Hi, thanks for this great python package.

I noticed the follow error when I do this in jupyter on python-3.6.2

import lolviz
lolviz.objviz('1234567890')

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-4-7a8f0cba785c> in <module>()
      1 import lolviz
----> 2 lolviz.objviz('1234567890')

/usr/lib/python3.6/site-packages/lolviz.py in objviz(o, orientation)
    217 """ % orientation
    218     reachable = closure(o)
--> 219     s += obj_nodes(reachable)
    220     s += obj_edges(reachable)
    221     s += "}\n"

/usr/lib/python3.6/site-packages/lolviz.py in obj_nodes(nodes)
    229     # currently only making subgraph cluster for linked lists
    230     # otherwise it squishes trees.
--> 231     max_edges_for_type,subgraphs = connected_subgraphs(nodes)
    232     c = 1
    233     for g in subgraphs:

/usr/lib/python3.6/site-packages/lolviz.py in connected_subgraphs(reachable, varnames)
    785     of sets containing the id()s of all nodes in a specific subgraph
    786     """
--> 787     max_edges_for_type = max_edges_in_connected_subgraphs(reachable, varnames)
    788 
    789     reachable = closure(reachable, varnames)

/usr/lib/python3.6/site-packages/lolviz.py in max_edges_in_connected_subgraphs(reachable, varnames)
    839     """
    840     max_edges_for_type = defaultdict(int)
--> 841     reachable = closure(reachable, varnames)
    842     reachable = [p for p in reachable if isplainobj(p)]
    843     for p in reachable:

/usr/lib/python3.6/site-packages/lolviz.py in closure(p, varnames)
    706     from but don't include frame objects.
    707     """
--> 708     return closure_(p, varnames, set())
    709 
    710 

/usr/lib/python3.6/site-packages/lolviz.py in closure_(p, varnames, visited)
    710 
    711 def closure_(p, varnames, visited):
--> 712     if p is None or isatom(p):
    713         return []
    714     if id(p) in visited:

/usr/lib/python3.6/site-packages/lolviz.py in isatom(p)
    691 
    692 
--> 693 def isatom(p): return type(p) == int or type(p) == float or type(p) == str or type(p) == unicode
    694 
    695 

NameError: name 'unicode' is not defined

I find table in lolviz can not show values if cell in one row is a list, as:

T = [
    ['11','12','13','14',['a','b','c'],'16']
]
objviz(T)

Only the a, b, c are shown and 11, 12, 13, 14, and 16 are not shown, is this configurable? Thanks!

Contd. from https://github.com/parrt/lolviz/issues/11#issuecomment-326474125

Traceback (most recent call last):

  File "/Users/srid/code/ipython/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2862, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)

  File "<ipython-input-1-ae470ca34f62>", line 1, in <module>
    from lolviz import *

  File "/Users/srid/code/ipython/lib/python3.6/site-packages/lolviz.py", line 442
    print "hashcode =", hashcode(key)
                     ^
SyntaxError: invalid syntax

This package is currently registered as Python 2.7 only in PyPI. This is what happens if you try to install it on Python 3.6:

(venv) $ pip install lolviz
Collecting lolviz
  Using cached lolviz-1.2.tar.gz
lolviz requires Python '<3' but the running Python is 3.6.0

I think it's just a matter of adding the Python 3 classifier in setup.py, because as far as I can see the code is fine for Python 3.

from lolviz import *

dict_tuple_values = dictviz({'a': (1, 2)})
dict_tuple_values.render(view=True, cleanup=True)

Is rendered as:

This only happens with 2-tuples. Any other tuple length is rendered as expected.

This is the offending line: https://github.com/parrt/lolviz/blob/a6fc29b008a16993738416e793de71c3bff4175d/lolviz.py#L159

What is the significance of ... and len(el) == 2 ?

Hi, I was looking for a package to visualize Tree Search Algorithm Recently and I found this repository and really liked it. But for tree visualization, the treeviz function can only support binary tree with child name left and right.

So I made some modification to support multi children and specifying child name. I add 2 new parameters for treeviz(), childfields and show_all_children. The variable name in childfields will be recognized as child node. And if show_all_children=False, it will only visualize the child names exist, else it will show all the names in childfieds.

I know you may be busy and this repository hasn't been updated for a long time. You can check these modification anytime free. I am so glad to receive any suggestions from you.

Hello @parrt! I used your lolviz project a few years ago, and I rediscovered it today. It's awesome!

Could we add a "super" display function that chooses the best one based on the datatype?

