[ENH] Make the dash app load the images

This also means the app won't work if the images are not there

climate_emotions_map/app.py

@@ -15,7 +15,11 @@
 from dash.exceptions import PreventUpdate
 
 from . import utility as utils
-from .data_loader import NATIONAL_SAMPLE_SIZE, SURVEY_DATA
+from .data_loader import (
+    NATIONAL_SAMPLE_SIZE,
+    PRERENDERED_BARPLOTS,
+    SURVEY_DATA,
+)
 from .layout import MAP_LAYOUT, SINGLE_SUBQUESTION_FIG_KW, construct_layout
 from .make_descriptive_plots import make_descriptive_plots
 from .make_map import make_map
@@ -300,24 +304,18 @@ def update_stacked_bar_plots(
     show_all_responses_checked,
 ):
     """Update the stacked bar plots for all questions based on the selected criteria."""
+    if show_all_responses_checked:
+        threshold = None
+    elif not show_all_responses_checked:
+        threshold = DEFAULT_QUESTION["outcome"]
+
+    figure_lookup_key = (state, is_party_stratify_checked, threshold)
+
     figures = []
     for output in ctx.outputs_list:
         # Example: {'id': {'question': 'q2', 'type': 'stacked-bar-plot'}, 'property': 'figure'}
         question = output["id"]["question"]
-
-        if show_all_responses_checked:
-            threshold = None
-        elif not show_all_responses_checked:
-            threshold = DEFAULT_QUESTION["outcome"]
-
-        figure = make_stacked_bar(
-            question=question,
-            subquestion="all",
-            state=state,
-            stratify=is_party_stratify_checked,
-            threshold=threshold,
-        )
-        figures.append(figure)
+        figures.append(PRERENDERED_BARPLOTS[figure_lookup_key][question])
 
     return figures
 

climate_emotions_map/data_loader.py

@@ -6,15 +6,18 @@
 """
 
 import json
+import pickle as pkl
 from pathlib import Path
 
 import pandas as pd
 
+BASE_PATH = Path(__file__).parents[1]
+
 
 def load_data_file(file: str) -> pd.DataFrame:
     """Load a TSV data file into a dataframe."""
     return pd.read_csv(
-        Path(__file__).parents[1] / "data" / "survey_results" / file,
+        BASE_PATH / "data" / "survey_results" / file,
         sep="\t",
         dtype={"question": str, "sub_question": str, "outcome": str},
     )
@@ -23,7 +26,7 @@ def load_data_file(file: str) -> pd.DataFrame:
 def load_data_dictionary(file: str) -> pd.DataFrame:
     """Load a data dictionary TSV into a dataframe."""
     return pd.read_csv(
-        Path(__file__).parents[1] / "data" / "data_dictionaries" / file,
+        BASE_PATH / "data" / "data_dictionaries" / file,
         sep="\t",
         # Some data dictionaries have "None" as a meaningful value, so we have to prevent it
         # from being interpreted as a NaN by pandas
@@ -32,6 +35,21 @@ def load_data_dictionary(file: str) -> pd.DataFrame:
     )
 
 
+def load_prerendered_figures(file: str) -> dict:
+    """Load a pickle file containing a dictionary of prerendered plotly figures."""
+    target_file = BASE_PATH / "code/assets" / file
+    # Because this module always runs the loaders, even when imported by the create_prerendered_figures module
+    # we need to allow for the file to not exist yet when we want to run the script the first time
+    if not target_file.exists():
+        print(
+            "Prerendered figures not found. Run create_prerendered_figures.py to generate them."
+        )
+        return {}
+
+    print(f"Loading prerendered figures from {target_file}")
+    return pkl.load(target_file.open("rb"))
+
+
 def remove_ignored_rows(df: pd.DataFrame) -> pd.DataFrame:
     """Remove rows from a dataframe that have a value of TRUE in the "ignore" column."""
     return df[df["ignore"] == False]
@@ -40,7 +58,7 @@ def remove_ignored_rows(df: pd.DataFrame) -> pd.DataFrame:
 def load_geojson_object(file: str) -> dict:
     """Load a geojson file into a dataframe."""
     return json.loads(
-        (Path(__file__).parents[1] / "code" / "assets" / file).read_text(),
+        (BASE_PATH / "code" / "assets" / file).read_text(),
     )
 
 
@@ -155,3 +173,4 @@ def get_domain_text() -> dict[str, str]:
 
 NATIONAL_SAMPLE_SIZE = SURVEY_DATA["samplesizes_state.tsv"]["n"].sum()
 GEOJSON_OBJECTS = load_geojson_objects()
+PRERENDERED_BARPLOTS = load_prerendered_figures("prerendered_figures.pkl")

code/create_prerendered_figures.py

@@ -12,12 +12,13 @@
     make_stacked_bar,
 )
 
-unique_questions_list = (
+UNIQUE_QUESTIONS = (
     DATA_DICTIONARIES["question_dictionary.tsv"]["question"].unique().tolist()
 )
-unique_states_list = (
+UNIQUE_STATES = (
     DATA_DICTIONARIES["state_abbreviations.tsv"]["state"].unique().tolist()
 )
+OUTPUT_FILE = Path(__file__).parents[0] / "assets/prerendered_figures.pkl"
 
 
 def make_full_set_of_barplots(state=None, stratify=None, threshold=None):
@@ -27,7 +28,7 @@ def make_full_set_of_barplots(state=None, stratify=None, threshold=None):
     """
     return {
         question: make_stacked_bar(question, "all", state, stratify, threshold)
-        for question in unique_questions_list
+        for question in UNIQUE_QUESTIONS
     }
 
 
@@ -40,7 +41,7 @@ def make_all_figures():
     """
     figures = {}
     # A state of None means we are looking at national level questions
-    for state in unique_states_list + [None]:
+    for state in UNIQUE_STATES + [None]:
         for stratify in [False, True]:
             # For state level figures, we don't stratify by party
             if state is not None and stratify:
@@ -53,5 +54,7 @@ def make_all_figures():
 
 if __name__ == "__main__":
     figures = make_all_figures()
-    with open("prerendered_figures.pkl", "wb") as f:
+    with OUTPUT_FILE.open("wb") as f:
         pkl.dump(figures, f)
+
+    print(f"Done prerendering figures to {OUTPUT_FILE}!")