I have a question regarding external labels in pygraphviz. Sadly, I haven't found anything regarding this on the internet.
I want to use networkx to create/parse a graph in tree structure and then use pygraphviz/pydot to draw it. I need external labels on top of the normal labels for the nodes because I want to display values for the nodes + the node name itself.
Let's say I have the following graph (very simplified example of what I'm doing later):
g = nx.Graph()
g.add_edges_from([('A','B'), ('A','C')])
p = nx.drawing.nx_pydot.to_pydot(g)
So I'm using the last line to generate a tree like hraph and I need external labels for B and C.
How to do it?
For pygraphviz you can pass through arbitrary graphviz attributes, so you can do:
dot.node('point', '', shape='point', xlabel='Label')
Related
Does someone has a function handy that adds POIs retrieved with geometries_from_polygon (or geometries_from_place) as GDF to an existing graph via graph_from_gdfs?
Basically something simple like
G = ox.graph_from_place(query = "<query>", network_type='all', simplify=False, retain_all=True)
pois = ox.geometries.geometries_from_polygon(<geometry>, tags = {"amenity": [<amenity_tag>]})
> simple function that generates the nodes and edges from the pois GDF ?
G2 = ox.graph_from_gdfs(pois_nodes, pois_edges, <graph_attrs>)
> merge G and G2 ?
ox.save_graph_xml(<merged_G_and_G2>)
I think I can just iterate through the GDF and generate the nodes/edges myself, but I'm wondering if there isn't an easier/simpler way to do this, or even an existing function I'm missing?
I'm not looking into rendering that actual graph, I'm just looking for an easy way to generate a graph XML file that includes both, the street network and some POIs and I'm using OSMX to modify that data in some ways.
I use OSMnx for my project and I need to retrieve information about roads.
I know I can use osmnx.graph_from_place(place, network_type="all") to get the graph of highways of a certain type from Open Street Map. However it does not provides lots of features, only 15.
I know I can use osmnx.geometries_from_place(place, tags={"highway"=True}) , it gives more features however it provides different result than the previous function (less rows).
Using 'Etterbeek, Brussels, Belgium' as place, with graph_from_place, I get 12636 rows in my dataframe but with geometries_from_place, I get 3636
So can anyone explain me what is the difference between the two functions and why they provide different results?
Code :
import osmnx as ox
place = 'Etterbeek, Brussels, Belgium'
etterbeek_all_highway_info = ox.geometries_from_place(place, tags={"highway":True})
len(etterbeek_all_highway_info) # 3636
street_graph = ox.graph_from_place(place, network_type='all', simplify=False)
nodes , streets = ox.graph_to_gdfs(street_graph)
len(streets["highway"]) #12636
Documentation :
https://osmnx.readthedocs.io/en/stable/osmnx.html#osmnx.graph.graph_from_place
https://osmnx.readthedocs.io/en/stable/osmnx.html#osmnx.geometries.geometries_from_place
I am using OSMnx to get a graph and add a new edge attribute (w3) representing a custom weight for each edge. Then I can successfully find 2 different shortest paths between 2 points using NetworkX and 'length', 'w2'. Everything works fine, this is my code:
G = ox.graph_from_place(PLACE, network_type='all_private', retain_all = True, simplify=True,truncate_by_edge=False) ```
w3_dict = dict((zip(zip(lu, lv, lk),lw3)))
nx.set_edge_attributes(G, w3_dict, "w3")
route_1 = nx.shortest_path(G, node_start, node_stop, weight = 'length')
route_2 = nx.shortest_path(G, node_start, node_stop, weight = 'w3')
Now I would like to save G to disk and reopen it, to perform more navigation tasks later on. But after saving it with:
ox.save_graph_xml(G, filepath='DATA/network.osm')
and reopen it with:
G = ox.graph_from_xml('DATA/network.osm')
my custom attribute w3 has disappeared. I have followed the instructions in the docs but with no luck. It feels like I'm missing something really obvious but I don't understand what it is..
