For example, I generate the data with:
i=0; while [ "$i" -lt 10 ]; do echo "$i,$((2*i)),$((4*i))"; i=$((i+1)); done > main.csv
which contains:
0,0,0
1,2,4
2,4,8
3,6,12
4,8,16
5,10,20
6,12,24
7,14,28
8,16,32
9,18,36
Then for example in gnuplot, I get what I want with palette:
#!/usr/bin/env gnuplot
set terminal png size 1024,1024
set output "main.png"
set datafile separator ","
set key off
plot "main.csv" using 1:2:3 palette pt 7 pointsize 10
which gives the desired:
How to achieve this effect with Paraview?
I managed to make the scatter plot with a Line Chart View, but all points are red like this:
Also I could not resize the marker sizes, but for that I found an open issue: https://gitlab.kitware.com/paraview/paraview/issues/14169
I am initially learning the GUI for plotting, but if you have a scripting option that is good to know too.
The reason I am looking into Parasol is that I need to plot 10M points interactively, which I have found gnuplot and matplotlib not to handle well, so I'm curious if this VTK-based solution will cut it. More info at: Large plot: ~20 million samples, gigabytes of data
Tested in Ubuntu 18.10, Paraview 5.4.1.
Here is the python script to read the file and display the markers in the ParaView 3D Render View.
import paraview.simple as pvs
# create a new 'CSV Reader'
csvReader = pvs.CSVReader(FileName=['C:\\your_file.csv'])
csvReader.HaveHeaders = 0
# create a new 'Table To Points'
tableToPoints = pvs.TableToPoints(Input=csvReader)
tableToPoints.XColumn = 'Field 0'
tableToPoints.YColumn = 'Field 1'
tableToPoints.ZColumn = 'Field 2'
tableToPoints.KeepAllDataArrays = 1
tableToPoints.a2DPoints = 1
# create a new 'Glyph'
glyph = pvs.Glyph(Input=tableToPoints, GlyphType='Arrow')
glyph.Scalars = ['POINTS', 'Field 0']
glyph.Vectors = ['POINTS', 'None']
glyph.GlyphType = '2D Glyph'
glyph.GlyphType.GlyphType = 'Square'
glyph.GlyphType.Filled = 1
glyph.ScaleMode = 'off'
glyph.ScaleFactor = 1.0
glyph.GlyphMode = 'All Points'
### uncomment to scale markers by 'Field 2'
# glyph1.Scalars = ['POINTS', 'Field 2']
# glyph1.ScaleMode = 'scalar'
# show data in view
renderView = pvs.GetActiveView()
glyphDisplay = pvs.Show(glyph, renderView)
glyphDisplay.Representation = 'Surface'
pvs.ColorBy(glyphDisplay, ('POINTS', 'Field 2'))
glyphDisplay.SetScalarBarVisibility(renderView, True)
glyphDisplay.RescaleTransferFunctionToDataRange(True, False)
renderView.Update()
pvs.UpdatePipeline()
renderView.AxesGrid.Visibility = 1
renderView.ResetCamera()
pvs.Render(renderView)
And the result:
Related
I am trying to run the code below in a bokeh server to visualize the plots. the final_imdb_dataframe is in the same directory as the python code. these are the steps i take to get the server to run:
open my terminal
type in "cd" then a space and then my directory in for the map containing my python script and the final_imdb_dataframe
press enter
type: bokeh serve --show "filename.py"
press enter
in my terminal i am getting this error message however:
bokeh : The term 'bokeh' is not recognized as the name of a cmdlet, function, script file, or operable program. (the complete error message is attached as a picture)
when i type in "pip show bokeh" in the command promt in python it gives me this:
pip show bokeh
Name: bokeh
Version: 2.4.3
Summary: Interactive plots and applications in the browser from Python
Home-page: https://github.com/bokeh/bokeh
Author: Bokeh Team
Author-email: info#bokeh.org
License: BSD-3-Clause
Location: c:\users\fazan\anaconda\lib\site-packages
Requires: Jinja2, numpy, packaging, pillow, PyYAML, tornado, typing-extensions
Required-by: hvplot, panel
Note: you may need to restart the kernel to use updated packages.
