Merge pull request 'custom_dmc' (#37) from custom_dmc into master
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Reviewed-on: #37
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@@ -2,7 +2,7 @@ import pdme.model
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import pdme.measurement
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import pdme.measurement.input_types
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import pdme.subspace_simulation
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from typing import Tuple, Sequence
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from typing import Tuple, Dict, NewType, Any
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from dataclasses import dataclass
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import logging
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import numpy
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@@ -30,6 +30,20 @@ class DirectMonteCarloConfig:
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tag: str = ""
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# Aliasing dict as a generic data container
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DirectMonteCarloData = NewType("DirectMonteCarloData", Dict[str, Any])
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class DirectMonteCarloFilter:
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"""
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Abstract class for filtering out samples matching some criteria. Initialise with data as needed,
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then filter out samples as needed.
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"""
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def filter_samples(self, samples: numpy.ndarray) -> numpy.ndarray:
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raise NotImplementedError
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class DirectMonteCarloRun:
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"""
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A single model Direct Monte Carlo run, currently implemented only using single threading.
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@@ -65,25 +79,26 @@ class DirectMonteCarloRun:
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def __init__(
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self,
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model_name_pair: Tuple[str, pdme.model.DipoleModel],
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measurements: Sequence[pdme.measurement.DotRangeMeasurement],
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filter: DirectMonteCarloFilter,
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config: DirectMonteCarloConfig,
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):
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self.model_name, self.model = model_name_pair
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self.measurements = measurements
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self.dot_inputs = [(measure.r, measure.f) for measure in self.measurements]
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# self.measurements = measurements
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# self.dot_inputs = [(measure.r, measure.f) for measure in self.measurements]
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self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(
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self.dot_inputs
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)
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# self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(
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# self.dot_inputs
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# )
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self.config = config
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(
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self.lows,
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self.highs,
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) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
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self.measurements
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)
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self.filter = filter
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# (
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# self.lows,
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# self.highs,
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# ) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
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# self.measurements
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# )
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def _single_run(self, seed) -> numpy.ndarray:
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rng = numpy.random.default_rng(seed)
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@@ -93,18 +108,20 @@ class DirectMonteCarloRun:
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)
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current_sample = sample_dipoles
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for di, low, high in zip(self.dot_inputs_array, self.lows, self.highs):
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if len(current_sample) < 1:
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break
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vals = pdme.util.fast_v_calc.fast_vs_for_dipoleses(
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numpy.array([di]), current_sample
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)
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return self.filter.filter_samples(current_sample)
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# for di, low, high in zip(self.dot_inputs_array, self.lows, self.highs):
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current_sample = current_sample[
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numpy.all((vals > low) & (vals < high), axis=1)
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]
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return current_sample
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# if len(current_sample) < 1:
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# break
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# vals = pdme.util.fast_v_calc.fast_vs_for_dipoleses(
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# numpy.array([di]), current_sample
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# )
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# current_sample = current_sample[
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# numpy.all((vals > low) & (vals < high), axis=1)
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# ]
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# return current_sample
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def execute(self) -> DirectMonteCarloResult:
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step_count = 0
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143
deepdog/direct_monte_carlo/dmc_filters.py
Normal file
143
deepdog/direct_monte_carlo/dmc_filters.py
Normal file
@@ -0,0 +1,143 @@
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from numpy import ndarray
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from deepdog.direct_monte_carlo.direct_mc import DirectMonteCarloFilter
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from typing import Sequence
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import pdme.measurement
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import pdme.measurement.input_types
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import pdme.util.fast_nonlocal_spectrum
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import pdme.util.fast_v_calc
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import numpy
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class SingleDotPotentialFilter(DirectMonteCarloFilter):
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def __init__(self, measurements: Sequence[pdme.measurement.DotRangeMeasurement]):
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self.measurements = measurements
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self.dot_inputs = [(measure.r, measure.f) for measure in self.measurements]
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self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(
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self.dot_inputs
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)
