mimoo · January 11, 2025 03:00
diff --git a/whoop_rem.py b/whoop_rem.py
 import argparse
 import pandas as pd
 from typing import Optional
 from pydantic import BaseModel, field_validator, ValidationError
 from datetime import datetime


 # Define the Pydantic model for sleep data
 class SleepData(BaseModel):
    cycle_start_time: datetime | None
    cycle_end_time: datetime | None
    cycle_timezone: str
    sleep_onset: datetime
    wake_onset: datetime
    sleep_performance: int
    respiratory_rate: float
    asleep_duration: int
    in_bed_duration: int
    light_sleep_duration: int
    deep_sws_duration: int
    rem_duration: int
    awake_duration: int
    sleep_need: int
    sleep_debt: int | None
    sleep_efficiency: int
    sleep_consistency: int | None
    nap: bool

    # Validator for boolean fields represented as 'true/false' in the CSV
    @field_validator("nap", mode="before")
    def parse_boolean(cls, value):
        if isinstance(value, bool):
            return value
        return value.lower() == "true"

    # Validator to handle optional fields with NaN values
    @field_validator("cycle_end_time", "sleep_consistency", mode="before")
    def handle_optional_nan(cls, value):
        if pd.isna(value):
            return None
        return value

    def to_dict(self):
        """Convert the model instance to a dictionary."""
        return self.dict()


 def read_and_parse_csv(file_path: str):
    """Read the sleeps.csv file and parse it using the SleepData model."""
    try:
        # Read the CSV file into a Pandas DataFrame
        df = pd.read_csv(file_path)

        # Rename columns to match the Pydantic model's attributes
        df.columns = [
            "cycle_start_time",
            "cycle_end_time",
            "cycle_timezone",
            "sleep_onset",
            "wake_onset",
            "sleep_performance",
            "respiratory_rate",
            "asleep_duration",
            "in_bed_duration",
            "light_sleep_duration",
            "deep_sws_duration",
            "rem_duration",
            "awake_duration",
            "sleep_need",
            "sleep_debt",
            "sleep_efficiency",
            "sleep_consistency",
            "nap",
        ]

        # Parse each row using the Pydantic model
        sleep_data = []
        for row in df.to_dict(orient="records"):
            try:
                sleep_data.append(SleepData(**row))
            except ValidationError as e:
                print(f"Validation error for row {row}: {e}")
        return sleep_data

    except Exception as e:
        print(f"Error parsing the CSV file: {e}")
        return []


 def generate_html_report(sleep_data, output_file):
    """Generate an HTML report with two graphs."""
    dates = [data.sleep_onset.strftime("%Y-%m-%d") for data in sleep_data]
    rem_durations = [data.rem_duration for data in sleep_data]

    # Reverse the order of dates and REM durations for chronological display
    dates.reverse()
    rem_durations.reverse()

    # Calculate monthly averages
    df = pd.DataFrame({"date": dates, "rem_duration": rem_durations})
    df["date"] = pd.to_datetime(df["date"])
    df["month"] = df["date"].dt.to_period("M")
    monthly_avg = df.groupby("month")["rem_duration"].mean()

    # Convert to JavaScript-friendly formats
    monthly_avg_labels = [str(month) for month in monthly_avg.index]
    monthly_avg_values = [
        float(value) for value in monthly_avg.values
    ]  # Convert np.float64 to float

    html_template = f"""
    <!DOCTYPE html>
    <html>
    <head>
        <title>Sleep Data Report</title>
        <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
    </head>
    <body>
        <h1>Sleep Data Report</h1>
        
        <h2>Daily REM Durations</h2>
        <canvas id="remChart" width="800" height="400"></canvas>
        
        <h2>Monthly Average REM Durations</h2>
        <canvas id="monthlyAvgChart" width="800" height="400"></canvas>
        
