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python 3 how do i temporarily disable tracing variables

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  • Techknowledgy :

For example:

functionChoiceVar.trace_id = functionChoiceVar.trace("w", display_parameters)
   ...
   functionChoiceVar.trace_vdelete("w", functionChoiceVar.trace_id)

A1: how to remove tracing temporarily & return it afterwards

aWriteTracerID = presetChoiceVar.trace( "w", aWriteHANDLER ) # SAVE <<aTracer>> ID#
   # setup code continues

   presetChoiceVar.trace_vdelete( "w", aWriteTracerID ) # REMOVE <<aTracer>>
      # code
      # that needs the write-tracer
      # to have been removed
      # ...

      aWriteTracerID = presetChoiceVar.trace( "w", aWriteHANDLER ) # SET AGAIN

I ran into this myself while creating a new class which inherits from a base class taking a variable as an input keyword argument. So I developed the below routine which attempts to recover a named callback function from the globals() based on the fact that the Trace ID is nothing more than some numerical digits prepended to the original callback function name. I expect that those digits have some meaning, but I was not able to ascertain them. Here's the routine:

def get_observer_callback(id):
func = None
funcname = id.lstrip('0123456789')
if funcname != '<lambda>' and funcname in globals():
   func = globals().get(funcname)
   if type(func) is not type(lambda:()):
   func = None
   return func

While testing, a trace_id was 50360848my_callback and the callback function was identified as

  <function my_callback at 0x03064370>

The easiest way to remove a trace would be to say:

idx = 0 # This is the index of the first trace on tk_variable.
tk_variable.trace_remove( * tk_variable.trace_info()[idx])

To add the trace back, you can say:

tk_variable.trace_add('write', your_function)

To check if a variable is traced, you can say:

if tk_variable.trace_info():
   # do something
   pass

Suggestion : 2

Q1: How to temporarily disable tracing variables I have a traceable variable field and I would like to temporarily disable tracing so that I can change the value of the field without calling the trace function.,I am using Python 3.4.1 for windows, if that helps.,When you set up a trace, tkinter will return an identifier that you can use to remove the trace later using the method .trace_vdelete() . To restart tracing, simply follow what you did the first time.,What's the best way to work with Java String that has fields mapping inside it? - java

Here is the code:

import tkinter as tk

WORKINGWINDOWWIDTH = 800 # Width
for the working window
WORKINGWINDOWHEIGHT = 800 # Height
for the working window

root = tk.Tk()

w = tk.Canvas(root, width = WORKINGWINDOWWIDTH - 10, height = WORKINGWINDOWHEIGHT - 10, bg = "darkred")

def display_parameters( * args):
   print("args: {0}, and I have the following option: {1}".format(args, functionChoiceVar.get()))
if functionChoiceVar.get() == "Option 1":
   print("I picked the first one...")
print("How do I repack the presets?")
elif functionChoiceVar.get() == "Option 2":
   print("I picked the second one...")
return

def display_options( * args):
   print("args: {0}, and I have the following suboption: {1}".format(args, presetChoiceVar.get()))
return

functionChoiceVar = tk.StringVar(root)
functionChoices = ['Option 1', 'Option 2']
functionOption = tk.OptionMenu(root, functionChoiceVar, * functionChoices)
functionOption.pack(side = 'left', padx = 10, pady = 10)
functionOption.place(x = 10, y = 10)
functionChoiceVar.set('Option 1')
functionChoiceVar.trace("w", display_parameters)

presetChoiceVar = tk.StringVar(root)
presetChoices11 = ['Suboption 11', 'Suboption 12', 'Suboption 13', 'Suboption 14', 'Suboption 15']
presetChoices12 = ['Suboption 21', 'Suboption 22', 'Suboption 23', 'Suboption 24', 'Suboption 25']
presetOption = tk.OptionMenu(root, presetChoiceVar, * presetChoices11)
presetOption.pack(side = 'left', padx = 10, pady = 10)
presetOption.place(x = 100, y = 10)
presetChoiceVar.set('Suboption 11')
presetChoiceVar.trace("w", display_options)

