Add steps summary to jsonfitbit_mysql
JulioV
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# `fitbitjson_mysql`
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This [data stream](../../datastreams/data-streams-introduction) handles Fitbit sensor data downloaded using the [Fitbit Web API](https://dev.fitbit.com/build/reference/web-api/) and stored in a MySQL database. Please note that RAPIDS cannot query the API directly, you need to use other available tools or implement your own. Once you have your sensor data in a MySQL database, RAPIDS can process it.
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This [data stream](../../datastreams/data-streams-introduction) handles Fitbit sensor data downloaded using the [Fitbit Web API](https://dev.fitbit.com/build/reference/web-api/) and stored in a MySQL database. Please note that RAPIDS cannot query the API directly; you need to use other available tools or implement your own. Once you have your sensor data in a MySQL database, RAPIDS can process it.
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## Container
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A MySQL database with a table per sensor, each containing the data for all participants.
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The container should be a MySQL database with a table per sensor, each containing the data for all participants.
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The script to connect and download data from this container is at:
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```bash
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@ -17,16 +17,9 @@ The `format.yaml` maps and transforms columns in your raw data stream to the [ma
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src/data/streams/fitbitjson_mysql/format.yaml
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```
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If you want RAPIDS to process Fitbit sensor data using this stream, you will need to replace the following `RAPIDS_COLUMN_MAPPINGS` inside **each sensor** section in `format.yaml` to match your raw data column names:
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If you want RAPIDS to process Fitbit sensor data using this stream, you will need to replace `[RAPIDS_COLUMN_MAPPINGS]`/`[MUTATION][COLUMN_MAPPINGS]` inside **each sensor** section in `format.yaml` to match your raw data column names:
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| Column | Description |
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|-----------------|-----------------|
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| device_id | A string that uniquely identifies a device |
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| fitbit_data | A string column that contains the JSON objects downloaded from Fitbit's API |
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??? info "FITBIT_HEARTRATE_SUMMARY section"
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??? info "FITBIT_HEARTRATE_SUMMARY"
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**RAPIDS_COLUMN_MAPPINGS**
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@ -53,3 +46,40 @@ If you want RAPIDS to process Fitbit sensor data using this stream, you will nee
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"activities-heart":[{"dateTime":"2020-10-07","value":{"customHeartRateZones":[],"heartRateZones":[{"caloriesOut":1200.6102,"max":88,"min":31,"minutes":1058,"name":"Out of Range"},{"caloriesOut":760.3020,"max":120,"min":86,"minutes":366,"name":"Fat Burn"},{"caloriesOut":15.2048,"max":146,"min":120,"minutes":2,"name":"Cardio"},{"caloriesOut":0,"max":221,"min":148,"minutes":0,"name":"Peak"}],"restingHeartRate":72}}],"activities-heart-intraday":{"dataset":[{"time":"00:00:00","value":68},{"time":"00:01:00","value":67},{"time":"00:02:00","value":67},...],"datasetInterval":1,"datasetType":"minute"}}
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"activities-heart":[{"dateTime":"2020-10-08","value":{"customHeartRateZones":[],"heartRateZones":[{"caloriesOut":1100.1120,"max":89,"min":30,"minutes":921,"name":"Out of Range"},{"caloriesOut":660.0012,"max":118,"min":82,"minutes":361,"name":"Fat Burn"},{"caloriesOut":23.7088,"max":142,"min":108,"minutes":3,"name":"Cardio"},{"caloriesOut":0,"max":221,"min":148,"minutes":0,"name":"Peak"}],"restingHeartRate":70}}],"activities-heart-intraday":{"dataset":[{"time":"00:00:00","value":77},{"time":"00:01:00","value":75},{"time":"00:02:00","value":73},...],"datasetInterval":1,"datasetType":"minute"}}
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"activities-heart":[{"dateTime":"2020-10-09","value":{"customHeartRateZones":[],"heartRateZones":[{"caloriesOut":750.3615,"max":77,"min":30,"minutes":851,"name":"Out of Range"},{"caloriesOut":734.1516,"max":107,"min":77,"minutes":550,"name":"Fat Burn"},{"caloriesOut":131.8579,"max":130,"min":107,"minutes":29,"name":"Cardio"},{"caloriesOut":0,"max":220,"min":130,"minutes":0,"name":"Peak"}],"restingHeartRate":69}}],"activities-heart-intraday":{"dataset":[{"time":"00:00:00","value":90},{"time":"00:01:00","value":89},{"time":"00:02:00","value":88},...],"datasetInterval":1,"datasetType":"minute"}}
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??? info "FITBIT_STEPS_SUMMARY"
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**RAPIDS_COLUMN_MAPPINGS**
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| RAPIDS column | Stream column |
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|-----------------|-----------------|
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| TIMESTAMP | FLAG_TO_MUTATE |
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| DEVICE_ID | device_id |
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| LOCAL_DATE_TIME | FLAG_TO_MUTATE |
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| STEPS | FLAG_TO_MUTATE |
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**MUTATION**
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- **COLUMN_MAPPINGS**
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| Script column | Stream column |
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|-----------------|-----------------|
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| JSON_FITBIT_COLUMN | fitbit_data |
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- **SCRIPTS**
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```bash
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src/data/streams/mutations/fitbit/parse_steps_summary_json.py
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```
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!!! note