        <script>
            // Chart for Daily REM Durations
            const remCtx = document.getElementById('remChart').getContext('2d');
            const remChart = new Chart(remCtx, {{
                type: 'bar',
                data: {{
                    labels: {dates},
                    datasets: [{{
                        label: 'Daily REM Duration (min)',
                        data: {rem_durations},
                        backgroundColor: 'rgba(75, 192, 192, 0.2)',
                        borderColor: 'rgba(75, 192, 192, 1)',
                        borderWidth: 1
                    }}]
                }},
                options: {{
                    scales: {{
                        x: {{
                            type: 'category',
                            title: {{
                                display: true,
                                text: 'Date'
                            }}
                        }},
                        y: {{
                            beginAtZero: true,
                            title: {{
                                display: true,
                                text: 'REM Duration (min)'
                            }}
                        }}
                    }}
                }}
            }});

            // Chart for Monthly Average REM Durations
            const avgCtx = document.getElementById('monthlyAvgChart').getContext('2d');
            const avgChart = new Chart(avgCtx, {{
                type: 'line',
                data: {{
                    labels: {monthly_avg_labels},
                    datasets: [{{
                        label: 'Monthly Average REM Duration (min)',
                        data: {monthly_avg_values},
                        backgroundColor: 'rgba(153, 102, 255, 0.2)',
                        borderColor: 'rgba(153, 102, 255, 1)',
                        borderWidth: 2,
                        fill: true
                    }}]
                }},
                options: {{
                    scales: {{
                        x: {{
                            type: 'category',
                            title: {{
                                display: true,
                                text: 'Month'
                            }}
                        }},
                        y: {{
                            beginAtZero: true,
                            title: {{
                                display: true,
                                text: 'Average REM Duration (min)'
                            }}
                        }}
                    }}
                }}
            }});
        </script>
    </body>
    </html>
    """

    with open(output_file, "w") as file:
        file.write(html_template)


 def main():
    parser = argparse.ArgumentParser(
        description="Parse and validate a sleeps.csv file."
    )
    parser.add_argument("file", type=str, help="Path to the sleeps.csv file")
    parser.add_argument("output", type=str, help="Path to the output HTML file")

    args = parser.parse_args()

    sleep_data = read_and_parse_csv(args.file)
    if sleep_data:
        print("Generating HTML report...")
        generate_html_report(sleep_data, args.output)
        print(f"Report generated: {args.output}")
    else:
        print("No valid data found in the file.")


 if __name__ == "__main__":
    main()
	import argparse
	import pandas as pd
	from typing import Optional
	from pydantic import BaseModel, field_validator, ValidationError
	from datetime import datetime


	# Define the Pydantic model for sleep data
	class SleepData(BaseModel):
	cycle_start_time: datetime \| None
	cycle_end_time: datetime \| None
	cycle_timezone: str
	sleep_onset: datetime
	wake_onset: datetime
	sleep_performance: int
	respiratory_rate: float
	asleep_duration: int
	in_bed_duration: int
	light_sleep_duration: int
	deep_sws_duration: int
	rem_duration: int
	awake_duration: int
	sleep_need: int
	sleep_debt: int \| None
	sleep_efficiency: int
	sleep_consistency: int \| None
	nap: bool

	# Validator for boolean fields represented as 'true/false' in the CSV
	@field_validator("nap", mode="before")
	def parse_boolean(cls, value):
	if isinstance(value, bool):
	return value
	return value.lower() == "true"

	# Validator to handle optional fields with NaN values
	@field_validator("cycle_end_time", "sleep_consistency", mode="before")
	def handle_optional_nan(cls, value):
	if pd.isna(value):
	return None
	return value

	def to_dict(self):
	"""Convert the model instance to a dictionary."""
	return self.dict()


	def read_and_parse_csv(file_path: str):
	"""Read the sleeps.csv file and parse it using the SleepData model."""
	try:
	# Read the CSV file into a Pandas DataFrame
	df = pd.read_csv(file_path)

	# Rename columns to match the Pydantic model's attributes
	df.columns = [
	"cycle_start_time",
	"cycle_end_time",
	"cycle_timezone",
	"sleep_onset",
	"wake_onset",
	"sleep_performance",
	"respiratory_rate",
	"asleep_duration",
	"in_bed_duration",
	"light_sleep_duration",
	"deep_sws_duration",
	"rem_duration",
	"awake_duration",
	"sleep_need",
	"sleep_debt",
	"sleep_efficiency",
	"sleep_consistency",
	"nap",
	]

