From bee98b90dab8c31b2a651f53552242a31f368425 Mon Sep 17 00:00:00 2001
From: TheOriginalGraLargeShrimpakaReaper <graber-michael@hotmail.com>
Date: Wed, 16 Oct 2024 20:48:05 +0200
Subject: [PATCH 01/15] adding corelation.py, corelation_old.py, README.md,
 hr_gramic.csv and sleep_gramic.csv

---
 README.md                 | 17 ++++++++++++-
 code/corelation.py        | 40 +++++++++++++++++++++++++++++
 code/corelation_old.py    | 35 ++++++++++++++++++++++++++
 code/crawler.py           | 21 ++++++++++++++++
 data/raw/hr_gramic.csv    | 53 +++++++++++++++++++++++++++++++++++++++
 data/raw/sleep_gramic.csv | 53 +++++++++++++++++++++++++++++++++++++++
 6 files changed, 218 insertions(+), 1 deletion(-)
 create mode 100644 code/corelation.py
 create mode 100644 code/corelation_old.py
 create mode 100644 code/crawler.py
 create mode 100644 data/raw/hr_gramic.csv
 create mode 100644 data/raw/sleep_gramic.csv

diff --git a/README.md b/README.md
index 846c077..24f5817 100644
--- a/README.md
+++ b/README.md
@@ -18,4 +18,19 @@ Für die folgenden Aufträge dürfen Sie je nach Vorkenntnisse beliebige Tools (
 ## Problems
 - Garmin Weekly Algorithmus unknown.
   Reverse Engineering necessery
-- Comparition between Garmin (Instict Solar 2X) and Withings (Steel HR Sport) not necessarely given
\ No newline at end of file
+- Comparition between Garmin (Instict Solar 2X) and Withings (Steel HR Sport) not necessarely given
+
+## Python Depencencis
+- 
+
+```shell
+pip install --upgrade pip
+pip install matplotlib
+pip install pandas
+pip install pybtex
+pip install requests
+pip install pyyaml
+pip install setuptools
+pip install bs4
+pip install requests
+```
\ No newline at end of file
diff --git a/code/corelation.py b/code/corelation.py
new file mode 100644
index 0000000..8cbab6c
--- /dev/null
+++ b/code/corelation.py
@@ -0,0 +1,40 @@
+import pandas as pd
+
+# Lade die CSV-Dateien
+# Datei 1 mit Semikolon (;) separiert (HR-Daten)
+hr_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv', sep=';')
+
+# Datei 2 mit Komma (,) separiert (Schlafdaten)
+sleep_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv', sep=',')
+
+# Überprüfen der ersten Zeilen der Dateien
+print("HR-Daten (vor der Berechnung des Durchschnitts):")
+print(hr_data.head())
+
+# Berechne den Durchschnitt der HR-Daten (zwischen Ruhe und Hoch)
+# Erstelle eine neue Spalte 'avg_hr', die den Durchschnitt von 'Resting HR' und 'High HR' enthält
+hr_data['avg_hr'] = hr_data[['In Ruhe', 'Hoch']].mean(axis=1)
+
+# Überprüfen der ersten Zeilen nach dem Hinzufügen der 'avg_hr' Spalte
+print("\nHR-Daten (nach der Berechnung des Durchschnitts):")
+print(hr_data.head())
+
+# Überprüfen der ersten Zeilen der Schlafdaten
+print("\nSchlafdaten:")
+print(sleep_data.head())
+
+# Sicherstellen, dass beide Datensätze nach Woche sortiert sind
+hr_data = hr_data.sort_values(by='Datum')
+sleep_data = sleep_data.sort_values(by='Datum')
+
+# Kombinieren der beiden Datensätze anhand der 'week' Spalte
+combined_data = pd.merge(hr_data, sleep_data, on='Datum')
+
+# Überprüfen der kombinierten Daten
+print("\nKombinierte Daten:")
+print(combined_data.head())
+
+# Berechne die Korrelation zwischen dem durchschnittlichen Herzfrequenzwert ('avg_hr') und der Schlafdauer ('sleep_duration')
+correlation = combined_data['avg_hr'].corr(combined_data['Durchschnittliche Dauer'])
+
+print(f"\nDie Korrelation zwischen der durchschnittlichen Herzfrequenz und der Schlafdauer ist: {correlation}")
diff --git a/code/corelation_old.py b/code/corelation_old.py
new file mode 100644
index 0000000..a597b71
--- /dev/null
+++ b/code/corelation_old.py
@@ -0,0 +1,35 @@
+import pandas as pd
+
+# Lade die CSV-Dateien
+# Datei 1 mit Semikolon (;) separiert (HR-Daten)
+hr_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv', sep=';')
+
+# Datei 2 mit Komma (,) separiert (Schlafdaten)
+sleep_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv', sep=',')
+
+# Überprüfen der ersten Zeilen der Dateien
+print("HR-Daten:")
+print(hr_data.head())
+
+print("\nSchlafdaten:")
+print(sleep_data.head())
+
+# Annahme: Beide Datensätze enthalten eine Spalte für Wochen (z.B. 'week')
+# und die jeweiligen Werte (z.B. 'avg_hr' für Herzfrequenz und 'sleep_duration' für Schlafdauer)
+
+# Sicherstellen, dass beide Datensätze nach Woche sortiert sind
+hr_data = hr_data.sort_values(by='Datum')
+sleep_data = sleep_data.sort_values(by='week')
+
+# Kombinieren der beiden Datensätze anhand der 'week' Spalte
+combined_data = pd.merge(hr_data, sleep_data, on='Datum')
+
+# Überprüfen der kombinierten Daten
+print("\nKombinierte Daten:")
+print(combined_data.head())
+
+# Berechne die Korrelation zwischen der durchschnittlichen Herzfrequenz (z.B. 'avg_hr')
+# und der Schlafdauer (z.B. 'sleep_duration')
+correlation = combined_data['avg_hr'].corr(combined_data['Durchschnittliche Dauer'])
+
+print(f"\nDie Korrelation zwischen der Herzfrequenz und der Schlafdauer ist: {correlation}")
diff --git a/code/crawler.py b/code/crawler.py
new file mode 100644
index 0000000..b15145f
--- /dev/null
+++ b/code/crawler.py
@@ -0,0 +1,21 @@
+import requests
+from bs4 import BeautifulSoup
+import queue
+import re
+import time
+import random
+
+urls = queue.PriorityQueue()
+urls.put((0.5, "https://www.scrapingcourse.com/ecommerce/"))
+visited_urls = []
+
+while not urls.empty():
+    _, current_url = urls.get()
+    soup = BeautifulSoup(get_html(current_url), "html.parser")
+
+    visited_urls.append(current_url)
+    crawl_page(soup, current_url, visited_urls, urls)
+
+    # if it is a product page:
+    # scrape_page(soup, url, products)
+time.sleep(random.uniform(1, 3))
\ No newline at end of file
diff --git a/data/raw/hr_gramic.csv b/data/raw/hr_gramic.csv
new file mode 100644
index 0000000..3bf5a06
--- /dev/null
+++ b/data/raw/hr_gramic.csv
@@ -0,0 +1,53 @@
+Datum;In Ruhe;Hoch
+Okt 4-10;67 bpm;130 bpm
+Sep 27 - Okt 3;67 bpm;143 bpm
+Sep 20-26;66 bpm;149 bpm
+Sep 13-19;66 bpm;144 bpm
+Sep 6-12;62 bpm;132 bpm
+Aug 30 - Sep 5;64 bpm;141 bpm
+Aug 23-29;67 bpm;150 bpm
+Aug 16-22;63 bpm;143 bpm
+Aug 9-15;69 bpm;141 bpm
+Aug 2-8;67 bpm;140 bpm
+Jul 26 - Aug 1;67 bpm;147 bpm
+Jul 19-25;69 bpm;136 bpm
+Jul 12-18;66 bpm;151 bpm
+Jul 5-11;67 bpm;146 bpm
+Jun 28 - Jul 4;66 bpm;157 bpm
+Jun 21-27;64 bpm;141 bpm
+Jun 14-20;70 bpm;145 bpm
+Jun 7-13;69 bpm;134 bpm
+Mai 31 - Jun 6;70 bpm;139 bpm
+Mai 24-30;72 bpm;142 bpm
+Mai 17-23;72 bpm;135 bpm
+Mai 10-16;71 bpm;147 bpm
+Mai 3-9;73 bpm;142 bpm
+Apr 26 - Mai 2;69 bpm;151 bpm
+Apr 19-25;61 bpm;135 bpm
+Apr 12-18;58 bpm;140 bpm
+Apr 5-11;64 bpm;131 bpm
+Mrz 29 - Apr 4;63 bpm;139 bpm
+Mrz 22-28;65 bpm;135 bpm
+Mrz 15-21;66 bpm;137 bpm
+Mrz 8-14;62 bpm;136 bpm
+Mrz 1-7;70 bpm;134 bpm
+Feb 23-29;68 bpm;144 bpm
+Feb 16-22;71 bpm;132 bpm
+Feb 9-15;65 bpm;143 bpm
+Feb 2-8;66 bpm;133 bpm
+Jan 26 - Feb 1;59 bpm;142 bpm
+Jan 19-25;62 bpm;136 bpm
+Jan 12-18;60 bpm;134 bpm
+Jan 5-11;56 bpm;139 bpm
+Dez 29, 2023 - Jan 4, 2024;59 bpm;128 bpm
+Dez 22-28, 2023;52 bpm;124 bpm
+Dez 15-21, 2023;57 bpm;133 bpm
+Dez 8-14, 2023;65 bpm;133 bpm
+Dez 1-7, 2023;69 bpm;134 bpm
+Nov 24-30, 2023;68 bpm;139 bpm
+Nov 17-23, 2023;68 bpm;143 bpm
+Nov 10-16, 2023;64 bpm;144 bpm
+Nov 3-9, 2023;63 bpm;140 bpm
+Okt 27 - Nov 2, 2023;57 bpm;133 bpm
+Okt 20-26, 2023;55 bpm;138 bpm
+Okt 13-19, 2023;50 bpm;121 bpm
diff --git a/data/raw/sleep_gramic.csv b/data/raw/sleep_gramic.csv
new file mode 100644
index 0000000..90d5bfc
--- /dev/null
+++ b/data/raw/sleep_gramic.csv
@@ -0,0 +1,53 @@
+Datum,Ø Score,Ø Qualität,Durchschnittliche Dauer,Ø Schlafenszeit,Ø Aufstehzeit
+Okt 10-16,46,Schlecht,6h 11min,1:28,7:50
+Okt 3-9,56,Schlecht,6h 34min,0:33,7:22
+Sep 26 - Okt 2,52,Schlecht,6h 23min,0:00,6:37
+Sep 19-25,59,Schlecht,6h 41min,0:27,7:16
+Sep 12-18,59,Schlecht,6h 18min,0:48,7:12
+Sep 5-11,55,Schlecht,6h 19min,0:56,7:22
+Aug 29 - Sep 4,66,Ausreichend,7h 1min,0:21,7:31
+Aug 22-28,57,Schlecht,6h 17min,0:35,6:59
+Aug 15-21,72,Ausreichend,6h 36min,0:38,7:25
+Aug 8-14,56,Schlecht,5h 45min,0:16,6:06
+Aug 1-7,64,Ausreichend,7h 4min,0:39,8:10
+Jul 25-31,61,Ausreichend,6h 52min,0:40,7:56
+Jul 18-24,54,Schlecht,6h 26min,2:16,9:19
+Jul 11-17,74,Ausreichend,7h 10min,0:06,7:26
+Jul 4-10,58,Schlecht,7h 35min,0:45,9:05
+Jun 27 - Jul 3,60,Ausreichend,6h 8min,1:22,7:42
+Jun 20-26,69,Ausreichend,6h 30min,0:19,6:58
+Jun 13-19,59,Schlecht,6h 1min,0:25,6:33
+Jun 6-12,60,Ausreichend,6h 22min,0:02,6:30
+Mai 30 - Jun 5,60,Ausreichend,6h 33min,0:34,7:15
+Mai 23-29,51,Schlecht,6h 47min,0:56,7:54
+Mai 16-22,50,Schlecht,5h 51min,1:13,7:08
+Mai 9-15,55,Schlecht,6h 21min,0:58,7:23
+Mai 2-8,40,Schlecht,5h 36min,0:39,6:19
+Apr 25 - Mai 1,55,Schlecht,6h 2min,1:18,7:26
+Apr 18-24,56,Schlecht,6h 28min,3:11,9:55
+Apr 11-17,56,Schlecht,6h 17min,1:23,7:51
+Apr 4-10,62,Ausreichend,6h 18min,0:39,7:10
+Mrz 28 - Apr 3,68,Ausreichend,6h 50min,1:45,8:51
+Mrz 21-27,60,Ausreichend,6h 9min,0:56,7:13
+Mrz 14-20,54,Schlecht,6h 11min,1:03,7:24
+Mrz 7-13,54,Schlecht,6h 22min,0:49,7:17
+Feb 29 - Mrz 6,50,Schlecht,6h 4min,0:56,7:09
+Feb 22-28,61,Ausreichend,7h 11min,0:08,7:25
+Feb 15-21,44,Schlecht,6h 58min,0:19,7:51
+Feb 8-14,50,Schlecht,8h 17min,0:09,8:47
+Feb 1-7,43,Schlecht,6h 12min,1:06,7:25
+Jan 25-31,57,Schlecht,7h 21min,1:17,8:40
+Jan 18-24,51,Schlecht,6h 25min,0:56,7:26
+Jan 11-17,50,Schlecht,6h 27min,1:36,8:12
+Jan 4-10,59,Schlecht,6h 44min,1:26,8:24
+Dez 28, 2023 - Jan 3, 2024,54,Schlecht,7h 2min
+Dez 21-27, 2023,55,Schlecht,7h 16min,0:38
+Dez 14-20, 2023,56,Schlecht,7h 17min,0:31
+Dez 7-13, 2023,44,Schlecht,7h 53min,23:46
+Nov 30 - Dez 6, 2023,48,Schlecht,6h 51min,0:33
+Nov 23-29, 2023,48,Schlecht,7h 11min,0:21
+Nov 16-22, 2023,53,Schlecht,7h 7min,0:23
+Nov 9-15, 2023,47,Schlecht,7h 5min,0:19
+Nov 2-8, 2023,50,Schlecht,6h 2min,0:00
+Okt 26 - Nov 1, 2023,59,Schlecht,6h 39min,0:14
+Okt 19-25, 2023,48,Schlecht,6h 17min,0:46
\ No newline at end of file

From 6804054cc69f206f102b2aac29021037e5eb45b5 Mon Sep 17 00:00:00 2001
From: TheOriginalGraLargeShrimpakaReaper <graber-michael@hotmail.com>
Date: Wed, 16 Oct 2024 20:58:01 +0200
Subject: [PATCH 02/15] edit corelation.py and add hr_data_cleaned.csv

---
 code/corelation.py       | 40 ++++++++++++++++++------------
 code/hr_data_cleaned.csv | 53 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 78 insertions(+), 15 deletions(-)
 create mode 100644 code/hr_data_cleaned.csv

diff --git a/code/corelation.py b/code/corelation.py
index 8cbab6c..c2cf59a 100644
--- a/code/corelation.py
+++ b/code/corelation.py
@@ -1,40 +1,50 @@
 import pandas as pd
 
-# Lade die CSV-Dateien
-# Datei 1 mit Semikolon (;) separiert (HR-Daten)
+# Schritt 1: Lade die HR-Daten (mit Semikolon separiert) und entferne 'bpm'
 hr_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv', sep=';')
 
-# Datei 2 mit Komma (,) separiert (Schlafdaten)
-sleep_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv', sep=',')
-
-# Überprüfen der ersten Zeilen der Dateien
-print("HR-Daten (vor der Berechnung des Durchschnitts):")
+# Zeige die ersten Zeilen der HR-Daten vor der Bereinigung
+print("HR-Daten (vor der Bereinigung):")
 print(hr_data.head())
 
-# Berechne den Durchschnitt der HR-Daten (zwischen Ruhe und Hoch)
-# Erstelle eine neue Spalte 'avg_hr', die den Durchschnitt von 'Resting HR' und 'High HR' enthält
+# Entferne 'bpm' und konvertiere die Werte in numerische Daten
+hr_data['In Ruhe'] = hr_data['In Ruhe'].str.replace(' bpm', '').astype(float)
+hr_data['Hoch'] = hr_data['Hoch'].str.replace(' bpm', '').astype(float)
+
+# Zeige die bereinigten HR-Daten
+print("\nHR-Daten (nach der Entfernung von 'bpm'):")
+print(hr_data.head())
+
+# Speichere die bereinigten HR-Daten in eine neue CSV-Datei (Zwischendatei)
+hr_data.to_csv('hr_data_cleaned.csv', sep=';', index=False)
+print("\nBereinigte HR-Daten wurden in '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_data_cleaned.csv' gespeichert.")
+
+# Schritt 2: Berechne den Durchschnitt der Herzfrequenzdaten (Resting HR und High HR)
 hr_data['avg_hr'] = hr_data[['In Ruhe', 'Hoch']].mean(axis=1)
 
-# Überprüfen der ersten Zeilen nach dem Hinzufügen der 'avg_hr' Spalte
+# Zeige die HR-Daten nach der Berechnung des Durchschnitts
 print("\nHR-Daten (nach der Berechnung des Durchschnitts):")
 print(hr_data.head())
 
-# Überprüfen der ersten Zeilen der Schlafdaten
+# Schritt 3: Lade die Schlafdaten (mit Komma separiert)
+sleep_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv', sep=',')
+
+# Zeige die Schlafdaten
 print("\nSchlafdaten:")
 print(sleep_data.head())
 
-# Sicherstellen, dass beide Datensätze nach Woche sortiert sind
+# Schritt 4: Sicherstellen, dass beide Datensätze nach 'week' sortiert sind
 hr_data = hr_data.sort_values(by='Datum')
 sleep_data = sleep_data.sort_values(by='Datum')
 
-# Kombinieren der beiden Datensätze anhand der 'week' Spalte
+# Schritt 5: Kombiniere die beiden Datensätze anhand der 'week' Spalte
 combined_data = pd.merge(hr_data, sleep_data, on='Datum')
 
-# Überprüfen der kombinierten Daten
+# Zeige die kombinierten Daten
 print("\nKombinierte Daten:")
 print(combined_data.head())
 
-# Berechne die Korrelation zwischen dem durchschnittlichen Herzfrequenzwert ('avg_hr') und der Schlafdauer ('sleep_duration')
+# Schritt 6: Berechne die Korrelation zwischen dem durchschnittlichen Herzfrequenzwert ('avg_hr') und der Schlafdauer ('sleep_duration')
 correlation = combined_data['avg_hr'].corr(combined_data['Durchschnittliche Dauer'])
 
 print(f"\nDie Korrelation zwischen der durchschnittlichen Herzfrequenz und der Schlafdauer ist: {correlation}")
diff --git a/code/hr_data_cleaned.csv b/code/hr_data_cleaned.csv
new file mode 100644
index 0000000..e30f4a3
--- /dev/null
+++ b/code/hr_data_cleaned.csv
@@ -0,0 +1,53 @@
+Datum;In Ruhe;Hoch
+Okt 4-10;67.0;130.0
+Sep 27 - Okt 3;67.0;143.0
+Sep 20-26;66.0;149.0
+Sep 13-19;66.0;144.0
+Sep 6-12;62.0;132.0
+Aug 30 - Sep 5;64.0;141.0
+Aug 23-29;67.0;150.0
+Aug 16-22;63.0;143.0
+Aug 9-15;69.0;141.0
+Aug 2-8;67.0;140.0
+Jul 26 - Aug 1;67.0;147.0
+Jul 19-25;69.0;136.0
+Jul 12-18;66.0;151.0
+Jul 5-11;67.0;146.0
+Jun 28 - Jul 4;66.0;157.0
+Jun 21-27;64.0;141.0
+Jun 14-20;70.0;145.0
+Jun 7-13;69.0;134.0
+Mai 31 - Jun 6;70.0;139.0
+Mai 24-30;72.0;142.0
+Mai 17-23;72.0;135.0
+Mai 10-16;71.0;147.0
+Mai 3-9;73.0;142.0
+Apr 26 - Mai 2;69.0;151.0
+Apr 19-25;61.0;135.0
+Apr 12-18;58.0;140.0
+Apr 5-11;64.0;131.0
+Mrz 29 - Apr 4;63.0;139.0
+Mrz 22-28;65.0;135.0
+Mrz 15-21;66.0;137.0
+Mrz 8-14;62.0;136.0
+Mrz 1-7;70.0;134.0
+Feb 23-29;68.0;144.0
+Feb 16-22;71.0;132.0
+Feb 9-15;65.0;143.0
+Feb 2-8;66.0;133.0
+Jan 26 - Feb 1;59.0;142.0
+Jan 19-25;62.0;136.0
+Jan 12-18;60.0;134.0
+Jan 5-11;56.0;139.0
+Dez 29, 2023 - Jan 4, 2024;59.0;128.0
+Dez 22-28, 2023;52.0;124.0
+Dez 15-21, 2023;57.0;133.0
+Dez 8-14, 2023;65.0;133.0
+Dez 1-7, 2023;69.0;134.0
+Nov 24-30, 2023;68.0;139.0
+Nov 17-23, 2023;68.0;143.0
+Nov 10-16, 2023;64.0;144.0
+Nov 3-9, 2023;63.0;140.0
+Okt 27 - Nov 2, 2023;57.0;133.0
+Okt 20-26, 2023;55.0;138.0
+Okt 13-19, 2023;50.0;121.0

From 104b1dc6c6933079415c4741e66b5048ebff0b88 Mon Sep 17 00:00:00 2001
From: TheOriginalGraLargeShrimpakaReaper <graber-michael@hotmail.com>
Date: Wed, 16 Oct 2024 21:15:23 +0200
Subject: [PATCH 03/15] edit corelation.py and add hr_data_cleaned.csv

---
 code/corelation.py      | 82 +++++++++++++++++++++++++++++++----------
 code/corelation_old2.py | 50 +++++++++++++++++++++++++
 2 files changed, 113 insertions(+), 19 deletions(-)
 create mode 100644 code/corelation_old2.py

diff --git a/code/corelation.py b/code/corelation.py
index c2cf59a..15cad3a 100644
--- a/code/corelation.py
+++ b/code/corelation.py
@@ -1,4 +1,31 @@
 import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from datetime import datetime
+
+
+# Manuelle Zuordnung der Monatsnamen von Deutsch auf Englisch
+month_translation = {
+    'Jan': 'Jan', 'Feb': 'Feb', 'Mär': 'Mar', 'Apr': 'Apr', 'Mai': 'May', 'Jun': 'Jun',
+    'Jul': 'Jul', 'Aug': 'Aug', 'Sep': 'Sep', 'Okt': 'Oct', 'Nov': 'Nov', 'Dez': 'Dec'
+}
+
+# Funktion, um 'Okt 10-16' in eine Kalenderwoche zu konvertieren
+def convert_to_week_number(date_range_str):
+    # Splitte den Datumsbereich in Monat und Tage
+    month_str, day_range = date_range_str.split(" ")
+    start_day = int(day_range.split("-")[0])  # Nimm den Starttag
+    current_year = datetime.now().year
+
+    # Konvertiere den deutschen Monatsnamen in den englischen
+    if month_str in month_translation:
+        month_str = month_translation[month_str]
+
+    # Erstelle das Datum basierend auf dem Starttag und dem aktuellen Jahr
+    date_str = f"{month_str} {start_day} {current_year}"
+    start_date = datetime.strptime(date_str, "%b %d %Y")  # Konvertiere in ein Datum
+    return start_date.isocalendar()[1]  # Gib die Kalenderwoche zurück
+
 
 # Schritt 1: Lade die HR-Daten (mit Semikolon separiert) und entferne 'bpm'
 hr_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv', sep=';')
@@ -11,40 +38,57 @@ print(hr_data.head())
 hr_data['In Ruhe'] = hr_data['In Ruhe'].str.replace(' bpm', '').astype(float)
 hr_data['Hoch'] = hr_data['Hoch'].str.replace(' bpm', '').astype(float)
 
-# Zeige die bereinigten HR-Daten
-print("\nHR-Daten (nach der Entfernung von 'bpm'):")
+# Konvertiere die Spalte 'Datum' in Kalenderwoche (KW)
+hr_data['week'] = hr_data['Datum'].apply(convert_to_week_number)
+
+# Zeige die HR-Daten nach der Umwandlung in Wochen
+print("\nHR-Daten (nach der Umwandlung in KW):")
 print(hr_data.head())
 
-# Speichere die bereinigten HR-Daten in eine neue CSV-Datei (Zwischendatei)
-hr_data.to_csv('hr_data_cleaned.csv', sep=';', index=False)
-print("\nBereinigte HR-Daten wurden in '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_data_cleaned.csv' gespeichert.")
-
-# Schritt 2: Berechne den Durchschnitt der Herzfrequenzdaten (Resting HR und High HR)
+# Schritt 2: Berechne den Durchschnitt der Herzfrequenzdaten (In Ruhe und Hoch)
 hr_data['avg_hr'] = hr_data[['In Ruhe', 'Hoch']].mean(axis=1)
 
-# Zeige die HR-Daten nach der Berechnung des Durchschnitts
-print("\nHR-Daten (nach der Berechnung des Durchschnitts):")
-print(hr_data.head())
-
 # Schritt 3: Lade die Schlafdaten (mit Komma separiert)
 sleep_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv', sep=',')
 
-# Zeige die Schlafdaten
+# Zeige die ersten Zeilen der Schlafdaten
 print("\nSchlafdaten:")
 print(sleep_data.head())
 
-# Schritt 4: Sicherstellen, dass beide Datensätze nach 'week' sortiert sind
-hr_data = hr_data.sort_values(by='Datum')
-sleep_data = sleep_data.sort_values(by='Datum')
+# Konvertiere auch das 'Datum' der Schlafdaten in Kalenderwoche (KW)
+sleep_data['week'] = sleep_data['Datum'].apply(convert_to_week_number)
 
