def plot_dists(day='Fri'):
df = data.read_data(day, grouped=True)
dists = data.read_group_distances(day)
# y, x = np.histogram(dists, 100)
# plt.figure()
# plt.bar(x[:-1], y, x[1]-x[0])
#
# plt.figure()
# plt.pcolormesh(dists)
#
# plt.figure()
# ix = np.indices(dists.shape)
# plt.scatter(dists.flat, ix[0], s=2, lw=0)
close_dists = np.unique(dists)
for dist in close_dists[1:10]:
matches = np.where(dists == dist)
n_matches = len(matches)
if n_matches > 2:
print('More than 2 matches ({})'.format(n_matches))
ids = np.where(dists == dist)[0]
fig, ax = plt.subplots(subplot_kw={'projection': '3d'})
tc.plot_trajectories(ax=ax, df=df[df['group_id'].isin(ids)], id_str='group_id')
ax.set_title('group {} & {}\ndistance = {}'.format(ids[0], ids[1], dist))
def plot_group(day=None, group_id=0, ax=None, df=None):
if df is None:
df = data.read_data(day)
groups = data.read_groups(day)
ids = groups.loc[groups['group_id'] == group_id, 'id']
if ax is None:
fig, ax = plt.subplots(subplot_kw={'projection': '3d'})
tc.plot_trajectories(ax=ax, df=df[df['id'].isin(ids)])
return ax
def on_click(event):
if event.inaxes is not ax:
return
cutoff = event.ydata
ax.lines.clear()
ax.plot([0, 1], [cutoff, cutoff], color='red', lw=2)
ids_below = [id for i, id in enumerate(td.all_ids) if dists[i] <= cutoff]
ax3d.clear()
tc.plot_trajectories(ax=ax3d, df=td.df[td.df['id'].isin(ids_below)])
ax3d.set_title('{0} trajectories.\nCutoff = {1:.2f}'.format(
len(ids_below), cutoff))
fig.canvas.draw()
"""
Little script that draws two trajectories at a time.
Used to quickly find matching trajectories.
"""
import data
import matplotlib.pyplot as plt
import script.plot.timecube as tc
import itertools
df = data.read_data('Fri', 'subset-100')
ids = df['id'].unique()
for id1, id2 in itertools.combinations(ids, 2):
fig, ax = plt.subplots(1, 1, subplot_kw={'projection': '3d'})
df2 = df[df['id'].isin([id1, id2])]
tc.plot_trajectories(ax=ax, df=df2)
plt.title('id {} and id {}'.format(id1, id2))
plt.show()
import data
import script.plot.timecube as tc
import matplotlib.pyplot as plt
with open('C:/Users/zchen4/Desktop/data/outputlab.csv') as f1:
data1 = f1.read()
data1 = data1.split('\n')
x1 = [row for row in data1]
x1 = x1[:len(x1) - 1]
print(x1)
x1 = map(int, x1)
df = data.read_data('Fri')
tc.plot_trajectories(df[df['id'].isin(x1)])
plt.show()