Added graphs to show workouts & PR's per week on dashboard. However there is no tooltip on hover and I duplicated the svg spark line template (May combine the two)

app.py

@@ -7,11 +7,10 @@ import jinja_partials
 from jinja2_fragments import render_block
 from decorators import validate_person, validate_topset, validate_workout
 from db import DataBase
-from utils import flatten, get_date_info, get_people_and_exercise_rep_maxes, convert_str_to_date, get_earliest_and_latest_workout_date, filter_workout_topsets, get_exercise_ids_from_workouts, first_and_last_visible_days_in_month
+from utils import count_prs_over_time, get_date_info, get_people_and_exercise_rep_maxes, convert_str_to_date, get_earliest_and_latest_workout_date, filter_workout_topsets, first_and_last_visible_days_in_month, get_weekly_pr_graph_model, get_workout_counts
 from flask_htmx import HTMX
 import minify_html
-from urllib.parse import urlparse, unquote, quote
-import random
+from urllib.parse import quote
 
 app = Flask(__name__)
 app.config.from_pyfile('config.py')
@@ -61,9 +60,13 @@ def dashboard():
     people_and_exercise_rep_maxes = get_people_and_exercise_rep_maxes(
         all_topsets, selected_person_ids, selected_exercise_ids, min_date, max_date)
 
+    weekly_counts = get_workout_counts(all_topsets, 'week')
+    weekly_pr_counts = count_prs_over_time(all_topsets, 'week')
+    dashboard_graphs = [get_weekly_pr_graph_model('Workouts per week', weekly_counts), get_weekly_pr_graph_model('PRs per week', weekly_pr_counts)]
+
     if htmx:
-        return render_block(app.jinja_env, 'dashboard.html', 'content', model=people_and_exercise_rep_maxes, people=people, exercises=exercises, min_date=min_date, max_date=max_date, selected_person_ids=selected_person_ids, selected_exercise_ids=selected_exercise_ids, tags=tags)
-    return render_template('dashboard.html', model=people_and_exercise_rep_maxes, people=people, exercises=exercises, min_date=min_date, max_date=max_date, selected_person_ids=selected_person_ids, selected_exercise_ids=selected_exercise_ids, tags=tags)
+        return render_block(app.jinja_env, 'dashboard.html', 'content', model=people_and_exercise_rep_maxes, people=people, exercises=exercises, min_date=min_date, max_date=max_date, selected_person_ids=selected_person_ids, selected_exercise_ids=selected_exercise_ids, tags=tags, dashboard_graphs=dashboard_graphs)
+    return render_template('dashboard.html', model=people_and_exercise_rep_maxes, people=people, exercises=exercises, min_date=min_date, max_date=max_date, selected_person_ids=selected_person_ids, selected_exercise_ids=selected_exercise_ids, tags=tags, dashboard_graphs=dashboard_graphs)
 
 
 @ app.route("/person/list", methods=['GET'])

requirements.txt

@@ -9,3 +9,4 @@ minify-html==0.10.3
 jinja2-fragments==0.3.0
 Werkzeug==2.2.2
 numpy==1.19.5
+pandas==1.3.1

templates/dashboard.html

@@ -2,6 +2,14 @@
 
 {% block content %}
 
+<div class="w-full mb-4 grid grid-cols-1 xl:grid-cols-2 gap-4">
+    {% for graph in dashboard_graphs %}
+    <div class="bg-white shadow rounded-lg p-4 sm:p-6 xl:p-8">
+        {{ render_partial('partials/svg_line_graph.html', **graph) }}
+    </div>
+    {% endfor %}
+</div>
+
 <div class="bg-white shadow rounded-lg pt-4 p-3 md:p-4 w-full mb-4">
     <div class="flex flex-wrap">
         <div class="w-full lg:w-1/4 sm:w-full px-3 mb-6 md:mb-0">

