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 This repository contains the official implementation code of the paper Transformer-based Feature Reconstruction Network for Robust Multimodal Sentiment Analysis This repository contains the official implementation code of the paper Transformer-based Feature Reconstruction Network for Robust Multimodal Sentiment Analysis, accepted at ACMMM 2021. 10 Sep 30, 2022 Reference implementation of code generation projects from Facebook AI Research. General toolkit to apply machine learning to code, from dataset creation to model training and evaluation. Comes with pretrained models. This repository is a toolkit to do machine learning for programming languages. It implements tokenization, dataset preprocessing, model training and m 408 Jan 01, 2023 Face Recognition & AI Based Smart Attendance Monitoring System. In today’s generation, authentication is one of the biggest problems in our society. So, one of the most known techniques used for authentication is h 1 Jan 14, 2022 Implementation of various Vision Transformers I found interesting Implementation of various Vision Transformers I found interesting 78 Dec 06, 2022 This is a clean and robust Pytorch implementation of DQN and Double DQN. DQN/DDQN-Pytorch This is a clean and robust Pytorch implementation of DQN and Double DQN. Here is the training curve: All the experiments are trained 15 Dec 27, 2022 Learning To Have An Ear For Face Super-Resolution Learning To Have An Ear For Face Super-Resolution [Project Page] This repository contains demo code of our CVPR2020 paper. Training and evaluation on 50 Nov 16, 2022 yolov5 deepsort 行人 车辆 跟踪 检测 计数 yolov5 deepsort 行人 车辆 跟踪 检测 计数 实现了 出/入 分别计数。 默认是 南/北 方向检测，若要检测不同位置和方向，可在 main.py 文件第13行和21行，修改2个polygon的点。 默认检测类别：行人、自行车、小汽车、摩托车、公交车、卡车。 检测类别可在 detect 554 Dec 30, 2022 GAN JAX - A toy project to generate images from GANs with JAX GAN JAX - A toy project to generate images from GANs with JAX This project aims to bring the power of JAX, a Python framework developped by Google and 14 Nov 29, 2022 Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai Coursera-deep-learning-specialization - Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks an 1.7k Jan 08, 2023 Release of SPLASH: Dataset for semantic parse correction with natural language feedback in the context of text-to-SQL parsing SPLASH: Semantic Parsing with Language Assistance from Humans SPLASH is dataset for the task of semantic parse correction with natural language feedba 35 Oct 31, 2022 Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning Human-Level Control through Deep Reinforcement Learning Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning. This imp 2.4k Dec 26, 2022 Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data Real-ESRGAN Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data Ported from https://github.com/xinntao/Real-ESRGAN Depend 44 Dec 27, 2022 Dynamic Attentive Graph Learning for Image Restoration, ICCV2021 [PyTorch Code] Dynamic Attentive Graph Learning for Image Restoration This repository is for GATIR introduced in the following paper: Chong Mou, Jian Zhang, Zhuoyuan 84 Dec 09, 2022 This is the official repository of the paper Stocastic bandits with groups of similar arms (NeurIPS 2021). It contains the code that was used to compute the figures and experiments of the paper. Experiments How to reproduce experimental results of Stochastic bandits with groups of similar arms submitted paper ? Section 5 of the paper To reprod 0 Oct 25, 2021 Collapse by Conditioning: Training Class-conditional GANs with Limited Data Collapse by Conditioning: Training Class-conditional GANs with Limited Data Moha 33 Dec 06, 2022 D-NeRF: Neural Radiance Fields for Dynamic Scenes D-NeRF: Neural Radiance Fields for Dynamic Scenes [Project] [Paper] D-NeRF is a method for synthesizing novel views, at an arbitrary point in time, of 291 Jan 02, 2023 Implementation of the paper ''Implicit Feature Refinement for Instance Segmentation''. Implicit Feature Refinement for Instance Segmentation This repository is an official implementation of the ACM Multimedia 2021 paper Implicit Feature 17 Dec 28, 2022 You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling Transformer-based models are widely used in natural language processi 12 Jan 01, 2023 Implementation of SSMF: Shifting Seasonal Matrix Factorization SSMF Implementation of SSMF: Shifting Seasonal Matrix Factorization, Koki Kawabata, Siddharth Bhatia, Rui Liu, Mohit Wadhwa, Bryan Hooi. NeurIPS, 2021 9 Jun 10, 2022 Code for "Unsupervised State Representation Learning in Atari" Unsupervised State Representation Learning in Atari Ankesh Anand*, Evan Racah*, Sherjil Ozair*, Yoshua Bengio, Marc-Alexandre Côté, R Devon Hjelm This 217 Jan 03, 2023