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0.8.0 ... 1.0.1

Author SHA1 Message Date
d28c190816 chore(release): 1.0.1
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2024-05-01 21:08:00 -05:00
0262de060f nix: adds node for npx in dev shell 2024-05-01 21:07:48 -05:00
e25db1e0f6 fix: fixes issue of zero division error with no successes for anything
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2024-05-01 20:37:08 -05:00
8fdbe4d334 chore(release): 1.0.0
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2024-05-01 15:49:57 -05:00
406a1485da build: justfile allows version 2024-05-01 15:49:01 -05:00
6dc66b1c27 release: allows dynamic release as arg 2024-05-01 15:43:33 -05:00
f2b1a1dd3b feat: Adds more powerful direct mc runs to sub for old real spectrum run
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2024-05-01 15:22:59 -05:00
cb166a399d deps: updates pdme to 1.0.0 release yay 2024-04-28 21:43:11 -05:00
7108dd0111 feat!: allows new seed spec instead of cli arg, removes old cli arg
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2024-04-28 20:54:58 -05:00
2105754911 feat: adds additional file slug parsing 2024-04-28 20:54:15 -05:00
f3ba4cbfd3 fix: no longer throws error for overlapping keys, the warning should hopefully be enough? 2024-04-28 20:51:43 -05:00
e5f7085324 chore(release): 0.8.1
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2024-04-28 04:27:47 -05:00
578481324b chore(release): 0.8.1 2024-04-28 04:27:32 -05:00
bf8ac9850d release: fixes standard version updater which didn't allow minor version to be multidigit 2024-04-28 04:27:06 -05:00
ab408b6412 chore(release): 0.8.1 2024-04-28 04:19:08 -05:00
14 changed files with 583 additions and 325 deletions

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@@ -2,6 +2,35 @@
All notable changes to this project will be documented in this file. See [standard-version](https://github.com/conventional-changelog/standard-version) for commit guidelines. All notable changes to this project will be documented in this file. See [standard-version](https://github.com/conventional-changelog/standard-version) for commit guidelines.
### [1.0.1](https://gitea.deepak.science:2222/physics/deepdog/compare/1.0.0...1.0.1) (2024-05-02)
### Bug Fixes
* fixes issue of zero division error with no successes for anything ([e25db1e](https://gitea.deepak.science:2222/physics/deepdog/commit/e25db1e0f677e8d9a657fa1631305cc8f05ff9ff))
## [1.0.0](https://gitea.deepak.science:2222/physics/deepdog/compare/0.8.1...1.0.0) (2024-05-01)
### ⚠ BREAKING CHANGES
* allows new seed spec instead of cli arg, removes old cli arg
### Features
* adds additional file slug parsing ([2105754](https://gitea.deepak.science:2222/physics/deepdog/commit/2105754911c89bde9dcbea9866462225604a3524))
* Adds more powerful direct mc runs to sub for old real spectrum run ([f2b1a1d](https://gitea.deepak.science:2222/physics/deepdog/commit/f2b1a1dd3b3436e37d84f7843b9b2a202be4b51c))
* allows new seed spec instead of cli arg, removes old cli arg ([7108dd0](https://gitea.deepak.science:2222/physics/deepdog/commit/7108dd0111c7dfd6ec204df1d0058530cd3dcab9))
### Bug Fixes
* no longer throws error for overlapping keys, the warning should hopefully be enough? ([f3ba4cb](https://gitea.deepak.science:2222/physics/deepdog/commit/f3ba4cbfd36a9f08cdc4d8774a7f745f8c98bac3))
### [0.8.1](https://gitea.deepak.science:2222/physics/deepdog/compare/0.8.0...0.8.1) (2024-04-28)
### [0.8.1](https://gitea.deepak.science:2222/physics/deepdog/compare/0.8.0...0.8.1) (2024-04-28)
## [0.8.0](https://gitea.deepak.science:2222/physics/deepdog/compare/0.7.10...0.8.0) (2024-04-28) ## [0.8.0](https://gitea.deepak.science:2222/physics/deepdog/compare/0.7.10...0.8.0) (2024-04-28)

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@@ -30,18 +30,6 @@ def parse_args() -> argparse.Namespace:
help="A json file with the indexify config for parsing job indexes. Will skip if not present", help="A json file with the indexify config for parsing job indexes. Will skip if not present",
default="", default="",
) )
parser.add_argument(
"--seed-index",
type=int,
help='take an integer to append as a "seed" key with range at end of indexify dict. Skip if <= 0',
default=0,
)
parser.add_argument(
"--seed-fieldname",
type=str,
help='if --seed-index is set, the fieldname to append to the indexifier. "seed" by default',
default="seed",
)
parser.add_argument( parser.add_argument(
"--coalesced-keys", "--coalesced-keys",
type=str, type=str,

