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 FCOS: Fully Convolutional One-Stage Object Detection (ICCV'19) FCOS: Fully Convolutional One-Stage Object Detection This project hosts the code for implementing the FCOS algorithm for object detection, as presente 3.1k Jan 05, 2023 GenshinMapAutoMarkTools - Tools To add/delete/refresh resources mark in Genshin Impact Map 使用说明 适配 windows7以上 64位 原神1920x1080窗口(其他分辨率后续适配) 待更新渊下宫 English version is to be 209 Dec 28, 2022 Reviatalizing Optimization for 3D Human Pose and Shape Estimation: A Sparse Constrained Formulation Reviatalizing Optimization for 3D Human Pose and Shape Estimation: A Sparse Constrained Formulation This is the implementation of the approach describ 47 Nov 15, 2022 A curated list of resources for Image and Video Deblurring A curated list of resources for Image and Video Deblurring 1.7k Jan 01, 2023 Implementation of MA-Trace - a general-purpose multi-agent RL algorithm for cooperative environments. Off-Policy Correction For Multi-Agent Reinforcement Learning This repository is the official implementation of Off-Policy Correction For Multi-Agent R 4 Aug 18, 2022 Geometric Sensitivity Decomposition Geometric Sensitivity Decomposition This repo is the official implementation of A Geometric Perspective towards Neural Calibration via Sensitivity Dec 16 Dec 26, 2022 PyTorch implementations for our SIGGRAPH 2021 paper: Editable Free-viewpoint Video Using a Layered Neural Representation. st-nerf We provide PyTorch implementations for our paper: Editable Free-viewpoint Video Using a Layered Neural Representation SIGGRAPH 2021 Jiakai Zha 258 Jan 02, 2023 Tensorforce: a TensorFlow library for applied reinforcement learning Tensorforce: a TensorFlow library for applied reinforcement learning Introduction Tensorforce is an open-source deep reinforcement learning framework, 3.2k Jan 02, 2023 Yoga - Yoga asana classifier for python Yoga Asana Classifier Description Hi welcome to my new deep learning project "Yo 35 Dec 12, 2022 As a part of the HAKE project, includes the reproduced SOTA models and the corresponding HAKE-enhanced versions (CVPR2020). HAKE-Action HAKE-Action (TensorFlow) is a project to open the SOTA action understanding studies based on our Human Activity Knowledge Engine. It inclu 94 Nov 18, 2022 An elaborate and exhaustive paper list for Named Entity Recognition (NER) Named-Entity-Recognition-NER-Papers by Pengfei Liu, Jinlan Fu and other contributors. An elaborate and exhaustive paper list for Named Entity Recognit 388 Dec 18, 2022 DECA: Detailed Expression Capture and Animation (SIGGRAPH 2021) DECA: Detailed Expression Capture and Animation (SIGGRAPH2021) input image, aligned reconstruction, animation with various poses & expressions This is 1.5k Jan 02, 2023 The official PyTorch implementation for the paper "sMGC: A Complex-Valued Graph Convolutional Network via Magnetic Laplacian for Directed Graphs". Magnetic Graph Convolutional Networks About The official PyTorch implementation for the paper sMGC: A Complex-Valued Graph Convolutional Network via M 3 Feb 25, 2022 INSPIRED: A Transparent Dialogue Dataset for Interactive Semantic Parsing INSPIRED: A Transparent Dialogue Dataset for Interactive Semantic Parsing Existing studies on semantic parsing focus primarily on mapping a natural-la 7 Aug 22, 2022 Official PyTorch implementation of the paper "Deep Constrained Least Squares for Blind Image Super-Resolution", CVPR 2022. Deep Constrained Least Squares for Blind Image Super-Resolution [Paper] This is the official implementation of 'Deep Constrained Least Squares for Bli 141 Dec 30, 2022 Combinatorial model of ligand-receptor binding Combinatorial model of ligand-receptor binding The binding of ligands to receptors is the starting point for many import signal pathways within a cell 0 Jan 09, 2022 [CVPR 2022 Oral] Rethinking Minimal Sufficient Representation in Contrastive Learning Rethinking Minimal Sufficient Representation in Contrastive Learning PyTorch implementation of Rethinking Minimal Sufficient Representation in Contras 36 Nov 23, 2022 Self-Supervised Contrastive Learning of Music Spectrograms Self-Supervised Music Analysis Self-Supervised Contrastive Learning of Music Spectrograms Dataset Songs on the Billboard Year End Hot 100 were collect 27 Dec 10, 2022 Official PyTorch implementation of "The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person Pose Estimation" (ICCV 21). CenterGroup This the official implementation of our ICCV 2021 paper The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person P 43 Dec 25, 2022 imbalanced-DL: Deep Imbalanced Learning in Python imbalanced-DL: Deep Imbalanced Learning in Python Overview imbalanced-DL (imported as imbalanceddl) is a Python package designed to make deep imbalanc 19 Dec 28, 2022