I am trying to build a countdown timer with tkinter. I want to pass on the values from the entries onto the countdown(count) function. Here is what I tried:
def countdown(count):
label['text'] = count
if count > 0:
top.after(1000, countdown,count-1)
top = tkinter.Tk()
top.geometry("700x100")
hoursT=tkinter.Label(top, text="Hours:")
hoursE=tkinter.Entry(top)
minuteT=tkinter.Label(top, text="Minutes:")
minuteE=tkinter.Entry(top)
secondT=tkinter.Label(top, text="Seconds:")
secondE=tkinter.Entry(top)
hoursT.grid(row=1,column=1)
hoursE.grid(row=1,column=2)
minuteT.grid(row=1,column=3)
minuteE.grid(row=1,column=4)
secondT.grid(row=1,column=5)
secondE.grid(row=1,column=6)
label = tkinter.Label(top)
label.grid(row=3)
t=(int(hoursE.get())*360+int(minuteT.get())*60+int(secondE.get())
button=tkinter.Button(top,text="Start Timer",command=lambda count=t:countdown(count))
button.grid(row=2)
However, I get this error:
Traceback (most recent call last):
File "C:\Users\charley.ACER-PC\AppData\Local\Programs\Python\Python35- 32\tkinterTutorial.py", line 30, in <module>
t=(int(hoursE.get())*360+int(minuteT.get())*60+int(secondE.get()))
ValueError: invalid literal for int() with base 10: ''
How can I run this code:
t=(int(hoursE.get())*360+int(minuteT.get())*60+int(secondE.get())
only when the entries are filled with integers?
Thanks :)
One solution is to have the button there in the first place and change the command at run time with event listeners:
button=tkinter.Button(top,text="Start Timer",command=lambda:None)
button.grid(row=2)
def updateButton():
hour,min,sec=hoursE.get(),minuteT.get(),secondE.get()
if hour.isdigit() and min.isdigit() and sec.isdigit():
time=int(hour)*360+int(min)*60+int(sec)
button.configure(command=lambda count=time:countdown(count))
for widget in (hoursE,minuteT,secondE):
widget.bind("<FocusOut>", updateButton)
Related
So i tried to execute a code using the the tkdraw.basic library in jupyter notebook, and this is what i got :
Its just a simple program that is supposed to draw a line along the window :
import tkdraw.basic as graph
def ligne_horiz(y,larg):
for x in range(larg):
graph.plot(y,x)
graph.open_win(120,160)
ligne_horiz(50,160)
graph.wait()
hers the error :
AssertionError Traceback (most recent call last)
Cell In [12], line 7
4 for x in range(larg):
5 graph.plot(y,x)
----> 7 graph.open_win(120,160)
8 ligne_horiz(50,160)
9 graph.wait()
File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/tkdraw/basic.py:63, in open_win(height, width, zoom)
59 # pylint: disable=global-statement
60 # I really want to use a global in this module, to make those functions
61 # easier to use.
62 global _WINDOW
---> 63 assert not _WINDOW, "ERROR: function open() was called twice!"
64 _WINDOW = tkd.Screen((height, width), zoom, grid=False)
AssertionError: ERROR: function open() was called twice!
The code work perfectly on the terminal or in an another .py folder so i think the issue come from the Extension,
I tried to find any similar issue or an update in the library but nothing wrong apparently.(maybe a parameter in the extension it self ?)
I am trying to implement Hierarchical time series forecasting on azureautoml pipelines.
I followed this notebook for implementation
https://github.com/Azure/azureml-examples/blob/main/v1/python-sdk/tutorials/automl-with-azureml/forecasting-hierarchical-timeseries/auto-ml-forecasting-hierarchical-timeseries.ipynb
While I ran training pipeline on compute instance it worked, but when I am running the same on compute cluster it breaks at hts-proportion-calculation part.
