how to create a dot file in python?

You might consider pygraphviz.

>>>
import pygraphviz as pgv
   >>>
   G = pgv.AGraph() >>>
   G.add_node('a') >>>
   G.add_edge('b', 'c') >>>
   G
strict graph {
   a;
   b--c;
}

Example:

>>> from gvgen
import GvGen
   >>>
   g = GvGen() >>>
   a = g.newItem('a') >>>
   b = g.newItem('b') >>>
   c = g.newItem('c') >>>
   _ = g.newLink(b, c) >>>
   g.dot()
/* Generated by GvGen v.1.0 (https://www.github.com/stricaud/gvgen) */

digraph G {
   compound = true;
   node1[label = "a"];
   node2[label = "b"];
   node3[label = "c"];
   node2 - > node3;
}

Suggestion : 2

Feb 15, 2021 , Released: Feb 15, 2021 , Uploaded Feb 15, 2021 py2 py3 , Uploaded Feb 15, 2021 source

Use this method if you already have a DOT-file describing a graph, for example as output of another program. Let's say you already have this example.dot (based on an example from Wikipedia):

graph my_graph {
   bgcolor = "yellow";
   a[label = "Foo"];
   b[shape = circle];
   a--b--c[color = blue];
}

Just read the graph from the DOT-file:

import pydot

graphs = pydot.graph_from_dot_file('example.dot')
graph = graphs[0]

Use this method if you already have a DOT-string describing a graph in a Python variable:

import pydot

dot_string = ""
"graph my_graph {
bgcolor = "yellow";
a[label = "Foo"];
b[shape = circle];
a--b--c[color = blue];
}
""
"

graphs = pydot.graph_from_dot_data(dot_string)
graph = graphs[0]

Now this is where the cool stuff starts. Use this method if you want to build new graphs from Python.

import pydot

graph = pydot.Dot('my_graph', graph_type = 'graph', bgcolor = 'yellow')

# Add nodes
my_node = pydot.Node('a', label = 'Foo')
graph.add_node(my_node)
# Or, without using an intermediate variable:
   graph.add_node(pydot.Node('b', shape = 'circle'))

# Add edges
my_edge = pydot.Edge('a', 'b', color = 'blue')
graph.add_edge(my_edge)
# Or, without using an intermediate variable:
   graph.add_edge(pydot.Edge('b', 'c', color = 'blue'))

NetworkX has conversion methods for pydot graphs:

import networkx
import pydot

# See NetworkX documentation on how to build a NetworkX graph.

graph = networkx.drawing.nx_pydot.to_pydot(my_networkx_graph)

Add further nodes and edges:

graph.add_edge(pydot.Edge('b', 'd', style = 'dotted'))

Edit attributes of graph, nodes and edges:

graph.set_bgcolor('lightyellow')
graph.get_node('b')[0].set_shape('box')

Suggestion : 3

The graphviz package provides two main classes: graphviz.Graph and graphviz.Digraph. They create graph descriptions in the DOT language for undirected and directed graphs respectively. They have the same API.,Use the graph_attr, node_attr, and edge_attr arguments of the Graph and Digraph constuctors to change the default attributes for your graph, nodes, and edges.,graphviz provides a simple pure-Python interface for the Graphviz graph-drawing software. It runs under Python 3.7+. To install it with pip, run the following:,To directly access the raw results from the Graphviz dot layout command as binary bytes or as decoded str (for plain-text formats like SVG) instead of writing to a file, use the pipe() method of your Graph or Digraph object:

$ pip install graphviz

>>> import graphviz

>>> dot = graphviz.Digraph('round-table', comment='The Round Table')

>>> dot
<graphviz.graphs.Digraph object at 0x...>

>>> dot.node('A', 'King Arthur') >>>
   dot.node('B', 'Sir Bedevere the Wise') >>>
   dot.node('L', 'Sir Lancelot the Brave')

   >>>
   dot.edges(['AB', 'AL']) >>>
   dot.edge('B', 'L', constraint = 'false')

>>> print(dot.source)
// The Round Table
digraph "round-table" {
   A[label = "King Arthur"]
   B[label = "Sir Bedevere the Wise"]
   L[label = "Sir Lancelot the Brave"]
   A - > B
   A - > L
   B - > L[constraint = false]
}

>>> doctest_mark_exe()

   >>>
   dot.render(directory = 'doctest-output').replace('\\', '/')
'doctest-output/round-table.gv.pdf'

>>> doctest_mark_exe()

   >>>
   dot.render(directory = 'doctest-output', view = True)
'doctest-output/round-table.gv.pdf'

Suggestion : 4

How to select span with specific email attribute value with Puppeteer,I want to know how to just use the date portion of the Unix timestamp. I have left blank the portion I am having trouble trying to figure out,How to write text to temp file?

