Metadata-Version: 2.1
Name: memory_graph
Version: 0.1.25
Summary: Draws a graph of your data to analyze the structure of its references.
Home-page: https://github.com/bterwijn/memory_graph
Author: Bas Terwijn
Author-email: bterwijn@gmail.com
License: BSD 2-clause
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Education
Classifier: Topic :: Software Development :: Debuggers
Description-Content-Type: text/markdown
License-File: LICENSE.txt

## Installation ##
Install `memory_graph` using pip:
```
pip install memory-graph
```
Additionally [Graphviz](https://graphviz.org/download/) needs to be installed.


# Graph your Memory #
Does your Python code have a bug, is it behaving differently from what you expect? The problem could be a misunderstanding of the Python Data Model, and the first step to the solution could be drawing your data as a graph using `memory_graph.show( your_data )`, an example:
```python
import memory_graph

data = [ (1, 2), [3, 4], {5, 6}, {7:'seven', 8:'eight'} ]
memory_graph.show( data, block=True )
```
This shows a graph with the starting point of our 'data' drawn with thick lines, the program blocks until the ENTER key is pressed.

![image](https://raw.githubusercontent.com/bterwijn/memory_graph/main/images/example1.png)


Alternatively render the graph to an output file of our choosing using for example:
```python
memory_graph.render( data, "my_graph.png" )
```


## Python Data Model ##
The [Python Data Model](https://docs.python.org/3/reference/datamodel.html) makes a distiction between immutable and mutable types:

* **immutable**: bool, int, float, complex, str, tuple, bytes, frozenset
* **mutable**: list, dict, set, class, ... (all other types)


### immutable type ###
In the code below variable `a` and `b` both reference the same `int` value 10. An `int` is an immutable type and therefore when we change variable `a` its value can **not** be mutated in place, and thus a copy is made and `a` and `b` reference a different value afterwards.
```python
import memory_graph
memory_graph.rewrite_to_node.reduce_reference_children.remove("int") # shows references to 'int'

a = 10
b = a
memory_graph.render(locals(), 'immutable1.png')
a += 1
memory_graph.render(locals(), 'immutable2.png')
```
![image](https://raw.githubusercontent.com/bterwijn/memory_graph/main/images/immutable1.png)
![image](https://raw.githubusercontent.com/bterwijn/memory_graph/main/images/immutable2.png)


### mutable type ###
With mutable types the result is different. In the code below variable `a` and `b` both reference the same `list` value [4, 3, 2]. A `list` is a mutable type and therefore when we change variable `a` its value **can** be mutated in place and thus `a` and `b` both reference the same new value afterwards. The result is that changing `a` also changes `b` and vice versa. Sometimes we want this but other times we don't and then we will have to make a copy so that `b` is independent from `a`.
```python
import memory_graph

a = [4, 3, 2]
b = a
memory_graph.render(locals(), 'mutable1.png')
a.append(1)
memory_graph.render(locals(), 'mutable2.png')
```
![image](https://raw.githubusercontent.com/bterwijn/memory_graph/main/images/mutable1.png)
![image](https://raw.githubusercontent.com/bterwijn/memory_graph/main/images/mutable2.png)

Python makes this distiction between mutable and immutable types because a value of a mutable type generally could be large and therefore it would be slow to make a copy each time we change it. On the other hand, a value of a changable immutable type generally is small and therefore fast to copy.


### copying ###
Python offers three different "copy" options that we will demonstrate using a nested list:

```python
import memory_graph
import copy

a = [ [1, 2], ['a', 'b'] ] # a nested list (a list containing other lists)

# three different ways to make a "copy" of 'a':
c1 = a
c2 = copy.copy(a) # equivalent to:   a.copy() a[:] list(a)
c3 = copy.deepcopy(a)

memory_graph.render(locals(), 'copies.png')
```

* `c1` is an *assignment*, all the data is shared.
* `c2` is a *shallow copy*, only the data referenced by the first reference is copied and the underlying data is shared
* `c3` is a *deep copy*, all the data is copied

![image](https://raw.githubusercontent.com/bterwijn/memory_graph/main/images/copies.png)


### custom copy method ###
We can write our own custom copy function or method in case the three "copy" options don't do what we want. For example the copy() method of My_Class in the code below copies the `numbers` but shares the `letters` between the two objects.
```python
import memory_graph
import copy

class My_Class:

    def __init__(self):
        self.numbers = [1, 2]
        self.letters = ['a', 'b']

    def copy(self): # custom copy method copies the numbers but shares the letters
        c = copy.copy(self)
        c.numbers = copy.copy(self.numbers)
        return c

a = My_Class()
b = a.copy()

memory_graph.render(locals(), 'copy_method.png')
```
![image](https://raw.githubusercontent.com/bterwijn/memory_graph/main/images/copy_method.png)


## Graph all Local Variables ##
Often it is useful to graph all the local variables using:
```python
memory_graph.show( locals(), block=True )
```