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`TIMESTAMP`, `LOCAL_DATE_TIME`, and `STEPS` are parsed from `JSON_FITBIT_COLUMN`. `JSON_FITBIT_COLUMN` is a string column containing the JSON objects returned by Fitbit's API. See an example of the raw data RAPIDS expects for this data stream:
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??? example "Example of the expected raw data"
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|device_id |fitbit_data |
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|---------------------------------------- |--------------------------------------------------------- |
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |"activities-steps":[{"dateTime":"2020-10-07","value":"1775"}],"activities-steps-intraday":{"dataset":[{"time":"00:00:00","value":5},{"time":"00:01:00","value":3},{"time":"00:02:00","value":0},...],"datasetInterval":1,"datasetType":"minute"}}
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |"activities-steps":[{"dateTime":"2020-10-08","value":"3201"}],"activities-steps-intraday":{"dataset":[{"time":"00:00:00","value":14},{"time":"00:01:00","value":11},{"time":"00:02:00","value":10},...],"datasetInterval":1,"datasetType":"minute"}}
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |"activities-steps":[{"dateTime":"2020-10-09","value":"998"}],"activities-steps-intraday":{"dataset":[{"time":"00:00:00","value":0},{"time":"00:01:00","value":0},{"time":"00:02:00","value":0},...],"datasetInterval":1,"datasetType":"minute"}}
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@ -1,15 +1,24 @@
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# Mandatory Fitbit Format
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This is a description of the format RAPIDS needs to process data for the following PHONE sensors.
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This is a description of the format RAPIDS needs to process data for the following Fitbit\ sensors.
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??? info "FITBIT_HEARTRATE_SUMMARY"
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| RAPIDS column | Description |
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|-----------------|-----------------|
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| LOCAL_DATE_TIME | TODO |
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| DEVICE_ID | TODO |
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| LOCAL_DATE_TIME | Date time string with format `yyyy-mm-dd hh:mm:ss` |
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| DEVICE_ID | A string that uniquely identifies a device |
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| HEARTRATE_DAILY_RESTINGHR | TODO |
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| HEARTRATE_DAILY_CALORIESOUTOFRANGE | TODO |
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| HEARTRATE_DAILY_CALORIESFATBURN | TODO |
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| HEARTRATE_DAILY_CALORIESCARDIO | TODO |
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| HEARTRATE_DAILY_CALORIESPEAK | TODO |
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??? info "FITBIT_STEPS_SUMMARY"
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| RAPIDS column | Description |
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|-----------------|-----------------|
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| TIMESTAMP | An UNIX timestamp (13 digits) when a row of data was logged |
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| LOCAL_DATE_TIME | Date time string with format `yyyy-mm-dd hh:mm:ss` |
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| DEVICE_ID | A string that uniquely identifies a device |
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| STEPS | Daily step count |
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@ -0,0 +1,43 @@
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import json
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import pandas as pd
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from datetime import datetime
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STEPS_COLUMNS = ("device_id", "steps", "local_date_time", "timestamp")
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def parseStepsData(steps_data):
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if steps_data.empty:
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return pd.DataFrame(columns=STEPS_COLUMNS)
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device_id = steps_data["device_id"].iloc[0]
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records = []
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# Parse JSON into individual records
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for record in steps_data.fitbit_data:
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record = json.loads(record) # Parse text into JSON
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if "activities-steps" in record.keys():
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curr_date = datetime.strptime(record["activities-steps"][0]["dateTime"], "%Y-%m-%d")
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# Parse intraday data
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if "activities-steps-intraday" in record.keys():
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dataset = record["activities-steps-intraday"]["dataset"]
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for data in dataset:
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d_time = datetime.strptime(data["time"], '%H:%M:%S').time()
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d_datetime = datetime.combine(curr_date, d_time)
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row_intraday = (device_id,
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data["value"],
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d_datetime,
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0)
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records.append(row_intraday)
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parsed_data = pd.DataFrame(data=records, columns=STEPS_COLUMNS)
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return parsed_data
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def main(json_raw, stream_parameters):
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parsed_data = parseStepsData(json_raw)
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parsed_data["timestamp"] = None # this column is added at readable_datetime.R because we neeed to take into account multiple timezones
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return(parsed_data)
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