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| 1 | +# BSD 3-Clause License; see https://github.com/scikit-hep/uproot5/blob/main/LICENSE |
| 2 | +from __future__ import annotations |
| 3 | + |
| 4 | +from utils import run_test_in_pyodide |
| 5 | + |
| 6 | + |
| 7 | +# Taken from test_1347_rntuple_floats_suppressed_cols.py |
| 8 | +@run_test_in_pyodide(test_file="test_float_types_rntuple_v1-0-0-0.root") |
| 9 | +def test_rntuple_with_awkwardforth(selenium): |
| 10 | + import numpy as np |
| 11 | + |
| 12 | + import uproot |
| 13 | + |
| 14 | + def truncate_float(value, bits): |
| 15 | + a = np.float32(value).view(np.uint32) |
| 16 | + a &= np.uint32(0xFFFFFFFF) << (32 - bits) |
| 17 | + return a.astype(np.uint32).view(np.float32) |
| 18 | + |
| 19 | + def quantize_float(value, bits, min, max): |
| 20 | + min = np.float32(min) |
| 21 | + max = np.float32(max) |
| 22 | + if value < min or value > max: |
| 23 | + raise ValueError(f"Value {value} is out of range [{min}, {max}]") |
| 24 | + scaled_value = (value - min) * (2**bits - 1) / (max - min) |
| 25 | + int_value = np.round(scaled_value) |
| 26 | + quantized_float = min + int_value * (max - min) / ((1 << bits) - 1) |
| 27 | + return quantized_float.astype(np.float32) |
| 28 | + |
| 29 | + with uproot.open("test_float_types_rntuple_v1-0-0-0.root") as f: |
| 30 | + obj = f["ntuple"] |
| 31 | + |
| 32 | + arrays = obj.arrays() |
| 33 | + |
| 34 | + min_value = -2.0 |
| 35 | + max_value = 3.0 |
| 36 | + |
| 37 | + entry = arrays[0] |
| 38 | + true_value = 1.23456789 |
| 39 | + assert entry.trunc10 == truncate_float(true_value, 10) |
| 40 | + assert entry.trunc16 == truncate_float(true_value, 16) |
| 41 | + assert entry.trunc24 == truncate_float(true_value, 24) |
| 42 | + assert entry.trunc31 == truncate_float(true_value, 31) |
| 43 | + assert np.isclose( |
| 44 | + entry.quant1, quantize_float(true_value, 1, min_value, max_value) |
| 45 | + ) |
| 46 | + assert np.isclose( |
| 47 | + entry.quant8, quantize_float(true_value, 8, min_value, max_value) |
| 48 | + ) |
| 49 | + assert np.isclose( |
| 50 | + entry.quant16, quantize_float(true_value, 16, min_value, max_value) |
| 51 | + ) |
| 52 | + assert np.isclose( |
| 53 | + entry.quant20, quantize_float(true_value, 20, min_value, max_value) |
| 54 | + ) |
| 55 | + assert np.isclose( |
| 56 | + entry.quant24, quantize_float(true_value, 24, min_value, max_value) |
| 57 | + ) |
| 58 | + assert np.isclose( |
| 59 | + entry.quant25, quantize_float(true_value, 25, min_value, max_value) |
| 60 | + ) |
| 61 | + assert np.isclose( |
| 62 | + entry.quant32, quantize_float(true_value, 32, min_value, max_value) |
| 63 | + ) |
| 64 | + |
| 65 | + entry = arrays[1] |
| 66 | + true_value = 1.4660155e13 |
| 67 | + assert entry.trunc10 == truncate_float(true_value, 10) |
| 68 | + assert entry.trunc16 == truncate_float(true_value, 16) |
| 69 | + assert entry.trunc24 == truncate_float(true_value, 24) |
| 70 | + assert entry.trunc31 == truncate_float(true_value, 31) |
| 71 | + true_value = 1.6666666 |
| 72 | + assert np.isclose( |
| 73 | + entry.quant1, quantize_float(true_value, 1, min_value, max_value) |
| 74 | + ) |
| 75 | + assert np.isclose( |
