mirror of
https://github.com/aykhans/HuaweiHealthDataVisualization.git
synced 2025-04-18 23:09:43 +00:00
Data operations removed from main and added to data_access
This commit is contained in:
ayxan
parent
74079ef92d
commit
749c8c7bad
@ -3,59 +3,29 @@ from pathlib import Path
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main_path: Path = Path(__file__).resolve().parent.parent
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from sys import path
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path.append(main_path)
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if str(main_path) not in path:
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path.append(str(main_path))
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import pandas as pd
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import plotly.express as px
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import streamlit as st
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from datetime import (datetime,
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timedelta)
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from data_access import LocalTestData
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from data_access import LocalDataAccess
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st.set_page_config(page_title = 'Health Data Visualization',
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page_icon = ':bar_chart:',
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layout = 'wide')
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local_data = LocalTestData('data')
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data = local_data.get_data('huawei_health_data.json')
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heart_rate = []
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for d in data:
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if d['type'] == 7:
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heart_rate.append(
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{
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'rate': float(d['samplePoints'][0]['value']),
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'time': (datetime.fromtimestamp(
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int(str(d['samplePoints'][0]['endTime'])[:10])
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) + timedelta(
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hours = int(data[0]['timeZone'][1:].replace('0', ''))
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)
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)
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}
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)
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local_data = LocalDataAccess('data')
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_ = local_data.get_data('huawei_health_data.json')
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heart_rate = local_data.get_heart_rate()
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if st.sidebar.checkbox(f'All Data ({len(heart_rate)})', False):
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if st.sidebar.checkbox('Average of Days', False):
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heart_rate = pd.DataFrame(
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list(
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map(
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lambda t: {'rate': t['rate'], 'date': t['time'].strftime("%d-%m-%Y")}, heart_rate
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)
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)
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)
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heart_rate_grouped = heart_rate.groupby('date', sort=False).mean()['rate']
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x = heart_rate_grouped.keys()
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y = heart_rate_grouped.values
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x, y = local_data.get_average_heart_rate_for_days_as_axis()
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labels = {'x': 'Date of The Day', 'y': 'Average Heart Rate'}
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else:
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heart_rate = pd.DataFrame(heart_rate)
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x = list(map(
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lambda t: t.strftime("%d-%m-%Y %X"),
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heart_rate['time']
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))
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y = heart_rate['rate']
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x, y = local_data.get_heart_rate_for_all_days_as_axis()
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labels = {'x': 'Date and Time', 'y': 'Heart Rate'}
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else:
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@ -66,19 +36,7 @@ else:
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max_value = heart_rate[-1]['time']
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).strftime("%d-%m-%Y")
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heart_rate = pd.DataFrame(
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list(
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filter(
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lambda t: t['time'].strftime("%d-%m-%Y") == day, heart_rate
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)
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)
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)
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x = list(map(
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lambda t: t.strftime("%d-%m-%Y %X"),
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heart_rate['time']
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))
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y = heart_rate['rate']
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x, y = local_data.get_heart_rate_for_one_day(day)
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labels = {'x': 'Date and Time', 'y': 'Heart Rate'}
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st.sidebar.header('Split Data:')
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@ -90,14 +48,7 @@ average_number = st.sidebar.number_input(
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)
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if average_number > 1:
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heart_rate2 = []
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rates = list(y)
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for i in range(0, len(rates), average_number):
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t = rates[i:i + average_number]
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heart_rate2.append(sum(t) / len(t))
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x = range(len(heart_rate2))
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y = heart_rate2
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x, y = local_data.get_averages_of_heart_rates(y, average_number)
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labels = {'x': 'Number of Heart Rate', 'y': 'Heart Rate'}
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chart_type = st.sidebar.selectbox(
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@ -1 +1 @@
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from .local_data_access import LocalTestData
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from .local_data_access import LocalDataAccess
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97
src/data_access/data_opeartions.py
Normal file
97
src/data_access/data_opeartions.py
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@ -0,0 +1,97 @@
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import pandas as pd
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from datetime import (datetime,
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timedelta)
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from typing import (List,
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Tuple)
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class DataOperations:
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heart_rate = None
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def is_data_none_wrapper(func):
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def __is_data_none(self, *args, **kwargs) -> None | NameError:
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if self.data is None:
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raise NameError(f'data not found. You must call the get_data function before calling the {func.__name__} function')
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return func(self, *args, **kwargs)
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return __is_data_none
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def is_heart_rate_none_wrapper(func):
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def __is_heart_rate_none(self, *args, **kwargs) -> None | NameError:
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if self.heart_rate is None:
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raise NameError(f'heart_rate data not found. You must call the get_heart_rate function before calling the {func.__name__} function')
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return func(self, *args, **kwargs)
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return __is_heart_rate_none
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@is_data_none_wrapper
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def get_heart_rate(self) -> List:
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# sourcery skip: inline-immediately-returned-variable, list-comprehension
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self.heart_rate = []
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for d in self.data:
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if d['type'] == 7:
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self.heart_rate.append(
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{
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'rate': float(d['samplePoints'][0]['value']),
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'time': (datetime.fromtimestamp(
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int(str(d['samplePoints'][0]['endTime'])[:10])
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) + timedelta(
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hours = int(self.data[0]['timeZone'][1:].replace('0', ''))
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)
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)
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}
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)
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return self.heart_rate
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@is_heart_rate_none_wrapper
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def get_heart_rate_for_all_days_as_axis(self) -> Tuple:
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heart_rate = pd.DataFrame(self.heart_rate)
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return (
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list (
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map(
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lambda t: t.strftime("%d-%m-%Y %X"),
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heart_rate['time']
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)
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),
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heart_rate['rate']
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)
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@is_heart_rate_none_wrapper
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def get_average_heart_rate_for_days_as_axis(self) -> Tuple:
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heart_rate_grouped = pd.DataFrame(
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list(
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map(
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lambda t: {'rate': t['rate'], 'date': t['time'].strftime("%d-%m-%Y")}, self.heart_rate
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)
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)
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).groupby('date', sort=False).mean()['rate']
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return (heart_rate_grouped.keys(), heart_rate_grouped.values)
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@is_heart_rate_none_wrapper
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def get_heart_rate_for_one_day(self, day) -> Tuple:
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heart_rate = pd.DataFrame(
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list(
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filter(
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lambda t: t['time'].strftime("%d-%m-%Y") == day, self.heart_rate
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)
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)
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)
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return (
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list (
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map(
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lambda t: t.strftime("%d-%m-%Y %X"),
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heart_rate['time']
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)
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),
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heart_rate['rate']
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)
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def get_averages_of_heart_rates(self, y: pd.Series, average_number: int) -> Tuple:
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heart_rate2 = []
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rates = list(y)
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for i in range(0, len(rates), average_number):
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t = rates[i:i + average_number]
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heart_rate2.append(sum(t) / len(t))
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return (range(len(heart_rate2)), heart_rate2)
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@ -1,13 +1,16 @@
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from pathlib import Path
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import json
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from typing import List
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from .data_opeartions import DataOperations
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class LocalTestData:
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class LocalDataAccess(DataOperations):
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def __init__(self, data_folder_name: str) -> None:
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self.data_dir: Path = Path(__file__).resolve().parent.parent.parent / data_folder_name
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self.data = None
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self.data_dir: Path =\
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Path(__file__).resolve().parent.parent.parent / data_folder_name
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def get_data(self, file_name: str) -> List:
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with open(self.data_dir / file_name) as f:
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data: List = json.load(f)
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return data
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self.data: List = json.load(f)
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return self.data
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