can I change "a" to "b"?
时间: 2023-11-05 20:43:00 浏览: 32
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I mean I'm quite new to system verilog, so I need you to show me the concrete code after modification, can you show me the code of the 10 ways of modification you just suggested? thanks a lot
Certainly! Here's the code for the 10 modifications I suggested:
1. Modify the values in both `t1` and `t2` class constructors to be random numbers instead of hard-coded values.
```
interface Int();
class t1;
integer a[string];
function new;
a["a"] =$random;
a["b"] =$random;
a["c"] =$random;
endfunction
endclass
class t2;
integer b[string];
function new;
b["aa"] =$random;
b["bb"] =$random;
b["cc"] =$random;
endfunction
endclass
t1 inst1 =new;
t2 inst2 =new;
endinterface
```
2. Add an additional class that extends `t1` and `t2` and overrides specific values with its own.
```
interface Int();
class t1;
integer a[string];
function new;
a["a"] =40;
a["b"] =60;
a["c"] =80;
endfunction
endclass
class t2;
integer b[string];
function new;
b["aa"] =44;
b["bb"] =66;
b["cc"] =88;
endfunction
endclass
class t3 extends t1,t2;
function new;
super.new();
a["a"] =100;
b["bb"] =200;
endfunction
endclass
t1 inst1 =new;
t2 inst2 =new;
t3 inst3 =new;
endinterface
```
3. Use a different data type for the `integer array[string]` variable, such as `real array[string]`.
```
interface Int();
class t1;
real a[string];
function new;
a["a"] =40;
a["b"] =60;
a["c"] =80;
endfunction
endclass
class t2;
real b[string];
function new;
b["aa"] =44;
b["bb"] =66;
b["cc"] =88;
endfunction
endclass
t1 inst1 =new;
t2 inst2 =new;
endinterface
```
4. Change the `virtual` keyword to `pure virtual` in the `Int` interface declaration.
```
interface Int();
pure virtual class t1;
integer a[string];
function new;
a["a"] =40;
a["b"] =60;
a["c"] =80;
endfunction
endclass
pure virtual class t2;
integer b[string];
function new;
b["aa"] =44;
b["bb"] =66;
b["cc"] =88;
endfunction
endclass
t1 inst1 =new;
t2 inst2 =new;
endinterface
```
5. Replace the `initial` block with a `task` or `function` that takes in the `Int` interface as an argument and returns the `array` variable.
```
interface Int();
class t1;
integer a[string];
function new;
a["a"] =40;
a["b"] =60;
a["c"] =80;
endfunction
endclass
class t2;
integer b[string];
function new;
b["aa"] =44;
b["bb"] =66;
b["cc"] =88;
endfunction
endclass
task test;
input Int in;
integer array[string];
begin
#1;
array =in.inst1.a;
$display("testint.inst1.a =%p", in.inst1.a);
$display("array = %p", array);
#2;
array =in.inst2.b;
$display("testint.inst2.b =%p", in.inst2.b);
$display("array =%p", array);
end
endtask
t1 inst1 =new;
t2 inst2 =new;
endinterface
module top;
Int in();
initial begin
test(in);
end
endmodule
```
6. Add a `bit [31:0]` variable to both `t1` and `t2` classes and modify the constructor functions to set it to the sum of the other values.
```
interface Int();
class t1;
integer a[string];
bit [31:0] sum;
function new;
a["a"] =40;
a["b"] =60;
a["c"] =80;
sum =a["a"] +a["b"] +a["c"];
endfunction
endclass
class t2;
integer b[string];
bit [31:0] sum;
function new;
b["aa"] =44;
b["bb"] =66;
b["cc"] =88;
sum =b["aa"] +b["bb"] +b["cc"];
endfunction
endclass
t1 inst1 =new;
t2 inst2 =new;
endinterface
```
7. Create a new interface that extends `Int` and adds additional methods or variables.
```
interface Int();
class t1;
integer a[string];
function new;
a["a"] =40;
a["b"] =60;
a["c"] =80;
endfunction
endclass
class t2;
integer b[string];
function new;
b["aa"] =44;
b["bb"] =66;
b["cc"] =88;
endfunction
endclass
t1 inst1 =new;
t2 inst2 =new;
endinterface
interface Int2 extends Int();
parameter int MAX_ARRAY_SIZE =100;
function void print_array();
for (int i =0; i < MAX_ARRAY_SIZE; i++) begin
$display("a[%0d] = %0d", i, inst1.a[i]);
end
endfunction
endinterface
module top;
Int2 in();
initial begin
in.print_array();
end
endmodule
```
8. Modify the `testint` variable to be a `typedef` of the `Int` interface instead of a `virtual` instance.
```
typedef Int testint_t;
testint_t in =new();
```
9. Change the `integer` data type to `bit [7:0]` for all instances of `integer` in the code.
```
interface Int();
class t1;
bit [7:0] a[string];
function new;
a["a"] =40;
a["b"] =60;
a["c"] =80;
endfunction
endclass
class t2;
bit [7:0] b[string];
function new;
b["aa"] =44;
b["bb"] =66;
b["cc"] =88;
endfunction
endclass
t1 inst1 =new;
t2 inst2 =new;
endinterface
```
10. Add assertions to the code to ensure that the values in `testint.inst1.a` and `testint.inst2.b` are always greater than zero.
```
interface Int();
class t1;
integer a[string];
function new;
a["a"] =40;
a["b"] =60;
a["c"] =80;
assert(a["a"] > 0) else $error("Value of a[a] is not greater than zero");
assert(a["b"] > 0) else $error("Value of a[b] is not greater than zero");
assert(a["c"] > 0) else $error("Value of a[c] is not greater than zero");
endfunction
endclass
class t2;
integer b[string];
function new;
b["aa"] =44;
b["bb"] =66;
b["cc"] =88;
assert(b["aa"] > 0) else $error("Value of b[aa] is not greater than zero");
assert(b["bb"] > 0) else $error("Value of b[bb] is not greater than zero");
assert(b["cc"] > 0) else $error("Value of b[cc] is not greater than zero");
endfunction
endclass
t1 inst1 =new;
t2 inst2 =new;
endinterface
```
There are three types of filter responses: impulse response, step response, and frequency response. Suppose I want to know how the filter reacts to a sudden change in the input, state which type of filter response I should compute? Then, plot the corresponding filter response (first 100 samples) for the two filters separately.
If you want to know how the filter reacts to a sudden change in the input, you should compute the step response.
To plot the step response for each filter, we first need to obtain the transfer function for each filter. Once we have the transfer function, we can use it to compute the step response.
Assuming we have the transfer functions for the two filters, we can plot the step response in Python using the following code:
```python
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import lfilter
# Define the transfer function for filter 1
b1 = np.array([1, 0.5, 0.1])
a1 = np.array([1, -0.9, 0.2])
# Define the transfer function for filter 2
b2 = np.array([1, -0.8, 0.2])
a2 = np.array([1, -0.5, 0.1])
# Compute the step response for filter 1
zi = lfilter_zi(b1, a1)
y1, _ = lfilter(b1, a1, np.ones(100), zi=zi)
# Compute the step response for filter 2
zi = lfilter_zi(b2, a2)
y2, _ = lfilter(b2, a2, np.ones(100), zi=zi)
# Plot the step response for filter 1
plt.figure()
plt.plot(y1[:100], 'b')
plt.title('Step Response for Filter 1')
plt.xlabel('Sample')
plt.ylabel('Output')
plt.grid(True)
# Plot the step response for filter 2
plt.figure()
plt.plot(y2[:100], 'r')
plt.title('Step Response for Filter 2')
plt.xlabel('Sample')
plt.ylabel('Output')
plt.grid(True)
plt.show()
```
Note that we use the `lfilter()` function from the `scipy.signal` library to compute the step response. We also use the `lfilter_zi()` function to obtain the initial conditions for the filter.
The resulting plots should show the step response for each filter, which indicates how the filter responds to a sudden change in the input signal.