-# Schritt 5: Kombiniere die beiden Datensätze anhand der 'week' Spalte
-combined_data = pd.merge(hr_data, sleep_data, on='Datum')
+# Schritt 4: Sicherstellen, dass beide Datensätze nach 'week' sortiert sind
+hr_data = hr_data.sort_values(by='week')
+sleep_data = sleep_data.sort_values(by='week')
+
+# Schritt 5: Kombiniere die beiden Datensätze anhand der 'week' Spalte mit einem äußeren Join
+combined_data = pd.merge(hr_data, sleep_data, on='week', how='outer')
 
 # Zeige die kombinierten Daten
 print("\nKombinierte Daten:")
-print(combined_data.head())
+print(combined_data)
 
-# Schritt 6: Berechne die Korrelation zwischen dem durchschnittlichen Herzfrequenzwert ('avg_hr') und der Schlafdauer ('sleep_duration')
+# Schritt 6: Entferne alle Zeilen mit fehlenden Daten
+combined_data = combined_data.dropna()
+
+# Schritt 7: Berechne die Korrelation zwischen dem durchschnittlichen Herzfrequenzwert ('avg_hr') und der Schlafdauer ('Durchschnittliche Dauer')
 correlation = combined_data['avg_hr'].corr(combined_data['Durchschnittliche Dauer'])
 
 print(f"\nDie Korrelation zwischen der durchschnittlichen Herzfrequenz und der Schlafdauer ist: {correlation}")
+
+# Schritt 8: Visualisiere den Zusammenhang zwischen Herzfrequenz und Schlafdauer
+plt.figure(figsize=(10, 6))
+plt.scatter(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], color='blue', label='Datenpunkte')
+plt.title('Zusammenhang zwischen Herzfrequenz (Durchschnitt) und Schlafdauer')
+plt.xlabel('Durchschnittliche Herzfrequenz (bpm)')
+plt.ylabel('Schlafdauer (Stunden)')
+plt.grid(True)
+
+# Linie zur Visualisierung des Trends hinzufügen
+m, b = np.polyfit(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], 1)
+plt.plot(combined_data['avg_hr'], m * combined_data['avg_hr'] + b, color='red',
+         label=f'Trendlinie (Kor = {correlation:.2f})')
+
+plt.legend()
+plt.show()
diff --git a/code/corelation_old2.py b/code/corelation_old2.py
new file mode 100644
index 0000000..c2cf59a
--- /dev/null
+++ b/code/corelation_old2.py
@@ -0,0 +1,50 @@
+import pandas as pd
+
+# Schritt 1: Lade die HR-Daten (mit Semikolon separiert) und entferne 'bpm'
+hr_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv', sep=';')
+
+# Zeige die ersten Zeilen der HR-Daten vor der Bereinigung
+print("HR-Daten (vor der Bereinigung):")
+print(hr_data.head())
+
+# Entferne 'bpm' und konvertiere die Werte in numerische Daten
+hr_data['In Ruhe'] = hr_data['In Ruhe'].str.replace(' bpm', '').astype(float)
+hr_data['Hoch'] = hr_data['Hoch'].str.replace(' bpm', '').astype(float)
+
+# Zeige die bereinigten HR-Daten
+print("\nHR-Daten (nach der Entfernung von 'bpm'):")
+print(hr_data.head())
+
+# Speichere die bereinigten HR-Daten in eine neue CSV-Datei (Zwischendatei)
+hr_data.to_csv('hr_data_cleaned.csv', sep=';', index=False)
+print("\nBereinigte HR-Daten wurden in '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_data_cleaned.csv' gespeichert.")
+
+# Schritt 2: Berechne den Durchschnitt der Herzfrequenzdaten (Resting HR und High HR)
+hr_data['avg_hr'] = hr_data[['In Ruhe', 'Hoch']].mean(axis=1)
+
+# Zeige die HR-Daten nach der Berechnung des Durchschnitts
+print("\nHR-Daten (nach der Berechnung des Durchschnitts):")
+print(hr_data.head())
+
+# Schritt 3: Lade die Schlafdaten (mit Komma separiert)
+sleep_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv', sep=',')
+
+# Zeige die Schlafdaten
+print("\nSchlafdaten:")
+print(sleep_data.head())
+
+# Schritt 4: Sicherstellen, dass beide Datensätze nach 'week' sortiert sind
+hr_data = hr_data.sort_values(by='Datum')
+sleep_data = sleep_data.sort_values(by='Datum')
+
+# Schritt 5: Kombiniere die beiden Datensätze anhand der 'week' Spalte
+combined_data = pd.merge(hr_data, sleep_data, on='Datum')
+
+# Zeige die kombinierten Daten
+print("\nKombinierte Daten:")
+print(combined_data.head())
+
+# Schritt 6: Berechne die Korrelation zwischen dem durchschnittlichen Herzfrequenzwert ('avg_hr') und der Schlafdauer ('sleep_duration')
+correlation = combined_data['avg_hr'].corr(combined_data['Durchschnittliche Dauer'])
+
+print(f"\nDie Korrelation zwischen der durchschnittlichen Herzfrequenz und der Schlafdauer ist: {correlation}")

From 0cc75b78c76916de4c09ef9e6939126381014387 Mon Sep 17 00:00:00 2001
From: TheOriginalGraLargeShrimpakaReaper <graber-michael@hotmail.com>
Date: Thu, 17 Oct 2024 20:42:54 +0200
Subject: [PATCH 04/15] edit corelation.py and add hr_data_cleaned.csv

---
 code/corelation.py      | 246 ++++++++++++++++++++++++++++++++--------
 code/corelation_old3.py |  94 +++++++++++++++
 data/raw/Schlaf.csv     |  53 +++++++++
 3 files changed, 347 insertions(+), 46 deletions(-)
 create mode 100644 code/corelation_old3.py
 create mode 100644 data/raw/Schlaf.csv

diff --git a/code/corelation.py b/code/corelation.py
index 15cad3a..47917ad 100644
--- a/code/corelation.py
+++ b/code/corelation.py
@@ -2,82 +2,236 @@ import pandas as pd
 import matplotlib.pyplot as plt
 import numpy as np
 from datetime import datetime
-
+import os
 
+# Manuelle Zuordnung der Monatsnamen von Deutsch auf Englisch
+# month_translation = {
+#     'Jan': 'Jan', 'Feb': 'Feb', 'Mär': 'Mar', 'Apr': 'Apr', 'Mai': 'May', 'Jun': 'Jun',
+#     'Jul': 'Jul', 'Aug': 'Aug', 'Sep': 'Sep', 'Okt': 'Oct', 'Nov': 'Nov', 'Dez': 'Dec', 'Mrz': 'Mar'
+# }
+
+
+# Funktion, um 'Okt 10-16' in eine Kalenderwoche zu konvertieren und das Startdatum zurückzugeben
+# def convert_to_week_number_and_start_date(date_range_str):
+#     # Splitte den Datumsbereich in Monat und Tage
+#     start_part, end_part = date_range_str.split(" - ")
+#     start_month_str, start_day = start_part.split(" ")
+#     start_day = int(start_day)  # Nimm den Starttag
+#
+#     # Konvertiere den deutschen Monatsnamen in den englischen
+#     if start_month_str in month_translation:
+#         start_month_str = month_translation[start_month_str]
+#
+#     # Erstelle das Datum basierend auf dem Starttag und dem aktuellen Jahr
+#     current_year = datetime.now().year
+#     date_str = f"{start_month_str} {start_day} {current_year}"
+#
+#     try:
+#         start_date = datetime.strptime(date_str, "%b %d %Y")  # Konvertiere in ein Datum
+#     except ValueError:
+#         raise ValueError(f"Ungültiges Datum: {date_str}")
+#
+#     week_number = start_date.isocalendar()[1]  # Kalenderwoche ermitteln
+#
+#     return week_number, start_date.strftime('%Y-%m-%d')  # Gib KW und das Startdatum zurück
+# Funktion, um 'Okt 10-16' oder 'Okt 10 - 16' in eine Kalenderwoche zu konvertieren und das Startdatum zurückzugeben
+# def convert_to_week_number_and_start_date(date_range_str):
+#     # Entferne zusätzliche Leerzeichen um den Bindestrich herum
+#     date_range_str = date_range_str.replace(" - ", "-")
+#
+#     # Splitte den Datumsbereich in Start- und Endbereich
+#     start_part, end_part = date_range_str.split("-")
+#     start_month_str, start_day = start_part.split(" ")
+#     start_day = int(start_day.strip())  # Nimm den Starttag und entferne etwaige Leerzeichen
+#
+#     # Konvertiere den deutschen Monatsnamen in den englischen
+#     if start_month_str in month_translation:
+#         start_month_str = month_translation[start_month_str]
+#
+#     # Erstelle das Datum basierend auf dem Starttag und dem aktuellen Jahr
+#     current_year = datetime.now().year
+#     date_str = f"{start_month_str} {start_day} {current_year}"
+#
+#     try:
+#         start_date = datetime.strptime(date_str, "%b %d %Y")  # Konvertiere in ein Datum
+#     except ValueError:
+#         raise ValueError(f"Ungültiges Datum: {date_str}")
+#
+#     week_number = start_date.isocalendar()[1]  # Kalenderwoche ermitteln
+#
+#     return week_number, start_date.strftime('%Y-%m-%d')  # Gib KW und das Startdatum zurück
+
+# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4' oder 'Okt 10-16' in die Kalenderwoche zu konvertieren
+# und das Startdatum zurückzugeben
+# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4' oder 'Okt 10-16' in die Kalenderwoche zu konvertieren
+# und das Startdatum zurückzugeben
+# def convert_to_week_number_and_start_date(date_range_str):
+#     # Entferne zusätzliche Leerzeichen um den Bindestrich herum
+#     date_range_str = date_range_str.replace(" - ", "-")
+#
+#     # Splitte den Datumsbereich in Start- und Endteil
+#     start_part, end_part = date_range_str.split("-")
+#
+#     # Verarbeite den Startteil
+#     start_month_str, start_day = start_part.split(" ")
+#     start_day = int(start_day.strip())  # Nimm den Starttag und entferne etwaige Leerzeichen
+#
+#     # Wenn der Endteil einen Monat enthält, verarbeite ihn ebenfalls
+#     if " " in end_part:
+#         end_month_str, end_day = end_part.split(" ")
+#         end_day = int(end_day.strip())  # Nimm den Endtag
+#     else:
+#         # Wenn der Endteil keinen Monat enthält, verwenden wir denselben Monat wie im Startteil
+#         end_month_str = start_month_str
+#         end_day = int(end_part.strip())
+#
+#     # Konvertiere den deutschen Monatsnamen in den englischen für beide Monate
+#     if start_month_str in month_translation:
+#         start_month_str = month_translation[start_month_str]
+#     if end_month_str in month_translation:
+#         end_month_str = month_translation[end_month_str]
+#
+#     # Erstelle das Datum basierend auf dem Starttag und dem aktuellen Jahr
+#     current_year = datetime.now().year
+#     start_date_str = f"{start_month_str} {start_day} {current_year}"
+#
+#     try:
+#         # Konvertiere in ein Datum
+#         start_date = datetime.strptime(start_date_str, "%b %d %Y")
+#     except ValueError:
+#         raise ValueError(f"Ungültiges Startdatum: {start_date_str}")
+#
+#     # Berechne die Kalenderwoche basierend auf dem Startdatum
+#     week_number = start_date.isocalendar()[1]
+#
+#     return week_number, start_date.strftime('%Y-%m-%d')  # Gib KW und das Startdatum zurück
 # Manuelle Zuordnung der Monatsnamen von Deutsch auf Englisch
 month_translation = {
-    'Jan': 'Jan', 'Feb': 'Feb', 'Mär': 'Mar', 'Apr': 'Apr', 'Mai': 'May', 'Jun': 'Jun',
+    'Jan': 'Jan', 'Feb': 'Feb', 'Mär': 'Mar', 'Mrz': 'Mar', 'Apr': 'Apr', 'Mai': 'May', 'Jun': 'Jun',
     'Jul': 'Jul', 'Aug': 'Aug', 'Sep': 'Sep', 'Okt': 'Oct', 'Nov': 'Nov', 'Dez': 'Dec'
 }
 
-# Funktion, um 'Okt 10-16' in eine Kalenderwoche zu konvertieren
-def convert_to_week_number(date_range_str):
-    # Splitte den Datumsbereich in Monat und Tage
-    month_str, day_range = date_range_str.split(" ")
-    start_day = int(day_range.split("-")[0])  # Nimm den Starttag
-    current_year = datetime.now().year
 
-    # Konvertiere den deutschen Monatsnamen in den englischen
-    if month_str in month_translation:
-        month_str = month_translation[month_str]
+# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4' oder 'Dez 29, 2023-Jan 4, 2024' in die Kalenderwoche und das Startdatum zu konvertieren
+def convert_to_week_number_and_start_date(date_range_str):
+    # Entferne zusätzliche Leerzeichen um den Bindestrich herum
+    date_range_str = date_range_str.replace(" - ", "-").replace(",", "")
 
-    # Erstelle das Datum basierend auf dem Starttag und dem aktuellen Jahr
-    date_str = f"{month_str} {start_day} {current_year}"
-    start_date = datetime.strptime(date_str, "%b %d %Y")  # Konvertiere in ein Datum
-    return start_date.isocalendar()[1]  # Gib die Kalenderwoche zurück
+    # Splitte den Datumsbereich in Start- und Endteil
+    start_part, end_part = date_range_str.split("-")
+
+    # Verarbeite den Startteil
+    start_parts = start_part.split(" ")
+    start_month_str = start_parts[0]
+    start_day = int(start_parts[1].strip())  # Nimm den Starttag und entferne etwaige Leerzeichen
+
+    # Verarbeite den Endteil
+    end_parts = end_part.split(" ")
+
+    if len(end_parts) == 2:
+        end_month_str = end_parts[0]
+        end_day = int(end_parts[1].strip())  # Nimm den Endtag
+    else:
+        end_month_str = start_month_str  # Falls kein Monat angegeben, setze den Startmonat
+        end_day = int(end_parts[0].strip())
+
+    # Konvertiere den deutschen Monatsnamen in den englischen für beide Monate
+    if start_month_str in month_translation:
+        start_month_str = month_translation[start_month_str]
+    if end_month_str in month_translation:
+        end_month_str = month_translation[end_month_str]
+
+    # Wenn der Startteil ein Jahr enthält, nutze dieses
+    if len(start_parts) == 3:
+        current_year = int(start_parts[2])
+    else:
+        current_year = datetime.now().year
+
+    # Wenn der Datumsbereich über den Dezember hinausgeht, endet das Enddatum im nächsten Jahr
+    if start_month_str == 'Dec' and end_month_str == 'Jan':
+        end_year = current_year + 1
+    else:
+        end_year = current_year
+
+    try:
+        # Konvertiere das Startdatum in ein Datum
+        start_date = datetime.strptime(f"{start_month_str} {start_day} {current_year}", "%b %d %Y")
+    except ValueError:
+        raise ValueError(f"Ungültiges Startdatum: {start_month_str} {start_day} {current_year}")
+
+    try:
+        # Konvertiere das Enddatum in ein Datum
+        end_date = datetime.strptime(f"{end_month_str} {end_day} {end_year}", "%b %d %Y")
+    except ValueError:
+        raise ValueError(f"Ungültiges Enddatum: {end_month_str} {end_day} {end_year}")
+
+    # Berechne die Kalenderwoche basierend auf dem Startdatum
+    week_number = start_date.isocalendar()[1]
+
+    return week_number, start_date.strftime('%Y-%m-%d')  # Gib KW und das Startdatum zurück
 
 
-# Schritt 1: Lade die HR-Daten (mit Semikolon separiert) und entferne 'bpm'
-hr_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv', sep=';')
+# Funktion zum Bereinigen und Zwischenspeichern der Daten
+def clean_and_save_data(hr_data, sleep_data, output_dir='sandbox'):
+    # Entferne 'bpm' und konvertiere die Werte in numerische Daten
+    hr_data['In Ruhe'] = hr_data['In Ruhe'].str.replace(' bpm', '').astype(float)
+    hr_data['Hoch'] = hr_data['Hoch'].str.replace(' bpm', '').astype(float)
 
-# Zeige die ersten Zeilen der HR-Daten vor der Bereinigung
-print("HR-Daten (vor der Bereinigung):")
-print(hr_data.head())
+    # Konvertiere die Spalte 'Datum' in Kalenderwoche (KW) und Startdatum
+    hr_data[['week', 'start_date']] = hr_data['Datum'].apply(
+        lambda x: pd.Series(convert_to_week_number_and_start_date(x)))
 
-# Entferne 'bpm' und konvertiere die Werte in numerische Daten
-hr_data['In Ruhe'] = hr_data['In Ruhe'].str.replace(' bpm', '').astype(float)
-hr_data['Hoch'] = hr_data['Hoch'].str.replace(' bpm', '').astype(float)
+    # Berechne den Durchschnitt der Herzfrequenzdaten (In Ruhe und Hoch)
+    hr_data['avg_hr'] = hr_data[['In Ruhe', 'Hoch']].mean(axis=1)
 
-# Konvertiere die Spalte 'Datum' in Kalenderwoche (KW)
-hr_data['week'] = hr_data['Datum'].apply(convert_to_week_number)
+    # Konvertiere auch das 'Datum' der Schlafdaten in Kalenderwoche (KW) und Startdatum
+    sleep_data[['week', 'start_date']] = sleep_data['Datum'].apply(
+        lambda x: pd.Series(convert_to_week_number_and_start_date(x)))
 
-# Zeige die HR-Daten nach der Umwandlung in Wochen
-print("\nHR-Daten (nach der Umwandlung in KW):")
-print(hr_data.head())
+    # Speichere die bereinigten Daten in 'sandbox'
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
 
-# Schritt 2: Berechne den Durchschnitt der Herzfrequenzdaten (In Ruhe und Hoch)
-hr_data['avg_hr'] = hr_data[['In Ruhe', 'Hoch']].mean(axis=1)
+    hr_data_clean_path = os.path.join(output_dir, 'hr_data_clean.csv')
+    sleep_data_clean_path = os.path.join(output_dir, 'sleep_data_clean.csv')
 
-# Schritt 3: Lade die Schlafdaten (mit Komma separiert)
-sleep_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv', sep=',')
+    hr_data.to_csv(hr_data_clean_path, sep=';', index=False)
+    sleep_data.to_csv(sleep_data_clean_path, sep=',', index=False)
 
-# Zeige die ersten Zeilen der Schlafdaten
-print("\nSchlafdaten:")
-print(sleep_data.head())
+    print(f"HR-Daten wurden bereinigt und in {hr_data_clean_path} gespeichert.")
+    print(f"Schlafdaten wurden bereinigt und in {sleep_data_clean_path} gespeichert.")
 
-# Konvertiere auch das 'Datum' der Schlafdaten in Kalenderwoche (KW)
-sleep_data['week'] = sleep_data['Datum'].apply(convert_to_week_number)
+    return hr_data, sleep_data
 
-# Schritt 4: Sicherstellen, dass beide Datensätze nach 'week' sortiert sind
-hr_data = hr_data.sort_values(by='week')
-sleep_data = sleep_data.sort_values(by='week')
 
-# Schritt 5: Kombiniere die beiden Datensätze anhand der 'week' Spalte mit einem äußeren Join
-combined_data = pd.merge(hr_data, sleep_data, on='week', how='outer')
+# Schritt 1: Lade die HR- und Schlafdaten
+hr_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv',
+                      sep=';')
+sleep_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv',
+                         sep=',')
+
+# Bereinige und speichere die Daten
+hr_data, sleep_data = clean_and_save_data(hr_data, sleep_data)
+
+# Schritt 2: Sortiere beide Datensätze nach 'week' und 'start_date'
+hr_data = hr_data.sort_values(by=['week', 'start_date'])
+sleep_data = sleep_data.sort_values(by=['week', 'start_date'])
+
+# Schritt 3: Kombiniere die beiden Datensätze anhand der 'week' und 'start_date' Spalten mit einem äußeren Join
+combined_data = pd.merge(hr_data, sleep_data, on=['week', 'start_date'], how='outer')
 
 # Zeige die kombinierten Daten
 print("\nKombinierte Daten:")
 print(combined_data)
 
-# Schritt 6: Entferne alle Zeilen mit fehlenden Daten
+# Schritt 4: Entferne alle Zeilen mit fehlenden Daten
 combined_data = combined_data.dropna()
 
-# Schritt 7: Berechne die Korrelation zwischen dem durchschnittlichen Herzfrequenzwert ('avg_hr') und der Schlafdauer ('Durchschnittliche Dauer')
+# Schritt 5: Berechne die Korrelation zwischen 'avg_hr' und 'Durchschnittliche Dauer'
 correlation = combined_data['avg_hr'].corr(combined_data['Durchschnittliche Dauer'])
-
 print(f"\nDie Korrelation zwischen der durchschnittlichen Herzfrequenz und der Schlafdauer ist: {correlation}")
 
