mirror of
https://github.com/fjosw/pyerrors.git
synced 2025-05-14 11:33:42 +02:00
Merge branch 'develop' into feat/typehints
This commit is contained in:
Fabian Joswig
commit
6d80efd388
15 changed files with 156 additions and 93 deletions
12
.github/workflows/examples.yml
vendored
12
.github/workflows/examples.yml
vendored
|
|
@ -27,17 +27,17 @@ jobs:
|
|||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: ${{ matrix.python-version }}
|
||||
- name: uv
|
||||
uses: astral-sh/setup-uv@v5
|
||||
|
||||
- name: Install
|
||||
run: |
|
||||
sudo apt-get update
|
||||
sudo apt-get install dvipng texlive-latex-extra texlive-fonts-recommended cm-super
|
||||
python -m pip install --upgrade pip
|
||||
pip install wheel
|
||||
pip install .
|
||||
pip install pytest
|
||||
pip install nbmake
|
||||
pip install -U matplotlib!=3.7.0 # Exclude version 3.7.0 of matplotlib as this breaks local imports of style files.
|
||||
uv pip install wheel --system
|
||||
uv pip install . --system
|
||||
uv pip install pytest nbmake --system
|
||||
uv pip install -U matplotlib!=3.7.0 --system # Exclude version 3.7.0 of matplotlib as this breaks local imports of style files.
|
||||
|
||||
- name: Run tests
|
||||
run: pytest -vv --nbmake examples/*.ipynb
|
||||
|
|
|
|||
21
.github/workflows/pytest.yml
vendored
21
.github/workflows/pytest.yml
vendored
|
|
@ -20,7 +20,9 @@ jobs:
|
|||
python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
|
||||
include:
|
||||
- os: macos-latest
|
||||
python-version: "3.10"
|
||||
python-version: "3.12"
|
||||
- os: ubuntu-24.04-arm
|
||||
python-version: "3.12"
|
||||
|
||||
steps:
|
||||
- name: Checkout source
|
||||
|
|
@ -30,18 +32,15 @@ jobs:
|
|||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: ${{ matrix.python-version }}
|
||||
- name: uv
|
||||
uses: astral-sh/setup-uv@v5
|
||||
|
||||
- name: Install
|
||||
run: |
|
||||
python -m pip install --upgrade pip
|
||||
pip install wheel
|
||||
pip install .
|
||||
pip install pytest
|
||||
pip install pytest-cov
|
||||
pip install pytest-benchmark
|
||||
pip install hypothesis
|
||||
pip install typing_extensions
|
||||
pip freeze
|
||||
uv pip install wheel --system
|
||||
uv pip install . --system
|
||||
uv pip install pytest pytest-cov pytest-benchmark hypothesis typing_extensions --system
|
||||
uv pip freeze --system
|
||||
|
||||
- name: Run tests
|
||||
run: pytest --cov=pyerrors -vv
|
||||
run: pytest --cov=pyerrors -vv -Werror
|
||||
|
|
|
|||
15
CHANGELOG.md
15
CHANGELOG.md
|
|
@ -2,6 +2,21 @@
|
|||
|
||||
All notable changes to this project will be documented in this file.
|
||||
|
||||
## [2.14.0] - 2025-03-09
|
||||
|
||||
### Added
|
||||
- Explicit checks of the provided inverse matrix for correlated fits #259
|
||||
|
||||
### Changed
|
||||
- Compute derivative for pow explicitly instead of relying on autograd. This results in a ~4x speedup for pow operations #246
|
||||
- More explicit exception types #248
|
||||
|
||||
### Fixed
|
||||
- Removed the possibility to create an Obs from data on several replica #258
|
||||
- Fix range in `set_prange` #247
|
||||
- Fix ensemble name handling in sfcf input modules #253
|
||||
- Correct error message for fit shape mismatch #257
|
||||
|
||||
## [2.13.0] - 2024-11-03
|
||||
|
||||
### Added
|
||||
|
|
|
|||
|
|
@ -151,7 +151,7 @@
|
|||
"\n",
|
||||
"$$C_{\\textrm{projected}}(t)=v_1^T \\underline{C}(t) v_2$$\n",
|
||||
"\n",
|
||||
"If we choose the vectors to be $v_1=v_2=(0,1,0,0)$, we should get the same correlator as in the cell above. \n",
|
||||
"If we choose the vectors to be $v_1=v_2=(1,0,0,0)$, we should get the same correlator as in the cell above. \n",
|
||||
"\n",
|
||||
"Thinking about it this way is usefull in the Context of the generalized eigenvalue problem (GEVP), used to find the source-sink combination, which best describes a certain energy eigenstate.\n",
|
||||
"A good introduction is found in https://arxiv.org/abs/0902.1265."
