PyTraceToIX
Description
PyTraceToIX is an expression tracer designed for debugging Jinja2 templates, Flask web apps, lambdas, list comprehensions, method chaining, and expressions in general.
Code editors often cannot set breakpoints within these kinds of expressions, which requires significant code modifications to debug effectively.
For Jinja2 templates, the debug extension can be used, but it typically dumps the entire context, making it difficult to isolate specific issues. PyTraceToIX solves this by allowing developers to trace and write specific data directly to sys.stdout or a stream without altering the design or making any changes to the web application.
Additionally, PyTraceToIX can capture multiple inputs and their results, displaying them all in a single line, making it easier to view aggregated data and trace the flow of values.
PyTraceToIX offers a straightforward solution to these challenges, simplifying debugging while preserving the integrity of the original codebase.
It was designed to be simple, with easily identifiable functions that can be removed once the bug is found.
PyTraceToIX has 2 major functions:
c__
capture the input of an expression input. ex:c__(x)
d__
display the result of an expression and all the captured inputs. ex:d__(c__(x) + c__(y))
And 2 optional functions:
init__
initializes display format, output stream, multithreading, enable/disable processingc__
,d__
andt__
.t__
defines a name for the current thread.
If you find this project useful, please, read the Support this Project on how to contribute.
Features
- No external dependencies.
- Minimalist function names that are simple and short.
- Traces Results along with Inputs.
- Configurable Result and Input naming.
- Output to the
stdout
or a stream. - Supports multiple levels.
- Capture Input method with customizable
allow
andname
callbacks. - Display Result method with customizable
allow
,before
, andafter
callbacks. - Support to globally disable the processing
c__
,d__
,t__
. - Result and Inputs can be reformatted and overridden.
- Configurable formatting at global level and at function level.
- Multithreading support.
JavaScript Version
This package is also available in JavaScript for similar debugging purposes. The JavaScript version, called JsTraceToIX, allows tracing input and output values during debugging and can be found on JsTraceToIX.
It offers the same c__
and d__
tracing functionality for JavaScript, supporting React, Vue, browser and Node.js environments.
Installation
pip install pytracetoix
Jinja2 templates Usage
In this example:
- A flask web app uses a Jinja2 template
- It generates a shopping card html table with product, quantity and final price
Product | Qty | Final Price |
---|---|---|
Smartphone | 5 | 2500 |
Wireless B | 50 | 49960 |
Smartphone | 20 | 1990 |
- The product name is only the first 11 characters, but we need to know the full name.
- It only shows the final price which is Price * Qty - discount.
- The discount is dependent of the quantity.
c__
captures the complete name but doesn't change the design.c__
captures the qty and labels it as Qty.c__
captures the discount value.d__
outputs to sys.stdout all the captured inputs and the final price.
The stdout will display these lines:
i0:`Smartphone 128GB` | qty:`5` | i2:`500` | discount:`0` | _:`2500`
i0:`Wireless Bluetooth Headphones` | qty:`50` | i2:`1000` | discount:`40` | _:`49960`
i0:`Smartphone 64GB Black` | qty:`20` | i2:`100` | discount:`10` | _:`1990`
Jinja2 template:
<html lang="en">
<head><link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0-alpha3/dist/css/bootstrap.min.css" rel="stylesheet"></head>
<body>
<div class="container mt-5">
<h1>Shopping Cart</h1>
<table class="table table-striped">
<tr><th>Product</th><th>Qty</th><th>Final Price</th></tr>
{% for item in purchases %}
{% set product = products[item['product']] %}
<tr>
<td>{{ c__(product['name'])[0:10] }}</td>
<td>{{ c__(item['qty'], name='qty') }}</td>
<td>{{ d__(c__(product['price']) * item['qty']
- c__(discount(item['qty']), name='discount')) }}</td>
</tr>
{% endfor %}
</table>
</div>
</body>
</html>
app.py:
from flask import Flask, render_template
from pytracetoix import c__, d__
app = Flask(__name__)
app.Jinja2_env.globals['d__'] = d__
app.Jinja2_env.globals['c__'] = c__
DISCOUNTS = {50: 40, 20: 10, 10: 5, 0: 0}
PRODUCTS = {
'WB50CC': {'name': 'Wireless Bluetooth Headphones', 'price': 1000},
'PH20XX': {'name': 'Smartphone 128GB', 'price': 500},
'PH50YY': {'name': 'Smartphone 64GB Black', 'price': 100}
}
PURCHASES = [
{'product': 'PH20XX', 'qty': 5},
{'product': 'WB50CC', 'qty': 50},
{'product': 'PH50YY', 'qty': 20}
]
def discount(qty): return next((k, v) for k, v in DISCOUNTS.items() if k <= qty)[1]
@app.route('/', methods=['GET'])
def index():
return render_template('index.html', products=PRODUCTS, purchases=PURCHASES, discount=discount)
if __name__ == '__main__':
app.run(debug=True)
In the previous example, if we add c__
to the discount
function on app.py
:
def discount(qty): return c__(next((k, v) for k, v in DISCOUNTS.items() if k <= qty))[1]
It will add richer discount information to the output:
i0:`Smartphone 128GB` | qty:`5` | i2:`500` | i3:`(0, 0)` | discount:`0` | _:`2500`