-# Schritt 8: Visualisiere den Zusammenhang zwischen Herzfrequenz und Schlafdauer
+# Schritt 6: Visualisiere den Zusammenhang zwischen Herzfrequenz und Schlafdauer
 plt.figure(figsize=(10, 6))
 plt.scatter(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], color='blue', label='Datenpunkte')
 plt.title('Zusammenhang zwischen Herzfrequenz (Durchschnitt) und Schlafdauer')
diff --git a/code/corelation_old3.py b/code/corelation_old3.py
new file mode 100644
index 0000000..15cad3a
--- /dev/null
+++ b/code/corelation_old3.py
@@ -0,0 +1,94 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from datetime import datetime
+
+
+# Manuelle Zuordnung der Monatsnamen von Deutsch auf Englisch
+month_translation = {
+    'Jan': 'Jan', 'Feb': 'Feb', 'Mär': 'Mar', 'Apr': 'Apr', 'Mai': 'May', 'Jun': 'Jun',
+    'Jul': 'Jul', 'Aug': 'Aug', 'Sep': 'Sep', 'Okt': 'Oct', 'Nov': 'Nov', 'Dez': 'Dec'
+}
+
+# Funktion, um 'Okt 10-16' in eine Kalenderwoche zu konvertieren
+def convert_to_week_number(date_range_str):
+    # Splitte den Datumsbereich in Monat und Tage
+    month_str, day_range = date_range_str.split(" ")
+    start_day = int(day_range.split("-")[0])  # Nimm den Starttag
+    current_year = datetime.now().year
+
+    # Konvertiere den deutschen Monatsnamen in den englischen
+    if month_str in month_translation:
+        month_str = month_translation[month_str]
+
+    # Erstelle das Datum basierend auf dem Starttag und dem aktuellen Jahr
+    date_str = f"{month_str} {start_day} {current_year}"
+    start_date = datetime.strptime(date_str, "%b %d %Y")  # Konvertiere in ein Datum
+    return start_date.isocalendar()[1]  # Gib die Kalenderwoche zurück
+
+
+# Schritt 1: Lade die HR-Daten (mit Semikolon separiert) und entferne 'bpm'
+hr_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv', sep=';')
+
+# Zeige die ersten Zeilen der HR-Daten vor der Bereinigung
+print("HR-Daten (vor der Bereinigung):")
+print(hr_data.head())
+
+# Entferne 'bpm' und konvertiere die Werte in numerische Daten
+hr_data['In Ruhe'] = hr_data['In Ruhe'].str.replace(' bpm', '').astype(float)
+hr_data['Hoch'] = hr_data['Hoch'].str.replace(' bpm', '').astype(float)
+
+# Konvertiere die Spalte 'Datum' in Kalenderwoche (KW)
+hr_data['week'] = hr_data['Datum'].apply(convert_to_week_number)
+
+# Zeige die HR-Daten nach der Umwandlung in Wochen
+print("\nHR-Daten (nach der Umwandlung in KW):")
+print(hr_data.head())
+
+# Schritt 2: Berechne den Durchschnitt der Herzfrequenzdaten (In Ruhe und Hoch)
+hr_data['avg_hr'] = hr_data[['In Ruhe', 'Hoch']].mean(axis=1)
+
+# Schritt 3: Lade die Schlafdaten (mit Komma separiert)
+sleep_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv', sep=',')
+
+# Zeige die ersten Zeilen der Schlafdaten
+print("\nSchlafdaten:")
+print(sleep_data.head())
+
+# Konvertiere auch das 'Datum' der Schlafdaten in Kalenderwoche (KW)
+sleep_data['week'] = sleep_data['Datum'].apply(convert_to_week_number)
+
+# Schritt 4: Sicherstellen, dass beide Datensätze nach 'week' sortiert sind
+hr_data = hr_data.sort_values(by='week')
+sleep_data = sleep_data.sort_values(by='week')
+
+# Schritt 5: Kombiniere die beiden Datensätze anhand der 'week' Spalte mit einem äußeren Join
+combined_data = pd.merge(hr_data, sleep_data, on='week', how='outer')
+
+# Zeige die kombinierten Daten
+print("\nKombinierte Daten:")
+print(combined_data)
+
+# Schritt 6: Entferne alle Zeilen mit fehlenden Daten
+combined_data = combined_data.dropna()
+
+# Schritt 7: Berechne die Korrelation zwischen dem durchschnittlichen Herzfrequenzwert ('avg_hr') und der Schlafdauer ('Durchschnittliche Dauer')
+correlation = combined_data['avg_hr'].corr(combined_data['Durchschnittliche Dauer'])
+
+print(f"\nDie Korrelation zwischen der durchschnittlichen Herzfrequenz und der Schlafdauer ist: {correlation}")
+
+# Schritt 8: Visualisiere den Zusammenhang zwischen Herzfrequenz und Schlafdauer
+plt.figure(figsize=(10, 6))
+plt.scatter(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], color='blue', label='Datenpunkte')
+plt.title('Zusammenhang zwischen Herzfrequenz (Durchschnitt) und Schlafdauer')
+plt.xlabel('Durchschnittliche Herzfrequenz (bpm)')
+plt.ylabel('Schlafdauer (Stunden)')
+plt.grid(True)
+
+# Linie zur Visualisierung des Trends hinzufügen
+m, b = np.polyfit(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], 1)
+plt.plot(combined_data['avg_hr'], m * combined_data['avg_hr'] + b, color='red',
+         label=f'Trendlinie (Kor = {correlation:.2f})')
+
+plt.legend()
+plt.show()
diff --git a/data/raw/Schlaf.csv b/data/raw/Schlaf.csv
new file mode 100644
index 0000000..1296df2
--- /dev/null
+++ b/data/raw/Schlaf.csv
@@ -0,0 +1,53 @@
+Datum,Ø Score,Ø Qualität,Durchschnittliche Dauer,Ø Schlafenszeit,Ø Aufstehzeit
+Okt 11-17,45,Schlecht,5h 59min,1:40,7:50
+Okt 4-10,52,Schlecht,6h 38min,0:29,7:25
+Sep 27 - Okt 3,53,Schlecht,6h 5min,0:14,6:31
+Sep 20-26,60,Ausreichend,7h 5min,0:12,7:23
+Sep 13-19,61,Ausreichend,6h 27min,0:44,7:18
+Sep 6-12,53,Schlecht,6h 8min,1:08,7:22
+Aug 30 - Sep 5,66,Ausreichend,6h 59min,0:11,7:19
+Aug 23-29,61,Ausreichend,6h 39min,0:35,7:19
+Aug 16-22,67,Ausreichend,6h 25min,0:41,7:17
+Aug 9-15,57,Schlecht,5h 43min,0:17,6:04
+Aug 2-8,61,Ausreichend,6h 30min,0:31,7:24
+Jul 26 - Aug 1,62,Ausreichend,6h 55min,0:31,7:48
+Jul 19-25,55,Schlecht,7h 0min,2:35,10:14
+Jul 12-18,72,Ausreichend,6h 48min,0:03,7:05
+Jul 5-11,58,Schlecht,7h 42min,0:32,8:57
+Jun 28 - Jul 4,66,Ausreichend,6h 26min,1:24,7:57
+Jun 21-27,65,Ausreichend,6h 25min,0:39,7:17
+Jun 14-20,59,Schlecht,5h 43min,0:24,6:14
+Jun 7-13,61,Ausreichend,6h 34min,23:47,6:26
+Mai 31 - Jun 6,61,Ausreichend,6h 47min,0:35,7:33
+Mai 24-30,52,Schlecht,6h 33min,1:00,7:38
+Mai 17-23,51,Schlecht,6h 11min,1:12,7:30
+Mai 10-16,48,Schlecht,5h 44min,0:48,6:39
+Mai 3-9,45,Schlecht,5h 55min,0:49,6:48
+Apr 26 - Mai 2,56,Schlecht,6h 11min,1:26,7:41
+Apr 19-25,51,Schlecht,6h 19min,2:55,9:31
+Apr 12-18,57,Schlecht,6h 24min,1:28,8:03
+Apr 5-11,62,Ausreichend,6h 4min,0:56,7:09
+Mrz 29 - Apr 4,73,Ausreichend,6h 59min,1:34,8:53
+Mrz 22-28,59,Schlecht,5h 59min,1:05,7:11
+Mrz 15-21,55,Schlecht,6h 25min,0:47,7:23
+Mrz 8-14,53,Schlecht,6h 22min,0:56,7:25
+Mrz 1-7,47,Schlecht,5h 56min,0:51,6:56
+Feb 23-29,64,Ausreichend,7h 11min,0:09,7:27
+Feb 16-22,46,Schlecht,6h 56min,0:24,7:52
+Feb 9-15,50,Schlecht,8h 26min,23:58,8:48
+Feb 2-8,43,Schlecht,6h 13min,1:06,7:26
+Jan 26 - Feb 1,55,Schlecht,7h 17min,1:12,8:32
+Jan 19-25,55,Schlecht,6h 33min,0:59,7:37
+Jan 12-18,49,Schlecht,6h 16min,1:39,8:04
+Jan 5-11,54,Schlecht,6h 35min,1:28,8:14
+Dez 29, 2023 - Jan 4, 2024,55,Schlecht,7h 15min,0:52,8:34
+Dez 22-28, 2023,56,Schlecht,7h 10min,1:02,8:33
+Dez 15-21, 2023,61,Ausreichend,7h 40min,0:18,8:18
+Dez 8-14, 2023,43,Schlecht,7h 45min,23:45,8:52
+Dez 1-7, 2023,47,Schlecht,6h 51min,0:31,7:25
+Nov 24-30, 2023,48,Schlecht,7h 5min,0:29,8:01
+Nov 17-23, 2023,53,Schlecht,7h 1min,0:26,7:34
+Nov 10-16, 2023,47,Schlecht,7h 5min,0:19,7:28
+Nov 3-9, 2023,49,Schlecht,6h 10min,23:58,7:12
+Okt 27 - Nov 2, 2023,61,Ausreichend,6h 33min,0:08,6:50
+Okt 20-26, 2023,47,Schlecht,6h 11min,0:41,7:14
\ No newline at end of file

From 9005b4fbfbac208caefbb2e94e5923f884b1ee31 Mon Sep 17 00:00:00 2001
From: TheOriginalGraLargeShrimpakaReaper <graber-michael@hotmail.com>
Date: Thu, 17 Oct 2024 20:47:31 +0200
Subject: [PATCH 05/15] edit corelation.py and add hr_data_cleaned.csv

---
 code/corelation.py | 30 ++++++++++++++++++------------
 1 file changed, 18 insertions(+), 12 deletions(-)

diff --git a/code/corelation.py b/code/corelation.py
index 47917ad..c5ea7dc 100644
--- a/code/corelation.py
+++ b/code/corelation.py
@@ -114,7 +114,7 @@ month_translation = {
 
 # Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4' oder 'Dez 29, 2023-Jan 4, 2024' in die Kalenderwoche und das Startdatum zu konvertieren
 def convert_to_week_number_and_start_date(date_range_str):
-    # Entferne zusätzliche Leerzeichen um den Bindestrich herum
+    # Entferne zusätzliche Leerzeichen um den Bindestrich herum und entferne Kommas
     date_range_str = date_range_str.replace(" - ", "-").replace(",", "")
 
     # Splitte den Datumsbereich in Start- und Endteil
@@ -128,12 +128,20 @@ def convert_to_week_number_and_start_date(date_range_str):
     # Verarbeite den Endteil
     end_parts = end_part.split(" ")
 
-    if len(end_parts) == 2:
+    # Verarbeite das Enddatum, basierend darauf, ob ein Jahr enthalten ist oder nicht
+    if len(end_parts) == 3:  # Fall: 'Jan 4 2024'
         end_month_str = end_parts[0]
-        end_day = int(end_parts[1].strip())  # Nimm den Endtag
+        end_day = int(end_parts[1].strip())
+        end_year = int(end_parts[2].strip())  # Nimm das Jahr aus dem Endteil
+    elif len(end_parts) == 2:  # Fall: 'Jan 4' ohne Jahr
+        end_month_str = end_parts[0]
+        end_day = int(end_parts[1].strip())
+        end_year = datetime.now().year  # Nutze das aktuelle Jahr, wenn kein Jahr angegeben ist
     else:
-        end_month_str = start_month_str  # Falls kein Monat angegeben, setze den Startmonat
+        # Falls kein Monat angegeben, setze den Startmonat für den Endteil
+        end_month_str = start_month_str
         end_day = int(end_parts[0].strip())
+        end_year = datetime.now().year  # Nutze das aktuelle Jahr
 
     # Konvertiere den deutschen Monatsnamen in den englischen für beide Monate
     if start_month_str in month_translation:
@@ -143,21 +151,19 @@ def convert_to_week_number_and_start_date(date_range_str):
 
     # Wenn der Startteil ein Jahr enthält, nutze dieses
     if len(start_parts) == 3:
-        current_year = int(start_parts[2])
+        start_year = int(start_parts[2])
     else:
-        current_year = datetime.now().year
+        start_year = datetime.now().year
 
-    # Wenn der Datumsbereich über den Dezember hinausgeht, endet das Enddatum im nächsten Jahr
+    # Falls der Datumsbereich über den Dezember hinausgeht, endet das Enddatum im nächsten Jahr
     if start_month_str == 'Dec' and end_month_str == 'Jan':
-        end_year = current_year + 1
-    else:
-        end_year = current_year
+        end_year = start_year + 1
 
     try:
         # Konvertiere das Startdatum in ein Datum
-        start_date = datetime.strptime(f"{start_month_str} {start_day} {current_year}", "%b %d %Y")
+        start_date = datetime.strptime(f"{start_month_str} {start_day} {start_year}", "%b %d %Y")
     except ValueError:
-        raise ValueError(f"Ungültiges Startdatum: {start_month_str} {start_day} {current_year}")
+        raise ValueError(f"Ungültiges Startdatum: {start_month_str} {start_day} {start_year}")
 
     try:
         # Konvertiere das Enddatum in ein Datum

From 37ae6b437d30a4be83f5dce3666fff48fa159834 Mon Sep 17 00:00:00 2001
From: TheOriginalGraLargeShrimpakaReaper <graber-michael@hotmail.com>
Date: Thu, 17 Oct 2024 20:50:23 +0200
Subject: [PATCH 06/15] edit corelation.py and add hr_data_cleaned.csv

---
 code/corelation.py | 41 +++++++++++++++++------------------------
 1 file changed, 17 insertions(+), 24 deletions(-)

diff --git a/code/corelation.py b/code/corelation.py
index c5ea7dc..9ab54d2 100644
--- a/code/corelation.py
+++ b/code/corelation.py
@@ -112,11 +112,20 @@ month_translation = {
 }
 
 
-# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4' oder 'Dez 29, 2023-Jan 4, 2024' in die Kalenderwoche und das Startdatum zu konvertieren
+# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4', 'Dez 29, 2023-Jan 4, 2024' oder 'Dez 22-28 2023' in Kalenderwoche und Startdatum zu konvertieren
 def convert_to_week_number_and_start_date(date_range_str):
     # Entferne zusätzliche Leerzeichen um den Bindestrich herum und entferne Kommas
     date_range_str = date_range_str.replace(" - ", "-").replace(",", "")
 
+    # Prüfen, ob das Jahr am Ende steht (z.B. 'Dez 22-28 2023')
+    if date_range_str[-4:].isdigit():
+        # Jahr am Ende des Datums
+        year_str = date_range_str[-4:]
+        date_range_str = date_range_str[:-5]  # Entferne das Jahr aus der Datumsangabe
+    else:
+        # Kein Jahr am Ende -> nutze das aktuelle Jahr
+        year_str = str(datetime.now().year)
+
     # Splitte den Datumsbereich in Start- und Endteil
     start_part, end_part = date_range_str.split("-")
 
@@ -128,20 +137,14 @@ def convert_to_week_number_and_start_date(date_range_str):
     # Verarbeite den Endteil
     end_parts = end_part.split(" ")
 
-    # Verarbeite das Enddatum, basierend darauf, ob ein Jahr enthalten ist oder nicht
-    if len(end_parts) == 3:  # Fall: 'Jan 4 2024'
+    # Verarbeite das Enddatum, basierend darauf, ob der Endteil das Jahr enthält
+    if len(end_parts) == 2:  # Fall: 'Apr 4'
         end_month_str = end_parts[0]
         end_day = int(end_parts[1].strip())
-        end_year = int(end_parts[2].strip())  # Nimm das Jahr aus dem Endteil
-    elif len(end_parts) == 2:  # Fall: 'Jan 4' ohne Jahr
-        end_month_str = end_parts[0]
-        end_day = int(end_parts[1].strip())
-        end_year = datetime.now().year  # Nutze das aktuelle Jahr, wenn kein Jahr angegeben ist
     else:
-        # Falls kein Monat angegeben, setze den Startmonat für den Endteil
+        # Fall: kein Monat -> nur Tag (z.B. '28' im Format 'Dez 22-28 2023')
         end_month_str = start_month_str
         end_day = int(end_parts[0].strip())
-        end_year = datetime.now().year  # Nutze das aktuelle Jahr
 
     # Konvertiere den deutschen Monatsnamen in den englischen für beide Monate
     if start_month_str in month_translation:
@@ -149,27 +152,17 @@ def convert_to_week_number_and_start_date(date_range_str):
     if end_month_str in month_translation:
         end_month_str = month_translation[end_month_str]
 
-    # Wenn der Startteil ein Jahr enthält, nutze dieses
-    if len(start_parts) == 3:
-        start_year = int(start_parts[2])
-    else:
-        start_year = datetime.now().year
-
-    # Falls der Datumsbereich über den Dezember hinausgeht, endet das Enddatum im nächsten Jahr
-    if start_month_str == 'Dec' and end_month_str == 'Jan':
-        end_year = start_year + 1
-
     try:
         # Konvertiere das Startdatum in ein Datum
-        start_date = datetime.strptime(f"{start_month_str} {start_day} {start_year}", "%b %d %Y")
+        start_date = datetime.strptime(f"{start_month_str} {start_day} {year_str}", "%b %d %Y")
     except ValueError:
-        raise ValueError(f"Ungültiges Startdatum: {start_month_str} {start_day} {start_year}")
+        raise ValueError(f"Ungültiges Startdatum: {start_month_str} {start_day} {year_str}")
 
     try:
         # Konvertiere das Enddatum in ein Datum
-        end_date = datetime.strptime(f"{end_month_str} {end_day} {end_year}", "%b %d %Y")
+        end_date = datetime.strptime(f"{end_month_str} {end_day} {year_str}", "%b %d %Y")
     except ValueError:
-        raise ValueError(f"Ungültiges Enddatum: {end_month_str} {end_day} {end_year}")
+        raise ValueError(f"Ungültiges Enddatum: {end_month_str} {end_day} {year_str}")
 
     # Berechne die Kalenderwoche basierend auf dem Startdatum
     week_number = start_date.isocalendar()[1]

From 692099b36d3acc94245a673af7351f0d907129fc Mon Sep 17 00:00:00 2001
From: TheOriginalGraLargeShrimpakaReaper <graber-michael@hotmail.com>
Date: Thu, 17 Oct 2024 20:52:26 +0200
Subject: [PATCH 07/15] edit corelation.py and add hr_data_cleaned.csv

---
 code/corelation.py                | 21 +++++++++++-
 code/sandbox/hr_data_clean.csv    | 53 +++++++++++++++++++++++++++++++
 code/sandbox/sleep_data_clean.csv | 53 +++++++++++++++++++++++++++++++
 3 files changed, 126 insertions(+), 1 deletion(-)
 create mode 100644 code/sandbox/hr_data_clean.csv
 create mode 100644 code/sandbox/sleep_data_clean.csv

diff --git a/code/corelation.py b/code/corelation.py
index 9ab54d2..2b55ec6 100644
--- a/code/corelation.py
+++ b/code/corelation.py
@@ -112,11 +112,30 @@ month_translation = {
 }
 
 
-# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4', 'Dez 29, 2023-Jan 4, 2024' oder 'Dez 22-28 2023' in Kalenderwoche und Startdatum zu konvertieren
+# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4', 'Dez 29, 2023-Jan 4, 2024', 'Dez 22-28 2023' oder 'Dez 28' in Kalenderwoche und Startdatum zu konvertieren
 def convert_to_week_number_and_start_date(date_range_str):
     # Entferne zusätzliche Leerzeichen um den Bindestrich herum und entferne Kommas
     date_range_str = date_range_str.replace(" - ", "-").replace(",", "")
 
+    # Prüfen, ob das Format nur einen Tag und Monat enthält (z.B. 'Dez 28')
+    if "-" not in date_range_str and len(date_range_str.split(" ")) == 2:
+        # Nur ein Datum (Monat und Tag)
+        month_str, day_str = date_range_str.split(" ")
+        day = int(day_str.strip())
+        year_str = str(datetime.now().year)  # Verwende das aktuelle Jahr
+
+        # Konvertiere den deutschen Monatsnamen in den englischen
+        if month_str in month_translation:
+            month_str = month_translation[month_str]
+
+        # Konvertiere das Datum
+        start_date = datetime.strptime(f"{month_str} {day} {year_str}", "%b %d %Y")
+
+        # Berechne die Kalenderwoche
+        week_number = start_date.isocalendar()[1]
+
+        return week_number, start_date.strftime('%Y-%m-%d')
+
     # Prüfen, ob das Jahr am Ende steht (z.B. 'Dez 22-28 2023')
     if date_range_str[-4:].isdigit():
         # Jahr am Ende des Datums
diff --git a/code/sandbox/hr_data_clean.csv b/code/sandbox/hr_data_clean.csv
new file mode 100644
index 0000000..e098c31
--- /dev/null
+++ b/code/sandbox/hr_data_clean.csv
@@ -0,0 +1,53 @@
+Datum;In Ruhe;Hoch;week;start_date;avg_hr
+Okt 4-10;67.0;130.0;40;2024-10-04;98.5
+Sep 27 - Okt 3;67.0;143.0;39;2024-09-27;105.0
+Sep 20-26;66.0;149.0;38;2024-09-20;107.5
+Sep 13-19;66.0;144.0;37;2024-09-13;105.0
+Sep 6-12;62.0;132.0;36;2024-09-06;97.0
+Aug 30 - Sep 5;64.0;141.0;35;2024-08-30;102.5
+Aug 23-29;67.0;150.0;34;2024-08-23;108.5
+Aug 16-22;63.0;143.0;33;2024-08-16;103.0
+Aug 9-15;69.0;141.0;32;2024-08-09;105.0
+Aug 2-8;67.0;140.0;31;2024-08-02;103.5
+Jul 26 - Aug 1;67.0;147.0;30;2024-07-26;107.0
+Jul 19-25;69.0;136.0;29;2024-07-19;102.5
+Jul 12-18;66.0;151.0;28;2024-07-12;108.5
+Jul 5-11;67.0;146.0;27;2024-07-05;106.5
+Jun 28 - Jul 4;66.0;157.0;26;2024-06-28;111.5
+Jun 21-27;64.0;141.0;25;2024-06-21;102.5
+Jun 14-20;70.0;145.0;24;2024-06-14;107.5
+Jun 7-13;69.0;134.0;23;2024-06-07;101.5
+Mai 31 - Jun 6;70.0;139.0;22;2024-05-31;104.5
+Mai 24-30;72.0;142.0;21;2024-05-24;107.0
+Mai 17-23;72.0;135.0;20;2024-05-17;103.5
+Mai 10-16;71.0;147.0;19;2024-05-10;109.0
+Mai 3-9;73.0;142.0;18;2024-05-03;107.5
+Apr 26 - Mai 2;69.0;151.0;17;2024-04-26;110.0
+Apr 19-25;61.0;135.0;16;2024-04-19;98.0
+Apr 12-18;58.0;140.0;15;2024-04-12;99.0
+Apr 5-11;64.0;131.0;14;2024-04-05;97.5
+Mrz 29 - Apr 4;63.0;139.0;13;2024-03-29;101.0
+Mrz 22-28;65.0;135.0;12;2024-03-22;100.0
+Mrz 15-21;66.0;137.0;11;2024-03-15;101.5
+Mrz 8-14;62.0;136.0;10;2024-03-08;99.0
+Mrz 1-7;70.0;134.0;9;2024-03-01;102.0
+Feb 23-29;68.0;144.0;8;2024-02-23;106.0
+Feb 16-22;71.0;132.0;7;2024-02-16;101.5
+Feb 9-15;65.0;143.0;6;2024-02-09;104.0
+Feb 2-8;66.0;133.0;5;2024-02-02;99.5
+Jan 26 - Feb 1;59.0;142.0;4;2024-01-26;100.5
+Jan 19-25;62.0;136.0;3;2024-01-19;99.0
+Jan 12-18;60.0;134.0;2;2024-01-12;97.0
+Jan 5-11;56.0;139.0;1;2024-01-05;97.5
+Dez 29, 2023 - Jan 4, 2024;59.0;128.0;52;2024-12-29;93.5
+Dez 22-28, 2023;52.0;124.0;51;2023-12-22;88.0
+Dez 15-21, 2023;57.0;133.0;50;2023-12-15;95.0
+Dez 8-14, 2023;65.0;133.0;49;2023-12-08;99.0
+Dez 1-7, 2023;69.0;134.0;48;2023-12-01;101.5
+Nov 24-30, 2023;68.0;139.0;47;2023-11-24;103.5
+Nov 17-23, 2023;68.0;143.0;46;2023-11-17;105.5
+Nov 10-16, 2023;64.0;144.0;45;2023-11-10;104.0
+Nov 3-9, 2023;63.0;140.0;44;2023-11-03;101.5
+Okt 27 - Nov 2, 2023;57.0;133.0;43;2023-10-27;95.0
+Okt 20-26, 2023;55.0;138.0;42;2023-10-20;96.5
+Okt 13-19, 2023;50.0;121.0;41;2023-10-13;85.5
diff --git a/code/sandbox/sleep_data_clean.csv b/code/sandbox/sleep_data_clean.csv
new file mode 100644
index 0000000..0fc8250
--- /dev/null
+++ b/code/sandbox/sleep_data_clean.csv
@@ -0,0 +1,53 @@
+Datum,Ø Score,Ø Qualität,Durchschnittliche Dauer,Ø Schlafenszeit,Ø Aufstehzeit,week,start_date
+Okt 10-16,46,Schlecht,6h 11min,1:28,7:50,41,2024-10-10
+Okt 3-9,56,Schlecht,6h 34min,0:33,7:22,40,2024-10-03
+Sep 26 - Okt 2,52,Schlecht,6h 23min,0:00,6:37,39,2024-09-26
+Sep 19-25,59,Schlecht,6h 41min,0:27,7:16,38,2024-09-19
+Sep 12-18,59,Schlecht,6h 18min,0:48,7:12,37,2024-09-12
+Sep 5-11,55,Schlecht,6h 19min,0:56,7:22,36,2024-09-05
+Aug 29 - Sep 4,66,Ausreichend,7h 1min,0:21,7:31,35,2024-08-29
+Aug 22-28,57,Schlecht,6h 17min,0:35,6:59,34,2024-08-22
+Aug 15-21,72,Ausreichend,6h 36min,0:38,7:25,33,2024-08-15
+Aug 8-14,56,Schlecht,5h 45min,0:16,6:06,32,2024-08-08
+Aug 1-7,64,Ausreichend,7h 4min,0:39,8:10,31,2024-08-01
+Jul 25-31,61,Ausreichend,6h 52min,0:40,7:56,30,2024-07-25
+Jul 18-24,54,Schlecht,6h 26min,2:16,9:19,29,2024-07-18
+Jul 11-17,74,Ausreichend,7h 10min,0:06,7:26,28,2024-07-11
+Jul 4-10,58,Schlecht,7h 35min,0:45,9:05,27,2024-07-04
+Jun 27 - Jul 3,60,Ausreichend,6h 8min,1:22,7:42,26,2024-06-27
+Jun 20-26,69,Ausreichend,6h 30min,0:19,6:58,25,2024-06-20
+Jun 13-19,59,Schlecht,6h 1min,0:25,6:33,24,2024-06-13
+Jun 6-12,60,Ausreichend,6h 22min,0:02,6:30,23,2024-06-06
+Mai 30 - Jun 5,60,Ausreichend,6h 33min,0:34,7:15,22,2024-05-30
+Mai 23-29,51,Schlecht,6h 47min,0:56,7:54,21,2024-05-23
+Mai 16-22,50,Schlecht,5h 51min,1:13,7:08,20,2024-05-16
+Mai 9-15,55,Schlecht,6h 21min,0:58,7:23,19,2024-05-09
+Mai 2-8,40,Schlecht,5h 36min,0:39,6:19,18,2024-05-02
+Apr 25 - Mai 1,55,Schlecht,6h 2min,1:18,7:26,17,2024-04-25
+Apr 18-24,56,Schlecht,6h 28min,3:11,9:55,16,2024-04-18
+Apr 11-17,56,Schlecht,6h 17min,1:23,7:51,15,2024-04-11
+Apr 4-10,62,Ausreichend,6h 18min,0:39,7:10,14,2024-04-04
+Mrz 28 - Apr 3,68,Ausreichend,6h 50min,1:45,8:51,13,2024-03-28
+Mrz 21-27,60,Ausreichend,6h 9min,0:56,7:13,12,2024-03-21
+Mrz 14-20,54,Schlecht,6h 11min,1:03,7:24,11,2024-03-14
+Mrz 7-13,54,Schlecht,6h 22min,0:49,7:17,10,2024-03-07
+Feb 29 - Mrz 6,50,Schlecht,6h 4min,0:56,7:09,9,2024-02-29
+Feb 22-28,61,Ausreichend,7h 11min,0:08,7:25,8,2024-02-22
+Feb 15-21,44,Schlecht,6h 58min,0:19,7:51,7,2024-02-15
+Feb 8-14,50,Schlecht,8h 17min,0:09,8:47,6,2024-02-08
+Feb 1-7,43,Schlecht,6h 12min,1:06,7:25,5,2024-02-01
+Jan 25-31,57,Schlecht,7h 21min,1:17,8:40,4,2024-01-25
+Jan 18-24,51,Schlecht,6h 25min,0:56,7:26,3,2024-01-18
+Jan 11-17,50,Schlecht,6h 27min,1:36,8:12,2,2024-01-11
+Jan 4-10,59,Schlecht,6h 44min,1:26,8:24,1,2024-01-04
+Dez 28, 2023 - Jan 3, 2024,54,Schlecht,7h 2min,52,2024-12-28
+Dez 21-27, 2023,55,Schlecht,7h 16min,0:38,51,2024-12-21
+Dez 14-20, 2023,56,Schlecht,7h 17min,0:31,50,2024-12-14
+Dez 7-13, 2023,44,Schlecht,7h 53min,23:46,49,2024-12-07
+Nov 30 - Dez 6, 2023,48,Schlecht,6h 51min,0:33,48,2024-11-30
+Nov 23-29, 2023,48,Schlecht,7h 11min,0:21,47,2024-11-23
+Nov 16-22, 2023,53,Schlecht,7h 7min,0:23,46,2024-11-16
+Nov 9-15, 2023,47,Schlecht,7h 5min,0:19,45,2024-11-09
+Nov 2-8, 2023,50,Schlecht,6h 2min,0:00,44,2024-11-02
+Okt 26 - Nov 1, 2023,59,Schlecht,6h 39min,0:14,43,2024-10-26
+Okt 19-25, 2023,48,Schlecht,6h 17min,0:46,42,2024-10-19

