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Jure Šorn 5 years ago
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  1. 53
      README.md
  2. 44
      index.html
  3. 2
      parse.js

53
README.md
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@ -14,7 +14,7 @@ Contents
**   ** **4. System:** **        ** **[`Print`](#print)**__,__ **[`Input`](#input)**__,__ **[`Command_Line_Arguments`](#command-line-arguments)**__,__ **[`Open`](#open)**__,__ **[`Path`](#path)**__,__ **[`Command_Execution`](#command-execution)**__.__
**   ** **5. Data:** **             ** **[`CSV`](#csv)**__,__ **[`JSON`](#json)**__,__ **[`Pickle`](#pickle)**__,__ **[`SQLite`](#sqlite)**__,__ **[`Bytes`](#bytes)**__,__ **[`Struct`](#struct)**__,__ **[`Array`](#array)**__,__ **[`MemoryView`](#memory-view)**__,__ **[`Deque`](#deque)**__.__
**   ** **6. Advanced:** **   ** **[`Threading`](#threading)**__,__ **[`Operator`](#operator)**__,__ **[`Introspection`](#introspection)**__,__ **[`Metaprograming`](#metaprograming)**__,__ **[`Eval`](#eval)**__,__ **[`Coroutine`](#coroutine)**__.__
**   ** **7. Libraries:** **      ** **[`Progress_Bar`](#progress-bar)**__,__ **[`Plot`](#plot)**__,__ **[`Table`](#table)**__,__ **[`Curses`](#curses)**__,__ **[`Logging`](#logging)**__,__ **[`Scraping`](#scraping)**__,__ **[`Web`](#web)**__,__ **[`Profile`](#profile)**__,__
**   ** **7. Libraries:** **      ** **[`Progress_Bar`](#progress-bar)**__,__ **[`Plot`](#plot)**__,__ **[`Table`](#table)**__,__ **[`Curses`](#curses)**__,__ **[`Logging`](#logging)**__,__ **[`Scraping`](#scraping)**__,__ **[`Web`](#web)**__,__ **[`Profile`](#profiling)**__,__
**                                 ** **[`NumPy`](#numpy)**__,__ **[`Image`](#image)**__,__ **[`Animation`](#animation)**__,__ **[`Audio`](#audio)**__,__ **[`Synthesizer`](#synthesizer)**__.__
@ -2408,9 +2408,9 @@ def odds_handler(sport):
```
Profile
-------
### Basic
Profiling
---------
### Stopwatch
```python
from time import time
start_time = time() # Seconds since the Epoch.
@ -2418,7 +2418,7 @@ start_time = time() # Seconds since the Epoch.
duration = time() - start_time
```
### High Performance
#### High performance:
```python
from time import perf_counter
start_time = perf_counter() # Seconds since restart.
@ -2434,17 +2434,24 @@ duration = perf_counter() - start_time
0.34986
```
### Line Profiler
### Timing by Function
#### Generates a PNG image of a call graph with highlighted bottlenecks:
```python
# $ pip3 install line_profiler
@profile
def main():
a = [*range(10000)]
b = {*range(10000)}
main()
# $ pip3 install pycallgraph
from pycallgraph import output, PyCallGraph
from datetime import datetime
time_str = datetime.now().strftime('%Y%m%d%H%M%S')
filename = f'profile-{time_str}.png'
drawer = output.GraphvizOutput(output_file=filename)
with PyCallGraph(drawer):
<code_to_be_profiled>
```
### Profiling by Line
```text
$ pip3 install line_profiler memory_profiler
```
#### Usage:
```text
$ kernprof -lv test.py
Line # Hits Time Per Hit % Time Line Contents
@ -2455,18 +2462,14 @@ Line # Hits Time Per Hit % Time Line Contents
4 1 2994.0 2994.0 72.6 b = {*range(10000)}
```
### Call Graph
#### Generates a PNG image of a call graph with highlighted bottlenecks:
```python
# $ pip3 install pycallgraph
from pycallgraph import output, PyCallGraph
from datetime import datetime
time_str = datetime.now().strftime('%Y%m%d%H%M%S')
filename = f'profile-{time_str}.png'
drawer = output.GraphvizOutput(output_file=filename)
with PyCallGraph(drawer):
<code_to_be_profiled>
```text
$ python3 -m memory_profiler test.py
Line # Mem usage Increment Line Contents
================================================
1 35.387 MiB 35.387 MiB @profile
2 def main():
3 35.734 MiB 0.348 MiB a = [*range(10000)]
4 36.160 MiB 0.426 MiB b = {*range(10000)}
```

