I am trying to follow different papers and tutorials to learn how to solve optimization problems of modelica modells.
In http://www.syscop.de/files/2015ss/events/opcon-thermal-systems/optimization_tool_chain_in_jmodelica.org_toivo_henningsson.pdf I found a very simple tutorial. But when I execute it I get some very open error messages.
I am using Python 2.7 with jupyther.
Here is my Notepad:
from pyjmi import transfer_optimization_problem
import matplotlib.pyplot as plt
import os.path
file_path = os.path.join("D:\Studies", "Integrator.mop")
op = transfer_optimization_problem('optI', file_path)
res = op.optimize()
t = res['time']
x = res['x']
u = res['u']
plt.plot(t,x,t,u)
My modelica file:
package Integrator
model Integrator
Real x(start=2, fixed = true);
input Real u;
equation
der(x) = -u;
end Integrator;
optimization optI(objective = finalTime, objectiveIntegrand = x^2 + u^2, startTime = 0, finalTime(free = true, min = 0.5, max = 2, initialGuess = 1))
Real x (start = 2, fixed = true);
input Real u;
equation
der(x) = -u;
constraint
u <= 2;
x(finalTime) = 0;
end optI;
end Integrator;
When I excute the code I get an RuntimeError, telling me that a java error occured and details where printed. From the Traceback I do not know what the note
This file is compatible with both classic and new-style classes
mean. I know that my setup is working because I executed the CSTR tutorial given by modelon. But now, it try to use my own models and it is giving me that error.
Runtime Error desciption
Using same syntax like in Modelica for import
e.g.
import Modelica.SIunits.Temperature;
where the package structure is part of the model-identification should resolve the issue.
op = transfer_optimization_problem('Integrator.optI', file_path)
Related
I have provided the following python code,but the problem is that when it doesnot receive any input, it start showing error. how can i modify the code in a way that this error dosnot appear:
#!/usr/bin/env python from roslib import message import rospy import sensor_msgs.point_cloud2 as pc2 from sensor_msgs.msg import PointCloud2, PointField import numpy as np import ros_numpy from geometry_msgs.msg import Pose
#listener def listen():
rospy.init_node('listen', anonymous=True)
rospy.Subscriber("/Filtered_points_x", PointCloud2, callback_kinect)
def callback_kinect(data):
pub = rospy.Publisher('lidar_distance',Pose, queue_size=10)
data_lidar = Pose()
xyz_array = ros_numpy.point_cloud2.pointcloud2_to_xyz_array(data)
print(xyz_array)
mini_data = min(xyz_array[:,0])
print("mini_data", mini_data)
data_lidar.position.x = mini_data
pub.publish(data_lidar)
print("data_points", data_lidar.position.x)
height = int (data.height / 2)
middle_x = int (data.width / 2)
middle = read_depth (middle_x, height, data) # do stuff with middle
def read_depth(width, height, data) :
if (height >= data.height) or (width >= data.width) :
return -1
data_out = pc2.read_points(data, field_names= ('x','y','z'), skip_nans=True, uvs=[[width, height]])
int_data = next(data_out)
rospy.loginfo("int_data " + str(int_data))
return int_data
if __name__ == '__main__':
try:
listen()
rospy.spin()
except rospy.ROSInterruptException:
pass
the following is the error that i mentioned:
[[ 7.99410915 1.36072445 -0.99567264]]
('mini_data', 7.994109153747559)
('data_points', 7.994109153747559)
[INFO] [1662109961.035894]: int_data (7.994109153747559, 1.3607244491577148, -0.9956726431846619)
[]
[ERROR] [1662109961.135572]: bad callback: <function callback_kinect at 0x7f9346d44230>
Traceback (most recent call last):
File "/opt/ros/kinetic/lib/python2.7/dist-packages/rospy/topics.py", line 750, in _invoke_callback
cb(msg)
File "/home/masoumeh/catkin_ws/src/yocs_velocity_smoother/test4/distance_from_pointcloud.py", line 27, in callback_kinect
mini_data = min(xyz_array[:,0])
ValueError: min() arg is an empty sequence
The code is still receiving input, but specifically it’s receiving an empty array. You’re then trying to splice the empty array, causing the error. Instead you should check that the array has elements before the line min(xyz_array[:,0]). It can be as simple as:
if xyz_array == []:
return
As another note, you’re creating a publisher in the callback. You shouldn’t do this as a new publisher will be created every time it gets called. Instead create it as a global variable.
I think that the issue is silly.
I'd like to run the code on two computers and I need to use a list. I followed this Tutorials
I used my PC as a talker and computer of the robot as a listener.
when running the code on my PC, the output is good as I needed.
[INFO] [1574230834.705510]: [3.0, 2.1]
[INFO] [1574230834.805443]: [3.0, 2.1]
but once running the code on the computer of the robot, the output is:
Traceback (most recent call last):
File "/home/redhwan/learn.py", line 28, in <module>
talker()
File "/home/redhwan/learn.py", line 23, in talker
pub.publish(position.data)
File "/opt/ros/kinetic/lib/python2.7/dist-packages/rospy/topics.py", line 886, in publish
raise ROSSerializationException(str(e))
rospy.exceptions.ROSSerializationException: <class 'struct.error'>: 'required argument is not a float' when writing 'data: [3.0, 2.1]'
full code on PC:
#!/usr/bin/env python
import rospy
from std_msgs.msg import Float32
x = 3.0
y = 2.1
def talker():
# if a == None:
pub = rospy.Publisher('position', Float32, queue_size=10)
rospy.init_node('talker', anonymous=True)
# rospy.init_node('talker')
rate = rospy.Rate(10) # 10hz
while not rospy.is_shutdown():
position = Float32()
a = [x,y]
# a = x
position.data = list(a)
# position.data = a
# hello_str = [5.0 , 6.1]
rospy.loginfo(position.data)
pub.publish(position.data)
rate.sleep()
if __name__ == '__main__':
try:
talker()
except rospy.ROSInterruptException:
pass
full code on the computer of the robot:
#!/usr/bin/env python
import rospy
from std_msgs.msg import Float32
def callback(data):
# a = list(data)
a = data.data
print a
def listener():
rospy.init_node('listener', anonymous=True)
rospy.Subscriber("position", Float32, callback)
# spin() simply keeps python from exiting until this node is stopped
rospy.spin()
if __name__ == '__main__':
listener()
when using one number as float everything is OK.
I understand how to publish and subscribe to them separately as the float but I'd like to do it as list
Any ideas or suggestion, it would be appreciated.
When you exchange messages in ROS is preferred to adopt standard messages if there is something relatively simple. Of course, when you develop more sophisticated systems (or modules), you can implement your own custom messages.
So in the case of float array, Float32MultiArray is your friend.
Populating the message in one side will look like that (just an example using a 2 elements float32 array) in C++:
.
.
.
while (ros::ok())
{
std_msgs::Float32MultiArray velocities;
velocities.layout.dim.push_back(std_msgs::MultiArrayDimension());
velocities.layout.dim[0].label = "velocities";
velocities.layout.dim[0].size = 2;
velocities.layout.dim[0].stride = 1;
velocities.data.clear();
velocities.data.push_back(count % 255);
velocities.data.push_back(-(count % 255));
velocities_demo_pub.publish(velocities);
ros::spinOnce();
loop_rate.sleep();
++count;
}
.
.
.
in Python for 8 elements array an example will look like:
.
.
.
while not rospy.is_shutdown():
# compose the multiarray message
pwmVelocities = Float32MultiArray()
myLayout = MultiArrayLayout()
myMultiArrayDimension = MultiArrayDimension()
myMultiArrayDimension.label = "motion_cmd"
myMultiArrayDimension.size = 1
myMultiArrayDimension.stride = 8
myLayout.dim = [myMultiArrayDimension]
myLayout.data_offset = 0
pwmVelocities.layout = myLayout
pwmVelocities.data = [0, 10.0, 0, 10.0, 0, 10.0, 0, 10.0]
# publish the message and log in terminal
pub.publish(pwmVelocities)
rospy.loginfo("I'm publishing: [%f, %f, %f, %f, %f, %f, %f, %f]" % (pwmVelocities.data[0], pwmVelocities.data[1],
pwmVelocities.data[2], pwmVelocities.data[3], pwmVelocities.data[4], pwmVelocities.data[5],
pwmVelocities.data[6], pwmVelocities.data[7]))
# repeat
r.sleep()
.
.
.
and on the other side your callback (in C++), will look like:
.
.
.
void hardware_interface::velocity_callback(const std_msgs::Float32MultiArray::ConstPtr &msg) {
//velocities.clear();
if (velocities.size() == 0) {
velocities.push_back(msg->data[0]);
velocities.push_back(msg->data[1]);
} else {
velocities[0] = msg->data[0];
velocities[1] = msg->data[1];
}
vel1 = msg->data[0];
vel2 = msg->data[1];
//ROS_INFO("Vel_left: [%f] - Vel_right: [%f]", vel1 , vel2);
}
.
.
.
Hope that you got an idea...if you need something more drop me a line!
" I'm new in neural networks and DL4j, and I want to train neural network with CSV and build linear regression. How can I fix these errors "Cannot resolve method'.iterations and getFeatureMatrix()'"?
"Previously I'm tried to do that, but have another error in 'seed'".
import org.datavec.api.records.reader.RecordReader;
import org.datavec.api.records.reader.impl.csv.CSVRecordReader;
import org.datavec.api.split.FileSplit;
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.nd4j.evaluation.classification.Evaluation;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.DataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import java.io.File;
public class Data {
public static void main(String[] args) throws Exception {
Parameters:
int seed = 3000;
int batchSize = 200;
double learningRate = 0.001;
int nEpochs = 150;
int numInputs = 2;
int numOutputs = 2;
int numHiddenNodes = 100;
Load data:
//load data train
RecordReader rr = new CSVRecordReader();
rr.initialize(new FileSplit(new File("train.csv")));
DataSetIterator trainIter = new RecordReaderDataSetIterator(rr, batchSize, 0, 2);
//load test data
RecordReader rrTest = new CSVRecordReader();
rr.initialize(new FileSplit(new File("test.csv")));
DataSetIterator testIter = new RecordReaderDataSetIterator(rrTest, batchSize, 0, 2);
Network Configuration:
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(seed)
.iterations(1000)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.learningRate(learningRate)
.updater(Updater.NESTEROVS).momentum(0.9)
.list()
.layer(0, new DenseLayer.Builder()
.nIn(numInputs)
.nOut(numHiddenNodes)
.weightInit(WeightInit.XAVIER)
.activation(Activation.fromString("relu"))
.build())
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.weightInit(WeightInit.XAVIER)
.activation(Activation.fromString("softmax"))
.weightInit(WeightInit.XAVIER)
.nIn(numHiddenNodes)
.nOut(numOutputs)
.build()
)
.pretrain(false).backprop(true).build();
MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();
model.setListeners(new ScoreIterationListener((15)));
for (int n = 0; n < nEpochs; n++) {
model.fit((trainIter));
System.out.println(("--------------eval model"));
Evaluation eval = new Evaluation(numOutputs);
while (testIter.hasNext()) {
DataSet t = testIter.next();
INDArray features = getFeatureMatrix();
INDArray lables = t.getLabels();
INDArray predicted = model.output(features, false);
eval.eval(lables, predicted);
}
System.out.println(eval.stats());
}
}
}
Logs
Build
First you should consider to use more class (like one for the definition of the neural network, one for the training process etc, ...). Just a best practice stuff.
I do not know which version of DL4J you're using but we can notice that getFeatureMatrix() has been removed. One more thing is that this function should be called on a DataSet object and not "statically" like you seem to do. (you should do t.getFeatureMatrix()).
It is pretty same things about iterations() function of the neural network creation; This function has been removed since some DL4J releases. You can get more information about this function on this thread. Now you have to find an alternative to set up number of iteration, you can take a look at this thread. Hope it is answering your question !
I have got a file named test.xls which looks like this:
All
Index MaxProd
Nuclear 300,0
DomesticCoal_Anthracite 588,0
BrownLignite 203,1
ImportedCoal_SubBituminous 150,4
ImportedCoal_Bituminous 194,4
CCGT_1 500,0
CCGT_2 500,0
CCGT_3 500,0
CCGT_4 667,5
OCGT_1 400,0
OCGT_2 400,0
OCGT_3 400,0
FuelOilGas 441,8
Where All (and rest) is in column A and MaxProd column B. But when I try to read the data in with my little script:
from pyomo.environ import *
model = AbstractModel()
model.Index = Set()
model.MaxProd = Param(model.Index)
data = DataPortal(model=model)
data.load(filename="Test.xls", range="All", format='set', set=model.Index)
It gives me the error message:
OSError: Unknown range name All'
Does anyone know what I am doing wrong?
I would like to do a peak detection of a .wav file signal in Python with Octave library on a Raspberry pi 3 with Raspbian but there is a problem with the octave.findpeaks function. I had this error:findpeaks : argument 'MeanPeakHeight' did not match any valid parameter of the parser
I have installed all the packages concerning Octave so this is why i don't understand.
This is a part of my program :
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import wavfile as wav
from scipy.signal import find_peaks_cwt, butter, lfilter
from pylab import *
import os
from operator import truediv
from easygui import *
from oct2py import octave
"High and Low Frequency for the filter"
low = 100
high = 50
list_file = []
octave.eval("pkg load signal")
def display_wav(wav_file):
samplerate, beat = wav.read('/home/pi/heartbeat_project/heartbeat_songs/%s' %wav_file)
beat_resize = np.fromfile(open('/home/pi/heartbeat_project/heartbeat_songs/%s' %wav_file),np.int16)[4*samplerate:float(beat.shape[0])-4*samplerate]
beat_resize = beat_resize / (2.**15)
timeArray = arange(0,float(beat_resize.shape[0]),1)
timeArray = timeArray / samplerate
ylow = butter_lowpass_filter(samplerate, 5, low, beat_resize)
y = butter_highpass_filter(samplerate, 5, high, ylow)
peaks, indexes = octave.findpeaks(np.array(y),'DoubleSided','MeanPeakHeight',np.std(y))
findpeaks is part of the octave-forge signal package:source file
This function doesn't have a 'MeanPeakHeight' parameter. Is guess this is a typo and you want 'MinPeakHeight'