| 76 | + entry.quant8, quantize_float(true_value, 8, min_value, max_value) |
| 77 | + ) |
| 78 | + assert np.isclose( |
| 79 | + entry.quant16, quantize_float(true_value, 16, min_value, max_value) |
| 80 | + ) |
| 81 | + assert np.isclose( |
| 82 | + entry.quant20, quantize_float(true_value, 20, min_value, max_value) |
| 83 | + ) |
| 84 | + assert np.isclose( |
| 85 | + entry.quant24, quantize_float(true_value, 24, min_value, max_value) |
| 86 | + ) |
| 87 | + assert np.isclose( |
| 88 | + entry.quant25, quantize_float(true_value, 25, min_value, max_value) |
| 89 | + ) |
| 90 | + assert np.isclose( |
| 91 | + entry.quant32, quantize_float(true_value, 32, min_value, max_value) |
| 92 | + ) |
| 93 | + |
| 94 | + entry = arrays[2] |
| 95 | + true_value = -6.2875986e-22 |
| 96 | + assert entry.trunc10 == truncate_float(true_value, 10) |
| 97 | + assert entry.trunc16 == truncate_float(true_value, 16) |
| 98 | + assert entry.trunc24 == truncate_float(true_value, 24) |
| 99 | + assert entry.trunc31 == truncate_float(true_value, 31) |
| 100 | + assert np.isclose( |
| 101 | + entry.quant1, quantize_float(true_value, 1, min_value, max_value) |
| 102 | + ) |
| 103 | + assert np.isclose( |
| 104 | + entry.quant8, quantize_float(true_value, 8, min_value, max_value) |
| 105 | + ) |
| 106 | + assert np.isclose( |
| 107 | + entry.quant16, quantize_float(true_value, 16, min_value, max_value) |
| 108 | + ) |
| 109 | + assert np.isclose( |
| 110 | + entry.quant20, quantize_float(true_value, 20, min_value, max_value) |
| 111 | + ) |
| 112 | + assert np.isclose( |
| 113 | + entry.quant24, quantize_float(true_value, 24, min_value, max_value) |
| 114 | + ) |
| 115 | + assert np.isclose( |
| 116 | + entry.quant25, |
| 117 | + quantize_float(true_value, 25, min_value, max_value), |
| 118 | + atol=2e-07, |
| 119 | + ) |
| 120 | + assert np.isclose( |
| 121 | + entry.quant32, quantize_float(true_value, 32, min_value, max_value) |
| 122 | + ) |
| 123 | + |
| 124 | + entry = arrays[3] |
| 125 | + true_value = -1.9060668 |
| 126 | + assert entry.trunc10 == truncate_float(true_value, 10) |
| 127 | + assert entry.trunc16 == truncate_float(true_value, 16) |
| 128 | + assert entry.trunc24 == truncate_float(true_value, 24) |
| 129 | + assert entry.trunc31 == truncate_float(true_value, 31) |
| 130 | + assert np.isclose( |
| 131 | + entry.quant1, quantize_float(true_value, 1, min_value, max_value) |
| 132 | + ) |
| 133 | + assert np.isclose( |
| 134 | + entry.quant8, quantize_float(true_value, 8, min_value, max_value) |
| 135 | + ) |
| 136 | + assert np.isclose( |
| 137 | + entry.quant16, quantize_float(true_value, 16, min_value, max_value) |
| 138 | + ) |
| 139 | + assert np.isclose( |
| 140 | + entry.quant20, quantize_float(true_value, 20, min_value, max_value) |
| 141 | + ) |
| 142 | + assert np.isclose( |
| 143 | + entry.quant24, quantize_float(true_value, 24, min_value, max_value) |
| 144 | + ) |
| 145 | + assert np.isclose( |
| 146 | + entry.quant25, quantize_float(true_value, 25, min_value, max_value) |
| 147 | + ) |
| 148 | + assert np.isclose( |
| 149 | + entry.quant32, quantize_float(true_value, 32, min_value, max_value) |
| 150 | + ) |
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