Add several more complex animation examples

Author: Bergam0t

.gitignore

@@ -1 +1,4 @@
 /.quarto/
+*.pyc
+*output_generate_animation_df*.csv
+*output_reshape_for_animations*.csv

animation_examples/complex_community_booking/__init__.py

@@ -0,0 +1,12 @@
+,clinic_1,clinic_2,clinic_3,clinic_4,clinic_5,clinic_6,clinic_7,clinic_8,clinic_9,clinic_10,clinic_11
+clinic_1,1,1,1,0,0,0,0,0,0,0,0
+clinic_2,1,1,1,0,0,0,0,0,0,0,0
+clinic_3,1,1,1,1,1,0,0,0,1,0,0
+clinic_4,0,0,0,1,1,1,0,0,0,0,0
+clinic_5,0,0,0,1,1,1,0,0,0,0,0
+clinic_6,0,0,0,1,1,1,1,0,0,0,0
+clinic_7,0,0,0,0,0,1,1,0,0,0,0
+clinic_8,0,0,0,0,0,0,0,1,1,1,1
+clinic_9,0,0,1,0,0,0,0,1,1,1,0
+clinic_10,0,0,0,0,0,0,0,0,1,1,0
+clinic_11,0,0,0,0,0,0,0,1,0,0,1

animation_examples/complex_community_booking/data/partial_pooling.csv

@@ -0,0 +1,12 @@
+clinic,prop,referred_out,dna
+1,0.111491288098121,0.456,0.2
+2,0.026346265199768,0.428,0.25
+3,0.196004632310365,0.489,0.25
+3,0.062220350581671,0.296,0.2
+4,0.025451387061115,0.275,0.23
+5,0.081302310891193,0.091,0.21
+6,0.05714060114755,0.162,0.24
+7,0.04927093751645,0.129,0.17
+8,0.125625098699795,0.41,0.21
+9,0.255356108859294,0.361,0.3
+10,0.009791019634679,0.123,0.2

animation_examples/complex_community_booking/data/referrals.csv

@@ -0,0 +1,8 @@
+clinic_1,clinic_2,clinic_3,clinic_4,clinic_5,clinic_6,clinic_7,clinic_8,clinic_9,clinic_10,clinic_11
+4,1,7,3,1,5,3,3,5,11,1
+4,1,6,3,1,5,3,3,5,10,0
+4,1,7,3,2,5,4,3,5,11,1
+4,1,6,3,1,5,3,3,5,10,1
+4,1,6,2,1,4,3,2,4,10,0
+0,0,0,0,0,0,0,0,0,0,0
+0,0,0,0,0,0,0,0,0,0,0