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@@ -61,7 +61,7 @@ def write_uncoalesced_dict(
if not (set(model_field_names).isdisjoint(calculation_field_names)): if not (set(model_field_names).isdisjoint(calculation_field_names)):
_logger.info(f"Detected model field names {model_field_names}") _logger.info(f"Detected model field names {model_field_names}")
_logger.info(f"Detected calculation field names {calculation_field_names}") _logger.info(f"Detected calculation field names {calculation_field_names}")
raise ValueError( _logger.warning(
f"model field names {model_field_names} and calculation {calculation_field_names} have an overlap, which is possibly a problem" f"model field names {model_field_names} and calculation {calculation_field_names} have an overlap, which is possibly a problem"
) )
collected_fieldnames = list(model_field_names) collected_fieldnames = list(model_field_names)

View File

@@ -51,9 +51,13 @@ def main(args: argparse.Namespace):
indexifier = None indexifier = None
if args.indexify_json: if args.indexify_json:
with open(args.indexify_json, "r") as indexify_json_file: with open(args.indexify_json, "r") as indexify_json_file:
indexify_data = json.load(indexify_json_file) indexify_spec = json.load(indexify_json_file)
if args.seed_index > 0: indexify_data = indexify_spec["indexes"]
indexify_data[args.seed_fieldname] = list(range(args.seed_index)) if "seed_spec" in indexify_spec:
seed_spec = indexify_spec["seed_spec"]
indexify_data[seed_spec["field_name"]] = list(
range(seed_spec["num_seeds"])
)
# _logger.debug(f"Indexifier data looks like {indexify_data}") # _logger.debug(f"Indexifier data looks like {indexify_data}")
indexifier = deepdog.indexify.Indexifier(indexify_data) indexifier = deepdog.indexify.Indexifier(indexify_data)

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@@ -1,22 +1,28 @@
import csv
import pdme.model import pdme.model
import pdme.measurement import pdme.measurement
import pdme.measurement.input_types import pdme.measurement.input_types
import pdme.subspace_simulation import pdme.subspace_simulation
from typing import Tuple, Dict, NewType, Any import datetime
from typing import Tuple, Dict, NewType, Any, Sequence
from dataclasses import dataclass from dataclasses import dataclass
import logging import logging
import numpy import numpy
import numpy.random import numpy.random
import pdme.util.fast_v_calc import pdme.util.fast_v_calc
import multiprocessing
_logger = logging.getLogger(__name__) _logger = logging.getLogger(__name__)
ANTI_ZERO_SUCCESS_THRES = 0.1
@dataclass @dataclass
class DirectMonteCarloResult: class DirectMonteCarloResult:
successes: int successes: int
monte_carlo_count: int monte_carlo_count: int
likelihood: float likelihood: float
model_name: str
@dataclass @dataclass
@@ -28,6 +34,10 @@ class DirectMonteCarloConfig:
monte_carlo_seed: int = 1234 monte_carlo_seed: int = 1234
write_successes_to_file: bool = False write_successes_to_file: bool = False
tag: str = "" tag: str = ""
cap_core_count: int = 0 # 0 means cap at num cores - 1
chunk_size: int = 50
write_bayesrun_file = True
# chunk size of some kind
# Aliasing dict as a generic data container # Aliasing dict as a generic data container
@@ -51,8 +61,8 @@ class DirectMonteCarloRun:
Parameters Parameters
---------- ----------
model_name_pair : Sequence[Tuple(str, pdme.model.DipoleModel)] model_name_pairs : Sequence[Tuple(str, pdme.model.DipoleModel)]
The model to evaluate, with name. The models to evaluate, with names
measurements: Sequence[pdme.measurement.DotRangeMeasurement] measurements: Sequence[pdme.measurement.DotRangeMeasurement]
The measurements as dot ranges to use as the bounds for the Monte Carlo calculation. The measurements as dot ranges to use as the bounds for the Monte Carlo calculation.
@@ -78,11 +88,11 @@ class DirectMonteCarloRun:
def __init__( def __init__(
self, self,
model_name_pair: Tuple[str, pdme.model.DipoleModel], model_name_pairs: Sequence[Tuple[str, pdme.model.DipoleModel]],
filter: DirectMonteCarloFilter, filter: DirectMonteCarloFilter,
config: DirectMonteCarloConfig, config: DirectMonteCarloConfig,
): ):
self.model_name, self.model = model_name_pair self.model_name_pairs = model_name_pairs
# self.measurements = measurements # self.measurements = measurements
# self.dot_inputs = [(measure.r, measure.f) for measure in self.measurements] # self.dot_inputs = [(measure.r, measure.f) for measure in self.measurements]
@@ -100,10 +110,16 @@ class DirectMonteCarloRun:
# self.measurements # self.measurements
# ) # )
def _single_run(self, seed) -> numpy.ndarray: def _single_run(
self, model_name_pair: Tuple[str, pdme.model.DipoleModel], seed
) -> numpy.ndarray:
rng = numpy.random.default_rng(seed) rng = numpy.random.default_rng(seed)
sample_dipoles = self.model.get_monte_carlo_dipole_inputs( _, model = model_name_pair
# don't log here it's madness
# _logger.info(f"Executing for model {model_name}")
sample_dipoles = model.get_monte_carlo_dipole_inputs(
self.config.monte_carlo_count_per_cycle, -1, rng self.config.monte_carlo_count_per_cycle, -1, rng
) )
@@ -123,15 +139,38 @@ class DirectMonteCarloRun:
# ] # ]
# return current_sample # return current_sample
def execute(self) -> DirectMonteCarloResult: def _wrapped_single_run(self, args: Tuple):
step_count = 0 """
total_success = 0 single run wrapped up for multiprocessing call.
total_count = 0
takes in a tuple of arguments corresponding to
(model_name_pair, seed)
"""
# here's where we do our work
model_name_pair, seed = args
cycle_success_configs = self._single_run(model_name_pair, seed)
cycle_success_count = len(cycle_success_configs)
return cycle_success_count
def execute_no_multiprocessing(self) -> Sequence[DirectMonteCarloResult]:
count_per_step = ( count_per_step = (
self.config.monte_carlo_count_per_cycle * self.config.monte_carlo_cycles self.config.monte_carlo_count_per_cycle * self.config.monte_carlo_cycles
) )
seed_sequence = numpy.random.SeedSequence(self.config.monte_carlo_seed) seed_sequence = numpy.random.SeedSequence(self.config.monte_carlo_seed)
# core count etc. logic here
results = []
for model_name_pair in self.model_name_pairs:
step_count = 0
total_success = 0
total_count = 0
_logger.info(f"Working on model {model_name_pair[0]}")
# This is probably where multiprocessing logic should go
while (step_count < self.config.max_monte_carlo_cycles_steps) and ( while (step_count < self.config.max_monte_carlo_cycles_steps) and (
total_success < self.config.target_success total_success < self.config.target_success
): ):
@@ -139,13 +178,14 @@ class DirectMonteCarloRun:
for cycle_i, seed in enumerate( for cycle_i, seed in enumerate(
seed_sequence.spawn(self.config.monte_carlo_cycles) seed_sequence.spawn(self.config.monte_carlo_cycles)
): ):
cycle_success_configs = self._single_run(seed) # here's where we do our work
cycle_success_configs = self._single_run(model_name_pair, seed)
cycle_success_count = len(cycle_success_configs) cycle_success_count = len(cycle_success_configs)
if cycle_success_count > 0: if cycle_success_count > 0:
_logger.debug( _logger.debug(
f"For cycle {cycle_i} received {cycle_success_count} successes" f"For cycle {cycle_i} received {cycle_success_count} successes"
) )
_logger.debug(cycle_success_configs) # _logger.debug(cycle_success_configs)
if self.config.write_successes_to_file: if self.config.write_successes_to_file:
sorted_by_freq = numpy.array( sorted_by_freq = numpy.array(
[ [
@@ -163,12 +203,124 @@ class DirectMonteCarloRun:
delimiter=",", delimiter=",",
) )
total_success += cycle_success_count total_success += cycle_success_count
_logger.debug(f"At end of step {step_count} have {total_success} successes") _logger.debug(
f"At end of step {step_count} have {total_success} successes"
)
step_count += 1 step_count += 1
total_count += count_per_step total_count += count_per_step
return DirectMonteCarloResult( results.append(
DirectMonteCarloResult(
successes=total_success, successes=total_success,
monte_carlo_count=total_count, monte_carlo_count=total_count,
likelihood=total_success / total_count, likelihood=total_success / total_count,
model_name=model_name_pair[0],
) )
)
return results
def execute(self) -> Sequence[DirectMonteCarloResult]:
# set starting execution timestamp
timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
count_per_step = (
self.config.monte_carlo_count_per_cycle * self.config.monte_carlo_cycles
)
seed_sequence = numpy.random.SeedSequence(self.config.monte_carlo_seed)
# core count etc. logic here
core_count = multiprocessing.cpu_count() - 1 or 1
if (self.config.cap_core_count >= 1) and (
self.config.cap_core_count < core_count
):
core_count = self.config.cap_core_count
_logger.info(f"Using {core_count} cores")
results = []
with multiprocessing.Pool(core_count) as pool:
for model_name_pair in self.model_name_pairs:
_logger.info(f"Working on model {model_name_pair[0]}")
# This is probably where multiprocessing logic should go
step_count = 0
total_success = 0
total_count = 0
while (step_count < self.config.max_monte_carlo_cycles_steps) and (
total_success < self.config.target_success
):
step_count += 1
_logger.debug(f"Executing step {step_count}")
seeds = seed_sequence.spawn(self.config.monte_carlo_cycles)
pool_results = sum(
pool.imap_unordered(
self._wrapped_single_run,
[(model_name_pair, seed) for seed in seeds],
self.config.chunk_size,
)
)
_logger.debug(f"Pool results: {pool_results}")
total_success += pool_results
total_count += count_per_step
_logger.debug(
f"At end of step {step_count} have {total_success} successes"
)
results.append(
DirectMonteCarloResult(
successes=total_success,
monte_carlo_count=total_count,
likelihood=total_success / total_count,
model_name=model_name_pair[0],
)
)
if self.config.write_bayesrun_file:
filename = (
f"{timestamp}-{self.config.tag}.realdata.fast_filter.bayesrun.csv"
)
_logger.info(f"Going to write to file [{filename}]")
# row: Dict[str, Union[int, float, str]] = {}
row = {}
num_models = len(self.model_name_pairs)
success_weight = sum(
[
(
max(ANTI_ZERO_SUCCESS_THRES, res.successes)
/ res.monte_carlo_count
)
/ num_models
for res in results
]
)
for res in results:
row.update(
{
f"{res.model_name}_success": res.successes,
f"{res.model_name}_count": res.monte_carlo_count,
f"{res.model_name}_prob": (
max(ANTI_ZERO_SUCCESS_THRES, res.successes)
/ res.monte_carlo_count
)
/ (num_models * success_weight),
}
)
_logger.info(f"Writing row {row}")
fieldnames = list(row.keys())
with open(filename, "w", newline="") as outfile:
writer = csv.DictWriter(outfile, fieldnames=fieldnames, dialect="unix")
writer.writeheader()
writer.writerow(row)
return results

