Added 'save as image' feature

src/app/main.py

@@ -8,24 +8,36 @@ if str(main_path) not in path:
 
 import plotly.express as px
 import streamlit as st
-from data_access import LocalDataAccess
+from data_access import (LocalDataAccess,
+                        UploadDataAccess)
 
 
 st.set_page_config(page_title = 'Health Data Visualization',
                     page_icon = ':bar_chart:',
                     layout = 'wide')
 
-local_data = LocalDataAccess('data')
+upload_data = st.file_uploader('Choose a Huawei Health data',
-_ = local_data.get_data('huawei_health_data.json')
+                                    type=['json'])
-heart_rate = local_data.get_heart_rate()
+
+if upload_data is None:
+    st.sidebar.caption('Local test data is being used')
+    data_access = LocalDataAccess('data')
+    data_access.data = 'huawei_health_data.json'
+
+else:
+    st.sidebar.caption('Uploaded data is being used')
+    data_access = UploadDataAccess()
+    data_access.data = upload_data
+
+heart_rate = data_access.heart_rate
 
 if st.sidebar.checkbox(f'All Data ({len(heart_rate)})', False):
     if st.sidebar.checkbox('Average of Days', False):
-        x, y = local_data.get_average_heart_rate_for_days_as_axis()
+        x, y = data_access.get_average_heart_rate_for_days_as_axis()
         labels = {'x': 'Date of The Day', 'y': 'Average Heart Rate'}
 
     else:
-        x, y = local_data.get_heart_rate_for_all_days_as_axis()
+        x, y = data_access.get_heart_rate_for_all_days_as_axis()
         labels = {'x': 'Date and Time', 'y': 'Heart Rate'}
 
 else:
@@ -36,7 +48,7 @@ else:
         max_value = heart_rate[-1]['time']
     ).strftime("%d-%m-%Y")
 
-    x, y = local_data.get_heart_rate_for_one_day(day)
+    x, y = data_access.get_heart_rate_for_one_day(day)
     labels = {'x': 'Date and Time', 'y': 'Heart Rate'}
 
 st.sidebar.header('Split Data:')
@@ -48,7 +60,7 @@ average_number = st.sidebar.number_input(
 )
 
 if average_number > 1:
-    x, y = local_data.get_averages_of_heart_rates(y, average_number)
+    x, y = data_access.get_averages_of_heart_rates(y, average_number)
     labels = {'x': 'Number of Heart Rate', 'y': 'Heart Rate'}
 
 chart_type = st.sidebar.selectbox(
@@ -56,6 +68,12 @@ chart_type = st.sidebar.selectbox(
     ('Line', 'Scatter', 'Bar')
 )
 
+st.sidebar.header('Save as image')
+st.sidebar.caption('white pixels is min rate, black pixels is max rate, green is empty pixels')
+st.sidebar.download_button('Download',
+                        data=data_access.get_heart_rate_as_img(y),
+                        file_name='heart-rate.png')
+
 st.plotly_chart(
     {'Line': px.line, 'Scatter': px.scatter, 'Bar': px.bar}[chart_type](
         x = x, y = y,

src/data_access/__init__.py

@@ -1 +1,2 @@
 from .local_data_access import LocalDataAccess
+from .upload_data_access import UploadDataAccess

src/data_access/data_opeartions.py

@@ -1,4 +1,8 @@
 import pandas as pd
+from PIL import Image
+from io import BytesIO
+import numpy as np
+from abc import ABC, abstractmethod
 from datetime import (datetime,
                     timedelta)
 from typing import (List,
@@ -6,13 +10,22 @@ from typing import (List,
                     Union)
 
 
-class DataOperations:
+class DataOperations(ABC):
-    heart_rate = None
+    _heart_rate: Union[List, None] = None
+    _data: Union[List, None] = None
+
+    @property
+    @abstractmethod
+    def data(self): ...
+
+    @data.setter
+    @abstractmethod
+    def data(self): ...
 
     def is_data_none_wrapper(func):
         def __is_data_none(self, *args, **kwargs) -> Union[None, NameError]:
             if self.data is None:
-                raise NameError(f'data not found. You must call the get_data function before calling the {func.__name__} function')
+                raise NameError(f'data not found. You must set the data before calling the {func.__name__} function')
             return func(self, *args, **kwargs)
         return __is_data_none
 
@@ -23,13 +36,14 @@ class DataOperations:
             return func(self, *args, **kwargs)
         return __is_heart_rate_none
 
+    @property
     @is_data_none_wrapper
-    def get_heart_rate(self) -> List:
+    def heart_rate(self) -> List:
         # sourcery skip: inline-immediately-returned-variable, list-comprehension
-        self.heart_rate = []
+        self._heart_rate = []
         for d in self.data:
             if d['type'] == 7:
-                self.heart_rate.append(
+                self._heart_rate.append(
                     {
                         'rate': float(d['samplePoints'][0]['value']),
                         'time': (datetime.fromtimestamp(
@@ -40,11 +54,11 @@ class DataOperations:
                         )
                     }
                 )
-        return self.heart_rate
+        return self._heart_rate
 
     @is_heart_rate_none_wrapper
     def get_heart_rate_for_all_days_as_axis(self) -> Tuple:
-        heart_rate = pd.DataFrame(self.heart_rate)
+        heart_rate = pd.DataFrame(self._heart_rate)
         return (
             list (
                 map(
@@ -60,7 +74,7 @@ class DataOperations:
         heart_rate_grouped = pd.DataFrame(
             list(
                 map(
-                    lambda t: {'rate': t['rate'], 'date': t['time'].strftime("%d-%m-%Y")}, self.heart_rate
+                    lambda t: {'rate': t['rate'], 'date': t['time'].strftime("%d-%m-%Y")}, self._heart_rate
                 )
             )
         ).groupby('date', sort=False).mean()['rate']
@@ -72,7 +86,7 @@ class DataOperations:
         heart_rate = pd.DataFrame(
             list(
                 filter(
-                    lambda t: t['time'].strftime("%d-%m-%Y") == day, self.heart_rate
+                    lambda t: t['time'].strftime("%d-%m-%Y") == day, self._heart_rate
                 )
             )
         )
@@ -96,3 +110,36 @@ class DataOperations:
             heart_rate2.append(sum(t) / len(t))
 
         return (range(len(heart_rate2)), heart_rate2)
+
+    @classmethod
+    def __array_to_bytes_image(cls, img_array: np.array) -> bytes:
+        buf = BytesIO()
+        Image.fromarray(img_array, 'RGB').save(buf, format="PNG")
+        return buf.getvalue()
+
+    @staticmethod
+    def get_heart_rate_as_img(rates: List) -> bytes:
+        rates_len = len(rates)
+        old_max = max(rates)
+        old_min = min(rates)
+        new_max = 255
+        new_min = 0
+
+        old_range = (old_max - old_min)
+        new_range = (new_max - new_min)
+
+        for max_n in range(rates_len):
+            if max_n * max_n > rates_len: break
+
+        img_array = np.full((max_n, max_n, 3), [0, 255, 0], dtype=np.uint8)
+
+        x, y = 0, 0
+        for n in rates:
+            color = (255 - (((n - old_min) * new_range) / old_range) + new_min)
+            img_array[x][y] = [color, color, color]
+            if y < max_n-1: y += 1
+            else:
+                x += 1
+                y = 0
+
+        return DataOperations.__array_to_bytes_image(img_array)

src/data_access/local_data_access.py

@@ -1,16 +1,19 @@
 from pathlib import Path
-import json
 from typing import List
+import json
 from .data_opeartions import DataOperations
 
 
 class LocalDataAccess(DataOperations):
     def __init__(self, data_folder_name: str) -> None:
-        self.data = None
         self.data_dir: Path =\
             Path(__file__).resolve().parent.parent.parent / data_folder_name
 
-    def get_data(self, file_name: str) -> List:
+    @property
+    def data(self) -> List:
+        return self._data
+
+    @data.setter
+    def data(self, file_name: str):
         with open(self.data_dir / file_name) as f:
-            self.data: List = json.load(f)
+            self._data = json.load(f)
-        return self.data

src/data_access/upload_data_access.py

@@ -0,0 +1,17 @@
+import json
+from typing import List
+from streamlit.runtime.uploaded_file_manager import UploadedFile
+from .data_opeartions import DataOperations
+
+
+class UploadDataAccess(DataOperations):
+    def __init__(self) -> None:
+        super().__init__()
+
+    @property
+    def data(self) -> List:
+        return self._data
+
+    @data.setter
+    def data(self, uploaded_file: UploadedFile) -> None:
+        self._data = json.load(uploaded_file)