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|
%global _empty_manifest_terminate_build 0
Name: python-chronometry
Version: 2020.11.12
Release: 1
Summary: Python library for tracking time and displaying progress bars
License: MIT
URL: https://github.com/idin/chronometry
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/93/15/c445e164db7dcb2068653974931964433af911b17ece66f6923941ad3b3e/chronometry-2020.11.12.tar.gz
BuildArch: noarch
Requires: python3-pandas
Requires: python3-numpy
Requires: python3-slytherin
Requires: python3-colouration
Requires: python3-sklearn
Requires: python3-ravenclaw
Requires: python3-func-timeout
Requires: python3-matplotlib
%description
# *Chronometry*
## `ProgressBar`
## `Estimator`
`Estimator` is an object that estimates the running time of a single argument function.
You can use it to avoid running a script for too long.
For example, if you want to cluster a large dataset and running it might take too long,
and cost too much if you use cloud computing,
you can create a function with one argument `x` which takes a sample with `x` rows
and clusters it; then you can use `Estimator` to estimate how long it takes to run it
on the full dataset by providing the actual number of rows to the `estimate()` method.
`Estimator` uses a *Polynomial* *Linear Regression* model
and gives more weight to larger numbers for the training.
### Usage
```python
from chronometry import Estimator
from time import sleep
def multiply_with_no_delay(x, y):
return (x ** 2 + 0.1 * x ** 3 + 1) * 0.00001 + y * 0.001
def multiply(x, y):
sleep_time = multiply_with_no_delay(x, y)
if sleep_time > 30:
raise
sleep(sleep_time)
if y == 6:
sleep(12)
elif 7 < y < 15:
raise Exception()
return sleep_time
estimator = Estimator(function=multiply, polynomial_degree=3, timeout=5)
# the `unit` argument chooses the unit of time to be used. By default unit='s'
estimator.auto_explore()
estimator.predict_time(x=10000, y=10000)
```
The above code runs for about *53* seconds and then estimates that
`multiply(10000, 10000)` will take *1002371.7* seconds which is only slightly
smaller than the correct number: *1001010* seconds.
`max_time` is the maximum time allowed for the estimate function to run.
If you are using `Estimator` in *Jupyter*,
you can plot the measurements with the `plot()` method (no arguments needed) which
returns a `matplotlib` `AxesSubplot` object and displays it at the same time.
```python
estimator.plot('x')
estimator.plot('y')
```
%package -n python3-chronometry
Summary: Python library for tracking time and displaying progress bars
Provides: python-chronometry
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-chronometry
# *Chronometry*
## `ProgressBar`
## `Estimator`
`Estimator` is an object that estimates the running time of a single argument function.
You can use it to avoid running a script for too long.
For example, if you want to cluster a large dataset and running it might take too long,
and cost too much if you use cloud computing,
you can create a function with one argument `x` which takes a sample with `x` rows
and clusters it; then you can use `Estimator` to estimate how long it takes to run it
on the full dataset by providing the actual number of rows to the `estimate()` method.
`Estimator` uses a *Polynomial* *Linear Regression* model
and gives more weight to larger numbers for the training.
### Usage
```python
from chronometry import Estimator
from time import sleep
def multiply_with_no_delay(x, y):
return (x ** 2 + 0.1 * x ** 3 + 1) * 0.00001 + y * 0.001
def multiply(x, y):
sleep_time = multiply_with_no_delay(x, y)
if sleep_time > 30:
raise
sleep(sleep_time)
if y == 6:
sleep(12)
elif 7 < y < 15:
raise Exception()
return sleep_time
estimator = Estimator(function=multiply, polynomial_degree=3, timeout=5)
# the `unit` argument chooses the unit of time to be used. By default unit='s'
estimator.auto_explore()
estimator.predict_time(x=10000, y=10000)
```
The above code runs for about *53* seconds and then estimates that
`multiply(10000, 10000)` will take *1002371.7* seconds which is only slightly
smaller than the correct number: *1001010* seconds.
`max_time` is the maximum time allowed for the estimate function to run.
If you are using `Estimator` in *Jupyter*,
you can plot the measurements with the `plot()` method (no arguments needed) which
returns a `matplotlib` `AxesSubplot` object and displays it at the same time.
```python
estimator.plot('x')
estimator.plot('y')
```
%package help
Summary: Development documents and examples for chronometry
Provides: python3-chronometry-doc
%description help
# *Chronometry*
## `ProgressBar`
## `Estimator`
`Estimator` is an object that estimates the running time of a single argument function.
You can use it to avoid running a script for too long.
For example, if you want to cluster a large dataset and running it might take too long,
and cost too much if you use cloud computing,
you can create a function with one argument `x` which takes a sample with `x` rows
and clusters it; then you can use `Estimator` to estimate how long it takes to run it
on the full dataset by providing the actual number of rows to the `estimate()` method.
`Estimator` uses a *Polynomial* *Linear Regression* model
and gives more weight to larger numbers for the training.
### Usage
```python
from chronometry import Estimator
from time import sleep
def multiply_with_no_delay(x, y):
return (x ** 2 + 0.1 * x ** 3 + 1) * 0.00001 + y * 0.001
def multiply(x, y):
sleep_time = multiply_with_no_delay(x, y)
if sleep_time > 30:
raise
sleep(sleep_time)
if y == 6:
sleep(12)
elif 7 < y < 15:
raise Exception()
return sleep_time
estimator = Estimator(function=multiply, polynomial_degree=3, timeout=5)
# the `unit` argument chooses the unit of time to be used. By default unit='s'
estimator.auto_explore()
estimator.predict_time(x=10000, y=10000)
```
The above code runs for about *53* seconds and then estimates that
`multiply(10000, 10000)` will take *1002371.7* seconds which is only slightly
smaller than the correct number: *1001010* seconds.
`max_time` is the maximum time allowed for the estimate function to run.
If you are using `Estimator` in *Jupyter*,
you can plot the measurements with the `plot()` method (no arguments needed) which
returns a `matplotlib` `AxesSubplot` object and displays it at the same time.
```python
estimator.plot('x')
estimator.plot('y')
```
%prep
%autosetup -n chronometry-2020.11.12
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-chronometry -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 2020.11.12-1
- Package Spec generated
|
