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 YOLOv3 in PyTorch > ONNX > CoreML > TFLite This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices 9.3k Jan 07, 2023 Instant Real-Time Example-Based Style Transfer to Facial Videos FaceBlit: Instant Real-Time Example-Based Style Transfer to Facial Videos The official implementation of FaceBlit: Instant Real-Time Example-Based Sty 131 Dec 19, 2022 Repository for open research on optimizers. Open Optimizers Repository for open research on optimizers. This is a test in sharing research/exploration as it happens. If you use anything from thi 6 Jun 24, 2022 Unofficial PyTorch implementation of SimCLR by Google Brain Unofficial PyTorch implementation of SimCLR by Google Brain 2 Oct 13, 2021 A PyTorch re-implementation of Neural Radiance Fields nerf-pytorch A PyTorch re-implementation Project | Video | Paper NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis Ben Mildenhall 709 Jan 09, 2023 网络协议2天集训 网络协议2天集训 抓包工具安装 Wireshark wireshark下载地址 Tcpdump CentOS yum install tcpdump -y Ubuntu apt-get install tcpdump -y k8s抓包测试环境 查看虚拟网卡veth pair 查看 120 Dec 12, 2022 This is the repo for Uncertainty Quantification 360 Toolkit. UQ360 The Uncertainty Quantification 360 (UQ360) toolkit is an open-source Python package that provides a diverse set of algorithms to quantify uncert 207 Dec 30, 2022 PyTorch implementation of popular datasets and models in remote sensing PyTorch Remote Sensing (torchrs) (WIP) PyTorch implementation of popular datasets and models in remote sensing tasks (Change Detection, Image Super Re 222 Dec 28, 2022 The first public PyTorch implementation of Attentive Recurrent Comparators arc-pytorch PyTorch implementation of Attentive Recurrent Comparators by Shyam et al. A blog explaining Attentive Recurrent Comparators Visualizing At 150 Oct 14, 2022 Source Code of NeurIPS21 paper: Recognizing Vector Graphics without Rasterization YOLaT-VectorGraphicsRecognition This repository is the official PyTorch implementation of our NeurIPS-2021 paper: Recognizing Vector Graphics without 49 Dec 20, 2022 GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophily GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophily Abstract Graph Neural Networks (GNNs) are widely used on a 10 Dec 20, 2022 🏃‍♀️ A curated list about human motion capture, analysis and synthesis. Awesome Human Motion 🏃‍♀️ A curated list about human motion capture, analysis and synthesis. Contents Introduction Human Models Datasets Data Process 274 Dec 14, 2022 An implementation of quantum convolutional neural network with MindQuantum. Huawei, classifying MNIST dataset 关于实现的一点说明 山东大学 2020级 苏博南 www.subonan.com 文件说明 tools.py 这里面主要有两个函数： resize(a, lenb) 这其实是我找同学写的一个小算法hhh。给出一个$28\times 28$的方阵a，返回一个$lenb\times lenb$的方阵。因 2 Aug 29, 2022 Demos of essentia classifiers hosted on replicate.ai essentia-replicate-demos Demos of Essentia models hosted on replicate.ai's MTG site. The models Check our site for a complete list of the models avail 12 Nov 14, 2022 A New Approach to Overgenerating and Scoring Abstractive Summaries We provide the source code for the paper "A New Approach to Overgenerating and Scoring Abstractive Summaries" accepted at NAACL'21. If you find the code useful, please cite the following paper. 4 Apr 03, 2022 Official PyTorch implementation of "Evolving Search Space for Neural Architecture Search" Evolving Search Space for Neural Architecture Search Usage Install all required dependencies in requirements.txt and replace all ..path/..to in the co 10 Oct 24, 2022 An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters CNN-Filter-DB An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters Paul Gavrikov, Janis Keuper Paper: htt 18 Dec 30, 2022 MDETR: Modulated Detection for End-to-End Multi-Modal Understanding MDETR: Modulated Detection for End-to-End Multi-Modal Understanding Website • Colab • Paper This repository contains code and links to pre-trained mod 770 Dec 28, 2022 This repository is an official implementation of the paper MOTR: End-to-End Multiple-Object Tracking with TRansformer. MOTR: End-to-End Multiple-Object Tracking with TRansformer This repository is an official implementation of the paper MOTR: End-to-End Multiple-Object 348 Jan 07, 2023 FocusFace: Multi-task Contrastive Learning for Masked Face Recognition FocusFace This is the official repository of "FocusFace: Multi-task Contrastive Learning for Masked Face Recognition" accepted at IEEE International C 21 Nov 17, 2022