From 5e2c6e7187d6ace681abae75dea6c3766a927cf7 Mon Sep 17 00:00:00 2001
From: TheOriginalGraLargeShrimpakaReaper <graber-michael@hotmail.com>
Date: Fri, 18 Oct 2024 09:59:08 +0200
Subject: [PATCH 08/15] edit corelation.py and add hr_data_cleaned.csv

---
 code/corelation.py                | 202 +++++------------------
 code/corelation_old4.py           | 266 ++++++++++++++++++++++++++++++
 code/sandbox/hr_data_clean.csv    |  53 ------
 code/sandbox/sleep_data_clean.csv |  53 ------
 data/sandbox/combined_data.csv    |  42 +++++
 data/sandbox/hr_data_clean.csv    |  53 ++++++
 data/sandbox/sleep_data_clean.csv |  53 ++++++
 7 files changed, 454 insertions(+), 268 deletions(-)
 create mode 100644 code/corelation_old4.py
 delete mode 100644 code/sandbox/hr_data_clean.csv
 delete mode 100644 code/sandbox/sleep_data_clean.csv
 create mode 100644 data/sandbox/combined_data.csv
 create mode 100644 data/sandbox/hr_data_clean.csv
 create mode 100644 data/sandbox/sleep_data_clean.csv

diff --git a/code/corelation.py b/code/corelation.py
index 2b55ec6..79bc956 100644
--- a/code/corelation.py
+++ b/code/corelation.py
@@ -2,109 +2,7 @@ import pandas as pd
 import matplotlib.pyplot as plt
 import numpy as np
 from datetime import datetime
-import os
 
-# Manuelle Zuordnung der Monatsnamen von Deutsch auf Englisch
-# month_translation = {
-#     'Jan': 'Jan', 'Feb': 'Feb', 'Mär': 'Mar', 'Apr': 'Apr', 'Mai': 'May', 'Jun': 'Jun',
-#     'Jul': 'Jul', 'Aug': 'Aug', 'Sep': 'Sep', 'Okt': 'Oct', 'Nov': 'Nov', 'Dez': 'Dec', 'Mrz': 'Mar'
-# }
-
-
-# Funktion, um 'Okt 10-16' in eine Kalenderwoche zu konvertieren und das Startdatum zurückzugeben
-# def convert_to_week_number_and_start_date(date_range_str):
-#     # Splitte den Datumsbereich in Monat und Tage
-#     start_part, end_part = date_range_str.split(" - ")
-#     start_month_str, start_day = start_part.split(" ")
-#     start_day = int(start_day)  # Nimm den Starttag
-#
-#     # Konvertiere den deutschen Monatsnamen in den englischen
-#     if start_month_str in month_translation:
-#         start_month_str = month_translation[start_month_str]
-#
-#     # Erstelle das Datum basierend auf dem Starttag und dem aktuellen Jahr
-#     current_year = datetime.now().year
-#     date_str = f"{start_month_str} {start_day} {current_year}"
-#
-#     try:
-#         start_date = datetime.strptime(date_str, "%b %d %Y")  # Konvertiere in ein Datum
-#     except ValueError:
-#         raise ValueError(f"Ungültiges Datum: {date_str}")
-#
-#     week_number = start_date.isocalendar()[1]  # Kalenderwoche ermitteln
-#
-#     return week_number, start_date.strftime('%Y-%m-%d')  # Gib KW und das Startdatum zurück
-# Funktion, um 'Okt 10-16' oder 'Okt 10 - 16' in eine Kalenderwoche zu konvertieren und das Startdatum zurückzugeben
-# def convert_to_week_number_and_start_date(date_range_str):
-#     # Entferne zusätzliche Leerzeichen um den Bindestrich herum
-#     date_range_str = date_range_str.replace(" - ", "-")
-#
-#     # Splitte den Datumsbereich in Start- und Endbereich
-#     start_part, end_part = date_range_str.split("-")
-#     start_month_str, start_day = start_part.split(" ")
-#     start_day = int(start_day.strip())  # Nimm den Starttag und entferne etwaige Leerzeichen
-#
-#     # Konvertiere den deutschen Monatsnamen in den englischen
-#     if start_month_str in month_translation:
-#         start_month_str = month_translation[start_month_str]
-#
-#     # Erstelle das Datum basierend auf dem Starttag und dem aktuellen Jahr
-#     current_year = datetime.now().year
-#     date_str = f"{start_month_str} {start_day} {current_year}"
-#
-#     try:
-#         start_date = datetime.strptime(date_str, "%b %d %Y")  # Konvertiere in ein Datum
-#     except ValueError:
-#         raise ValueError(f"Ungültiges Datum: {date_str}")
-#
-#     week_number = start_date.isocalendar()[1]  # Kalenderwoche ermitteln
-#
-#     return week_number, start_date.strftime('%Y-%m-%d')  # Gib KW und das Startdatum zurück
-
-# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4' oder 'Okt 10-16' in die Kalenderwoche zu konvertieren
-# und das Startdatum zurückzugeben
-# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4' oder 'Okt 10-16' in die Kalenderwoche zu konvertieren
-# und das Startdatum zurückzugeben
-# def convert_to_week_number_and_start_date(date_range_str):
-#     # Entferne zusätzliche Leerzeichen um den Bindestrich herum
-#     date_range_str = date_range_str.replace(" - ", "-")
-#
-#     # Splitte den Datumsbereich in Start- und Endteil
-#     start_part, end_part = date_range_str.split("-")
-#
-#     # Verarbeite den Startteil
-#     start_month_str, start_day = start_part.split(" ")
-#     start_day = int(start_day.strip())  # Nimm den Starttag und entferne etwaige Leerzeichen
-#
-#     # Wenn der Endteil einen Monat enthält, verarbeite ihn ebenfalls
-#     if " " in end_part:
-#         end_month_str, end_day = end_part.split(" ")
-#         end_day = int(end_day.strip())  # Nimm den Endtag
-#     else:
-#         # Wenn der Endteil keinen Monat enthält, verwenden wir denselben Monat wie im Startteil
-#         end_month_str = start_month_str
-#         end_day = int(end_part.strip())
-#
-#     # Konvertiere den deutschen Monatsnamen in den englischen für beide Monate
-#     if start_month_str in month_translation:
-#         start_month_str = month_translation[start_month_str]
-#     if end_month_str in month_translation:
-#         end_month_str = month_translation[end_month_str]
-#
-#     # Erstelle das Datum basierend auf dem Starttag und dem aktuellen Jahr
-#     current_year = datetime.now().year
-#     start_date_str = f"{start_month_str} {start_day} {current_year}"
-#
-#     try:
-#         # Konvertiere in ein Datum
-#         start_date = datetime.strptime(start_date_str, "%b %d %Y")
-#     except ValueError:
-#         raise ValueError(f"Ungültiges Startdatum: {start_date_str}")
-#
-#     # Berechne die Kalenderwoche basierend auf dem Startdatum
-#     week_number = start_date.isocalendar()[1]
-#
-#     return week_number, start_date.strftime('%Y-%m-%d')  # Gib KW und das Startdatum zurück
 # Manuelle Zuordnung der Monatsnamen von Deutsch auf Englisch
 month_translation = {
     'Jan': 'Jan', 'Feb': 'Feb', 'Mär': 'Mar', 'Mrz': 'Mar', 'Apr': 'Apr', 'Mai': 'May', 'Jun': 'Jun',
@@ -112,8 +10,8 @@ month_translation = {
 }
 
 
-# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4', 'Dez 29, 2023-Jan 4, 2024', 'Dez 22-28 2023' oder 'Dez 28' in Kalenderwoche und Startdatum zu konvertieren
-def convert_to_week_number_and_start_date(date_range_str):
+# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4', 'Dez 22-28 2023' oder 'Dez 28' in Kalenderwoche und Jahr zu konvertieren
+def convert_to_week_and_year(date_range_str):
     # Entferne zusätzliche Leerzeichen um den Bindestrich herum und entferne Kommas
     date_range_str = date_range_str.replace(" - ", "-").replace(",", "")
 
@@ -131,10 +29,11 @@ def convert_to_week_number_and_start_date(date_range_str):
         # Konvertiere das Datum
         start_date = datetime.strptime(f"{month_str} {day} {year_str}", "%b %d %Y")
 
-        # Berechne die Kalenderwoche
+        # Berechne die Kalenderwoche und das Jahr
         week_number = start_date.isocalendar()[1]
+        year = start_date.year
 
-        return week_number, start_date.strftime('%Y-%m-%d')
+        return f"W{week_number}-{year}"
 
     # Prüfen, ob das Jahr am Ende steht (z.B. 'Dez 22-28 2023')
     if date_range_str[-4:].isdigit():
@@ -177,79 +76,58 @@ def convert_to_week_number_and_start_date(date_range_str):
     except ValueError:
         raise ValueError(f"Ungültiges Startdatum: {start_month_str} {start_day} {year_str}")
 
-    try:
-        # Konvertiere das Enddatum in ein Datum
-        end_date = datetime.strptime(f"{end_month_str} {end_day} {year_str}", "%b %d %Y")
-    except ValueError:
-        raise ValueError(f"Ungültiges Enddatum: {end_month_str} {end_day} {year_str}")
-
-    # Berechne die Kalenderwoche basierend auf dem Startdatum
+    # Berechne die Kalenderwoche und das Jahr basierend auf dem Startdatum
     week_number = start_date.isocalendar()[1]
+    year = start_date.year
 
-    return week_number, start_date.strftime('%Y-%m-%d')  # Gib KW und das Startdatum zurück
+    return f"W{week_number}-{year}"  # Gib Kalenderwoche und Jahr im Format W-YYYY zurück
 
 
-# Funktion zum Bereinigen und Zwischenspeichern der Daten
-def clean_and_save_data(hr_data, sleep_data, output_dir='sandbox'):
-    # Entferne 'bpm' und konvertiere die Werte in numerische Daten
-    hr_data['In Ruhe'] = hr_data['In Ruhe'].str.replace(' bpm', '').astype(float)
-    hr_data['Hoch'] = hr_data['Hoch'].str.replace(' bpm', '').astype(float)
+# Datei Pfade
+hr_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv'
+sleep_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv'
+hr_clean_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/sandbox/hr_data_clean.csv'
+sleep_clean_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/sandbox/sleep_data_clean.csv'
+combined_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/sandbox/combined_data.csv'
 
-    # Konvertiere die Spalte 'Datum' in Kalenderwoche (KW) und Startdatum
-    hr_data[['week', 'start_date']] = hr_data['Datum'].apply(
-        lambda x: pd.Series(convert_to_week_number_and_start_date(x)))
+# Schritt 1: Lade die HR-Daten (mit Semikolon separiert) und entferne 'bpm'
+hr_data = pd.read_csv(hr_data_path, sep=';')
 
-    # Berechne den Durchschnitt der Herzfrequenzdaten (In Ruhe und Hoch)
-    hr_data['avg_hr'] = hr_data[['In Ruhe', 'Hoch']].mean(axis=1)
+# Entferne 'bpm' und konvertiere die Werte in numerische Daten
+hr_data['In Ruhe'] = hr_data['In Ruhe'].str.replace(' bpm', '').astype(float)
+hr_data['Hoch'] = hr_data['Hoch'].str.replace(' bpm', '').astype(float)
 
-    # Konvertiere auch das 'Datum' der Schlafdaten in Kalenderwoche (KW) und Startdatum
-    sleep_data[['week', 'start_date']] = sleep_data['Datum'].apply(
-        lambda x: pd.Series(convert_to_week_number_and_start_date(x)))
+# Konvertiere die Spalte 'Datum' in Kalenderwoche (KW und Jahr)
+hr_data['Woche'] = hr_data['Datum'].apply(convert_to_week_and_year)
 
-    # Speichere die bereinigten Daten in 'sandbox'
-    if not os.path.exists(output_dir):
-        os.makedirs(output_dir)
+# Berechne den Durchschnitt der Herzfrequenzdaten (In Ruhe und Hoch)
+hr_data['avg_hr'] = hr_data[['In Ruhe', 'Hoch']].mean(axis=1)
 
-    hr_data_clean_path = os.path.join(output_dir, 'hr_data_clean.csv')
-    sleep_data_clean_path = os.path.join(output_dir, 'sleep_data_clean.csv')
+# Speichere nur die Spalten 'Woche' und 'avg_hr' in einer neuen CSV-Datei
+hr_data_clean = hr_data[['Woche', 'avg_hr']]
+hr_data_clean.to_csv(hr_clean_path, index=False)
 
-    hr_data.to_csv(hr_data_clean_path, sep=';', index=False)
-    sleep_data.to_csv(sleep_data_clean_path, sep=',', index=False)
+# Schritt 2: Lade die Schlafdaten (mit Komma separiert)
+sleep_data = pd.read_csv(sleep_data_path, sep=',')
 
-    print(f"HR-Daten wurden bereinigt und in {hr_data_clean_path} gespeichert.")
-    print(f"Schlafdaten wurden bereinigt und in {sleep_data_clean_path} gespeichert.")
+# Konvertiere die Spalte 'Datum' in Kalenderwoche (KW und Jahr)
+sleep_data['Woche'] = sleep_data['Datum'].apply(convert_to_week_and_year)
 
-    return hr_data, sleep_data
+# Speichere nur die Spalten 'Woche' und 'Durchschnittliche Dauer' in einer neuen CSV-Datei
+sleep_data_clean = sleep_data[['Woche', 'Durchschnittliche Dauer']]
+sleep_data_clean.to_csv(sleep_clean_path, index=False)
 
+# Schritt 3: Kombiniere die HR- und Schlafdaten basierend auf der 'Woche'
+combined_data = pd.merge(hr_data_clean, sleep_data_clean, on='Woche', how='inner')
 
-# Schritt 1: Lade die HR- und Schlafdaten
-hr_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv',
-                      sep=';')
-sleep_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv',
-                         sep=',')
+# Speichere das kombinierte Dataset in einer neuen CSV-Datei
+combined_data.to_csv(combined_data_path, index=False)
 
-# Bereinige und speichere die Daten
-hr_data, sleep_data = clean_and_save_data(hr_data, sleep_data)
-
-# Schritt 2: Sortiere beide Datensätze nach 'week' und 'start_date'
-hr_data = hr_data.sort_values(by=['week', 'start_date'])
-sleep_data = sleep_data.sort_values(by=['week', 'start_date'])
-
-# Schritt 3: Kombiniere die beiden Datensätze anhand der 'week' und 'start_date' Spalten mit einem äußeren Join
-combined_data = pd.merge(hr_data, sleep_data, on=['week', 'start_date'], how='outer')
-
-# Zeige die kombinierten Daten
-print("\nKombinierte Daten:")
-print(combined_data)
-
-# Schritt 4: Entferne alle Zeilen mit fehlenden Daten
-combined_data = combined_data.dropna()
-
-# Schritt 5: Berechne die Korrelation zwischen 'avg_hr' und 'Durchschnittliche Dauer'
+# Schritt 4: Berechne die Korrelation zwischen avg_hr und der Schlafdauer
 correlation = combined_data['avg_hr'].corr(combined_data['Durchschnittliche Dauer'])
-print(f"\nDie Korrelation zwischen der durchschnittlichen Herzfrequenz und der Schlafdauer ist: {correlation}")
+print(f"Die Korrelation zwischen der durchschnittlichen Herzfrequenz und der Schlafdauer ist: {correlation}")
 
