I have this problem.
I need order this points 1-7
1(4,2), 2(3, 5), 3(1,4), 4(1,1), 5(2,2), 6(1,3), 7(1,5)
and get this result
4 , 6 , 3 , 5 , 2 , 1 , 7.
I am using a python script for sort with x reference and is ok, but the sort in y is wrong.
I have tried with sorted(dicts,key=itemgetter(1,2))
Someone can help me please ?
Try this:
sorted(dicts,key=itemgetter(1,0))
Indexing in python starts at 0. itemgetter(1,0) is sorting by the second element and then by the first element
This sorts the code based on ordering the first coordinate of the tuple, and then sub-ordering by the second coordinate of the tuple. I.e. Like alphabetically where "Aa", then "Ab", then "Ba", then "Bb". More literall (1,1), (1,2), (2,1), (2,2), etc.
This will work IF (and only if) the tuple value pair associated with #7 is actually out of order in your question (and should actually be between #3 and #5.)
If this is NOT the case, See my other answer.
# Make it a dictionary, with the VALUETUPLES as the KEYS, and the designator as the value
d = {(1,1):4, (1,3):6, (1,4):3, (2,2):5, (3,5):2, (4,2):1,(1,5):7}
# ALSO make a list of just the value tuples
l = [ (1,1), (1,3), (1,4), (2,2), (3,5), (4,2), (1,5)]
# Sort the list by the first element in each tuple. ignoring the second
new = sorted(l, key=lambda x: x[0])
# Create a new dictionary, basically for temp sorting
new_d = {}
# This iterates through the first sorted list "new"
# and creates a dictionary where the key is the first number of value tuples
count = 0
# The extended range is because we don't know if any of the Tuple Values share any same numbers
for r in range(0, len(new)+1,1):
count += 1
new_d[r] = []
for item in new:
if item[0] == r:
new_d[r].append(item)
print(new_d) # So it makes sense
# Make a final list to capture the rdered TUPLES VALUES
final_list = []
# Go through the same rage as above
for r in range(0, len(new)+1,1):
_list = new_d[r] # Grab the first list item from the dic. Order does not matter here
if len(_list) > 0: # If the list has any values...
# Sort that list now by the SECOND tuple value
_list = sorted(_list, key=lambda x: x[1])
# Lists are ordered. So we can now just tack that ordered list onto the final list.
# The order remains
for item in _list:
final_list.append(item)
# This is all the tuple values in order
print(final_list)
# If you need them correlated to their original numbers
by_designator_num = []
for i in final_list: # The the first tupele value
by_designator_num.append(d[i]) # Use the tuple value as the key, to get the original designator number from the original "d" dictionary
print(by_designator_num)
OUTPUT:
[(1, 1), (1, 3), (1, 4), (1, 5), (2, 2), (3, 5), (4, 2)]
[4, 6, 3, 7, 5, 2, 1]
Since you're searching visually from top-to-bottom, then left-to-right, this code is much simpler and provides the correct result. It basically does the equivalent of a visual scan, by checking for all tuples that are at each "y=n" position, and then sorting any "y=n" tuples based on the second number (left-to-right).
Just to be more consistent with the Cartesian number system, I've converted the points on the graph to (x,y) coordinates, with X-positive (increasing to the right) and y-negative (decreasing as they go down).
d = {(2,-4):1, (5,-3):2, (4,-1):3, (1,-1):4, (2,-2):5, (3,-1):6, (1,-5):7}
l = [(2,-4), (5,-3), (4,-1), (1,-1), (2,-2), (3,-1), (1,-5)]
results = []
# Use the length of the list. Its more than needed, but guarantees enough loops
for y in range(0, -len(l), -1):
# For ONLY the items found at the specified y coordinate
temp_list = []
for i in l: # Loop through ALL the items in the list
if i[1] == y: # If tuple is at this "y" coordinate then...
