def read_time_data(url):
"""
Read the time of day each incident occurred at.
Arguments:
url: A string representing the path to the database.
Returns:
A `pandas` dataframe with two columns: `time` and `hour`. `time`
contains Python `datetime.time` objects with times at midnight filtered
out (most of these indicate a date was available, but not time). `hour`
is `time` in hours (a float between 0 and 24, exclusive).
`time` is derived from `Incident.datetime`.
"""
engine, session = database.initialize(url)
df = tabulate(session.query(Incident.datetime))
database.terminate(engine, session)
df = df.assign(time=[datetime.time() for datetime in df.datetime])
df = df[df.time != datetime.time(0)]
df = df.assign(hour=[time.hour + time.minute/60 + time.second/3600
for time in df.time])
return df
def test_tabulation(self):
query = self.session.query(Subject.survived, Subject.age)
df = tabulate(query)
self.assertEqual(list(df.columns), ['survived', 'age'])
self.assertEqual(len(df), self.session.query(Subject).count())
for columns in df.itertuples(False):
for value in columns:
self.assertIsNotNone(value)
def read_data(url):
engine, session = database.initialize(url)
query = session.query(Incident.total_hours, Subject.survived,
Group.category).join(Group, Subject)
df = tabulate(query)
database.terminate(engine, session)
return df
def read_time_data(url):
engine, session = database.initialize(url)
df = tabulate(session.query(Incident.datetime))
database.terminate(engine, session)
df = df.assign(time=[datetime.time() for datetime in df.datetime])
df = df[df.time != datetime.time(0)]
df = df.assign(hour=[time.hour + time.minute/60 + time.second/3600
for time in df.time])
return df
def read_simple_data(url, exclude_singles=False, exclude_groups=False):
"""
Read incident duration, survival, and category data. A useful shorthand.
Arguments:
url: A string representing the database URL to connect to.
exclude_singles: A boolean indicating whether the query should exclude
subjects from groups with exactly one member.
exclude_groups: A boolean indicating whether the query should exclude
subjects from groups with more than one members.
Returns:
A pandas dataframe containing the lost person data. The columns include
`total_hours`, `survived`, `category`, `days` (the incident duration in
days, as taken from `total_hours`), and `doa` (a boolean that is `True`
is the subject did not survive). Cases with a negative timedelta
`Incident.total_hours` are filtered out.
Warning:
If `exclude_singles` is `True` or `exclude_groups` is `True`, the
function also needs to query the size of each `Group`, which may take
a while (perhaps a minute).
"""
engine, session = database.initialize(url)
columns = Incident.total_hours, Subject.survived, Group.category, Group.id
query = session.query(*columns).join(Group, Subject)
df = tabulate(query)
database.terminate(engine, session)
if exclude_singles or exclude_groups:
df['size'] = [Group.query.get(int(id_)).size for id_ in df.id] # Hack
if exclude_singles:
df = df[df['size'] > 1]
if exclude_groups:
df = df[df['size'] == 1]
if 'size' in df:
df.drop('size', 1, inplace=True)
df.drop('id', 1, inplace=True)
df = df.assign(days=[total_hours.total_seconds()/3600/24
for total_hours in df.total_hours],
doa=[not survived for survived in df.survived])
df = df[0 <= df.days]
return df
#!/usr/bin/env python3
import matplotlib.pyplot as plt
import numpy as np
import Orange
from pomegranate import *
import database
from database.models import Subject, Group, Incident, Weather
from database.processing import survival_rate, tabulate, export_as_orange
engine, session = database.initialize('sqlite:///../data/isrid-master.db')
query = session.query(Subject.survived, Group.size, Weather.avg_temp)
query = query.join(Group, Incident, Weather)
df = tabulate(query, [True, True, True])
database.terminate(engine, session)
print(sum(df.survived)/len(df))
import pandas as pd
import database
from database.models import Incident, Group, Subject
from database.processing import tabulate
# Get data
# Path may vary based on your current working directory
engine, session = database.initialize('sqlite:///../../data/isrid-master.db')
query = session.query(Subject.survived, Incident.total_hours, Group.category,
Group.id, Subject.age, Subject.sex)
query = query.join(Group, Incident)
df = tabulate(query)
df['size'] = [Group.query.get(int(id)).size for id in df.id] # Bad hack
df['days'] = [hours.total_seconds()/3600/24 for hours in df.total_hours]
database.terminate(engine, session)
# Build UI
plot = figure(y_range=Range1d(bounds='auto', start=0, end=1 + 1e-3),
plot_width=1000, title='Lost Person Survival Over Time')
status = Paragraph() # Used for notifying the user when the constraints
# exclude all cases, rather than failing silently.
# Create a list of checkboxes for enabling each category
def execute():
matplotlib.rc("font", size=20)
engine, session = database.initialize("sqlite:///../data/isrid-master.db")
# Query with Group.size may take awhile, at least for Charles
# Not sure why
query = session.query(Incident.total_hours, Subject.survived, Group.category, Group.size).join(Group, Subject)
print("Tabulating query... may take awhile for unknown reasons.")
df = tabulate(query)
print("Done tabulating.")
