrix:
result = nb.groupby_apply_matrix_nb(self.to_2d_array(), groups, apply_func_nb, *args)
else:
result = nb.groupby_apply_nb(self.to_2d_array(), groups, apply_func_nb, *args)
return self.wrap_reduced(result, index=list(regrouped.indices.keys()))
Example #7
| Source File: accessors.py From vectorbt with GNU General Public License v3.0 | 5 votes |
|
def resample_apply(self, freq, apply_func_nb, *args, on_matrix=False, **kwargs):
"""See `vectorbt.tseries.nb.groupby_apply_nb` and
`vectorbt.tseries.nb.groupby_apply_matrix_nb` for `on_matrix=True`.
For `freq`, see `pandas.DataFrame.resample`.
Example:
```python-repl
>>> mean_nb = njit(lambda col, i, a: np.nanmean(a))
>>> print(df.vbt.tseries.resample_apply('2d', mean_nb))
a b c
2020-01-01 1.5 4.5 1.5
2020-01-03 3.5 2.5 2.5
2020-01-05 5.0 1.0 1.0
>>> mean_matrix_nb = njit(lambda i, a: np.nanmean(a))
>>> print(df.vbt.tseries.resample_apply('2d',
... mean_matrix_nb, on_matrix=True))
a b c
2020-01-01 2.500000 2.500000 2.500000
2020-01-03 2.833333 2.833333 2.833333
2020-01-05 2.333333 2.333333 2.333333
```"""
checks.assert_numba_func(apply_func_nb)
resampled = self._obj.resample(freq, axis=0, **kwargs)
groups = Dict()
for i, (k, v) in enumerate(resampled.indices.items()):
groups[i] = np.asarray(v)
if on_matrix:
result = nb.groupby_apply_matrix_nb(self.to_2d_array(), groups, apply_func_nb, *args)
else:
result = nb.groupby_apply_nb(self.to_2d_array(), groups, apply_func_nb, *args)
result_obj = self.wrap(result, index=list(resampled.indices.keys()))
resampled_arr = np.full((resampled.ngroups, self.to_2d_array().shape[1]), np.nan)
resampled_obj = self.wrap(resampled_arr, index=pd.Index(list(resampled.groups.keys()), freq=freq))
resampled_obj.loc[result_obj.index] = result_obj.values
return resampled_obj
Example #8
| Source File: accessors.py From vectorbt with GNU General Public License v3.0 | 5 votes |
|
def mean(self, **kwargs):
"""Return mean of non-NaN elements."""
return self.wrap_reduced(nanmean(self.to_2d_array(), axis=0), **kwargs)
Example #9
| Source File: frame_align.py From sima with GNU General Public License v2.0 | 5 votes |
|
def shifted_corr(reference, image, displacement):
"""Calculate the correlation between the reference and the image shifted
by the given displacement.
Parameters
----------
reference : np.ndarray
image : np.ndarray
displacement : np.ndarray
Returns
-------
correlation : float
"""
ref_cuts = np.maximum(0, displacement)
ref = reference[ref_cuts[0]:, ref_cuts[1]:, ref_cuts[2]:]
im_cuts = np.maximum(0, -displacement)
im = image[im_cuts[0]:, im_cuts[1]:, im_cuts[2]:]
s = np.minimum(im.shape, ref.shape)
ref = ref[:s[0], :s[1], :s[2]]
im = im[:s[0], :s[1], :s[2]]
ref -= nanmean(ref.reshape(-1, ref.shape[-1]), axis=0)
ref = np.nan_to_num(ref)
im -= nanmean(im.reshape(-1, im.shape[-1]), axis=0)
im = np.nan_to_num(im)
assert np.all(np.isfinite(ref)) and np.all(np.isfinite(im))
corr = nanmean(
[old_div(np.sum(i * r), np.sqrt(np.sum(i * i) * np.sum(r * r))) for
i, r in zip(np.rollaxis(im, -1), np.rollaxis(ref, -1))])
return corr
Example #10
| Source File: boost_experiment.py From mHTM with MIT License | 4 votes |
|
def _phase3(self): """ Normal phase 3, but with tracking the boost changes. Double commented lines are new. """ # Update permanences self.p = np.clip(self.p + (self.c_pupdate * self.y[:, 0:1] * self.x[self.syn_map] - self.pdec * self.y[:, 0:1]), 0, 1) if self.disable_boost is False: # Update the boosting mechanisms if self.global_inhibition: min_dc = np.zeros(self.ncolumns) min_dc.fill(self.c_mdc * bn.nanmax(self.active_dc)) else: min_dc = self.c_mdc * bn.nanmax(self.neighbors * self.active_dc, 1) ## Save pre-overlap boost info boost = list(self.boost) # Update boost self._update_active_duty_cycle() self._update_boost(min_dc) self._update_overlap_duty_cycle() ## Write out overlap boost changes with open(os.path.join(self.out_path, 'overlap_boost.csv'), 'ab') as f: writer = csv.writer(f) writer.writerow([self.iter, bn.nanmean(boost != self.boost)]) # Boost permanences mask = self.overlap_dc < min_dc mask.resize(self.ncolumns, 1) self.p = np.clip(self.p + self.c_sboost * mask, 0, 1) ## Write out permanence boost info with open(os.path.join(self.out_path, 'permanence_boost.csv'), 'ab') \ as f: writer = csv.writer(f) writer.writerow([self.iter, bn.nanmean(mask)]) # Trim synapses if self.trim is not False: self.p[self.p < self.trim] = 0
