├── .codecov.yml ├── src └── perfplot │ ├── __about__.py │ ├── _exceptions.py │ ├── __init__.py │ └── _main.py ├── renovate.json ├── .gitignore ├── .flake8 ├── tox.ini ├── tests ├── test_live.py └── test_perfplot.py ├── .pre-commit-config.yaml ├── example └── concat.py ├── justfile ├── .github ├── ISSUE_TEMPLATE │ ├── bug_report.md │ └── feature_request.md └── workflows │ └── ci.yml ├── pyproject.toml ├── README.md └── LICENSE /.codecov.yml: -------------------------------------------------------------------------------- 1 | comment: no 2 | -------------------------------------------------------------------------------- /src/perfplot/__about__.py: -------------------------------------------------------------------------------- 1 | __version__ = "0.10.2" 2 | -------------------------------------------------------------------------------- /renovate.json: -------------------------------------------------------------------------------- 1 | { 2 | "extends": [ 3 | "config:base" 4 | ] 5 | } 6 | -------------------------------------------------------------------------------- /src/perfplot/_exceptions.py: -------------------------------------------------------------------------------- 1 | class PerfplotError(Exception): 2 | pass 3 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .cache/ 2 | dist/ 3 | *.egg-info/ 4 | README.rst 5 | build/ 6 | *.png 7 | .pytest_cache/ 8 | .coverage 9 | .tox/ 10 | -------------------------------------------------------------------------------- /.flake8: -------------------------------------------------------------------------------- 1 | [flake8] 2 | ignore = E203, E266, E501, W503, C901 3 | max-line-length = 80 4 | max-complexity = 18 5 | select = B,C,E,F,W,T4,B9 6 | -------------------------------------------------------------------------------- /src/perfplot/__init__.py: -------------------------------------------------------------------------------- 1 | from .__about__ import __version__ 2 | from ._main import bench, live, plot, save, show 3 | 4 | __all__ = ["bench", "plot", "show", "save", "live", "__version__"] 5 | -------------------------------------------------------------------------------- /tox.ini: -------------------------------------------------------------------------------- 1 | [tox] 2 | envlist = py3 3 | isolated_build = True 4 | 5 | [testenv] 6 | deps = 7 | pytest 8 | pytest-codeblocks 9 | pytest-cov 10 | commands = 11 | pytest {posargs} --codeblocks 12 | -------------------------------------------------------------------------------- /tests/test_live.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | import perfplot 4 | 5 | 6 | def test_live(): 7 | kernels = [lambda a: np.c_[a, a]] 8 | r = [2**k for k in range(4)] 9 | 10 | perfplot.live( 11 | setup=np.random.rand, 12 | kernels=kernels, 13 | labels=["c_"], 14 | n_range=r, 15 | xlabel="len(a)", 16 | ) 17 | -------------------------------------------------------------------------------- /.pre-commit-config.yaml: -------------------------------------------------------------------------------- 1 | repos: 2 | - repo: https://github.com/PyCQA/isort 3 | rev: 5.10.1 4 | hooks: 5 | - id: isort 6 | 7 | - repo: https://github.com/psf/black 8 | rev: 22.3.0 9 | hooks: 10 | - id: black 11 | language_version: python3 12 | 13 | - repo: https://github.com/PyCQA/flake8 14 | rev: 4.0.1 15 | hooks: 16 | - id: flake8 17 | -------------------------------------------------------------------------------- /example/concat.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | import perfplot 4 | 5 | perfplot.show( 6 | setup=np.random.rand, 7 | kernels=[ 8 | lambda a: np.c_[a, a], 9 | lambda a: np.stack([a, a]).T, 10 | lambda a: np.vstack([a, a]).T, 11 | lambda a: np.column_stack([a, a]), 12 | lambda a: np.concatenate([a[:, None], a[:, None]], axis=1), 13 | ], 14 | labels=["c_", "stack", "vstack", "column_stack", "concat"], 15 | n_range=[2**k for k in range(15)], 16 | xlabel="len(a)", 17 | ) 18 | -------------------------------------------------------------------------------- /justfile: -------------------------------------------------------------------------------- 1 | version := `python3 -c "from src.perfplot.__about__ import __version__; print(__version__)"` 2 | 3 | default: 4 | @echo "\"just publish\"?" 