44
index.html
View File

@ -216,7 +216,7 @@ pre.prettyprint {
<strong><span class="hljs-string"><span class="hljs-string">'4. System'</span></span></strong>: [<a href="#print">Print</a>, <a href="#input">Input</a>, <a href="#commandlinearguments">Command_Line_Arguments</a>, <a href="#open">Open</a>, <a href="#path">Path</a>, <a href="#commandexecution">Command_Execution</a>],
<strong><span class="hljs-string"><span class="hljs-string">'5. Data'</span></span></strong>: [<a href="#csv">CSV</a>, <a href="#json">JSON</a>, <a href="#pickle">Pickle</a>, <a href="#sqlite">SQLite</a>, <a href="#bytes">Bytes</a>, <a href="#struct">Struct</a>, <a href="#array">Array</a>, <a href="#memoryview">MemoryView</a>, <a href="#deque">Deque</a>],
<strong><span class="hljs-string"><span class="hljs-string">'6. Advanced'</span></span></strong>: [<a href="#threading">Threading</a>, <a href="#operator">Operator</a>, <a href="#introspection">Introspection</a>, <a href="#metaprograming">Metaprograming</a>, <a href="#eval">Eval</a>, <a href="#coroutine">Coroutine</a>],
<strong><span class="hljs-string"><span class="hljs-string">'7. Libraries'</span></span></strong>: [<a href="#progressbar">Progress_Bar</a>, <a href="#plot">Plot</a>, <a href="#table">Table</a>, <a href="#curses">Curses</a>, <a href="#logging">Logging</a>, <a href="#scraping">Scraping</a>, <a href="#web">Web</a>, <a href="#profile">Profile</a>,
<strong><span class="hljs-string"><span class="hljs-string">'7. Libraries'</span></span></strong>: [<a href="#progressbar">Progress_Bar</a>, <a href="#plot">Plot</a>, <a href="#table">Table</a>, <a href="#curses">Curses</a>, <a href="#logging">Logging</a>, <a href="#scraping">Scraping</a>, <a href="#web">Web</a>, <a href="#profiling">Profile</a>,
<a href="#numpy">NumPy</a>, <a href="#image">Image</a>, <a href="#animation">Animation</a>, <a href="#audio">Audio</a>, <a href="#synthesizer">Synthesizer</a>]
}
</code></pre></div></div>
@ -2070,14 +2070,14 @@ run(host=<span class="hljs-string">'0.0.0.0'</span>, port=<span class="hljs-numb
[<span class="hljs-string">'arsenal f.c.'</span>, <span class="hljs-number">2.44</span>, <span class="hljs-number">3.29</span>]
</code></pre></div>
<div><h2 id="profile"><a href="#profile" name="profile">#</a>Profile</h2><div><h3 id="basic">Basic</h3><pre><code class="python language-python hljs"><span class="hljs-keyword">from</span> time <span class="hljs-keyword">import</span> time
<div><h2 id="profiling"><a href="#profiling" name="profiling">#</a>Profiling</h2><div><h3 id="stopwatch">Stopwatch</h3><pre><code class="python language-python hljs"><span class="hljs-keyword">from</span> time <span class="hljs-keyword">import</span> time
start_time = time() <span class="hljs-comment"># Seconds since the Epoch.</span>
...
duration = time() - start_time
</code></pre></div></div>
<div><h3 id="highperformance">High Performance</h3><pre><code class="python language-python hljs"><span class="hljs-keyword">from</span> time <span class="hljs-keyword">import</span> perf_counter
<div><h4 id="highperformance">High performance:</h4><pre><code class="python language-python hljs"><span class="hljs-keyword">from</span> time <span class="hljs-keyword">import</span> perf_counter
start_time = perf_counter() <span class="hljs-comment"># Seconds since restart.</span>
...
duration = perf_counter() - start_time
@ -2089,24 +2089,7 @@ duration = perf_counter() - start_time
<span class="hljs-number">0.34986</span>
</code></pre></div>
<div><h3 id="lineprofiler">Line Profiler</h3><pre><code class="python language-python hljs"><span class="hljs-comment"># $ pip3 install line_profiler</span>
<span class="hljs-meta">@profile</span>
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">main</span><span class="hljs-params">()</span>:</span>
a = [*range(<span class="hljs-number">10000</span>)]
b = {*range(<span class="hljs-number">10000</span>)}
main()
</code></pre></div>
<div><h4 id="usage">Usage:</h4><pre><code class="text language-text">$ kernprof -lv test.py
Line # Hits Time Per Hit % Time Line Contents
==============================================================
1 @profile
2 def main():
3 1 1128.0 1128.0 27.4 a = [*range(10000)]
4 1 2994.0 2994.0 72.6 b = {*range(10000)}
</code></pre></div>
<div><h3 id="callgraph">Call Graph</h3><div><h4 id="generatesapngimageofacallgraphwithhighlightedbottlenecks">Generates a PNG image of a call graph with highlighted bottlenecks:</h4><pre><code class="python language-python hljs"><span class="hljs-comment"># $ pip3 install pycallgraph</span>
<div><h3 id="timingbyfunction">Timing by Function</h3><div><h4 id="generatesapngimageofacallgraphwithhighlightedbottlenecks">Generates a PNG image of a call graph with highlighted bottlenecks:</h4><pre><code class="python language-python hljs"><span class="hljs-comment"># $ pip3 install pycallgraph</span>
<span class="hljs-keyword">from</span> pycallgraph <span class="hljs-keyword">import</span> output, PyCallGraph
<span class="hljs-keyword">from</span> datetime <span class="hljs-keyword">import</span> datetime
time_str = datetime.now().strftime(<span class="hljs-string">'%Y%m%d%H%M%S'</span>)
@ -2117,6 +2100,25 @@ drawer = output.GraphvizOutput(output_file=filename)
</code></pre></div></div>
<div><h3 id="profilingbyline">Profiling by Line</h3><pre><code class="text language-text">$ pip3 install line_profiler memory_profiler
</code></pre></div>
<pre><code class="text language-text">$ kernprof -lv test.py
Line # Hits Time Per Hit % Time Line Contents
==============================================================
1 @profile
2 def main():
3 1 1128.0 1128.0 27.4 a = [*range(10000)]
4 1 2994.0 2994.0 72.6 b = {*range(10000)}
</code></pre>
<pre><code class="text language-text">$ python3 -m memory_profiler test.py
Line # Mem usage Increment Line Contents
================================================
1 35.387 MiB 35.387 MiB @profile
2 def main():
3 35.734 MiB 0.348 MiB a = [*range(10000)]
4 36.160 MiB 0.426 MiB b = {*range(10000)}
</code></pre>
<div><h2 id="numpy"><a href="#numpy" name="numpy">#</a>NumPy</h2><p><strong>Array manipulation mini language. Can run up to one hundred times faster than equivalent Python code.</strong></p><pre><code class="python language-python hljs"><span class="hljs-comment"># $ pip3 install numpy</span>
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
</code></pre></div>