|
||||
|
|
|
|||
|
|
@ -316,7 +316,7 @@ def least_squares(x: Any, y: Union[dict[str, ndarray], list[Obs], ndarray, dict[
|
|||
if len(key_ls) > 1:
|
||||
for key in key_ls:
|
||||
if np.asarray(yd[key]).shape != funcd[key](np.arange(n_parms), xd[key]).shape:
|
||||
raise ValueError(f"Fit function {key} returns the wrong shape ({funcd[key](np.arange(n_parms), xd[key]).shape} instead of {xd[key].shape})\nIf the fit function is just a constant you could try adding x*0 to get the correct shape.")
|
||||
raise ValueError(f"Fit function {key} returns the wrong shape ({funcd[key](np.arange(n_parms), xd[key]).shape} instead of {np.asarray(yd[key]).shape})\nIf the fit function is just a constant you could try adding x*0 to get the correct shape.")
|
||||
|
||||
if not silent:
|
||||
print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1))
|
||||
|
|
@ -387,6 +387,8 @@ def least_squares(x: Any, y: Union[dict[str, ndarray], list[Obs], ndarray, dict[
|
|||
if (chol_inv[1] != key_ls):
|
||||
raise ValueError('The keys of inverse covariance matrix are not the same or do not appear in the same order as the x and y values.')
|
||||
chol_inv = chol_inv[0]
|
||||
if np.any(np.diag(chol_inv) <= 0) or (not np.all(chol_inv == np.tril(chol_inv))):
|
||||
raise ValueError('The inverse covariance matrix inv_chol_cov_matrix[0] has to be a lower triangular matrix constructed from a Cholesky decomposition.')
|
||||
else:
|
||||
corr = covariance(y_all, correlation=True, **kwargs)
|
||||
inverrdiag = np.diag(1 / np.asarray(dy_f))
|
||||
|
|
|
|||
|
|
@ -538,7 +538,8 @@ def import_dobs_string(content: bytes, full_output: bool=False, separator_insert
|
|||
deltas.append(repdeltas)
|
||||
idl.append(repidl)
|
||||
|
||||
res.append(Obs(deltas, obs_names, idl=idl))
|
||||
obsmeans = [np.average(deltas[j]) for j in range(len(deltas))]
|
||||
res.append(Obs([np.array(deltas[j]) - obsmeans[j] for j in range(len(obsmeans))], obs_names, idl=idl, means=obsmeans))
|
||||
res[-1]._value = mean[i]
|
||||
_check(len(e_names) == ne)
|
||||
|
||||
|
|
|
|||
|
|
@ -135,10 +135,11 @@ def create_json_string(ol: list, description: Union[str, dict]='', indent: int=1
|
|||
names = []
|
||||
idl = []
|
||||
for key, value in obs.idl.items():
|
||||
samples.append([np.nan] * len(value))
|
||||
samples.append(np.array([np.nan] * len(value)))
|
||||
names.append(key)
|
||||
idl.append(value)
|
||||
my_obs = Obs(samples, names, idl)
|
||||
my_obs = Obs(samples, names, idl, means=[np.nan for n in names])
|
||||
my_obs._value = np.nan
|
||||
my_obs._covobs = obs._covobs
|
||||
for name in obs._covobs:
|
||||
my_obs.names.append(name)
|
||||
|
|
@ -334,7 +335,8 @@ def _parse_json_dict(json_dict: dict[str, Any], verbose: bool=True, full_output:
|
|||
cd = _gen_covobsd_from_cdatad(o.get('cdata', {}))
|
||||
|
||||
if od:
|
||||
ret = Obs([[ddi[0] + values[0] for ddi in di] for di in od['deltas']], od['names'], idl=od['idl'])
|
||||
r_offsets = [np.average([ddi[0] for ddi in di]) for di in od['deltas']]
|
||||
ret = Obs([np.array([ddi[0] for ddi in od['deltas'][i]]) - r_offsets[i] for i in range(len(od['deltas']))], od['names'], idl=od['idl'], means=[ro + values[0] for ro in r_offsets])
|
||||
ret._value = values[0]
|
||||
else:
|
||||
ret = Obs([], [], means=[])
|
||||
|
|
@ -359,7 +361,8 @@ def _parse_json_dict(json_dict: dict[str, Any], verbose: bool=True, full_output:
|
|||
taglist = o.get('tag', layout * [None])
|
||||
for i in range(layout):
|
||||
if od:
|
||||
ret.append(Obs([list(di[:, i] + values[i]) for di in od['deltas']], od['names'], idl=od['idl']))
|
||||
r_offsets = np.array([np.average(di[:, i]) for di in od['deltas']])
|
||||
