create a scheduling method to display custom labels on 7 day rotation in Objective C (Xcode)
Date : March 29 2020, 07:55 AM
Does that help You can view the source code for the entire category here with comments: https://gist.github.com/1590955#import "NSDate+RotationScheduleFormatter.h" in every implementation file in which you would like to use the category. Then use the following method call to get the current rotation day: RotationDay rotationDayInt = [NSDate JL_currentRotationDayWithSchoolYearBeginningOnDateString:@"2011-08-01"];
if (rotationDayInt == kRotationDayA) {
[rotationLetter setText:@"A"];
}
else if (rotationDayInt == kRotationDayB) {
[rotationLetter setText:@"B"];
}
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java method that takes in generic class, create new instance of the class and calls its method
Date : March 29 2020, 07:55 AM
I wish this helpful for you First, you should define some type bound to the T parameter. Otherwise it will become Object at run-time. class YourClass <T extends BaseEmailClass>
public void generateEmailData(int numOfMessage, Class<T> emailClass) {
for (int i = 0; i < numOfMessage; i++) {
String message = emailClass.newInstance().generateRandomMessage();
System.out.println(message);
}
}
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How to dynamically create content (labels/grids) from Object method that returns List<string>
Date : March 29 2020, 07:55 AM
hope this fix your issue You can display the content of your collection with any type of ItemsControl. Some examples are ListBox, TreeView, HeaderedItemsControl, TabControl, and even ItemsControl itself. These controls take a list of items and display them according to the ItemTemplate. In your example, you would edit the UCRobots.xaml file to have the following <!-- The ItemsSource property defines the list of items.
Here we are binding directly to the DataContext of the UCRobots class.
You could also bind to a property of the object that is set as the DataContext -->
<ItemsControl ItemsSource="{Binding}">
<!--
The below is commented out because the default DataTemplate for the ItemTemplate
property is to display a TextBlock that binds directly to the item
<ItemsControl.ItemTemplate>
<DataTemplate>
<TextBlock Text="{Binding}"/>
The Text property is binding directly to the item in the list.
If your list contained objects with properties, you could bind to one of those properties.
For example, if your list contained a list of People objects, you could bind the
Text property to the Name property of your class
</DataTemplate>
</ItemsControl.ItemTemplate>
-->
</ItemsControl>
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Create tkinter Labels, Buttons, ... in a class
Date : March 29 2020, 07:55 AM
With these it helps I am trying to improve my structure of tkinter apps. , Here's something that should give you the general idea: import tkinter as tk
class Statusbar(tk.Frame):
def __init__(self, parent, *args, **kwargs):
tk.Frame.__init__(self, parent, *args, **kwargs)
lbl1 = tk.Label(self, text='Status1', fg='yellow', bg='blue')
lbl1.pack(side='left')
lbl2 = tk.Label(self, text='Status2', fg='white', bg='green')
lbl2.pack(side='left')
class Main(tk.Frame):
def __init__(self, parent, *args, **kwargs):
tk.Frame.__init__(self, parent, *args, **kwargs)
txt = tk.Text(self, width=15, height=5)
txt.insert(tk.END, 'Hello world')
txt.pack()
class MainApplication(tk.Frame):
def __init__(self, parent, *args, **kwargs):
tk.Frame.__init__(self, parent, *args, **kwargs)
self.parent = parent
self.statusbar = Statusbar(self)
self.main = Main(self)
self.statusbar.grid()
self.main.grid()
if __name__ == "__main__":
root = tk.Tk()
MainApplication(root).grid()
root.mainloop()
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Method for feeding multi-class image data-set where folders name can be used as labels in Pytorch?
Date : September 19 2020, 01:00 AM
I hope this helps you . To split your dataset into train and test datasets you could use random_split function: import torch
from torchvision import datasets, transforms
from torch.utils import data
import numpy as np
dataset = datasets.ImageFolder('path_to_dataset', transform=transforms.ToTensor())
lengths = [int(np.ceil(0.5*len(dataset))),
int(np.floor(0.5*len(dataset)))]
train_set, test_set = data.random_split(dataset, lengths)
train_dataloader = data.DataLoader(train_set, batch_size=...)
test_dataloader = data.DataLoader(test_set, batch_size=...)
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