Enter a number to calculate the number of days from now, and use python code to implement
时间: 2023-03-13 15:38:02 浏览: 90
我可以使用Python代码来实现从现在起计算天数的功能,例如:from datetime import datetime, timedelta n = int(input("Enter a number: ")) days_from_now = datetime.now() + timedelta(days=n) print(f"{n}天后的日期是:{days_from_now.strftime('%d/%m/%Y')}")
相关问题
Please write a code in Python to calculate the composition of fuzzy matrix
Sure, here's an example Python code to calculate the composition of fuzzy matrix:
```python
import numpy as np
# Define two fuzzy matrices A and B
A = np.array([[0.8, 0.2],
[0.4, 0.6]])
B = np.array([[0.7, 0.3],
[0.5, 0.5]])
# Define a function to calculate the composition of two fuzzy matrices
def fuzzy_composition(A, B):
C = np.zeros_like(A)
for i in range(A.shape[0]):
for j in range(B.shape[1]):
max_val = 0
for k in range(A.shape[1]):
val = min(A[i,k], B[k,j])
if val > max_val:
max_val = val
C[i,j] = max_val
return C
# Calculate the composition of matrices A and B
C = fuzzy_composition(A, B)
print("Matrix A:")
print(A)
print("Matrix B:")
print(B)
print("Composition of A and B:")
print(C)
```
In this example, we define two fuzzy matrices `A` and `B` using NumPy arrays. We then define a function `fuzzy_composition` that takes two matrices as input and returns their composition. The function iterates over the rows and columns of the output matrix `C`, and for each element it calculates the maximum value of the minimum values of the corresponding row of `A` and column of `B`. Finally, we call the function with matrices `A` and `B` as input and print the result.
Write me a python code to use weibull distribution. To fit a group of data and calculate the quantile values
Sure, here's an example Python code using the `scipy.stats` module to fit a group of data to a Weibull distribution and calculate the quantile values:
``` python
import numpy as np
from scipy.stats import weibull_min
# generate some sample data
data = weibull_min.rvs(1.5, size=1000)
# fit the data to a Weibull distribution
shape, loc, scale = weibull_min.fit(data)
# calculate the 90th percentile (i.e. the value below which 90% of the data falls)
pct = 0.9
q = weibull_min.ppf(pct, shape, loc, scale)
print("Shape parameter:", shape)
print("Location parameter:", loc)
print("Scale parameter:", scale)
print("90th percentile:", q)
```
In this example, we first generate a sample of 1000 data points from a Weibull distribution with shape parameter 1.5. We then fit these data to a Weibull distribution using the `weibull_min.fit()` function, which returns the estimated shape, location, and scale parameters of the distribution. Finally, we calculate the 90th percentile of the distribution using the `weibull_min.ppf()` function, which takes as input the desired percentile (in this case, 0.9), as well as the shape, location, and scale parameters of the distribution.