memory-graph 0.1.19

Draw a graph of your data to see the structure of its references.

Graph your Memory

Want to draw a graph of your data in Python to better understand its structure or the Python memory model in general?

Just call memory_graph.show( your_data ), an example:

data = [ (1, 2), [3, 4], {5:'five', 6:'six'} ]

import memory_graph
memory_graph.show( data, block=True )

This shows the graph with the starting point of your 'data' drawn using thick lines, the program blocks until the ENTER key is pressed.

If show() doesn't work well on your system (the PDF viewer integration is platform specific) use render() to output the graph in the format of your choosing and open it yourself.

memory_graph.render( data, "my_graph.png", block=True )

Install

Install using pip:

pip install memory-graph

Additionally Graphviz needs to be installed.

Graph all Local Variables

Often it is useful to graph all the local variables using:

memory_graph.show( locals(), block=True )

So much so that function d() is available as alias for easier debugging. Additionally it logs all locals by printing them which allows for comparing them over time. For example:

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:

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 'watch' in a debugger tool and open the output file:

memory_graph.render( locals(), "my_debug_graph.pdf" )

Larger Example

This larger example shows objects that share a class (static) variable and also shows we can handle recursive references although the graph layout might suffer a bit.

my_list = [10, 20, 10]

class My_Class:
    my_class_var = 20 # class variable: shared by different objects
    
    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() )

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
• memory_graph.graphviz_nodes.node_attr : dict
  • allows to set various graphviz node attributes
• memory_graph.graphviz_nodes.edge_attr : dict
  • allows to set various graphviz edges attributes

See for color names: graphviz colors

To configure more about the visualization use:

digraph = memory_graph.create_graph( locals() )

and see the graphviz api 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 defined how to read various 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:

Custom Accessor Functions

For any type a custom accessor function can be introduced. For example Panda DataFrames and Series are not visualized correctly by default. This can be fixed by adding custom accessor functions:

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:

Troubleshooting

When edges overlap it can be hard to distinguish them. Using an interactive graphviz viewer, such as xdot, on a '*.gv' output file will help.

Author

Bas Terwijn

Inspiration

Inspired by PythonTutor.