I applied facebook network sample from this documentation on my work, to get this code:
edges_df = pd.read_csv('rel.csv', delimiter= ";")
nodes_df = pd.read_csv('monfichier.csv', delimiter= ";")
padding = dict(x=(-1.1, 1.1), y=(-1.1, 1.1))
fb_nodes = hv.Dataset(nodes_df, 'index')
fb_graph = hv.Graph((edges_df, fb_nodes)).redim.range(**padding)
colors = ['#000000'] + hv.Cycle('Category20').values
fb_graph.opts(color_index='age', show_frame=False,
xaxis=True, yaxis=True, node_size=10, edge_line_width=1, cmap=colors)
renderer = hv.renderer('bokeh')
plot = renderer.get_plot(fb_graph).state
show(plot)
It works fine. But the resulted network was a graph without a specific layout (as shown in attached figure). I want to specify the network layout as in networkx. How to do that ?
I found, this instruction:
hv.Graph.from_networkx(G, nx.layout.spring_layout).opts(tools=['hover'])
But I did not find how to use it with my code, since my G is already an holoview and not a networkx graph.
Do you have any suggestion ?
There is a function called layout_nodes in HoloViews which can apply networkx (and other) layouts to an existing graph:
N = 8
node_indices = np.arange(N)
source = np.zeros(N)
target = node_indices
padding = dict(x=(-1.2, 1.2), y=(-1.2, 1.2))
simple_graph = hv.Graph(((source, target),)).redim.range(**padding)
hv.element.graphs.layout_nodes(simple_graph, layout=nx.spring_layout)
Related
maybe you can help me out with a right comment or hint for my problem.
Pretty easy, I would like to plot a 2D slice vtp file (OpenFOAM) via matplotlib as tricontourf plot.
1.) Creating the vtp slice by Paraview and saving as vtp file works like a charme
2.) Using the runtime vtp file, created by cuttingPlane - libsampling OpenFOAM creates a weird triangle order.
What am I missing?
Best,
def loadVTPFile(filename):
import vtk
from vtk.util.numpy_support import vtk_to_numpy
from vtk.util import numpy_support as npvtk
reader = vtk.vtkXMLPolyDataReader()
reader.SetFileName(filename)
reader.Update()
data = reader.GetOutput()
points = data.GetPoints()
npts = points.GetNumberOfPoints()
x = vtk_to_numpy(points.GetData())
triangles= vtk_to_numpy(data.GetPolys().GetData())
ntri = triangles.size // 4 # number of cells
tri = np.take(triangles,[n for n in range(triangles.size) if n%4 != 0]).reshape(ntri,3)
n_arrays = reader.GetNumberOfPointArrays()
for i in range(n_arrays):
print(reader.GetPointArrayName(i))
X = vtk_to_numpy(points.GetData())
x=X[:,0]
y=X[:,1]
z=X[:,2]
# Define the velocity components U=(u,v,w)
U = vtk_to_numpy(data.GetPointData().GetArray('UMean'))
u = U[:,0]
v = U[:,1]
w = U[:,2]
magU=np.sqrt(u**2+v**2+w**2)
p = vtk_to_numpy(data.GetPointData().GetArray('pMean'))
Ma = vtk_to_numpy(data.GetPointData().GetArray('MaMean'))
rho = vtk_to_numpy(data.GetPointData().GetArray('rhoMean'))
return x,y,z,u,v,w,magU,p,Ma,rho,tri
1st: Paraview vtp slice via matplotlib:
Paraview vtp slice via matplotlib
2nd image OpenFOAM cut via libsampling
2nd image OpenFOAM cut via libsampling
Thanks for your help
OpenFOAM vtp slice export:
cellPoint, triangulated true/false, interpolated true/false and so on...