ret.append(Obs([od['deltas'][j][:, i] - r_offsets[j] for j in range(len(od['deltas']))], od['names'], idl=od['idl'], means=[ro + values[i] for ro in r_offsets]))
|
||||
ret[-1]._value = values[i]
|
||||
else:
|
||||
ret.append(Obs([], [], means=[]))
|
||||
|
|
@ -386,7 +389,8 @@ def _parse_json_dict(json_dict: dict[str, Any], verbose: bool=True, full_output:
|
|||
taglist = o.get('tag', N * [None])
|
||||
for i in range(N):
|
||||
if od:
|
||||
ret.append(Obs([di[:, i] + values[i] for di in od['deltas']], od['names'], idl=od['idl']))
|
||||
r_offsets = np.array([np.average(di[:, i]) for di in od['deltas']])
|
||||
ret.append(Obs([od['deltas'][j][:, i] - r_offsets[j] for j in range(len(od['deltas']))], od['names'], idl=od['idl'], means=[ro + values[i] for ro in r_offsets]))
|
||||
ret[-1]._value = values[i]
|
||||
else:
|
||||
ret.append(Obs([], [], means=[]))
|
||||
|
|
@ -570,7 +574,6 @@ def _ol_from_dict(ind: dict, reps: str='DICTOBS') -> tuple[list, dict]:
|
|||
counter = 0
|
||||
|
||||
def dict_replace_obs(d):
|
||||
nonlocal ol
|
||||
nonlocal counter
|
||||
x = {}
|
||||
for k, v in d.items():
|
||||
|
|
@ -591,7 +594,6 @@ def _ol_from_dict(ind: dict, reps: str='DICTOBS') -> tuple[list, dict]:
|
|||
return x
|
||||
|
||||
def list_replace_obs(li):
|
||||
nonlocal ol
|
||||
nonlocal counter
|
||||
x = []
|
||||
for e in li:
|
||||
|
|
@ -612,7 +614,6 @@ def _ol_from_dict(ind: dict, reps: str='DICTOBS') -> tuple[list, dict]:
|
|||
return x
|
||||
|
||||
def obslist_replace_obs(li):
|
||||
nonlocal ol
|
||||
nonlocal counter
|
||||
il = []
|
||||
for e in li:
|
||||
|
|
@ -693,7 +694,6 @@ def _od_from_list_and_dict(ol: list, ind: dict, reps: str='DICTOBS') -> dict[str
|
|||
|
||||
def dict_replace_string(d):
|
||||
nonlocal counter
|
||||
nonlocal ol
|
||||
x = {}
|
||||
for k, v in d.items():
|
||||
if isinstance(v, dict):
|
||||
|
|
@ -709,7 +709,6 @@ def _od_from_list_and_dict(ol: list, ind: dict, reps: str='DICTOBS') -> dict[str
|
|||
|
||||
def list_replace_string(li):
|
||||
nonlocal counter
|
||||
nonlocal ol
|
||||
x = []
|
||||
for e in li:
|
||||
if isinstance(e, list):
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@ from __future__ import annotations
|
|||
import warnings
|
||||
import gzip
|
||||
import sqlite3
|
||||
from contextlib import closing
|
||||
import pandas as pd
|
||||
from ..obs import Obs
|
||||
from ..correlators import Corr
|
||||
|
|
@ -32,9 +33,8 @@ def to_sql(df: DataFrame, table_name: str, db: str, if_exists: str='fail', gz: b
|
|||
None
|
||||
"""
|
||||
se_df = _serialize_df(df, gz=gz)
|
||||
con = sqlite3.connect(db)
|
||||
se_df.to_sql(table_name, con, if_exists=if_exists, index=False, **kwargs)
|
||||
con.close()
|
||||
with closing(sqlite3.connect(db)) as con:
|
||||
se_df.to_sql(table_name, con=con, if_exists=if_exists, index=False, **kwargs)
|
||||
|
||||
|
||||
def read_sql(sql: str, db: str, auto_gamma: bool=False, **kwargs) -> DataFrame:
|
||||
|
|
@ -55,9 +55,8 @@ def read_sql(sql: str, db: str, auto_gamma: bool=False, **kwargs) -> DataFrame:
|
|||
data : pandas.DataFrame
|
||||
Dataframe with the content of the sqlite database.
|
||||
"""
|
||||
con = sqlite3.connect(db)
|
||||
extract_df = pd.read_sql(sql, con, **kwargs)
|
||||
con.close()
|
||||
with closing(sqlite3.connect(db)) as con:
|
||||
extract_df = pd.read_sql(sql, con=con, **kwargs)
|
||||
return _deserialize_df(extract_df, auto_gamma=auto_gamma)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -95,6 +95,8 @@ class Obs:
|
|||
raise ValueError('Names are not unique.')
|
||||
if not all(isinstance(x, str) for x in names):
|
||||
raise TypeError('All names have to be strings.')