temp_list.append(i) # ... append it to the temp list
# Now sort the list based on the "x" position of the coordinate
temp_list = sorted(temp_list, key=lambda x: x[0])
results += temp_list # And just append it to the final result list
# Final TUPLES in order
print(results)
# If you need them correlated to their original numbers
by_designator_num = []
for i in results: # The the first tupele value
by_designator_num.append(d[i]) # Use the tuple value as the key, to get the original designator number from the original "d" dictionary
print(by_designator_num)
OR if you want it faster and more compact
d = {(2,-4):1, (5,-3):2, (4,-1):3, (1,-1):4, (2,-2):5, (3,-1):6, (1,-5):7}
l = [(2,-4), (5,-3), (4,-1), (1,-1), (2,-2), (3,-1), (1,-5)]
results = []
for y in range(0, -len(l), -1):
results += sorted([i for i in l if i[1] == y ], key=lambda x: x[0])
print(results)
by_designator_num = [d[i] for i in results]
print(by_designator_num)
OUTPUT:
[(1, -1), (3, -1), (4, -1), (2, -2), (5, -3), (2, -4), (1, -5)]
[4, 6, 3, 5, 2, 1, 7]
Related
Having the following problem. I'm reading the data from stdin and save it in list that I convert to tuple the following way:
x = int(input())
f = []
for i in range(x):
a, b = map(int, input().split())
f.append([a,b])
def to_tuple(lst):
return tuple(to_tuple(i) if isinstance(i, list) else i for i in lst)
After this I receive two tuples of tuples looking something like that:
f = ((0, 1), (1, 2), (0, 2), (0, 3))
s = (((0,), (1, 2, 3)), ((0, 1), (2, 3)), ((0, 1, 2), (3,)))
What I'm trying to do is to find the number of intersections between all inner tuples of f and each tuple of s. In my case "intersection" should be considered as an "edges" between tuples (so in f we have all possible "edges" and checking if there will be an edge between inner tuples in particular tuple of s). So for the example it should print [3,3,1].
Basically, I know how to do in the simple case of intersection - so one can just use set() and then apply a.intersection(b) But how should I proceed in my case?
Many thanks and sorry if the question was already asked before :=)
I am sure this can be solve by different ways. but I believe this is the easiest.
out = set() # holds the output
for ff in f: # loop through f tuple
ff = set(ff) # convert to set
for ss1,ss2 in s: # loop through s tuple
# you can select which tuple to do the intersection on.
# here I am doing the intersection on both inner tuples in the s tuple.
ss1 = set(ss1) # convert to set
ss2 = set(ss2)
out.update(ff.intersection(ss1)) # intersection and add to out
out.update(ff.intersection(ss2)) # intersection and add to out
#if you want your output to be in list format
out = list(out)
This is an example of how you can proceed
a = ((1,1),(1,2))
b = (((1,2),(3,1)),((3,2),(1,2)),((1,4),))
l=[]
for t in b:
c=[i for i in a for j in t if i==j]
l.append(c)
print(l)
General answer for overall amount of edges:
def cnt_edges(a,b):
edge_cnt = 0
for i in range(len(a)):
node1 = a[i][0]
node2 = a[i][1]
for j in range(len(b)):
inner_node1 = b[j][0]
inner_node2 = b[j][1]
if (node1 in inner_node1 and node2 in inner_node2) or (node1 in inner_node2 and node2 in inner_node1):
edge_cnt += 1
return edge_cnt
a = ((0, 1),(0, 2), (0,3))
b = (((0),(1,2,3)), ((0,1),(2,3)), ((0,1,2),(3)))
cnt_edges(a,b)
I have a list of tuples:
x = [(2, 10), (4, 5), (8, 10), (9, 11), (10, 15)]
I'm trying to compare the first values in all the tuples to see if they are within 1 from each other. If they are within 1, I want to aggregate (sum) the second value of the tuple, and take the mean of the first value.
The output list would look like this:
[(2, 10), (4, 5), (9, 36)]
Notice that the 8 and 10 have a difference of 2, but they're both only 1 away from 9, so they all 3 get aggregated.