I am trying to create a DOT file from python anycodings_python and an error shows up saying :

[Errno 13 permission denied]

This is the code i am using:

Credit_tree_file = open('c:\credit_tree.dot', 'w')

As a normal user, you are usually not anycodings_python allowed (and, you should not be allowed) anycodings_python to write into the root directory of your anycodings_python disk drive. However, you of course have anycodings_python access to other locations like your own anycodings_python home directory or the temp directory. anycodings_python You can try to create the file in your anycodings_python temp directory, for example like

import tempfile
import os

tmpdir = tempfile.gettempdir()
Credit_tree_file = open(os.path.join(tmpdir, 'credit_tree.dot'), 'w')
   ...

Suggestion : 5

Use this method if you already have a DOT-string describing a graph in a Python variable:,Use this method if you already have a DOT-file describing a graph, for example as output of another program. Let's say you already have this example.dot (based on an example from Wikipedia):,Development occurs at GitHub, where you can report issues and contribute code., or: Parse a graph from an existing DOT-string. Use this method if you already have a DOT-string describing a graph in a Python variable: import pydot dot_string = """graph my_graph { bgcolor="yellow"; a [label="Foo"]; b [shape=circle]; a -- b -- c [color=blue]; }""" graphs = pydot.graph_from_dot_data(dot_string) graph = graphs[0]

-Module structure following example from NetworkX and other projects.
This adds an `import pydot`
to `core.py`, which is cyclic / circular,
   but necessary to make the exceptions available.Cyclic imports are
not uncommon and Python can handle this.Using the exceptions in
   pydot source code now requires prefixing `pydot.`,
   for example:

   raise pydot.Error("Message")

   -
   Exception hierarchy changes.See ChangeLog and class docstrings in
   this commit
for details.Additional background notes:

   -Removal of the unused exception class `InvocationException`: It was
introduced in 842173 c of 2008(v1 .0 .2), but got in disuse when
commits 9 b3c1a1 and bc639e7 of 2016(v1 .2 .0) fell back to using `Exception`
and `assert`(`AssertionError`) again.If we ever need
a custom class like this again in the future, it would probably
have a different signature
for context data(e.g.a DOT string,
   input and output), different parent class(or classes, e.g.
   `PydotException, CalledProcessError`) and perhaps a different name(e.g.ending in `...Error`), so no need to keep the old class
around
for that.

Further additions to the exception hierarchy are likely before the
final release of pydot 2.0.

Discussed in #171, # 230 and #271.

graph my_graph {
   bgcolor = "yellow";
   a[label = "Foo"];
   b[shape = circle];
   a--b--c[color = blue];
}

import pydot

graphs = pydot.graph_from_dot_file("example.dot")
graph = graphs[0]

import pydot

dot_string = ""
"graph my_graph {
bgcolor = "yellow";
a[label = "Foo"];
b[shape = circle];
a--b--c[color = blue];
}
""
"

graphs = pydot.graph_from_dot_data(dot_string)
graph = graphs[0]

import pydot

graph = pydot.Dot("my_graph", graph_type = "graph", bgcolor = "yellow")

# Add nodes
my_node = pydot.Node("a", label = "Foo")
graph.add_node(my_node)
# Or, without using an intermediate variable:
   graph.add_node(pydot.Node("b", shape = "circle"))

# Add edges
my_edge = pydot.Edge("a", "b", color = "blue")
graph.add_edge(my_edge)
# Or, without using an intermediate variable:
   graph.add_edge(pydot.Edge("b", "c", color = "blue"))

import networkx
import pydot

# See NetworkX documentation on how to build a NetworkX graph.

graph = networkx.drawing.nx_pydot.to_pydot(my_networkx_graph)