When reading the documentation, it shows like 8 different functions, and I don't want to spend my time thinking about the name of one or another function for one or another datatype. What is described in this documentation is almost trivially translated to Python code.

modes = [ "str", "matrix", "call", "calls", "obj", "tree", "lol", "list" ]

def unified_lolviz(obj, mode=None):
    """ Unified function to display `obj` with lolviz, in Jupyter notebook only."""
    if mode == "str" or isinstance(obj):
        return strviz(obj)
    if mode == "matrix" or "<class 'numpy.ndarray'>" == str(type(obj)):
        # can't use isinstance(obj, np.ndarray) without import numpy!
        return matrixviz(obj)
    if mode == "call": return callviz()
    if mode == "calls": return callviz()
    if mode == "lol" or isinstance(obj, list) and obj and isinstance(obj[0], list):
        # obj is a list, is non empty, and obj[0] is a list!
        return lolviz(obj)
    if mode == "list" or isinstance(obj, list):
        return listviz(obj)
    return objviz(obj)  # default

So I'm opening this ticket: if you think this could be added to the library, can we discuss it here, and then I can take care of writing it, testing it, sending a pull-request, and you can merge, and then update on Pypi! What do you think?

Regards from France, @Naereen

This isn't a bug, but a question for help / hints.

I would like to use lolviz to create a graph of my python class structure. Every class-attribute is type annotated, so it should be possible to like their interactions without creating class instances. Here is a minimal example:

from copy import deepcopy
from lolviz import *
class Workout:
    def __init__(
        self,
        date: dt.date,
        name: str = "",
        duration: int = 0
    ):
        # assert isinstance(tss, (np.number, int))
        self.date: dt.date = date
        self.name: str = name
        self.duration: int = duration  # in seconds
        self.done: boolean = False

class Athlete:
    def __init__(
        self,
        name: str,
        birthday: dt.date,
        sports: List[str]
    ):
        self.name: str = name
        self.birthday: dt.date = birthday
        self.sports: List[str] = sports


class DataContainer:
    def __init__(self, 
                 athlete: Athlete, 
                 tasks: List[Workout] = [], 
                 fulfilled: List[Workout] = []):
        self.athelete: Athlete = athlete
        self.tasks: List[Workout] = [w for w in tasks if isinstance(w, Workout)]
        self.fulfilled: List[Workout] = [w for w in fulfilled if isinstance(w, Workout)]

me = Athlete("nico", dt(1990,3,1).date(), sports=["running","climbing"])
t1 = Workout(date=dt(2020,1,1).date(), name="5k Run")
f1 = deepcopy(t1)
f1.done = True
dc1 = DataContainer(me, tasks=[t1], fulfilled=[f1])

Now I can use objviz(dc1) to create the following diagram:

What I actually would like to achieve is a command like classviz(DataContainer) which will give me a similar chart, but not with the actual attribute values but their types. For sure there will be other small changes, but that's the basic idea.

What I already can do is something like:

def get_types(annotated_class):
    return (annotated_class.__name__, {k: v.__name__ for k,v in annotated_class.__init__.__annotations__.items()})

get_types(Workout)

which gives me something like: ('Workout', {'date': 'date', 'name': 'str', 'duration': 'int'}). How ever I don't find a proper way to create similar table-elements which contain Workout in the header and the name-attribute mapping in it's body.

Can someone give me a hint, how to create such tables manually? I am also happy for any additional advices

from lolviz import objviz a={"hello":"<"} objviz(a).render() Error: Source.gv: syntax error in line 16 scanning a HTML string (missing '>'? bad nesting? longer than 16384?) String starting:<