	# Parse each row using the Pydantic model
	sleep_data = []
	for row in df.to_dict(orient="records"):
	try:
	sleep_data.append(SleepData(**row))
	except ValidationError as e:
	print(f"Validation error for row {row}: {e}")
	return sleep_data

	except Exception as e:
	print(f"Error parsing the CSV file: {e}")
	return []


	def generate_html_report(sleep_data, output_file):
	"""Generate an HTML report with two graphs."""
	dates = [data.sleep_onset.strftime("%Y-%m-%d") for data in sleep_data]
	rem_durations = [data.rem_duration for data in sleep_data]

	# Reverse the order of dates and REM durations for chronological display
	dates.reverse()
	rem_durations.reverse()

	# Calculate monthly averages
	df = pd.DataFrame({"date": dates, "rem_duration": rem_durations})
	df["date"] = pd.to_datetime(df["date"])
	df["month"] = df["date"].dt.to_period("M")
	monthly_avg = df.groupby("month")["rem_duration"].mean()

	# Convert to JavaScript-friendly formats
	monthly_avg_labels = [str(month) for month in monthly_avg.index]
	monthly_avg_values = [
	float(value) for value in monthly_avg.values
	] # Convert np.float64 to float

	html_template = f"""
	<!DOCTYPE html>
	<html>
	<head>
	<title>Sleep Data Report</title>
	<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
	</head>
	<body>
	<h1>Sleep Data Report</h1>

	<h2>Daily REM Durations</h2>
	<canvas id="remChart" width="800" height="400"></canvas>

	<h2>Monthly Average REM Durations</h2>
	<canvas id="monthlyAvgChart" width="800" height="400"></canvas>

	<script>
	// Chart for Daily REM Durations
	const remCtx = document.getElementById('remChart').getContext('2d');
	const remChart = new Chart(remCtx, {{
	type: 'bar',
	data: {{
	labels: {dates},
	datasets: [{{
	label: 'Daily REM Duration (min)',
	data: {rem_durations},
	backgroundColor: 'rgba(75, 192, 192, 0.2)',
	borderColor: 'rgba(75, 192, 192, 1)',
	borderWidth: 1
	}}]
	}},
	options: {{
	scales: {{
	x: {{
	type: 'category',
	title: {{
	display: true,
	text: 'Date'
	}}
	}},
	y: {{
	beginAtZero: true,
	title: {{
	display: true,
	text: 'REM Duration (min)'
	}}
	}}
	}}
	}}
	}});

	// Chart for Monthly Average REM Durations
	const avgCtx = document.getElementById('monthlyAvgChart').getContext('2d');
	const avgChart = new Chart(avgCtx, {{
	type: 'line',
	data: {{
	labels: {monthly_avg_labels},
	datasets: [{{
	label: 'Monthly Average REM Duration (min)',
	data: {monthly_avg_values},
	backgroundColor: 'rgba(153, 102, 255, 0.2)',
	borderColor: 'rgba(153, 102, 255, 1)',
	borderWidth: 2,
	fill: true
	}}]
	}},
	options: {{
	scales: {{
	x: {{
	type: 'category',
	title: {{
	display: true,
	text: 'Month'
	}}
	}},
	y: {{
	beginAtZero: true,
	title: {{
	display: true,
	text: 'Average REM Duration (min)'
	}}
	}}
	}}
	}}
	}});
	</script>
	</body>
	</html>
	"""

	with open(output_file, "w") as file:
	file.write(html_template)


	def main():
	parser = argparse.ArgumentParser(
	description="Parse and validate a sleeps.csv file."
	)
	parser.add_argument("file", type=str, help="Path to the sleeps.csv file")
	parser.add_argument("output", type=str, help="Path to the output HTML file")

	args = parser.parse_args()

	sleep_data = read_and_parse_csv(args.file)
	if sleep_data:
	print("Generating HTML report...")
	generate_html_report(sleep_data, args.output)
	print(f"Report generated: {args.output}")
	else:
	print("No valid data found in the file.")


	if __name__ == "__main__":
	main()