Suggestion : 6

For the same data, we show below how to create a dot plot using either px.scatter or go.Scatter.,Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures.,Dot plots (also known as Cleveland dot plots) are scatter plots with one categorical axis and one continuous axis. They can be used to show changes between two (or more) points in time or between two (or more) conditions. Compared to a bar chart, dot plots can be less cluttered and allow for an easier comparison between conditions.,Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.express as px
df = px.data.medals_long()

fig = px.scatter(df, y = "nation", x = "count", color = "medal", symbol = "medal")
fig.update_traces(marker_size = 10)
fig.show()

import plotly.express as px
import pandas as pd

schools = ["Brown", "NYU", "Notre Dame", "Cornell", "Tufts", "Yale",
   "Dartmouth", "Chicago", "Columbia", "Duke", "Georgetown",
   "Princeton", "U.Penn", "Stanford", "MIT", "Harvard"
]
n_schools = len(schools)

women_salary = [72, 67, 73, 80, 76, 79, 84, 78, 86, 93, 94, 90, 92, 96, 94, 112]
men_salary = [92, 94, 100, 107, 112, 114, 114, 118, 119, 124, 131, 137, 141, 151, 152, 165]

df = pd.DataFrame(dict(school = schools * 2, salary = men_salary + women_salary,
   gender = ["Men"] * n_schools + ["Women"] * n_schools))

# Use column names of df
for the different parameters x, y, color, ...
fig = px.scatter(df, x = "salary", y = "school", color = "gender",
   title = "Gender Earnings Disparity",
   labels = {
      "salary": "Annual Salary (in thousands)"
   }
   # customize axis label
)

fig.show()

import plotly.graph_objects as go

schools = ["Brown", "NYU", "Notre Dame", "Cornell", "Tufts", "Yale",
   "Dartmouth", "Chicago", "Columbia", "Duke", "Georgetown",
   "Princeton", "U.Penn", "Stanford", "MIT", "Harvard"
]

fig = go.Figure()
fig.add_trace(go.Scatter(
   x = [72, 67, 73, 80, 76, 79, 84, 78, 86, 93, 94, 90, 92, 96, 94, 112],
   y = schools,
   marker = dict(color = "crimson", size = 12),
   mode = "markers",
   name = "Women",
))

fig.add_trace(go.Scatter(
   x = [92, 94, 100, 107, 112, 114, 114, 118, 119, 124, 131, 137, 141, 151, 152, 165],
   y = schools,
   marker = dict(color = "gold", size = 12),
   mode = "markers",
   name = "Men",
))

fig.update_layout(title = "Gender Earnings Disparity",
   xaxis_title = "Annual Salary (in thousands)",
   yaxis_title = "School")

fig.show()

import plotly.graph_objects as go

country = ['Switzerland (2011)', 'Chile (2013)', 'Japan (2014)',
   'United States (2012)', 'Slovenia (2014)', 'Canada (2011)',
   'Poland (2010)', 'Estonia (2015)', 'Luxembourg (2013)', 'Portugal (2011)'
]
voting_pop = [40, 45.7, 52, 53.6, 54.1, 54.2, 54.5, 54.7, 55.1, 56.6]
reg_voters = [49.1, 42, 52.7, 84.3, 51.7, 61.1, 55.3, 64.2, 91.1, 58.9]

fig = go.Figure()

fig.add_trace(go.Scatter(
   x = voting_pop,
   y = country,
   name = 'Percent of estimated voting age population',
   marker = dict(
      color = 'rgba(156, 165, 196, 0.95)',
      line_color = 'rgba(156, 165, 196, 1.0)',
   )
))
fig.add_trace(go.Scatter(
   x = reg_voters, y = country,
   name = 'Percent of estimated registered voters',
   marker = dict(
      color = 'rgba(204, 204, 204, 0.95)',
      line_color = 'rgba(217, 217, 217, 1.0)'
   )
))

fig.update_traces(mode = 'markers', marker = dict(line_width = 1, symbol = 'circle', size = 16))

fig.update_layout(
   title = "Votes cast for ten lowest voting age population in OECD countries",
   xaxis = dict(
      showgrid = False,
      showline = True,
      linecolor = 'rgb(102, 102, 102)',
      tickfont_color = 'rgb(102, 102, 102)',
      showticklabels = True,
      dtick = 10,
      ticks = 'outside',
      tickcolor = 'rgb(102, 102, 102)',
   ),
   margin = dict(l = 140, r = 40, b = 50, t = 80),
   legend = dict(
      font_size = 10,
      yanchor = 'middle',
      xanchor = 'right',
   ),
   width = 800,
   height = 600,
   paper_bgcolor = 'white',
   plot_bgcolor = 'white',
   hovermode = 'closest',
)
fig.show()

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express
function e.g.px.bar(...)
# fig.add_trace(...)
# fig.update_layout(...)

import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash()
app.layout = html.Div([
   dcc.Graph(figure = fig)
])

app.run_server(debug = True, use_reloader = False) # Turn off reloader
if inside Jupyter