animation_examples/complex_community_booking/data/shifts.csv

@@ -0,0 +1,219 @@
+import pandas as pd
+import math
+from animation_examples.complex_community_booking.model_classes import Scenario, generate_seed_vector
+from animation_examples.complex_community_booking.simulation_execution_functions import single_run
+from vidigi.prep import reshape_for_animations, generate_animation_df
+from vidigi.animation import generate_animation
+import plotly.io as pio
+pio.renderers.default = "notebook"
+
+shifts = pd.read_csv("animation_examples/complex_community_booking/data/shifts.csv")
+# if scenario_choice == "As-is" or scenario_choice == "With Pooling":
+# prop_carve_out = [0.0, 0.9, 0.15, 0.01]
+prop_carve_out = 0.15
+
+#depending on settings and CPU this model takes around 15-20 seconds to run
+RESULTS_COLLECTION = 90 * 1
+
+# We use a warm-up period
+# because the model starts up empty which doesn't reflect reality
+WARM_UP = 60 * 1
+RUN_LENGTH = RESULTS_COLLECTION + WARM_UP
+
+# Set up the scenario for the model to run.
+scenarios = {}
+
+scenarios['as-is'] = Scenario(
+    RUN_LENGTH,
+    WARM_UP,
+    prop_carve_out=prop_carve_out,
+    seeds=generate_seed_vector(),
+    slots_file=shifts
+    )
+
+scenarios['pooled'] = Scenario(
+    RUN_LENGTH,
+    WARM_UP,
+    prop_carve_out=prop_carve_out,
+    pooling=True,
+    seeds=generate_seed_vector(),
+    slots_file=shifts
+    )
+
+scenarios['no_carve_out'] = Scenario(
+    RUN_LENGTH,
+    WARM_UP,
+    pooling=True,
+    prop_carve_out=0.0,
+    seeds=generate_seed_vector(),
+    slots_file=shifts
+    )
+
+clinic_lkup_df = pd.DataFrame([
+    {'clinic': 0, 'icon': "🟠"},
+    {'clinic': 1, 'icon': "🟡"},
+    {'clinic': 2, 'icon': "🟢"},
+    {'clinic': 3, 'icon': "🔵"},
+    {'clinic': 4, 'icon': "🟣"},
+    {'clinic': 5, 'icon': "🟤"},
+    {'clinic': 6, 'icon': "⚫"},
+    {'clinic': 7, 'icon': "⚪"},
+    {'clinic': 8, 'icon': "🔶"},
+    {'clinic': 9, 'icon': "🔷"},
+    {'clinic': 10, 'icon': "🟩"}
+])
+
+
+def show_home_clinic(row):
+        if "more" not in row["icon"]:
+            if row["home_clinic"] == 0:
+                return "🟠"
+            if row["home_clinic"] == 1:
+                return "🟡"
+            if row["home_clinic"] == 2:
+                return "🟢"
+            if row["home_clinic"] == 3:
+                return "🔵"
+            if row["home_clinic"] == 4:
+                return "🟣"
+            if row["home_clinic"] == 5:
+                return "🟤"
+            if row["home_clinic"] == 6:
+                return "⚫"
+            if row["home_clinic"] == 7:
+                return "⚪"
+            if row["home_clinic"] == 8:
+                return "🔶"
+            if row["home_clinic"] == 9:
+                return "🔷"
+            if row["home_clinic"] == 10:
+                return "🟩"
+            else:
+                return row["icon"]
+        else:
+            return row["icon"]
+
+def show_priority_icon(row):
+    if "more" not in row["icon"]:
+        if row["pathway"] == 2:
+            return "🚨"
+        else:
+            return row["icon"]
+    else:
+        return row["icon"]
+
+def add_los_to_icon(row):
+    if row["event_original"] == "have_appointment":
+        return f'{row["icon"]}<br>{int(row["wait"])}'
+    else:
+        return row["icon"]
+
+def generate_scenario_results(scenario):
+    results_all, results_low, results_high, event_log = single_run(scenarios[scenario])
+    event_log_df = pd.DataFrame(event_log)
+    event_log_df['event_original'] = event_log_df['event']
+    event_log_df['event'] = event_log_df.apply(lambda x: f"{x['event']}{f'_{int(x.booked_clinic)}' if pd.notna(x['booked_clinic']) else ''}", axis=1)
+
+    full_patient_df = reshape_for_animations(
+        event_log_df,
+        entity_col_name="patient",
+        limit_duration=WARM_UP+180,
+        every_x_time_units=1,
+        step_snapshot_max=50,
+        )
+
+    # Remove the warm-up period from the event log
+    full_patient_df = full_patient_df[full_patient_df["snapshot_time"] >= WARM_UP]
+
+    clinics =  [x for x in event_log_df['booked_clinic'].sort_values().unique().tolist() if not math.isnan(x)]
+
+    clinic_waits = [{'event': f'appointment_booked_waiting_{int(clinic)}',
+        'y':  950-(clinic+1)*80,
+        'x': 625,
+        'label': f"Booked into<br>clinic {int(clinic)}",
+        'clinic': int(clinic)}
+        for clinic in clinics]
+
+    clinic_attends = [{'event': f'have_appointment_{int(clinic)}',
+        'y':  950-(clinic+1)*80,
+        'x': 850,
+        'label': f"Attending appointment<br>at clinic {int(clinic)}"}
+        for clinic in clinics]
+
+    event_position_df = pd.concat([pd.DataFrame(clinic_waits),(pd.DataFrame(clinic_attends))])
+
+    referred_out = [{'event': f'referred_out_{int(clinic)}',
+        'y':  950-(clinic+1)*80,
+        'x': 125,
+        'label': f"Referred Out From <br>clinic {int(clinic)}"}
+        for clinic in clinics]
+
+    event_position_df = pd.concat([event_position_df,(pd.DataFrame(referred_out))])
+
+    # if scenario == "pooled" or "no_carve_out":
+    #     event_position_df = event_position_df.merge(clinic_lkup_df, how="left")
+    #     event_position_df["label"] = event_position_df.apply(
+    #         lambda x: f"{x['label']} {x['icon']}" if pd.notna(x['icon']) else x['label'],
+    #         axis=1
+    #         )
+    #     event_position_df = event_position_df.drop(columns="icon")
+
+    event_position_df.drop(columns="clinic")
+
+    full_patient_df_plus_pos = generate_animation_df(
+                    full_entity_df=full_patient_df,
+                    entity_col_name="patient",
+                    event_position_df=event_position_df,
+                    wrap_queues_at=25,
+                    step_snapshot_max=50,
+                    gap_between_entities=15,
+                    gap_between_queue_rows=15,
+                    debug_mode=False
+            )
+
+    return full_patient_df, full_patient_df_plus_pos, event_position_df
+
+
+full_patient_df, full_patient_df_plus_pos, event_position_df = generate_scenario_results(
+    'as-is'
+    )
+
+def generate_clinic_animation(final_df):
+    fig = generate_animation(
+        full_entity_df_plus_pos=final_df,
+        event_position_df=event_position_df,
+        scenario=None,
+        entity_col_name="patient",
+        plotly_height=800,
+        plotly_width=1000,
+        override_x_max=1000,
+        override_y_max=1000,
+        entity_icon_size=10,
+        text_size=10,
+        include_play_button=True,
+        add_background_image=None,
+        display_stage_labels=True,
+        time_display_units="d",
+        simulation_time_unit="days",
+        start_date="2022-06-27",
+        setup_mode=False,
+        frame_duration=1500, #milliseconds
+        frame_transition_duration=1000, #milliseconds
+        debug_mode=False
+    )
+
+    return fig
+
+    #TODO
+    # Add in additional trace that shows the number of available slots per day
+    # using the slot df
+
+    #TODO
+    # Pooled booking version where being in non-home clinic makes you one colour
+    # and home clinic makes you another
+
+    #TODO
+    # Investigate adding a priority attribute to event log
+    # that can be considered when ranking queues if present
+
+generate_clinic_animation(full_patient_df_plus_pos)