def test_with_run_71(self):
places = read.read_tidy_csv(
filename=occ_test_files.files.RUN_71_PLACES,
node_type="place",
drop_non_coloured_sums=True)
transitions = read.read_tidy_csv(
filename=occ_test_files.files.RUN_71_TRANSITIONS,
node_type="transition",
drop_non_coloured_sums=True)
graph_medium = nx.read_gml(occ_test_files.files.RUN_71_NETWORK_GML,
destringizer=int)
places = occ.reduction.read.prepend_tidy_frame_with_tstep(places)
transitions = occ.reduction.read.prepend_tidy_frame_with_tstep(
transitions)
place_change_events = generate_place_increased_events(places)
transition_events = generate_transition_events(transitions)
causal_graph = generate_causal_graph(place_change_events,
transition_events, 0.1)
fig = occasion_graph.generate_causal_graph_figure(causal_graph,
graph_medium,
style=Style.ORIG)
if PLOT:
url = py.plot(fig)
print(url)
def _load_run_77():
graph_medium = _gen_loop_graph(6)
places = read.read_tidy_csv(PLACES, 'place', drop_non_coloured_sums=True)
places = occ.reduction.read.prepend_tidy_frame_with_tstep(places)
place_change_events = generate_place_increased_events(places)
transitions = read.read_tidy_csv(TRANSITIONS, 'transition', drop_non_coloured_sums=True)
transitions = occ.reduction.read.prepend_tidy_frame_with_tstep(transitions)
transition_events = generate_transition_events(transitions)
return graph_medium, place_change_events, transition_events
def _load_run_100():
graph_medium = nx.read_gml(occ_test_files.files.RUN_100_GML, destringizer=int)
places = read.read_tidy_csv(occ_test_files.files.RUN_100_PLACES, 'place', drop_non_coloured_sums=True)
places = occ.reduction.read.prepend_tidy_frame_with_tstep(places)
place_change_events = generate_place_increased_events(places)
transitions = read.read_tidy_csv(occ_test_files.files.RUN_100_TRANSITIONS, 'transition', drop_non_coloured_sums=True)
transitions = occ.reduction.read.prepend_tidy_frame_with_tstep(transitions)
transition_events = generate_transition_events(transitions)
return graph_medium, place_change_events, transition_events
def test_generate_network_animation_figure_with_slider():
places = read_tidy_csv(RUN_71_PLACES, 'place', drop_non_coloured_sums=True)
places = occ.reduction.read.prepend_tidy_frame_with_tstep(places)
places.query('time < 8', inplace=True)
medium_graph = nx.read_gml(RUN_71_NETWORK_GML, destringizer=int)
medium_layout = nx.spring_layout(medium_graph, dim=2)
fig = generate_network_animation_figure_with_slider(
places, medium_graph, medium_layout)
if PLOT:
py.plot(fig)
def generate_sums_by_state_figure(places_path: Path = None,
places=None,
run_id=None):
if places_path:
assert not places
assert places_path.exists()
places = read.read_tidy_csv(str(places_path),
'place',
drop_non_coloured_sums=False)
elif places is not None:
assert not places_path
else:
raise ValueError("Only one of places_path or places should be set")
non_coloured_sums = places[places.num.isna()]
state_name_list = list(non_coloured_sums['name'].unique())
ordered_state_list, color_dict = pyspike.util.generate_state_order_and_colours(
state_name_list)
data = []
for state_name in ordered_state_list:
color = color_dict[state_name]
df = non_coloured_sums.query(f"name == '{state_name}'")
df = df[df['count'].diff() != 0]
data.append(
go.Scatter(
name=render_name(state_name),
x=df['time'],
y=df['count'],
line=dict(color=color,
# shape='hv'
),
mode='lines+markers',
))
title = f"Non coloured sums for run {run_id}" if run_id else "Non coloured sums"
layout = go.Layout(
title=title,
yaxis=dict(title='count'),
xaxis=dict(
title='time',
rangeslider=dict(visible=True),
),
clickmode='event+select',
)
# fig = go.Figure(data=data, layout=layout)
# return fig
return dict(data=data, layout=layout)
def test_with_are_both_neighbours__func_used(self):
transitions = read.read_tidy_csv(
occ_test_files.files.RUN_90_TRANSITIONS, 'transition')
log([transitions])
# follow1(a, A, 0.2),
# follow1(A, a, 0.2),
follow2(a, A, 2),
follow2(A, a, 2),
"""
RUN_162_PATH = '/Users/walton/Documents/DPhil/proof-of-concept/runs/162'
# %% Plot run 160 data
RUN_DIR = RUN_161_PATH
n_runs = 100
# Load time column
places_path = os.path.join(RUN_DIR, "places.csv")
places = read.read_tidy_csv(str(places_path),
'place',
drop_non_coloured_sums=False)
time_list = places['time'].unique()
# with columns time, run1
state_name_list = sorted(places['name'].unique())
_, color_dict = pyspike.util.generate_state_order_and_colours(state_name_list)
data = []
for run_num in range(n_runs):
places_path = os.path.join(RUN_DIR, f"places_{run_num:04d}.csv")
assert os.path.isfile(places_path)
places = read.read_tidy_csv(str(places_path),
'place',
drop_non_coloured_sums=False)
places_a = places.query("name=='a'")