In [1]:
%load_ext autoreload
%autoreload 2
%matplotlib inline
import sqlite3
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
from flight_safety.queries import (get_events_accidents,
get_aircrafts_accidents, get_occurrences_accidents,
get_seq_of_events_accidents, get_flight_time_accidents,
get_flight_crew_accidents)
In [2]:
mpl.rcParams['figure.figsize'] = 10, 6
mpl.rcParams['font.size'] = 20
con = sqlite3.connect('data/avall.db')
events = get_events_accidents(con)
aircraft = get_aircrafts_accidents(con)
occurrences = get_occurrences_accidents(con)
seq_of_events = get_seq_of_events_accidents(con)
flight_time = get_flight_time_accidents(con)
In [3]:
aircraft_with_phase = aircraft[aircraft.phase_flt_spec_gross != 0]
In [4]:
aircraft_with_phase.phase_flt_spec_gross.value_counts()[:10].plot(kind='barh');
In [5]:
try:
# Just in case it was created before
aircraft.damage.cat.add_categories('TOTAL', inplace=True)
except ValueError:
pass
damage_per_phase = pd.crosstab(aircraft_with_phase.phase_flt_spec_gross,
aircraft.damage)
damage_per_phase['TOTAL'] = damage_per_phase.sum(axis=1)
damage_per_phase.sort_values('TOTAL', inplace=True, ascending=False)
damage_per_phase.iloc[:10,:5 ]
Out[5]:
In [6]:
damage_per_phase.loc[damage_per_phase.index[:10], ['DEST', 'SUBS', 'MINR', 'NONE']].plot.barh(stacked=True);
In [7]:
# f: fatal
# m: medium
# n: none
# s: serious
# t: f+s+m
# TODO: borrar injuries
injury_types = ['inj_tot_f', 'inj_tot_s', 'inj_tot_m', 'inj_tot_n', 'inj_tot_t']
aircraft_2 = aircraft_with_phase.join(events, on='ev_id', how='inner', rsuffix='e')
injuries_per_phase = aircraft_2[injury_types + ['phase_flt_spec_gross']].groupby('phase_flt_spec_gross').sum()
injuries_per_phase.sort_values('inj_tot_t', inplace=True, ascending=False)
injuries_per_phase = injuries_per_phase.iloc[:10]
injuries_per_phase['inj_tot_s+m'] = injuries_per_phase['inj_tot_s'] + injuries_per_phase['inj_tot_m']
injuries_per_phase
Out[7]:
In [8]:
injuries_per_phase[['inj_tot_t', 'inj_tot_n']].plot.barh();
In [9]:
injuries_per_phase[['inj_tot_f', 'inj_tot_s+m']].plot.barh();
In [10]:
try:
occurrences.phase_flt_spec_gross.cat.add_categories('TOTAL', inplace=True)
except ValueError:
pass
phases_per_occurence = pd.crosstab(occurrences.Occurrence_Code, occurrences.phase_flt_spec_gross)
phases_per_occurence['TOTAL'] = phases_per_occurence.sum(axis=1)
phases_per_occurence.sort_values('TOTAL', inplace=True, ascending=False)
phases_per_occurence.iloc[:10].iloc[:, 1:-1]
Out[10]:
In [11]:
phases_per_occurence.iloc[:10].loc[:, 'TOTAL'].plot.barh();
In [12]:
aux = occurrences.Occurrence_Code.value_counts().iloc[0:10]
aux
occurrences_red = occurrences[occurrences['Occurrence_Code'].isin(aux.index)]
occurrences_per_phase = pd.crosstab(occurrences_red.phase_flt_spec_gross, occurrences_red.Occurrence_Code)
occurrences_per_phase = occurrences_per_phase.loc[['Standing', 'Taxi', 'Takeoff', 'Climb', 'Cruise', 'Descent', 'Approach',
'Landing', 'Maneuvering']]
occurrences_per_phase
Out[12]:
In [13]:
plt.figure(figsize=(20, 20))
occurrences_per_phase_ = occurrences_per_phase.iloc
plt.matshow(occurrences_per_phase.values, cmap=plt.cm.Blues)
plt.xticks(np.arange(occurrences_per_phase.shape[1]),
occurrences_per_phase.columns.tolist(),
rotation='vertical')
plt.yticks(np.arange(occurrences_per_phase.shape[0]),
occurrences_per_phase.index.tolist());
In [14]:
occurrences_ = pd.merge(occurrences, events, left_on='ev_id', right_index=True)
inj_cols = ['inj_tot_f', 'inj_tot_s', 'inj_tot_m', 'inj_tot_n', 'inj_tot_t']
inj_per_occ = occurrences_.groupby('Occurrence_Code')[inj_cols].sum()
inj_per_occ.sort_values('inj_tot_t', inplace=True, ascending=False)
In [15]:
inj_per_occ.loc[inj_per_occ.index[:10], ['inj_tot_t', 'inj_tot_n']].plot.barh(stacked=True);
In [16]:
inj_per_occ['inj_tot_s+m'] = inj_per_occ['inj_tot_s'] + inj_per_occ['inj_tot_m']
In [17]:
inj_per_occ.loc[inj_per_occ.index[:10], ['inj_tot_f', 'inj_tot_s+m']].plot.barh(stacked=True);
In [18]:
try:
aircraft.damage.cat.add_categories(['TOTAL'], inplace=True)
except ValueError:
pass
damage_per_Occurrence = pd.crosstab(occurrences.Occurrence_Code, aircraft.damage)
damage_per_Occurrence['TOTAL'] = damage_per_Occurrence.sum(axis=1)
damage_per_Occurrence.sort_values('TOTAL', inplace=True, ascending=False)
damage_per_Occurrence.head()
Out[18]:
In [19]:
damage_per_Occurrence.loc[damage_per_Occurrence.index[1:10], ['DEST', 'SUBS', 'MINR', 'NONE']].plot.barh(stacked=True);
In [20]:
def tot_events_and_mean_injuries_by_factor(factor):
ac_ = aircraft_2[[
'ev_id',
'inj_tot_f', 'inj_tot_m', 'inj_tot_n', 'inj_tot_s', 'inj_tot_t',
'apt_dist', 'apt_dir', 'light_cond', 'sky_cond_nonceil',
'gust_ind', 'gust_kts',
'damage', 'acft_fire', 'acft_expl', 'acft_make', 'acft_model', 'acft_category',
'afm_hrs', 'afm_hrs_last_insp', 'num_eng', 'far_part',
]]
gby = ac_.groupby([factor])
r = gby.agg({'ev_id': 'count',
'inj_tot_f':'mean',
'inj_tot_m':'mean',
'inj_tot_n':'mean',
'inj_tot_s':'mean',
'inj_tot_t':'mean'}
)
if factor in ('acft_fire', 'acft_expl'):
r = r.loc[['GRD ', 'IFLT', 'NONE', 'UNK ']]
elif factor in ('gust_ind'):
r = r.loc[['N', 'Y']]
return r.loc[:, ['ev_id', 'inj_tot_f']]
# EN acft_fire u acft_expl drop blaco y BOTH
# EN gust_ind drop blanco
# EVALUAR light_cond y sky_con_nonceil
In [21]:
tot_events_and_mean_injuries_by_factor('acft_fire')
Out[21]:
In [22]:
gby_year = events.groupby(events.ev_date.dt.year)
injured_per_year = gby_year[['inj_tot_f', 'inj_tot_s', 'inj_tot_m']].sum()
injured_per_year.tail()
Out[22]: