我正在尝试使用Python https://en.wikipedia.org/wiki/Hertzsprung%E2%80%93Russell_diagram#/media/File:HRDiagram.png从Wikipedia复制Hertzsprung图的图。
我是Python的新手，正在尝试使用嵌套字典来完成一小部分数据的代码：
32349 379.21 -1.44 0.009
30438 10.43 -0.62 0.164
69673 88.85 -0.05 1.239
71683 742.12 -0.01 0.710
91262 128.93 0.03 -0.001
24608 77.29 0.08 0.795
24436 4.22 0.18 -0.030
37279 285.93 0.40 0.432
7588 22.68 0.45 -0.158
27989 7.63 0.45 1.500
68702 6.21 0.61 -0.231
97649 194.44 0.76 0.221
60718 10.17 0.77 -0.243
21421 50.09 0.87 1.538
65474 12.44 0.98 -0.235
80763 5.40 1.06 1.865
37826 96.74 1.16 0.991
113368 130.08 1.17 0.145
62434 9.25 1.25 -0.238
102098 1.01 1.25 0.092
如果可能的话，请问如何找到一个适用于嵌套字典的循环？另外，我能否在创建轴时像显示在图表中一样获得帮助？
到目前为止，我的代码：
import numpy as np
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
astronomical_unit = 1.495978707e11 # meters
meters_to_light_years = 1./9.4607e15
def star_colormap(star_b_minus_vs):
# Create color map from B-V = -0.33 (#7070ff) to 1.40 (#ff7f7f)
# yellow = #ffff7f at B-V = 0.81
number_of_gradient_points = 256
white_index = int((0.33 / (0.33 + 1.40)) * number_of_gradient_points)
yellow_index = int(((0.81 + .33) / (0.33 + 1.40)) * number_of_gradient_points)
print(white_index, yellow_index, number_of_gradient_points)
color_values = np.ones((number_of_gradient_points, 4))
# Red values
color_values[:white_index, 0] = np.linspace(112 / number_of_gradient_points,
255 / number_of_gradient_points,
white_index)
color_values[white_index:yellow_index, 0] = np.linspace(255 / number_of_gradient_points,
255 / number_of_gradient_points,
yellow_index - white_index)
color_values[yellow_index:, 0] = np.linspace(255 / number_of_gradient_points,
255 / number_of_gradient_points,
number_of_gradient_points - yellow_index)
# Green values
color_values[:white_index, 1] = np.linspace(112 / number_of_gradient_points,
255 / number_of_gradient_points,
white_index)
color_values[white_index:yellow_index, 1] = np.linspace(255 / number_of_gradient_points,
255 / number_of_gradient_points,
yellow_index - white_index)
color_values[yellow_index:, 1] = np.linspace(255 / number_of_gradient_points,
127 / number_of_gradient_points,
number_of_gradient_points - yellow_index)
# Blue values
color_values[:white_index, 2] = np.linspace(255 / number_of_gradient_points,
255 / number_of_gradient_points,
white_index)
color_values[white_index:yellow_index, 2] = np.linspace(255 / number_of_gradient_points,
127 / number_of_gradient_points,
yellow_index - white_index)
color_values[yellow_index:, 2] = np.linspace(127 / number_of_gradient_points,
127 / number_of_gradient_points,
number_of_gradient_points - yellow_index)
new_colormap = ListedColormap(color_values)
# Scale B-V values from 0 to 1
scaled_b_minus_vs = (star_b_minus_vs - np.amin(star_b_minus_vs)) / (
np.amax(star_b_minus_vs) - np.amin(star_b_minus_vs))
return scaled_b_minus_vs, new_colormap
def parallax_to_distance(parallax):
"""Take parallax in milliarcseconds and convert to distance in meters"""
parallax_in_radians = (parallax / 1000. / 3600.) * (2 * np.pi / 360.)
distance = astronomical_unit / np.tan(parallax_in_radians)
return distance
def apparent_to_absolute_magnitude(apparent_magnitude, distance):
"""Calculate absolute magnitude from apparent magnitude and distance in meters"""
distance_in_parsecs = distance / (648000. * astronomical_unit / np.pi)
absolute_magnitude = apparent_magnitude - 5*np.log10(distance_in_parsecs) + 5
return absolute_magnitude
def read_file(filename):
"""Read four column data from HIPPARCOS satellite and return a nested dictionary"""
# Read in as nested dictionary
hipparcos_data = {'(star catalog number':
{ 'parallax' : [] , 'apparent_magnitude' : [] , 'blue_minus_visual' : [] }}
return hipparcos_data
# Apply read function to the data file and produce a nested dictionary
# Loop over star catalog number key
# Call parallax_to_distance on parallax value and assign to new value
# Call apparent_to_absolute_magnitude on apparent magnitude value and assign to new value
# Append absolute magnitude for current star into NumPy array of absolute magnitudes
# named star_absolute_magnitudes
# Append B-V value for current star into NumPy array of B-V values
# named star_b_minus_vs
# Use dark style for plot
plt.style.use('dark_background')
# Reverse the absolute magnitude so that negative values appear on top
star_absolute_magnitudes = np.negative(star_absolute_magnitudes)
# Get color map to match star colors
scaled_b_minus_v, hr_diagram_colormap = star_colormap(star_b_minus_vs)
# Create axes labels
# Scatter plot of B-V vs absolute magnitude
plt.scatter(star_b_minus_vs, star_absolute_magnitudes, c=scaled_b_minus_v, cmap=hr_diagram_colormap)
plt.show()
答案 0 :(得分：0)
def read_file(filename):
with open(filename) as fp:
res = {}
for line in fp:
star, parallax, magnitude, bmv = line.split()
res[star] = {
'parallax': float(parallax),
'magnitude': float(magnitude),
'blue_minus_visual': float(bmv)
}
return res
stars = read_file('data.txt')
# Loop over star catalog number key
for star in stars:
# Call parallax_to_distance on parallax value and assign to new value
stars[star]['parallax'] = parallax_to_distance(stars[star]['parallax'])
# Call apparent_to_absolute_magnitude on apparent magnitude value and assign to new value
stars[star]['magnitude'] = apparent_to_absolute_magnitude(stars[star]['magnitude'])