so it should work right?
i don't know what to do anymore...
code:
import pandas as pd
from bokeh.plotting import figure, show
import numpy as np
from bokeh.models import ColumnDataSource, HoverTool, Slider, RangeSlider, Div, Select
from bokeh.io import output_file, curdoc
from bokeh.layouts import column, row, layout
#EERSTE PLOT:
#line diagram of amount of movies released each year en avg rating displayed over the years
df = pd.read_csv("final_imdb_dataframe")
#"unnamed column weghalen
df.drop(df.columns[df.columns.str.contains('unnamed',case = False)],axis = 1, inplace = True)
#creating plot 2
plot2 = figure(plot_width = 1000,
plot_height = 400,
x_axis_label= "year",
title = "watchtime and movie rating over the years")
#creating a dropdown menu
source2 = ColumnDataSource(data={'x': df["release_date"], 'y': df["watchtime"]})
plot2.circle(x="x", y="y", size = 3, source = source2, alpha = 0.2)
def update_plot(attr, old, new):
if new == 'watchtime':
source2.data = {'x' : df["release_date"], 'y' : df["watchtime"]}
else:
source2.data = {'x' : df["release_date"], 'y' : df["movie_rating"]}
select = Select(title = "keuze menu", options=["watchtime", "movie_rating"], value = 'watchtime')
#code for updating the plot when the value is changed
select.on_change('value', update_plot)
#TWEEDE PLOT:
#creating a list of all the years a movie came out
dfrelease = df.release_date.drop_duplicates().tolist()
#sorting the list with release dates to get them in chronological order
dfrelease1 = sorted(dfrelease)
#creating an empty list
total_releases_per_year = []
#adding the amount of movies that came out every year to the list
for i in range(len(dfrelease1)):
total_releases_per_year.append(len(df[df["release_date"] == dfrelease1[i]]))
#creating a columndatasource of the two lists created above to make it possible for the hovertool to be used
source1 = ColumnDataSource(data=dict(year = dfrelease1, amount_of_movies_made = total_releases_per_year))
#making a hovertool
hover2 = HoverTool(tooltips=[("year", "#year"), ("amount of movies made", "#amount_of_movies_made")])
#creating plot 3
plot3 = figure(plot_width = 1000,
plot_height = 400,
x_axis_label= "year",
y_axis_label = "amount of movies",
title = "amount of movies per year")
plot3.line("year", "amount_of_movies_made", source = source1)
plot3.add_tools(hover2)
#DERDE PLOT:
#creating the ColumnDataSource
df_sel = df[df["release_date"] == 2020]
source1 = ColumnDataSource(df_sel)
#scatter plot
p1 = figure(title = "relation 'watchtime' and 'movie rating' over the years",
x_axis_label= "watchtime",
y_axis_label= "rating (0-10)",
plot_width = 1000,
plot_height = 400)
p1.circle(x="watchtime", y="movie_rating", alpha = 0.2, source=source1)
#making the rangeslider
slider = RangeSlider(title="release date", value= (1919, 2021), start=1919, end=2020, step=1)
#making the hover tool
hover1 = HoverTool(tooltips=[("title", "#movie_name"),("gross", "#gross_collection"),("votes", "#votes"),("genre", "#genre"), ("watchtime", "#watchtime"),("rating", "#movie_rating"),("release date", "#release_date")])
#making a def callback for the rangeslider to work when moved
def callback(attr, old, new):
year = slider.value
source1.data = df[(df["release_date"] >= year[0]) & (df["release_date"] <= year[1])]
slider.on_change("value", callback)
layout = column(slider, p1) #create a layout with slider on top of plot
p1.add_tools(hover1) #adding hover tool
layout3 = column(select, plot2, plot3, slider, p1) #making the layout for the dashboard
curdoc().add_root(layout3)*
i tried what i wrote above and expected acces to the bokeh server that would show me my plots. i have done this before on my previous computer (mac) and it worked then. now on my lenovo it is not working.`
I want to use object detection using tensorflow lite in order to detect a clear face or a covered face where the statement "door opens" is printed when a clear face is detected. I could run this code smoothly previously but later after rebooting raspberry pi 4, although the tensorflow lite runtime is initialized, the raspberry pi 4 disconnects with the ssh completely. The following is the code:
######## Webcam Object Detection Using Tensorflow-trained Classifier #########
#
# Author: Evan Juras
# Date: 10/27/19
# Description:
# This program uses a TensorFlow Lite model to perform object detection on a live webcam
# feed. It draws boxes and scores around the objects of interest in each frame from the
# webcam. To improve FPS, the webcam object runs in a separate thread from the main program.
# This script will work with either a Picamera or regular USB webcam.
#
# This code is based off the TensorFlow Lite image classification example at:
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/examples/python/label_image.py
#
# I added my own method of drawing boxes and labels using OpenCV.
# Import packages
import os
import argparse
import cv2
import numpy as np
import sys
import time
from threading import Thread
import importlib.util
import simpleaudio as sa
# Define VideoStream class to handle streaming of video from webcam in separate processing thread
# Source - Adrian Rosebrock, PyImageSearch: https://www.pyimagesearch.com/2015/12/28/increasing-raspberry-pi-fps-with-python-and-opencv/
class VideoStream:
"""Camera object that controls video streaming from the Picamera"""
def __init__(self,resolution=(640,480),framerate=30):
# Initialize the PiCamera and the camera image stream
self.stream = cv2.VideoCapture(0)
ret = self.stream.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))
ret = self.stream.set(3,resolution[0])
ret = self.stream.set(4,resolution[1])
# Read first frame from the stream
(self.grabbed, self.frame) = self.stream.read()
# Variable to control when the camera is stopped
self.stopped = False
def start(self):
# Start the thread that reads frames from the video stream
Thread(target=self.update,args=()).start()
return self
def update(self):
# Keep looping indefinitely until the thread is stopped
while True:
# If the camera is stopped, stop the thread
if self.stopped:
# Close camera resources
self.stream.release()
return
# Otherwise, grab the next frame from the stream
(self.grabbed, self.frame) = self.stream.read()
def read(self):
# Return the most recent frame
return self.frame
def stop(self):
# Indicate that the camera and thread should be stopped
self.stopped = True
# Define and parse input arguments
parser = argparse.ArgumentParser()
parser.add_argument('--modeldir', help='Folder the .tflite file is located in',
required=True)
parser.add_argument('--graph', help='Name of the .tflite file, if different than detect.tflite',
default='masktracker.tflite')
parser.add_argument('--labels', help='Name of the labelmap file, if different than labelmap.txt',
default='facelabelmap.txt')
parser.add_argument('--threshold', help='Minimum confidence threshold for displaying detected objects',
default=0.5)
parser.add_argument('--resolution', help='Desired webcam resolution in WxH. If the webcam does not support the resolution entered, errors may occur.',
default='640x480')
parser.add_argument('--edgetpu', help='Use Coral Edge TPU Accelerator to speed up detection',
action='store_true')
args = parser.parse_args()
MODEL_NAME = args.modeldir
GRAPH_NAME = args.graph
LABELMAP_NAME = args.labels
min_conf_threshold = float(args.threshold)
resW, resH = args.resolution.split('x')
imW, imH = int(resW), int(resH)
use_TPU = args.edgetpu
# Import TensorFlow libraries
# If tflite_runtime is installed, import interpreter from tflite_runtime, else import from regular tensorflow
# If using Coral Edge TPU, import the load_delegate library
pkg = importlib.util.find_spec('tflite_runtime')
if pkg:
from tflite_runtime.interpreter import Interpreter
if use_TPU:
from tflite_runtime.interpreter import load_delegate
else:
from tensorflow.lite.python.interpreter import Interpreter
if use_TPU:
from tensorflow.lite.python.interpreter import load_delegate
# If using Edge TPU, assign filename for Edge TPU model
if use_TPU:
# If user has specified the name of the .tflite file, use that name, otherwise use default 'edgetpu.tflite'
if (GRAPH_NAME == 'masktracker.tflite'):
GRAPH_NAME = 'edgetpu.tflite'
# Get path to current working directory
CWD_PATH = os.getcwd()
# Path to .tflite file, which contains the model that is used for object detection
PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,GRAPH_NAME)
# Path to label map file
PATH_TO_LABELS = os.path.join(CWD_PATH,MODEL_NAME,LABELMAP_NAME)
# Load the label map
with open(PATH_TO_LABELS, 'r') as f:
labels = [line.strip() for line in f.readlines()]