print(df.describe())
database.terminate(engine, session)
df = df.assign(
days=[total_hours.total_seconds() / 3600 / 24 for total_hours in df.total_hours],
doa=[not survived for survived in df.survived],
)
df = df[0 <= df.days]
rows, columns = 2, 2
grid, axes = plt.subplots(rows, columns, figsize=(15, 10))
categories = Counter(df.category)
plot = 0
kmfs = []
options = {"show_censors": True, "censor_styles": {"marker": "|", "ms": 6}, "censor_ci_force_lines": False}
for category, count in categories.most_common()[: rows * columns]:
print("Category:", category)
ax = axes[plot // columns, plot % columns]
df_ = df[df.category == category]
N, Ndoa = len(df_), sum(df_.doa)
Srate = 100 * (1 - Ndoa / N)
grp = df_[df_.size > 1]
sng = df_[df_.size == 1]
kmf = KaplanMeierFitter()
# kmf.fit(df_.days, event_observed=df_.doa, label=category)
# kmf.plot(ax=ax, ci_force_lines=True)
kmf.fit(grp.days, event_observed=grp.doa, label=category + " Groups")
kmf.plot(ax=ax, **options)
kmf.fit(sng.days, event_observed=sng.doa, label=category + " Singles")
kmf.plot(ax=ax, **options)
kmfs.append(kmf)
ax.set_xlim(0, min(30, 1.05 * ax.get_xlim()[1]))
ax.set_ylim(0, 1)
ax.set_title("{}, N = {}, DOA = {}, {:.0f}% surv".format(category, N, Ndoa, Srate))
ax.set_xlabel("Total Incident Time (days)")
ax.set_ylabel("Probability of Survival")
# ax.legend_.remove()
# ax.grid(True)
plot += 1
grid.suptitle("Kaplan-Meier Survival Curves", fontsize=25)
grid.tight_layout()
grid.subplots_adjust(top=0.9)
grid.savefig("../doc/figures/kaplan-meier/km-grid-large.svg", transparent=True)
combined = plt.figure(figsize=(15, 10))
ax = combined.add_subplot(1, 1, 1)
for kmf in kmfs[: rows * columns]:
kmf.plot(ci_show=False, show_censors=True, censor_styles={"marker": "|", "ms": 6}, ax=ax)
ax.set_xlim(0, 15)
ax.set_ylim(0, 1)
ax.set_xlabel("Total Incident Time (days)")
ax.set_ylabel("Probability of Survival")
ax.set_title("Kaplan-Meier Survival Curves", fontsize=25)
ax.grid(True)
combined.savefig("../doc/figures/kaplan-meier/km-combined-large.svg", transparent=True)
plt.show()
def main():
"""
Plot the profile (size and survival rate) of the most common categories.
"""
## Read data
engine, session = database.initialize('sqlite:///../data/isrid-master.db')
query = session.query(Subject.age, Group.category, Subject.survived)
query = query.join(Group)
df = tabulate(query)
database.terminate(engine, session)
## Process subjects by category and age
selected_categories = df.category.value_counts()[:10].index.tolist()
df = df[df.category.isin(selected_categories)]
age_bins = np.linspace(0, 100, 11)
survival_rates = np.full((10, 10), np.nan, dtype=np.float64)
subgroup_sizes = np.full((10, 10), 0, dtype=np.float64)
min_subgroup_size = 10
for category, group in df.groupby('category'):
group.insert(len(group.columns), 'age_bin',
np.digitize(group.age, age_bins))
for age_index, subgroup in group.groupby('age_bin'):
survivals = subgroup.survived.values.tolist()
key = age_index - 1, selected_categories.index(category)
if len(survivals) > min_subgroup_size:
survival_rates[key] = sum(survivals)/len(survivals)
subgroup_sizes[key] = len(survivals)
# Debugging
lower, upper = age_bins[age_index - 1], age_bins[age_index]
print('{}, {} - {} years old'.format(category, int(lower),
int(upper)))
print(' Survival rate: {:.3f}%'.format(
100*survival_rates[key]))
print(' Number of subjects: {}'.format(
int(subgroup_sizes[key])))
## Plot survival rates and subgroup sizes
canvas = plt.matshow(survival_rates, fignum=False, cmap='RdYlGn',
origin='lower')
colorbar = plt.colorbar(canvas)
colorbar.solids.set_edgecolor('face')
colorbar.set_label('Survival Rate')
x_positions = y_positions = np.arange(0, 10)
for x in x_positions:
for y in y_positions:
plt.text(x, y, int(subgroup_sizes[y, x]) or '',
horizontalalignment='center', verticalalignment='center')
plt.title('Lost Person Category Profiles')
plt.ylabel('Age (years)')
plt.xlabel('Category')
ax = plt.gca()
ax.xaxis.tick_bottom()
plt.yticks(np.linspace(0, 10, 11) - 0.5, age_bins.astype(np.int))
plt.xticks(x_positions, selected_categories, rotation=60)
plt.subplots_adjust(bottom=0.2)
plt.tight_layout()
plt.savefig('../doc/figures/subject-data/category-profiles.svg',
transparent=True)
plt.show()
"""
plots -- Plots of subject data
"""
import matplotlib.pyplot as plt
import database
from database.models import Subject
from database.processing import tabulate
## Fetch data
engine, session = database.initialize('sqlite:///../data/isrid-master.db')
query = session.query(Subject.age, Subject.weight, Subject.height)
query = query.filter(Subject.age != None)
df = tabulate(query, not_null=False)
database.terminate(engine, session)
## Make weight vs. age plot
color = '#177788'
df_filtered = df[df.weight.notnull()]
plt.figure(1)
plt.scatter(df_filtered.age, df_filtered.weight, c=color, alpha=0.5)
plt.xlim(0, df_filtered.age.max() + 5)
plt.ylim(0, df_filtered.weight.max() + 5)
plt.title('Weight vs. Age')
plt.xlabel('Age (year)')
plt.ylabel('Weight (kg)')
plt.tight_layout()
plt.savefig('../doc/figures/subject-data/weight-vs-age-plot.svg',