I'm trying to create stimuli that consist of 100 small lines in the centre of the screen, with orientations sampled from a Gaussian distribution (please see the image link below):
Orientation stimuli
I've managed to achieve something that almost fits the bill, but this code only works in isolation:
from psychopy import visual, core, event
import numpy as np
from numpy.random import random
import random
Lines = visual.Rect(
win=win, name='Lines',
width=(0.015, 0.0025)[0], height=(0.015, 0.0025)[1],
lineWidth=1, lineColor=[1,1,1], lineColorSpace='rgb',
fillColor=[1,1,1], fillColorSpace='rgb',
opacity=1, depth=-2.0, interpolate=True)
lines_hpos = np.random.uniform(-0.49,0.49,100)
mu = 315
sigma = 15
for i in range(100):
Lines.pos = [lines_hpos[i],np.random.uniform(-0.49,0.49)]
Lines.ori = random.gauss(mu, sigma)
I've tried to manipulate this code so that I can integrate it into the full experiment I'm designing in PsychoPy's experiment builder. I run the below code in the experiment builder's coding window calling 'gdist' and 'loc' as values for the 'Orientation' and 'Position' of the rectangles, respectively:
import random
gdist =[]
loc = []
lines_hpos = np.random.uniform(-0.49,0.49,100)
mu = 90
sigma = 20
for i in range(100):
rloc = [lines_hpos[i],np.random.uniform(-0.49,0.49)]
loc.append(rloc)
gauss = random.gauss(mu, sigma)
gdist.append(gauss)
When I attempt to run the experiment, I get an error return and the experiment fails to start:
File "C:\Users\r02mj20\AppData\Local\PsychoPy3\lib\site-packages\psychopy\visual\image.py", line 238, in __del__
File "C:\Users\r02mj20\AppData\Local\PsychoPy3\lib\site-packages\pyglet\gl\lib.py", line 97, in errcheck
ImportError: sys.meta_path is None, Python is likely shutting down
I'm assuming this has something to do with pyglet not liking the idea of there being 100 rectangles all at once (side note: the script works fine if range(1)). If anyone has any suggestions for how I might fix or work around this problem, I'd be eternally grateful.
i don't see any problem with this idea, except you better use visual.Line instead of Rect, and your units of measure are not described; the key to preserving video memory is BufferImageStim, btw
from psychopy import visual, core, event, monitors
from psychopy.iohub.client import launchHubServer
import random
import numpy as np
MU = 315; SIGMA = 15
num_lines = 100
io = launchHubServer(iohub_config_name='iohub_config.yaml')
display = io.devices.display
mon = monitors.Monitor(name = display.getPsychopyMonitorName())
win = visual.Window([640, 480], units='pix', viewScale = 1.0,
monitor = mon, winType='pyglet',
fullScr = False, waitBlanking = True, useFBO = True, useLights = False,
allowStencil=False, allowGui = True,
screen = display.getIndex(), colorSpace = 'rgb255', color = [128,128,128],
name = 'my_win01')
rects = []
lines_hpos = np.random.uniform(-0.49, 0.49, num_lines)
for i in range(num_lines):
line_rect = visual.Rect(win=win, size=(0.001, 1.0), units='norm',
pos=(0,0), lineWidth=1, lineColor=[1,1,1], fillColor=[1,1,1], opacity=1, depth=-2.0,
name='lines_rect', interpolate=True, autoLog=False, autoDraw=False)
line_rect.pos = [lines_hpos[i], np.random.uniform(-0.49,0.49)]
line_rect.ori = random.gauss(MU, SIGMA)
rects.append(line_rect)
rect_buffer = visual.BufferImageStim(win, buffer='back', stim=rects, sqPower2=False, interpolate=False, name='rect-buffer', autoLog=True)
rect_buffer.draw()
win.flip()
event.waitKeys()
I'm working on line plotting a metric for a course module as well as each of its questions within a Jupyter Notebook using %matplotlib notebook. That part is no problem. A module has typically 20-35 questions, so it results in a lot of lines on a chart. Therefore, I am plotting the metric for each question in a low alpha and I want to change the alpha and display the question name when I hover over the line, then reverse those when no longer hovering over the line.
The thing is, I've tried every test version of interactivity from the matplotlib documentation on event handling, as well as those in this question. It seems like the mpl_connect event is never firing, whether I use click or hover.