5 | 6 | publish: 7 | @if [ "$(git rev-parse --abbrev-ref HEAD)" != "main" ]; then exit 1; fi 8 | gh release create "v{{version}}" 9 | flit publish 10 | 11 | clean: 12 | @find . | grep -E "(__pycache__|\.pyc|\.pyo$)" | xargs rm -rf 13 | @rm -rf src/*.egg-info/ build/ dist/ .tox/ 14 | 15 | format: 16 | isort . 17 | black . 18 | blacken-docs README.md 19 | 20 | lint: 21 | black --check . 22 | flake8 . 23 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/bug_report.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Bug report 3 | about: Create a report to help us improve 4 | title: "[BUG]" 5 | labels: Needs triage 6 | assignees: "" 7 | --- 8 | 9 | **Describe the bug** 10 | A clear and concise description of what the bug is. 11 | 12 | **To Reproduce** 13 | A minimal code example that reproduces the problem would be a big help if you can provide it. 14 | 15 | **Diagnose** 16 | I may ask you to cut and paste the output of the following command. 17 | 18 | ``` 19 | pip freeze | grep perfplot 20 | ``` 21 | 22 | **Did I help?** 23 | 24 | If I was able to resolve your problem, consider [sponsoring](https://github.com/sponsors/nschloe) my work on perfplot, or [buy me a coffee](https://ko-fi.com/nschloe) to say thanks. 25 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/feature_request.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Feature request 3 | about: Suggest an idea for this project 4 | title: "[REQUEST]" 5 | labels: Needs triage 6 | assignees: "" 7 | --- 8 | 9 | Consider posting in https://github.com/nschloe/perfplot/discussions for feedback before raising a feature request. 10 | 11 | **How would you improve perfplot?** 12 | 13 | Give as much detail as you can. Example code of how you would like it to work would help. 14 | 15 | **What problem does it solved for you?** 16 | 17 | What problem do you have that this feature would solve? I may be able to suggest an existing way of solving it. 18 | 19 | **Did I help** 20 | 21 | If I was able to resolve your problem, consider [sponsoring](https://github.com/sponsors/nschloe) my work on perfplot, or [buy me a coffee](https://ko-fi.com/nschloe) to say thanks. 22 | -------------------------------------------------------------------------------- /.github/workflows/ci.yml: -------------------------------------------------------------------------------- 1 | name: ci 2 | 3 | on: 4 | push: 5 | branches: 6 | - main 7 | pull_request: 8 | branches: 9 | - main 10 | 11 | jobs: 12 | lint: 13 | runs-on: ubuntu-latest 14 | steps: 15 | - name: Check out repo 16 | uses: actions/checkout@v3 17 | - name: Set up Python 18 | uses: actions/setup-python@v3 19 | - name: Run pre-commit 20 | uses: pre-commit/action@v3.0.0 21 | 22 | build: 23 | runs-on: ubuntu-latest 24 | strategy: 25 | matrix: 26 | python-version: ["3.7", "3.8", "3.9", "3.10"] 27 | steps: 28 | - uses: actions/setup-python@v3 29 | with: 30 | python-version: ${{ matrix.python-version }} 31 | - uses: actions/checkout@v3 32 | - name: Test with tox 33 | run: | 34 | pip install tox 35 | tox -- --cov perfplot --cov-report xml --cov-report term 36 | - uses: codecov/codecov-action@v3 37 | if: ${{ matrix.python-version == '3.10' }} 38 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [build-system] 2 | requires = ["flit_core >=3.2,<4"] 3 | build-backend = "flit_core.buildapi" 4 | 5 | [project] 6 | name = "perfplot" 7 | authors = [{name = "Nico Schlömer", email = "nico.schloemer@gmail.com"}] 8 | description = "Performance plots for Python code snippets" 9 | readme = "README.md" 10 | license = {file = "LICENSE"} 11 | classifiers = [ 12 | "Development Status :: 5 - Production/Stable", 13 | "Intended Audience :: Developers", 14 | "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)", 15 | "Operating System :: OS Independent", 16 | "Programming Language :: Python", 17 | "Programming Language :: Python :: 3", 18 | "Programming Language :: Python :: 3.7", 19 | "Programming Language :: Python :: 3.8", 20 | "Programming Language :: Python :: 3.9", 21 | "Programming Language :: Python :: 3.10", 22 | "Topic :: Software Development", 23 | "Topic :: Utilities", 24 | ] 25 | keywords = ["performance", "profile"] 26 | dynamic = ["version"] 27 | requires-python = ">=3.7" 28 | dependencies = [ 29 | "matplotlib", 30 | "matplotx", 31 | "numpy", 32 | "rich", 33 | "typing_extensions;python_version<'3.8'", 34 | ] 35 | 36 | [project.urls] 37 | Code = "https://github.com/nschloe/perfplot" 38 | Issues = "https://github.com/nschloe/perfplot/issues" 39 | Funding = "https://github.com/sponsors/nschloe" 40 | -------------------------------------------------------------------------------- /tests/test_perfplot.