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functionChoiceVar.trace_id = functionChoiceVar.trace("w", display_parameters)
   ...
   functionChoiceVar.trace_vdelete("w", functionChoiceVar.trace_id)

(adsbygoogle = window.adsbygoogle || []).push({});

aWriteTracerID = presetChoiceVar.trace( "w", aWriteHANDLER )  # SAVE <<aTracer>> ID#
# setup code continues

presetChoiceVar.trace_vdelete(          "w", aWriteTracerID ) # REMOVE <<aTracer>>
# code
# that needs the write-tracer
# to have been removed
# ...

aWriteTracerID = presetChoiceVar.trace( "w", aWriteHANDLER )  # SET AGAIN

      
        
        
        
          (adsbygoogle = window.adsbygoogle || []).push({});

I ran into this myself while creating a new class that inherits from the base class, taking a variable as a keyword argument. So I developed a subroutine below that tries to reconstruct a named callback function from global variables () based on the fact that the trace ID is nothing more than some numeric digits preceding the original name of the callback function. I expect these numbers to make some sense, but I have not been able to establish them. Here's the procedure:

def get_observer_callback(id):
    func = None
    funcname = id.lstrip('0123456789')
    if funcname != '<lambda>' and funcname in globals():
        func = globals().get(funcname)
        if type(func) is not type(lambda:()):
            func = None
    return func

      
        
        
        
          (adsbygoogle = window.adsbygoogle || []).push({});

During testing, the trace_id was 50360848my_callback and the callback function was identified as

  <function my_callback at 0x03064370>

      
        
        
        
          (adsbygoogle = window.adsbygoogle || []).push({});

Suggestion : 3

Last Updated : 12 Dec, 2021,GATE CS 2021 Syllabus

  1.  
  2. trace_info(): 
    The trace_info() method has replaced trace_vinfo() method and trace() method. It returns the name of the callback. This is generally used to find the name of the callback that is to be deleted. This method takes no argument other than the tkinter variable itself.
     
Syntax: trace_info(self)

Suggestion : 4

Indicates if a message of severity level would be processed by this logger. This method checks first the module-level level set by logging.disable(level) and then the logger’s effective level as determined by getEffectiveLevel().,Sets the threshold for this handler to level. Logging messages which are less severe than level will be ignored. When a handler is created, the level is set to NOTSET (which causes all messages to be processed).,Handles a record by passing it to all handlers associated with this logger and its ancestors (until a false value of propagate is found). This method is used for unpickled records received from a socket, as well as those created locally. Logger-level filtering is applied using filter().,Logs a message with level ERROR on this logger. The arguments are interpreted as for debug(). Exception info is added to the logging message. This method should only be called from an exception handler.

>>>
import logging
   >>>
   logging.warning('Watch out!')
WARNING: root: Watch out!
Stack(most recent call last):
FORMAT = '%(asctime)s %(clientip)-15s %(user)-8s %(message)s'
logging.basicConfig(format = FORMAT)
d = {
   'clientip': '192.168.0.1',
   'user': 'fbloggs'
}
logger = logging.getLogger('tcpserver')
logger.warning('Protocol problem: %s', 'connection reset', extra = d)
2006 - 02 - 08 22: 20: 02, 165 192.168 .0 .1 fbloggs Protocol problem: connection reset
old_factory = logging.getLogRecordFactory()

def record_factory( * args, ** kwargs):
   record = old_factory( * args, ** kwargs)
record.custom_attribute = 0xdecafbad
return record

logging.setLogRecordFactory(record_factory)
class MyLogger(logging.getLoggerClass()):
   #...override behaviour here

Suggestion : 5

Last updated 2022-07-28 UTC.

