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https://github.com/ramonsaraiva/timy
Minimalist measurement of python code time
https://github.com/ramonsaraiva/timy
measurement python time timy
Last synced: 3 days ago
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Minimalist measurement of python code time
- Host: GitHub
- URL: https://github.com/ramonsaraiva/timy
- Owner: ramonsaraiva
- License: mit
- Created: 2017-01-07T20:05:52.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2020-07-17T01:55:52.000Z (over 4 years ago)
- Last Synced: 2024-04-14T12:21:49.442Z (7 months ago)
- Topics: measurement, python, time, timy
- Language: Python
- Homepage:
- Size: 29.3 KB
- Stars: 278
- Watchers: 9
- Forks: 13
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# timy
Minimalist measurement of python code time
> **timy** comes with a different idea of the built-in module [timeit](https://docs.python.org/2.7/library/timeit.html). It adds flexibility and different ways of measuring code time, using simple context managers and function decorators.
## Installing
```
pip install timy
```
## Usage
### Decorating a function
Let's say you have a `calculate` function and you want to keep track of its execution time
```python
import timy
@timy.timer()
def calculate(n, r):
"""
Divide, multiply and sum 'n' to every number in range 'r'
returning the result list
"""
return [i / n * n + n for i in range(r)]
```
Whenever you call that function, the execution time will be tracked
```python
calculate(5, 10000000)
>> Timy executed (calculate) for 1 time(s) in 1.529540 seconds
>> Timy best time was 1.529540 seconds
```
Changing the **ident** and adding **loops** to the execution
```python
import timy
@timy.timer(ident='My calculation', loops=10)
def calculate(n, r):
return [i / n * n + n for i in range(r)]
calculate(5, 10000000)
>> My calculation executed (calculate) for 10 time(s) in 15.165313 seconds
>> My calculation best time was 1.414186 seconds
```
### Tracking **specific points** along your code
The `with` statement can also be used to measure code time
> Named tracking points can be added with the `track` function
```python
import timy
with timy.Timer() as timer:
N = 10000000
for i in range(N):
if i == N/2:
timer.track('Half way')
>> Timy (Half way) 0.557577 seconds
>> Timy 0.988087 seconds
```
Another usage of tracking in a prime factors function
```python
def prime_factors(n):
with timy.Timer('Factors') as timer:
i = 2
factors = []
def add_factor(n):
factors.append(n)
timer.track('Found a factor')
while i * i <= n:
if n % i == 0:
add_factor(i)
n //= i
else:
i += 1
return factors + [n]
factors = prime_factors(600851475143)
print(factors)
>> Factors (Found a factor) 0.000017 seconds
>> Factors (Found a factor) 0.000376 seconds
>> Factors (Found a factor) 0.001547 seconds
>> Factors 0.001754 seconds
>> [71, 839, 1471, 6857]
```
### Configuring
#### Importing timy config
```python
from timy.settings import timy_config
```
#### Enable or disable timy trackings
You can enable or disable timy trackings with the `tracking` value.
> The default value of `tracking` is `True`
```python
timy_config.tracking = False
```
#### Changing the way timy outputs information
You can choose between print or logging for all timy outputs by setting the
value of `tracking_mode`.
> The default value of `tracking_mode` is `TrackingMode.PRINTING`.
```python
from timy.settings import (
timy_config,
TrackingMode
)
timy_config.tracking_mode = TrackingMode.LOGGING
```
timy logs at the INFO level, which is not printed or stored by default. To
configure the logging system to print all INFO messages do
```
import logging
logging.basicConfig(level=logging.INFO)
```
or to configure the logging system to print only timy's INFO messages do
```
import logging
logging.basicConfig()
logging.getLogger('timy').level=logging.INFO
```
## Contribute
Contributions are **always** welcome, but keep it simple and small.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details