|
||||
if len(set([o.split('|')[0] for o in names])) > 1:
|
||||
raise ValueError('Cannot initialize Obs based on multiple ensembles. Please average separate Obs from each ensemble.')
|
||||
else:
|
||||
if not isinstance(names[0], str):
|
||||
raise TypeError('All names have to be strings.')
|
||||
|
|
@ -1434,6 +1436,8 @@ def reweight(weight: Obs, obs: Union[ndarray, list[Obs]], **kwargs) -> list[Obs]
|
|||
raise ValueError('Error: Not possible to reweight an Obs that contains covobs!')
|
||||
if not set(obs[i].names).issubset(weight.names):
|
||||
raise ValueError('Error: Ensembles do not fit')
|
||||
if len(obs[i].mc_names) > 1 or len(weight.mc_names) > 1:
|
||||
raise ValueError('Error: Cannot reweight an Obs that contains multiple ensembles.')
|
||||
for name in obs[i].names:
|
||||
if not set(obs[i].idl[name]).issubset(weight.idl[name]):
|
||||
raise ValueError('obs[%d] has to be defined on a subset of the configs in weight.idl[%s]!' % (i, name))
|
||||
|
|
@ -1469,9 +1473,12 @@ def correlate(obs_a: Obs, obs_b: Obs) -> Obs:
|
|||
-----
|
||||
Keep in mind to only correlate primary observables which have not been reweighted
|
||||
yet. The reweighting has to be applied after correlating the observables.
|
||||
Currently only works if ensembles are identical (this is not strictly necessary).
|
||||
Only works if a single ensemble is present in the Obs.
|
||||
Currently only works if ensemble content is identical (this is not strictly necessary).
|
||||
"""
|
||||
|
||||
if len(obs_a.mc_names) > 1 or len(obs_b.mc_names) > 1:
|
||||
raise ValueError('Error: Cannot correlate Obs that contain multiple ensembles.')
|
||||
if sorted(obs_a.names) != sorted(obs_b.names):
|
||||
raise ValueError(f"Ensembles do not fit {set(sorted(obs_a.names)) ^ set(sorted(obs_b.names))}")
|
||||
if len(obs_a.cov_names) or len(obs_b.cov_names):
|
||||
|
|
@ -1781,8 +1788,13 @@ def import_bootstrap(boots: ndarray, name: str, random_numbers: ndarray) -> Obs:
|
|||
return ret
|
||||
|
||||
|
||||
<<<<<<< HEAD
|
||||
def merge_obs(list_of_obs: list[Obs]) -> Obs:
|
||||
"""Combine all observables in list_of_obs into one new observable
|
||||
"""Combine all observables in list_of_obs into one new observable.
|
||||
This allows to merge Obs that have been computed on multiple replica
|
||||
of the same ensemble.
|
||||
If you like to merge Obs that are based on several ensembles, please
|
||||
average them yourself.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
__version__ = "2.14.0-dev"
|
||||
__version__ = "2.15.0-dev"
|
||||
|
|
|
|||
1
setup.py
1
setup.py
|
|
@ -30,7 +30,6 @@ setup(name='pyerrors',
|
|||
classifiers=[
|
||||
'Development Status :: 5 - Production/Stable',
|
||||
'Intended Audience :: Science/Research',
|
||||
'License :: OSI Approved :: MIT License',
|
||||
'Programming Language :: Python :: 3',
|
||||
'Programming Language :: Python :: 3.9',
|
||||
'Programming Language :: Python :: 3.10',
|
||||
|
|
|
|||
|
|
@ -223,6 +223,9 @@ def test_inv_cov_matrix_input_least_squares():
|
|||
diff_inv_cov_combined_fit.gamma_method()
|
||||
assert(diff_inv_cov_combined_fit.is_zero(atol=1e-12))
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
pe.least_squares(x_dict, data_dict, fitf_dict, correlated_fit = True, inv_chol_cov_matrix = [corr,chol_inv_keys_combined_fit])
|
||||
|
||||
def test_least_squares_invalid_inv_cov_matrix_input():
|
||||
xvals = []
|
||||
yvals = []
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ def test_jsonio():
|
|||
o = pe.pseudo_Obs(1.0, .2, 'one')
|
||||
o2 = pe.pseudo_Obs(0.5, .1, 'two|r1')
|
||||
o3 = pe.pseudo_Obs(0.5, .1, 'two|r2')
|
||||
o4 = pe.merge_obs([o2, o3])
|
||||
o4 = pe.merge_obs([o2, o3, pe.pseudo_Obs(0.5, .1, 'two|r3', samples=3221)])
|
||||
otag = 'This has been merged!'
