0 Posted 2022-03-07Updated 2024-01-11Python / Data6 minutes read (About 929 words)

Python: Data Calculating Skills

2D points cloud

Distance of two points

# from math
math.dist()

# from scipy
from scipy.spatial import distance
a = (1, 2, 3)
b = (4, 5, 6)
print(distance.euclidean(a, b))

Longest distance of points in a point cloud

The longest distance of points between any two points.

Yann, 2015

from numpy import random, nanmax, argmax, unravel_index
from scipy.spatial.distance import pdist, squareform

A = random.randint(-5,5, (500,2))
D = pdist(A)
D = squareform(D);
N, [I_row, I_col] = nanmax(D), unravel_index( argmax(D), D.shape )

# result:
# Point 1:
A[I_row]
# Point 2:
A[I_col]

Nearest adjacent point

import numpy as np
import math

p1 = [1,2]
p_list = [[2,1], [1,2], [0,0]]

# np.array([math.dist(p1, i) for i in p_list]).argmin() can return the index rather than value

p_list[np.array([math.dist(p1, i) for i in p_list]).argmin()]

Distance from points to line

from numpy.linalg import norm

p1=(x1,y1)
p2=(x2,y2)
p3=(x3,y3)

d = norm(np.cross(p2-p1, p1-p3))/norm(p2-p1)

Distance from points to rectangle

p1----p2
|  p5 |
|     |
p4----p3

from point p5 to rectangle p1,p2,p3,p4

from numpy.linalg import norm

def lin_dist(p1, p2, p3):
	d = norm(np.cross(p2-p1, p1-p3))/norm(p2-p1)
	return d
def p_rect(p1,p2,p3,p4,p5):
	d1 = lin_dist(p1,p2,p5)
	d2 = lin_dist(p1,p4,p5)
	d3 = lin_dist(p3,p2,p5)
	d4 = lin_dist(p3,p4,p5)
	return min([d1,d2,d3,d4])

p1=(x1,y1)
p2=(x2,y2)
p3=(x3,y3)
p4=(x4,y4)
p5=(x5,y5)

p_rect(p1,p2,p3,p4,p5)

Points rotation

P1 and P2 are points in points. Rotate the P1 to the same level as P2, so as the rest of others.

import numpy as np
def rotate(point, origin, degrees):
    radians = np.deg2rad(degrees)
    x,y = point
    offset_x, offset_y = origin
    adjusted_x = (x - offset_x)
    adjusted_y = (y - offset_y)
    cos_rad = np.cos(radians)
    sin_rad = np.sin(radians)
    qx = offset_x + cos_rad * adjusted_x + sin_rad * adjusted_y
    qy = offset_y + -sin_rad * adjusted_x + cos_rad * adjusted_y
    return qx, qy

Angles of two points

This codes works perfect to me.
© sabbahillel; 2017

import math
myradians = math.atan2(targetY-gunY, targetX-gunX)
mydegrees = math.degrees(myradians)
myradians = math.radians(mydegrees)

def point2agl(P1, P2):
	myradians = math.atan2(P1[1]-P2[1], P1[0]-P2[0])
	mydegrees = math.degrees(myradians)
	return mydegrees

Angle of three points

import math

def getAngle(a, b, c):
    ang = math.degrees(math.atan2(c[1]-b[1], c[0]-b[0]) - math.atan2(a[1]-b[1], a[0]-b[0]))
    return return ang - 360 if ang >= 360 else ang


print(getAngle((5, 0), (0, 0), (0, 5)))

Angle of two vectors

import numpy as np
import math

vector_1 = [0, 1]
vector_2 = [1, 0]

def Vector_angle(vector_1, vector_2):
	unit_vector_1 = vector_1 / np.linalg.norm(vector_1)
	unit_vector_2 = vector_2 / np.linalg.norm(vector_2)
	dot_product = np.dot(unit_vector_1, unit_vector_2)
	angle = np.arccos(dot_product)
	mydegrees = math.degrees(angle)
	return mydegrees

ang = Vector_angle(vector_1, vector_2)
print(ang)

SO, we can have angle of 4 points:

P1 = np.array([0,1])
P2 = np.array([1,1])
P3 = np.array([1,2])
P4 = np.array([3,1])

vector_1 = P2 - P1
vector_2 = P3 - P2


ang = Vector_angle(vector_1, vector_2)
print(ang)

# or:

def Points4_angle(P1, P2, P3, P4):
	P1 = np.array(P1)
	P2 = np.array(P2)
	P3 = np.array(P3)
	P4 = np.array(P4)
	vector_1 = P2 - P1
	vector_2 = P4 - P3
	unit_vector_1 = vector_1 / np.linalg.norm(vector_1)
	unit_vector_2 = vector_2 / np.linalg.norm(vector_2)
	dot_product = np.dot(unit_vector_1, unit_vector_2)
	angle = np.arccos(dot_product)
	mydegrees = math.degrees(angle)
	return mydegrees

Find the end point by angele and length

import math
import matplotlib.pyplot as plt

Angle = 30
Radian = 0.5
Origin = (0.5, 0.5)
End_x = math.cos(math.radians(Angle)) * Radian
End_y = math.sin(math.radians(Angle)) * Radian

plt.arrow(Origin[0], Origin[0], End_x, End_y,
    head_width = 0.1,
    width = 0.03)

plt.show()

Test

import cv2
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

NP_result = np.load("/mnt/8A26661926660713/Deng/Cell_segmentation/CellSegmentation/Results/220214_all_channels/images/lgl.3d.casp.1.lsm2_seg.npy", allow_pickle=True)

A = NP_result.all()['masks']

# Create the multiindex we'll need for the series
index = pd.MultiIndex.from_product(
    (*map(range, A.shape[:2]), (['r'])),
    names=('col','row',  None)
)

# Can be chained but separated for use in explanation
df = pd.Series(A.flatten(), index=index)
df = df.unstack()
df = df.reset_index().reindex(columns=['row', 'col', 'r'])

sns.scatterplot(data=df[df.r!=0], x= "row", y="col", hue = "r")
plt.show()

Time format

Second to format Hour and min
studytonight.com

seconds = 12601

seconds = seconds % (24 * 3600)
hour = seconds // 3600
seconds %= 3600
minutes = seconds // 60
seconds %= 60

print("%d:%02d:%02d" % (hour, minutes, seconds))