View File

@@ -39,6 +39,135 @@ class SingleDotPotentialFilter(DirectMonteCarloFilter):
return current_sample return current_sample
class SingleDotSpinQubitFrequencyFilter(DirectMonteCarloFilter):
def __init__(self, measurements: Sequence[pdme.measurement.DotRangeMeasurement]):
self.measurements = measurements
self.dot_inputs = [(measure.r, measure.f) for measure in self.measurements]
self.dot_inputs_array = pdme.measurement.input_types.dot_inputs_to_array(
self.dot_inputs
)
(
self.lows,
self.highs,
) = pdme.measurement.input_types.dot_range_measurements_low_high_arrays(
self.measurements
)
# oh no not this again
def fast_s_spin_qubit_tarucha_apsd_dipoleses(
self, dot_inputs: numpy.ndarray, dipoleses: numpy.ndarray
) -> numpy.ndarray:
"""
No error correction here baby.
"""
# We're going to annotate the indices on this class.
# Let's define some indices:
# A -> index of dipoleses configurations
# j -> within a particular configuration, indexes dipole j
# measurement_index -> if we have 100 frequencies for example, indexes which one of them it is
# If we need to use numbers, let's use A -> 2, j -> 10, measurement_index -> 9 for consistency with
# my other notes
# axes are [dipole_config_idx A, dipole_idx j, {px, py, pz}3]
ps = dipoleses[:, :, 0:3]
# axes are [dipole_config_idx A, dipole_idx j, {sx, sy, sz}3]
ss = dipoleses[:, :, 3:6]
# axes are [dipole_config_idx A, dipole_idx j, w], last axis is just 1
ws = dipoleses[:, :, 6]
# dot_index is either 0 or 1 for dot1 or dot2
# hopefully this adhoc grammar is making sense, with the explicit labelling of the values of the last axis in cartesian space
# axes are [measurement_idx, {dot_index}, {rx, ry, rz}] where the inner {dot_index} is gone
# [measurement_idx, cartesian3]
rs = dot_inputs[:, 0:3]
# axes are [measurement_idx]
fs = dot_inputs[:, 3]
# first operation!
# r1s has shape [measurement_idx, rxs]
# None inserts an extra axis so the r1s[:, None] has shape
# [measurement_idx, 1]([rxs]) with the last rxs hidden
#
# ss has shape [ A, j, {sx, sy, sz}3], so second term has shape [A, 1, j]([sxs])
# these broadcast from right to left
# [ measurement_idx, 1, rxs]
# [A, 1, j, sxs]
# resulting in [A, measurement_idx, j, cart3] sxs rxs are both cart3
diffses = rs[:, None] - ss[:, None, :]
# norms takes out axis 3, the last one, giving [A, measurement_idx, j]
norms = numpy.linalg.norm(diffses, axis=3)
# _logger.info(f"norms1: {norms1}")
# _logger.info(f"norms1 shape: {norms1.shape}")
#
# diffses1 (A, measurement_idx, j, xs)
# ps: (A, j, px)
# result is (A, measurement_idx, j)
# intermediate_dot_prod = numpy.einsum("abcd,acd->abc", diffses1, ps)
# _logger.info(f"dot product shape: {intermediate_dot_prod.shape}")
# transpose makes it (j, measurement_idx, A)
# transp_intermediate_dot_prod = numpy.transpose(numpy.einsum("abcd,acd->abc", diffses1, ps) / (norms1**3))
# transpose of diffses has shape (xs, j, measurement_idx, A)
# numpy.transpose(diffses1)
# _logger.info(f"dot product shape: {transp_intermediate_dot_prod.shape}")
# inner transpose is (j, measurement_idx, A) * (xs, j, measurement_idx, A)
# next transpose puts it back to (A, measurement_idx, j, xs)
# p_dot_r_times_r_term = 3 * numpy.transpose(numpy.transpose(numpy.einsum("abcd,acd->abc", diffses1, ps) / (norms1**3)) * numpy.transpose(diffses1))
# _logger.info(f"p_dot_r_times_r_term: {p_dot_r_times_r_term.shape}")
# only x axis puts us at (A, measurement_idx, j)
# p_dot_r_times_r_term_x_only = p_dot_r_times_r_term[:, :, :, 0]
# _logger.info(f"p_dot_r_times_r_term_x_only.shape: {p_dot_r_times_r_term_x_only.shape}")
# now to complete the numerator we subtract the ps, which are (A, j, px):
# slicing off the end gives us (A, j), so we newaxis to get (A, 1, j)
# _logger.info(ps[:, numpy.newaxis, :, 0].shape)
alphses = (
(
3
* numpy.transpose(
numpy.transpose(
numpy.einsum("abcd,acd->abc", diffses, ps) / (norms**2)
)
* numpy.transpose(diffses)
)[:, :, :, 0]
)
- ps[:, numpy.newaxis, :, 0]
) / (norms**3)
bses = (
2
* numpy.pi
* ws[:, None, :]
/ ((2 * numpy.pi * fs[:, None]) ** 2 + 4 * ws[:, None, :] ** 2)
)
return numpy.einsum("...j->...", alphses * alphses * bses)
def filter_samples(self, samples: ndarray) -> ndarray:
current_sample = samples
for di, low, high in zip(self.dot_inputs_array, self.lows, self.highs):
if len(current_sample) < 1:
break
vals = self.fast_s_spin_qubit_tarucha_apsd_dipoleses(
numpy.array([di]), current_sample
)
# _logger.info(vals)
current_sample = current_sample[
numpy.all((vals > low) & (vals < high), axis=1)
]
# _logger.info(f"leaving with {len(current_sample)}")
return current_sample
class DoubleDotSpinQubitFrequencyFilter(DirectMonteCarloFilter): class DoubleDotSpinQubitFrequencyFilter(DirectMonteCarloFilter):
def __init__( def __init__(
self, self,
@@ -59,59 +188,6 @@ class DoubleDotSpinQubitFrequencyFilter(DirectMonteCarloFilter):
self.pair_phase_measurements self.pair_phase_measurements
) )
def fast_s_spin_qubit_tarucha_nonlocal_dipoleses(
self, dot_pair_inputs: numpy.ndarray, dipoleses: numpy.ndarray
) -> numpy.ndarray:
"""
No error correction here baby.
"""
ps = dipoleses[:, :, 0:3]
ss = dipoleses[:, :, 3:6]
ws = dipoleses[:, :, 6]
r1s = dot_pair_inputs[:, 0, 0:3]
r2s = dot_pair_inputs[:, 1, 0:3]
f1s = dot_pair_inputs[:, 0, 3]
# Don't actually need this
# f2s = dot_pair_inputs[:, 1, 3]
diffses1 = r1s[:, None] - ss[:, None, :]
diffses2 = r2s[:, None] - ss[:, None, :]
norms1 = numpy.linalg.norm(diffses1, axis=3)
norms2 = numpy.linalg.norm(diffses2, axis=3)
alphses1 = (
(
3
* numpy.transpose(
numpy.transpose(
numpy.einsum("abcd,acd->abc", diffses1, ps) / (norms1**2)
)
* numpy.transpose(diffses1)
)[:, :, :, 0]
)
- ps[:, :, 0, numpy.newaxis]
) / (norms1**3)
alphses2 = (
(
3
* numpy.transpose(
numpy.transpose(
numpy.einsum("abcd,acd->abc", diffses2, ps) / (norms2**2)
)
* numpy.transpose(diffses2)
)[:, :, :, 0]
)
- ps[:, :, 0, numpy.newaxis]
) / (norms2**3)
bses = (1 / numpy.pi) * (
ws[:, None, :] / (f1s[:, None] ** 2 + ws[:, None, :] ** 2)
)
return numpy.einsum("...j->...", alphses1 * alphses2 * bses)
def filter_samples(self, samples: ndarray) -> ndarray: def filter_samples(self, samples: ndarray) -> ndarray:
current_sample = samples current_sample = samples
@@ -121,16 +197,8 @@ class DoubleDotSpinQubitFrequencyFilter(DirectMonteCarloFilter):
if len(current_sample) < 1: if len(current_sample) < 1:
break break
###
# This should be abstracted out, but we're going to dump it here for time pressure's sake
###
# vals = pdme.util.fast_nonlocal_spectrum.signarg(
# pdme.util.fast_nonlocal_spectrum.fast_s_nonlocal_dipoleses(
# numpy.array([pi]), current_sample
# )
#
vals = pdme.util.fast_nonlocal_spectrum.signarg( vals = pdme.util.fast_nonlocal_spectrum.signarg(
self.fast_s_spin_qubit_tarucha_nonlocal_dipoleses( pdme.util.fast_nonlocal_spectrum.fast_s_spin_qubit_tarucha_nonlocal_dipoleses(
numpy.array([pi]), current_sample numpy.array([pi]), current_sample
) )
) )