templates/partials/svg_line_graph.html

@@ -0,0 +1,88 @@
+{% set stroke_width = 4 %}
+{% set margin = 2 %}
+
+{% macro path(data_points, vb_height) %}
+{% for value, position in data_points %}
+{% set x = (position * vb_width)+margin %}
+{% set y = (vb_height - value)+margin %}
+{% if loop.first %}M{{ x | int }} {{ y | int }}{% else %} L{{ x | int }} {{ y | int }}{% endif %}
+{% endfor %}
+{% endmacro %}
+
+{% macro path_best_fit(best_fit_points, vb_height) %}
+{% for value, position in best_fit_points %}
+{% set x = (position * vb_width)+margin %}
+{% set y = (vb_height - value)+margin %}
+{% if loop.first %}M{{ x | int }} {{ y | int }}{% else %} L{{ x | int }} {{ y | int }}{% endif %}
+{% endfor %}
+{% endmacro %}
+
+{% macro circles(data_points, color) %}
+    {% for i in range(data_points|length) %}
+    {% set current_value, current_position = data_points[i] %}
+        {% set prev_value = data_points[i - 1][0] if i > 0 else None %}
+        {% set next_value = data_points[i + 1][0] if i < data_points|length - 1 else None %}
+        {# Plot the circle only if the current value is different from both previous and next values #}
+        {% if next_value != prev_value or (next_value == prev_value and next_value != current_value)  %} 
+            {% set x=(current_position * vb_width) + margin %} 
+            {% set y=(vb_height - current_value) + margin %}
+            <circle cx="{{ x | int }}" cy="{{ y | int }}" r="1" fill="{{ color }}"></circle>
+        {% endif %}
+    {% endfor %}
+{% endmacro %}
+
+    {% macro plot_line(points, color) %}
+    <path d="{{ path(points, vb_height) }}" stroke="{{ color }}" fill="none" />
+    {{ circles(points, color) }}
+    {% endmacro %}
+
+    <!-- HubSpot doesn't escape whitespace. -->
+    {% macro random_int() %}{% for n in [0,1,2,3,4,5] %}{{ [0,1,2,3,4,5,6,7,8,9]|random }}{% endfor %}{% endmacro %}
+
+    <!-- You have to manually call the macros in a list. -->
+    {% set parts = [random_int()] %}
+    {% set unique_id = parts|join('-') %}
+
+    <div class="relative" id="svg-plot-{{ unique_id }}" _="
+    on mouseover from .pnt-{{ unique_id }}
+        put event.target @data-msg into #popover-{{ unique_id }}
+        then remove .hidden from #popover-{{ unique_id }}
+    on mouseout from .pnt-{{ unique_id }}
+        add .hidden to #popover-{{ unique_id }}">
+        <div id="popover-{{ unique_id }}" class="absolute t-0 r-0 hidden bg-white border border-gray-300 p-2 z-10">
+            <!-- Popover content will be dynamically inserted here -->
+        </div>
+        <h4 class="text-l font-semibold text-blue-400 mb-2 text-center">{{ title }}</h4>
+        <svg viewBox="0 0 {{ (vb_width + 2*margin) | int }} {{ (vb_height + 2*margin) | int }}"
+            preserveAspectRatio="none">
+            {% for plot in plots %}
+            <g class="{{ plot.label }}" style="fill: {{ plot.color }}; stroke: {{ plot.color }};">
+                {{ plot_line(plot.points, plot.color) }}
+            </g>
+            {% endfor %}
+
+            <g style="fill-opacity: 0%">
+                {% for pos, message in plot_labels %}
+                {% set x = (pos * vb_width) - (stroke_width/2) + margin %}
+                {% set y = 0 %}
+                {% set width = stroke_width %}
+                {% set height = vb_height + margin %}
+                <rect x="{{ x | int }}" y="{{ y | int }}" width="{{ width | int }}" height="{{ height | int }}"
+                    class="pnt-{{ unique_id }}" data-msg="{{ message }}"></rect>
+                {% endfor %}
+            </g>
+
+            <path d="{{ path_best_fit(best_fit_points, vb_height) }}" stroke="gray" stroke-dasharray="2,1" fill="none"
+                stroke-opacity="60%" />
+        </svg>
+        <div class="flex justify-center pt-2">
+            {% for plot in plots %}
+            <div class="flex items-center px-2 select-none cursor-pointer" _="on load put document.querySelector('#svg-plot-{{ unique_id }} g.{{plot.label}}') into my.plot_line
+            on click toggle .hidden on my.plot_line then toggle .line-through on me">
+                <div class="w-3 h-3 mr-1" style="background-color: {{ plot.color }};"></div>
+                <div class="text-xs">{{ plot.label }}</div>
+            </div>
+            {% endfor %}
+        </div>
+
+    </div>

utils.py

@@ -1,7 +1,8 @@
+import colorsys
 from datetime import datetime, date, timedelta
+import random
 import numpy as np
-import json
-
+import pandas as pd
 