Use the ox.save_graphml and ox.load_graphml functions to save/load full-featured OSMnx/NetworkX graphs to/from disk for later use. The save xml function exists only to allow serialization to the .osm file format for applications that require it, and has many constraints to conform to that.
import networkx as nx
import osmnx as ox
ox.config(use_cache=True, log_console=True)
# get a graph, set 'w3' edge attribute
G = ox.graph_from_place('Piedmont, CA, USA', network_type='drive')
nx.set_edge_attributes(G, 100, 'w3')
# save graph to disk
ox.save_graphml(G, './data/graph.graphml')
# load graph from disk and confirm 'w3' edge attribute is there
G2 = ox.load_graphml('./data/graph.graphml')
nx.get_edge_attributes(G2, 'w3')
Following the instructions of ipycitoscape I am not able to plot a graph using ipycitoscape.
according to: https://github.com/QuantStack/ipycytoscape/blob/master/examples/Test%20NetworkX%20methods.ipynb
this should work:
import networkx as nx
import ipycytoscape
G2 = nx.Graph()
G2.add_nodes_from([*'ABCDEF'])
G2.add_edges_from([('A','B'),('B','C'),('C','D'),('E','F')])
print(G2.nodes)
print(G2.edges)
cytoscapeobj = ipycytoscape.CytoscapeWidget()
cytoscapeobj.graph.add_graph_from_networkx(nx_graph)
G2 is a networkx graph example and it looks ok since print(G2) gives the networkx object back and G2.nodes and G2.edges can be printed.
The error:
ValueError: invalid literal for int() with base 10: 'A'
Why should a node be an integer?
More general what to do if the starting data point if a pandas dataframe with a million rows edges those being strings like ProcessA-ProcessB, processC-processD etc
Also having a look to the examples it is to be noted that the list of nodes is composed of a dictionary data for every node. that data including an "id" per node and also "Atribute". The surprise here is that the networkx Graph should have all those properties.
thanks
This problem was fixed. See attachment.
Please let me know if it's still happening. Feel free to open an issue: https://github.com/QuantStack/ipycytoscape/
I'm just playing around with ipycytoscape myself, so I could be way off-base, but, shouldn't the line be:
cytoscapeobj.graph.add_graph_from_networkx(G2) # your graph name goes here
Trying to generate a cytoscape object built on a graph that doesn't exist might trigger a ValueError because it can't find any nodes.
I am downloading all the images from the Neurotransmitter study of the Allen Brain Atlas using this script:
from allensdk.api.queries.image_download_api import ImageDownloadApi
from allensdk.config.manifest import Manifest
import pandas as pd
import os
#getting transmitter study
#product id from http://api.brain-map.org/api/v2/data/query.json?criteria=model::Product
nt_datasets = image_api.get_section_data_sets_by_product([27])
#an instance of Image Api for downloading
image_api = ImageDownloadApi()
for index, row in df_nt.iterrows():
#get section dataset id
section_dataset_id= row['id']
#each section dataset id has multiple image sections
section_images = pd.DataFrame(
image_api.section_image_query(
section_data_set_id=section_dataset_id)
)
for section_image_id in section_images['id'].tolist():
file_path = os.path.join('/path/to/save/dir/',
str(section_image_id) + '.jpg' )
Manifest.safe_make_parent_dirs(file_path)
image_api.download_section_image(section_image_id,
file_path=file_path,
downsample=downsample)
This script downloads presumably all the available ISH experiments. However, I am wondering what would be the best way to get more of the metadata as follows:
1) type of ISH experiment, known as "gene" (for example whether an image is MBP-stained, Nissl-stained or etc). Shown in red circle below.
2) Structure and correspondence to the atlas image (annotations, for example to see to which part of brain a section belongs to). I think this could be acquired with tree_search but not sure how. Shown in red circles below from two different webpages on Allen website.
3) The scale of the image, for example how big one pixel is in the downloaded image (e.g., 0.001x0.001 mm). I would require this for image analysis with respect to MRI, for example. Shown below in the red circle.
All the above information are somehow available on the website, my question is whether you could help me to do this programmatically via the SDK.
EDIT:
Also would be great to download "Nissl" stains programmatically, as they do not show using the above loop iteration. The picture is shown below.
To access this information, you'll need to formulate a somewhat complex API query.
from allensdk.api.queries.rma_api import RmaApi
api = RmaApi()
data_set_id = 146586401
data = api.model_query('SectionDataSet',
criteria='[id$eq%d]' % data_set_id,
include='section_images,treatments,probes(gene),specimen(structure)')
print("gene symbol: %s" % data[0]['probes'][0]['gene']['acronym'])
print("treatment name: %s" % data[0]['treatments'][0]['name'])
print("specimen location: %s" % data[0]['specimen']['structure']['name'])
print("section xy resolution: %f um" % data[0]['section_images'][0]['resolution'])
gene symbol: MBP
treatment name: ISH
specimen location: Cingulate Cortex
section xy resolution: 1.008000 um
Without doing a deep dive on the API data model, SectionDataSets have constituent SectionImages, Treatments, Probes, and source Specimens. Probes target Genes, and Specimens can be associated with a Structure. The query is downloading all of that information for a single SectionDataSet into a nested dictionary.
I don't remember how to find the specimen block extent. I'll update the answer if I find it.