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pytest 3 | 4 | import perfplot 5 | 6 | kernels = [lambda a: np.c_[a, a]] 7 | r = [2**k for k in range(4)] 8 | 9 | 10 | def test0(): 11 | perfplot.show( 12 | setup=np.random.rand, 13 | kernels=kernels, 14 | labels=["c_"], 15 | n_range=r, 16 | xlabel="len(a)", 17 | ) 18 | 19 | 20 | def test1(): 21 | perfplot.show( 22 | setup=np.random.rand, 23 | kernels=kernels, 24 | labels=["c_"], 25 | n_range=r, 26 | xlabel="len(a)", 27 | logx=True, 28 | logy=False, 29 | ) 30 | 31 | 32 | def test2(): 33 | out = perfplot.bench( 34 | setup=np.random.rand, 35 | kernels=kernels, 36 | labels=["c_"], 37 | n_range=r, 38 | xlabel="len(a)", 39 | ) 40 | print(out) 41 | 42 | 43 | def test3(): 44 | def setup(n): 45 | assert isinstance(n, int) 46 | return np.random.rand(n) 47 | 48 | perfplot.show( 49 | setup=setup, 50 | kernels=kernels, 51 | labels=["c_"], 52 | n_range=r, 53 | xlabel="len(a)", 54 | logx=True, 55 | logy=True, 56 | ) 57 | 58 | 59 | def test_no_labels(): 60 | perfplot.plot(setup=np.random.rand, kernels=kernels, n_range=r, xlabel="len(a)") 61 | 62 | 63 | # (expected_unit, time in nanoseconds, expected_timing, time_unit) format 64 | @pytest.mark.parametrize( 65 | "exp_unit, time_ns, time_unit", 66 | [ 67 | # Dealing w/ edge-case when timing < nanosecond 68 | # ("ns", 0.125, "auto"), 69 | # Almost a millisecond 70 | ("us", 9.999e5, "auto"), 71 | # Equal exactly to a millisecond 72 | ("ms", 1e6, "auto"), 73 | # Over 1 second 74 | ("s", 1.5e9, "auto"), 75 | # Checking if providing 's' for time_unit yields seconds 76 | ("s", 1e9, "s"), 77 | ], 78 | ) 79 | def test_automatic_scale(exp_unit, time_ns, time_unit): 80 | import re 81 | 82 | import matplotlib.pyplot as plt 83 | 84 | from perfplot._main import PerfplotData 85 | 86 | timings = np.full((1, 1), time_ns * 1.0e-9) 87 | 88 | data = PerfplotData( 89 | n_range=[1], 90 | # Converting timings to ns 91 | timings_s=timings, 92 | labels=["."], # Suppress no handle error # TODO fix this 93 | xlabel="", 94 | title="", 95 | flop=None, 96 | ) 97 | # Has the correct unit been applied to the y_label? 98 | data.plot(time_unit=time_unit) 99 | ax = plt.gca() 100 | 101 | # Regular Expression that retrieves the plot unit from label 102 | unit_re = re.compile(r"\[([musn]?[s])\]") 103 | m = unit_re.search(ax.get_ylabel()) 104 | assert m is not None 105 | plot_unit = m.groups()[0] 106 | assert plot_unit == exp_unit 107 | 108 | 109 | def test_save(): 110 | perfplot.save( 111 | "out.png", 112 | setup=np.random.rand, 113 | kernels=kernels, 114 | n_range=r, 115 | xlabel="len(a)", 116 | relative_to=0, 117 | ) 118 | 119 | 120 | def test_flops(): 121 | perfplot.show( 122 | setup=np.random.rand, 123 | kernels=kernels, 124 | labels=["c_"], 125 | n_range=r, 126 | xlabel="len(a)", 127 | flops=lambda n: n, 128 | ) 129 | 130 | 131 | def test_no_setup(): 132 | perfplot.show(kernels=[], n_range=r) 133 | 134 | 135 | def test_n_setups(): 136 | setups = [np.random.rand] * len(kernels) 137 | perfplot.show(setup=setups, kernels=kernels, n_range=r) 138 | 139 | 140 | def test_return_tuples(): 141 | def setup(n): 142 | return np.ones(n), np.full(n + 2, 3.1) 143 | 144 | def times(a, b): 145 | return 2 * a, 3 * b 146 | 147 | def times_reversed(a, b): 148 | return a * 2, b * 3 149 | 150 | perfplot.show( 151 | setup=setup, kernels=[times, times_reversed], n_range=[2**k for k in range(3)] 152 | ) 153 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |

2 | perfplot 3 |