|
||||
o4.tag = otag
|
||||
do = o - .2 * o4
|
||||
|
|
@ -101,8 +101,8 @@ def test_json_string_reconstruction():
|
|||
|
||||
|
||||
def test_json_corr_io():
|
||||
my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']) for o in range(8)]
|
||||
rw_list = pe.reweight(pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']), my_list)
|
||||
my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100), np.random.normal(1.0, 0.1, 321)], ['ens1|r1', 'ens1|r2'], idl=[range(1, 201, 2), range(321)]) for o in range(8)]
|
||||
rw_list = pe.reweight(pe.Obs([np.random.normal(1.0, 0.1, 100), np.random.normal(1.0, 0.1, 321)], ['ens1|r1', 'ens1|r2'], idl=[range(1, 201, 2), range(321)]), my_list)
|
||||
|
||||
for obs_list in [my_list, rw_list]:
|
||||
for tag in [None, "test"]:
|
||||
|
|
@ -111,7 +111,8 @@ def test_json_corr_io():
|
|||
for corr_tag in [None, 'my_Corr_tag']:
|
||||
for prange in [None, [3, 6]]:
|
||||
for gap in [False, True]:
|
||||
my_corr = pe.Corr(obs_list, padding=[pad, pad], prange=prange)
|
||||
for mult in [1., pe.cov_Obs([12.22, 1.21], [.212**2, .11**2], 'renorm')[0]]:
|
||||
my_corr = mult * pe.Corr(obs_list, padding=[pad, pad], prange=prange)
|
||||
my_corr.tag = corr_tag
|
||||
if gap:
|
||||
my_corr.content[4] = None
|
||||
|
|
@ -128,13 +129,23 @@ def test_json_corr_io():
|
|||
|
||||
|
||||
def test_json_corr_2d_io():
|
||||
obs_list = [np.array([[pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test'), pe.pseudo_Obs(0.0, 0.1 * i, 'test')], [pe.pseudo_Obs(0.0, 0.1 * i, 'test'), pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test')]]) for i in range(4)]
|
||||
obs_list = [np.array([
|
||||
[
|
||||
pe.merge_obs([pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test|r2'), pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test|r1', samples=321)]),
|
||||
pe.merge_obs([pe.pseudo_Obs(0.0, 0.1 * i, 'test|r2'), pe.pseudo_Obs(0.0, 0.1 * i, 'test|r1', samples=321)]),
|
||||
],
|
||||
[
|
||||
pe.merge_obs([pe.pseudo_Obs(0.0, 0.1 * i, 'test|r2'), pe.pseudo_Obs(0.0, 0.1 * i, 'test|r1', samples=321),]),
|
||||
pe.merge_obs([pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test|r2'), pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test|r1', samples=321)]),
|
||||
],
|
||||
]) for i in range(4)]
|
||||
|
||||
for tag in [None, "test"]:
|
||||
obs_list[3][0, 1].tag = tag
|
||||
for padding in [0, 1]:
|
||||
for prange in [None, [3, 6]]:
|
||||
my_corr = pe.Corr(obs_list, padding=[padding, padding], prange=prange)
|
||||
for mult in [1., pe.cov_Obs([12.22, 1.21], [.212**2, .11**2], 'renorm')[0]]:
|
||||
my_corr = mult * pe.Corr(obs_list, padding=[padding, padding], prange=prange)
|
||||
my_corr.tag = tag
|
||||
pe.input.json.dump_to_json(my_corr, 'corr')
|
||||
recover = pe.input.json.load_json('corr')
|
||||
|
|
@ -211,6 +222,7 @@ def test_json_dict_io():
|
|||
'd': pe.pseudo_Obs(.01, .001, 'testd', samples=10) * pe.cov_Obs(1, .01, 'cov1'),
|
||||
'se': None,
|
||||
'sf': 1.2,
|
||||
'k': pe.cov_Obs(.1, .001**2, 'cov') * pe.merge_obs([pe.pseudo_Obs(1.0, 0.1, 'test|r2'), pe.pseudo_Obs(1.0, 0.1, 'test|r1', samples=321)]),
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -314,7 +326,7 @@ def test_dobsio():
|
|||
|
||||
o2 = pe.pseudo_Obs(0.5, .1, 'two|r1')
|
||||
o3 = pe.pseudo_Obs(0.5, .1, 'two|r2')
|
||||
o4 = pe.merge_obs([o2, o3])
|
||||
o4 = pe.merge_obs([o2, o3, pe.pseudo_Obs(0.5, .1, 'two|r3', samples=3221)])
|
||||
otag = 'This has been merged!'
