def main():
text1 = tknzr.tokenize_file("sample_input.txt")
processed_text1 = preprocess_text(text1)
v11 = build_vector(processed_text1)
v12 = build_vector(processed_text1, type="bigram")
print(v11)
print(v12)
text2 = tknzr.tokenize_file("sample_input2.txt")
processed_text2 = preprocess_text(text2)
v21 = build_vector(processed_text2)
v22 = build_vector(processed_text2, type="bigram")
print(v21)
print(v22)
print(ang.angle_between(v11, v21))
print(ang.angle_between(v12, v22))
def cng_fw(file1, file2):
vc1 = cng.get_vector(file1)
vc2 = cng.get_vector(file2)
vf1 = fw.get_vector(file1)
vf2 = fw.get_vector(file2)
v1 = vc1 + vf1
v2 = vc2 + vf2
return ang.angle_between(v1, v2)
def png_fw(file1, file2):
vp1 = png.get_vector(file1)
vp2 = png.get_vector(file2)
vf1 = fw.get_vector(file1)
vf2 = fw.get_vector(file2)
v1 = vp1 + vf1
v2 = vp2 + vf2
return ang.angle_between(v1, v2)
def cng_png(file1, file2):
vc1 = cng.get_vector(file1)
vc2 = cng.get_vector(file2)
vp1 = png.get_vector(file1)
vp2 = png.get_vector(file2)
v1 = vc1 + vp1
v2 = vc2 + vp2
return ang.angle_between(v1, v2)
def png_cng_fw_freq(file1, file2):
vc1 = cng.get_vector(file1)
vc2 = cng.get_vector(file2)
vp1 = png.get_vector(file1)
vp2 = png.get_vector(file2)
vf1 = fw.get_vector(file1)
vf2 = fw.get_vector(file2)
f1 = freqd.get_freq(file1, local_thre)
f2 = freqd.get_freq(file2, local_thre)
v1 = vc1 + vp1 + vf1
v1.append(f1 / 50)
v2 = vc2 + vp2 + vf2
v2.append(f2 / 50)
return ang.angle_between(v1, v2)
ref_vectors = pd.DataFrame(data_dict_list)
print("\nWE RCVD:")
print(ref_vectors)
# Build a simple list with numpy vectors for easier processing
obs_positions = [
np.array([rowt[1]['x'], rowt[1]['y'], rowt[1]['z']])
for rowt in ref_vectors.iterrows()
]
matches = []
for i, obs_position in enumerate(obs_positions):
# Calculate the angles between this observation and all other observations.
# Find the two obervations which have the smallest angle.
angels = [
angle_between(obs_position, other_obs_pos)
for other_obs_pos in obs_positions
]
closest_obs = np.argsort(angels)[:3]
# Get the angles as well as the angle between the two closest observations.
a1 = angels[closest_obs[1]]
a2 = angels[closest_obs[2]]
ab = angle_between(obs_positions[closest_obs[1]],
obs_positions[closest_obs[2]])
print(
f"Two closest observations next to observation {i} are {closest_obs[1]} and {closest_obs[2]}"
)
index, error = stars_improved.find_by_angles(a1, a2, ab)
print(f"Matched star from catalog: {index} (Error: {error:.6})")
def fw_only(file1, file2):
v1 = fw.get_vector(file1)
v2 = fw.get_vector(file2)
return ang.angle_between(v1, v2)
def png_only(file1, file2):
v1 = png.get_vector(file1)
v2 = png.get_vector(file2)
return ang.angle_between(v1, v2)
def main():
vec1 = get_vector("sample_input.txt")
vec2 = get_vector("sample_input2.txt")
print(ang.angle_between(vec1, vec2))
#!/usr/bin/env python3
import pandas as pd
import numpy as np
import angle
ref_catalog = pd.read_csv('test.csv') # import catalog reference vectors
for i, rowt in ref_catalog.iterrows():
star_cat = np.array([rowt['x'], rowt['y'], rowt['z']])
next_1_a = np.inf
next_1_i = None
next_2_a = np.inf
next_2_i = None
for i_t, rowt_t in ref_catalog.iterrows():
if i == i_t:
continue
star_cat_t = np.array([rowt_t['x'], rowt_t['y'], rowt_t['z']])
a = angle.angle_between(star_cat, star_cat_t)
if a < next_1_a:
next_2_a = next_1_a
next_2_i = next_1_i
next_1_a = a
next_1_i = i_t
elif a < next_2_a:
next_2_a = a
next_2_i = i_t
print("Two closest stars next to", i, ":", next_1_i, next_1_a, next_2_i,
next_2_a)
print(ref_vectors)
indexes = []
for i, rowt in ref_vectors.iterrows():
star_obs = np.array([rowt['x'], rowt['y'], rowt['z']])
# Find the closest two stars in the observation
next_1_a = np.inf
next_1_i = None
next_2_a = np.inf
next_2_i = None
for i_t, rowt_t in ref_vectors.iterrows():
if i == i_t:
continue
star_obs_t = np.array([rowt_t['x'], rowt_t['y'], rowt_t['z']])
a = angle_between(star_obs, star_obs_t)
if a < next_1_a:
next_2_a = next_1_a
next_2_i = next_1_i
next_1_a = a
next_1_i = i_t
elif a < next_2_a:
next_2_a = a
next_2_i = i_t
print("Two closest stars next to", i, "are :", next_1_i, next_1_a,
next_2_i, next_2_a)
index = stars.find_by_angles(next_1_a, next_2_a)
print("Matched star:", index)
indexes.append(index)
# Remove duplicates, take a random subset and build a string