Setup

# Update TensorFlow, as this notebook requires version 2.9 or later
   !pip install - q - U tensorflow >= 2.9 .0
import tensorflow as tf

Define a helper function to demonstrate the kinds of errors you might encounter:

import traceback
import contextlib

# Some helper code to demonstrate the kinds of errors you might encounter.
@contextlib.contextmanager
def assert_raises(error_class):
   try:
   yield
except error_class as e:
   print('Caught expected exception \n  {}:'.format(error_class))
traceback.print_exc(limit = 2)
except Exception as e:
   raise e
else:
   raise Exception('Expected {} to be raised but no error was raised!'.format(
      error_class))

A Function you define (for example by applying the @tf.function decorator) is just like a core TensorFlow operation: You can execute it eagerly; you can compute gradients; and so on.

@tf.function # The decorator converts `add`
into a `Function`.
def add(a, b):
   return a + b

add(tf.ones([2, 2]), tf.ones([2, 2])) #[[2., 2.], [2., 2.]]
<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[2., 2.],
       [2., 2.]], dtype=float32)>

v = tf.Variable(1.0)
with tf.GradientTape() as tape:
   result = add(v, 1.0)
tape.gradient(result, v)
v = tf.Variable(1.0)
with tf.GradientTape() as tape:
  result = add(v, 1.0)
tape.gradient(result, v)

<tf.Tensor: shape=(), dtype=float32, numpy=1.0>

Suggestion : 6

Django uses and extends Python’s builtin logging module to perform system logging. This module is discussed in detail in Python’s own documentation; this section provides a quick overview.,Next we can add more fine-grained logging. Here’s an example of how to make the logging system print more messages from just the django named logger:,A logger is the entry point into the logging system. Each logger is a named bucket to which messages can be written for processing.,A logger is configured to have a log level. This log level describes the severity of the messages that the logger will handle. Python defines the following log levels:

import os

LOGGING = {
   'version': 1,
   'disable_existing_loggers': False,
   'handlers': {
      'console': {
         'class': 'logging.StreamHandler',
      },
   },
   'root': {
      'handlers': ['console'],
      'level': 'WARNING',
   },
}
import os

LOGGING = {
   'version': 1,
   'disable_existing_loggers': False,
   'handlers': {
      'console': {
         'class': 'logging.StreamHandler',
      },
   },
   'root': {
      'handlers': ['console'],
      'level': 'WARNING',
   },
   'loggers': {
      'django': {
         'handlers': ['console'],
         'level': os.getenv('DJANGO_LOG_LEVEL', 'INFO'),
         'propagate': False,
      },
   },
}
LOGGING = {
   'version': 1,
   'disable_existing_loggers': False,
   'handlers': {
      'file': {
         'level': 'DEBUG',
         'class': 'logging.FileHandler',
         'filename': '/path/to/django/debug.log',
      },
   },
   'loggers': {
      'django': {
         'handlers': ['file'],
         'level': 'DEBUG',
         'propagate': True,
      },
   },
}
LOGGING = {
   'version': 1,
   'disable_existing_loggers': False,
   'formatters': {
      'verbose': {
         'format': '{levelname} {asctime} {module} {process:d} {thread:d} {message}',
         'style': '{',
      },
      'simple': {
         'format': '{levelname} {message}',
         'style': '{',
      },
   },
   'filters': {
      'special': {
         '()': 'project.logging.SpecialFilter',
         'foo': 'bar',
      },
      'require_debug_true': {
         '()': 'django.utils.log.RequireDebugTrue',
      },
   },
   'handlers': {
      'console': {
         'level': 'INFO',
         'filters': ['require_debug_true'],
         'class': 'logging.StreamHandler',
         'formatter': 'simple'
      },
      'mail_admins': {
         'level': 'ERROR',
         'class': 'django.utils.log.AdminEmailHandler',
         'filters': ['special']
      }
   },
   'loggers': {
      'django': {
         'handlers': ['console'],
         'propagate': True,
      },
      'django.request': {
         'handlers': ['mail_admins'],
         'level': 'ERROR',
         'propagate': False,
      },
      'myproject.custom': {
         'handlers': ['console', 'mail_admins'],
         'level': 'INFO',
         'filters': ['special']
      }
   }
}
LOGGING_CONFIG = None

import logging.config
logging.config.dictConfig(...)


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