View File

@@ -112,59 +112,6 @@ def get_a_result_fast_filter_tarucha_spin_qubit_pair_phase_only(input) -> int:
seed, seed,
) = input ) = input
def fast_s_spin_qubit_tarucha_nonlocal_dipoleses(
dot_pair_inputs: numpy.ndarray, dipoleses: numpy.ndarray
) -> numpy.ndarray:
"""
No error correction here baby.
"""
ps = dipoleses[:, :, 0:3]
ss = dipoleses[:, :, 3:6]
ws = dipoleses[:, :, 6]
r1s = dot_pair_inputs[:, 0, 0:3]
r2s = dot_pair_inputs[:, 1, 0:3]
f1s = dot_pair_inputs[:, 0, 3]
# don't actually need, because we're assuming they're the same frequencies across the pair
# f2s = dot_pair_inputs[:, 1, 3]
diffses1 = r1s[:, None] - ss[:, None, :]
diffses2 = r2s[:, None] - ss[:, None, :]
norms1 = numpy.linalg.norm(diffses1, axis=3)
norms2 = numpy.linalg.norm(diffses2, axis=3)
alphses1 = (
(
3
* numpy.transpose(
numpy.transpose(
numpy.einsum("abcd,acd->abc", diffses1, ps) / (norms1**2)
)
* numpy.transpose(diffses1)
)[:, :, :, 0]
)
- ps[:, numpy.newaxis, :, 0]
) / (norms1**3)
alphses2 = (
(
3
* numpy.transpose(
numpy.transpose(
numpy.einsum("abcd,acd->abc", diffses2, ps) / (norms2**2)
)
* numpy.transpose(diffses2)
)[:, :, :, 0]
)
- ps[:, numpy.newaxis, :, 0]
) / (norms2**3)
bses = (1 / numpy.pi) * (
ws[:, None, :] / (f1s[:, None] ** 2 + ws[:, None, :] ** 2)
)
return numpy.einsum("...j->...", alphses1 * alphses2 * bses)
rng = numpy.random.default_rng(seed) rng = numpy.random.default_rng(seed)
# TODO: A long term refactor is to pull the frequency stuff out from here. The None stands for max_frequency, which is unneeded in the actually useful models. # TODO: A long term refactor is to pull the frequency stuff out from here. The None stands for max_frequency, which is unneeded in the actually useful models.
sample_dipoles = model.get_monte_carlo_dipole_inputs( sample_dipoles = model.get_monte_carlo_dipole_inputs(
@@ -186,7 +133,7 @@ def get_a_result_fast_filter_tarucha_spin_qubit_pair_phase_only(input) -> int:
# ) # )
# #
vals = pdme.util.fast_nonlocal_spectrum.signarg( vals = pdme.util.fast_nonlocal_spectrum.signarg(
fast_s_spin_qubit_tarucha_nonlocal_dipoleses( pdme.util.fast_nonlocal_spectrum.fast_s_spin_qubit_tarucha_nonlocal_dipoleses(
numpy.array([pi]), current_sample numpy.array([pi]), current_sample
) )
) )