This is the error I am getting,
system error:
Encountered an internal AutoML error. Error Message/Code: ClientException. Additional Info: ClientException:
Message: No objects to concatenate
InnerException: None
ErrorResponse
{
"error": {
"message": "No objects to concatenate"
}
}
logs :
Loading arguments for scenario proportions-calculation
adding argument --input-medatadata
adding argument --hts-graph
adding argument --enable-event-logger
Input arguments dict is {'--input-medatadata': '/mnt/azureml/cr/j/85509be625484b6caa3c1d97b7ab2e33/cap/data-capability/wd/INPUT_automl_training_workspaceblobstore/azureml/17ca5ae7-7269-4246-888f-e781071e3f5c/automl_training', '--hts-graph': '/mnt/azureml/cr/j/85509be625484b6caa3c1d97b7ab2e33/cap/data-capability/wd/INPUT_hts_graph_workspaceblobstore/azureml/a2c1b15a-c895-41e8-b6a6-1ca37ebe9e77/hts_graph', '--enable-event-logger': None}
Unknown file to proceed outputs.txt
processing: outputs.txt with type None.
Cleaning up all outstanding Run operations, waiting 300.0 seconds
3 items cleaning up...
Cleanup took 0.001676321029663086 seconds
Traceback (most recent call last):
File "proportions_calculation_wrapper.py", line 47, in <module>
runtime_wrapper.run()
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/azureml/train/automl/runtime/_many_models/automl_pipeline_step_wrapper.py", line 63, in run
self._run()
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/azureml/train/automl/runtime/_hts/proportions_calculation.py", line 44, in _run
proportions_calculation(self.arguments_dict, self.event_logger, script_run=self.step_run)
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/azureml/train/automl/runtime/_hts/proportions_calculation.py", line 173, in proportions_calculation
proportion_files_list, forecasting_parameters.time_column_name, graph.label_column_name
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/azureml/train/automl/runtime/_hts/proportions_calculation.py", line 92, in calculate_time_agg_sum_for_all_files
df = pd.concat(pool.map(concat_func, files_batches), ignore_index=True)
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/pandas/util/_decorators.py", line 311, in wrapper
return func(*args, **kwargs)
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/pandas/core/reshape/concat.py", line 304, in concat
sort=sort,
File "/azureml-envs/azureml_e34d0633ffc4cb2fa25d91e3da5f59be/lib/python3.7/site-packages/pandas/core/reshape/concat.py", line 351, in __init__
raise ValueError("No objects to concatenate")
ValueError: No objects to concatenate
Please let me know how can I resolve this issue ?
This error was incurred as Iteration timeout was not less than experiment timeout , but the system error & logs are a kind of misleading.
df = pd.concat(pool.map(concat_func, files_batches), ignore_index=True)
logs was pointing to pandas "No objects to concatenate"
This error can be overcome by setting iterationtimeout value less than experimenttime out value.
I had set iteration_timeout_minutes=60 which caused the error.
automl_settings = AutoMLConfig(
task="forecasting",
primary_metric="normalized_root_mean_squared_error",
experiment_timeout_hours=1,
label_column_name=label_column_name,
track_child_runs=False,
forecasting_parameters=forecasting_parameters,
pipeline_fetch_max_batch_size=15,
model_explainability=model_explainability,
n_cross_validations="auto", # Feel free to set to a small integer (>=2) if runtime is an issue.
cv_step_size="auto",
# The following settings are specific to this sample and should be adjusted according to your own needs.
iteration_timeout_minutes=10,
iterations=15,
)
We are able to run the sample successfully using the compute cluster as given below.
from azureml.core.compute import ComputeTarget, AmlCompute
# Name your cluster
compute_name = "hts-compute"
if compute_name in ws.compute_targets:
compute_target = ws.compute_targets[compute_name]
if compute_target and type(compute_target) is AmlCompute:
print("Found compute target: " + compute_name)
else:
print("Creating a new compute target...")
provisioning_config = AmlCompute.provisioning_configuration(
vm_size="STANDARD_D16S_V3", max_nodes=20
)
# Create the compute target
compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)
