# Python numpy.histogram_bin_edges() Examples

The following are *13*
code examples for showing how to use *numpy.histogram_bin_edges()*.
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Example 1

Project: Carnets Author: holzschu File: test_quantity_non_ufuncs.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |

def test_histogram_bin_edges(self): x = np.array([1.1, 1.2, 1.3, 2.1, 5.1]) * u.m out_b = np.histogram_bin_edges(x) expected_b = np.histogram_bin_edges(x.value) * x.unit assert np.all(out_b == expected_b) # With bins out2_b = np.histogram_bin_edges(x, [125, 200] * u.cm) expected2_b = np.histogram_bin_edges(x.value, [1.25, 2.]) * x.unit assert np.all(out2_b == expected2_b) with pytest.raises(u.UnitsError): np.histogram_bin_edges(x, [125, 200] * u.s) with pytest.raises(u.UnitsError): np.histogram_bin_edges(x, [125, 200]) with pytest.raises(u.UnitsError): np.histogram_bin_edges(x.value, [125, 200] * u.s)

Example 2

Project: Carnets Author: holzschu File: function_helpers.py License: BSD 3-Clause "New" or "Revised" License | 5 votes |

def histogram_bin_edges(a, bins=10, range=None, weights=None): # weights is currently unused a = _as_quantity(a) if not isinstance(bins, str): bins = _check_bins(bins, a.unit) return (a.value, bins, range, weights), {}, a.unit, None

Example 3

Project: recruit Author: Frank-qlu File: histograms.py License: Apache License 2.0 | 4 votes |

def _hist_bin_auto(x, range): """ Histogram bin estimator that uses the minimum width of the Freedman-Diaconis and Sturges estimators if the FD bandwidth is non zero and the Sturges estimator if the FD bandwidth is 0. The FD estimator is usually the most robust method, but its width estimate tends to be too large for small `x` and bad for data with limited variance. The Sturges estimator is quite good for small (<1000) datasets and is the default in the R language. This method gives good off the shelf behaviour. .. versionchanged:: 1.15.0 If there is limited variance the IQR can be 0, which results in the FD bin width being 0 too. This is not a valid bin width, so ``np.histogram_bin_edges`` chooses 1 bin instead, which may not be optimal. If the IQR is 0, it's unlikely any variance based estimators will be of use, so we revert to the sturges estimator, which only uses the size of the dataset in its calculation. Parameters ---------- x : array_like Input data that is to be histogrammed, trimmed to range. May not be empty. Returns ------- h : An estimate of the optimal bin width for the given data. See Also -------- _hist_bin_fd, _hist_bin_sturges """ fd_bw = _hist_bin_fd(x, range) sturges_bw = _hist_bin_sturges(x, range) del range # unused if fd_bw: return min(fd_bw, sturges_bw) else: # limited variance, so we return a len dependent bw estimator return sturges_bw # Private dict initialized at module load time

Example 4

Project: mars Author: mars-project File: test_statistics_execute.py License: Apache License 2.0 | 4 votes |

def testHistogramBinEdgesExecution(self): rs = np.random.RandomState(0) raw = rs.randint(10, size=(20,)) a = tensor(raw, chunk_size=3) # range provided for range_ in [(0, 10), (3, 11), (3, 7)]: bin_edges = histogram_bin_edges(a, range=range_) result = self.executor.execute_tensor(bin_edges)[0] expected = np.histogram_bin_edges(raw, range=range_) np.testing.assert_array_equal(result, expected) ctx, executor = self._create_test_context(self.executor) with ctx: raw2 = rs.randint(10, size=(1,)) b = tensor(raw2) raw3 = rs.randint(10, size=(0,)) c = tensor(raw3) for t, r in [(a, raw), (b, raw2), (c, raw3), (sort(a), raw)]: test_bins = [10, 'stone', 'auto', 'doane', 'fd', 'rice', 'scott', 'sqrt', 'sturges'] for bins in test_bins: bin_edges = histogram_bin_edges(t, bins=bins) if r.size > 0: with self.assertRaises(TilesError): executor.execute_tensor(bin_edges) result = executor.execute_tensors([bin_edges])[0] expected = np.histogram_bin_edges(r, bins=bins) np.testing.assert_array_equal(result, expected) test_bins = [[0, 4, 8], tensor([0, 4, 8], chunk_size=2)] for bins in test_bins: bin_edges = histogram_bin_edges(t, bins=bins) result = executor.execute_tensors([bin_edges])[0] expected = np.histogram_bin_edges(r, bins=[0, 4, 8]) np.testing.assert_array_equal(result, expected) raw = np.arange(5) a = tensor(raw, chunk_size=3) bin_edges = histogram_bin_edges(a) result = executor.execute_tensors([bin_edges])[0] expected = np.histogram_bin_edges(raw) self.assertEqual(bin_edges.shape, expected.shape) np.testing.assert_array_equal(result, expected)

Example 5

Project: lambda-packs Author: ryfeus File: histograms.py License: MIT License | 4 votes |

def _hist_bin_auto(x): """ Histogram bin estimator that uses the minimum width of the Freedman-Diaconis and Sturges estimators if the FD bandwidth is non zero and the Sturges estimator if the FD bandwidth is 0. The FD estimator is usually the most robust method, but its width estimate tends to be too large for small `x` and bad for data with limited variance. The Sturges estimator is quite good for small (<1000) datasets and is the default in the R language. This method gives good off the shelf behaviour. .. versionchanged:: 1.15.0 If there is limited variance the IQR can be 0, which results in the FD bin width being 0 too. This is not a valid bin width, so ``np.histogram_bin_edges`` chooses 1 bin instead, which may not be optimal. If the IQR is 0, it's unlikely any variance based estimators will be of use, so we revert to the sturges estimator, which only uses the size of the dataset in its calculation. Parameters ---------- x : array_like Input data that is to be histogrammed, trimmed to range. May not be empty. Returns ------- h : An estimate of the optimal bin width for the given data. See Also -------- _hist_bin_fd, _hist_bin_sturges """ fd_bw = _hist_bin_fd(x) sturges_bw = _hist_bin_sturges(x) if fd_bw: return min(fd_bw, sturges_bw) else: # limited variance, so we return a len dependent bw estimator return sturges_bw # Private dict initialized at module load time

Example 6

Project: Mastering-Elasticsearch-7.0 Author: PacktPublishing File: histograms.py License: MIT License | 4 votes |

Example 7

Project: GraphicDesignPatternByPython Author: Relph1119 File: histograms.py License: MIT License | 4 votes |

Example 8

Project: predictive-maintenance-using-machine-learning Author: awslabs File: histograms.py License: Apache License 2.0 | 4 votes |

Example 9

Project: pySINDy Author: luckystarufo File: histograms.py License: MIT License | 4 votes |

Example 10

Project: coffeegrindsize Author: jgagneastro File: histograms.py License: MIT License | 4 votes |

Example 11

Project: Carnets Author: holzschu File: histograms.py License: BSD 3-Clause "New" or "Revised" License | 4 votes |

Example 12

Project: Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda Author: PacktPublishing File: histograms.py License: MIT License | 4 votes |

Example 13

Project: twitter-stock-recommendation Author: alvarobartt File: histograms.py License: MIT License | 4 votes |