opened by ami-navon 1 Releases(1.4) 1.4(Jul 11, 2018) Added numpy 1D, 2D array support Source code(tar.gz) Source code(zip) 1.3.3(Jul 4, 2018) Fixed bug that prevented dictionaries from being displayed as such. Source code(tar.gz) Source code(zip) 1.3.1(Sep 19, 2017) Source code(tar.gz) Source code(zip) 1.3(Sep 13, 2017) Source code(tar.gz) Source code(zip) 1.2.2(Sep 1, 2017) Source code(tar.gz) Source code(zip) 1.2.1(Sep 1, 2017) Source code(tar.gz) Source code(zip) 1.2(Aug 29, 2017) Source code(tar.gz) Source code(zip) 1.1.1(Aug 28, 2017) Source code(tar.gz) Source code(zip) 1.1(Aug 27, 2017) Source code(tar.gz) Source code(zip) 1.0.2(Aug 26, 2017) Source code(tar.gz) Source code(zip) 1.0.1(Aug 26, 2017) Source code(tar.gz) Source code(zip) 1.0(Aug 26, 2017) Source code(tar.gz) Source code(zip) Owner Terence Parr Creator of the ANTLR parser generator. Professor at Univ of San Francisco, computer science and data science. Working mostly on machine learning stuff now. GitHub Repository 3D Generative Adversarial Network Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling This repository contains pre-trained models and sampling 791 Dec 20, 2022 Extracting knowledge graphs from language models as a diagnostic benchmark of model performance. Interpreting Language Models Through Knowledge Graph Extraction Idea: How do we interpret what a language model learns at various stages of training? 9 Oct 25, 2022 Node-level Graph Regression with Deep Gaussian Process Models Node-level Graph Regression with Deep Gaussian Process Models Prerequests our implementation is mainly based on tensorflow 1.x and gpflow 1.x: python 1 Jan 16, 2022 Recognize numbers from an (28 x 28) image using neural networks Number recognition Recognize numbers from a 28 x 28 image using neural networks Usage This is an example of a simple usage of number-recognition NOTE: 2 Dec 29, 2021 Invariant Causal Prediction for Block MDPs MISA Abstract Generalization across environments is critical to the successful application of reinforcement learning algorithms to real-world challeng 41 Sep 17, 2022 CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation CoTr: Efficient 3D Medical Image Segmentation by bridging CNN and Transformer This is the official pytorch implementation of the CoTr: Paper: CoTr: Ef 218 Dec 25, 2022 A new data augmentation method for extreme lighting conditions. Random Shadows and Highlights This repo has the source code for the paper: Random Shadows and Highlights: A new data augmentation method for extreme l 35 Nov 26, 2022 PyTorch implementation of CloudWalk's recent work DenseBody densebody_pytorch PyTorch implementation of CloudWalk's recent paper DenseBody. Note: For most recent updates, please check out the dev branch. Update 401 Nov 19, 2022 Implement some metaheuristics and cost functions Metaheuristics This repot implement some metaheuristics and cost functions. Metaheuristics JAYA Implement Jaya optimizer without constraints. Cost fun 1 Mar 23, 2022 A nutritional label for food for thought. Lexiscore As a first effort in tackling the theme of information overload in content consumption, I've been working on the lexiscore: a nutritional la 34 Nov 08, 2022 Industrial Image Anomaly Localization Based on Gaussian Clustering of Pre-trained Feature Industrial Image Anomaly Localization Based on Gaussian Clustering of Pre-trained Feature Q. Wan, L. Gao, X. Li and L. Wen, "Industrial Image Anomaly 6 Dec 25, 2022 Official PyTorch implementation of StyleGAN3 Modified StyleGAN3 Repo Changes Made tied to python 3.7 syntax .jpgs instead of .pngs for training sample seeds to recreate the 1024 training grid wit 83 Dec 15, 2022 This repository will be a summary and outlook on all our open, medical, AI advancements. medical by LAION This repository will be a summary and outlook on all our open, medical, AI advancements. See the medical-general channel in the medic 18 Dec 30, 2022 Code for reproducible experiments presented in KSD Aggregated Goodness-of-fit Test. Code for KSDAgg: a KSD aggregated goodness-of-fit test This GitHub repository contains the code for the reproducible experiments presented in our pape 5 Dec 15, 2022 Lowest memory consumption and second shortest runtime in NTIRE 2022 challenge on Efficient Super-Resolution FMEN Lowest memory consumption and second shortest runtime in NTIRE 2022 on Efficient Super-Resolution. Our paper: Fast and Memory-Efficient Network T 33 Dec 01, 2022 DirectVoxGO reconstructs a scene representation from a set of calibrated images capturing the scene. DirectVoxGO reconstructs a scene representation from a set of calibrated images capturing the scene. We achieve NeRF-comparable novel-view synthesis quality with super-fast convergence. 709 Dec 31, 2022 Software Platform for solving and manipulating multiparametric programs in Python PPOPT Python Parametric OPtimization Toolbox (PPOPT) is a software platform for solving and manipulating multiparametric programs in Python. This pack 10 Sep 13, 2022 Code for classifying international patents based on the text of their titles/abstracts Patent Classification Goal: To train a machine learning classifier that can automatically classify international patents downloaded from the WIPO webs 1 Nov 08, 2022 Neural Caption Generator with Attention Neural Caption Generator with Attention Tensorflow implementation of "Show 510 Nov 30, 2022 RETRO-pytorch - Implementation of RETRO, Deepmind's Retrieval based Attention net, in Pytorch RETRO - Pytorch (wip) Implementation of RETRO, Deepmind's Retrieval based Attent 556 Jan 04, 2023