View File

@@ -16,6 +16,7 @@ MODEL_REGEXES = [
FILE_SLUG_REGEXES = [ FILE_SLUG_REGEXES = [
r"mock_tarucha-(?P<job_index>\d+)", r"mock_tarucha-(?P<job_index>\d+)",
r"(?:(?P<mock>mock)_)?tarucha(?:_(?P<tarucha_run_id>\d+))?-(?P<job_index>\d+)",
] ]

View File

@@ -36,6 +36,7 @@
self.packages.${system}.deepdogEnv self.packages.${system}.deepdogEnv
self.packages.${system}.deepdogApp self.packages.${system}.deepdogApp
pkgs.just pkgs.just
pkgs.nodejs
]; ];
shellHook = '' shellHook = ''
export DO_NIX_CUSTOM=1 export DO_NIX_CUSTOM=1

View File

@@ -46,9 +46,15 @@ fmt:
find deepdog -type f -name "*.py" -exec sed -i -e 's/ /\t/g' {} \; find deepdog -type f -name "*.py" -exec sed -i -e 's/ /\t/g' {} \;
find tests -type f -name "*.py" -exec sed -i -e 's/ /\t/g' {} \; find tests -type f -name "*.py" -exec sed -i -e 's/ /\t/g' {} \;
# release the app, checking that our working tree is clean and ready for release # release the app, checking that our working tree is clean and ready for release, optionally takes target version
release: release version="":
#!/usr/bin/env bash
set -euxo pipefail
if [[ -n "{{version}}" ]]; then
./scripts/release.sh {{version}}
else
./scripts/release.sh ./scripts/release.sh
fi
htmlcov: htmlcov:
poetry run pytest --cov-report=html poetry run pytest --cov-report=html