I have been trying something along these lines, but It's not capturing the sequenced values like 8, 9, and 10. It's also still preserving the original values, even if they've been aggregated together.
tuple_list = [(2, 10), (4, 5), (8, 10), (9, 11), (10, 15)]
output_list = []
for x1,y1 in tuple_list:
for x2,y2 in tuple_list:
if x1==x2:
continue
if np.abs(x1-x2) <= 1:
output_list.append((np.mean([x1,x2]), y1+y2))
else:
output_list.append((x1,y1))
output_list = list(set(output_list))
You can do it in a list comprehension using groupby (from itertools). The grouping key will be the difference between the first value and the tuple's index in the list. When the values are 1 apart, this difference will be constant and the tuples will be part of the same group.
For example: [2, 4, 8, 9, 10] minus their indexes [0, 1, 2, 3, 4] will give [2, 3, 6, 6, 6] forming 3 groups: [2], [4] and [8 ,9, 10].
from itertools import groupby
x = [(2, 10), (4, 5), (8, 10), (9, 11), (10, 15)]
y = [ (sum(k)/len(k),sum(v)) # output tuple
for i in [enumerate(x)] # sequence iterator
for _,g in groupby(x,lambda t:t[0]-next(i)[0]) # group by sequence
for k,v in [list(zip(*g))] ] # lists of keys & values
print(y)
[(2.0, 10), (4.0, 5), (9.0, 36)]
The for k,v in [list(zip(*g))] part is a bit tricky but what it does it transform a list of tuples (in a group) into two lists (k and v) with k containing the first item of each tuple and v containing the second items.
e.g. if g is ((8,10),(9,11),(10,15)) then k will be (8,9,10) and v will be (10,11,15)
By sorting the list first, and then using itertools.pairwise to iterate over the next and previous days, this problem starts to become much easier. On sequential days, instead of adding a new item to our final list, we modify the last item added to it. Figuring out the new sum is easy enough, and figuring out the new average is actually super easy because we're averaging sequential numbers. We just need to keep track of how many sequential days have passed and we can use that to get the average.
def on_neighboring_days_sum_occurrances(tuple_list):
tuple_list.sort()
ret = []
sequential_days = 1
# We add the first item now
# And then when we start looping we begin looping on the second item
# This way the loop will always be able to modify ret[-1]
ret.append(tuple_list[0])
# Python 3.10+ only, in older versions do
# for prev, current in zip(tuple_list, tuple_list[1:]):
for prev, current in itertools.pairwise(tuple_list):
day = current[0]
prev_day = prev[0]
is_sequential_day = day - prev_day <= 1
if is_sequential_day:
sequential_days += 1
avg_day = day - sequential_days/2
summed_vals = ret[-1][1] + current[1]
ret[-1] = (avg_day, summed_vals)
else:
sequential_days = 1
ret.append(current)
return ret
You can iterate through the list and keep track of a single tuple, and iterate from the tuple next to the one that you're tracking till the penultimate tuple in the list because, when the last tuple comes into tracking there is no tuple after that and thus it is a waste iteration; and find if the difference between the 1st elements is equal to the difference in indices of the tuples, if so sum up the 2nd as well as 1st elements, when this condition breaks, divide the sum of 1st elements with the difference in indices so as to get the average of them, and append them to the result list, now to make sure the program doesn't consider the same tuples again, jump to the index where the condition broke... like this
x = [(2, 10), (4, 5), (8, 10), (9, 11), (10, 15)]
x.sort()
res, i = [], 0
while i<len(x)-1:
sum2, avg1 = x[i][1], x[i][0]
for j in range(i+1, len(x)):
if abs(x[j][0]-x[i][0]) == (j-i):
sum2 += x[j][1]
avg1 += x[j][0]
else:
res.append(x[i])
i+=1
break
else:
avg1 /= len(x)-i
res.append((int(avg1), sum2))
i = j+1
print(res)
Here the while loop iterates from the start of the list till the penultimate tuple in the list, the sum2, avg1 keeps track of the 2nd and 1st elements of the current tuple respectively. The for loop iterates through the next tuple to the current tuple till the end. The if checks the condition, and if it is met, it adds the elements of the tuple from the for loop since the variables are intialized with the elements of current tuple, else it appends the tuple from the for loop directly to the result list res, increments the while loop variable and breaks out of the iteration. When the for loop culminates without a break, it means that the condition breaks, thus it finds the average of the 1st element and appends the tuple (avg1, sum2) to res and skips to the tuple which is next to the one that broke the condition.