4 | 5 | [![PyPi Version](https://img.shields.io/pypi/v/perfplot.svg?style=flat-square)](https://pypi.org/project/perfplot) 6 | [![PyPI pyversions](https://img.shields.io/pypi/pyversions/perfplot.svg?style=flat-square)](https://pypi.org/pypi/perfplot/) 7 | [![GitHub stars](https://img.shields.io/github/stars/nschloe/perfplot.svg?style=flat-square&logo=github&label=Stars&logoColor=white)](https://github.com/nschloe/perfplot) 8 | [![Downloads](https://pepy.tech/badge/perfplot/month)](https://pepy.tech/project/perfplot) 9 | 10 | 11 | 12 | [![Discord](https://img.shields.io/static/v1?logo=discord&logoColor=white&label=chat&message=on%20discord&color=7289da&style=flat-square)](https://discord.gg/hnTJ5MRX2Y) 13 | 14 | [![gh-actions](https://img.shields.io/github/workflow/status/nschloe/perfplot/ci?style=flat-square)](https://github.com/nschloe/perfplot/actions?query=workflow%3Aci) 15 | [![codecov](https://img.shields.io/codecov/c/github/nschloe/perfplot.svg?style=flat-square)](https://codecov.io/gh/nschloe/perfplot) 16 | [![LGTM](https://img.shields.io/lgtm/grade/python/github/nschloe/perfplot.svg?style=flat-square)](https://lgtm.com/projects/g/nschloe/perfplot) 17 | [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square)](https://github.com/psf/black) 18 | 19 | perfplot extends Python's [timeit](https://docs.python.org/3/library/timeit.html) by 20 | testing snippets with input parameters (e.g., the size of an array) and plotting the 21 | results. 22 | 23 | For example, to compare different NumPy array concatenation methods, the script 24 | 25 | ```python 26 | import numpy as np 27 | import perfplot 28 | 29 | perfplot.show( 30 | setup=lambda n: np.random.rand(n), # or setup=np.random.rand 31 | kernels=[ 32 | lambda a: np.c_[a, a], 33 | lambda a: np.stack([a, a]).T, 34 | lambda a: np.vstack([a, a]).T, 35 | lambda a: np.column_stack([a, a]), 36 | lambda a: np.concatenate([a[:, None], a[:, None]], axis=1), 37 | ], 38 | labels=["c_", "stack", "vstack", "column_stack", "concat"], 39 | n_range=[2**k for k in range(25)], 40 | xlabel="len(a)", 41 | # More optional arguments with their default values: 42 | # logx="auto", # set to True or False to force scaling 43 | # logy="auto", 44 | # equality_check=np.allclose, # set to None to disable "correctness" assertion 45 | # show_progress=True, 46 | # target_time_per_measurement=1.0, 47 | # max_time=None, # maximum time per measurement 48 | # time_unit="s", # set to one of ("auto", "s", "ms", "us", or "ns") to force plot units 49 | # relative_to=1, # plot the timings relative to one of the measurements 50 | # flops=lambda n: 3*n, # FLOPS plots 51 | ) 52 | ``` 53 | 54 | produces 55 | 56 | | ![](https://nschloe.github.io/perfplot/concat.svg) | ![](https://nschloe.github.io/perfplot/relative.svg) | 57 | | -------------------------------------------------- | ---------------------------------------------------- | 58 | 59 | Clearly, `stack` and `vstack` are the best options for large arrays. 60 | 61 | (By default, perfplot asserts the equality of the output of all snippets, too.) 62 | 63 | If your plot takes a while to generate, you can also use 64 | 65 | 66 | 67 | ```python 68 | perfplot.live( 69 | # ... 70 | ) 71 | ``` 72 | 73 | live 74 | 75 | with the same arguments as above. It will plot the updates live. 76 | 77 | Benchmarking and plotting can be separated. This allows multiple plots of the same data, 78 | for example: 79 | 80 | 81 | 82 | ```python 83 | out = perfplot.bench( 84 | # same arguments as above (except the plot-related ones, like time_unit or log*) 85 | ) 86 | out.show() 87 | out.save("perf.png", transparent=True, bbox_inches="tight") 88 | ``` 89 | 90 | Other examples: 91 | 92 | - [Making a flat list out of list of lists in Python](https://stackoverflow.com/a/45323085/353337) 93 | - [Most efficient way to map function over numpy array](https://stackoverflow.com/a/46470401/353337) 94 | - [numpy: most efficient frequency counts for unique values in an array](https://stackoverflow.com/a/43096495/353337) 95 | - [Most efficient way to reverse a numpy array](https://stackoverflow.com/a/44921013/353337) 96 | - [How to add an extra column to an numpy array](https://stackoverflow.com/a/40218298/353337) 97 | - [Initializing numpy matrix to something other than zero or one](https://stackoverflow.com/a/45006691/353337) 98 | 99 | ### Installation 100 | 101 | perfplot is [available from the Python Package 102 | Index](https://pypi.org/project/perfplot/), so simply do 103 | 104 | ``` 105 | pip install perfplot 106 | ``` 107 | 108 | to install. 109 | 110 | ### Testing 111 | 112 | To run the perfplot unit tests, check out this repository and type 113 | 114 | ``` 115 | tox 116 | ``` 117 | 118 | ### License 119 | 120 | This software is published under the [GPLv3 license](https://www.gnu.org/licenses/gpl-3.0.en.html). 121 | -------------------------------------------------------------------------------- /src/perfplot/_main.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | 3 | import io 4 | import time 5 | import timeit 6 | from typing import Callable 7 | 8 | try: 9 | # Python 3.8+ 10 | from typing import Literal 11 | except ImportError: 12 | from typing_extensions import Literal 13 | 14 | import matplotlib.animation as animation 15 | import matplotlib.pyplot as plt 16 | import matplotx 17 | import numpy as np 18 | import numpy.typing as npt 19 | from rich.console import Console 20 | from rich.progress import Progress 21 | from rich.table import Table 22 | 23 | from ._exceptions import PerfplotError 24 | 25 | plt.style.use(matplotx.styles.duftify(matplotx.styles.tab20r)) 26 | 27 | # Orders of Magnitude for SI time units in {unit: magnitude} format 28 | si_time = { 29 | "s": 1e0, # second 30 | "ms": 1e-3, # millisecond 31 | "us": 1e-6, # microsecond 32 | "ns": 1e-9, # nanosecond 33 | } 34 | 35 | 36 | def default_equality_check(a, b): 37 | if isinstance(a, str): 38 | return a == b 39 | 40 | return np.allclose(a, b) 41 | 42 | 43 | def _auto_time_unit(time_s: float) -> str: 44 | """Automatically obtains a readable unit at which to plot :py:attr:`timings` of the 45 | benchmarking process. This is accomplished by converting the minimum measured 46 | execution time into SI second and iterating over the plausible SI time units (s, ms, 47 | us, ns) to find the first one whose magnitude is smaller than the minimum execution 48 | time. 49 | 50 | :rtype: str 51 | 52 | .. note:: 53 | This is the same algorithm used by the timeit module 54 | """ 55 | # Converting minimum timing into seconds from nanoseconds 56 | time_unit = None 57 | for time_unit, magnitude in si_time.items(): 58 | if time_s >= magnitude: 59 | break 60 | assert time_unit is not None 61 | return time_unit 62 | 63 | 64 | class PerfplotData: 65 | def __init__( 66 | self, 67 | n_range: list[int], 68 | timings_s, 69 | flop, 70 | labels: list[str], 71 | xlabel: str | None, 72 | title: str | None, 73 | ): 74 | self.n_range = np.asarray(n_range) 75 | self.timings_s = timings_s 76 | self.flop = flop 77 | self.labels = labels 78 | self.xlabel = xlabel 79 | self.title = title 80 | 81 | def plot( # noqa: C901 82 | self, 83 | time_unit: str = "s", 84 | relative_to: int | None = None, 85 | logx: str | bool = "auto", 86 | logy: str | bool = "auto", 87 | ): 88 | if logx == "auto": 89 | # Check if the x values are approximately equally spaced in log 90 | if np.any(self.n_range <= 0): 91 | logx = False 92 | else: 93 | log_n_range = np.log(self.n_range) 94 | linlog = np.linspace(log_n_range[0], log_n_range[-1], len(log_n_range)) 95 | # don't consider first and last, they are equal anyway 96 | rel_diff = (log_n_range - linlog)[1:-1] / log_n_range[1:-1] 97 | logx = np.all(np.abs(rel_diff) <= 0.1) 98 | 99 | if logy == "auto": 100 | if relative_to is not None: 101 | logy = False 102 | elif self.flop is not None: 103 | logy = False 104 | else: 105 | logy = logx 106 | 107 | if logx and logy: 108 | plotfun = plt.loglog 109 | elif logx: 110 | plotfun = plt.semilogx 111 | elif logy: 112 | plotfun = plt.semilogy 113 | else: 114 | plotfun = plt.plot 115 | 116 | if self.flop is None: 117 | if relative_to is None: 118 | # Set time unit of plots. 119 | # Allowed values: ("s", "ms", "us", "ns", "auto") 120 | if time_unit == "auto": 121 | time_unit = _auto_time_unit(np.min(self.timings_s)) 122 | else: 123 | assert time_unit in si_time, "Provided `time_unit` is not valid" 124 | 125 | scaled_timings = self.timings_s / si_time[time_unit] 126 | ylabel = f"Runtime [{time_unit}]" 127 | else: 128 | scaled_timings = self.timings_s / self.timings_s[relative_to] 129 | ylabel = f"Runtime\nrelative to {self.labels[relative_to]}" 130 | 131 | for t, label in zip(scaled_timings, self.labels): 132 | plotfun(self.n_range, t, label=label) 133 | 134 | matplotx.ylabel_top(ylabel) 135 | else: 136 | if relative_to is None: 137 | flops = self.flop / self.timings_s 138 | plt.title("FLOPS") 139 | else: 140 | flops = self.timings_s[relative_to] / self.timings_s 141 | plt.title(f"FLOPS relative to {self.labels[relative_to]}") 142 | 143 | for fl, label in zip(flops, self.labels): 144 | plotfun(self.n_range, fl, label=label) 145 | 146 | if self.xlabel: 147 | plt.xlabel(self.xlabel) 148 | if self.title: 149 | plt.title(self.title) 150 | if relative_to is not None and not logy: 151 | plt.gca().set_ylim(bottom=0) 152 | 153 | matplotx.line_labels() 154 | 155 | def show(self, **kwargs): 156 | self.plot(**kwargs) 157 | plt.show() 158 | 159 | def save(self, filename, transparent=True, bbox_inches="tight", **kwargs): 160 | self.plot(**kwargs) 161 | plt.savefig(filename, transparent=transparent, bbox_inches=bbox_inches) 162 | plt.close() 163 | 164 | def __repr__(self): 165 | table = Table(show_header=True) 166 | table.add_column("n") 167 | for label in self.labels: 168 | table.add_column(label) 169 | 170 | for n, t in zip(self.n_range, self.timings_s.T): 171 | lst = [str(n)] + [str(tt) for tt in t] 172 | table.add_row(*lst) 173 | 174 | f = io.StringIO() 175 | console = Console(file=f) 176 | console.print(table) 177 | return f.getvalue() 178 | 179 | 180 | # iterator 181 | class Bench: 182 | def __init__( 183 | self, 184 | setup, 185 | kernels, 186 | equality_check, 187 | n_range, 188 | target_time_per_measurement, 189 | max_time, 190 | labels, 191 | cutoff_reached, 192 | callback=None, 193 | ): 194 | self.setup = setup 195 | self.kernels = kernels 196 | self.equality_check = equality_check 197 | # Make n_range a list of "scalar" np.arrays. This makes it possible for the 198 | # setup function to override the value with 199 | # n[...] = something_else. 200 | # That is useful if you want to plot the graphs against something else than the 201 | # input n. 202 | assert len(np.asarray(n_range).shape) == 1 203 | self.n_range = [np.array(item) for item in n_range] 204 | self.target_time_per_measurement = target_time_per_measurement 205 | self.max_time = max_time 206 | self.labels = labels 207 | self.cutoff_reached = cutoff_reached 208 | self.callback = callback 209 | 210 | self.idx = 0 211 | 212 | def __iter__(self): 213 | return self 214 | 215 | def __next__(self): 216 | if self.idx >= len(self.n_range): 217 | raise StopIteration 218 | 219 | # Explicitly convert to Python int. One difference between a numpy.array(int) 220 | # and a native Python int are in expressions like `n * [2.0]`. 221 | n = int(self.n_range[self.idx]) 222 | 223 | self.idx += 1 224 | 225 | data = None 226 | if callable(self.setup): 227 | data = self.setup(n) 228 | if not isinstance(data, tuple): 229 | data = (data,) 230 | 231 | reference = None 232 | timings = [] 233 | for k, kernel in enumerate(self.kernels): 234 | if self.cutoff_reached[k]: 235 | timings.append(np.nan) 236 | if self.callback is not None: 237 | self.callback() 238 | continue 239 | 240 | if isinstance(self.setup, list): 241 | data = self.setup[k](n) 242 | if not isinstance(data, tuple): 243 | data = (data,) 244 | 245 | # First let the function run once. The value is used for the equality_check 246 | # and the time for gauging how many more repetitions are to be done. If the 247 | # initial time doesn't exceed the target time, append as many repetitions as 248 | # the first measurement suggests. If the kernel is fast, the measurement 249 | # with one repetition only can be somewhat off because the CPU needs to spin 250 | # up first. The actual times are only reached after a few hundred 251 | # nanoseconds of computation. Most of the time it's okay though. 252 | t0_ns = time.perf_counter_ns() 253 | val = kernel(*data) 254 | t1_ns = time.perf_counter_ns() 255 | t_ns = t1_ns - t0_ns 256 | 257 | if t_ns == 0: 258 | raise RuntimeError("Measured 0 ns for a function call. Try again?") 259 | 260 | if self.equality_check: 261 | if k == 0: 262 | reference = val 263 | else: 264 | try: 265 | if isinstance(reference, tuple): 266 | assert isinstance(val, tuple) 267 | assert len(reference) == len(val) 268 | is_equal = True 269 | for r, v in zip(reference, val): 270 | if not self.equality_check(r, v): 271 | is_equal = False 272 | break 273 | else: 274 | is_equal = self.equality_check(reference, val) 275 | except TypeError: 276 | raise PerfplotError( 277 | "Error in equality_check. " 278 | + "Try setting equality_check=None." 279 | ) 280 | else: 281 | if not is_equal: 282 | raise PerfplotError( 283 | "Equality check failure.