|
||||
o4.tag = otag
|
||||
do = o - .2 * o4
|
||||
|
|
@ -328,7 +340,7 @@ def test_dobsio():
|
|||
o5 /= co2[0]
|
||||
o5.tag = 2 * otag
|
||||
|
||||
tt1 = pe.Obs([np.random.rand(100), np.random.rand(100)], ['t|r1', 't|r2'], idl=[range(2, 202, 2), range(22, 222, 2)])
|
||||
tt1 = pe.Obs([np.random.rand(100), np.random.rand(102)], ['t|r1', 't|r2'], idl=[range(2, 202, 2), range(22, 226, 2)])
|
||||
tt3 = pe.Obs([np.random.rand(102)], ['qe|r1'])
|
||||
|
||||
tt = tt1 + tt3
|
||||
|
|
@ -337,7 +349,7 @@ def test_dobsio():
|
|||
|
||||
tt4 = pe.Obs([np.random.rand(100), np.random.rand(100)], ['t|r1', 't|r2'], idl=[range(1, 101, 1), range(2, 202, 2)])
|
||||
|
||||
ol = [o2, o3, o4, do, o5, tt, tt4, np.log(tt4 / o5**2), np.exp(o5 + np.log(co3 / tt3 + o4) / tt)]
|
||||
ol = [o2, o3, o4, do, o5, tt, tt4, np.log(tt4 / o5**2), np.exp(o5 + np.log(co3 / tt3 + o4) / tt), o4.reweight(o4)]
|
||||
print(ol)
|
||||
fname = 'test_rw'
|
||||
|
||||
|
|
@ -362,9 +374,12 @@ def test_dobsio():
|
|||
|
||||
|
||||
def test_reconstruct_non_linear_r_obs(tmp_path):
|
||||
to = pe.Obs([np.random.rand(500), np.random.rand(500), np.random.rand(111)],
|
||||
["e|r1", "e|r2", "my_new_ensemble_54^£$|8'[@124435%6^7&()~#"],
|
||||
idl=[range(1, 501), range(0, 500), range(1, 999, 9)])
|
||||
to = (
|
||||
pe.Obs([np.random.rand(500), np.random.rand(1200)],
|
||||
["e|r1", "e|r2", ],
|
||||
idl=[range(1, 501), range(0, 1200)])
|
||||
+ pe.Obs([np.random.rand(111)], ["my_new_ensemble_54^£$|8'[@124435%6^7&()~#"], idl=[range(1, 999, 9)])
|
||||
)
|
||||
to = np.log(to ** 2) / to
|
||||
to.dump((tmp_path / "test_equality").as_posix())
|
||||
ro = pe.input.json.load_json((tmp_path / "test_equality").as_posix())
|
||||
|
|
@ -372,9 +387,12 @@ def test_reconstruct_non_linear_r_obs(tmp_path):
|
|||
|
||||
|
||||
def test_reconstruct_non_linear_r_obs_list(tmp_path):
|
||||
to = pe.Obs([np.random.rand(500), np.random.rand(500), np.random.rand(111)],
|
||||
["e|r1", "e|r2", "my_new_ensemble_54^£$|8'[@124435%6^7&()~#"],
|
||||
idl=[range(1, 501), range(0, 500), range(1, 999, 9)])
|
||||
to = (
|
||||
pe.Obs([np.random.rand(500), np.random.rand(1200)],
|
||||
["e|r1", "e|r2", ],
|
||||
idl=[range(1, 501), range(0, 1200)])
|
||||
+ pe.Obs([np.random.rand(111)], ["my_new_ensemble_54^£$|8'[@124435%6^7&()~#"], idl=[range(1, 999, 9)])
|
||||
)
|
||||
to = np.log(to ** 2) / to
|
||||
for to_list in [[to, to, to], np.array([to, to, to])]:
|
||||
pe.input.json.dump_to_json(to_list, (tmp_path / "test_equality_list").as_posix())
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@ def test_matmul():
|
|||
my_list = []
|
||||
length = 100 + np.random.randint(200)
|
||||
for i in range(dim ** 2):
|
||||
my_list.append(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']))
|
||||
my_list.append(pe.Obs([np.random.rand(length)], ['t1']) + pe.Obs([np.random.rand(length + 1)], ['t2']))
|
||||
my_array = const * np.array(my_list).reshape((dim, dim))
|
||||
tt = pe.linalg.matmul(my_array, my_array) - my_array @ my_array
|
||||
for t, e in np.ndenumerate(tt):
|
||||
|
|
@ -43,8 +43,8 @@ def test_matmul():
|
|||
my_list = []
|
||||
length = 100 + np.random.randint(200)
|
||||
for i in range(dim ** 2):
|
||||
my_list.append(pe.CObs(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']),
|
||||
pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2'])))
|
||||
my_list.append(pe.CObs(pe.Obs([np.random.rand(length)], ['t1']) + pe.Obs([np.random.rand(length + 1)], ['t2']),