# Can poll for a minimum number of nodes and for a specific timeout.
# If no min node count is provided it will use the scale settings for the cluster
compute_target.wait_for_completion(
show_output=True, min_node_count=None, timeout_in_minutes=20
)
# For a more detailed view of current cluster status, use the 'status' property
print(compute_target.status.serialize())
I am trying to iterate this list with words as
CTCCTC TCCTCT CCTCTC CTCTCC TCTCCC CTCCCA TCCCAA CCCAAA CCAAAC CAAACT
CTGGGC TGGGCC GGGCCA GGCCAA GCCAAT CCAATG CAATGC AATGCC ATGCCT TGCCTG GCCTGC
TGCCAG GCCAGG CCAGGA CAGGAG AGGAGG GGAGGG GAGGGG AGGGGC GGGGCT GGGCTG GGCTGG GCTGGT CTGGTC
TGGTCT GGTCTG GTCTGG TCTGGA CTGGAC TGGACA GGACAC GACACT ACACTA CACTAT
ATTCAG TTCAGC TCAGCC CAGCCA AGCCAG GCCAGT CCAGTC CAGTCA AGTCAA GTCAAC TCAACA CAACAC AACACA
ACACAA CACAAG ACAAGG AGGTGG GGTGGC GTGGCC TGGCCT GGCCTG GCCTGC CCTGCA CTGCAC
TGCACT GCACTC CACTCG ACTCGA CTCGAG TCGAGG CGAGGT GAGGTT AGGTTC GGTTCC
TATATA ATATAC TATACC ATACCT TACCTG ACCTGG CCTGGT CTGGTA TGGTAA GGTAAT GTAATG TAATGG AATGGA
I am trying for loop to read each item in the list and parse it through mk_model.vector
the code used is as follows
for x in all_seq_sentences[:]:
mk_model.vector(x)
print(x)
Usually, mk_model.vector("AGT") will give an array corresponding to defines dna2vec model, But here rather than actually performing the model run it throws error as
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-144-77c47b13e98a> in <module>
1 for x in all_seq_sentences[:]:
----> 2 mk_model.vector(x)
3 print(x)
4
~/Desktop/DNA2vec/dna2vec/dna2vec/multi_k_model.py in vector(self, vocab)
35
36 def vector(self, vocab):
---> 37 return self.data[len(vocab)].model[vocab]
38
39 def unitvec(self, vec):
KeyError: 664
Looking forward to some help here
The above problem was having issues because the for loop took all items in first line as one item, which is why .split() was best solution of it. To read follow https://python-reference.readthedocs.io/en/latest/docs/str/split.html
working code:
for i in all_seq_sentences:
word = i.split()
print(word[0])
and then later implement another loop to access the model.vector function
vec_of_all_seq = []
for sentence in all_seq_sentences:
sentence = sentence.split()
for word in sentence:
vec_of_all_seq.append(mk_model.vector(word))
vector representation derived from model.vector will be saved in numpy array named vec_of_all_seq.
This question already has an answer here:
How to catch all exceptions in Try/Catch Block Python?
(1 answer)
Closed 3 years ago.
My code below goes through each .m4v file in the list and converts them to a .wav file using FFmpeg, and it works. I use python 3 jupyter environment.
for fpath in list:
if (fpath.endswith(".m4v")):
cdir=os.path.dirname(fpath)
os.chdir(cdir)
filename=os.path.basename(fpath)
os.system("ffmpeg -i {0} temp_name.wav".format(filename))
ofnamepath=os.path.splitext(fpath)[0]
temp_name=os.path.join(cdir, "temp_name.wav")
new_name = os.path.join(ofnamepath+'.wav')
os.rename(temp_name,new_name)
old_name=os.path.join(ofnamepath+'.m4v')
os.remove(old_name)
However, for this particular dataset I get the following error;
> UnicodeEncodeError Traceback (most recent call
> last) <ipython-input-10-bd3b17e409fa> in <module>()
>
>
> > 7 os.chdir(cdir)
> > 8 filename=os.path.basename(fpath)
> > ----> 9 os.system("ffmpeg -i {0} temp_name.wav".format(filename))
> > 10 ofnamepath=os.path.splitext(fpath)[0]
> > 11 temp_name=os.path.join(cdir, "temp_name.wav")
>
> UnicodeEncodeError: 'ascii' codec can't encode characters in position
> 10-16: ordinal not in range(128)
Is it possible to do add an if comment line in the code something like;
if 'UnicodeEncodeError: 'ascii' codec can't encode'
delete that file and continue to the next file?
You can use a try and except block.
If an exception occurs while inside a try block, it will jump to the exception block. What's better is that you can even specify the exception.
Adding this to your code would look something like:
for fpath in list:
if (fpath.endswith(".m4v")):
cdir=os.path.dirname(fpath)
os.chdir(cdir)
filename=os.path.basename(fpath)
try:
os.system("ffmpeg -i {0} temp_name.wav".format(filename))
except UnicodeEncodeError:
print("Some failure message.. Continuing to next..")