So much so that function `d()` is available as alias for this for easier debugging. Additionally it logs all locals by printing them which helps comparing them over time. For example:
```python
from memory_graph import d

my_squares = []
my_squares_ref = my_squares
for i in range(5):
    my_squares.append(i**2)
    d()                                    # 'd' for debug, logs and graphs all local variables and blocks
my_squares_copy = my_squares.copy()
d(block=False)                             # debug without blocking
d(log=False,block=False)                   # debug without logging and blocking

import memory_graph
memory_graph.log_file=open("log.txt","w")  # now log to file instead of screen (sys.stdout)
d(graph=False)                             # debug without showing the graph
```

Which in the end results in:

![image](https://raw.githubusercontent.com/bterwijn/memory_graph/main/images/example2.png)
```
my_squares: [0, 1, 4, 9, 16]
my_squares_ref: [0, 1, 4, 9, 16]
i: 4
my_squares_copy: [0, 1, 4, 9, 16]
```

Notice that in the graph it is clear that `my_squares` and `my_squares_ref` share their data while `my_squares_copy` has its own copy. This can not be observed in the log and shows the benefit of the graph.

Alternatively debug by setting this expression as a 'watch' in a debugger tool and open the output file:
```
memory_graph.render( locals(), "my_debug_graph.pdf" )
```


## Larger Example ##
This larger example shows a (static) class variable and recursive references.
```python
my_list = [10, 20, 10]

class My_Class:
    my_class_var = 20 # class variable
    
    def __init__(self):
        self.var1 = "foo"
        self.var2 = "bar"
        self.var3 = 20

obj1 = My_Class()
obj2 = My_Class()

data=[my_list, my_list, obj1, obj2]

my_list.append(data) # recursive reference

import memory_graph
memory_graph.show( locals() )
```
![image](https://raw.githubusercontent.com/bterwijn/memory_graph/main/images/example3.png)


## Call Stack ##

Function ```memory_graph.get_call_stack()``` returns the full call stack that holds for each called function all the local variables. This enables us to visualize the local variables of different called functions simultaneously. This helps to visualize if different called functions share the same data or not. Here we call function ```add_one()``` with arguments ```a, b, c``` and add one to them.

```python
import memory_graph

def add_one(a, b, c):
    a += 1
    b.append(1)
    c.append(1)
    memory_graph.show(memory_graph.get_call_stack())

a = 1
b = [4, 3, 2]
c = [4, 3, 2]

add_one(a, b, c.copy())
print(f"a:{a} b:{b} c:{c}")
```
![image](https://raw.githubusercontent.com/bterwijn/memory_graph/main/images/add_one.png)

The visualization shows only ```b``` is shared so only ```b``` is changed in the calling stack frame as reflected in the printed output:
```
a:1 b:[4, 3, 2, 1] c:[4, 3, 2]
```
### recursion ###
The call stack also helps to visualize how recursion works. Here we show each step of how recursively ```factorial(3)``` is computed:

```python
import memory_graph

def factorial(n):
    if n==0:
        return 1
    memory_graph.show( memory_graph.get_call_stack(), block=True )
    result = n*factorial(n-1)
    memory_graph.show( memory_graph.get_call_stack(), block=True )
    return result

factorial(3)
```
  <div><img src="https://raw.githubusercontent.com/bterwijn/memory_graph/main/images/factorial1.png" /></div>
  <div><img src="https://raw.githubusercontent.com/bterwijn/memory_graph/main/images/factorial2.png" /></div>
  <div><img src="https://raw.githubusercontent.com/bterwijn/memory_graph/main/images/factorial3.png" /></div>
  <div><img src="https://raw.githubusercontent.com/bterwijn/memory_graph/main/images/factorial4.png" /></div>
  <div><img src="https://raw.githubusercontent.com/bterwijn/memory_graph/main/images/factorial5.png" /></div>
  <div><img src="https://raw.githubusercontent.com/bterwijn/memory_graph/main/images/factorial6.png" /></div>
and the final result is: 3 x 2 x 1 = 6

