def generateRotation(axis, angle, radians=True):
if not radians:
angle = np.radian(angle)
sqr_a = axis.x*axis.x
sqr_b = axis.y*axis.y
sqr_c = axis.z*axis.z
len2 = sqr_a+sqr_b+sqr_c
k2 = math.cos(angle)
k1 = (1.0-k2)/len2
k3 = math.sin(angle)/math.sqrt(len2)
k1ab = k1*axis.x*axis.y
k1ac = k1*axis.x*axis.z
k1bc = k1*axis.y*axis.z
k3a = k3*axis.x
k3b = k3*axis.y
k3c = k3*axis.z
rotation = np.matrix([[k1*sqr_a+k2, k1ab-k3c, k1ac+k3b],
[k1ab+k3c, k1*sqr_b+k2, k1bc-k3a],
[k1ac-k3b, k1bc+k3a, k1*sqr_c+k2]],
dtype=np.float)
rotation[np.abs(rotation) < 1.e-15] = 0.
checkRotation(rotation)
return rotation
def dip_rand_samp(dip, err, num, threshold=30, output='degrees'):
"""
Creates a uniform random sample between min(dip+err, threshold)
and returns both the sample and the fraction (decimal) of the total
dip range that is below the threshold (this fraction should be multiplied
by any final probabilities of observing an earthquake on a dip below the
threshold value).
Assumes input dips are in degrees (it's dip...) but output can be in
radians, if desired.
Returns: tuple [samp, fraction]
"""
if threshold != None:
dip_min = dip - err
dip_max = min((dip + err), threshold)
dip_samp = np.random.rand(num) * (dip_max - dip_min) + dip_min
frac = (dip_max - dip_min) / float(2 * err)
else:
dip_min = dip - err
dip_max = dip + err
dip_samp = np.random.rand(num) * (dip_max - dip_min) + dip_min
frac = 1
if output == 'radians':
dip_samp = np.radian(dip_samp)
return [dip_samp, frac]
def dip_rand_samp(dip, err, num, threshold=30, output='degrees'):
"""
Creates a uniform random sample between min(dip+err, threshold)
and returns both the sample and the fraction (decimal) of the total
dip range that is below the threshold (this fraction should be multiplied
by any final probabilities of observing an earthquake on a dip below the
threshold value).
Assumes input dips are in degrees (it's dip...) but output can be in
radians, if desired.
Returns: tuple [samp, fraction]
"""
if threshold != None:
dip_min = dip - err
dip_max = min((dip + err), threshold)
dip_samp = np.random.rand(num) * (dip_max - dip_min) + dip_min
frac = (dip_max-dip_min) / float(2 * err)
else:
dip_min = dip - err
dip_max = dip + err
dip_samp = np.random.rand(num) * (dip_max - dip_min) + dip_min
frac = 1
if output == 'radians':
dip_samp = np.radian(dip_samp)
return [dip_samp, frac]
def generateRotation(axis, angle, radians=True):
if not radians:
angle = np.radian(angle)
sqr_a = axis.x*axis.x
sqr_b = axis.y*axis.y
sqr_c = axis.z*axis.z
len2 = sqr_a+sqr_b+sqr_c
k2 = math.cos(angle)
k1 = (1.0-k2)/len2
k3 = math.sin(angle)/math.sqrt(len2)
k1ab = k1*axis.x*axis.y
k1ac = k1*axis.x*axis.z
k1bc = k1*axis.y*axis.z
k3a = k3*axis.x
k3b = k3*axis.y
k3c = k3*axis.z
rotation = np.matrix([[k1*sqr_a+k2, k1ab-k3c, k1ac+k3b],
[k1ab+k3c, k1*sqr_b+k2, k1bc-k3a],
[k1ac-k3b, k1bc+k3a, k1*sqr_c+k2]],
dtype=np.float)
rotation[np.abs(rotation) < 1.e-15] = 0.
checkRotation(rotation)
return rotation
def filter_curve(self, angle):
angle = np.radian(angle)
mask = np.ones(self.dim)
mask[self.index_nan] = np.nan
# !!! En lugar de self.teta utilizar (psi-psi0)
index = np.where(np.abs(self.teta * mask - np.pi / 2) > angle)
index_red = np.where((self.teta * mask - np.pi / 2) > angle)
index_blue = np.where((np.pi / 2 - self.teta * mask) > angle)
filterrc = self.sort_curve(self.arcsec, self.Vc, self.sigmaVc, index)
appr = self.sort_curve(self.arcsec, self.Vc, self.sigmaVc, index_blue)
receed = self.sort_curve(self.arcsec, self.Vc, self.sigmaVc, index_red)
# Those output velocity curves will be in [km/s],[arcsec]
return filterrc, appr, receed
import numpy as np
import pandas as pd
from sklearn.cluster import DBSCAN
lat = np.array([22.45, 29.45, 36.567])
lon = np.array([73.345, 75.34, 95.567])
minimum_samples = 2
minimum_dist = 2 / 6371.0088 #distance in KM / 6371.0088
X = np.column_stack([np.radian(lat), np.radians(lon)])
db = DBSCAN(eps=minimum_dist,
min_samples=minimum_samples,
algorithm='ball_tree',
metric='haversine').fit(X)
db.labels_
#This code was in a Tableau workbook to find hot spots in a geography
# db.labels_ gives the cluster labels as 0,1,.. .Label -1 is noise.
from numpy import genfromtxt
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import os.path
import math
from math import pi
from helper import *
#%%
position_data = genfromtxt('Coordinate_Meas_1', delimiter=',')
polar_data = np.zeros((position_data.shape))
for i in range(0, position_data.shape[1]):
polar_data[:, i] = cart2pol(position_data[0, i], position_data[1, i])
# %% Polar data
np.radian(2)
heading = pi
import numpy as np
a = np.ones(10)
selfPosition = np.array((0, 0))
r = polar_data[0, :]
t = polar_data[1, :]
r.shape = (r.size, 1)
t.shape = (t.size, 1)
type(heading)
t = t + heading * np.ones((t.size, 1))
self_x = selfPosition[0] * np.ones((t.size, 1))
self_y = selfPosition[1] * np.ones((t.size, 1))
self_x.shape = (t.size, 1)