# os.remove(filename)
continue # This skips the rest of the current iteration and jumps to the top of the loop.
ofnamepath=os.path.splitext(fpath)[0]
temp_name=os.path.join(cdir, "temp_name.wav")
new_name = os.path.join(ofnamepath+'.wav')
os.rename(temp_name,new_name)
old_name=os.path.join(ofnamepath+'.m4v')
os.remove(old_name)
Uncomment the # os.remove(filename) to have your files deleted. Are you sure you want to permanently delete them?
I can get SIFT keypoints and descriptors from two, seperate, large images (~2GB) when I run sift.detectAndCompute from the command line. I run it on one image, wait a very long time, but eventually get the keypoints and descriptors. Then I repeat for the second image, and again it takes a long time, but I DO eventually get my keypoints and descriptors. Here are the two lines I run from the IPython console in Spyder, which I am running on my machine with 32 GB of RAM. (MAX_MATCHES = 50000 in the code below):
sift = cv2.xfeatures2d.SIFT_create(MAX_MATCHES)
keypoints, descriptors = sift.detectAndCompute(imgGray, None)
This takes 10 minutes to finish, but it does finish. Next, I run this:
keypoints2, descriptors2 = sift.detectAndCompute(refimgGray, None)
When done, keypoints and keypoints2 DO contain 50000 keypoint objects.
However, if I run my script, which calls a function that uses sift.detectAndCompute and returns keypoints and descriptors, the process takes a long time, uses 100% of my memory and ~95% of my disk BW and then fails with this traceback:
runfile('C:/AV GIS/python scripts/img_align_w_geo_w_mask_refactor_ret_1.py', wdir='C:/AV GIS/python scripts')
Reading reference image : C:\Users\kellett\Downloads\3074_transparent_mosaic_group1.tif
xfrm for image = (584505.1165100001, 0.027370000000000002, 0.0, 4559649.608440001, 0.0, -0.027370000000000002)
Reading image to align : C:\Users\kellett\Downloads\3071_transparent_mosaic_group1.tif
xfrm for image = (584499.92168, 0.02791, 0.0, 4559648.80372, 0.0, -0.02791)
Traceback (most recent call last):
File "<ipython-input-75-571660ddab7f>", line 1, in <module>
runfile('C:/AV GIS/python scripts/img_align_w_geo_w_mask_refactor_ret_1.py', wdir='C:/AV GIS/python scripts')
File "C:\Users\kellett\AppData\Local\Continuum\anaconda3\envs\testgdal\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 668, in runfile
execfile(filename, namespace)
File "C:\Users\kellett\AppData\Local\Continuum\anaconda3\envs\testgdal\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 108, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/AV GIS/python scripts/img_align_w_geo_w_mask_refactor_ret_1.py", line 445, in <module>
matches = find_matches(refKP, refDesc, imgKP, imgDesc)
File "C:/AV GIS/python scripts/img_align_w_geo_w_mask_refactor_ret_1.py", line 301, in find_matches
matches = matcher.match(dsc1, dsc2)
error: C:\ci\opencv_1512688052760\work\modules\core\src\stat.cpp:4024: error: (-215) (type == 0 && dtype == 4) || dtype == 5 in function cv::batchDistance
The function is simply called once for each image thusly:
print("Reading image to align : ", imFilename);
img, imgGray, imgEdgmask, imgXfrm, imgGeoInfo = read_ortho4align(imFilename)
refKP, refDesc = extractKeypoints(refimgGray, refEdgmask)
imgKP, imgDesc = extractKeypoints(imgGray, imgEdgmask)
HERE IS MY QUESTION (sorry for shouting): Do you think Python tries to run the two lines above concurrently in some way? If so, how can I force it to run serially? If not, do you have any idea why the two keypoint detections would work individually, but not when they come one after another in a script?
One more clue - I put in a statement to see if the script proceeds to the second detectAndCompute statement before it fails, and it does. (I just put a print statement in between the two.)
My error was coming later in my script where I was finding matches.
I have no reason to believe the two SIFT keypoint finding processes are occurring at the same time.
I downsampled the images I was searching for SIFT keypoints and was able to iterate my troubleshooting more quickly and found my error.
I will look at my error more closely next time before asking a question.