def get_max_move_size(prev, next, ids):
group_info = data.read_group_info('Fri').set_index('group_id')
# places = pd.DataFrame(data={'prev': prev, 'next': next}).dropna().astype('int64')
places = pd.DataFrame(data={'prev': prev, 'next': next}, index=ids)
# drop any rows with 0 for the place id, as we can't plot that.
places = places.loc[(places != 0).all(axis=1)]
places['size'] = group_info['size']
p2 = places.groupby(['next', 'prev']).sum().reset_index().sort_values('size')
# remove the small slices
# p2 = p2[p2['size'] >= 8]
max_size = p2['size'].max()
return max_size
def get_max_move_size(prev, next, ids):
group_info = data.read_group_info('Fri').set_index('group_id')
# places = pd.DataFrame(data={'prev': prev, 'next': next}).dropna().astype('int64')
places = pd.DataFrame(data={'prev': prev, 'next': next}, index=ids)
# drop any rows with 0 for the place id, as we can't plot that.
places = places.loc[(places != 0).all(axis=1)]
places['size'] = group_info['size']
p2 = places.groupby(['next', 'prev']).sum().reset_index().sort_values('size')
# remove the small slices
# p2 = p2[p2['size'] >= 8]
max_size = p2['size'].max()
return max_size
def plot_next_place(prev, next, ids, ax=None, max_size=None):
if ax is None:
fig, ax = plt.subplots()
kp = data.read_key_points().set_index('place_id')
group_info = data.read_group_info('Fri').set_index('group_id')
# places = pd.DataFrame(data={'prev': prev, 'next': next}).dropna().astype('int64')
places = pd.DataFrame(data={'prev': prev, 'next': next}, index=ids)
# drop any rows with 0 for the place id, as we can't plot that.
places = places.loc[(places != 0).all(axis=1)]
places['size'] = group_info['size']
p2 = places.groupby(['next',
'prev']).sum().reset_index().sort_values('size')
# remove the small slices
# p2 = p2[p2['size'] >= 8]
if max_size is None:
max_size = p2['size'].max()
# print(max_size)
im = data.read_image('Grey')
ax.imshow(im, extent=[0, 100, 0, 100])
cmap = plt.get_cmap('plasma')
for i, row in enumerate(p2.itertuples()):
# index_amt = i / (len(p2) - 1)
size_amt = row.size / max_size
prev_xy = kp.loc[row.prev, ['X', 'Y']].values
next_xy = kp.loc[row.next, ['X', 'Y']].values
arrowprops = {
'arrowstyle': 'simple',
'mutation_scale': 50 * size_amt,
'alpha': 0.2 + 0.8 * size_amt,
'lw': 0,
'color': cmap(0.5 * size_amt),
'connectionstyle': "arc3,rad=-0.1"
}
ax.annotate('', xy=next_xy, xytext=prev_xy, arrowprops=arrowprops)
ax.xaxis.set_ticks([])
ax.yaxis.set_ticks([])
def plot_next_place(prev, next, ids, ax=None, max_size=None):
if ax is None:
fig, ax = plt.subplots()
kp = data.read_key_points().set_index('place_id')
group_info = data.read_group_info('Fri').set_index('group_id')
# places = pd.DataFrame(data={'prev': prev, 'next': next}).dropna().astype('int64')
places = pd.DataFrame(data={'prev': prev, 'next': next}, index=ids)
# drop any rows with 0 for the place id, as we can't plot that.
places = places.loc[(places != 0).all(axis=1)]
places['size'] = group_info['size']
p2 = places.groupby(['next', 'prev']).sum().reset_index().sort_values('size')
# remove the small slices
# p2 = p2[p2['size'] >= 8]
if max_size is None:
max_size = p2['size'].max()
# print(max_size)
im = data.read_image('Grey')
ax.imshow(im, extent=[0, 100, 0, 100])
cmap = plt.get_cmap('plasma')
for i, row in enumerate(p2.itertuples()):
# index_amt = i / (len(p2) - 1)
size_amt = row.size / max_size
prev_xy = kp.loc[row.prev, ['X', 'Y']].values
next_xy = kp.loc[row.next, ['X', 'Y']].values
arrowprops = {'arrowstyle': 'simple',
'mutation_scale': 50 * size_amt,
'alpha': 0.2 + 0.8 * size_amt,
'lw': 0,
'color': cmap(0.5 * size_amt),
'connectionstyle': "arc3,rad=-0.1"}
ax.annotate('', xy=next_xy, xytext=prev_xy, arrowprops=arrowprops)
ax.xaxis.set_ticks([])
ax.yaxis.set_ticks([])
import data
import numpy as np
import matplotlib.pyplot as plt
kp = data.read_visited_key_points('Fri', extra=['X', 'Y'], grouped=True)
groups = data.read_group_info('Fri')
# the group that visited the least things
shortest_group = kp.groupby('group_id').size().argmin()
# the group that visited the most things
longest_group = kp.groupby('group_id').size().argmax()
# the largest group
largest_group = groups['size'].argmax()
df = kp[kp['group_id'] == longest_group]
im = data.read_image('grey')
plt.imshow(im, extent=[0, 100, 0, 100])
cmap = plt.get_cmap('rainbow')
for i in range(len(df) - 1):
amt = i / (len(df) - 2)
color = np.array(cmap(amt))
color[:-1] *= 0.8
prev = df.iloc[i]
next = df.iloc[i + 1]
# tdiff = next.Timestamp - prev.Timestamp
# size = 0.001 * (tdiff / np.timedelta64(1, 's'))
# text = '{} -> {}'.format(prev.place_id, next.place_id)
arrowprops = {
'arrowstyle': 'simple',