Here's a test version with a reduced dataset using the solution to the question linked above. Am I missing something necessary to get events to fire?
def update_annot(ind):
x,y = line.get_data()
annot.xy = (x[ind["ind"][0]], y[ind["ind"][0]])
text = "{}, {}".format(" ".join(list(map(str,ind["ind"]))),
" ".join([names[n] for n in ind["ind"]]))
annot.set_text(text)
annot.get_bbox_patch().set_alpha(0.4)
def hover(event):
vis = annot.get_visible()
if event.inaxes == ax:
cont, ind = line.contains(event)
if cont:
update_annot(ind)
annot.set_visible(True)
fig.canvas.draw_idle()
else:
if vis:
annot.set_visible(False)
fig.canvas.draw_idle()
module = 'bd2bc472-ee0d-466f-8557-788cc6de3018'
module_metrics[module] = {
'q_count': 31,
'sequence_pks': [0.5274546300604932,0.5262044653349001,0.5360993905297703,0.5292329279700655,0.5268691588785047,0.5319099014547161,0.5305164319248826,0.5268235294117647,0.573648805381582,0.5647933116581514,0.5669839795681448,0.5646591970121382,0.5663157894736842,0.5646976090014064,0.5659005628517824,0.5693634879925391,0.5728268468888371,0.5668834184858337,0.5687237026647967,0.5795640965549567,0.5877684407096172,0.585690904839841,0.5766899766899767,0.5971341320178529,0.6059972105997211,0.6055516678329834,0.6209865053513262,0.6203121360354065,0.6153666510976179,0.6236909471724459,0.6387654898293196],
'q_pks': {
'0da04f02-4aad-4ac8-91a5-214862b5c0d0': [0.6686046511627907,0.6282051282051282,0.76,0.6746987951807228,0.7092198581560284,0.71875,0.6585365853658537,0.7070063694267515,0.7171052631578947,0.7346938775510204,0.7737226277372263,0.7380952380952381,0.6774193548387096,0.7142857142857143,0.7,0.6962962962962963,0.723404255319149,0.6737588652482269,0.7232704402515723,0.7142857142857143,0.7164179104477612,0.7317073170731707,0.6333333333333333,0.75,0.7217391304347827,0.7017543859649122,0.7333333333333333,0.7641509433962265,0.6869565217391305,0.75,0.794392523364486],
'10bd29aa-3a26-49e6-bc2c-50fd503d7ab5': [0.64375,0.6014492753623188,0.5968992248062015,0.5059523809523809,0.5637583892617449,0.5389221556886228,0.5576923076923077,0.51875,0.4931506849315068,0.5579710144927537,0.577922077922078,0.5467625899280576,0.5362318840579711,0.6095890410958904,0.5793103448275863,0.5159235668789809,0.6196319018404908,0.6143790849673203,0.5035971223021583,0.5897435897435898,0.5857142857142857,0.5851851851851851,0.6164383561643836,0.6054421768707483,0.5714285714285714,0.627906976744186,0.5826771653543307,0.6504065040650406,0.5864661654135338,0.6333333333333333,0.6851851851851852]
}}
suptitle_size = 24
title_size = 18
tick_size = 12
axis_label_size = 15
legend_size = 14
fig, ax = plt.subplots(figsize=(15,8))
fig.suptitle('PK by Sequence Order', fontsize=suptitle_size)
module_name = 'Test'
q_count = module_metrics[module]['q_count']
y_ticks = [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]
x_ticks = np.array([x for x in range(0,q_count)])
x_labels = x_ticks + 1
# Plot it
ax.set_title(module_name, fontsize=title_size)
ax.set_xticks(x_ticks)
ax.set_yticks(y_ticks)
ax.set_xticklabels(x_labels, fontsize=tick_size)
ax.set_yticklabels(y_ticks, fontsize=tick_size)
ax.set_xlabel('Sequence', fontsize=axis_label_size)
ax.set_xlim(-0.5,q_count-0.5)
ax.set_ylim(0,1)
ax.grid(which='major',axis='y')
# Output module PK by sequence
ax.plot(module_metrics[module]['sequence_pks'])
# Output PK by sequence for each question
for qid in module_metrics[module]['q_pks']:
ax.plot(module_metrics[module]['q_pks'][qid], alpha=0.15, label=qid)
annot = ax.annotate("", xy=(0,0), xytext=(-20,20),textcoords="offset points", bbox=dict(boxstyle="round", fc="w"), arrowprops=dict(arrowstyle="->"))
annot.set_visible(False)
mpl_id = fig.canvas.mpl_connect('motion_notify_event', hover)
Since there are dozens of modules, I created an ipywidgets dropdown to select the module, which then runs a function to output the chart. Nonetheless, whether running it hardcoded as here or from within the function, mpl_connect never seems to fire.