i0:`Wireless Bluetooth Headphones` | qty:`50` | i2:`1000` | i3:`(50, 40)` | discount:`40` | _:`49960`
i0:`Smartphone 64GB Black` | qty:`20` | i2:`100` | i3:`(20, 10)` | discount:`10` | _:`1990`
Detailed Usage and Examples
from pytracetoix import d__, c__
[x, y, w, k, u] = [1, 2, 3, 4 + 4, lambda x:x]
# expression
z = x + y * w + (k * u(5))
# Display expression with no inputs
z = d__(x + y * w + (k * u(5)))
# Output:
# _:`47`
# Display expression result with inputs
z = d__(c__(x) + y * c__(w) + (k * u(5)))
# Output:
# i0:`1` | i1:`3` | _:`47`
# Display expression result with inputs within an expression
z = d__(c__(x) + y * c__(w) + d__(k * c__(u(5), level=1)))
# Output:
# i0:`5` | _:`40`
# i0:`1` | i1:`3` | _:`47`
# lambda function
f = lambda x, y: x + (y + 1)
f(5, 6)
# Display lambda function result and inputs
f = lambda x, y: d__(c__(x) + c__(y + 1))
f(5, 6)
# Output:
# i0:`5` | i1:`7` | _:`12`
# Display lambda function inputs and result with input and result names
f = lambda x, y: d__(c__(x, name='x') + c__(y + 1, name='y+1'), name='f')
f(5, 6)
# Output:
# x:`5` | y+1:`7` | f:`12`
# list comprehension
l = [5 * y * x for x, y in [(10, 20), (30, 40)]]
# Display list comprehension with input and result names
l = d__([5 * c__(y, name=f"y{y}") * c__(x, name=lambda index, _, __: f'v{index}') for x, y in [(10, 20), (30, 40)]])
# Output:
# y20:`20` | v1:`10` | y40:`40` | v3:`30` | _:`[1000, 6000]`
# Display expression if `input count` is 2
d__(c__(x) + c__(y), allow=lambda data: data['input_count__'] == 2)
# Output:
# i0:`1` | i1:`2` | _:`3`
# Display expression if the first input value is 10.0
d__(c__(x) + c__(y), allow=lambda data: data['i0'] == 10.0)
# No output
# Display expression if the `allow_input_count` is 2, in this case if `x > 10`
d__(c__(x, allow=lambda index, name, value: value > 10) + c__(y),
allow=lambda data: data['allow_input_count__'] == 2)
# No output
# Display list comprehension if the generated output has the text 10
d__([c__(x) for x in ['10', '20']], before=lambda data: '10' in data['output__'])
# Output:
# i0:`10` | i1:`20` | _:`['10', '20']`
# Display list comprehension and after call `call_after` if it was allowed to display
d__([c__(x) for x in ['10', '20']], allow=lambda data: data['allow_input_count__'] == 2,
after=lambda data: call_after(data) if data['allow__'] else "")
# Display list comprehension with allow input override
d__([c__(x, allow=lambda index, name, value:value[0]) \
for x in [('10', '20'), ('30', '40'), ('50', '60')]])
# i0:`10` | i1:`30` | i2:`50` | _:`[('10', '20'), ('30', '40'), ('50', '60')]`
# Display list comprehension with allow result override
d__([c__(x) for x in [('10', '20'), ('30', '40'), ('50', '60')]], \
allow=lambda data:data['_'][0:2])
# i0:`('10', '20')` | i1:`('30', '40')` | i2:`('50', '60')` | _:`[('10', '20'), ('30', '40')]`
class Chain:
def __init__(self, data):
self.data = data
def map(self, func):
self.data = list(map(func, self.data))
return self
def filter(self, func):
self.data = list(filter(func, self.data))
return self
# A class with chain methods
Chain([10, 20, 30, 40, 50]).map(lambda x: x * 2).filter(lambda x: x > 70)
# Display the result and capture the map and filter inputs
d__(Chain([10, 20, 30, 40, 50]).map(lambda x: c__(x * 2)).filter(lambda x: c__(x > 70)).data)
# Output:
# i0:`20` | i1:`40` | i2:`60` | i3:`80` | i4:`100` | i5:`False` | i6:`False` | i7:`False` | i8:`True` | i9:`True` | _:`[80, 100]`
Formatting
The d__
function can override the default formatting, and it can also be defined at global level.
from pytracetoix import init__
init__(format={
'result': '{name}:`{value}`',
'input': '{name}:`{value}`',
'thread': '{id}: ',
'sep': ' | ',
'new_line': True
})
Formatting parameters:
result
: The result value format will be displayed.input
: The input value format will be displayed.sep
: The separator text between each input and the result.new_line
: IfTrue
it will add a new line at the end of output.
Multithreading
To activate the multithreading support:
from pytracetoix import d__, c__, t__, init__
init__(multithreading=True)
## It displays the threadId: i0: `4` | _: `5`
def thread_function():
d__(c__(4) + 1)
## It displays the something: i0: `4` | _: `5`
def thread_function_with_name():
t__("something")
d__(c__(4) + 1)
threads = []
for _ in range(5):
thread = threading.Thread(target=thread_function)
threads.append(thread)
threads.append(threading.Thread(target=thread_function_with_name))
for thread in threads:
thread.start()
for thread in threads:
thread.join()
Metadata
The d__
function callbacks allow
, before
and after
will receive a parameter data
with the allowed inputs plus the following meta
items:
meta__
: list of meta keys including the name key.thread_id__
: thread_id being executedallow_input_count__
: total number of inputs that are allowed.input_count__
: total number of inputs being captured.allow__
: If False it wasn't allowed. Use this forafter
callback.output__
: Text passed tobefore
withoutnew_line
.- name: name parameter
Documentation
Support this Project
If you find this project useful, consider supporting it:
- Donate:
- Visit the project homepage
- Give the project a ⭐ on Github
- Spread the word
- Follow me:
License
MIT License
Copyrights
(c) 2024 Alexandre Bento Freire
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