327
poetry.lock generated
View File

@@ -1,5 +1,20 @@
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. # This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
[[package]]
name = "backports-tarfile"
version = "1.1.1"
description = "Backport of CPython tarfile module"
optional = false
python-versions = ">=3.8"
files = [
{file = "backports.tarfile-1.1.1-py3-none-any.whl", hash = "sha256:73e0179647803d3726d82e76089d01d8549ceca9bace469953fcb4d97cf2d417"},
{file = "backports_tarfile-1.1.1.tar.gz", hash = "sha256:9c2ef9696cb73374f7164e17fc761389393ca76777036f5aad42e8b93fcd8009"},
]
[package.extras]
docs = ["furo", "jaraco.packaging (>=9.3)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
testing = ["jaraco.test", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)"]
[[package]] [[package]]
name = "black" name = "black"
version = "22.12.0" version = "22.12.0"
@@ -250,63 +265,63 @@ files = [
[[package]] [[package]]
name = "coverage" name = "coverage"
version = "7.4.3" version = "7.5.0"
description = "Code coverage measurement for Python" description = "Code coverage measurement for Python"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
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@@ -557,27 +609,29 @@ trio = ["async_generator", "trio"]
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name = "keyring" name = "keyring"
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name = "mccabe" name = "mccabe"
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description = "Core utilities for Python packages" description = "Core utilities for Python packages"
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python-versions = ">=3.7" python-versions = ">=3.7"
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@@ -732,13 +786,13 @@ files = [
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version = "0.9.3" version = "1.0.0"
description = "Python dipole model evaluator" description = "Python dipole model evaluator"
optional = false optional = false
python-versions = ">=3.8.1,<3.10" python-versions = "<3.10,>=3.8.1"
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@@ -747,42 +801,43 @@ scipy = ">=1.10,<1.11"
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name = "pkginfo" name = "pkginfo"
version = "1.9.6" version = "1.10.0"
description = "Query metadata from sdists / bdists / installed packages." description = "Query metadata from sdists / bdists / installed packages."
optional = false optional = false
python-versions = ">=3.6" python-versions = ">=3.6"
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[[package]] [[package]]
name = "platformdirs" name = "platformdirs"
version = "4.2.0" version = "4.2.1"
description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
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python-versions = ">=3.8" python-versions = ">=3.8"
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name = "pluggy" name = "pluggy"
version = "1.4.0" version = "1.5.0"
description = "plugin and hook calling mechanisms for python" description = "plugin and hook calling mechanisms for python"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
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version = "2.21" version = "2.22"
description = "C parser in Python" description = "C parser in Python"
optional = false optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" python-versions = ">=3.8"
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@@ -839,13 +894,13 @@ windows-terminal = ["colorama (>=0.4.6)"]
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name = "pytest" name = "pytest"
version = "8.0.2" version = "8.2.0"
description = "pytest: simple powerful testing with Python" description = "pytest: simple powerful testing with Python"
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python-versions = ">=3.8" python-versions = ">=3.8"
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packaging = "*" packaging = "*"
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tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""} tomli = {version = ">=1", markers = "python_version < \"3.11\""}
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name = "pytest-cov" name = "pytest-cov"
@@ -1162,13 +1217,13 @@ urllib3 = ">=1.26.0"
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@@ -1204,20 +1259,20 @@ test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
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python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
{file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"}, {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
{file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"}, {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
] ]
[package.extras] [package.extras]
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"] docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = ">=3.8.1,<3.10" python-versions = ">=3.8.1,<3.10"
content-hash = "828610d9447294e707a6df2affb6ee7947e2be3b567371217265a8b94a9768f6" content-hash = "a28054e255cbd49396795127380c2b7a0cfd742b15cba2184322f3c4894ed041"

View File

@@ -1,12 +1,12 @@
[tool.poetry] [tool.poetry]
name = "deepdog" name = "deepdog"
version = "0.7.10" version = "1.0.1"
description = "" description = ""
authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"] authors = ["Deepak Mallubhotla <dmallubhotla+github@gmail.com>"]
[tool.poetry.dependencies] [tool.poetry.dependencies]
python = ">=3.8.1,<3.10" python = ">=3.8.1,<3.10"
pdme = "^0.9.3" pdme = "^1.0.0"
numpy = "1.22.3" numpy = "1.22.3"
scipy = "1.10" scipy = "1.10"
tqdm = "^4.66.2" tqdm = "^4.66.2"

View File

@@ -25,15 +25,22 @@ if [ -z "$(git status --porcelain)" ]; then
exit 0 exit 0
fi fi
std_version_args=()
if [[ -n "${1:-}" ]]; then
std_version_args+=( "--release-as" "$1" )
echo "Parameter $1 was supplied, so we should use release-as"
else
echo "No release-as parameter specifed."
fi
# Working directory clean # Working directory clean
echo "Doing a dry run..." echo "Doing a dry run..."
npx standard-version --dry-run npx standard-version --dry-run "${std_version_args[@]}"
read -p "Does that look good? [y/N] " -n 1 -r read -p "Does that look good? [y/N] " -n 1 -r
echo # (optional) move to a new line echo # (optional) move to a new line
if [[ $REPLY =~ ^[Yy]$ ]] if [[ $REPLY =~ ^[Yy]$ ]]
then then
# do dangerous stuff # do dangerous stuff
npx standard-version npx standard-version "${std_version_args[@]}"
git push --follow-tags origin master git push --follow-tags origin master
else else
echo "okay, never mind then..." echo "okay, never mind then..."

View File

@@ -1,4 +1,4 @@
const pattern = /(\[tool\.poetry\]\nname = "deepdog"\nversion = ")(?<vers>\d+\.\d+\.\d)(")/mg; const pattern = /(\[tool\.poetry\]\nname = "deepdog"\nversion = ")(?<vers>\d+\.\d+\.\d+)(")/mg;
module.exports.readVersion = function (contents) { module.exports.readVersion = function (contents) {
const result = pattern.exec(contents); const result = pattern.exec(contents);