def get_orbit_tally(data, orbit, pre=True):
"""
set pre = False if you already added orbit columns to data
tallys up the count of objects per year in orbit
returns a dataframe w/ columns orbit (containing counts)
and 'LaunchYear', orbit may be LEO, MEO, GEO or HighEarthOrbit
"""
if pre:
data = get_launch_years_column(data)
data = get_orbit_columns(data)
data = data[data[orbit]]
data = data[['LaunchYear', orbit]]
data = data.groupby(['LaunchYear'], as_index=False).count()
for i in range(1, len(data)):
data.loc[i, orbit] += data.loc[i - 1, orbit]
return data
def compare_by_alt(data):
"""
takes a dataframe of orbital debris data and adds columns
with information about orbits. Creates a scatter plot of
altitude vs launch year
"""
data = get_launch_years_column(data)
data = get_orbit_columns(data)
data.plot(kind='scatter',
x='LaunchYear',
y='Apoapsis',
figsize=(7, 5),
color="#0f4c81")
plt.title("Altitude vs Year Launched")
plt.xlabel('Launch Year')
plt.ylabel('Altitude (km)')
plt.savefig('visualizations/alt_vs_launch_year.png')
def bar_plot_LEO(data):
"""
takes a dataframe from process_data and creates a
bar plot for count by launch year from dataframe
(for LEO)
"""
data = get_orbit_columns(data)
data = data[data['LEO']]
data = data['InternationalDesignator']
data = data.dropna()
data = data.apply(get_launch_year)
data = data.groupby(data).count()
data.plot(kind='bar', figsize=(17, 10), color='#0f4c81')
plt.title("Orbital Debris Count Increase Per Year (LEO)")
plt.xlabel('Launch Year')
plt.ylabel('Count')
plt.savefig('visualizations/count_by_launch_year.png')
def polynomial_fit_probability(data):
"""
from a dataframe, makes a polynomial
best fit model of the probability of impact
per year. Creates a plot of the model
overtop a scatter plot of the data points.
Prints some information about the model,
includung the R-squared value. Creates a
plot of the residuals.
"""
# get the data in the right format
data = get_orbit_columns(data)
data = data[data['LEO']]
data = get_probability_tally(data)
x = np.array(list(data['LaunchYear']))
y = np.array(list(data['Probability']))
# create the model
prob_func = np.poly1d(np.polyfit(x, y, 2))
# plot the scatter plot & line of best fit
# plt.scatter(x, y)
fig, ax = plt.subplots(1)
plt.scatter(x, y, color='#ffa372')
line = np.linspace(1963, 2020, 6)
# m = (line*0) + (1/10000)
plt.plot(line, prob_func(line), color='#0f4c81')
# plt.plot(line, m, color='red')
plt.title("Polynomial Model of Probability of Impact Per Year (LEO)")
plt.xlabel('Year')
plt.ylabel('Count')
fig.savefig('poly_fit_probability.png')
# print some information about the model
print()
print('Polynomial fit for probability:')
print('Eq: ')
print(prob_func)
print('R-squared: ', r2_score(y, prob_func(x)))
# plot the residuals
fig, ax = plt.subplots(1)
res = y - prob_func(x)
d = {'x': x, 'res': res}
df = pd.DataFrame(d)
df.plot(kind='scatter', x='x', y='res', ax=ax, color='#0f4c81')
plt.title("Probability Model Residuals")
plt.xlabel('Year')
plt.ylabel('Error')
fig.savefig('probability_residuals.png')
def polynomial_fit_count(data):
"""
from a dataframe, makes a polynomial
best fit model of the count of objects
per year. Creates a plot of the model
overtop a scatter plot of the data points.
Prints some information about the model,
includung the R-squared value. Creates a
plot of the residuals.