|
||||
pe.Obs([np.random.rand(length)], ['t1']) + pe.Obs([np.random.rand(length + 1)], ['t2'])))
|
||||
my_array = np.array(my_list).reshape((dim, dim)) * const
|
||||
tt = pe.linalg.matmul(my_array, my_array) - my_array @ my_array
|
||||
for t, e in np.ndenumerate(tt):
|
||||
|
|
@ -151,7 +151,7 @@ def test_multi_dot():
|
|||
my_list = []
|
||||
length = 1000 + np.random.randint(200)
|
||||
for i in range(dim ** 2):
|
||||
my_list.append(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']))
|
||||
my_list.append(pe.Obs([np.random.rand(length)], ['t1']) + pe.Obs([np.random.rand(length + 1)], ['t2']))
|
||||
my_array = pe.cov_Obs(1.0, 0.002, 'cov') * np.array(my_list).reshape((dim, dim))
|
||||
tt = pe.linalg.matmul(my_array, my_array, my_array, my_array) - my_array @ my_array @ my_array @ my_array
|
||||
for t, e in np.ndenumerate(tt):
|
||||
|
|
@ -160,8 +160,8 @@ def test_multi_dot():
|
|||
my_list = []
|
||||
length = 1000 + np.random.randint(200)
|
||||
for i in range(dim ** 2):
|
||||
my_list.append(pe.CObs(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']),
|
||||
pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2'])))
|
||||
my_list.append(pe.CObs(pe.Obs([np.random.rand(length)], ['t1']) + pe.Obs([np.random.rand(length + 1)], ['t2']),
|
||||
pe.Obs([np.random.rand(length)], ['t1']) + pe.Obs([np.random.rand(length + 1)], ['t2'])))
|
||||
my_array = np.array(my_list).reshape((dim, dim)) * pe.cov_Obs(1.0, 0.002, 'cov')
|
||||
tt = pe.linalg.matmul(my_array, my_array, my_array, my_array) - my_array @ my_array @ my_array @ my_array
|
||||
for t, e in np.ndenumerate(tt):
|
||||
|
|
@ -209,7 +209,7 @@ def test_irregular_matrix_inverse():
|
|||
for idl in [range(8, 508, 10), range(250, 273), [2, 8, 19, 20, 78, 99, 828, 10548979]]:
|
||||
irregular_array = []
|
||||
for i in range(dim ** 2):
|
||||
irregular_array.append(pe.Obs([np.random.normal(1.1, 0.2, len(idl)), np.random.normal(0.25, 0.1, 10)], ['ens1', 'ens2'], idl=[idl, range(1, 11)]))
|
||||
irregular_array.append(pe.Obs([np.random.normal(1.1, 0.2, len(idl))], ['ens1'], idl=[idl]) + pe.Obs([np.random.normal(0.25, 0.1, 10)], ['ens2'], idl=[range(1, 11)]))
|
||||
irregular_matrix = np.array(irregular_array).reshape((dim, dim)) * pe.cov_Obs(1.0, 0.002, 'cov') * pe.pseudo_Obs(1.0, 0.002, 'ens2|r23')
|
||||
|
||||
invertible_irregular_matrix = np.identity(dim) + irregular_matrix @ irregular_matrix.T
|
||||
|
|
|
|||
|
|
@ -333,7 +333,7 @@ def test_derived_observables():
|
|||
|
||||
def test_multi_ens():
|
||||
names = ['A0', 'A1|r001', 'A1|r002']
|
||||
test_obs = pe.Obs([np.random.rand(50), np.random.rand(50), np.random.rand(50)], names)
|
||||
test_obs = pe.Obs([np.random.rand(50)], names[:1]) + pe.Obs([np.random.rand(50), np.random.rand(50)], names[1:])
|
||||
assert test_obs.e_names == ['A0', 'A1']
|
||||
assert test_obs.e_content['A0'] == ['A0']
|
||||
assert test_obs.e_content['A1'] == ['A1|r001', 'A1|r002']
|
||||
|
|
@ -345,6 +345,9 @@ def test_multi_ens():
|
|||
ensembles.append(str(i))
|
||||
assert my_sum.e_names == sorted(ensembles)
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
test_obs = pe.Obs([np.random.rand(50), np.random.rand(50), np.random.rand(50)], names)
|
||||
|
||||
|
||||
def test_multi_ens2():
|
||||
names = ['ens', 'e', 'en', 'e|r010', 'E|er', 'ens|', 'Ens|34', 'ens|r548984654ez4e3t34terh']
|
||||
|
|
@ -499,18 +502,25 @@ def test_reweighting():
|
|||
