(self, value)
| 2953 | return t_value[0] if is_scalar else t_value |
| 2954 | |
| 2955 | def inverse(self, value): |
| 2956 | if not self.scaled(): |
| 2957 | raise ValueError("Not invertible until scaled") |
| 2958 | if self.vmin > self.vmax: |
| 2959 | raise ValueError("vmin must be less or equal to vmax") |
| 2960 | t_vmin, t_vmax = self._trf.transform([self.vmin, self.vmax]) |
| 2961 | if not np.isfinite([t_vmin, t_vmax]).all(): |
| 2962 | raise ValueError("Invalid vmin or vmax") |
| 2963 | value, is_scalar = self.process_value(value) |
| 2964 | rescaled = value * (t_vmax - t_vmin) |
| 2965 | rescaled += t_vmin |
| 2966 | value = (self._trf |
| 2967 | .inverted() |
| 2968 | .transform(rescaled) |
| 2969 | .reshape(np.shape(value))) |
| 2970 | return value[0] if is_scalar else value |
| 2971 | |
| 2972 | def autoscale_None(self, A): |
| 2973 | # i.e. A[np.isfinite(...)], but also for non-array A's |
nothing calls this directly
no test coverage detected