 def get_workouts(topsets):
     # Get all unique workout_ids (No duplicates)
@@ -306,3 +307,135 @@ def get_exercise_graph_model(title, estimated_1rm, repetitions, weight, start_da
         'best_fit_points': best_fit_points,
         'plot_labels': plot_labels
     }
+
+def get_workout_counts(workouts, period='week'):
+    # Convert to DataFrame
+    df = pd.DataFrame(workouts)
+    
+    # Convert 'StartDate' to datetime
+    df['StartDate'] = pd.to_datetime(df['StartDate'])
+
+    # Determine the range of periods to cover
+    min_date = df['StartDate'].min()
+    max_date = pd.Timestamp(datetime.now())
+
+    # Generate a complete range of periods
+    freq = 'W-MON' if period == 'week' else 'MS'
+    period_range = pd.date_range(start=min_date, end=max_date, freq=freq)
+
+    # Initialize a dictionary to store workout counts and person names
+    workout_counts = {
+        person_id: {
+            "PersonName": person_name,
+            "PRCounts": {p: 0 for p in period_range}
+        } for person_id, person_name in df[['PersonId', 'PersonName']].drop_duplicates().values
+    }
+
+    # Process the workouts
+    for person_id, person_data in workout_counts.items():
+        person_df = df[df['PersonId'] == person_id]
+
+        for period_start in person_data["PRCounts"]:
+            period_end = period_start + pd.DateOffset(weeks=1) if period == 'week' else period_start + pd.DateOffset(months=1)
+            period_workouts = person_df[(person_df['StartDate'] >= period_start) & (person_df['StartDate'] < period_end)]
+            person_data["PRCounts"][period_start] = len(period_workouts)
+
+    return workout_counts
+
+def count_prs_over_time(workouts, period='week'):
+    # Convert to DataFrame
+    df = pd.DataFrame(workouts)
+    
+    # Convert 'StartDate' to datetime
+    df['StartDate'] = pd.to_datetime(df['StartDate'])
+
+    # Determine the range of periods to cover
+    min_date = df['StartDate'].min()
+    max_date = pd.Timestamp(datetime.now())
+
+    # Generate a complete range of periods
+    period_range = pd.date_range(start=min_date, end=max_date, freq='W-MON' if period == 'week' else 'MS')
+
+    # Initialize a dictionary to store PR counts and names
+    pr_counts = {
+        person_id: {
+            "PersonName": person_name,
+            "PRCounts": {p: 0 for p in period_range}
+        } for person_id, person_name in df[['PersonId', 'PersonName']].drop_duplicates().values
+    }
+
+    # Process the workouts
+    for person_id, person_data in pr_counts.items():
+        person_df = df[df['PersonId'] == person_id]
+
+        for period_start in person_data["PRCounts"]:
+            period_end = period_start + pd.DateOffset(weeks=1) if period == 'week' else period_start + pd.DateOffset(months=1)
+            period_workouts = person_df[(person_df['StartDate'] >= period_start) & (person_df['StartDate'] < period_end)]
+
+            for exercise_id in period_workouts['ExerciseId'].unique():
+                exercise_max = period_workouts[period_workouts['ExerciseId'] == exercise_id]['Estimated1RM'].max()
+
+                # Check if this is a PR
+                previous_max = person_df[(person_df['StartDate'] < period_start) & 
+                                         (person_df['ExerciseId'] == exercise_id)]['Estimated1RM'].max()
+                
+                if pd.isna(previous_max) or exercise_max > previous_max:
+                    person_data["PRCounts"][period_start] += 1
+
+    return pr_counts
+
+
+def get_weekly_pr_graph_model(title, weekly_pr_data):
+    # Assuming weekly_pr_data is in the format {1: {"PersonName": "Alice", "PRCounts": {Timestamp('2022-01-01', freq='W-MON'): 0, ...}}, 2: {...}, ...}
+
+    # Find the overall date range for all users
+    all_dates = [date for user_data in weekly_pr_data.values() for date in user_data["PRCounts"].keys()]
+    min_date, max_date = min(all_dates), max(all_dates)
+    total_span = (max_date - min_date).days or 1
+    relative_positions = [(date - min_date).days / total_span for date in all_dates]
+
+    # Calculate viewBox dimensions
+    max_pr_count = max(max(user_data["PRCounts"].values()) for user_data in weekly_pr_data.values()) or 1
+    vb_width, vb_height = total_span, max_pr_count
+    vb_width *= 200 / vb_width  # Scale to 200px width
+    vb_height *= 75 / vb_height  # Scale to 75px height
+
+    plots = []
+    colors = get_distinct_colors(len(weekly_pr_data.items()))
+    for count, (user_id, user_data) in enumerate(weekly_pr_data.items()):
+        pr_counts = user_data["PRCounts"]
+        person_name = user_data["PersonName"]
+
+        values = pr_counts.values()
+        min_value, max_value = min(values), max(values)
+        value_range = (max_value - min_value) or 1
+
+        values_scaled = [((value - min_value) / value_range) * vb_height for value in values]
+        plot_points = list(zip(values_scaled, relative_positions))
+
+        # Create a plot for each user
+        plot = {
+            'label': person_name,  # Use PersonName instead of User ID
+            'color': colors[count],
+            'points': plot_points
+        }
+        plots.append(plot)
+
+    # Return workout data with SVG dimensions and data points
+    return {
+        'title': title,
+        'vb_width': vb_width,
+        'vb_height': vb_height,
+        'plots': plots
+    }
+
+def get_distinct_colors(n):
+    colors = []
+    for i in range(n):
+        # Divide the color wheel into n parts
+        hue = i / n
+        # Convert HSL (Hue, Saturation, Lightness) to RGB and then to a Hex string
+        rgb = colorsys.hls_to_rgb(hue, 0.6, 0.4)  # Fixed lightness and saturation
+        hex_color = '#{:02x}{:02x}{:02x}'.format(int(rgb[0]*255), int(rgb[1]*255), int(rgb[2]*255))
+        colors.append(hex_color)
+    return colors