with pytest.raises(ValueError):
|
||||
pe.reweight(my_irregular_obs, [my_obs])
|
||||
|
||||
my_merged_obs = my_obs + pe.Obs([np.random.rand(1000)], ['q'])
|
||||
with pytest.raises(ValueError):
|
||||
pe.reweight(my_merged_obs, [my_merged_obs])
|
||||
|
||||
|
||||
def test_merge_obs():
|
||||
my_obs1 = pe.Obs([np.random.rand(100)], ['t'])
|
||||
my_obs2 = pe.Obs([np.random.rand(100)], ['q'], idl=[range(1, 200, 2)])
|
||||
my_obs1 = pe.Obs([np.random.normal(1, .1, 100)], ['t|1'])
|
||||
my_obs2 = pe.Obs([np.random.normal(1, .1, 100)], ['t|2'], idl=[range(1, 200, 2)])
|
||||
merged = pe.merge_obs([my_obs1, my_obs2])
|
||||
diff = merged - my_obs2 - my_obs1
|
||||
assert diff == -(my_obs1.value + my_obs2.value) / 2
|
||||
diff = merged - (my_obs2 + my_obs1) / 2
|
||||
assert np.isclose(0, diff.value, atol=1e-16)
|
||||
with pytest.raises(ValueError):
|
||||
pe.merge_obs([my_obs1, my_obs1])
|
||||
my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
|
||||
with pytest.raises(ValueError):
|
||||
pe.merge_obs([my_obs1, my_covobs])
|
||||
my_obs3 = pe.Obs([np.random.rand(100)], ['q|2'], idl=[range(1, 200, 2)])
|
||||
with pytest.raises(ValueError):
|
||||
pe.merge_obs([my_obs1, my_obs3])
|
||||
|
||||
|
||||
|
||||
|
|
@ -543,6 +553,9 @@ def test_correlate():
|
|||
my_obs6 = pe.Obs([np.random.rand(100)], ['t'], idl=[range(5, 505, 5)])
|
||||
corr3 = pe.correlate(my_obs5, my_obs6)
|
||||
assert my_obs5.idl == corr3.idl
|
||||
my_obs7 = pe.Obs([np.random.rand(99)], ['q'])
|
||||
with pytest.raises(ValueError):
|
||||
pe.correlate(my_obs1, my_obs7)
|
||||
|
||||
my_new_obs = pe.Obs([np.random.rand(100)], ['q3'])
|
||||
with pytest.raises(ValueError):
|
||||
|
|
@ -682,14 +695,14 @@ def test_gamma_method_irregular():
|
|||
assert (a.dvalue - 5 * a.ddvalue < expe and expe < a.dvalue + 5 * a.ddvalue)
|
||||
|
||||
arr2 = np.random.normal(1, .2, size=N)
|
||||
afull = pe.Obs([arr, arr2], ['a1', 'a2'])
|
||||
afull = pe.Obs([arr], ['a1']) + pe.Obs([arr2], ['a2'])
|
||||
|
||||
configs = np.ones_like(arr2)
|
||||
for i in np.random.uniform(0, len(arr2), size=int(.8*N)):
|
||||
configs[int(i)] = 0
|
||||
zero_arr2 = [arr2[i] for i in range(len(arr2)) if not configs[i] == 0]
|
||||
idx2 = [i + 1 for i in range(len(configs)) if configs[i] == 1]
|
||||
a = pe.Obs([zero_arr, zero_arr2], ['a1', 'a2'], idl=[idx, idx2])
|
||||
a = pe.Obs([zero_arr], ['a1'], idl=[idx]) + pe.Obs([zero_arr2], ['a2'], idl=[idx2])
|
||||
|
||||
afull.gamma_method()
|
||||
a.gamma_method()
|
||||
|
|
@ -1023,7 +1036,7 @@ def test_correlation_intersection_of_idls():
|
|||
|
||||
|
||||
def test_covariance_non_identical_objects():
|
||||
obs1 = pe.Obs([np.random.normal(1.0, 0.1, 1000), np.random.normal(1.0, 0.1, 1000), np.random.normal(1.0, 0.1, 732)], ["ens|r1", "ens|r2", "ens2"])
|
||||
obs1 = pe.Obs([np.random.normal(1.0, 0.1, 1000), np.random.normal(1.0, 0.1, 1000)], ["ens|r1", "ens|r2"]) + pe.Obs([np.random.normal(1.0, 0.1, 732)], ['ens2'])
|
||||
obs1.gamma_method()
|
||||
obs2 = obs1 + 1e-18
|
||||
obs2.gamma_method()
|
||||
|
|
@ -1107,6 +1120,9 @@ def test_reweight_method():
|
|||
obs1 = pe.pseudo_Obs(0.2, 0.01, 'test')
|
||||
rw = pe.pseudo_Obs(0.999, 0.001, 'test')
|
||||
assert obs1.reweight(rw) == pe.reweight(rw, [obs1])[0]
|
||||
rw2 = pe.pseudo_Obs(0.999, 0.001, 'test2')
|
||||
with pytest.raises(ValueError):
|
||||
obs1.reweight(rw2)
|
||||
|
||||
|
||||
def test_jackknife():
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue