Hi I am developing a plugin for Qgis in python, I need to connect the sselectionChanged signal emitted when a feature of the layer is selected, I could not find any examples on internet, here is what I have done as far
QObject.connect(self.iface.mapCanvas(),SIGNAL("selectionChanged(QgsMapLayer)"), self.test)
on the log of qgis I get this warning
Warning: Object::connect: (receiver name: 'MainWindow')
What you have to do is to create a signal every time you select a layer:
def initGui(self):
# ... your other code here
self.handler = None
self.selected_layer = None
QObject.connect(self.iface,SIGNAL("currentLayerChanged(QgsMapLayer *)") ,self.layerChanged)
def layerChanged(self, layer):
if self.handler:
QObject.disconnect(self.selected_layer, SIGNAL("selectionChanged()"),self.select_changed)
self.handler = False
self.selected_layer = None
if layer is not None:
if layer.isValid():
QObject.connect(layer,SIGNAL("selectionChanged()"),self.select_changed)
self.selected_layer = layer
def select_changed(self):
# whatever
pass
edit: disconnect(wrong layer) fixed
Related
I have successfully added and simulated my MMIO perihperal coupled to a normal sized rocket core before.
But now I want to try to add it to a small core (so called TinyCore), and this is the part where I am having problems. Also, just in case it is relevant, the conexions with my peripheral are all trough FIFOs.
First, the error I am getting when trying to generate the design:
[error] java.lang.IllegalArgumentException: requirement failed: Ports cannot overlap: AddressSet(0x80000000, 0x3fff) AddressSet(0x80000000, 0xfffffff)
I imagine this comes from the fact that the small rocket config has a different memory map, which I don't know, and I am trying to add the peripheral to an address that doesn't exist in this configuration.
Here it is the configuration I am using:
class myTinyRocketConfig2 extends Config(
new freechips.rocketchip.subsystem.WithInclusiveCache(nBanks=1, nWays=4, capacityKB=128) ++
new freechips.rocketchip.subsystem.With1TinyCore ++ // single tiny rocket-core
new chipyard.config.AbstractConfig)
And this is how I added the peripheral, it shows the address and some other parameters:
class TLTxWriteQueue
(
depth: Int = 4,
csrAddress: AddressSet = AddressSet(0x2000, 0xff),
beatBytes: Int = 4,
)(implicit p: Parameters) extends TxWriteQueue(depth) with TLHasCSR {
val devname = "tlQueueIn"
val devcompat = Seq("ucb-art", "dsptools")
val device = new SimpleDevice(devname, devcompat) {
override def describe(resources: ResourceBindings): Description = {
val Description(name, mapping) = super.describe(resources)
Description(name, mapping)
}
}
// make diplomatic TL node for regmap
override val mem = Some(TLRegisterNode(address = Seq(csrAddress), device = device, beatBytes = beatBytes))
}
I apologize in advance for any stupid mistake, as I am a beginner trying to go trough with his first project. Thanks
The the Rocket TinyCore uses a default scratchpad instead of a backing memory. This scratchpad 0x80000000 to 0x80003fff is overlapping with the memport's address range.
You'll have to remove the memport. This is what chipyard's TinyRocketConfig does. This config should generate a design (just without an L2 Cache or backing memory).
class TinyRocketConfig extends Config(
new chipyard.config.WithTLSerialLocation(
freechips.rocketchip.subsystem.FBUS,
freechips.rocketchip.subsystem.PBUS) ++ // attach TL serial adapter to f/p busses
new chipyard.WithMulticlockIncoherentBusTopology ++ // use incoherent bus topology
new freechips.rocketchip.subsystem.WithNBanks(0) ++ // remove L2$
new freechips.rocketchip.subsystem.WithNoMemPort ++ // remove backing memory
new freechips.rocketchip.subsystem.With1TinyCore ++ // single tiny rocket-core
new chipyard.config.AbstractConfig)
If you wanted to include an InclusiveCache in your design, you can try using a modified version of chipyard's TinyRocketConfig. Though currently, it doesn't seem like you're addressing the entire L2 Cache, and I think it's microarchitecturally unused with TinyCore. If you simply need a larger scratchpad, you can modify the scratchpad to contain more sets:
class WithModifiedScratchPad extends Config((site, here, up) => {
case RocketTilesKey => up(RocketTilesKey, site) map { r =>
// each set is currently 64 bytes
r.copy(dcache = r.dcache.map(_.copy(nSets = 2048 /*128KiB scratchpad*/))) }
})
So I have some code which uses xlwings for writing data in Excel file, xlsm.
after i've done writing, I press a certain button to calculate.
sometimes, an error/message pops in the Excel, which is fine, but i want to catch this message to python, and write it later to a log/print it.
also, i need to interact with this message, in this case to press "Ok" in the message box
Attached image of the message box
So guys, I've been able to solve this with an external python library.
here is the code:
from pywinauto import application as autoWin
app = autoWin.Application()
con = app.connect(title = 'Configuration Error')
msgText = con.Dialog.Static2.texts()[0]
con.Dialog.Button.click()
con.Dialog.Button.click()
print(msgText)
basically, what it does, is connecting to the app, and searching for the title.
in this case "Configuration Error"
it needs to perform double click in order to press "Ok" to close the message.
Secondly, it gets the text from the message, and can forward it wherever i want.
important part to remember though, because this should be an automated task, it should run concurrently, which means Threading.
so, a simple Thread class below:
class ButtonClicker(Thread):
def __init__(self):
Thread.__init__(self)
self._stop_event = Event()
def stop(self):
self._stop_event.set()
def stopped(self):
return self._stop_event.is_set()
def run(self) -> None:
while True:
time.sleep(3)
try:
app = autoWin.Application()
con = app.connect(title='Configuration Error')
msg_data = con.Dialog.Static2.texts()[0]
while True:
con.Dialog.Button.click()
# con.Dialog.Button.click()
# print(msg_data)
return msg_data
except Exception as e:
print('Excel didnt stuck')
break
and of course to actually use it:
event_handle = ButtonClicker()
event_handle.start()
some manipulation is needed in order to work in different codes/scenarios, but at least I hope i will help others in the future, because this seems to be very common question.
#Danny's solution, i.e. pywinauto and Thread, works perfectly in my local machine, but it seems can't catch the message box when Excel is running in server mode, e.g. in my case, the automation is triggered in local and started by a system service installed in the server.
pywinauto.findwindows.ElementNotFoundError:
{'title': '<my-wanted-title>', 'backend': 'win32', 'visible_only': False}
It is finally solved with another python third-party library pywin32, so providing a backup solution here.
'''
Keep finding message box with specified title and clicking button to close it,
until stopped by the main thread.
'''
import time
from threading import Thread, Event
import win32gui
import win32con
class ButtonClicker(Thread):
def __init__(self, title:str, interval:int):
Thread.__init__(self)
self._title = title
self._interval = interval
self._stop_event = Event()
def stop(self):
'''Stop thread.'''
self._stop_event.set()
#property
def stopped(self):
return self._stop_event.is_set()
def run(self):
while not self.stopped:
try:
time.sleep(self._interval)
self._close_msgbox()
except Exception as e:
print(e, flush=True)
def _close_msgbox(self):
# find the top window by title
hwnd = win32gui.FindWindow(None, self._title)
if not hwnd: return
# find child button
h_btn = win32gui.FindWindowEx(hwnd, None,'Button', None)
if not h_btn: return
# show text
text = win32gui.GetWindowText(h_btn)
print(text)
# click button
win32gui.PostMessage(h_btn, win32con.WM_LBUTTONDOWN, None, None)
time.sleep(0.2)
win32gui.PostMessage(h_btn, win32con.WM_LBUTTONUP, None, None)
time.sleep(0.2)
if __name__=='__main__':
t = ButtonClicker('Configuration Error', 3)
t.start()
time.sleep(10)
t.stop()
I need to understand how to deploy models on Google Cloud ML. My first task is to deploy a very simple text classifier on the service. I do it in the following steps (could perhaps be shortened to fewer steps, if so, feel free to let me know):
Define the model using Keras and export to YAML
Load up YAML and export as a Tensorflow SavedModel
Upload model to Google Cloud Storage
Deploy model from storage to Google Cloud ML
Set the upload model version as default on the models website.
Run model with a sample input
I've finally made step 1-5 work, but now I get this strange error seen below when running the model. Can anyone help? Details on the steps is below. Hopefully, it can also help others that are stuck on one of the previous steps. My model works fine locally.
I've seen Deploying Keras Models via Google Cloud ML and Export a basic Tensorflow model to Google Cloud ML, but they seem to be stuck on other steps of the process.
Error
Prediction failed: Exception during model execution: AbortionError(code=StatusCode.INVALID_ARGUMENT, details="In[0] is not a matrix
[[Node: MatMul = MatMul[T=DT_FLOAT, _output_shapes=[[-1,64]], transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/cpu:0"](Mean, softmax_W/read)]]")
Step 1
# import necessary classes from Keras..
model_input = Input(shape=(maxlen,), dtype='int32')
embed = Embedding(input_dim=nb_tokens,
output_dim=256,
mask_zero=False,
input_length=maxlen,
name='embedding')
x = embed(model_input)
x = GlobalAveragePooling1D()(x)
outputs = [Dense(nb_classes, activation='softmax', name='softmax')(x)]
model = Model(input=[model_input], output=outputs, name="fasttext")
# export to YAML..
Step 2
from __future__ import print_function
import sys
import os
import tensorflow as tf
from tensorflow.contrib.session_bundle import exporter
import keras
from keras import backend as K
from keras.models import model_from_config, model_from_yaml
from optparse import OptionParser
EXPORT_VERSION = 1 # for us to keep track of different model versions (integer)
def export_model(model_def, model_weights, export_path):
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
K.set_learning_phase(0) # all new operations will be in test mode from now on
yaml_file = open(model_def, 'r')
yaml_string = yaml_file.read()
yaml_file.close()
model = model_from_yaml(yaml_string)
# force initialization
model.compile(loss='categorical_crossentropy',
optimizer='adam')
Wsave = model.get_weights()
model.set_weights(Wsave)
# weights are not loaded as I'm just testing, not really deploying
# model.load_weights(model_weights)
print(model.input)
print(model.output)
pred_node_names = output_node_names = 'Softmax:0'
num_output = 1
export_path_base = export_path
export_path = os.path.join(
tf.compat.as_bytes(export_path_base),
tf.compat.as_bytes('initial'))
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
# Build the signature_def_map.
x = model.input
y = model.output
values, indices = tf.nn.top_k(y, 5)
table = tf.contrib.lookup.index_to_string_table_from_tensor(tf.constant([str(i) for i in xrange(5)]))
prediction_classes = table.lookup(tf.to_int64(indices))
classification_inputs = tf.saved_model.utils.build_tensor_info(model.input)
classification_outputs_classes = tf.saved_model.utils.build_tensor_info(prediction_classes)
classification_outputs_scores = tf.saved_model.utils.build_tensor_info(values)
classification_signature = (
tf.saved_model.signature_def_utils.build_signature_def(inputs={tf.saved_model.signature_constants.CLASSIFY_INPUTS: classification_inputs},
outputs={tf.saved_model.signature_constants.CLASSIFY_OUTPUT_CLASSES: classification_outputs_classes, tf.saved_model.signature_constants.CLASSIFY_OUTPUT_SCORES: classification_outputs_scores},
method_name=tf.saved_model.signature_constants.CLASSIFY_METHOD_NAME))
tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
tensor_info_y = tf.saved_model.utils.build_tensor_info(y)
prediction_signature = (tf.saved_model.signature_def_utils.build_signature_def(
inputs={'images': tensor_info_x},
outputs={'scores': tensor_info_y},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))
legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={'predict_images': prediction_signature,
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: classification_signature,},
legacy_init_op=legacy_init_op)
builder.save()
print('Done exporting!')
raise SystemExit
if __name__ == '__main__':
usage = "usage: %prog [options] arg"
parser = OptionParser(usage)
(options, args) = parser.parse_args()
if len(args) < 3:
raise ValueError("Too few arguments!")
model_def = args[0]
model_weights = args[1]
export_path = args[2]
export_model(model_def, model_weights, export_path)
Step 3
gsutil cp -r fasttext_cloud/ gs://quiet-notch-xyz.appspot.com
Step 4
from __future__ import print_function
from oauth2client.client import GoogleCredentials
from googleapiclient import discovery
from googleapiclient import errors
import time
projectID = 'projects/{}'.format('quiet-notch-xyz')
modelName = 'fasttext'
modelID = '{}/models/{}'.format(projectID, modelName)
versionName = 'Initial'
versionDescription = 'Initial release.'
trainedModelLocation = 'gs://quiet-notch-xyz.appspot.com/fasttext/'
credentials = GoogleCredentials.get_application_default()
ml = discovery.build('ml', 'v1', credentials=credentials)
# Create a dictionary with the fields from the request body.
requestDict = {'name': modelName, 'description': 'Online predictions.'}
# Create a request to call projects.models.create.
request = ml.projects().models().create(parent=projectID, body=requestDict)
# Make the call.
try:
response = request.execute()
except errors.HttpError as err:
# Something went wrong, print out some information.
print('There was an error creating the model.' +
' Check the details:')
print(err._get_reason())
# Clear the response for next time.
response = None
raise
time.sleep(10)
requestDict = {'name': versionName,
'description': versionDescription,
'deploymentUri': trainedModelLocation}
# Create a request to call projects.models.versions.create
request = ml.projects().models().versions().create(parent=modelID,
body=requestDict)
# Make the call.
try:
print("Creating model setup..", end=' ')
response = request.execute()
# Get the operation name.
operationID = response['name']
print('Done.')
except errors.HttpError as err:
# Something went wrong, print out some information.
print('There was an error creating the version.' +
' Check the details:')
print(err._get_reason())
raise
done = False
request = ml.projects().operations().get(name=operationID)
print("Adding model from storage..", end=' ')
while (not done):
response = None
# Wait for 10000 milliseconds.
time.sleep(10)
# Make the next call.
try:
response = request.execute()
# Check for finish.
done = True # response.get('done', False)
except errors.HttpError as err:
# Something went wrong, print out some information.
print('There was an error getting the operation.' +
'Check the details:')
print(err._get_reason())
done = True
raise
print("Done.")
Step 5
Use website.
Step 6
def predict_json(instances, project='quiet-notch-xyz', model='fasttext', version=None):
"""Send json data to a deployed model for prediction.
Args:
project (str): project where the Cloud ML Engine Model is deployed.
model (str): model name.
instances ([Mapping[str: Any]]): Keys should be the names of Tensors
your deployed model expects as inputs. Values should be datatypes
convertible to Tensors, or (potentially nested) lists of datatypes
convertible to tensors.
version: str, version of the model to target.
Returns:
Mapping[str: any]: dictionary of prediction results defined by the
model.
"""
# Create the ML Engine service object.
# To authenticate set the environment variable
# GOOGLE_APPLICATION_CREDENTIALS=<path_to_service_account_file>
service = googleapiclient.discovery.build('ml', 'v1')
name = 'projects/{}/models/{}'.format(project, model)
if version is not None:
name += '/versions/{}'.format(version)
response = service.projects().predict(
name=name,
body={'instances': instances}
).execute()
if 'error' in response:
raise RuntimeError(response['error'])
return response['predictions']
Then run function with test input: predict_json({'inputs':[[18, 87, 13, 589, 0]]})
There is now a sample demonstrating the use of Keras on CloudML engine, including prediction. You can find the sample here:
https://github.com/GoogleCloudPlatform/cloudml-samples/tree/master/census/keras
I would suggest comparing your code to that code.
Some additional suggestions that will still be relevant:
CloudML Engine currently only supports using a single signature (the default signature). Looking at your code, I think prediction_signature is more likely to lead to success, but you haven't made that the default signature. I suggest the following:
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: prediction_signature,},
legacy_init_op=legacy_init_op)
If you are deploying to the service, then you would invoke prediction like so:
predict_json({'images':[[18, 87, 13, 589, 0]]})
If you are testing locally using gcloud ml-engine local predict --json-instances the input data is slightly different (matches that of the batch prediction service). Each newline-separated line looks like this (showing a file with two lines):
{'images':[[18, 87, 13, 589, 0]]}
{'images':[[21, 85, 13, 100, 1]]}
I don't actually know enough about the shape of model.x to ensure the data being sent is correct for your model.
By way of explanation, it may be insightful to consider the difference between the Classification and Prediction methods in SavedModel. One difference is that, when using tensorflow_serving, which is based on gRPC, which is strongly typed, Classification provides a strongly-typed signature that most classifiers can use. Then you can reuse the same client on any classifier.
That's not overly useful when using JSON since JSON isn't strongly typed.
One other difference is that, when using tensorflow_serving, Prediction accepts column-based inputs (a map from feature name to every value for that feature in the whole batch) whereas Classification accepts row based inputs (each input instance/example is a row).
CloudML abstracts that away a bit and always requires row-based inputs (a list of instances). We even though we only officially support Prediction, but Classification should work as well.
is there a way to use the #incremental plugin like described att Pytest: how to skip the rest of tests in the class if one has failed? mixed with #pytest.mark.parametrize like below:
#pytest.mark.incremental
Class TestClass:
#pytest.mark.parametrize("input", data)
def test_preprocess_check(self,input):
# prerequisite for test
#pytest.mark.parametrize("input",data)
def test_process_check(self,input):
# test only if test_preprocess_check succeed
The problem i encountered is, at the first fail of test_preprocess_check with a given input of my data set, the following test_preprocess_check and test_process_check are labeled "xfail".
The behaviour i expect will be, at each new "input" of my parametrized data set, the test will act in an incremental fashion.
ex: data = [0,1,2]
if only test_preprocess_check(0) failed:
i got the following report:
1 failed, 5 xfailed
but i expect the report:
1 failed, 1 xfailed, 4 passed
Thanks
After some experiments i found a way to generalize the #incremental to works with parametrize annotation. Simply rewrite the _previousfailed argument to make it unique for each input. The argument _genid was excactly the need.
I added a #pytest.mark.incrementalparam to achieve this.
Code become:
def pytest_runtest_setup(item):
previousfailed_attr = getattr(item, "_genid",None)
if previousfailed_attr is not None:
previousfailed = getattr(item.parent, previousfailed_attr, None)
if previousfailed is not None:
pytest.xfail("previous test failed (%s)" %previousfailed.name)
previousfailed = getattr(item.parent, "_previousfailed", None)
if previousfailed is not None:
pytest.xfail("previous test failed (%s)" %previousfailed.name)
def pytest_runtest_makereport(item, call):
if "incrementalparam" in item.keywords:
if call.excinfo is not None:
previousfailed_attr = item._genid
setattr(item.parent,previousfailed_attr, item)
if "incremental" in item.keywords:
if call.excinfo is not None:
parent = item.parent
parent._previousfailed = item
It's interesting to mention that's it can't be used without parametrize cause parametrize annotation creates automatically _genid variable.
Hope this can helps others than me.
I am working on a WX-MPL app to display and interactively process data based on user input. I am having a difficulties setting up the MPL mouse events to work within the WX app.
The goal is to have a series of editable vertical lines that delineate the begin and end times for features which have been identified in earlier processing. Users should be able to drag the lines along the x-axis, delete unwanted lines, and insert new lines.
So far I have been able to achieve this functionality using the standard MPL plot figure (I think its Tk). When I try to integrate these classes into my app, something is going astray with the event handling and I am not able to interact with the line objects or create new line objects. So I backed up a step and have been trying to add complexity into my working example to determine where the problems are coming into the picture.
When running the working example as a simple WX app, the NewVLineListener class appears to no longer receive button_press_events. Below is the code related to the the specific problem I am experiencing.
This is the first time I have worked with mouse events and I am surely missing something... Any advice would be greatly appreciated.
Also, I am running WX 2.8.12 and MPL 1.1.1rc.
from matplotlib.figure import Figure
import wx
from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg
class NewVLineListener:
global castLog, count, dvls, ax1
lock = None # only one can be animated at a time
def __init__(self, fig):
self.fig = fig
def connect(self):
'connect to all the events we need'
self.cidpressright = self.fig.canvas.mpl_connect(
'button_press_event', self.on_press_right)
def on_press_right(self, event):
global count, castLog, dvls, ax1
print event.button
if event.button != 3: return
tag = 'Begin'
# Increase castLog Key to accomodate new Vline
count += 1
print 'count: ', count
# Set castLog values to x-value of triggering event
castLog[str(count)] = { tag:event.xdata }
# Spawn a new DraggableVline instance
color_map = {'Begin':'g', 'End':'r'}
dvl = DraggableVline(self.fig, ax1.axvline(x=castLog[str(count)][tag], linewidth=2, color=color_map[tag]), count, tag)
dvl.connect()
dvls.append(dvl)
canvas = self.fig.canvas
canvas.draw()
class MainFrame(wx.Frame):
def __init__(self):
wx.Frame.__init__(self, None, wx.ID_ANY)
global castLog, count, ax1, dvls
fig = Figure()
canvas = FigureCanvasWxAgg(self, -1, fig)
ax1 = fig.add_subplot(111)
# Create empty dict that will hold new V-line Data
castLog = { }
dvls = [ ]
count = 0
# Instantiate New Vline Listener
NVL = NewVLineListener(fig)
NVL.connect()
if __name__ == '__main__':
app = wx.PySimpleApp()
app.frame = MainFrame()
app.frame.Show()
app.MainLoop()
I got it!
I think the reference to the NewVlineListener object was being garbage collected so the events were never being received. By adding the NVL object reference to the array of references to draggable vline objects, it sticks around and receives the events as expected.
class MainFrame(wx.Frame):
def __init__(self):
wx.Frame.__init__(self, None, wx.ID_ANY)
global castLog, count, ax1, dvls
fig = Figure()
canvas = FigureCanvasWxAgg(self, -1, fig)
ax1 = fig.add_subplot(111)
# Create empty dict that will hold new V-line Data
castLog = { }
dvls = [ ]
count = 0
# Instantiate New Vline Listener
NVL = NewVLineListener(fig)
NVL.connect()
dvls.append(NVL) # This keeps NVL reference from being garbage collected?
I still find it interesting that naming the NewVlineListener object is not enough to keep the reference around, and that it works fine with pyplot but now with wx, however this seems to work.