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perforce Tagging and labelling files


perforce Tagging and labelling files

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perforce Tagging and labelling files
Tag : development , By : user179190
Date : November 28 2020, 01:01 AM


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Perforce integration with visual studio without project files being checked in to perforce


Tag : visual-studio , By : Matt Leacock
Date : March 29 2020, 07:55 AM
I hope this helps . In your case, I'd suggest not using the visual studio integration for Perforce.
You can either add Perforce commands to the Tools Menu, or try Nifty Perforce from Google:

Labelling files with spaces in ClearCase


Tag : development , By : Jarques
Date : March 29 2020, 07:55 AM
will be helpful for those in need You have tow approaches with cleartool mklabel:
 cleartool mklabel -recurse -replace -follow VPCEUM_9.0.0.99
# Windows syntax
cleartool find . -cview -exec "cleartool mklabel -replace TEST_LABEL \"%CLEARCASE_XPN%\""

# Unix syntax
cleartool find . -cview -exec 'cleartool mklabel -replace TEST_LABEL "$CLEARCASE_XPN"'

Tensorflow shuffle_batch from multiple files breaks labelling


Tag : python , By : Oli
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , I think the problem arises because you evaluate image and label_batch[1] in separate Tensor.eval() calls. This means that you are getting values from two different batches. If instead you write:
im, lbl = sess.run([image_batch[1], label_batch[1]])

Pandas Preprocessing: Labelling time series data using 2 other files


Tag : python , By : kakashi_
Date : March 29 2020, 07:55 AM
hope this fix your issue After many weeks of working on this problem, I have managed to come up with a solution. This solution, however, only uses pandas and therefore, I shall run it on lots of smaller files then combine them at the end.
Loading up the data
path = '/media/peter/ElementsSE/Labelling3/Small/prepared_collar3_0.csv'
times = pd.read_csv(path)

path = '/media/peter/Elements SE/Labelling3/Small/3labels.csv'
labels = pd.read_csv(path)

path = '/media/peter/ElementsSE/Labelling3/Small/3TimesForLabels.csv'
label_times = pd.read_csv(path)
labels['Date'] = pd.to_datetime(labels['Date(M/DD/YY)'])
labels['Timestamp'] = labels.apply(lambda row: str(row['Date'].date()) + ' ' + str(row['Time']),axis=1) 
#Casting from string to timestamp
labels['Timestamp'] = pd.to_datetime(labels['Timestamp'])

label_times['Date'] = pd.to_datetime(label_times['Date(M/DD/YY)'])
label_times['Timestamp'] = label_times.apply(lambda row: str(row['Date'].date()) + ' ' + str(row['Time']),axis=1) 
#Casting from string to timestamp
label_times['Timestamp'] = pd.to_datetime(label_times['Timestamp'])
times['behav'] = None
times.Timestamp = pd.to_datetime(times.Timestamp)
times.head()
#Iterating though labeltimes to get the right start and end times
for i in range(len(label_times)-1):
    date = label_times.loc[i,'Date']
    focal_start_time = label_times.loc[i,'Timestamp']
    focal_end_time = label_times.loc[i+1,'Timestamp']


    #Now we iterate though the labels
    day_labels = labels.loc[labels.Date == date].reset_index()
    for j in range(len(day_labels)-1):
        time = day_labels.loc[j,'Timestamp']
        next_time = day_labels.loc[j+1,'Timestamp']
        behav = day_labels.loc[j,'Focal Behavioral States']

        if(time.date() == focal_start_time.date()):

            #We have data of the same date 
            if(time.time() <= focal_end_time.time()):
                if(time.time() >= focal_start_time.time()):

                    #Our start time is in the time range, now we need to check the end time
                    if(next_time.time() <= focal_end_time.time()):
                        if(next_time.time() >= focal_start_time.time()):
                            times.behav.loc[(times.Timestamp >= time) & (times.Timestamp <= next_time)] = behav
times.behav.value_counts()

Labelling of text files in bash loop


Tag : bash , By : Dov
Date : March 29 2020, 07:55 AM
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