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(
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self.lows,
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self.highs,
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) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
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self.measurements
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)
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def filter_samples(self, samples: ndarray) -> ndarray:
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current_sample = samples
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for di, low, high in zip(self.dot_inputs_array, self.lows, self.highs):
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if len(current_sample) < 1:
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break
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vals = pdme.util.fast_v_calc.fast_vs_for_dipoleses(
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numpy.array([di]), current_sample
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)
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current_sample = current_sample[
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numpy.all((vals > low) & (vals < high), axis=1)
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]
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return current_sample
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class DoubleDotSpinQubitFrequencyFilter(DirectMonteCarloFilter):
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def __init__(
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self,
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pair_phase_measurements: Sequence[pdme.measurement.DotPairRangeMeasurement],
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):
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self.pair_phase_measurements = pair_phase_measurements
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self.dot_pair_inputs = [
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(measure.r1, measure.r2, measure.f)
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for measure in self.pair_phase_measurements
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]
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self.dot_pair_inputs_array = (
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pdme.measurement.input_types.dot_pair_inputs_to_array(self.dot_pair_inputs)
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)
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(
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self.pair_phase_lows,
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self.pair_phase_highs,
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) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
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self.pair_phase_measurements
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)
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def fast_s_spin_qubit_tarucha_nonlocal_dipoleses(
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self, dot_pair_inputs: numpy.ndarray, dipoleses: numpy.ndarray
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) -> numpy.ndarray:
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"""
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No error correction here baby.
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"""
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ps = dipoleses[:, :, 0:3]
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ss = dipoleses[:, :, 3:6]
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ws = dipoleses[:, :, 6]
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r1s = dot_pair_inputs[:, 0, 0:3]
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r2s = dot_pair_inputs[:, 1, 0:3]
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f1s = dot_pair_inputs[:, 0, 3]
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# Don't actually need this
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# f2s = dot_pair_inputs[:, 1, 3]
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diffses1 = r1s[:, None] - ss[:, None, :]
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diffses2 = r2s[:, None] - ss[:, None, :]
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norms1 = numpy.linalg.norm(diffses1, axis=3)
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norms2 = numpy.linalg.norm(diffses2, axis=3)
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alphses1 = (
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(
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3
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* numpy.transpose(
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numpy.transpose(
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numpy.einsum("abcd,acd->abc", diffses1, ps) / (norms1**2)
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)
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* numpy.transpose(diffses1)
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)[:, :, :, 0]
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)
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- ps[:, :, 0, numpy.newaxis]
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) / (norms1**3)
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alphses2 = (
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(
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3
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* numpy.transpose(
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numpy.transpose(
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numpy.einsum("abcd,acd->abc", diffses2, ps) / (norms2**2)
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)
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* numpy.transpose(diffses2)
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)[:, :, :, 0]
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)
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- ps[:, :, 0, numpy.newaxis]
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) / (norms2**3)
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bses = (1 / numpy.pi) * (
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ws[:, None, :] / (f1s[:, None] ** 2 + ws[:, None, :] ** 2)
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)
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return numpy.einsum("...j->...", alphses1 * alphses2 * bses)
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def filter_samples(self, samples: ndarray) -> ndarray:
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current_sample = samples
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for pi, plow, phigh in zip(
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self.dot_pair_inputs_array, self.pair_phase_lows, self.pair_phase_highs
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):
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if len(current_sample) < 1:
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break
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###
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# This should be abstracted out, but we're going to dump it here for time pressure's sake
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###
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# vals = pdme.util.fast_nonlocal_spectrum.signarg(
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# pdme.util.fast_nonlocal_spectrum.fast_s_nonlocal_dipoleses(
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# numpy.array([pi]), current_sample
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# )
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#
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vals = pdme.util.fast_nonlocal_spectrum.signarg(
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self.fast_s_spin_qubit_tarucha_nonlocal_dipoleses(
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numpy.array([pi]), current_sample
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)
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)
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current_sample = current_sample[
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numpy.all(
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((vals > plow) & (vals < phigh)) | ((vals < plow) & (vals > phigh)),
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axis=1,
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)
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]
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return current_sample
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