Here's what this one looks like when run
I got an error when try to use structural.em in "bnlearn" package
This is the code:
cut.learn<- structural.em(cut.df, maximize = "hc",
+ maximize.args = "restart",
+ fit="mle", fit.args = list(),
+ impute = "parents", impute.args = list(), return.all = FALSE,
+ max.iter = 5, debug = FALSE)
Error in check.data(x, allow.levels = TRUE, allow.missing = TRUE,
warn.if.no.missing = TRUE, : at least one variable has no observed
values.
Did anyone have the same problems, please tell me how to fix it.
Thank you.
I got structural.em working. I am currently working on a python interface to bnlearn that I call pybnl. I also ran into the problem you desecribe above.
Here is a jupyter notebook that shows how to use StructuralEM from python marks.
The gist of it is described in slides-bnshort.pdf on page 135, "The MARKS Example, Revisited".
You have to create an inital fit with an inital imputed dataframe by hand and then provide the arguments to structural.em like so (ldmarks is the latent-discrete-marks dataframe where the LAT column only contains missing/NA values):
library(bnlearn)
data('marks')
dmarks = discretize(marks, breaks = 2, method = "interval")
ldmarks = data.frame(dmarks, LAT = factor(rep(NA, nrow(dmarks)), levels = c("A", "B")))
imputed = ldmarks
# Randomly set values of the unobserved variable in the imputed data.frame
imputed$LAT = sample(factor(c("A", "B")), nrow(dmarks2), replace = TRUE)
# Fit the parameters over an empty graph
dag = empty.graph(nodes = names(ldmarks))
fitted = bn.fit(dag, imputed)
# Although we've set imputed values randomly, nonetheless override them with a uniform distribution
fitted$LAT = array(c(0.5, 0.5), dim = 2, dimnames = list(c("A", "B")))
# Use whitelist to enforce arcs from the latent node to all others
r = structural.em(ldmarks, fit = "bayes", impute="bayes-lw", start=fitted, maximize.args=list(whitelist = data.frame(from = "LAT", to = names(dmarks))), return.all = TRUE)
You have to use bnlearn 4.4-20180620 or later, because it fixes a bug in the underlying impute function.
I have list of point feature class. I am trying to write a python script to perform Krigging interpolation. I am getting error massage in this code "Point_Num" is not defined,
Below script i am working
import arcpy
from arcpy import env
from arcpy.sa import *
arcpy.env.overwriteOutput = True
# Check out the ArcGIS Spatial Analyst extension license
arcpy.CheckOutExtension("Spatial")
In_Point = r"D:\NPP_Test\MERRA_TEMP_2012C" #(Point feature name:r001_mean, r002_mean.....r012_mean )
Out_Raster = r"D:\NPP_Test\MERRA_TEMP_2012D"
points = arcpy.ListFeatureClasses()
zFields = "GRID_CODE"
#Kriging Veriable
cellSize = 0.05
lagSize = 0.5780481172534
majorRange = 6
partialSill = 3.304292110
nugget = 0.002701348
kRadius = RadiusFixed(20000, 1)
#Mask region of interest
mask = r"D:\Gujarta Shape file\GUJARATSTATE.shp"
for zField in zFields:
Point = Point_Num[:3]
kModelUniversalObj = KrigingModelUniversal("LINEARDRIFT", lagSize, majorRange, partialSill, nugget)
OutKriging = Kriging(inPointFeatures, zField, kModelUniversalObj, cellSize, kRadius)
#IDWMASk = ExtractByMask(outIDW, mask)
KrigMask = ExtractByMask(OutKriging, mask)
#Save outraster as the same name of input
KrigMask.save("r{}.tif".format(Point_Num))