Given a list of sorted lists, I wish to produce a sorted list of output.
This would be easy:
nums : List[List[int]]
h = heapq.merge(nums)
However, I also wish to tag each element of the output, with the index of the inner list from which it originated. Eg.
nums = [[1,3,5], [2,6], [4]]
h = _ # ???
for x in h:
print(x)
# Outputs
# (1,0)
# (2,1)
# (3,0)
# (4,2)
# (5,0)
# (6,1)
I have written a version that works,
h = heapq.merge(*map(lambda l: map(lambda x: (x,l[0]), l[1]), enumerate(nums)))
But I'm afraid I might have lost a desirable space-complexity guarantee; how can I know whether the (transformed) inner lists are being manifested or not? (and what exactly does the * do in my attempt?)
Since default tuple comparison is lexicographic, you can convert the innermost-element x to (x, i) where i is the index of the list containing x in nums. For example,
import heapq
from itertools import repeat
nums = [[1,3,5], [2,6], [4]]
nums_with_inds = (zip(lst, repeat(i)) for i, lst in enumerate(nums))
res = heapq.merge(*nums_with_inds)
for tup in res:
print(tup)
# (1, 0)
# (2, 1)
# (3, 0)
# (4, 2)
# (5, 0)
# (6, 1)
Assume, I have a NumPy-array of integers, as:
[34,2,3,22,22,22,22,22,22,18,90,5,-55,-19,22,6,6,6,6,6,6,6,6,23,53,1,5,-42,82]
I want to find the start and end indices of the array, where a value is more than x-times (say 5-times) repeated. So in the case above, it is the value 22 and 6. Start index of the repeated 22 is 3 and end-index is 8. Same for the repeatening 6.
Is there a special tool in Python that is helpful?
Otherwise, I would loop through the array index for index and compare the actual value with the previous.
Regards.
Using np.diff and the method given here by #WarrenWeckesser for finding runs of zeros in an array:
import numpy as np
def zero_runs(a): # from link
iszero = np.concatenate(([0], np.equal(a, 0).view(np.int8), [0]))
absdiff = np.abs(np.diff(iszero))
ranges = np.where(absdiff == 1)[0].reshape(-1, 2)
return ranges
a = [34,2,3,22,22,22,22,22,22,18,90,5,-55,-19,22,6,6,6,6,6,6,6,6,23,53,1,5,-42,82]
zero_runs(np.diff(a))
Out[87]:
array([[ 3, 8],
[15, 22]], dtype=int32)
This can then be filtered on the difference between the start & end of the run:
runs = zero_runs(np.diff(a))
runs[runs[:, 1]-runs[:, 0]>5] # runs of 7 or more, to illustrate filter
Out[96]: array([[15, 22]], dtype=int32)
Here is a solution using Python's native itertools.
Code
import itertools as it
def find_ranges(lst, n=2):
"""Return ranges for `n` or more repeated values."""
groups = ((k, tuple(g)) for k, g in it.groupby(enumerate(lst), lambda x: x[-1]))
repeated = (idx_g for k, idx_g in groups if len(idx_g) >=n)
return ((sub[0][0], sub[-1][0]) for sub in repeated)
lst = [34,2,3,22,22,22,22,22,22,18,90,5,-55,-19,22,6,6,6,6,6,6,6,6,23,53,1,5,-42,82]
list(find_ranges(lst, 5))
# [(3, 8), (15, 22)]
Tests
import nose.tools as nt
def test_ranges(f):
"""Verify list results identifying ranges."""
nt.eq_(list(f([])), [])
nt.eq_(list(f([0, 1,1,1,1,1,1, 2], 5)), [(1, 6)])
nt.eq_(list(f([1,1,1,1,1,1, 2,2, 1, 3, 1,1,1,1,1,1], 5)), [(0, 5), (10, 15)])
nt.eq_(list(f([1,1, 2, 1,1,1,1, 2, 1,1,1], 3)), [(3, 6), (8, 10)])
nt.eq_(list(f([1,1,1,1, 2, 1,1,1, 2, 1,1,1,1], 3)), [(0, 3), (5, 7), (9, 12)])
test_ranges(find_ranges)
This example captures (index, element) pairs in lst, and then groups them by element. Only repeated pairs are retained. Finally, first and last pairs are sliced, yielding (start, end) indices from each repeated group.
