Mudanças entre as edições de "Teste do highlight de códigos"

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<syntaxhighlight lang=bash>
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$ cat /etc/passwd | grep home | wc -l
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</syntaxhighlight>
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<syntaxhighlight lang=python>
 
<syntaxhighlight lang=python>
 
def quick_sort(arr):
 
def quick_sort(arr):
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else:
 
else:
 
pass   
 
pass   
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</syntaxhighlight>
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<syntaxhighlight lang=matlab>
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%% Signal in Time Domain
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% Use Fourier transforms to find the frequency components of a signal buried in noise.
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% Specify the parameters of a signal with a sampling frequency of 1 kHz and a signal duration of 1.5 seconds
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Fs = 1000;            % Sampling frequency                   
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T = 1/Fs;            % Sampling period     
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L = 1500;            % Length of signal
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t = (0:L-1)*T;        % Time vector
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% Form a signal containing a 50 Hz sinusoid of amplitude 0.7 and a 120 Hz sinusoid of amplitude 1.
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S = 0.7*sin(2*pi*50*t) + sin(2*pi*120*t);
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% Corrupt the signal with zero-mean white noise with a variance of 4.
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X = S + 2*randn(size(t));
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% Plot the noisy signal in the time domain. It is difficult to identify the frequency components by looking at the signal X(t).
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subplot(311);
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plot(1000*t(1:200),X(1:200), 'b')
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title('Signal Corrupted with Zero-Mean Random Noise')
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xlabel('t (milliseconds)')
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ylabel('X(t)')
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hold on
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plot(1000*t(1:200),S(1:200),'r')
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hold off
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</syntaxhighlight>
 
</syntaxhighlight>

Edição atual tal como às 16h08min de 9 de março de 2020

$ cat /etc/passwd | grep home | wc -l
def quick_sort(arr):
	less = []
	pivot_list = []
	more = []
	if len(arr) <= 1:
		return arr
	else:
		pass
%% Signal in Time Domain 
% Use Fourier transforms to find the frequency components of a signal buried in noise.
% Specify the parameters of a signal with a sampling frequency of 1 kHz and a signal duration of 1.5 seconds
Fs = 1000;            % Sampling frequency                    
T = 1/Fs;             % Sampling period       
L = 1500;             % Length of signal
t = (0:L-1)*T;        % Time vector

% Form a signal containing a 50 Hz sinusoid of amplitude 0.7 and a 120 Hz sinusoid of amplitude 1.
S = 0.7*sin(2*pi*50*t) + sin(2*pi*120*t);

% Corrupt the signal with zero-mean white noise with a variance of 4.
X = S + 2*randn(size(t));

% Plot the noisy signal in the time domain. It is difficult to identify the frequency components by looking at the signal X(t).
subplot(311);
plot(1000*t(1:200),X(1:200), 'b')
title('Signal Corrupted with Zero-Mean Random Noise')
xlabel('t (milliseconds)')
ylabel('X(t)')
hold on
plot(1000*t(1:200),S(1:200),'r')
hold off