"""
# get the data in the right format
data = get_orbit_columns(data)
data = data[data['LEO']]
data = get_launch_year_tally(data)
x = np.array(list(data['Year']))
y = np.array(list(data['Total']))
# create the model
count_func = np.poly1d(np.polyfit(x, y, 2))
# plot the scatter plot & line of best fit
fig, ax = plt.subplots(1)
line = np.linspace(1958, 2020, 18000)
plt.scatter(x, y, color='#ffa372')
plt.plot(line, count_func(line), color='#0f4c81')
plt.title("Polynomial Model of Count Per Year (LEO)")
plt.xlabel('Year')
plt.ylabel('Count')
fig.savefig('poly_fit_count.png')
# print some information about the model
print('Polynomial fit for number of objects:')
print('Eq: ')
print(count_func)
print('R-squared: ', r2_score(y, count_func(x)))
# plot the residuals
fig, ax = plt.subplots(1)
res = y - count_func(x)
d = {'x': x, 'res': res}
df = pd.DataFrame(d)
df.plot(kind='scatter', x='x', y='res', ax=ax, color='#0f4c81')
plt.title("Object Count Model Residuals")
plt.xlabel('Year')
plt.ylabel('Error')
fig.savefig('count_residuals.png')
def total_bar_stacked_LEO(data):
'''
from a dataframe of orbital data
creates a stacked bar chart that shows
the total count of objects per year
highlighting the increase each year in
a different color
(for LEO)
'''
data = get_orbit_columns(data)
data = data[data['LEO']]
data = get_launch_year_tally(data)
data['PrevSum'] = data['Total'] - data['Count']
fig, ax = plt.subplots(1, figsize=(17, 10))
data.plot(y='Total', x='Year', kind='bar', color="#ed6663", ax=ax)
data.plot(y='PrevSum', x='Year', kind='bar', color="#0f4c81", ax=ax)
plt.title("Orbital Debris Total Count Over Time (LEO)")
plt.xlabel('Year')
plt.ylabel('Count')
fig.savefig('visualizations/stacked_bar_count.png')
def total_line_and_scatter_plot_LEO(data):
'''
takes a dataframe from process_data
creates line and bar plots for the
total count of objects present per year
(for LEO)
'''
data = get_orbit_columns(data)
data = data[data['LEO']]
data = get_launch_year_tally(data)
data.plot(kind='line', x='Year', y='Total', color='#0f4c81')
plt.title("Orbital Debris Total Count Over Time (LEO)")
plt.xlabel('Year')
plt.ylabel('Count')
plt.savefig('visualizations/total_count_over_years_line.png')
data.plot(kind='scatter', x='Year', y='Total', color='#0f4c81')
plt.title("Orbital Debris Total Count Over Time (LEO)")
plt.xlabel('Year')
plt.ylabel('Count')
plt.savefig('visualizations/total_count_scatter.png', figsize=(17, 10))
def compare_years_by_orbit(data):
"""
takes a dataframe of orbital debris data and adds columns
with information about orbits. Creates three visualizations:
1. A set of four bar graphs showing objects per year by orbit
2. A bar graph showing total count per orbit
3. A stacked bar showing objects per year colored by orbit
"""
# processing
data = get_launch_years_column(data)
data = get_orbit_columns(data)
data = data[['LaunchYear', 'LEO', 'MEO', 'GEO', 'HighEarthOrbit']]
data = data.dropna()
data_LEO = get_orbit_tally(data, 'LEO', False)
data_MEO = get_orbit_tally(data, 'MEO', False)
data_GEO = get_orbit_tally(data, 'GEO', False)
data_HEO = get_orbit_tally(data, 'HighEarthOrbit', False)
df = data_LEO.merge(data_MEO,
left_on='LaunchYear',
right_on='LaunchYear',
how='outer')
df = df.merge(data_GEO,
left_on='LaunchYear',
right_on='LaunchYear',
how='outer')
df = df.merge(data_HEO,
left_on='LaunchYear',
right_on='LaunchYear',
how='outer')
df = df.fillna(0)
# first plot
fig, [ax1, ax2, ax3, ax4] = plt.subplots(4,
figsize=(17, 30),
subplot_kw={'ylim': (0, 16000)})
df.plot(kind='bar', y='LEO', x='LaunchYear', ax=ax1, color="#0f4c81")
df.plot(kind='bar', y='MEO', x='LaunchYear', ax=ax2, color="#0f4c81")
df.plot(kind='bar', y='GEO', x='LaunchYear', ax=ax3, color="#0f4c81")
df.plot(kind='bar',
y='HighEarthOrbit',
x='LaunchYear',
ax=ax4,
color="#0f4c81")
ax1.set_title('LEO')
ax2.set_title('MEO')
ax3.set_title('GEO')
ax4.set_title('High Earth Orbit')
plt.setp([ax1, ax2, ax3, ax4], xlabel='Year')
plt.setp([ax1, ax2, ax3, ax4], ylabel='Count')
fig.savefig('visualizations/orbit_years_comp.png')
# second plot
fig, ax = plt.subplots(1)
leo = len(data[data['LEO']])
meo = len(data[data['MEO']])
geo = len(data[data['GEO']])
heo = len(data[data['HighEarthOrbit']])
y = np.arange(4)
x = [leo, meo, geo, heo]
plt.bar(y, x)
fig.savefig('visualizations/orbit_bar.png')
# third plot
fig, ax = plt.subplots(1, figsize=(17, 10))
df['HighEarthOrbit'] += (df['LEO'] + df['MEO'] + df['GEO'])
df['GEO'] = df['LEO'] + df['MEO'] + df['GEO']
df['MEO'] = df['LEO'] + df['MEO']
df.plot(y='HighEarthOrbit',
x='LaunchYear',
kind='bar',
color="#ffa372",
ax=ax)
df.plot(y='GEO', x='LaunchYear', kind='bar', color="#ed6663", ax=ax)
df.plot(y='MEO', x='LaunchYear', kind='bar', color="#3282b8", ax=ax)
df.plot(y='LEO', x='LaunchYear', kind='bar', color="#0f4c81", ax=ax)
plt.title("Orbital Debris Total Count Over Time, by Orbit")
plt.xlabel('Year')
plt.ylabel('Count')
fig.savefig('visualizations/stacked_bar_orbits.png')