\n" 284 | + f"{self.labels[0]}:\n" 285 | + f"{reference}:\n\n" 286 | + f"{self.labels[k]}:\n" 287 | + f"{val}:\n" 288 | ) 289 | 290 | # First try with one repetition only. 291 | remaining_time_ns = int(self.target_time_per_measurement / si_time["ns"]) 292 | 293 | if self.max_time is not None and t_ns * si_time["ns"] > self.max_time: 294 | self.cutoff_reached[k] = True 295 | 296 | remaining_time_ns -= t_ns 297 | repeat = remaining_time_ns // t_ns 298 | if repeat > 0: 299 | t2 = _b(data, kernel, repeat) 300 | t_ns = min(t_ns, t2) 301 | 302 | timings.append(t_ns) 303 | if self.callback is not None: 304 | self.callback() 305 | 306 | return np.array(timings) * 1.0e-9 307 | 308 | 309 | def _b(data, kernel: Callable, repeat: int): 310 | # Make sure that the statement is executed at least so often that the timing exceeds 311 | # 10 times the resolution of the clock. `number` is larger than 1 only for the 312 | # fastest computations. Hardly ever happens. 313 | number = 1 314 | required_timing_ns = 10 315 | min_timing_ns = 0 316 | tm = None 317 | 318 | while min_timing_ns <= required_timing_ns: 319 | tm = np.array( 320 | timeit.repeat( 321 | stmt=lambda: kernel(*data), 322 | repeat=repeat, 323 | number=number, 324 | timer=time.perf_counter_ns, 325 | ) 326 | ) 327 | min_timing_ns = np.min(tm) 328 | tm //= number 329 | # Adapt the number of runs for the next iteration such that the 330 | # required_timing_ns is just exceeded. If the required timing and minimal timing 331 | # are just equal, `number` remains the same (up to an allowance of 0.2). 332 | allowance = 0.2 333 | max_factor = 100 334 | factor = max_factor 335 | if min_timing_ns > 0: 336 | # The next expression is 337 | # min(max_factor, required_timing_ns / min_timing_ns + allowance) 338 | # with avoiding division by 0 if min_timing_ns is too small. 339 | factor = ( 340 | required_timing_ns / min_timing_ns + allowance 341 | if min_timing_ns > required_timing_ns / (max_factor - allowance) 342 | else max_factor 343 | ) 344 | 345 | number = int(factor * number) + 1 346 | 347 | assert tm is not None 348 | # Only return the minimum time; everything else just measures how slow the system 349 | # can go. 350 | return np.min(tm) 351 | 352 | 353 | def live( 354 | setup: Callable, 355 | kernels: list[Callable], 356 | n_range: npt.ArrayLike, 357 | labels: list[str] | None = None, 358 | xlabel: str | None = None, 359 | target_time_per_measurement: float = 1.0, 360 | max_time: float | None = None, 361 | equality_check: Callable | None = default_equality_check, 362 | show_progress: bool = True, 363 | logx: Literal["auto"] | bool = "auto", 364 | logy: Literal["auto"] | bool = "auto", 365 | ): 366 | if labels is None: 367 | labels = [k.__name__ for k in kernels] 368 | 369 | # for data type of n_range, see above 370 | assert len(np.asarray(n_range).shape) == 1 371 | n_range = [np.array(item) for item in n_range] 372 | 373 | if logx == "auto": 374 | # Check if the x values are approximately equally spaced in log 375 | if any(n <= 0 for n in n_range): 376 | logx = False 377 | else: 378 | log_n_range = np.log(n_range) 379 | diff = log_n_range - np.linspace( 380 | log_n_range[0], log_n_range[-1], len(log_n_range) 381 | ) 382 | logx = np.all(np.abs(diff) < 1.0e-5) 383 | 384 | if logy == "auto": 385 | logy = logx 386 | 387 | # animation adapted from 388 | # 389 | with Progress() as progress: 390 | if show_progress: 391 | task1 = progress.add_task("Overall", total=len(n_range)) 392 | task2 = progress.add_task("Kernels", total=len(kernels)) 393 | 394 | def init(): 395 | for line, yd in zip(lines, ydata): 396 | line.set_data(xdata, yd) 397 | return lines 398 | 399 | fig, ax = plt.subplots() 400 | 401 | if logx and logy: 402 | plotfun = ax.loglog 403 | elif logx: 404 | plotfun = ax.semilogx 405 | elif logy: 406 | plotfun = ax.semilogy 407 | else: 408 | plotfun = ax.plot 409 | 410 | lines = [] 411 | for label in labels: 412 | lines.append(plotfun([], [], label=label)[0]) 413 | 414 | ax.legend() 415 | matplotx.ylabel_top("Runtime [s]") 416 | if xlabel: 417 | ax.set_xlabel(xlabel) 418 | xdata = [] 419 | ydata = [] 420 | for _ in range(len(kernels)): 421 | ydata.append([]) 422 | 423 | def run(data): 424 | # update the data 425 | for t, yd in zip(data, ydata): 426 | yd.append(t) 427 | 428 | xdata = n_range[: len(ydata[0])] 429 | 430 | if len(xdata) > 1: 431 | ax.set_xlim(np.nanmin(xdata), np.nanmax(xdata)) 432 | 433 | yd = np.asarray(ydata) 434 | if yd.size > 1: 435 | ax.set_ylim(np.nanmin(yd), np.nanmax(yd)) 436 | 437 | ax.figure.canvas.draw() 438 | 439 | for line, yd in zip(lines, ydata): 440 | line.set_data(xdata, yd) 441 | 442 | if show_progress: 443 | progress.update(task1, advance=1) 444 | progress.reset(task2) 445 | return lines 446 | 447 | def callback(): 448 | if show_progress: 449 | progress.update(task2, advance=1) 450 | 451 | bench = Bench( 452 | setup, 453 | kernels, 454 | equality_check, 455 | n_range, 456 | target_time_per_measurement, 457 | max_time, 458 | labels, 459 | cutoff_reached=np.zeros(len(n_range), dtype=bool), 460 | callback=callback, 461 | ) 462 | # Assign FuncAnimation to a dummy variable to avoid it being destroyed before 463 | # the animation has completed. This is mpl's recommendation. 464 | _ = animation.FuncAnimation( 465 | fig, run, bench, interval=10, init_func=init, repeat=False 466 | ) 467 | 468 | # anim.save("anim.gif", fps=5) 469 | plt.show() 470 | 471 | 472 | def bench( 473 | kernels: list[Callable], 474 | n_range: list[int], 475 | setup: Callable | list[Callable] | None = None, 476 | flops: Callable | None = None, 477 | labels: list[str] | None = None, 478 | xlabel: str | None = None, 479 | title: str | None = None, 480 | target_time_per_measurement: float = 1.0, 481 | max_time: float | None = None, 482 | equality_check: Callable | None = default_equality_check, 483 | show_progress: bool = True, 484 | ): 485 | if labels is None: 486 | labels = [k.__name__ for k in kernels] 487 | 488 | timings_s = np.full((len(n_range), len(kernels)), np.nan) 489 | cutoff_reached = np.zeros(len(kernels), dtype=bool) 490 | 491 | flop = None if flops is None else np.array([flops(n) for n in n_range]) 492 | 493 | task1 = None 494 | task2 = None 495 | 496 | # inner iterator 497 | with Progress() as progress: 498 | try: 499 | if show_progress: 500 | task1 = progress.add_task("Overall", total=len(n_range)) 501 | task2 = progress.add_task("Kernels", total=len(kernels)) 502 | 503 | def callback(): 504 | if show_progress: 505 | assert task2 is not None 506 | progress.update(task2, advance=1) 507 | 508 | b = Bench( 509 | setup, 510 | kernels, 511 | equality_check, 512 | n_range, 513 | target_time_per_measurement, 514 | max_time, 515 | labels, 516 | cutoff_reached, 517 | callback=callback, 518 | ) 519 | 520 | for i in range(len(n_range)): 521 | timings_s[i] = next(b) 522 | # override n_rane in case it got overridden in next() 523 | n_range = b.n_range 524 | 525 | if show_progress: 526 | assert task1 is not None 527 | assert task2 is not None 528 | progress.update(task1, advance=1) 529 | progress.reset(task2) 530 | 531 | except KeyboardInterrupt: 532 | timings_s = timings_s[:i] 533 | n_range = n_range[:i] 534 | 535 | return PerfplotData(n_range, timings_s.T, flop, labels, xlabel, title) 536 | 537 | 538 | # For backward compatibility: 539 | def plot( 540 | *args, 541 | time_unit: str = "s", 542 | logx: Literal["auto"] | bool = "auto", 543 | logy: Literal["auto"] | bool = "auto", 544 | relative_to: int | None = None, 545 | **kwargs, 546 | ): 547 | out = bench(*args, **kwargs) 548 | out.plot( 549 | time_unit=time_unit, 550 | logx=logx, 551 | logy=logy, 552 | relative_to=relative_to, 553 | ) 554 | 555 | 556 | def show( 557 | *args, 558 | time_unit: str = "s", 559 | relative_to: int | None = None, 560 | logx: bool | Literal["auto"] = "auto", 561 | logy: bool | Literal["auto"] = "auto", 562 | **kwargs, 563 | ): 564 | out = bench(*args, **kwargs) 565 | out.show( 566 | time_unit=time_unit, 567 | relative_to=relative_to, 568 | logx=logx, 569 | logy=logy, 570 | ) 571 | 572 | 573 | def save( 574 | filename, 575 | transparent=True, 576 | *args, 577 | time_unit: str = "s", 578 | logx: bool | Literal["auto"] = "auto", 579 | logy: bool | Literal["auto"] = "auto", 580 | relative_to: int | None = None, 581 | **kwargs, 582 | ): 583 | out = bench(*args, **kwargs) 584 | out.save( 585 | filename, 586 | transparent, 587 | time_unit=time_unit, 588 | logx=logx, 589 | logy=logy, 590 | relative_to=relative_to, 591 | ) 592 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------