2
parse.js
View File

@ -25,7 +25,7 @@ const TOC =
' <strong><span class="hljs-string">\'4. System\'</span></strong>: [<a href="#print">Print</a>, <a href="#input">Input</a>, <a href="#commandlinearguments">Command_Line_Arguments</a>, <a href="#open">Open</a>, <a href="#path">Path</a>, <a href="#commandexecution">Command_Execution</a>],\n' +
' <strong><span class="hljs-string">\'5. Data\'</span></strong>: [<a href="#csv">CSV</a>, <a href="#json">JSON</a>, <a href="#pickle">Pickle</a>, <a href="#sqlite">SQLite</a>, <a href="#bytes">Bytes</a>, <a href="#struct">Struct</a>, <a href="#array">Array</a>, <a href="#memoryview">MemoryView</a>, <a href="#deque">Deque</a>],\n' +
' <strong><span class="hljs-string">\'6. Advanced\'</span></strong>: [<a href="#threading">Threading</a>, <a href="#operator">Operator</a>, <a href="#introspection">Introspection</a>, <a href="#metaprograming">Metaprograming</a>, <a href="#eval">Eval</a>, <a href="#coroutine">Coroutine</a>],\n' +
' <strong><span class="hljs-string">\'7. Libraries\'</span></strong>: [<a href="#progressbar">Progress_Bar</a>, <a href="#plot">Plot</a>, <a href="#table">Table</a>, <a href="#curses">Curses</a>, <a href="#logging">Logging</a>, <a href="#scraping">Scraping</a>, <a href="#web">Web</a>, <a href="#profile">Profile</a>,\n' +
' <strong><span class="hljs-string">\'7. Libraries\'</span></strong>: [<a href="#progressbar">Progress_Bar</a>, <a href="#plot">Plot</a>, <a href="#table">Table</a>, <a href="#curses">Curses</a>, <a href="#logging">Logging</a>, <a href="#scraping">Scraping</a>, <a href="#web">Web</a>, <a href="#profiling">Profile</a>,\n' +
' <a href="#numpy">NumPy</a>, <a href="#image">Image</a>, <a href="#animation">Animation</a>, <a href="#audio">Audio</a>, <a href="#synthesizer">Synthesizer</a>]\n' +
'}\n' +
'</code></pre>\n';

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