-# Schritt 6: Visualisiere den Zusammenhang zwischen Herzfrequenz und Schlafdauer
+# Schritt 5: Visualisiere den Zusammenhang zwischen Herzfrequenz und Schlafdauer
 plt.figure(figsize=(10, 6))
 plt.scatter(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], color='blue', label='Datenpunkte')
 plt.title('Zusammenhang zwischen Herzfrequenz (Durchschnitt) und Schlafdauer')
diff --git a/code/corelation_old4.py b/code/corelation_old4.py
new file mode 100644
index 0000000..2b55ec6
--- /dev/null
+++ b/code/corelation_old4.py
@@ -0,0 +1,266 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from datetime import datetime
+import os
+
+# Manuelle Zuordnung der Monatsnamen von Deutsch auf Englisch
+# month_translation = {
+#     'Jan': 'Jan', 'Feb': 'Feb', 'Mär': 'Mar', 'Apr': 'Apr', 'Mai': 'May', 'Jun': 'Jun',
+#     'Jul': 'Jul', 'Aug': 'Aug', 'Sep': 'Sep', 'Okt': 'Oct', 'Nov': 'Nov', 'Dez': 'Dec', 'Mrz': 'Mar'
+# }
+
+
+# Funktion, um 'Okt 10-16' in eine Kalenderwoche zu konvertieren und das Startdatum zurückzugeben
+# def convert_to_week_number_and_start_date(date_range_str):
+#     # Splitte den Datumsbereich in Monat und Tage
+#     start_part, end_part = date_range_str.split(" - ")
+#     start_month_str, start_day = start_part.split(" ")
+#     start_day = int(start_day)  # Nimm den Starttag
+#
+#     # Konvertiere den deutschen Monatsnamen in den englischen
+#     if start_month_str in month_translation:
+#         start_month_str = month_translation[start_month_str]
+#
+#     # Erstelle das Datum basierend auf dem Starttag und dem aktuellen Jahr
+#     current_year = datetime.now().year
+#     date_str = f"{start_month_str} {start_day} {current_year}"
+#
+#     try:
+#         start_date = datetime.strptime(date_str, "%b %d %Y")  # Konvertiere in ein Datum
+#     except ValueError:
+#         raise ValueError(f"Ungültiges Datum: {date_str}")
+#
+#     week_number = start_date.isocalendar()[1]  # Kalenderwoche ermitteln
+#
+#     return week_number, start_date.strftime('%Y-%m-%d')  # Gib KW und das Startdatum zurück
+# Funktion, um 'Okt 10-16' oder 'Okt 10 - 16' in eine Kalenderwoche zu konvertieren und das Startdatum zurückzugeben
+# def convert_to_week_number_and_start_date(date_range_str):
+#     # Entferne zusätzliche Leerzeichen um den Bindestrich herum
+#     date_range_str = date_range_str.replace(" - ", "-")
+#
+#     # Splitte den Datumsbereich in Start- und Endbereich
+#     start_part, end_part = date_range_str.split("-")
+#     start_month_str, start_day = start_part.split(" ")
+#     start_day = int(start_day.strip())  # Nimm den Starttag und entferne etwaige Leerzeichen
+#
+#     # Konvertiere den deutschen Monatsnamen in den englischen
+#     if start_month_str in month_translation:
+#         start_month_str = month_translation[start_month_str]
+#
+#     # Erstelle das Datum basierend auf dem Starttag und dem aktuellen Jahr
+#     current_year = datetime.now().year
+#     date_str = f"{start_month_str} {start_day} {current_year}"
+#
+#     try:
+#         start_date = datetime.strptime(date_str, "%b %d %Y")  # Konvertiere in ein Datum
+#     except ValueError:
+#         raise ValueError(f"Ungültiges Datum: {date_str}")
+#
+#     week_number = start_date.isocalendar()[1]  # Kalenderwoche ermitteln
+#
+#     return week_number, start_date.strftime('%Y-%m-%d')  # Gib KW und das Startdatum zurück
+
+# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4' oder 'Okt 10-16' in die Kalenderwoche zu konvertieren
+# und das Startdatum zurückzugeben
+# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4' oder 'Okt 10-16' in die Kalenderwoche zu konvertieren
+# und das Startdatum zurückzugeben
+# def convert_to_week_number_and_start_date(date_range_str):
+#     # Entferne zusätzliche Leerzeichen um den Bindestrich herum
+#     date_range_str = date_range_str.replace(" - ", "-")
+#
+#     # Splitte den Datumsbereich in Start- und Endteil
+#     start_part, end_part = date_range_str.split("-")
+#
+#     # Verarbeite den Startteil
+#     start_month_str, start_day = start_part.split(" ")
+#     start_day = int(start_day.strip())  # Nimm den Starttag und entferne etwaige Leerzeichen
+#
+#     # Wenn der Endteil einen Monat enthält, verarbeite ihn ebenfalls
+#     if " " in end_part:
+#         end_month_str, end_day = end_part.split(" ")
+#         end_day = int(end_day.strip())  # Nimm den Endtag
+#     else:
+#         # Wenn der Endteil keinen Monat enthält, verwenden wir denselben Monat wie im Startteil
+#         end_month_str = start_month_str
+#         end_day = int(end_part.strip())
+#
+#     # Konvertiere den deutschen Monatsnamen in den englischen für beide Monate
+#     if start_month_str in month_translation:
+#         start_month_str = month_translation[start_month_str]
+#     if end_month_str in month_translation:
+#         end_month_str = month_translation[end_month_str]
+#
+#     # Erstelle das Datum basierend auf dem Starttag und dem aktuellen Jahr
+#     current_year = datetime.now().year
+#     start_date_str = f"{start_month_str} {start_day} {current_year}"
+#
+#     try:
+#         # Konvertiere in ein Datum
+#         start_date = datetime.strptime(start_date_str, "%b %d %Y")
+#     except ValueError:
+#         raise ValueError(f"Ungültiges Startdatum: {start_date_str}")
+#
+#     # Berechne die Kalenderwoche basierend auf dem Startdatum
+#     week_number = start_date.isocalendar()[1]
+#
+#     return week_number, start_date.strftime('%Y-%m-%d')  # Gib KW und das Startdatum zurück
+# Manuelle Zuordnung der Monatsnamen von Deutsch auf Englisch
+month_translation = {
+    'Jan': 'Jan', 'Feb': 'Feb', 'Mär': 'Mar', 'Mrz': 'Mar', 'Apr': 'Apr', 'Mai': 'May', 'Jun': 'Jun',
+    'Jul': 'Jul', 'Aug': 'Aug', 'Sep': 'Sep', 'Okt': 'Oct', 'Nov': 'Nov', 'Dez': 'Dec'
+}
+
+
+# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4', 'Dez 29, 2023-Jan 4, 2024', 'Dez 22-28 2023' oder 'Dez 28' in Kalenderwoche und Startdatum zu konvertieren
+def convert_to_week_number_and_start_date(date_range_str):
+    # Entferne zusätzliche Leerzeichen um den Bindestrich herum und entferne Kommas
+    date_range_str = date_range_str.replace(" - ", "-").replace(",", "")
+
+    # Prüfen, ob das Format nur einen Tag und Monat enthält (z.B. 'Dez 28')
+    if "-" not in date_range_str and len(date_range_str.split(" ")) == 2:
+        # Nur ein Datum (Monat und Tag)
+        month_str, day_str = date_range_str.split(" ")
+        day = int(day_str.strip())
+        year_str = str(datetime.now().year)  # Verwende das aktuelle Jahr
+
+        # Konvertiere den deutschen Monatsnamen in den englischen
+        if month_str in month_translation:
+            month_str = month_translation[month_str]
+
+        # Konvertiere das Datum
+        start_date = datetime.strptime(f"{month_str} {day} {year_str}", "%b %d %Y")
+
+        # Berechne die Kalenderwoche
+        week_number = start_date.isocalendar()[1]
+
+        return week_number, start_date.strftime('%Y-%m-%d')
+
+    # Prüfen, ob das Jahr am Ende steht (z.B. 'Dez 22-28 2023')
+    if date_range_str[-4:].isdigit():
+        # Jahr am Ende des Datums
+        year_str = date_range_str[-4:]
+        date_range_str = date_range_str[:-5]  # Entferne das Jahr aus der Datumsangabe
+    else:
+        # Kein Jahr am Ende -> nutze das aktuelle Jahr
+        year_str = str(datetime.now().year)
+
+    # Splitte den Datumsbereich in Start- und Endteil
+    start_part, end_part = date_range_str.split("-")
+
+    # Verarbeite den Startteil
+    start_parts = start_part.split(" ")
+    start_month_str = start_parts[0]
+    start_day = int(start_parts[1].strip())  # Nimm den Starttag und entferne etwaige Leerzeichen
+
+    # Verarbeite den Endteil
+    end_parts = end_part.split(" ")
+
+    # Verarbeite das Enddatum, basierend darauf, ob der Endteil das Jahr enthält
+    if len(end_parts) == 2:  # Fall: 'Apr 4'
+        end_month_str = end_parts[0]
+        end_day = int(end_parts[1].strip())
+    else:
+        # Fall: kein Monat -> nur Tag (z.B. '28' im Format 'Dez 22-28 2023')
+        end_month_str = start_month_str
+        end_day = int(end_parts[0].strip())
+
+    # Konvertiere den deutschen Monatsnamen in den englischen für beide Monate
+    if start_month_str in month_translation:
+        start_month_str = month_translation[start_month_str]
+    if end_month_str in month_translation:
+        end_month_str = month_translation[end_month_str]
+
+    try:
+        # Konvertiere das Startdatum in ein Datum
+        start_date = datetime.strptime(f"{start_month_str} {start_day} {year_str}", "%b %d %Y")
+    except ValueError:
+        raise ValueError(f"Ungültiges Startdatum: {start_month_str} {start_day} {year_str}")
+
+    try:
+        # Konvertiere das Enddatum in ein Datum
+        end_date = datetime.strptime(f"{end_month_str} {end_day} {year_str}", "%b %d %Y")
+    except ValueError:
+        raise ValueError(f"Ungültiges Enddatum: {end_month_str} {end_day} {year_str}")
+
+    # Berechne die Kalenderwoche basierend auf dem Startdatum
+    week_number = start_date.isocalendar()[1]
+
+    return week_number, start_date.strftime('%Y-%m-%d')  # Gib KW und das Startdatum zurück
+
+
+# Funktion zum Bereinigen und Zwischenspeichern der Daten
+def clean_and_save_data(hr_data, sleep_data, output_dir='sandbox'):
+    # Entferne 'bpm' und konvertiere die Werte in numerische Daten
+    hr_data['In Ruhe'] = hr_data['In Ruhe'].str.replace(' bpm', '').astype(float)
+    hr_data['Hoch'] = hr_data['Hoch'].str.replace(' bpm', '').astype(float)
+
+    # Konvertiere die Spalte 'Datum' in Kalenderwoche (KW) und Startdatum
+    hr_data[['week', 'start_date']] = hr_data['Datum'].apply(
+        lambda x: pd.Series(convert_to_week_number_and_start_date(x)))
+
+    # Berechne den Durchschnitt der Herzfrequenzdaten (In Ruhe und Hoch)
+    hr_data['avg_hr'] = hr_data[['In Ruhe', 'Hoch']].mean(axis=1)
+
+    # Konvertiere auch das 'Datum' der Schlafdaten in Kalenderwoche (KW) und Startdatum
+    sleep_data[['week', 'start_date']] = sleep_data['Datum'].apply(
+        lambda x: pd.Series(convert_to_week_number_and_start_date(x)))
+
+    # Speichere die bereinigten Daten in 'sandbox'
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    hr_data_clean_path = os.path.join(output_dir, 'hr_data_clean.csv')
+    sleep_data_clean_path = os.path.join(output_dir, 'sleep_data_clean.csv')
+
+    hr_data.to_csv(hr_data_clean_path, sep=';', index=False)
+    sleep_data.to_csv(sleep_data_clean_path, sep=',', index=False)
+
+    print(f"HR-Daten wurden bereinigt und in {hr_data_clean_path} gespeichert.")
+    print(f"Schlafdaten wurden bereinigt und in {sleep_data_clean_path} gespeichert.")
+
+    return hr_data, sleep_data
+
+
+# Schritt 1: Lade die HR- und Schlafdaten
+hr_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv',
+                      sep=';')
+sleep_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv',
+                         sep=',')
+
+# Bereinige und speichere die Daten
+hr_data, sleep_data = clean_and_save_data(hr_data, sleep_data)
+
+# Schritt 2: Sortiere beide Datensätze nach 'week' und 'start_date'
+hr_data = hr_data.sort_values(by=['week', 'start_date'])
+sleep_data = sleep_data.sort_values(by=['week', 'start_date'])
+
+# Schritt 3: Kombiniere die beiden Datensätze anhand der 'week' und 'start_date' Spalten mit einem äußeren Join
+combined_data = pd.merge(hr_data, sleep_data, on=['week', 'start_date'], how='outer')
+
+# Zeige die kombinierten Daten
+print("\nKombinierte Daten:")
+print(combined_data)
+
+# Schritt 4: Entferne alle Zeilen mit fehlenden Daten
+combined_data = combined_data.dropna()
+
+# Schritt 5: Berechne die Korrelation zwischen 'avg_hr' und 'Durchschnittliche Dauer'
+correlation = combined_data['avg_hr'].corr(combined_data['Durchschnittliche Dauer'])
+print(f"\nDie Korrelation zwischen der durchschnittlichen Herzfrequenz und der Schlafdauer ist: {correlation}")
+
+# Schritt 6: Visualisiere den Zusammenhang zwischen Herzfrequenz und Schlafdauer
+plt.figure(figsize=(10, 6))
+plt.scatter(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], color='blue', label='Datenpunkte')
+plt.title('Zusammenhang zwischen Herzfrequenz (Durchschnitt) und Schlafdauer')
+plt.xlabel('Durchschnittliche Herzfrequenz (bpm)')
+plt.ylabel('Schlafdauer (Stunden)')
+plt.grid(True)
+
+# Linie zur Visualisierung des Trends hinzufügen
+m, b = np.polyfit(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], 1)
+plt.plot(combined_data['avg_hr'], m * combined_data['avg_hr'] + b, color='red',
+         label=f'Trendlinie (Kor = {correlation:.2f})')
+
+plt.legend()
+plt.show()
diff --git a/code/sandbox/hr_data_clean.csv b/code/sandbox/hr_data_clean.csv
deleted file mode 100644
index e098c31..0000000
--- a/code/sandbox/hr_data_clean.csv
+++ /dev/null
@@ -1,53 +0,0 @@
-Datum;In Ruhe;Hoch;week;start_date;avg_hr
-Okt 4-10;67.0;130.0;40;2024-10-04;98.5
-Sep 27 - Okt 3;67.0;143.0;39;2024-09-27;105.0
-Sep 20-26;66.0;149.0;38;2024-09-20;107.5
-Sep 13-19;66.0;144.0;37;2024-09-13;105.0
-Sep 6-12;62.0;132.0;36;2024-09-06;97.0
-Aug 30 - Sep 5;64.0;141.0;35;2024-08-30;102.5
-Aug 23-29;67.0;150.0;34;2024-08-23;108.5
-Aug 16-22;63.0;143.0;33;2024-08-16;103.0
-Aug 9-15;69.0;141.0;32;2024-08-09;105.0
-Aug 2-8;67.0;140.0;31;2024-08-02;103.5
-Jul 26 - Aug 1;67.0;147.0;30;2024-07-26;107.0
-Jul 19-25;69.0;136.0;29;2024-07-19;102.5
-Jul 12-18;66.0;151.0;28;2024-07-12;108.5
-Jul 5-11;67.0;146.0;27;2024-07-05;106.5
-Jun 28 - Jul 4;66.0;157.0;26;2024-06-28;111.5
-Jun 21-27;64.0;141.0;25;2024-06-21;102.5
-Jun 14-20;70.0;145.0;24;2024-06-14;107.5
-Jun 7-13;69.0;134.0;23;2024-06-07;101.5
-Mai 31 - Jun 6;70.0;139.0;22;2024-05-31;104.5
-Mai 24-30;72.0;142.0;21;2024-05-24;107.0
-Mai 17-23;72.0;135.0;20;2024-05-17;103.5
-Mai 10-16;71.0;147.0;19;2024-05-10;109.0
-Mai 3-9;73.0;142.0;18;2024-05-03;107.5
-Apr 26 - Mai 2;69.0;151.0;17;2024-04-26;110.0
-Apr 19-25;61.0;135.0;16;2024-04-19;98.0
-Apr 12-18;58.0;140.0;15;2024-04-12;99.0
-Apr 5-11;64.0;131.0;14;2024-04-05;97.5
-Mrz 29 - Apr 4;63.0;139.0;13;2024-03-29;101.0
-Mrz 22-28;65.0;135.0;12;2024-03-22;100.0
-Mrz 15-21;66.0;137.0;11;2024-03-15;101.5
-Mrz 8-14;62.0;136.0;10;2024-03-08;99.0
-Mrz 1-7;70.0;134.0;9;2024-03-01;102.0
-Feb 23-29;68.0;144.0;8;2024-02-23;106.0
-Feb 16-22;71.0;132.0;7;2024-02-16;101.5
-Feb 9-15;65.0;143.0;6;2024-02-09;104.0
-Feb 2-8;66.0;133.0;5;2024-02-02;99.5
-Jan 26 - Feb 1;59.0;142.0;4;2024-01-26;100.5
-Jan 19-25;62.0;136.0;3;2024-01-19;99.0
-Jan 12-18;60.0;134.0;2;2024-01-12;97.0
-Jan 5-11;56.0;139.0;1;2024-01-05;97.5
-Dez 29, 2023 - Jan 4, 2024;59.0;128.0;52;2024-12-29;93.5
-Dez 22-28, 2023;52.0;124.0;51;2023-12-22;88.0
-Dez 15-21, 2023;57.0;133.0;50;2023-12-15;95.0
-Dez 8-14, 2023;65.0;133.0;49;2023-12-08;99.0
-Dez 1-7, 2023;69.0;134.0;48;2023-12-01;101.5
-Nov 24-30, 2023;68.0;139.0;47;2023-11-24;103.5
-Nov 17-23, 2023;68.0;143.0;46;2023-11-17;105.5
-Nov 10-16, 2023;64.0;144.0;45;2023-11-10;104.0
-Nov 3-9, 2023;63.0;140.0;44;2023-11-03;101.5
-Okt 27 - Nov 2, 2023;57.0;133.0;43;2023-10-27;95.0
-Okt 20-26, 2023;55.0;138.0;42;2023-10-20;96.5
-Okt 13-19, 2023;50.0;121.0;41;2023-10-13;85.5
diff --git a/code/sandbox/sleep_data_clean.csv b/code/sandbox/sleep_data_clean.csv
deleted file mode 100644
index 0fc8250..0000000
--- a/code/sandbox/sleep_data_clean.csv
+++ /dev/null
@@ -1,53 +0,0 @@
-Datum,Ø Score,Ø Qualität,Durchschnittliche Dauer,Ø Schlafenszeit,Ø Aufstehzeit,week,start_date
-Okt 10-16,46,Schlecht,6h 11min,1:28,7:50,41,2024-10-10
-Okt 3-9,56,Schlecht,6h 34min,0:33,7:22,40,2024-10-03
-Sep 26 - Okt 2,52,Schlecht,6h 23min,0:00,6:37,39,2024-09-26
-Sep 19-25,59,Schlecht,6h 41min,0:27,7:16,38,2024-09-19
-Sep 12-18,59,Schlecht,6h 18min,0:48,7:12,37,2024-09-12
-Sep 5-11,55,Schlecht,6h 19min,0:56,7:22,36,2024-09-05
-Aug 29 - Sep 4,66,Ausreichend,7h 1min,0:21,7:31,35,2024-08-29
-Aug 22-28,57,Schlecht,6h 17min,0:35,6:59,34,2024-08-22
-Aug 15-21,72,Ausreichend,6h 36min,0:38,7:25,33,2024-08-15
-Aug 8-14,56,Schlecht,5h 45min,0:16,6:06,32,2024-08-08
-Aug 1-7,64,Ausreichend,7h 4min,0:39,8:10,31,2024-08-01
-Jul 25-31,61,Ausreichend,6h 52min,0:40,7:56,30,2024-07-25
-Jul 18-24,54,Schlecht,6h 26min,2:16,9:19,29,2024-07-18
-Jul 11-17,74,Ausreichend,7h 10min,0:06,7:26,28,2024-07-11
-Jul 4-10,58,Schlecht,7h 35min,0:45,9:05,27,2024-07-04
-Jun 27 - Jul 3,60,Ausreichend,6h 8min,1:22,7:42,26,2024-06-27
-Jun 20-26,69,Ausreichend,6h 30min,0:19,6:58,25,2024-06-20
-Jun 13-19,59,Schlecht,6h 1min,0:25,6:33,24,2024-06-13
-Jun 6-12,60,Ausreichend,6h 22min,0:02,6:30,23,2024-06-06
-Mai 30 - Jun 5,60,Ausreichend,6h 33min,0:34,7:15,22,2024-05-30
-Mai 23-29,51,Schlecht,6h 47min,0:56,7:54,21,2024-05-23
-Mai 16-22,50,Schlecht,5h 51min,1:13,7:08,20,2024-05-16
-Mai 9-15,55,Schlecht,6h 21min,0:58,7:23,19,2024-05-09
-Mai 2-8,40,Schlecht,5h 36min,0:39,6:19,18,2024-05-02
-Apr 25 - Mai 1,55,Schlecht,6h 2min,1:18,7:26,17,2024-04-25
-Apr 18-24,56,Schlecht,6h 28min,3:11,9:55,16,2024-04-18
-Apr 11-17,56,Schlecht,6h 17min,1:23,7:51,15,2024-04-11
-Apr 4-10,62,Ausreichend,6h 18min,0:39,7:10,14,2024-04-04
-Mrz 28 - Apr 3,68,Ausreichend,6h 50min,1:45,8:51,13,2024-03-28
-Mrz 21-27,60,Ausreichend,6h 9min,0:56,7:13,12,2024-03-21
-Mrz 14-20,54,Schlecht,6h 11min,1:03,7:24,11,2024-03-14
-Mrz 7-13,54,Schlecht,6h 22min,0:49,7:17,10,2024-03-07
-Feb 29 - Mrz 6,50,Schlecht,6h 4min,0:56,7:09,9,2024-02-29
-Feb 22-28,61,Ausreichend,7h 11min,0:08,7:25,8,2024-02-22
-Feb 15-21,44,Schlecht,6h 58min,0:19,7:51,7,2024-02-15
-Feb 8-14,50,Schlecht,8h 17min,0:09,8:47,6,2024-02-08
-Feb 1-7,43,Schlecht,6h 12min,1:06,7:25,5,2024-02-01
-Jan 25-31,57,Schlecht,7h 21min,1:17,8:40,4,2024-01-25
-Jan 18-24,51,Schlecht,6h 25min,0:56,7:26,3,2024-01-18
-Jan 11-17,50,Schlecht,6h 27min,1:36,8:12,2,2024-01-11
-Jan 4-10,59,Schlecht,6h 44min,1:26,8:24,1,2024-01-04
-Dez 28, 2023 - Jan 3, 2024,54,Schlecht,7h 2min,52,2024-12-28
-Dez 21-27, 2023,55,Schlecht,7h 16min,0:38,51,2024-12-21
-Dez 14-20, 2023,56,Schlecht,7h 17min,0:31,50,2024-12-14
-Dez 7-13, 2023,44,Schlecht,7h 53min,23:46,49,2024-12-07
-Nov 30 - Dez 6, 2023,48,Schlecht,6h 51min,0:33,48,2024-11-30
-Nov 23-29, 2023,48,Schlecht,7h 11min,0:21,47,2024-11-23
-Nov 16-22, 2023,53,Schlecht,7h 7min,0:23,46,2024-11-16
-Nov 9-15, 2023,47,Schlecht,7h 5min,0:19,45,2024-11-09
-Nov 2-8, 2023,50,Schlecht,6h 2min,0:00,44,2024-11-02
-Okt 26 - Nov 1, 2023,59,Schlecht,6h 39min,0:14,43,2024-10-26
-Okt 19-25, 2023,48,Schlecht,6h 17min,0:46,42,2024-10-19
diff --git a/data/sandbox/combined_data.csv b/data/sandbox/combined_data.csv
new file mode 100644
index 0000000..743d87c
--- /dev/null
+++ b/data/sandbox/combined_data.csv
@@ -0,0 +1,42 @@
+Woche,avg_hr,Durchschnittliche Dauer
+W40-2024,98.5,6h 34min
+W39-2024,105.0,6h 23min
+W38-2024,107.5,6h 41min
+W37-2024,105.0,6h 18min
+W36-2024,97.0,6h 19min
+W35-2024,102.5,7h 1min
+W34-2024,108.5,6h 17min
+W33-2024,103.0,6h 36min
+W32-2024,105.0,5h 45min
+W31-2024,103.5,7h 4min
+W30-2024,107.0,6h 52min
+W29-2024,102.5,6h 26min
+W28-2024,108.5,7h 10min
+W27-2024,106.5,7h 35min
+W26-2024,111.5,6h 8min
+W25-2024,102.5,6h 30min
+W24-2024,107.5,6h 1min
+W23-2024,101.5,6h 22min
+W22-2024,104.5,6h 33min
+W21-2024,107.0,6h 47min
+W20-2024,103.5,5h 51min
+W19-2024,109.0,6h 21min
+W18-2024,107.5,5h 36min
+W17-2024,110.0,6h 2min
+W16-2024,98.0,6h 28min
+W15-2024,99.0,6h 17min
+W14-2024,97.5,6h 18min
+W13-2024,101.0,6h 50min
+W12-2024,100.0,6h 9min
+W11-2024,101.5,6h 11min
+W10-2024,99.0,6h 22min
+W9-2024,102.0,6h 4min
+W8-2024,106.0,7h 11min
+W7-2024,101.5,6h 58min
+W6-2024,104.0,8h 17min
+W5-2024,99.5,6h 12min
+W4-2024,100.5,7h 21min
+W3-2024,99.0,6h 25min
+W2-2024,97.0,6h 27min
+W1-2024,97.5,6h 44min
+W52-2024,93.5,54
diff --git a/data/sandbox/hr_data_clean.csv b/data/sandbox/hr_data_clean.csv
new file mode 100644
index 0000000..8e535c3
--- /dev/null
+++ b/data/sandbox/hr_data_clean.csv
@@ -0,0 +1,53 @@
+Woche,avg_hr
+W40-2024,98.5
+W39-2024,105.0
+W38-2024,107.5
+W37-2024,105.0
+W36-2024,97.0
+W35-2024,102.5
+W34-2024,108.5
+W33-2024,103.0
+W32-2024,105.0
+W31-2024,103.5
+W30-2024,107.0
+W29-2024,102.5
+W28-2024,108.5
+W27-2024,106.5
+W26-2024,111.5
+W25-2024,102.5
+W24-2024,107.5
+W23-2024,101.5
+W22-2024,104.5
+W21-2024,107.0
+W20-2024,103.5
+W19-2024,109.0
+W18-2024,107.5
+W17-2024,110.0
+W16-2024,98.0
+W15-2024,99.0
+W14-2024,97.5
+W13-2024,101.0
+W12-2024,100.0
+W11-2024,101.5
+W10-2024,99.0
+W9-2024,102.0
+W8-2024,106.0
+W7-2024,101.5
+W6-2024,104.0
+W5-2024,99.5
+W4-2024,100.5
+W3-2024,99.0
+W2-2024,97.0
+W1-2024,97.5
+W52-2024,93.5
+W51-2023,88.0
+W50-2023,95.0
+W49-2023,99.0
+W48-2023,101.5
+W47-2023,103.5
+W46-2023,105.5
+W45-2023,104.0
+W44-2023,101.5
+W43-2023,95.0
+W42-2023,96.5
+W41-2023,85.5
diff --git a/data/sandbox/sleep_data_clean.csv b/data/sandbox/sleep_data_clean.csv
new file mode 100644
index 0000000..5416f58
--- /dev/null
+++ b/data/sandbox/sleep_data_clean.csv
@@ -0,0 +1,53 @@
+Woche,Durchschnittliche Dauer
+W41-2024,6h 11min
+W40-2024,6h 34min
+W39-2024,6h 23min
+W38-2024,6h 41min
+W37-2024,6h 18min
+W36-2024,6h 19min
+W35-2024,7h 1min
+W34-2024,6h 17min
+W33-2024,6h 36min
+W32-2024,5h 45min
+W31-2024,7h 4min
+W30-2024,6h 52min
+W29-2024,6h 26min
+W28-2024,7h 10min
+W27-2024,7h 35min
+W26-2024,6h 8min
+W25-2024,6h 30min
+W24-2024,6h 1min
+W23-2024,6h 22min
+W22-2024,6h 33min
+W21-2024,6h 47min
+W20-2024,5h 51min
+W19-2024,6h 21min
+W18-2024,5h 36min
+W17-2024,6h 2min
+W16-2024,6h 28min
+W15-2024,6h 17min
+W14-2024,6h 18min
+W13-2024,6h 50min
+W12-2024,6h 9min
+W11-2024,6h 11min
+W10-2024,6h 22min
+W9-2024,6h 4min
+W8-2024,7h 11min
+W7-2024,6h 58min
+W6-2024,8h 17min
+W5-2024,6h 12min
+W4-2024,7h 21min
+W3-2024,6h 25min
+W2-2024,6h 27min
+W1-2024,6h 44min
+W52-2024,54
+W51-2024,Schlecht
+W50-2024,Schlecht
+W49-2024,Schlecht
+W48-2024,Schlecht
+W47-2024,Schlecht
+W46-2024,Schlecht
+W45-2024,Schlecht
+W44-2024,Schlecht
+W43-2024,Schlecht
+W42-2024,Schlecht

From b8c420621b71b35a177fde4d7f76544b12a717f9 Mon Sep 17 00:00:00 2001
From: TheOriginalGraLargeShrimpakaReaper <graber-michael@hotmail.com>
Date: Fri, 18 Oct 2024 10:40:27 +0200
Subject: [PATCH 09/15] edit corelation.py and add hr_data_cleaned.csv

---
 code/corelation.py                         |  47 ++++++-
 code/corelation_old5.py                    | 144 +++++++++++++++++++++
 data/final/gramic_sleep_hr_correlation.png | Bin 0 -> 39156 bytes
 data/final/weekly_hr_sleep.png             | Bin 0 -> 43004 bytes
 data/sandbox/combined_data.csv             |  92 +++++++------
 data/sandbox/sleep_data_clean.csv          | 104 +++++++--------
 data/sandbox/sleep_gramic.csv              |  53 ++++++++
 7 files changed, 345 insertions(+), 95 deletions(-)
 create mode 100644 code/corelation_old5.py
 create mode 100644 data/final/gramic_sleep_hr_correlation.png
 create mode 100644 data/final/weekly_hr_sleep.png
 create mode 100644 data/sandbox/sleep_gramic.csv

diff --git a/code/corelation.py b/code/corelation.py
index 79bc956..a1f68c0 100644
--- a/code/corelation.py
+++ b/code/corelation.py
@@ -9,6 +9,18 @@ month_translation = {
     'Jul': 'Jul', 'Aug': 'Aug', 'Sep': 'Sep', 'Okt': 'Oct', 'Nov': 'Nov', 'Dez': 'Dec'
 }
 
+# Funktion zur Umwandlung von '6h 11min' in numerische Stundenwerte
+def convert_sleep_duration(sleep_duration_str):
+    print(sleep_duration_str)
+    hours = 0
+    minutes = 0
+    if 'h' in sleep_duration_str:
+        hours_part = sleep_duration_str.split('h')[0].strip()
+        hours = int(hours_part)
+    if 'min' in sleep_duration_str:
+        minutes_part = sleep_duration_str.split('h')[-1].replace('min', '').strip()
+        minutes = int(minutes_part)
+    return hours + (minutes / 60)
 
 # Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4', 'Dez 22-28 2023' oder 'Dez 28' in Kalenderwoche und Jahr zu konvertieren
 def convert_to_week_and_year(date_range_str):
@@ -85,10 +97,13 @@ def convert_to_week_and_year(date_range_str):
 
 # Datei Pfade
 hr_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv'
-sleep_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv'
+sleep_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/sandbox/sleep_gramic.csv'
 hr_clean_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/sandbox/hr_data_clean.csv'
 sleep_clean_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/sandbox/sleep_data_clean.csv'
 combined_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/sandbox/combined_data.csv'
+graphic_corr_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/final/gramic_sleep_hr_correlation.png'
+graphic_weekly_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/final/weekly_hr_sleep.png'
+
 
 # Schritt 1: Lade die HR-Daten (mit Semikolon separiert) und entferne 'bpm'
 hr_data = pd.read_csv(hr_data_path, sep=';')
@@ -108,11 +123,15 @@ hr_data_clean = hr_data[['Woche', 'avg_hr']]
 hr_data_clean.to_csv(hr_clean_path, index=False)
 
 # Schritt 2: Lade die Schlafdaten (mit Komma separiert)
-sleep_data = pd.read_csv(sleep_data_path, sep=',')
+sleep_data = pd.read_csv(sleep_data_path, sep=';')
 
 # Konvertiere die Spalte 'Datum' in Kalenderwoche (KW und Jahr)
 sleep_data['Woche'] = sleep_data['Datum'].apply(convert_to_week_and_year)
 
