معرفی شرکت ها
TAMPPA-0.0.1
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Time And Memory Profile Parser
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | TAMPPA-0.0.1 |
| نام | TAMPPA |
| نسخه کتابخانه | 0.0.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Prashant Dandriyal |
| ایمیل نویسنده | prashantdandriyal7@gmail.com |
| آدرس صفحه اصلی | https://github.com/pra-dan/TAMPPA |
| آدرس اینترنتی | https://pypi.org/project/TAMPPA/ |
| مجوز | MPL |
# TAMPPA: Time And Memory Profile PArser
---
> As a maiden attampt, I hope to make it super useful for the community. Please report bugs and make pull requests to improve it.
---
## Introduction:
TAMPPA is a supporting package for the popular profilers
* [line_profiler and kernprof](https://github.com/pyutils/line_profiler/blob/master/README.rst), and
* [memory_profiler](https://github.com/pythonprofilers/memory_profiler)
Both the packages do an excellent job by providing profiling results on the terminal.
```python3
Total time: 0.181071 s
File: main.py
Function: linearRegressionfit at line 35
Line # Hits Time Per Hit % Time Line Contents
==============================================================
35 @profile
36 def linearRegressionfit(Xt,Yt,Xts,Yts):
37 1 52.0 52.0 0.1 lr=LinearRegression()
38 1 28942.0 28942.0 75.2 model=lr.fit(Xt,Yt)
39 1 1347.0 1347.0 3.5 predict=lr.predict(Xts)
40
41 1 4924.0 4924.0 12.8 print("train Accuracy",lr.score(Xt,Yt))
42 1 3242.0 3242.0 8.4 print("test Accuracy",lr.score(Xts,Yts))
```
But, there seems to be no method to get these stats in a exportable file that can be used with flexibility.
On dumping the logs to a `.txt` file still requires an individual to parse data from the text by self and then convert the content into a `.csv` file; which is a common format for sharing statistical data and plotting using MATPLOTLIB.
This is exactly what **TAMPPA** does ! It outputs one `.csv` file per function and another text file `func_names.txt` and `again_func_names.txt` for accessing these files easily.
## Pre-requisites:
**Note: Both the parsers need a .txt file to parse results from**
* Run both the profilers or the profiler whose results you need as a `csv`, and save the logs on the console to a `.txt` file. For e.g saving the memory profiling results of the python application `mainm.py` and saving the results to `mem_res_1.txt`
```python3
$ python -m memory_profiler mainm.py > mem_res_1.txt
```
* Avoid printing anything on the console. Try it with `python main.py` and nothing should be printed to the console. So, comment out all the print and log statements.
## Installation
Any particular release can be installed using `pip`:
```python3
$ pip install tamppa
```
To enter development mode,
```python3
$ git clone https://github.com/pra-dan/TAMPPA.git
```
## Usage
Refer to the following once the Installation is over.
### Time Profile Parser
Initially, if we have only the `.txt` file.
```python3
.
└── tim_prof_results.txt
0 directories, 1 file
```
Run `tim_parse` or time parser, in a Python environment (`$ python`)
```python3
>>> from tamppa import tim_parse
>>> tim_parse("tim_prof_results.txt")
```
On successful execution, the lonely directory is populated as
```python3
.
├── again_func_names.txt
├── import_data_tim_.csv
├── linearRegressionfit_tim_.csv
├── parse_data_tim_.csv
├── randForestRegressorfit_tim_.csv
└── tim_prof_results.txt
0 directories, 6 files
```
Additionally, a plot is also generated as
### Memory Profile Parser
Similarly, if we have only the `.txt` file for the `memory_profiler`.
```python3
.
└── mem_res_1.txt
0 directories, 1 file
```
Run `mem_parse` or time parser, in a Python environment (`$ python`)
```python3
>>> from tamppa import mem_parse
>>> mem_parse("mem_res_1.txt")
```
On successful execution, the lonely directory is populated as
```python3
.
├── func_names.txt
├── function_wise_time_results.csv
├── import_data_mem_.csv
├── linearRegressionfit_mem_.csv
├── mem_res_1.txt
├── parse_data_mem_.csv
└── randForestRegressorfit_mem_.csv
0 directories, 7 files
```
Additionally, a plot is also generated as
## TODOs:
- [ ] (Provide the entire package a executable-like interface; such that the parsers can be called simply as `$ mem_parse file.txt -plot true`)
- [ ] (Add flags to toggle plots for both parsers)
## References:
* [Packaging Python Projects](https://packaging.python.org/tutorials/packaging-projects/)
* [Jacob Tomlinson's Blogs](https://www.jacobtomlinson.co.uk/series/creating-an-open-source-python-project-from-scratch/)
نیازمندی
| مقدار | نام |
|---|---|
| - | matplotlib |
| - | pandas |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl TAMPPA-0.0.1:
pip install TAMPPA-0.0.1.whl
نصب پکیج tar.gz TAMPPA-0.0.1:
pip install TAMPPA-0.0.1.tar.gz