# Have to do a weird fix for label map if using the COCO "starter model" from
# https://www.tensorflow.org/lite/models/object_detection/overview
# First label is '???', which has to be removed.
if labels[0] == '???':
del(labels[0])
# Load the Tensorflow Lite model.
# If using Edge TPU, use special load_delegate argument
if use_TPU:
interpreter = Interpreter(model_path=PATH_TO_CKPT,
experimental_delegates=[load_delegate('libedgetpu.so.1.0')])
print(PATH_TO_CKPT)
else:
interpreter = Interpreter(model_path=PATH_TO_CKPT)
interpreter.allocate_tensors()
# Get model details
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
height = input_details[0]['shape'][1]
width = input_details[0]['shape'][2]
floating_model = (input_details[0]['dtype'] == np.float32)
input_mean = 127.5
input_std = 127.5
# Initialize frame rate calculation
frame_rate_calc = 1
freq = cv2.getTickFrequency()
global image_capture
image_capture = 0
# Initialize video stream
videostream = VideoStream(resolution=(imW,imH),framerate=30).start()
time.sleep(1)
#for frame1 in camera.capture_continuous(rawCapture, format="bgr",use_video_port=True):
while True:
# Start timer (for calculating frame rate)
t1 = cv2.getTickCount()
# Grab frame from video stream
frame1 = videostream.read()
# Acquire frame and resize to expected shape [1xHxWx3]
frame = frame1.copy()
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_resized = cv2.resize(frame_rgb, (width, height))
input_data = np.expand_dims(frame_resized, axis=0)
# Normalize pixel values if using a floating model (i.e. if model is non-quantized)
if floating_model:
input_data = (np.float32(input_data) - input_mean) / input_std
# Perform the actual detection by running the model with the image as input
interpreter.set_tensor(input_details[0]['index'],input_data)
interpreter.invoke()
# Retrieve detection results
boxes = interpreter.get_tensor(output_details[0]['index'])[0] # Bounding box coordinates of detected objects
classes = interpreter.get_tensor(output_details[1]['index'])[0] # Class index of detected objects
scores = interpreter.get_tensor(output_details[2]['index'])[0] # Confidence of detected objects
#num = interpreter.get_tensor(output_details[3]['index'])[0] # Total number of detected objects (inaccurate and not needed)
# Loop over all detections and draw detection box if confidence is above minimum threshold
for i in range(len(scores)):
if ((scores[i] > min_conf_threshold) and (scores[i] <= 1.0)):
# Get bounding box coordinates and draw box
# Interpreter can return coordinates that are outside of image dimensions, need to force them to be within image using max() and min()
ymin = int(max(1,(boxes[i][0] * imH)))
xmin = int(max(1,(boxes[i][1] * imW)))
ymax = int(min(imH,(boxes[i][2] * imH)))
xmax = int(min(imW,(boxes[i][3] * imW)))
# Draw label
object_name = labels[int(classes[i])] # Look up object name from "labels" array using class index
if (object_name=="face unclear" ):
color = (0, 255, 0)
cv2.rectangle(frame, (xmin,ymin), (xmax,ymax),color, 2)
print("Face Covered: Door Not Opened")
if(image_capture == 0):
path = r'/home/pi/Desktop/tflite_1/photographs'
date_string = time.strftime("%Y-%m-%d_%H%M%S")
#print(date_string)
cv2.imwrite(os.path.join(path, (date_string + ".jpg")),frame)
#cv2.imshow("Photograph",frame)
#mp3File = input(alert_audio.mp3)
print("Photo Taken")
#w_object = sa.WaveObject.from_wave_file('alert_audio.wav')
#p_object = w_object.play()
#p_object.wait_done()
image_capture = 1
else:
color = (0, 0, 255)
cv2.rectangle(frame, (xmin,ymin), (xmax,ymax),color, 2)
print("Face Clear: Door Opened")
image_capture = 0
#cv2.rectangle(frame, (xmin,ymin), (xmax,ymax),color, 2)
#image = np.asarray(ImageGrab.grab())
label = '%s: %d%%' % (object_name, int(scores[i]*100)) # Example: 'person: 72%'
labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2) # Get font size
label_ymin = max(ymin, labelSize[1] + 10) # Make sure not to draw label too close to top of window
cv2.rectangle(frame, (xmin, label_ymin-labelSize[1]-10), (xmin+labelSize[0], label_ymin+baseLine-10), (255, 255, 255), cv2.FILLED) # Draw white box to put label text in
cv2.putText(frame, label, (xmin, label_ymin-7), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2) # Draw label text
if ((scores[0] < min_conf_threshold)):
cv2.putText(frame,"No Face Detected",(260,260),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, color=(255,0,0))
print("No Face Detected")
image_capture = 0
# Draw framerate in corner of frame
cv2.putText(frame,'FPS: {0:.2f}'.format(frame_rate_calc),(30,50),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,0),2,cv2.LINE_AA)
# All the results have been drawn on the frame, so it's time to display it.
cv2.imshow('Object detector', frame)
# Calculate framerate
t2 = cv2.getTickCount()
time1 = (t2-t1)/freq
frame_rate_calc= 1/time1
# Press 'q' to quit
if cv2.waitKey(1) == ord('q'):
break
# Clean up
cv2.destroyAllWindows()
videostream.stop()
Any help is appreciated.
Regards,
MD
This code is specified to visualize the CALIPSO satellite atmospheric profiles
The input files are .HDF
The code is copyrighted to the HDF group.
In the begining, I struggled with installing the basemap,
finally I installed it using .whl file on my windows10.
Now, this error is reached when I run the script:
SystemError:
execution of module _geoslib raised unreported exception.
I have looked a lot in google, but nothing done.
Can you please help me?
Cheers
"Copyright (C) 2014-2019 The HDF Group
Copyright (C) 2014 John Evans
This example code illustrates how to access and visualize a LaRC CALIPSO file
in file in Python.
If you have any questions, suggestions, or comments on this example, please use
the HDF-EOS Forum (http://hdfeos.org/forums). If you would like to see an
example of any other NASA HDF/HDF-EOS data product that is not listed in the
HDF-EOS Comprehensive Examples page (http://hdfeos.org/zoo), feel free to
contact us at eoshelp#hdfgroup.org or post it at the HDF-EOS Forum
(http://hdfeos.org/forums).
Usage: save this script and run
$python CAL_LID_L2_VFM-ValStage1-V3-02.2011-12-31T23-18-11ZD.hdf.py
The HDF file must either be in your current working directory
or in a directory specified by the environment variable HDFEOS_ZOO_DIR.
Tested under: Python 2.7.15::Anaconda custom (64-bit)
Last updated: 2019-01-25
"""
import os
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.basemap import Basemap
from matplotlib import colors
USE_NETCDF4 = False
def run(FILE_NAME):
# Identify the data field.
DATAFIELD_NAME = 'Feature_Classification_Flags'
if USE_NETCDF4:
from netCDF4 import Dataset
nc = Dataset(FILE_NAME)
# Subset the data to match the size of the swath geolocation fields.
# Turn off autoscaling, we'll handle that ourselves due to presence of
# a valid range.
var = nc.variables[DATAFIELD_NAME]
data = var[:,1256]
# Read geolocation datasets.
lat = nc.variables['Latitude'][:]
lon = nc.variables['Longitude'][:]
else:
from pyhdf.SD import SD, SDC
hdf = SD(FILE_NAME, SDC.READ)
# Read dataset.
data2D = hdf.select(DATAFIELD_NAME)
data = data2D[:,1256]
# Read geolocation datasets.
latitude = hdf.select('Latitude')
lat = latitude[:]
longitude = hdf.select('Longitude')
lon = longitude[:]
# Subset data. Otherwise, all points look black.
lat = lat[::10]
lon = lon[::10]
data = data[::10]
# Extract Feature Type only through bitmask.
data = data & 7
# Make a color map of fixed colors.
cmap = colors.ListedColormap(['black', 'blue', 'yellow', 'green', 'red', 'purple', 'gray', 'white'])
# The data is global, so render in a global projection.
m = Basemap(projection='cyl', resolution='l',
llcrnrlat=-90, urcrnrlat=90,
llcrnrlon=-180, urcrnrlon=180)
m.drawcoastlines(linewidth=0.5)
m.drawparallels(np.arange(-90.,90,45))
m.drawmeridians(np.arange(-180.,180,45), labels=[True,False,False,True])
x,y = m(lon, lat)
i = 0
for feature in data:
m.plot(x[i], y[i], 'o', color=cmap(feature), markersize=3)
i = i+1
long_name = 'Feature Type at Altitude = 2500m'
basename = os.path.basename(FILE_NAME)
plt.title('{0}\n{1}'.format(basename, long_name))
fig = plt.gcf()
# define the bins and normalize
bounds = np.linspace(0,8,9)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
# create a second axes for the colorbar
ax2 = fig.add_axes([0.93, 0.2, 0.01, 0.6])
cb = mpl.colorbar.ColorbarBase(ax2, cmap=cmap, norm=norm, spacing='proportional', ticks=bounds, boundaries=bounds, format='%1i')
cb.ax.set_yticklabels(['invalid', 'clear', 'cloud', 'aerosol', 'strato', 'surface', 'subsurf', 'no signal'], fontsize=5)
# plt.show()
pngfile = "{0}.py.png".format(basename)
fig.savefig(pngfile)
if __name__ == "__main__":
# If a certain environment variable is set, look there for the input
# file, otherwise look in the current directory.
hdffile = 'CAL_LID_L2_VFM-ValStage1-V3-02.2011-12-31T23-18-11ZD.hdf'
try:
fname = os.path.join(os.environ['HDFEOS_ZOO_DIR'], ncfile)
except KeyError:
fname = hdffile
run(fname)
Please try miniconda and use basemap from conda-forge:
conda install -c conda-forge basemap
I would like to use my own pdf() plot device in an .Rnw document converted to a PDF with knitr. After the PDF of a figure is generated
it should call pdfCrop.off() instead of dev.off() (or whatever knitr calls); this would
perfectly crop the resulting figures. How can this be done?
The following MWE works (but without cropping) if (*) is commented out (and the line before properly closed).
\documentclass{article}
\begin{document}
<<knitr_options, echo = FALSE, results = "hide", purl = FALSE>>=
## Custom graphics device (for cropping .pdf):
pdfCrop <- function(file, width, height, ...)
{
f <- file
grDevices::pdf(f, width = width, height = height, onefile = FALSE)
assign(".pdfCrop.file", f, envir = globalenv())
}
pdfCrop.off <- function() # used automagically
{
grDevices::dev.off() # closing the pdf device
f <- get(".pdfCrop.file", envir = globalenv())
system(paste("pdfcrop --pdftexcmd pdftex", f, f, "1>/dev/null 2>&1"),
intern = FALSE) # crop the file (relies on PATH)
}
## knitr options
knitr::opts_chunk$set(fig.path = "./fig_", background = "#FFFFFF",
dev = "pdfCrop", fig.ext = "pdf") # (*) => how to use pdfCrop.off() instead of dev.off()?