+# Konvertiere die Spalte 'Durchschnittliche Dauer' in numerische Stunden
+sleep_data['Durchschnittliche Dauer'] = sleep_data['Durchschnittliche Dauer'].apply(convert_sleep_duration)
+
+
 # Speichere nur die Spalten 'Woche' und 'Durchschnittliche Dauer' in einer neuen CSV-Datei
 sleep_data_clean = sleep_data[['Woche', 'Durchschnittliche Dauer']]
 sleep_data_clean.to_csv(sleep_clean_path, index=False)
@@ -141,4 +160,28 @@ plt.plot(combined_data['avg_hr'], m * combined_data['avg_hr'] + b, color='red',
          label=f'Trendlinie (Kor = {correlation:.2f})')
 
 plt.legend()
+# Speichern der Grafik
+plt.savefig(graphic_corr_path)
+plt.show()
+
+# Schritt 6: Erstelle eine Grafik pro Kalenderwoche (Herzfrequenz und Schlafdaten)
+plt.figure(figsize=(12, 6))
+
+# Plot für die Herzfrequenz
+plt.bar(combined_data['Woche'], combined_data['avg_hr'], width=0.4, label='Durchschnittliche Herzfrequenz', align='center')
+
+# Plot für die Schlafdauer (mit Verschiebung, um Überlappung zu vermeiden)
+plt.bar(combined_data['Woche'], combined_data['Durchschnittliche Dauer'], width=0.4, label='Schlafdauer', align='edge')
+
+# Diagrammtitel und Achsenbeschriftungen
+plt.title('Durchschnittliche Herzfrequenz und Schlafdauer pro Kalenderwoche')
+plt.xlabel('Kalenderwoche')
+plt.ylabel('Wert')
+
+# Legende und Rotation der x-Achse
+plt.legend()
+plt.xticks(rotation=45, ha='right')
+
+# Speichern der zweiten Grafik
+plt.savefig(graphic_weekly_path)
 plt.show()
diff --git a/code/corelation_old5.py b/code/corelation_old5.py
new file mode 100644
index 0000000..79bc956
--- /dev/null
+++ b/code/corelation_old5.py
@@ -0,0 +1,144 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from datetime import datetime
+
+# Manuelle Zuordnung der Monatsnamen von Deutsch auf Englisch
+month_translation = {
+    'Jan': 'Jan', 'Feb': 'Feb', 'Mär': 'Mar', 'Mrz': 'Mar', 'Apr': 'Apr', 'Mai': 'May', 'Jun': 'Jun',
+    'Jul': 'Jul', 'Aug': 'Aug', 'Sep': 'Sep', 'Okt': 'Oct', 'Nov': 'Nov', 'Dez': 'Dec'
+}
+
+
+# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4', 'Dez 22-28 2023' oder 'Dez 28' in Kalenderwoche und Jahr zu konvertieren
+def convert_to_week_and_year(date_range_str):
+    # Entferne zusätzliche Leerzeichen um den Bindestrich herum und entferne Kommas
+    date_range_str = date_range_str.replace(" - ", "-").replace(",", "")
+
+    # Prüfen, ob das Format nur einen Tag und Monat enthält (z.B. 'Dez 28')
+    if "-" not in date_range_str and len(date_range_str.split(" ")) == 2:
+        # Nur ein Datum (Monat und Tag)
+        month_str, day_str = date_range_str.split(" ")
+        day = int(day_str.strip())
+        year_str = str(datetime.now().year)  # Verwende das aktuelle Jahr
+
+        # Konvertiere den deutschen Monatsnamen in den englischen
+        if month_str in month_translation:
+            month_str = month_translation[month_str]
+
+        # Konvertiere das Datum
+        start_date = datetime.strptime(f"{month_str} {day} {year_str}", "%b %d %Y")
+
+        # Berechne die Kalenderwoche und das Jahr
+        week_number = start_date.isocalendar()[1]
+        year = start_date.year
+
+        return f"W{week_number}-{year}"
+
+    # Prüfen, ob das Jahr am Ende steht (z.B. 'Dez 22-28 2023')
+    if date_range_str[-4:].isdigit():
+        # Jahr am Ende des Datums
+        year_str = date_range_str[-4:]
+        date_range_str = date_range_str[:-5]  # Entferne das Jahr aus der Datumsangabe
+    else:
+        # Kein Jahr am Ende -> nutze das aktuelle Jahr
+        year_str = str(datetime.now().year)
+
+    # Splitte den Datumsbereich in Start- und Endteil
+    start_part, end_part = date_range_str.split("-")
+
+    # Verarbeite den Startteil
+    start_parts = start_part.split(" ")
+    start_month_str = start_parts[0]
+    start_day = int(start_parts[1].strip())  # Nimm den Starttag und entferne etwaige Leerzeichen
+
+    # Verarbeite den Endteil
+    end_parts = end_part.split(" ")
+
+    # Verarbeite das Enddatum, basierend darauf, ob der Endteil das Jahr enthält
+    if len(end_parts) == 2:  # Fall: 'Apr 4'
+        end_month_str = end_parts[0]
+        end_day = int(end_parts[1].strip())
+    else:
+        # Fall: kein Monat -> nur Tag (z.B. '28' im Format 'Dez 22-28 2023')
+        end_month_str = start_month_str
+        end_day = int(end_parts[0].strip())
+
+    # Konvertiere den deutschen Monatsnamen in den englischen für beide Monate
+    if start_month_str in month_translation:
+        start_month_str = month_translation[start_month_str]
+    if end_month_str in month_translation:
+        end_month_str = month_translation[end_month_str]
+
+    try:
+        # Konvertiere das Startdatum in ein Datum
+        start_date = datetime.strptime(f"{start_month_str} {start_day} {year_str}", "%b %d %Y")
+    except ValueError:
+        raise ValueError(f"Ungültiges Startdatum: {start_month_str} {start_day} {year_str}")
+
+    # Berechne die Kalenderwoche und das Jahr basierend auf dem Startdatum
+    week_number = start_date.isocalendar()[1]
+    year = start_date.year
+
+    return f"W{week_number}-{year}"  # Gib Kalenderwoche und Jahr im Format W-YYYY zurück
+
+
+# Datei Pfade
+hr_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv'
+sleep_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv'
+hr_clean_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/sandbox/hr_data_clean.csv'
+sleep_clean_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/sandbox/sleep_data_clean.csv'
+combined_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/sandbox/combined_data.csv'
+
+# Schritt 1: Lade die HR-Daten (mit Semikolon separiert) und entferne 'bpm'
+hr_data = pd.read_csv(hr_data_path, sep=';')
+
+# Entferne 'bpm' und konvertiere die Werte in numerische Daten
+hr_data['In Ruhe'] = hr_data['In Ruhe'].str.replace(' bpm', '').astype(float)
+hr_data['Hoch'] = hr_data['Hoch'].str.replace(' bpm', '').astype(float)
+
+# Konvertiere die Spalte 'Datum' in Kalenderwoche (KW und Jahr)
+hr_data['Woche'] = hr_data['Datum'].apply(convert_to_week_and_year)
+
+# Berechne den Durchschnitt der Herzfrequenzdaten (In Ruhe und Hoch)
+hr_data['avg_hr'] = hr_data[['In Ruhe', 'Hoch']].mean(axis=1)
+
+# Speichere nur die Spalten 'Woche' und 'avg_hr' in einer neuen CSV-Datei
+hr_data_clean = hr_data[['Woche', 'avg_hr']]
+hr_data_clean.to_csv(hr_clean_path, index=False)
+
+# Schritt 2: Lade die Schlafdaten (mit Komma separiert)
+sleep_data = pd.read_csv(sleep_data_path, sep=',')
+
+# Konvertiere die Spalte 'Datum' in Kalenderwoche (KW und Jahr)
+sleep_data['Woche'] = sleep_data['Datum'].apply(convert_to_week_and_year)
+
+# Speichere nur die Spalten 'Woche' und 'Durchschnittliche Dauer' in einer neuen CSV-Datei
+sleep_data_clean = sleep_data[['Woche', 'Durchschnittliche Dauer']]
+sleep_data_clean.to_csv(sleep_clean_path, index=False)
+
+# Schritt 3: Kombiniere die HR- und Schlafdaten basierend auf der 'Woche'
+combined_data = pd.merge(hr_data_clean, sleep_data_clean, on='Woche', how='inner')
+
+# Speichere das kombinierte Dataset in einer neuen CSV-Datei
+combined_data.to_csv(combined_data_path, index=False)
+
+# Schritt 4: Berechne die Korrelation zwischen avg_hr und der Schlafdauer
+correlation = combined_data['avg_hr'].corr(combined_data['Durchschnittliche Dauer'])
+print(f"Die Korrelation zwischen der durchschnittlichen Herzfrequenz und der Schlafdauer ist: {correlation}")
+
+# Schritt 5: Visualisiere den Zusammenhang zwischen Herzfrequenz und Schlafdauer
+plt.figure(figsize=(10, 6))
+plt.scatter(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], color='blue', label='Datenpunkte')
+plt.title('Zusammenhang zwischen Herzfrequenz (Durchschnitt) und Schlafdauer')
+plt.xlabel('Durchschnittliche Herzfrequenz (bpm)')
+plt.ylabel('Schlafdauer (Stunden)')
+plt.grid(True)
+
+# Linie zur Visualisierung des Trends hinzufügen
+m, b = np.polyfit(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], 1)
+plt.plot(combined_data['avg_hr'], m * combined_data['avg_hr'] + b, color='red',
+         label=f'Trendlinie (Kor = {correlation:.2f})')
+
+plt.legend()
+plt.show()
diff --git a/data/final/gramic_sleep_hr_correlation.png b/data/final/gramic_sleep_hr_correlation.png
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diff --git a/data/sandbox/combined_data.csv b/data/sandbox/combined_data.csv
index 743d87c..208422d 100644
--- a/data/sandbox/combined_data.csv
+++ b/data/sandbox/combined_data.csv
@@ -1,42 +1,52 @@
 Woche,avg_hr,Durchschnittliche Dauer
-W40-2024,98.5,6h 34min
-W39-2024,105.0,6h 23min
-W38-2024,107.5,6h 41min
-W37-2024,105.0,6h 18min
-W36-2024,97.0,6h 19min
-W35-2024,102.5,7h 1min
-W34-2024,108.5,6h 17min
-W33-2024,103.0,6h 36min
-W32-2024,105.0,5h 45min
-W31-2024,103.5,7h 4min
-W30-2024,107.0,6h 52min
-W29-2024,102.5,6h 26min
-W28-2024,108.5,7h 10min
-W27-2024,106.5,7h 35min
-W26-2024,111.5,6h 8min
-W25-2024,102.5,6h 30min
-W24-2024,107.5,6h 1min
-W23-2024,101.5,6h 22min
-W22-2024,104.5,6h 33min
-W21-2024,107.0,6h 47min
-W20-2024,103.5,5h 51min
-W19-2024,109.0,6h 21min
-W18-2024,107.5,5h 36min
-W17-2024,110.0,6h 2min
-W16-2024,98.0,6h 28min
-W15-2024,99.0,6h 17min
-W14-2024,97.5,6h 18min
-W13-2024,101.0,6h 50min
-W12-2024,100.0,6h 9min
-W11-2024,101.5,6h 11min
-W10-2024,99.0,6h 22min
-W9-2024,102.0,6h 4min
-W8-2024,106.0,7h 11min
-W7-2024,101.5,6h 58min
-W6-2024,104.0,8h 17min
-W5-2024,99.5,6h 12min
-W4-2024,100.5,7h 21min
-W3-2024,99.0,6h 25min
-W2-2024,97.0,6h 27min
-W1-2024,97.5,6h 44min
-W52-2024,93.5,54
+W40-2024,98.5,6.566666666666666
+W39-2024,105.0,6.383333333333334
+W38-2024,107.5,6.683333333333334
+W37-2024,105.0,6.3
+W36-2024,97.0,6.316666666666666
+W35-2024,102.5,7.016666666666667
+W34-2024,108.5,6.283333333333333
+W33-2024,103.0,6.6
+W32-2024,105.0,5.75
+W31-2024,103.5,7.066666666666666
+W30-2024,107.0,6.866666666666667
+W29-2024,102.5,6.433333333333334
+W28-2024,108.5,7.166666666666667
+W27-2024,106.5,7.583333333333333
+W26-2024,111.5,6.133333333333334
+W25-2024,102.5,6.5
+W24-2024,107.5,6.016666666666667
+W23-2024,101.5,6.366666666666666
+W22-2024,104.5,6.55
+W21-2024,107.0,6.783333333333333
+W20-2024,103.5,5.85
+W19-2024,109.0,6.35
+W18-2024,107.5,5.6
+W17-2024,110.0,6.033333333333333
+W16-2024,98.0,6.466666666666667
+W15-2024,99.0,6.283333333333333
+W14-2024,97.5,6.3
+W13-2024,101.0,6.833333333333333
+W12-2024,100.0,6.15
+W11-2024,101.5,6.183333333333334
+W10-2024,99.0,6.366666666666666
+W9-2024,102.0,6.066666666666666
+W8-2024,106.0,7.183333333333334
+W7-2024,101.5,6.966666666666667
+W6-2024,104.0,8.283333333333333
+W5-2024,99.5,6.2
+W4-2024,100.5,7.35
+W3-2024,99.0,6.416666666666667
+W2-2024,97.0,6.45
+W1-2024,97.5,6.733333333333333
+W52-2024,93.5,7.033333333333333
+W51-2023,88.0,7.266666666666667
+W50-2023,95.0,7.283333333333333
+W49-2023,99.0,7.883333333333333
+W48-2023,101.5,6.85
+W47-2023,103.5,7.183333333333334
+W46-2023,105.5,7.116666666666666
+W45-2023,104.0,7.083333333333333
+W44-2023,101.5,6.033333333333333
+W43-2023,95.0,6.65
+W42-2023,96.5,6.283333333333333
diff --git a/data/sandbox/sleep_data_clean.csv b/data/sandbox/sleep_data_clean.csv
index 5416f58..2065d88 100644
--- a/data/sandbox/sleep_data_clean.csv
+++ b/data/sandbox/sleep_data_clean.csv
@@ -1,53 +1,53 @@
 Woche,Durchschnittliche Dauer
-W41-2024,6h 11min
-W40-2024,6h 34min
-W39-2024,6h 23min
-W38-2024,6h 41min
-W37-2024,6h 18min
-W36-2024,6h 19min
-W35-2024,7h 1min
-W34-2024,6h 17min
-W33-2024,6h 36min
-W32-2024,5h 45min
-W31-2024,7h 4min
-W30-2024,6h 52min
-W29-2024,6h 26min
-W28-2024,7h 10min
-W27-2024,7h 35min
-W26-2024,6h 8min
-W25-2024,6h 30min
-W24-2024,6h 1min
-W23-2024,6h 22min
-W22-2024,6h 33min
-W21-2024,6h 47min
-W20-2024,5h 51min
-W19-2024,6h 21min
-W18-2024,5h 36min
-W17-2024,6h 2min
-W16-2024,6h 28min
-W15-2024,6h 17min
-W14-2024,6h 18min
-W13-2024,6h 50min
-W12-2024,6h 9min
-W11-2024,6h 11min
-W10-2024,6h 22min
-W9-2024,6h 4min
-W8-2024,7h 11min
-W7-2024,6h 58min
-W6-2024,8h 17min
-W5-2024,6h 12min
-W4-2024,7h 21min
-W3-2024,6h 25min
-W2-2024,6h 27min
-W1-2024,6h 44min
-W52-2024,54
-W51-2024,Schlecht
-W50-2024,Schlecht
-W49-2024,Schlecht
-W48-2024,Schlecht
-W47-2024,Schlecht
-W46-2024,Schlecht
-W45-2024,Schlecht
-W44-2024,Schlecht
-W43-2024,Schlecht
-W42-2024,Schlecht
+W41-2024,6.183333333333334
+W40-2024,6.566666666666666
+W39-2024,6.383333333333334
+W38-2024,6.683333333333334
+W37-2024,6.3
+W36-2024,6.316666666666666
+W35-2024,7.016666666666667
+W34-2024,6.283333333333333
+W33-2024,6.6
+W32-2024,5.75
+W31-2024,7.066666666666666
+W30-2024,6.866666666666667
+W29-2024,6.433333333333334
+W28-2024,7.166666666666667
+W27-2024,7.583333333333333
+W26-2024,6.133333333333334
+W25-2024,6.5
+W24-2024,6.016666666666667
+W23-2024,6.366666666666666
+W22-2024,6.55
+W21-2024,6.783333333333333
+W20-2024,5.85
+W19-2024,6.35
+W18-2024,5.6
+W17-2024,6.033333333333333
+W16-2024,6.466666666666667
+W15-2024,6.283333333333333
+W14-2024,6.3
+W13-2024,6.833333333333333
+W12-2024,6.15
+W11-2024,6.183333333333334
+W10-2024,6.366666666666666
+W9-2024,6.066666666666666
+W8-2024,7.183333333333334
+W7-2024,6.966666666666667
+W6-2024,8.283333333333333
+W5-2024,6.2
+W4-2024,7.35
+W3-2024,6.416666666666667
+W2-2024,6.45
+W1-2024,6.733333333333333
+W52-2024,7.033333333333333
+W51-2023,7.266666666666667
+W50-2023,7.283333333333333
+W49-2023,7.883333333333333
+W48-2023,6.85
+W47-2023,7.183333333333334
+W46-2023,7.116666666666666
+W45-2023,7.083333333333333
+W44-2023,6.033333333333333
+W43-2023,6.65
+W42-2023,6.283333333333333
diff --git a/data/sandbox/sleep_gramic.csv b/data/sandbox/sleep_gramic.csv
new file mode 100644
index 0000000..74ff3b6
--- /dev/null
+++ b/data/sandbox/sleep_gramic.csv
@@ -0,0 +1,53 @@
+Datum;Ø Score;Ø Qualität;Durchschnittliche Dauer;Ø Schlafenszeit;Ø Aufstehzeit
+Okt 10-16;46;Schlecht;6h 11min;1:28;7:50
+Okt 3-9;56;Schlecht;6h 34min;0:33;7:22
+Sep 26 - Okt 2;52;Schlecht;6h 23min;0:00;6:37
+Sep 19-25;59;Schlecht;6h 41min;0:27;7:16
+Sep 12-18;59;Schlecht;6h 18min;0:48;7:12
+Sep 5-11;55;Schlecht;6h 19min;0:56;7:22
+Aug 29 - Sep 4;66;Ausreichend;7h 1min;0:21;7:31
+Aug 22-28;57;Schlecht;6h 17min;0:35;6:59
+Aug 15-21;72;Ausreichend;6h 36min;0:38;7:25
+Aug 8-14;56;Schlecht;5h 45min;0:16;6:06
+Aug 1-7;64;Ausreichend;7h 4min;0:39;8:10
+Jul 25-31;61;Ausreichend;6h 52min;0:40;7:56
+Jul 18-24;54;Schlecht;6h 26min;2:16;9:19
+Jul 11-17;74;Ausreichend;7h 10min;0:06;7:26
+Jul 4-10;58;Schlecht;7h 35min;0:45;9:05
+Jun 27 - Jul 3;60;Ausreichend;6h 8min;1:22;7:42
+Jun 20-26;69;Ausreichend;6h 30min;0:19;6:58
+Jun 13-19;59;Schlecht;6h 1min;0:25;6:33
+Jun 6-12;60;Ausreichend;6h 22min;0:02;6:30
+Mai 30 - Jun 5;60;Ausreichend;6h 33min;0:34;7:15
+Mai 23-29;51;Schlecht;6h 47min;0:56;7:54
+Mai 16-22;50;Schlecht;5h 51min;1:13;7:08
+Mai 9-15;55;Schlecht;6h 21min;0:58;7:23
+Mai 2-8;40;Schlecht;5h 36min;0:39;6:19
+Apr 25 - Mai 1;55;Schlecht;6h 2min;1:18;7:26
+Apr 18-24;56;Schlecht;6h 28min;3:11;9:55
+Apr 11-17;56;Schlecht;6h 17min;1:23;7:51
+Apr 4-10;62;Ausreichend;6h 18min;0:39;7:10
+Mrz 28 - Apr 3;68;Ausreichend;6h 50min;1:45;8:51
+Mrz 21-27;60;Ausreichend;6h 9min;0:56;7:13
+Mrz 14-20;54;Schlecht;6h 11min;1:03;7:24
+Mrz 7-13;54;Schlecht;6h 22min;0:49;7:17
+Feb 29 - Mrz 6;50;Schlecht;6h 4min;0:56;7:09
+Feb 22-28;61;Ausreichend;7h 11min;0:08;7:25
+Feb 15-21;44;Schlecht;6h 58min;0:19;7:51
+Feb 8-14;50;Schlecht;8h 17min;0:09;8:47
+Feb 1-7;43;Schlecht;6h 12min;1:06;7:25
+Jan 25-31;57;Schlecht;7h 21min;1:17;8:40
+Jan 18-24;51;Schlecht;6h 25min;0:56;7:26
+Jan 11-17;50;Schlecht;6h 27min;1:36;8:12
+Jan 4-10;59;Schlecht;6h 44min;1:26;8:24
+Dez 28, 2023 - Jan 3, 2024;54;Schlecht;7h 2min
+Dez 21-27, 2023;55;Schlecht;7h 16min;0:38
+Dez 14-20, 2023;56;Schlecht;7h 17min;0:31
+Dez 7-13, 2023;44;Schlecht;7h 53min;23:46
+Nov 30 - Dez 6, 2023;48;Schlecht;6h 51min;0:33
+Nov 23-29, 2023;48;Schlecht;7h 11min;0:21
+Nov 16-22, 2023;53;Schlecht;7h 7min;0:23
+Nov 9-15, 2023;47;Schlecht;7h 5min;0:19
+Nov 2-8, 2023;50;Schlecht;6h 2min;0:00
+Okt 26 - Nov 1, 2023;59;Schlecht;6h 39min;0:14
+Okt 19-25, 2023;48;Schlecht;6h 17min;0:46
\ No newline at end of file

From 568c13053c3ee482726155a28c3fcdebd2a07c4f Mon Sep 17 00:00:00 2001
From: TheOriginalGraLargeShrimpakaReaper <graber-michael@hotmail.com>
Date: Fri, 18 Oct 2024 10:58:36 +0200
Subject: [PATCH 10/15] edit pre-final

---
 code/corelation.py                         | 102 ++++++---------------
 data/final/gramic_sleep_hr_correlation.png | Bin 39156 -> 38104 bytes
 data/final/weekly_hr_sleep.png             | Bin 43004 -> 45884 bytes
 3 files changed, 27 insertions(+), 75 deletions(-)

diff --git a/code/corelation.py b/code/corelation.py
index a1f68c0..1660b7a 100644
--- a/code/corelation.py
+++ b/code/corelation.py
@@ -11,7 +11,6 @@ month_translation = {
 
 # Funktion zur Umwandlung von '6h 11min' in numerische Stundenwerte
 def convert_sleep_duration(sleep_duration_str):
-    print(sleep_duration_str)
     hours = 0
     minutes = 0
     if 'h' in sleep_duration_str:
@@ -22,78 +21,53 @@ def convert_sleep_duration(sleep_duration_str):
         minutes = int(minutes_part)
     return hours + (minutes / 60)
 
-# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4', 'Dez 22-28 2023' oder 'Dez 28' in Kalenderwoche und Jahr zu konvertieren
+# Funktion, um Datumsbereiche in Kalenderwoche und Jahr zu konvertieren
 def convert_to_week_and_year(date_range_str):
-    # Entferne zusätzliche Leerzeichen um den Bindestrich herum und entferne Kommas
     date_range_str = date_range_str.replace(" - ", "-").replace(",", "")
 
-    # Prüfen, ob das Format nur einen Tag und Monat enthält (z.B. 'Dez 28')
     if "-" not in date_range_str and len(date_range_str.split(" ")) == 2:
-        # Nur ein Datum (Monat und Tag)
         month_str, day_str = date_range_str.split(" ")
         day = int(day_str.strip())
-        year_str = str(datetime.now().year)  # Verwende das aktuelle Jahr
+        year_str = str(datetime.now().year)
 
-        # Konvertiere den deutschen Monatsnamen in den englischen
         if month_str in month_translation:
             month_str = month_translation[month_str]
 
-        # Konvertiere das Datum
         start_date = datetime.strptime(f"{month_str} {day} {year_str}", "%b %d %Y")
-
-        # Berechne die Kalenderwoche und das Jahr
         week_number = start_date.isocalendar()[1]
         year = start_date.year
 
         return f"W{week_number}-{year}"
 
-    # Prüfen, ob das Jahr am Ende steht (z.B. 'Dez 22-28 2023')
     if date_range_str[-4:].isdigit():
-        # Jahr am Ende des Datums
         year_str = date_range_str[-4:]
-        date_range_str = date_range_str[:-5]  # Entferne das Jahr aus der Datumsangabe
+        date_range_str = date_range_str[:-5]
     else:
-        # Kein Jahr am Ende -> nutze das aktuelle Jahr
         year_str = str(datetime.now().year)
 
-    # Splitte den Datumsbereich in Start- und Endteil
     start_part, end_part = date_range_str.split("-")
-
-    # Verarbeite den Startteil
     start_parts = start_part.split(" ")
     start_month_str = start_parts[0]
-    start_day = int(start_parts[1].strip())  # Nimm den Starttag und entferne etwaige Leerzeichen
-
-    # Verarbeite den Endteil
+    start_day = int(start_parts[1].strip())
     end_parts = end_part.split(" ")
 
-    # Verarbeite das Enddatum, basierend darauf, ob der Endteil das Jahr enthält
-    if len(end_parts) == 2:  # Fall: 'Apr 4'
+    if len(end_parts) == 2:
         end_month_str = end_parts[0]
         end_day = int(end_parts[1].strip())
     else:
-        # Fall: kein Monat -> nur Tag (z.B. '28' im Format 'Dez 22-28 2023')
         end_month_str = start_month_str
         end_day = int(end_parts[0].strip())
 
-    # Konvertiere den deutschen Monatsnamen in den englischen für beide Monate
     if start_month_str in month_translation:
         start_month_str = month_translation[start_month_str]
     if end_month_str in month_translation:
         end_month_str = month_translation[end_month_str]
 
-    try:
-        # Konvertiere das Startdatum in ein Datum
-        start_date = datetime.strptime(f"{start_month_str} {start_day} {year_str}", "%b %d %Y")
-    except ValueError:
-        raise ValueError(f"Ungültiges Startdatum: {start_month_str} {start_day} {year_str}")
-
-    # Berechne die Kalenderwoche und das Jahr basierend auf dem Startdatum
+    start_date = datetime.strptime(f"{start_month_str} {start_day} {year_str}", "%b %d %Y")
     week_number = start_date.isocalendar()[1]
     year = start_date.year
 
-    return f"W{week_number}-{year}"  # Gib Kalenderwoche und Jahr im Format W-YYYY zurück
-
+    return f"W{week_number}-{year}"
 
 # Datei Pfade
 hr_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv'
@@ -104,84 +78,62 @@ combined_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_as
 graphic_corr_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/final/gramic_sleep_hr_correlation.png'
 graphic_weekly_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/final/weekly_hr_sleep.png'
 
-
-# Schritt 1: Lade die HR-Daten (mit Semikolon separiert) und entferne 'bpm'
+# Schritt 1: Lade die HR-Daten und entferne 'bpm'
 hr_data = pd.read_csv(hr_data_path, sep=';')
-
-# Entferne 'bpm' und konvertiere die Werte in numerische Daten
 hr_data['In Ruhe'] = hr_data['In Ruhe'].str.replace(' bpm', '').astype(float)
 hr_data['Hoch'] = hr_data['Hoch'].str.replace(' bpm', '').astype(float)
-
-# Konvertiere die Spalte 'Datum' in Kalenderwoche (KW und Jahr)
 hr_data['Woche'] = hr_data['Datum'].apply(convert_to_week_and_year)
-
-# Berechne den Durchschnitt der Herzfrequenzdaten (In Ruhe und Hoch)
 hr_data['avg_hr'] = hr_data[['In Ruhe', 'Hoch']].mean(axis=1)
-
-# Speichere nur die Spalten 'Woche' und 'avg_hr' in einer neuen CSV-Datei
 hr_data_clean = hr_data[['Woche', 'avg_hr']]
 hr_data_clean.to_csv(hr_clean_path, index=False)
 
