I have some .nc data that I use xarray to analyze. I wanted to overlay a shapefile on the plot and came across hv.plot which seems to have the interactive component I have been needing. However, when I tried to plot, it does not seem to work. Then I found the following thread: Why doesn't holoviews show histogram in Spyder? but could not get Maxime's answer to work and Sander's only partly works, however I have trouble "connecting to the local host" consistently. Does anyone have a solution?
ds_path = 'Z:/MODIS-LAADS-DAAC/'
ds_file = 'anom_2016_v1.nc'
ds_anom = xr.open_dataset(ds_path+ds_file)
x = ds_anom.Rrs_667.isel(time=0)
# create holoviews plot
hv_map = x.hvplot()
# display graph in browser
# a bokeh server is automatically started
bokeh_server = pn.Row(hv_map).show(port=12345)
# stop the bokeh server (when needed)
bokeh_server.stop()
However, I get this 95% of the time ERR_CONNECTION_REFUSED but occasionally get it to work and look like this (however I don't know why!!!) sample ocean data plot
IN SUMMARY:
What I want to do:
https://tutorial.xarray.dev/scipy-tutorial/04_plotting_and_visualization.html#interactive-bokeh-plots-using-hvplot
, https://hvplot.holoviz.org/user_guide/Geographic_Data.html#declaring-an-output-projection
What I have tried:
https://discourse.holoviz.org/t/whats-the-most-efficient-way-to-overlay-a-shapefile-onto-a-hvplot-xarray-plot/397, Why doesn't holoviews show histogram in Spyder?
What I get: ERR_CONNECTION_REFUSED but occasionally (don't know why) sample ocean data plot
Related
I am implementing a small monitoring system to compute the error between (actual, desired) for a controller (and also recording them for quick and simple analysis). In some of my code, I've added my Systems to my DiagramBuilder and have connected everything using Connect().
I have a controller system that takes in desired input, then produces the actual as output.
Rather than try to remember the inputs connected to the desired output, I'd rather just trace it back.
How do I do that?
From quick perusal, there is both DiagramBuilder.connection_map() and Diagram.connection_map().
The following code seems to work as of v1.11.0:
def trace_to_output(diagram_or_builder, input_port):
system = input_port.get_system()
input_locator = (system, input_port.get_index())
connection_map = diagram_or_builder.connection_map()
output_system, output_index = connection_map[input_locator]
output_port = output_system.get_output_port(output_index)
return output_port
EDIT: I had dict_inverse() in there incorrectly. Fixed.
I have some existing PyTorch codes with cuda() as below, while net is a MainModel.KitModel object:
net = torch.load(model_path)
net.cuda()
and
im = cv2.imread(image_path)
im = Variable(torch.from_numpy(im).unsqueeze(0).float().cuda())
I want to test the code in a machine without any GPU, so I want to convert the cuda-code into CPU version. I tried to look at some relevant posts regarding the CPU/GPU switch of PyTorch, but they are related to the usage of device and thus doesn't apply to my case.
As pointed out by kHarshit in his comment, you can simply replace .cuda() call with .cpu():
net.cpu()
# ...
im = torch.from_numpy(im).unsqueeze(0).float().cpu()
However, this requires changing the code in multiple places every time you want to move from GPU to CPU and vice versa.
To alleviate this difficulty, pytorch has a more "general" method .to().
You may have a device variable defining where you want pytorch to run, this device can also be the CPU (!).
for instance:
if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
Once you determined once in your code where you want/can run, simply use .to() to send your model/variables there:
net.to(device)
# ...
im = torch.from_numpy(im).unsqueeze(0).float().to(device)
BTW,
You can use .to() to control the data type (.float()) as well:
im = torch.from_numpy(im).unsqueeze(0).to(device=device, dtype=torch.float)
PS,
Note that the Variable API has been deprecated and is no longer required.
net = torch.load(model_path, map_location=torch.device('cpu'))
Pytorch docs: https://pytorch.org/tutorials/beginner/saving_loading_models.html#save-on-cpu-load-on-gpu
I installed ROS melodic version in Ubuntu 18.04.
I'm running a rosbag in the background to mock cameras in messages rostopics.
I set the camera names in rosparams and iterated through it to capture each camera topics.
I'm using message_filter ApproximateTimeSynchronizer to get time synchronized data as mentioned in the official documentation,
http://wiki.ros.org/message_filters
But most of the time the callback function to ApproximateTimeSynchronizer is not being called/is having delay. The code snippet I'm using is given below:
What am I doing wrong here?
def camera_callback(*args):
pass # Other logic comes here
rospy.init_node('my_listener', anonymous=True)
camera_object_data = []
for camera_name in rospy.get_param('/my/cameras'):
camera_object_data.append(message_filters.Subscriber(
'/{}/hd/camera_info'.format(camera_name), CameraInfo))
camera_object_data.append(message_filters.Subscriber(
'/{}/hd/image_color_rect'.format(camera_name), Image))
camera_object_data.append(message_filters.Subscriber(
'/{}/qhd/image_depth_rect'.format(camera_name), Image))
camera_object_data.append(message_filters.Subscriber(
'/{}/qhd/points'.format(camera_name), PointCloud2)
topic_list = [filter_obj for filter_obj in camera_object_data]
ts = message_filters.ApproximateTimeSynchronizer(topic_list, 10, 1, allow_headerless=True)
ts.registerCallback(camera_callback)
rospy.spin()
Looking at your code, it seems correct. There is, however, a trouble with perhaps bad timestamps and ergo this synchronizer as well, see http://wiki.ros.org/message_filters/ApproximateTime for algorithm assumptions.
My recommendation is to write a corresponding node that publishes empty versions of these four msgs all at the same time. If it's still not working in this perfect scenario, there is an issue with the code above. If it is working just fine, then you need to pay attention to the headers.
Given that you have it as a bag file, you can step through the msgs on the command line and observe the timestamps as well. (Can also step within python).
$ rosbag play --pause recorded1.bag # step through msgs by pressing 's'
On time-noisy msgs with small payloads, I've just written a node to listen to all these msgs, and republish them all with the latest time found on any of them (for sync'd logging to csv). Not optimal, but it should reveal where the issue lies.
During my test on Pepper, I found some difficulties in realizing continuative collaborative dialog.
In particular, after about 10 minutes, it seems that the ALSpeechRecognition engine stops working.
In other words, Pepper dialog panel remains empty and/or the robot does not understand my words, even if the structure worked some minute before.
I tried to stop and restart it (i.e., the engine) via SSH terminal, by using:
qicli call ALSpeechRecognition.pause 1
qicli call ALSpeechRecognition.pause 0
It should restart the engine according to the guidelines shown here, but it does not work.
Thank you so much guys.
Sincerely,
Giovanni
According to the tutorial, starting and stopping the speech recognition engine is done by subscribing/unsubscribing it.
The recommended way to do this is unsubscribing and subscribing back to it. For me it also worked changing the speech reco language and chaging it back to the one you had previously.
Luis is right and to do so just create a function as below given and call it if ActiveListenning event comes false from ALSpeechRecognition module. Note: Use ALMemory module to get data from ALSpeechRecogntion.
asr_service = ALProxy("ALSpeechRecognition",ip,port)
memory = ALProxy("ALMemory",ip,port)
def reset():
asr_service.unsubscribe("ASR_Engine")
asr_service.subscribe("ASR_Engine")
ALS = memory.getData("ALSpeechRecognition/ActiveListening")
if ALS==False:
reset()
I'm trying to make CTR (Click through rate) prediction using a python module named 'xlearn'.
It enables me to implement a FFM (field-aware factorisation machine) quite easily.
However, I have a problem with the fit function ( supposed to train the model) which crashes the kernel of my jupyter notebook without any error messages.
Here is the code :
import xlearn as xl
ffm_model = xl.create_ffm()
param = {'task':'binary', 'lr':0.2, 'lambda':0.002, 'metric':'acc'}
ffm_model.setTrain('ffm_train.txt')
ffm_model.fit(param, "./model.out") #this line crashes the kernel
I've already tried to fit the model just after python ffm_model = xl.create_ffm() this also crashes the kernel without any error messages ...
Don't hesitate to share your ideas I'm really stuck here.
I didn't realize the xLearn module was showing error messages in the terminal :
Xlearn Imgae Error Messages