See also this post for finding ranges of indices using itertools.groupby.
There really isn't a great short-cut for this. You can do something like:
mult = 5
for elem in val_list:
target = [elem] * mult
found_at = val_list.index(target)
I leave the not-found exceptions and longer sequence detection to you.
If you're looking for value repeated n times in list L, you could do something like this:
def find_repeat(value, n, L):
look_for = [value for _ in range(n)]
for i in range(len(L)):
if L[i] == value and L[i:i+n] == look_for:
return i, i+n
Here is a relatively quick, errorless solution which also tells you how many copies were in the run. Some of this code was borrowed from KAL's solution.
# Return the start and (1-past-the-end) indices of the first instance of
# at least min_count copies of element value in container l
def find_repeat(value, min_count, l):
look_for = [value for _ in range(min_count)]
for i in range(len(l)):
count = 0
while l[i + count] == value:
count += 1
if count >= min_count:
return i, i + count
I had a similar requirement. This is what I came up with, using only comprehension lists:
A=[34,2,3,22,22,22,22,22,22,18,90,5,-55,-19,22,6,6,6,6,6,6,6,6,23,53,1,5,-42,82]
Find unique and return their indices
_, ind = np.unique(A,return_index=True)
np.unique sorts the array, sort the indices to get the indices in the original order
ind = np.sort(ind)
ind contains the indices of the first element in the repeating group, visible by non-consecutive indices
Their diff gives the number of elements in a group. Filtering using np.diff(ind)>5 shall give a boolean array with True at the starting indices of groups. The ind array contains the end indices of each group just after each True in the filtered list
Create a dict with the key as the repeating element and the values as a tuple of start and end indices of that group
rep_groups = dict((A[ind[i]], (ind[i], ind[i+1]-1)) for i,v in enumerate(np.diff(ind)>5) if v)
I want to get the top n keys of a dictionary with tuples as keys, where the first value of the tuple is a particular number (1 in the example below):
a = {}
a[1,2] = 3
a[1,0] =4
a[1,5] = 1
a[2,3] = 9
I want [1,0] and [1,2] to be returned, where the first element of the tuple/key = 1
this
import heapq
k = heapq.nlargest(2, a, key=a.get(1,))
returns [1,4] and [1,3], the highest keys/tuples with first element = 1, though if I make it
k = heapq.nlargest(2, a, key=a.get(2,))
it returns the same thing?
First you should take only the keys with first coordinate 1. Otherwise, there is the chance if there are a few elements with 1 as first coordinate, to get other tuples also. Then you can use heapq normally. For example:
a = {
(1, 2): 3,
(1, 0): 4,
(1, 5): 1,
(2, 3): 9
}
import heapq
print heapq.nlargest(2, (k for k in a if k[0] == 1), key=lambda k: a[k])
print heapq.nlargest(2, (k for k in a if k[0] == 2), key=lambda k: a[k])
Output:
[(1, 0), (1, 2)]
[(2, 3)]
The key parameter should be a function. But you are passing in a.get(1,). What this does is calling a.get(1,) which is the same as a.get(1) which is the same as a.get(1, None).
The dictionary doesn't have a 1 key so it returns None which means you are doing the equivalent of passing key=None which is the same as not passing a key at all: you are using the identity function as key.
Then heapq.nlargest returns the top 2 elements which are, correctly, [1, 4] and [1, 3].
This explains why using a.get(1,) and a.get(2,) does the same thing. The above reasoning works for both values and you end up with key=None in both cases.
To achieve what you want use something like:
key=lambda x: (x[0] == 1, a[x])
If you find yourself using this kind of keys often you can create a key maker function:
def make_key(value, container):
def key(x):
return x[0] == value, container[x]
return key
using it as:
nlargest(2, a, key=make_key(1, a))
nlargest(2, a, key=make_key(2, a))