def __init__(self, rtd, **kwargs):
self.data = cached_table_fetch(
'per_platform',
table_generator=lambda: self.generate_data(rtd),
push=True,
**kwargs)
self.data = self.group_uncommon(self.data)
def hafas_obstacles_to_sql_table(self):
obstacles = cached_table_fetch('obstacle', **self.kwargs)
simpler_obstacles = {
'from_time': [],
'to_time': [],
'from_id': [],
'to_id': [],
'from_station': [],
'to_station': [],
'dir': [],
'type': [],
'summary': [],
'text': [],
'category': [],
'modified': [],
'priority': [],
'priority_text': [],
'icon_title': [],
'icon_type': [],
}
for i, obstacle in obstacles.iterrows():
obstacle = obstacle['data']
if 'edges' not in obstacle:
print('no edges')
continue
for edge in obstacle['edges']:
for event in obstacle['events']:
simpler_obstacles['from_time'].append(event['start'])
simpler_obstacles['to_time'].append(event['end'])
simpler_obstacles['from_station'].append(
edge['fromLoc']['name'])
simpler_obstacles['to_station'].append(
edge['toLoc']['name'])
simpler_obstacles['from_id'].append(edge['fromLoc']['id'])
simpler_obstacles['to_id'].append(edge['toLoc']['id'])
simpler_obstacles['dir'].append(edge['dir'])
simpler_obstacles['icon_title'].append(
edge['icon']['title'])
simpler_obstacles['icon_type'].append(edge['icon']['type'])
simpler_obstacles['type'].append(obstacle['type'])
simpler_obstacles['summary'].append(obstacle['summary'])
simpler_obstacles['text'].append(obstacle['text'])
simpler_obstacles['category'].append(obstacle['category'])
simpler_obstacles['modified'].append(obstacle['modified'])
simpler_obstacles['priority'].append(obstacle['priority'])
simpler_obstacles['priority_text'].append(
priorities_text[obstacle['priority']])
self.simpler_obstacles = pd.DataFrame(simpler_obstacles)
self.simpler_obstacles['from_time'] = pd.to_datetime(
self.simpler_obstacles['from_time'])
self.simpler_obstacles['to_time'] = pd.to_datetime(
self.simpler_obstacles['to_time'])
def __init__(self, rtd, **kwargs):
self.data = cached_table_fetch(
'per_train_type',
index_col='c',
table_generator=lambda: self.generate_data(rtd),
push=True,
**kwargs)
self.data = self.group_uncommon(self.data)
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.kwargs = kwargs
self.ds100_regex = r".*\(([A-Z\s]+)\)"
self.betriebsstellen = BetriebsstellenBill(**kwargs)
self.obstacles = cached_table_fetch('parsed_obstacles',
**kwargs).set_index('edge_id')
self.dict_index = {
edge_id: index
for index, edge_id in enumerate(self.obstacles.index)
}
self.np_obstacles = self.obstacles[[
'priority', 'length', 'from_time', 'to_time'
]].to_numpy()
self.simpler_obstacles = cached_table_fetch('simpler_obstacles',
**kwargs)
def __init__(self, rtd, **kwargs):
super().__init__(**kwargs)
# The cache from an older version of this class on potentially older data should
# not be used. Thus, we create a random version that is attached to the filenames
# in the cache.
self.version = f'{id(self):x}'
self.data = cached_table_fetch(
'per_station_over_time',
table_generator=lambda: self.generate_data(rtd),
push=True,
**kwargs
)
# Setup Plot https://stackoverflow.com/questions/9401658/how-to-animate-a-scatter-plot
self.fig, self.ax = dark_fig_ax_germany(crs=self.MAP_CRS)
self.cmap = matplotlib.colors.LinearSegmentedColormap.from_list(
"", ["green", "yellow", "red"]
)
self.sc = self.ax.scatter(
np.zeros(1),
np.zeros(1),
c=np.zeros(1),
s=np.zeros(1),
cmap=self.cmap,
vmin=0,
vmax=7,
alpha=0.5,
zorder=10,
transform=ccrs.PlateCarree()
)
self.colorbar = self.fig.colorbar(self.sc)
self.colorbar.solids.set_edgecolor("face")
self.colorbar.outline.set_linewidth(0)
self.colorbar.ax.get_yaxis().labelpad = 15
self.colorbar.ax.set_ylabel("Ø Verspätung in Minuten", rotation=270)
for plot_name in self.DEFAULT_PLOTS:
if not os.path.isfile(f"{CACHE_PATH}/plot_cache/{self.version}_{plot_name}.webp"):
if plot_name == 'default':
self.ax.set_title('', fontsize=16)
else:
self.ax.set_title(plot_name, fontsize=16)
memory_buffer = io.BytesIO()
self.fig.savefig(memory_buffer, dpi=300, transparent=True)
image_to_webp(memory_buffer, f"{CACHE_PATH}/plot_cache/{self.version}_{plot_name}.webp")
def __init__(self, **kwargs):
if 'generate' in kwargs:
kwargs['generate'] = False
print('StationPhillip does not support generate')
self.name_index_stations = cached_table_fetch('stations',
index_col='name',
**kwargs)
self.name_index_stations['eva'] = self.name_index_stations[
'eva'].astype(int)
self.eva_index_stations = self.name_index_stations.reset_index(
).set_index('eva')
self.ds100_index_stations = self.name_index_stations.reset_index(
).set_index('ds100')
self.sta_list = self.name_index_stations.sort_values(
by='number_of_events', ascending=False).index.to_list()
def __init__(self, **kwargs):
if 'generate' in kwargs:
kwargs['generate'] = False
print('StreckennetzSteffi does not support generate')
super().__init__(**kwargs)
streckennetz_df = cached_table_fetch('minimal_streckennetz', **kwargs)
tuples = [
tuple(x) for x in streckennetz_df[['u', 'v', 'length']].values
]
self.streckennetz_igraph = igraph.Graph.TupleList(
tuples, directed=False, edge_attrs=['length'])
self.get_length = lambda edge: self.streckennetz_igraph.es[edge][
'length']
def __init__(self, **kwargs):
self.name_index_betriebsstellen = cached_table_fetch('betriebstellen', index_col='name', **kwargs)
self.ds100_index_betriebsstellen = self.name_index_betriebsstellen.reset_index().set_index('ds100')
self.betriebsstellen_list = self.name_index_betriebsstellen.dropna(subset=['lat', 'lon']).index.to_list()
self.NoLocationError = NoLocationError