## Config ##
Different aspects of memory_graph can be configured.

### Config Visualization, graphviz_nodes ###
Configure how the nodes of the graph are visualized with:

- ***memory_graph.graphviz_nodes.linear_layout_vertical*** : bool
  - if False, linear node layout is horizontal
- ***memory_graph.graphviz_nodes.linear_any_ref_layout_vertical*** : bool
  - if False, linear node layout is horizontal if any of its elements is a refence
- ***memory_graph.graphviz_nodes.linear_all_ref_layout_vertical*** : bool
  - if False, linear node layout is horizontal if all elements are reference
- ***memory_graph.graphviz_nodes.key_value_layout_vertical*** : bool
  - if False, key_value node layout is horizontal
- ***memory_graph.graphviz_nodes.key_value_any_ref_layout_vertical*** : bool
  - if False, key_value node layout is horizontal if any of its elements is a refence
- ***memory_graph.graphviz_nodes.key_value_all_ref_layout_vertical*** : bool
  - if False, key_value node layout is horizontal if all elements are reference
- ***memory_graph.graphviz_nodes.padding*** : int
  - the padding in nodes
- ***memory_graph.graphviz_nodes.padding*** : int
  - the spacing in nodes
- ***memory_graph.graphviz_nodes.join_references_count*** : int
  - minimum number of reference we join together
- ***memory_graph.graphviz_nodes.join_circle_size*** : string
  - size of the join circle
- ***memory_graph.graphviz_nodes.join_circle_minlen*** : string
  - extra space for references above a join circle
- ***memory_graph.graphviz_nodes.max_string_length*** : int
  - maximum string length where the string is cut off
- ***memory_graph.graphviz_nodes.category_to_color_map*** : dict
  - mapping van type/caterogries to node colors
- ***memory_graph.graphviz_nodes.uncategorized_color*** : dict
  - color for unkown types/categories
- ***memory_graph.graphviz_nodes.graph_attr*** : dict
  - allows to set various [graphviz graph attributes](https://graphviz.org/docs/graph/)
- ***memory_graph.graphviz_nodes.node_attr*** : dict
  - allows to set various [graphviz node attributes](https://graphviz.org/docs/nodes/)
- ***memory_graph.graphviz_nodes.edge_attr*** : dict
  - allows to set various [graphviz edges attributes](https://graphviz.org/docs/edges/)

See for color names: [graphviz colors](https://graphviz.org/doc/info/colors.html)

To configure more about the visualization use:
```
digraph = memory_graph.create_graph( locals() )
```
and see the [graphviz api](https://graphviz.readthedocs.io/en/stable/api.html) to render it in many different ways.

### Config Graph Structure, rewrite_to_node ###

Configure the structure of the nodes in the graph with:

- ***memory_graph.rewrite_to_node.reduce_reference_parents*** : set
  - the node types/categories for which we remove the reference to children
- ***memory_graph.rewrite_to_node.reduce_reference_children*** : bool
  - the node types/categories for which we remove the reference from parents
  
### Config Node Creation, rewrite ###

Configure what nodes are created based on reading the given data structure:

- ***memory_graph.rewrite.ignore_types*** : dict
  - all types that we ignore, these will not be in the graph
- ***memory_graph.rewrite.singular_types*** : set
  - all types rewritten to node as singular values (bool, int, float, ...)
- ***memory_graph.rewrite.linear_types*** : set
  - all types rewritten to node as linear values (tuple, list, set, ...)
- ***memory_graph.rewrite.dict_types*** : set
  - all types rewritten to node as dictionary values (dict, mappingproxy)
- ***memory_graph.rewrite.dict_ignore_dunder_keys*** : bool
  - determines if we ignore dunder keys ('`__example`') in dict_types
- ***memory_graph.rewrite.custom_accessor_functions*** : dict
  - custom accessor functions to define how to read various data types


### Config Examples ###
With configuration:
```
memory_graph.graphviz_nodes.linear_layout_vertical = False           # draw lists,tuples,sets,... horizontally
memory_graph.graphviz_nodes.category_to_color_map['list'] = 'yellow' # change color of 'list' type
memory_graph.graphviz_nodes.spacing=15                               # more spacing in each node
memory_graph.graphviz_nodes.graph_attr['ranksep']='1.2'              # more vertical separation
memory_graph.graphviz_nodes.graph_attr['nodesep']='1.2'              # more horizontal separation
memory_graph.rewrite_to_node.reduce_reference_children.remove("int") # draw references to 'int' type
```

the last example looks like:

![image](https://raw.githubusercontent.com/bterwijn/memory_graph/main/images/example4.png)


### Custom Accessor Functions ###
For any type a custom accessor function can be introduced. For example Pandas DataFrames and Series are not visualized correctly by default. This can be fixed by adding custom accessor functions:
```python
import pandas as pd

data = {'Name':['Tom', 'Anna', 'Steve', 'Lisa'],
        'Age':[28,34,29,42],
        'Length':[1.70,1.66,1.82,1.73] }
df = pd.DataFrame(data)

import memory_graph
memory_graph.rewrite.custom_accessor_functions[pd.DataFrame] = lambda d: list(d.items())
memory_graph.rewrite.custom_accessor_functions[pd.Series] = lambda d: list(d.items())
memory_graph.rewrite_to_node.reduce_reference_parents.add("DataFrame")
memory_graph.rewrite_to_node.reduce_reference_parents.add("Series")
memory_graph.graphviz_nodes.category_to_color_map['Series'] = 'lightskyblue'
memory_graph.show( locals() )
```

which results in:

![image](https://raw.githubusercontent.com/bterwijn/memory_graph/main/images/example5.png)


## Troubleshooting ##
* When graph edges overlap it can be hard to distinguish them. Using an interactive graphviz viewer, such as [xdot](https://github.com/jrfonseca/xdot.py), on a '*.gv' output file will help.


## Author ##
Bas Terwijn


## Inspiration ##
Inspired by [PythonTutor](https://pythontutor.com/).


