## Numpy: Test of an array items are above some value, x consecutive number of times?

check if array elements are consecutive javascript
find consecutive numbers in an array python
how to count consecutive numbers in java
check if array elements are consecutive javascript
check if array elements are consecutive python
javascript check if array is sequential
find consecutive numbers in an array c#
check if array elements are consecutive leetcode

I am trying to test to see if the values in an array are above some value a consecutive number of times.

For example

```arr1 = np.array([1,2,1,3,4,5,6,7])
arr2 = np.array([1,2,1,3,4,2,6,7])
```

Say I want to test to see if an item in the array is `>=3` for four consecutive periods. The test would return `true` for `arr1` but `false` for `arr2`.

Here's one way with `convolution` -

```def cross_thresh_convolve(arr, thresh, N):
# Detect if arr crosses thresh for N consecutive times anywhere
return (np.convolve(arr>=thresh,np.ones(N,dtype=int))==N).any()
```

Alternatively with `binary-dilation` -

```from scipy.ndimage.morphology import binary_erosion

def cross_thresh_erosion(arr, thresh, N):
return binary_erosion(arr>=thresh, np.ones(N)).any()
```

Sample runs -

```In [43]: arr1 = np.array([1,2,1,3,4,5,6,7])
...: arr2 = np.array([1,2,1,3,4,2,6,7])

In [44]: print cross_thresh_convolve(arr1, thresh=3, N=4)
...: print cross_thresh_erosion(arr1, thresh=3, N=4)
...: print cross_thresh_convolve(arr2, thresh=3, N=4)
...: print cross_thresh_erosion(arr2, thresh=3, N=4)
True
True
False
False
```

Generic comparisons

To cover generic comparisons, say if we want to look for `greater` or `less-than` or even simply compare for equality against a value, we could use NumPy builtin comparison functions to replace the `arr>=thresh` part from earlier solutions and hence give ourselves generic implementations, like so -

```def consecutive_comp_convolve(arr, comp, N, comparison=np.greater_equal):
return (np.convolve(comparison(arr,comp),np.ones(N,dtype=int))==N).any()

def consecutive_comp_erosion(arr, comp, N, comparison=np.greater_equal):
return binary_erosion(comparison(arr,comp), np.ones(N)).any()
```

Hence, our specific case runs would be -

```consecutive_comp_convolve(arr1, comp=3, N=4, comparison=np.greater_equal)
consecutive_comp_erosion(arr1, comp=3, N=4, comparison=np.greater_equal)
consecutive_comp_convolve(arr2, comp=3, N=4, comparison=np.greater_equal)
consecutive_comp_erosion(arr2, comp=3, N=4, comparison=np.greater_equal)
```

Check if array elements are consecutive, Given an unsorted array of numbers, write a function that returns true if array Note that, calloc is used here so that all values are initialized If visited first time, then mark the element as visited */ Driver program to test above functions */ Given an array A[] and a number x, check for pair in A[] with sum as x · Search an​  First let's discuss some useful array attributes. We'll start by defining three random arrays, a one-dimensional, two-dimensional, and three-dimensional array. We'll use NumPy's random number generator, which we will seed with a set value in order to ensure that the same random arrays are generated each time this code is run:

Here is a lowtech but fast method. Construct the boolean array, form the cumsum() and compare each element to the one n places away. If the difference is n this must be a streak of `True`s.

```def check_streak(a, th, n):
ps = (a>=th).cumsum()
return (ps[n:]-ps[:ps.size-n] == n).any()
```

Deep Learning With Python: Develop Deep Learning Models on Theano , We will phrase the time series prediction problem as a regression problem. That is, given the number of passengers (in units of thousands) this month, what is import math from keras.models import Sequential from keras.layers import Dense . the NumPy array from the dataframe and convert the integer values to floating​  numpy.append() : How to append elements at the end of a Numpy Array in Python; Find the index of value in Numpy Array using numpy.where() How to Reverse a 1D & 2D numpy array using np.flip() and [] operator in Python; Python: Check if all values are same in a Numpy Array (both 1D and 2D)

Another solution (but slower than the others)

```import numpy as np
from numpy.lib.stride_tricks import as_strided

def f(arr, threshold=3, n=4):
arr = as_strided(arr, shape=(arr.shape[0]-n+1, n), strides=2*arr.strides)
return (arr >= threshold).all(axis=1).any()

# How it works:
# arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])
# n = 4
# threshold = 3

# arr = as_strided(arr, shape=(arr.shape[0]-n+1, n), strides=2*arr.strides)
# print(arr)
# [[1 2 3 4]
#  [2 3 4 5]
#  [3 4 5 6]
#  [4 5 6 7]
#  [5 6 7 8]]

# print(arr >= threshold)
# [[False False  True  True]
#  [False  True  True  True]
#  [ True  True  True  True]
#  [ True  True  True  True]
#  [ True  True  True  True]]

# print((arr >= threshold).all(axis=1))
# [False False  True  True  True]

# print((arr >= threshold).all(axis=1).any())
# True
```

Check if array contains contiguous integers with duplicates allowed , Print “Yes” if it is a set of contiguous integers else print “No”. Examples: is to first sort the array. Then traverse the array to check if all consecutive elements differ at most by one. Driver program to test above function */ Time Complexity : O(n Log n). Efficient 4) Traverse visited array and return true if all values are true. I am trying to test to see if the values in an array are above some value a consecutive number of times. For example. arr1 = np.array([1,2,1,3,4,5,6,7]) arr2 = np.array([1,2,1,3,4,2,6,7]) Say I want to test to see if an item in the array is >=3 for four consecutive periods. The test would return true for arr1 but false for arr2.

NumPy: Average of every consecutive triplet of elements of a given , NumPy Array Object Exercises, Practice and Solution: Write a NumPy Original array: [ 1 2 3 2 4 6 1 2 12 0 -12 6] Average of every consecutive triplet of Test your Python skills with w3resource's quiz. ﻿. Python: Tips of the Day. Maps the values of a list to a dictionary using a function, where the key-value  NumPy is, just like SciPy, Scikit-Learn, Pandas, etc. one of the packages that you just can’t miss when you’re learning data science, mainly because this library provides you with an array data structure that holds some benefits over Python lists, such as: being more compact, faster access in reading and writing items, being more convenient and more efficient.

NumPy: Array Object - Exercises, Practice, Solution, Write a NumPy program to create an array with values ranging from 12 to 38. Write a NumPy program to test whether each element of a 1-D array is also present in a second array. Repeating 2 times Sample array: x = np.arange(4, dtype=np.int64) Number of non zero elements in the above array: 5 Use fancy indexing on the left and array creation on the right to assign values into an array, for instance by setting parts of the array in the diagram above to zero. Table Of Contents The NumPy array object

NumPy arange(): How to Use np.arange() – Real Python, You can define the interval of the values contained in an array, space between start is the number (integer or decimal) that defines the first value in the between each two consecutive values in the array and defaults to 1 Let's see a first example of how to use NumPy arange() : > Unsubscribe any time. You can add a NumPy array element by using the append() method of the NumPy module. The syntax of append is as follows: numpy.append(array, value, axis) The values will be appended at the end of the array and a new ndarray will be returned with new and old values as shown above.