behold 0.0.2

Behold
===
Working with real-world code inevitably involves a lot of debugging time. In
order to get a full understanding of what your code is doing, you will need to
monitor its internal state as it is executed. There are two main ways to do
this: step-debuggers, and print statements. Behold is a tool that can be used
for either one of these work flows, but is focused mainly on logging program
state to the console as it is being run. Think of it as a tool to help you
remove a lot of boiler-plate code when you want to drop into your code and
examine state.

Contextual Debugging
---
Behold enables you to do contextual debugging. What this means is that you will
be able to control when you dump state in a module depending on the context of
how it is being used in another module. For large code bases such as Django
projects, where your state can be spread across multiple apps and even the
database, such context-aware debugging becomes quite valuable.

Perhaps this is best explained with an example. Let's say you have the
following module. You've defined it in its own module because it is used all
over your code base and you've factored it out to maximize your code reuse.
You have added a Behold state logger that will show the `value` attribute of the
metric, but only if processing in the `action='handler'` context.
```python
#####################################################################
# processor.py
#####################################################################

from behold import Behold
from my_complicated_module import do_something_hard

def process(metric):
# Adding this line to your cold will log the metric value to the console
# but only if in the proper context
#
# Note that we could also filter on metric properties and show multiple
# metric attributes like this.
# Behold().when(metric.entity='Boston').show(t=metric.time, v=metric.value)
#
Behold().when_in_context(action='handler').show(value=metric.value)

do_something_hard(metric)
```

In a completely different package of your code base you call your processor
on two different sets of metrics, and you want to be able to log those metrics
in the processor, but only when being processed in the `handler` context.

```python
#####################################################################
# master.py
#####################################################################
from processor import process
from behold import in_context

# Here we decorate a handler with a named context
@in_context(action='handler')
def handle_vendors():
metrics = get_vendor_metrics()
process(metrics)

def handle_customers():
metrics = get_customer_metrics()

# here we set the context using a with statement
with in_context(action='handler'):
process(metrics)
```

When you program is run, you would then see output like the following printed to
your console

```bash
value: 0.1
value: 0.2
value: 0.3
```