Calculate the normalized intensity for each element peak. Parameters ---------- threshv : float No value is shown when smaller than the threshold value
(self, threshv=0.0)
| 114 | self.update_norm() |
| 115 | |
| 116 | def update_norm(self, threshv=0.0): |
| 117 | """ |
| 118 | Calculate the normalized intensity for each element peak. |
| 119 | |
| 120 | Parameters |
| 121 | ---------- |
| 122 | threshv : float |
| 123 | No value is shown when smaller than the threshold value |
| 124 | """ |
| 125 | # Do nothing if no elements are selected |
| 126 | if not self.element_dict: |
| 127 | return |
| 128 | |
| 129 | max_dict = np.max([v.maxv for v in self.element_dict.values()]) |
| 130 | |
| 131 | for v in self.element_dict.values(): |
| 132 | v.norm = v.maxv / max_dict * 100 |
| 133 | v.lbd_stat = bool(v.norm > threshv) |
| 134 | |
| 135 | # also delete smaller values |
| 136 | # there is some bugs in plotting when values < 0.0 |
| 137 | self.delete_peaks_below_threshold(threshv=threshv) |
| 138 | |
| 139 | def delete_all(self): |
| 140 | self.element_dict.clear() |
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