-# Schritt 2: Lade die Schlafdaten (mit Komma separiert)
+# Schritt 2: Lade die Schlafdaten
 sleep_data = pd.read_csv(sleep_data_path, sep=';')
-
-# Konvertiere die Spalte 'Datum' in Kalenderwoche (KW und Jahr)
 sleep_data['Woche'] = sleep_data['Datum'].apply(convert_to_week_and_year)
-
-# Konvertiere die Spalte 'Durchschnittliche Dauer' in numerische Stunden
 sleep_data['Durchschnittliche Dauer'] = sleep_data['Durchschnittliche Dauer'].apply(convert_sleep_duration)
-
-
-# Speichere nur die Spalten 'Woche' und 'Durchschnittliche Dauer' in einer neuen CSV-Datei
 sleep_data_clean = sleep_data[['Woche', 'Durchschnittliche Dauer']]
 sleep_data_clean.to_csv(sleep_clean_path, index=False)
 
-# Schritt 3: Kombiniere die HR- und Schlafdaten basierend auf der 'Woche'
+# Schritt 3: Kombiniere die HR- und Schlafdaten
 combined_data = pd.merge(hr_data_clean, sleep_data_clean, on='Woche', how='inner')
-
-# Speichere das kombinierte Dataset in einer neuen CSV-Datei
 combined_data.to_csv(combined_data_path, index=False)
 
-# Schritt 4: Berechne die Korrelation zwischen avg_hr und der Schlafdauer
+# Schritt 4: Berechne die Korrelation
 correlation = combined_data['avg_hr'].corr(combined_data['Durchschnittliche Dauer'])
 print(f"Die Korrelation zwischen der durchschnittlichen Herzfrequenz und der Schlafdauer ist: {correlation}")
 
-# Schritt 5: Visualisiere den Zusammenhang zwischen Herzfrequenz und Schlafdauer
+# Schritt 5: Visualisiere den Zusammenhang zwischen Herzfrequenz und Schlafdauer (invertierte x-Achse)
 plt.figure(figsize=(10, 6))
 plt.scatter(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], color='blue', label='Datenpunkte')
 plt.title('Zusammenhang zwischen Herzfrequenz (Durchschnitt) und Schlafdauer')
 plt.xlabel('Durchschnittliche Herzfrequenz (bpm)')
 plt.ylabel('Schlafdauer (Stunden)')
 plt.grid(True)
-
-# Linie zur Visualisierung des Trends hinzufügen
 m, b = np.polyfit(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], 1)
-plt.plot(combined_data['avg_hr'], m * combined_data['avg_hr'] + b, color='red',
-         label=f'Trendlinie (Kor = {correlation:.2f})')
-
+plt.plot(combined_data['avg_hr'], m * combined_data['avg_hr'] + b, color='red', label=f'Trendlinie (Kor = {correlation:.2f})')
+plt.gca().invert_xaxis()  # X-Achse invertieren
 plt.legend()
-# Speichern der Grafik
 plt.savefig(graphic_corr_path)
 plt.show()
 
-# Schritt 6: Erstelle eine Grafik pro Kalenderwoche (Herzfrequenz und Schlafdaten)
-plt.figure(figsize=(12, 6))
+# Schritt 6: Erstelle eine Grafik pro Kalenderwoche (HR und Schlafdaten)
+fig, ax1 = plt.subplots(figsize=(12, 6))
 
-# Plot für die Herzfrequenz
-plt.bar(combined_data['Woche'], combined_data['avg_hr'], width=0.4, label='Durchschnittliche Herzfrequenz', align='center')
+# Erste Achse: Herzfrequenz
+ax1.bar(combined_data['Woche'], combined_data['avg_hr'], width=0.4, label='Durchschnittliche Herzfrequenz', align='center', color='b')
+ax1.set_xlabel('Kalenderwoche')
+ax1.set_ylabel('Durchschnittliche Herzfrequenz (bpm)', color='b')
+ax1.tick_params(axis='y', labelcolor='b')
 
-# Plot für die Schlafdauer (mit Verschiebung, um Überlappung zu vermeiden)
-plt.bar(combined_data['Woche'], combined_data['Durchschnittliche Dauer'], width=0.4, label='Schlafdauer', align='edge')
+# Zweite Achse: Schlafdauer
+ax2 = ax1.twinx()
+ax2.bar(combined_data['Woche'], combined_data['Durchschnittliche Dauer'], width=0.4, label='Schlafdauer', align='edge', color='g')
+ax2.set_ylabel('Schlafdauer (Stunden)', color='g')
+ax2.tick_params(axis='y', labelcolor='g')
 
-# Diagrammtitel und Achsenbeschriftungen
 plt.title('Durchschnittliche Herzfrequenz und Schlafdauer pro Kalenderwoche')
-plt.xlabel('Kalenderwoche')
-plt.ylabel('Wert')
-
-# Legende und Rotation der x-Achse
-plt.legend()
 plt.xticks(rotation=45, ha='right')
+fig.tight_layout()
 
-# Speichern der zweiten Grafik
 plt.savefig(graphic_weekly_path)
 plt.show()
diff --git a/data/final/gramic_sleep_hr_correlation.png b/data/final/gramic_sleep_hr_correlation.png
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From ac7ab0c3399b2b1927cfc9eed81f3ae6b23dbe0a Mon Sep 17 00:00:00 2001
From: TheOriginalGraLargeShrimpakaReaper <graber-michael@hotmail.com>
Date: Fri, 18 Oct 2024 11:03:51 +0200
Subject: [PATCH 11/15] final branch commit

---
 code/corelation.py             |  10 ++++++++--
 data/final/weekly_hr_sleep.png | Bin 45884 -> 46318 bytes
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/code/corelation.py b/code/corelation.py
index 1660b7a..45a587e 100644
--- a/code/corelation.py
+++ b/code/corelation.py
@@ -117,7 +117,7 @@ plt.savefig(graphic_corr_path)
 plt.show()
 
 # Schritt 6: Erstelle eine Grafik pro Kalenderwoche (HR und Schlafdaten)
-fig, ax1 = plt.subplots(figsize=(12, 6))
+fig, ax1 = plt.subplots(figsize=(30, 8))  # Breitere Darstellung
 
 # Erste Achse: Herzfrequenz
 ax1.bar(combined_data['Woche'], combined_data['avg_hr'], width=0.4, label='Durchschnittliche Herzfrequenz', align='center', color='b')
@@ -132,7 +132,13 @@ ax2.set_ylabel('Schlafdauer (Stunden)', color='g')
 ax2.tick_params(axis='y', labelcolor='g')
 
 plt.title('Durchschnittliche Herzfrequenz und Schlafdauer pro Kalenderwoche')
-plt.xticks(rotation=45, ha='right')
+
+# Anpassung der x-Achse für bessere Lesbarkeit
+plt.xticks(rotation=90, ha='center', fontsize=12)  # Schriftgröße auf 12 erhöht
+
+# Zeige nur jede zweite Woche
+ax1.set_xticks(ax1.get_xticks()[::2])
+
 fig.tight_layout()
 
 plt.savefig(graphic_weekly_path)
diff --git a/data/final/weekly_hr_sleep.png b/data/final/weekly_hr_sleep.png
index 2f5bd92f0efbe547f6a550ae910e4df5b092f4ac..f703abbced1c4269f4ece85f77a713ceea4ecce9 100644
GIT binary patch
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From ea8280ba0e560b4a17a0e64adb947448b0b29c2a Mon Sep 17 00:00:00 2001
From: TheOriginalGraLargeShrimpakaReaper <graber-michael@hotmail.com>
Date: Fri, 18 Oct 2024 15:29:07 +0200
Subject: [PATCH 12/15] final branch commit

---
 code/corelation.py                         |  34 ++++++++++++++++-----
 data/final/gramic_sleep_hr_correlation.png | Bin 38104 -> 39782 bytes
 2 files changed, 26 insertions(+), 8 deletions(-)

diff --git a/code/corelation.py b/code/corelation.py
index 45a587e..3e2eef0 100644
--- a/code/corelation.py
+++ b/code/corelation.py
@@ -102,20 +102,38 @@ combined_data.to_csv(combined_data_path, index=False)
 correlation = combined_data['avg_hr'].corr(combined_data['Durchschnittliche Dauer'])
 print(f"Die Korrelation zwischen der durchschnittlichen Herzfrequenz und der Schlafdauer ist: {correlation}")
 
-# Schritt 5: Visualisiere den Zusammenhang zwischen Herzfrequenz und Schlafdauer (invertierte x-Achse)
+# # Schritt 5: Visualisiere den Zusammenhang zwischen Herzfrequenz und Schlafdauer (invertierte x-Achse)
+# plt.figure(figsize=(10, 6))
+# plt.scatter(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], color='blue', label='Datenpunkte')
+# plt.title('Zusammenhang zwischen Herzfrequenz (Durchschnitt) und Schlafdauer')
+# plt.xlabel('Durchschnittliche Herzfrequenz (bpm)')
+# plt.ylabel('Schlafdauer (Stunden)')
+# plt.grid(True)
+# m, b = np.polyfit(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], 1)
+# plt.plot(combined_data['avg_hr'], m * combined_data['avg_hr'] + b, color='red', label=f'Trendlinie (Kor = {correlation:.2f})')
+# plt.gca().invert_xaxis()  # X-Achse invertieren
+# plt.legend()
+# plt.savefig(graphic_corr_path)
+# plt.show()
+
+# Schritt 5: Visualisiere den Zusammenhang zwischen Herzfrequenz und Schlafdauer (invertierte y-Achse)
 plt.figure(figsize=(10, 6))
-plt.scatter(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], color='blue', label='Datenpunkte')
-plt.title('Zusammenhang zwischen Herzfrequenz (Durchschnitt) und Schlafdauer')
-plt.xlabel('Durchschnittliche Herzfrequenz (bpm)')
-plt.ylabel('Schlafdauer (Stunden)')
+plt.scatter(combined_data['Durchschnittliche Dauer'], combined_data['avg_hr'], color='blue', label='Datenpunkte')
+plt.title('Zusammenhang zwischen Schlafdauer und Herzfrequenz (Durchschnitt)')
+plt.xlabel('Schlafdauer (Stunden)')
+plt.ylabel('Durchschnittliche Herzfrequenz (bpm)')
 plt.grid(True)
-m, b = np.polyfit(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], 1)
-plt.plot(combined_data['avg_hr'], m * combined_data['avg_hr'] + b, color='red', label=f'Trendlinie (Kor = {correlation:.2f})')
-plt.gca().invert_xaxis()  # X-Achse invertieren
+
+# Berechne und zeichne die Trendlinie (umgekehrt)
+m, b = np.polyfit(combined_data['Durchschnittliche Dauer'], combined_data['avg_hr'], 1)
+plt.plot(combined_data['Durchschnittliche Dauer'], m * combined_data['Durchschnittliche Dauer'] + b, color='red', label=f'Trendlinie (Kor = {correlation:.2f})')
+
+plt.gca().invert_yaxis()  # Y-Achse invertieren, da die Herzfrequenz auf der Y-Achse ist
 plt.legend()
 plt.savefig(graphic_corr_path)
 plt.show()
 
+
 # Schritt 6: Erstelle eine Grafik pro Kalenderwoche (HR und Schlafdaten)
 fig, ax1 = plt.subplots(figsize=(30, 8))  # Breitere Darstellung
 
diff --git a/data/final/gramic_sleep_hr_correlation.png b/data/final/gramic_sleep_hr_correlation.png
index 5a5d6f9520d0be657a6eb0adc0de0ceffa7f28d2..4157e7fc7332c0e95bf115878818141034657b46 100644
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From 1507a506ba362c9a31eadc8c553bdbea935dcd3d Mon Sep 17 00:00:00 2001
From: TheOriginalGraLargeShrimpakaReaper <graber-michael@hotmail.com>
Date: Fri, 18 Oct 2024 15:35:22 +0200
Subject: [PATCH 13/15] final branch commit

---
 code/corelation.py                         |   6 ++----
 data/final/gramic_sleep_hr_correlation.png | Bin 39782 -> 40753 bytes
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/code/corelation.py b/code/corelation.py
index 3e2eef0..96d8126 100644
--- a/code/corelation.py
+++ b/code/corelation.py
@@ -116,7 +116,7 @@ print(f"Die Korrelation zwischen der durchschnittlichen Herzfrequenz und der Sch
 # plt.savefig(graphic_corr_path)
 # plt.show()
 
-# Schritt 5: Visualisiere den Zusammenhang zwischen Herzfrequenz und Schlafdauer (invertierte y-Achse)
+# Schritt 5: Visualisiere den Zusammenhang zwischen Schlafdauer und Herzfrequenz
 plt.figure(figsize=(10, 6))
 plt.scatter(combined_data['Durchschnittliche Dauer'], combined_data['avg_hr'], color='blue', label='Datenpunkte')
 plt.title('Zusammenhang zwischen Schlafdauer und Herzfrequenz (Durchschnitt)')
@@ -124,16 +124,14 @@ plt.xlabel('Schlafdauer (Stunden)')
 plt.ylabel('Durchschnittliche Herzfrequenz (bpm)')
 plt.grid(True)
 
-# Berechne und zeichne die Trendlinie (umgekehrt)
+# Berechne und zeichne die Trendlinie
 m, b = np.polyfit(combined_data['Durchschnittliche Dauer'], combined_data['avg_hr'], 1)
 plt.plot(combined_data['Durchschnittliche Dauer'], m * combined_data['Durchschnittliche Dauer'] + b, color='red', label=f'Trendlinie (Kor = {correlation:.2f})')
 
-plt.gca().invert_yaxis()  # Y-Achse invertieren, da die Herzfrequenz auf der Y-Achse ist
 plt.legend()
 plt.savefig(graphic_corr_path)
 plt.show()
 
-
 # Schritt 6: Erstelle eine Grafik pro Kalenderwoche (HR und Schlafdaten)
 fig, ax1 = plt.subplots(figsize=(30, 8))  # Breitere Darstellung
 
diff --git a/data/final/gramic_sleep_hr_correlation.png b/data/final/gramic_sleep_hr_correlation.png
index 4157e7fc7332c0e95bf115878818141034657b46..7dc0aa2dc494fdeed252dbbbcbd2584d56d86c29 100644
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From ebc0d684e6b8952c1dff479f2a3149fac8b32be2 Mon Sep 17 00:00:00 2001
From: TheOriginalGraLargeShrimpakaReaper <graber-michael@hotmail.com>
Date: Fri, 18 Oct 2024 15:35:55 +0200
Subject: [PATCH 14/15] final branch commit with cleanup

---
 code/corelation_old.py   |  35 ------
 code/corelation_old2.py  |  50 --------
 code/corelation_old3.py  |  94 --------------
 code/corelation_old4.py  | 266 ---------------------------------------
 code/corelation_old5.py  | 144 ---------------------
 code/crawler.py          |  21 ----
 code/hr_data_cleaned.csv |  53 --------
 7 files changed, 663 deletions(-)
 delete mode 100644 code/corelation_old.py
 delete mode 100644 code/corelation_old2.py
 delete mode 100644 code/corelation_old3.py
 delete mode 100644 code/corelation_old4.py
 delete mode 100644 code/corelation_old5.py
 delete mode 100644 code/crawler.py
 delete mode 100644 code/hr_data_cleaned.csv