#
<<MWE>>=
<<fig-MWE, eval = FALSE, echo = FALSE>>=
plot(1:10, 10:1)
#
\setkeys{Gin}{width=\textwidth}
\begin{figure}[htbp]
\centering
\framebox{
<<figMWE, echo = FALSE, fig.width=6, fig.height=6>>=
<<fig-MWE>>
#
}
\caption{Just some text to show the actual textwidth in order to see that the
figure is not perfectly horizontally aligned due to some white space which can
be avoided by properly crop the figure with an adequate pdf crop device.}
\end{figure}
\end{document}
knitr already provides a crop device based on pdfcrop, so we can use that via a hook:
\documentclass{article}
\begin{document}
<<knitr_options, echo = FALSE, results = "hide", purl = FALSE>>=
## knitr options
library(knitr)
knit_hooks$set(crop = hook_pdfcrop)
knitr::opts_chunk$set(fig.path = "./fig_", # all figures are saved as fig_*
background = "#FFFFFF", # avoid color
crop = TRUE) # always crop
#
<<MWE>>=
<<fig-MWE, eval = FALSE, echo = FALSE>>=
plot(1:10, 10:1)
#
\setkeys{Gin}{width=\textwidth}
\begin{figure}[htbp]
\centering
<<figMWE, echo = FALSE, fig.width=6, fig.height=6>>=
<<fig-MWE>>
#
\caption{Just some text to show the actual textwidth in order to see that the
figure is not perfectly horizontally aligned due to some white space which can
be avoided by properly crop the figure with an adequate pdf crop device.}
\end{figure}
\end{document}
I would like to use the chaco tools ScatterInspector and/or ScatterInspectorOverlay with enaml. I've set up a very simple controller and view (source below) but cannot determine how to proceed. I have tried unsuccessfully to follow the minimal and old examples I've found.
If I uncomment the overlay part for ScatterInspectorOverlay, the code fails to run with
File ".../chaco/scatter_inspector_overlay.py", line 51, in overlay if not plot or not plot.index or not getattr(plot, "value", True):
If I comment out the overlay part, I of course don't get the overlay behavior I want and also, on moving the mouse, get
File ".../chaco/tools/scatter_inspector.py", line 48, in normal_mouse_move index = plot.map_index((event.x, event.y), threshold=self.threshold)
view.enaml source:
from enaml.widgets.api import (
Window, Container, EnableCanvas,
)
enamldef ScatterView(Window):
attr controller
title = "Scatter Inspector Test"
initial_size = (640,480)
Container:
EnableCanvas:
component = controller.scatter_plot
controller.py source:
import enaml
from enaml.stdlib.sessions import show_simple_view
from traits.api import HasTraits, Instance
from chaco.api import Plot, ArrayPlotData, ScatterInspectorOverlay
from chaco.tools.api import ScatterInspector
from numpy import linspace, sin
class ScatterController(HasTraits):
scatter_plot = Instance(Plot)
def _scatter_plot_default(self):
# data
x = linspace(-14, 14, 100)
y = sin(x) * x**3
plotdata = ArrayPlotData(x = x, y = y)
# plot
scatter_plot = Plot(plotdata)
renderer = scatter_plot.plot(("x", "y"), type="scatter", color="red")
# inspector
scatter_plot.tools.append(ScatterInspector(scatter_plot))
# overlay
# scatter_plot.overlays.append( ScatterInspectorOverlay(
# scatter_plot,
# hover_color = 'red',
# hover_marker_size = 6,
# selection_marker_size = 6,
# selection_color = 'yellow',
# selection_outline_color='purple',
# selection_line_width = 3
# ))
#return
return scatter_plot
if __name__ == "__main__":
with enaml.imports():
from view import ScatterView
main_controller = ScatterController()
window = ScatterView(controller=ScatterController())
show_simple_view(window)
The problem with my above code was that I was adding ScatterInspector to scatter_plot rather than to renderer and that I was missing the [0] index to get renderer.
The key thing I was really wanting to do, though, was to be notified when the mouse was hovering over a data point and/or a data point was selected. I added when_hover_or_selection_changes which shows how to do that.
Working controller.py:
...
# plot
scatter_plot = Plot(plotdata)
renderer = scatter_plot.plot(("x", "y"), type="scatter", color="lightblue")[0]
# inspector
renderer.tools.append(ScatterInspector(renderer))
# overlay
renderer.overlays.append(ScatterInspectorOverlay(renderer,
hover_color="red",
hover_marker_size=6,
selection_marker_size=6,
selection_color="yellow",
selection_outline_color="purple",
selection_line_width=3))
...
# get notified when hover or selection changes
#on_trait_change('renderer.index.metadata')
def when_hover_or_selection_changes(self):
print 'renderer.index.metadata = ', self.renderer.index.metadata