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',