diff --git a/code/corelation_old.py b/code/corelation_old.py
deleted file mode 100644
index a597b71..0000000
--- a/code/corelation_old.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import pandas as pd
-
-# Lade die CSV-Dateien
-# Datei 1 mit Semikolon (;) separiert (HR-Daten)
-hr_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv', sep=';')
-
-# Datei 2 mit Komma (,) separiert (Schlafdaten)
-sleep_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv', sep=',')
-
-# Überprüfen der ersten Zeilen der Dateien
-print("HR-Daten:")
-print(hr_data.head())
-
-print("\nSchlafdaten:")
-print(sleep_data.head())
-
-# Annahme: Beide Datensätze enthalten eine Spalte für Wochen (z.B. 'week')
-# und die jeweiligen Werte (z.B. 'avg_hr' für Herzfrequenz und 'sleep_duration' für Schlafdauer)
-
-# Sicherstellen, dass beide Datensätze nach Woche sortiert sind
-hr_data = hr_data.sort_values(by='Datum')
-sleep_data = sleep_data.sort_values(by='week')
-
-# Kombinieren der beiden Datensätze anhand der 'week' Spalte
-combined_data = pd.merge(hr_data, sleep_data, on='Datum')
-
-# Überprüfen der kombinierten Daten
-print("\nKombinierte Daten:")
-print(combined_data.head())
-
-# Berechne die Korrelation zwischen der durchschnittlichen Herzfrequenz (z.B. 'avg_hr')
-# und der Schlafdauer (z.B. 'sleep_duration')
-correlation = combined_data['avg_hr'].corr(combined_data['Durchschnittliche Dauer'])
-
-print(f"\nDie Korrelation zwischen der Herzfrequenz und der Schlafdauer ist: {correlation}")
diff --git a/code/corelation_old2.py b/code/corelation_old2.py
deleted file mode 100644
index c2cf59a..0000000
--- a/code/corelation_old2.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import pandas as pd
-
-# Schritt 1: Lade die HR-Daten (mit Semikolon separiert) und entferne 'bpm'
-hr_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv', sep=';')
-
-# Zeige die ersten Zeilen der HR-Daten vor der Bereinigung
-print("HR-Daten (vor der Bereinigung):")
-print(hr_data.head())
-
-# Entferne 'bpm' und konvertiere die Werte in numerische Daten
-hr_data['In Ruhe'] = hr_data['In Ruhe'].str.replace(' bpm', '').astype(float)
-hr_data['Hoch'] = hr_data['Hoch'].str.replace(' bpm', '').astype(float)
-
-# Zeige die bereinigten HR-Daten
-print("\nHR-Daten (nach der Entfernung von 'bpm'):")
-print(hr_data.head())
-
-# Speichere die bereinigten HR-Daten in eine neue CSV-Datei (Zwischendatei)
-hr_data.to_csv('hr_data_cleaned.csv', sep=';', index=False)
-print("\nBereinigte HR-Daten wurden in '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_data_cleaned.csv' gespeichert.")
-
-# Schritt 2: Berechne den Durchschnitt der Herzfrequenzdaten (Resting HR und High HR)
-hr_data['avg_hr'] = hr_data[['In Ruhe', 'Hoch']].mean(axis=1)
-
-# Zeige die HR-Daten nach der Berechnung des Durchschnitts
-print("\nHR-Daten (nach der Berechnung des Durchschnitts):")
-print(hr_data.head())
-
-# Schritt 3: Lade die Schlafdaten (mit Komma separiert)
-sleep_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv', sep=',')
-
-# Zeige die Schlafdaten
-print("\nSchlafdaten:")
-print(sleep_data.head())
-
-# Schritt 4: Sicherstellen, dass beide Datensätze nach 'week' sortiert sind
-hr_data = hr_data.sort_values(by='Datum')
-sleep_data = sleep_data.sort_values(by='Datum')
-
-# Schritt 5: Kombiniere die beiden Datensätze anhand der 'week' Spalte
-combined_data = pd.merge(hr_data, sleep_data, on='Datum')
-
-# Zeige die kombinierten Daten
-print("\nKombinierte Daten:")
-print(combined_data.head())
-
-# Schritt 6: Berechne die Korrelation zwischen dem durchschnittlichen Herzfrequenzwert ('avg_hr') und der Schlafdauer ('sleep_duration')
-correlation = combined_data['avg_hr'].corr(combined_data['Durchschnittliche Dauer'])
-
-print(f"\nDie Korrelation zwischen der durchschnittlichen Herzfrequenz und der Schlafdauer ist: {correlation}")
diff --git a/code/corelation_old3.py b/code/corelation_old3.py
deleted file mode 100644
index 15cad3a..0000000
--- a/code/corelation_old3.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import pandas as pd
-import matplotlib.pyplot as plt
-import numpy as np
-from datetime import datetime
-
-
-# Manuelle Zuordnung der Monatsnamen von Deutsch auf Englisch
-month_translation = {
-    'Jan': 'Jan', 'Feb': 'Feb', 'Mär': 'Mar', 'Apr': 'Apr', 'Mai': 'May', 'Jun': 'Jun',
-    'Jul': 'Jul', 'Aug': 'Aug', 'Sep': 'Sep', 'Okt': 'Oct', 'Nov': 'Nov', 'Dez': 'Dec'
-}
-
-# Funktion, um 'Okt 10-16' in eine Kalenderwoche zu konvertieren
-def convert_to_week_number(date_range_str):
-    # Splitte den Datumsbereich in Monat und Tage
-    month_str, day_range = date_range_str.split(" ")
-    start_day = int(day_range.split("-")[0])  # Nimm den Starttag
-    current_year = datetime.now().year
-
-    # Konvertiere den deutschen Monatsnamen in den englischen
-    if month_str in month_translation:
-        month_str = month_translation[month_str]
-
-    # Erstelle das Datum basierend auf dem Starttag und dem aktuellen Jahr
-    date_str = f"{month_str} {start_day} {current_year}"
-    start_date = datetime.strptime(date_str, "%b %d %Y")  # Konvertiere in ein Datum
-    return start_date.isocalendar()[1]  # Gib die Kalenderwoche zurück
-
-
-# Schritt 1: Lade die HR-Daten (mit Semikolon separiert) und entferne 'bpm'
-hr_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv', sep=';')
-
-# Zeige die ersten Zeilen der HR-Daten vor der Bereinigung
-print("HR-Daten (vor der Bereinigung):")
-print(hr_data.head())
-
-# Entferne 'bpm' und konvertiere die Werte in numerische Daten
-hr_data['In Ruhe'] = hr_data['In Ruhe'].str.replace(' bpm', '').astype(float)
-hr_data['Hoch'] = hr_data['Hoch'].str.replace(' bpm', '').astype(float)
-
-# Konvertiere die Spalte 'Datum' in Kalenderwoche (KW)
-hr_data['week'] = hr_data['Datum'].apply(convert_to_week_number)
-
-# Zeige die HR-Daten nach der Umwandlung in Wochen
-print("\nHR-Daten (nach der Umwandlung in KW):")
-print(hr_data.head())
-
-# Schritt 2: Berechne den Durchschnitt der Herzfrequenzdaten (In Ruhe und Hoch)
-hr_data['avg_hr'] = hr_data[['In Ruhe', 'Hoch']].mean(axis=1)
-
-# Schritt 3: Lade die Schlafdaten (mit Komma separiert)
-sleep_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv', sep=',')
-
-# Zeige die ersten Zeilen der Schlafdaten
-print("\nSchlafdaten:")
-print(sleep_data.head())
-
-# Konvertiere auch das 'Datum' der Schlafdaten in Kalenderwoche (KW)
-sleep_data['week'] = sleep_data['Datum'].apply(convert_to_week_number)
-
-# Schritt 4: Sicherstellen, dass beide Datensätze nach 'week' sortiert sind
-hr_data = hr_data.sort_values(by='week')
-sleep_data = sleep_data.sort_values(by='week')
-
-# Schritt 5: Kombiniere die beiden Datensätze anhand der 'week' Spalte mit einem äußeren Join
-combined_data = pd.merge(hr_data, sleep_data, on='week', how='outer')
-
-# Zeige die kombinierten Daten
-print("\nKombinierte Daten:")
-print(combined_data)
-
-# Schritt 6: Entferne alle Zeilen mit fehlenden Daten
-combined_data = combined_data.dropna()
-
-# Schritt 7: Berechne die Korrelation zwischen dem durchschnittlichen Herzfrequenzwert ('avg_hr') und der Schlafdauer ('Durchschnittliche Dauer')
-correlation = combined_data['avg_hr'].corr(combined_data['Durchschnittliche Dauer'])
-
-print(f"\nDie Korrelation zwischen der durchschnittlichen Herzfrequenz und der Schlafdauer ist: {correlation}")
-
-# Schritt 8: Visualisiere den Zusammenhang zwischen Herzfrequenz und Schlafdauer
-plt.figure(figsize=(10, 6))
-plt.scatter(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], color='blue', label='Datenpunkte')
-plt.title('Zusammenhang zwischen Herzfrequenz (Durchschnitt) und Schlafdauer')
-plt.xlabel('Durchschnittliche Herzfrequenz (bpm)')
-plt.ylabel('Schlafdauer (Stunden)')
-plt.grid(True)
-
-# Linie zur Visualisierung des Trends hinzufügen
-m, b = np.polyfit(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], 1)
-plt.plot(combined_data['avg_hr'], m * combined_data['avg_hr'] + b, color='red',
-         label=f'Trendlinie (Kor = {correlation:.2f})')
-
-plt.legend()
-plt.show()
diff --git a/code/corelation_old4.py b/code/corelation_old4.py
deleted file mode 100644
index 2b55ec6..0000000
--- a/code/corelation_old4.py
+++ /dev/null
@@ -1,266 +0,0 @@
-import pandas as pd
-import matplotlib.pyplot as plt
-import numpy as np
-from datetime import datetime
-import os
-
-# Manuelle Zuordnung der Monatsnamen von Deutsch auf Englisch
-# month_translation = {
-#     'Jan': 'Jan', 'Feb': 'Feb', 'Mär': 'Mar', 'Apr': 'Apr', 'Mai': 'May', 'Jun': 'Jun',
-#     'Jul': 'Jul', 'Aug': 'Aug', 'Sep': 'Sep', 'Okt': 'Oct', 'Nov': 'Nov', 'Dez': 'Dec', 'Mrz': 'Mar'
-# }
-
-
-# Funktion, um 'Okt 10-16' in eine Kalenderwoche zu konvertieren und das Startdatum zurückzugeben
-# def convert_to_week_number_and_start_date(date_range_str):
-#     # Splitte den Datumsbereich in Monat und Tage
-#     start_part, end_part = date_range_str.split(" - ")
-#     start_month_str, start_day = start_part.split(" ")
-#     start_day = int(start_day)  # Nimm den Starttag
-#
-#     # Konvertiere den deutschen Monatsnamen in den englischen
-#     if start_month_str in month_translation:
-#         start_month_str = month_translation[start_month_str]
-#
-#     # Erstelle das Datum basierend auf dem Starttag und dem aktuellen Jahr
-#     current_year = datetime.now().year
-#     date_str = f"{start_month_str} {start_day} {current_year}"
-#
-#     try:
-#         start_date = datetime.strptime(date_str, "%b %d %Y")  # Konvertiere in ein Datum
-#     except ValueError:
-#         raise ValueError(f"Ungültiges Datum: {date_str}")
-#
-#     week_number = start_date.isocalendar()[1]  # Kalenderwoche ermitteln
-#
-#     return week_number, start_date.strftime('%Y-%m-%d')  # Gib KW und das Startdatum zurück
-# Funktion, um 'Okt 10-16' oder 'Okt 10 - 16' in eine Kalenderwoche zu konvertieren und das Startdatum zurückzugeben
-# def convert_to_week_number_and_start_date(date_range_str):
-#     # Entferne zusätzliche Leerzeichen um den Bindestrich herum
-#     date_range_str = date_range_str.replace(" - ", "-")
-#
-#     # Splitte den Datumsbereich in Start- und Endbereich
-#     start_part, end_part = date_range_str.split("-")
-#     start_month_str, start_day = start_part.split(" ")
-#     start_day = int(start_day.strip())  # Nimm den Starttag und entferne etwaige Leerzeichen
-#
-#     # Konvertiere den deutschen Monatsnamen in den englischen
-#     if start_month_str in month_translation:
-#         start_month_str = month_translation[start_month_str]
-#
-#     # Erstelle das Datum basierend auf dem Starttag und dem aktuellen Jahr
-#     current_year = datetime.now().year
-#     date_str = f"{start_month_str} {start_day} {current_year}"
-#
-#     try:
-#         start_date = datetime.strptime(date_str, "%b %d %Y")  # Konvertiere in ein Datum
-#     except ValueError:
-#         raise ValueError(f"Ungültiges Datum: {date_str}")
-#
-#     week_number = start_date.isocalendar()[1]  # Kalenderwoche ermitteln
-#
-#     return week_number, start_date.strftime('%Y-%m-%d')  # Gib KW und das Startdatum zurück
-
-# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4' oder 'Okt 10-16' in die Kalenderwoche zu konvertieren
-# und das Startdatum zurückzugeben
-# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4' oder 'Okt 10-16' in die Kalenderwoche zu konvertieren
-# und das Startdatum zurückzugeben
-# def convert_to_week_number_and_start_date(date_range_str):
-#     # Entferne zusätzliche Leerzeichen um den Bindestrich herum
-#     date_range_str = date_range_str.replace(" - ", "-")
-#
-#     # Splitte den Datumsbereich in Start- und Endteil
-#     start_part, end_part = date_range_str.split("-")
-#
-#     # Verarbeite den Startteil
-#     start_month_str, start_day = start_part.split(" ")
-#     start_day = int(start_day.strip())  # Nimm den Starttag und entferne etwaige Leerzeichen
-#
-#     # Wenn der Endteil einen Monat enthält, verarbeite ihn ebenfalls
-#     if " " in end_part:
-#         end_month_str, end_day = end_part.split(" ")
-#         end_day = int(end_day.strip())  # Nimm den Endtag
-#     else:
-#         # Wenn der Endteil keinen Monat enthält, verwenden wir denselben Monat wie im Startteil
-#         end_month_str = start_month_str
-#         end_day = int(end_part.strip())
-#
-#     # Konvertiere den deutschen Monatsnamen in den englischen für beide Monate
-#     if start_month_str in month_translation:
-#         start_month_str = month_translation[start_month_str]
-#     if end_month_str in month_translation:
-#         end_month_str = month_translation[end_month_str]
-#
-#     # Erstelle das Datum basierend auf dem Starttag und dem aktuellen Jahr
-#     current_year = datetime.now().year
-#     start_date_str = f"{start_month_str} {start_day} {current_year}"
-#
-#     try:
-#         # Konvertiere in ein Datum
-#         start_date = datetime.strptime(start_date_str, "%b %d %Y")
-#     except ValueError:
-#         raise ValueError(f"Ungültiges Startdatum: {start_date_str}")
-#
-#     # Berechne die Kalenderwoche basierend auf dem Startdatum
-#     week_number = start_date.isocalendar()[1]
-#
-#     return week_number, start_date.strftime('%Y-%m-%d')  # Gib KW und das Startdatum zurück
-# Manuelle Zuordnung der Monatsnamen von Deutsch auf Englisch
-month_translation = {
-    'Jan': 'Jan', 'Feb': 'Feb', 'Mär': 'Mar', 'Mrz': 'Mar', 'Apr': 'Apr', 'Mai': 'May', 'Jun': 'Jun',
-    'Jul': 'Jul', 'Aug': 'Aug', 'Sep': 'Sep', 'Okt': 'Oct', 'Nov': 'Nov', 'Dez': 'Dec'
-}
-
-
-# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4', 'Dez 29, 2023-Jan 4, 2024', 'Dez 22-28 2023' oder 'Dez 28' in Kalenderwoche und Startdatum zu konvertieren
-def convert_to_week_number_and_start_date(date_range_str):
-    # Entferne zusätzliche Leerzeichen um den Bindestrich herum und entferne Kommas
-    date_range_str = date_range_str.replace(" - ", "-").replace(",", "")
-
-    # Prüfen, ob das Format nur einen Tag und Monat enthält (z.B. 'Dez 28')
-    if "-" not in date_range_str and len(date_range_str.split(" ")) == 2:
-        # Nur ein Datum (Monat und Tag)
-        month_str, day_str = date_range_str.split(" ")
-        day = int(day_str.strip())
-        year_str = str(datetime.now().year)  # Verwende das aktuelle Jahr
-
-        # Konvertiere den deutschen Monatsnamen in den englischen
-        if month_str in month_translation:
-            month_str = month_translation[month_str]
-
-        # Konvertiere das Datum
-        start_date = datetime.strptime(f"{month_str} {day} {year_str}", "%b %d %Y")
-
-        # Berechne die Kalenderwoche
-        week_number = start_date.isocalendar()[1]
-
-        return week_number, start_date.strftime('%Y-%m-%d')
-
-    # Prüfen, ob das Jahr am Ende steht (z.B. 'Dez 22-28 2023')
-    if date_range_str[-4:].isdigit():
-        # Jahr am Ende des Datums
-        year_str = date_range_str[-4:]
-        date_range_str = date_range_str[:-5]  # Entferne das Jahr aus der Datumsangabe
-    else:
-        # Kein Jahr am Ende -> nutze das aktuelle Jahr
-        year_str = str(datetime.now().year)
-
-    # Splitte den Datumsbereich in Start- und Endteil
-    start_part, end_part = date_range_str.split("-")
-
-    # Verarbeite den Startteil
-    start_parts = start_part.split(" ")
-    start_month_str = start_parts[0]
-    start_day = int(start_parts[1].strip())  # Nimm den Starttag und entferne etwaige Leerzeichen
-
-    # Verarbeite den Endteil
-    end_parts = end_part.split(" ")
-
-    # Verarbeite das Enddatum, basierend darauf, ob der Endteil das Jahr enthält
-    if len(end_parts) == 2:  # Fall: 'Apr 4'
-        end_month_str = end_parts[0]
-        end_day = int(end_parts[1].strip())
-    else:
-        # Fall: kein Monat -> nur Tag (z.B. '28' im Format 'Dez 22-28 2023')
-        end_month_str = start_month_str
-        end_day = int(end_parts[0].strip())
-
-    # Konvertiere den deutschen Monatsnamen in den englischen für beide Monate
-    if start_month_str in month_translation:
-        start_month_str = month_translation[start_month_str]
-    if end_month_str in month_translation:
-        end_month_str = month_translation[end_month_str]
-
-    try:
-        # Konvertiere das Startdatum in ein Datum
-        start_date = datetime.strptime(f"{start_month_str} {start_day} {year_str}", "%b %d %Y")
-    except ValueError:
-        raise ValueError(f"Ungültiges Startdatum: {start_month_str} {start_day} {year_str}")
-
-    try:
-        # Konvertiere das Enddatum in ein Datum
-        end_date = datetime.strptime(f"{end_month_str} {end_day} {year_str}", "%b %d %Y")
-    except ValueError:
-        raise ValueError(f"Ungültiges Enddatum: {end_month_str} {end_day} {year_str}")
-
-    # Berechne die Kalenderwoche basierend auf dem Startdatum
-    week_number = start_date.isocalendar()[1]
-
-    return week_number, start_date.strftime('%Y-%m-%d')  # Gib KW und das Startdatum zurück
-
-
-# Funktion zum Bereinigen und Zwischenspeichern der Daten
-def clean_and_save_data(hr_data, sleep_data, output_dir='sandbox'):
-    # Entferne 'bpm' und konvertiere die Werte in numerische Daten
-    hr_data['In Ruhe'] = hr_data['In Ruhe'].str.replace(' bpm', '').astype(float)
-    hr_data['Hoch'] = hr_data['Hoch'].str.replace(' bpm', '').astype(float)
-
-    # Konvertiere die Spalte 'Datum' in Kalenderwoche (KW) und Startdatum
-    hr_data[['week', 'start_date']] = hr_data['Datum'].apply(
-        lambda x: pd.Series(convert_to_week_number_and_start_date(x)))
-
-    # Berechne den Durchschnitt der Herzfrequenzdaten (In Ruhe und Hoch)
-    hr_data['avg_hr'] = hr_data[['In Ruhe', 'Hoch']].mean(axis=1)
-
-    # Konvertiere auch das 'Datum' der Schlafdaten in Kalenderwoche (KW) und Startdatum
-    sleep_data[['week', 'start_date']] = sleep_data['Datum'].apply(
-        lambda x: pd.Series(convert_to_week_number_and_start_date(x)))
-
-    # Speichere die bereinigten Daten in 'sandbox'
-    if not os.path.exists(output_dir):
-        os.makedirs(output_dir)
-
-    hr_data_clean_path = os.path.join(output_dir, 'hr_data_clean.csv')
-    sleep_data_clean_path = os.path.join(output_dir, 'sleep_data_clean.csv')
-
-    hr_data.to_csv(hr_data_clean_path, sep=';', index=False)
-    sleep_data.to_csv(sleep_data_clean_path, sep=',', index=False)
-
-    print(f"HR-Daten wurden bereinigt und in {hr_data_clean_path} gespeichert.")
-    print(f"Schlafdaten wurden bereinigt und in {sleep_data_clean_path} gespeichert.")
-
-    return hr_data, sleep_data
-
-
-# Schritt 1: Lade die HR- und Schlafdaten
-hr_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv',
-                      sep=';')
-sleep_data = pd.read_csv('/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv',
-                         sep=',')
-
-# Bereinige und speichere die Daten
-hr_data, sleep_data = clean_and_save_data(hr_data, sleep_data)
-
-# Schritt 2: Sortiere beide Datensätze nach 'week' und 'start_date'
-hr_data = hr_data.sort_values(by=['week', 'start_date'])
-sleep_data = sleep_data.sort_values(by=['week', 'start_date'])
-
-# Schritt 3: Kombiniere die beiden Datensätze anhand der 'week' und 'start_date' Spalten mit einem äußeren Join
-combined_data = pd.merge(hr_data, sleep_data, on=['week', 'start_date'], how='outer')
-
-# Zeige die kombinierten Daten
-print("\nKombinierte Daten:")
-print(combined_data)
-
-# Schritt 4: Entferne alle Zeilen mit fehlenden Daten
-combined_data = combined_data.dropna()
-
-# Schritt 5: Berechne die Korrelation zwischen 'avg_hr' und 'Durchschnittliche Dauer'
-correlation = combined_data['avg_hr'].corr(combined_data['Durchschnittliche Dauer'])
-print(f"\nDie Korrelation zwischen der durchschnittlichen Herzfrequenz und der Schlafdauer ist: {correlation}")
-
-# Schritt 6: Visualisiere den Zusammenhang zwischen Herzfrequenz und Schlafdauer
-plt.figure(figsize=(10, 6))
-plt.scatter(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], color='blue', label='Datenpunkte')
-plt.title('Zusammenhang zwischen Herzfrequenz (Durchschnitt) und Schlafdauer')
-plt.xlabel('Durchschnittliche Herzfrequenz (bpm)')
-plt.ylabel('Schlafdauer (Stunden)')
-plt.grid(True)
-
-# Linie zur Visualisierung des Trends hinzufügen
-m, b = np.polyfit(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], 1)
-plt.plot(combined_data['avg_hr'], m * combined_data['avg_hr'] + b, color='red',
-         label=f'Trendlinie (Kor = {correlation:.2f})')
-
-plt.legend()
-plt.show()
diff --git a/code/corelation_old5.py b/code/corelation_old5.py
deleted file mode 100644
index 79bc956..0000000
--- a/code/corelation_old5.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import pandas as pd
-import matplotlib.pyplot as plt
-import numpy as np
-from datetime import datetime
-
-# Manuelle Zuordnung der Monatsnamen von Deutsch auf Englisch
-month_translation = {
-    'Jan': 'Jan', 'Feb': 'Feb', 'Mär': 'Mar', 'Mrz': 'Mar', 'Apr': 'Apr', 'Mai': 'May', 'Jun': 'Jun',
-    'Jul': 'Jul', 'Aug': 'Aug', 'Sep': 'Sep', 'Okt': 'Oct', 'Nov': 'Nov', 'Dez': 'Dec'
-}
-
-
-# Funktion, um Datumsbereiche wie 'Mrz 29-Apr 4', 'Dez 22-28 2023' oder 'Dez 28' in Kalenderwoche und Jahr zu konvertieren
-def convert_to_week_and_year(date_range_str):
-    # Entferne zusätzliche Leerzeichen um den Bindestrich herum und entferne Kommas
-    date_range_str = date_range_str.replace(" - ", "-").replace(",", "")
-
-    # Prüfen, ob das Format nur einen Tag und Monat enthält (z.B. 'Dez 28')
-    if "-" not in date_range_str and len(date_range_str.split(" ")) == 2:
-        # Nur ein Datum (Monat und Tag)
-        month_str, day_str = date_range_str.split(" ")
-        day = int(day_str.strip())
-        year_str = str(datetime.now().year)  # Verwende das aktuelle Jahr
-
-        # Konvertiere den deutschen Monatsnamen in den englischen
-        if month_str in month_translation:
-            month_str = month_translation[month_str]
-
-        # Konvertiere das Datum
-        start_date = datetime.strptime(f"{month_str} {day} {year_str}", "%b %d %Y")
-
-        # Berechne die Kalenderwoche und das Jahr
-        week_number = start_date.isocalendar()[1]
-        year = start_date.year
-
-        return f"W{week_number}-{year}"
-
-    # Prüfen, ob das Jahr am Ende steht (z.B. 'Dez 22-28 2023')
-    if date_range_str[-4:].isdigit():
-        # Jahr am Ende des Datums
-        year_str = date_range_str[-4:]
-        date_range_str = date_range_str[:-5]  # Entferne das Jahr aus der Datumsangabe
-    else:
-        # Kein Jahr am Ende -> nutze das aktuelle Jahr
-        year_str = str(datetime.now().year)
-
-    # Splitte den Datumsbereich in Start- und Endteil
-    start_part, end_part = date_range_str.split("-")
-
-    # Verarbeite den Startteil
-    start_parts = start_part.split(" ")
-    start_month_str = start_parts[0]
-    start_day = int(start_parts[1].strip())  # Nimm den Starttag und entferne etwaige Leerzeichen
-
-    # Verarbeite den Endteil
-    end_parts = end_part.split(" ")
-
-    # Verarbeite das Enddatum, basierend darauf, ob der Endteil das Jahr enthält
-    if len(end_parts) == 2:  # Fall: 'Apr 4'
-        end_month_str = end_parts[0]
-        end_day = int(end_parts[1].strip())
-    else:
-        # Fall: kein Monat -> nur Tag (z.B. '28' im Format 'Dez 22-28 2023')
-        end_month_str = start_month_str
-        end_day = int(end_parts[0].strip())
-
-    # Konvertiere den deutschen Monatsnamen in den englischen für beide Monate
-    if start_month_str in month_translation:
-        start_month_str = month_translation[start_month_str]
-    if end_month_str in month_translation:
-        end_month_str = month_translation[end_month_str]
-
-    try:
-        # Konvertiere das Startdatum in ein Datum
-        start_date = datetime.strptime(f"{start_month_str} {start_day} {year_str}", "%b %d %Y")
-    except ValueError:
-        raise ValueError(f"Ungültiges Startdatum: {start_month_str} {start_day} {year_str}")
-
-    # Berechne die Kalenderwoche und das Jahr basierend auf dem Startdatum
-    week_number = start_date.isocalendar()[1]
-    year = start_date.year
-
-    return f"W{week_number}-{year}"  # Gib Kalenderwoche und Jahr im Format W-YYYY zurück
-
-
-# Datei Pfade
-hr_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/hr_gramic.csv'
-sleep_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/raw/sleep_gramic.csv'
-hr_clean_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/sandbox/hr_data_clean.csv'
-sleep_clean_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/sandbox/sleep_data_clean.csv'
-combined_data_path = '/home/gra/PycharmProjects/cds_introduction_data_science_assignment/data/sandbox/combined_data.csv'
-
-# Schritt 1: Lade die HR-Daten (mit Semikolon separiert) und entferne 'bpm'
-hr_data = pd.read_csv(hr_data_path, sep=';')
-
-# Entferne 'bpm' und konvertiere die Werte in numerische Daten
-hr_data['In Ruhe'] = hr_data['In Ruhe'].str.replace(' bpm', '').astype(float)
-hr_data['Hoch'] = hr_data['Hoch'].str.replace(' bpm', '').astype(float)
-
-# Konvertiere die Spalte 'Datum' in Kalenderwoche (KW und Jahr)
-hr_data['Woche'] = hr_data['Datum'].apply(convert_to_week_and_year)
-
-# Berechne den Durchschnitt der Herzfrequenzdaten (In Ruhe und Hoch)
-hr_data['avg_hr'] = hr_data[['In Ruhe', 'Hoch']].mean(axis=1)
-
-# Speichere nur die Spalten 'Woche' und 'avg_hr' in einer neuen CSV-Datei
-hr_data_clean = hr_data[['Woche', 'avg_hr']]
-hr_data_clean.to_csv(hr_clean_path, index=False)
-
-# Schritt 2: Lade die Schlafdaten (mit Komma separiert)
-sleep_data = pd.read_csv(sleep_data_path, sep=',')
-
-# Konvertiere die Spalte 'Datum' in Kalenderwoche (KW und Jahr)
-sleep_data['Woche'] = sleep_data['Datum'].apply(convert_to_week_and_year)
-
-# Speichere nur die Spalten 'Woche' und 'Durchschnittliche Dauer' in einer neuen CSV-Datei
-sleep_data_clean = sleep_data[['Woche', 'Durchschnittliche Dauer']]
-sleep_data_clean.to_csv(sleep_clean_path, index=False)
-
-# Schritt 3: Kombiniere die HR- und Schlafdaten basierend auf der 'Woche'
-combined_data = pd.merge(hr_data_clean, sleep_data_clean, on='Woche', how='inner')
-
-# Speichere das kombinierte Dataset in einer neuen CSV-Datei
-combined_data.to_csv(combined_data_path, index=False)
-
-# Schritt 4: Berechne die Korrelation zwischen avg_hr und der Schlafdauer
-correlation = combined_data['avg_hr'].corr(combined_data['Durchschnittliche Dauer'])
-print(f"Die Korrelation zwischen der durchschnittlichen Herzfrequenz und der Schlafdauer ist: {correlation}")
-
-# Schritt 5: Visualisiere den Zusammenhang zwischen Herzfrequenz und Schlafdauer
-plt.figure(figsize=(10, 6))
-plt.scatter(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], color='blue', label='Datenpunkte')
-plt.title('Zusammenhang zwischen Herzfrequenz (Durchschnitt) und Schlafdauer')
-plt.xlabel('Durchschnittliche Herzfrequenz (bpm)')
-plt.ylabel('Schlafdauer (Stunden)')
-plt.grid(True)
-
-# Linie zur Visualisierung des Trends hinzufügen
-m, b = np.polyfit(combined_data['avg_hr'], combined_data['Durchschnittliche Dauer'], 1)
-plt.plot(combined_data['avg_hr'], m * combined_data['avg_hr'] + b, color='red',
-         label=f'Trendlinie (Kor = {correlation:.2f})')
-
-plt.legend()
-plt.show()
diff --git a/code/crawler.py b/code/crawler.py
deleted file mode 100644
index b15145f..0000000
--- a/code/crawler.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import requests
-from bs4 import BeautifulSoup
-import queue
-import re
-import time
-import random
-
-urls = queue.PriorityQueue()
-urls.put((0.5, "https://www.scrapingcourse.com/ecommerce/"))
-visited_urls = []
-
-while not urls.empty():
-    _, current_url = urls.get()
-    soup = BeautifulSoup(get_html(current_url), "html.parser")
-
-    visited_urls.append(current_url)
-    crawl_page(soup, current_url, visited_urls, urls)
-
-    # if it is a product page:
-    # scrape_page(soup, url, products)
-time.sleep(random.uniform(1, 3))
\ No newline at end of file
diff --git a/code/hr_data_cleaned.csv b/code/hr_data_cleaned.csv
deleted file mode 100644
index e30f4a3..0000000
--- a/code/hr_data_cleaned.csv
+++ /dev/null
@@ -1,53 +0,0 @@
-Datum;In Ruhe;Hoch
-Okt 4-10;67.0;130.0
-Sep 27 - Okt 3;67.0;143.0
-Sep 20-26;66.0;149.0
-Sep 13-19;66.0;144.0
-Sep 6-12;62.0;132.0
-Aug 30 - Sep 5;64.0;141.0
-Aug 23-29;67.0;150.0
-Aug 16-22;63.0;143.0
-Aug 9-15;69.0;141.0
-Aug 2-8;67.0;140.0
-Jul 26 - Aug 1;67.0;147.0
-Jul 19-25;69.0;136.0
-Jul 12-18;66.0;151.0
-Jul 5-11;67.0;146.0
-Jun 28 - Jul 4;66.0;157.0
-Jun 21-27;64.0;141.0
-Jun 14-20;70.0;145.0
-Jun 7-13;69.0;134.0
-Mai 31 - Jun 6;70.0;139.0
-Mai 24-30;72.0;142.0
-Mai 17-23;72.0;135.0
-Mai 10-16;71.0;147.0
-Mai 3-9;73.0;142.0
-Apr 26 - Mai 2;69.0;151.0
-Apr 19-25;61.0;135.0
-Apr 12-18;58.0;140.0
-Apr 5-11;64.0;131.0
-Mrz 29 - Apr 4;63.0;139.0
-Mrz 22-28;65.0;135.0
-Mrz 15-21;66.0;137.0
-Mrz 8-14;62.0;136.0
-Mrz 1-7;70.0;134.0
-Feb 23-29;68.0;144.0
-Feb 16-22;71.0;132.0
-Feb 9-15;65.0;143.0
-Feb 2-8;66.0;133.0
-Jan 26 - Feb 1;59.0;142.0
-Jan 19-25;62.0;136.0
-Jan 12-18;60.0;134.0
-Jan 5-11;56.0;139.0
-Dez 29, 2023 - Jan 4, 2024;59.0;128.0
-Dez 22-28, 2023;52.0;124.0
-Dez 15-21, 2023;57.0;133.0
-Dez 8-14, 2023;65.0;133.0
-Dez 1-7, 2023;69.0;134.0
-Nov 24-30, 2023;68.0;139.0
-Nov 17-23, 2023;68.0;143.0
-Nov 10-16, 2023;64.0;144.0
-Nov 3-9, 2023;63.0;140.0
-Okt 27 - Nov 2, 2023;57.0;133.0
-Okt 20-26, 2023;55.0;138.0
-Okt 13-19, 2023;50.0;121.0

From b71c248e98c31b1476a7a04d617426b4aae10ec3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Oliver=20Sch=C3=BCtz?=
 <78213692+DotNaos@users.noreply.github.com>
Date: Sat, 19 Oct 2024 15:32:51 +0000
Subject: [PATCH 15/15] Restructuring of files to comply with master

---
 .../gra}/gramic_sleep_hr_correlation.png            | Bin
 {data/final => media/gra}/weekly_hr_sleep.png       | Bin
 {code => src/gra}/corelation.py                     |   0
 3 files changed, 0 insertions(+), 0 deletions(-)
 rename {data/final => media/gra}/gramic_sleep_hr_correlation.png (100%)
 rename {data/final => media/gra}/weekly_hr_sleep.png (100%)
 rename {code => src/gra}/corelation.py (100%)

diff --git a/data/final/gramic_sleep_hr_correlation.png b/media/gra/gramic_sleep_hr_correlation.png
similarity index 100%
rename from data/final/gramic_sleep_hr_correlation.png
rename to media/gra/gramic_sleep_hr_correlation.png
diff --git a/data/final/weekly_hr_sleep.png b/media/gra/weekly_hr_sleep.png
similarity index 100%
rename from data/final/weekly_hr_sleep.png
rename to media/gra/weekly_hr_sleep.png
diff --git a/code/corelation.py b/src/gra/corelation.py
similarity index 100%
rename from code/corelation.py
rename to src/gra/corelation.py