Upload files to "/"

CalendarVisualization.py

@@ -0,0 +1,83 @@
+from datetime import datetime, timedelta
+import json
+
+import MySQLdb                                  #Version 2.2.4
+import pandas as pd                             #Version 2.2.2
+import plotly.express as px                     #Version 5.22.0
+
+db = MySQLdb.connect(host="localhost",user="root",passwd="admin",db="heiraterei")
+cur = db.cursor()
+
+cur.execute("SELECT JSON_EXTRACT(header, '$.Date') "
+            "FROM extractions "
+            "WHERE type='calendar' AND property_id = 200;")
+dateoutput = cur.fetchall()
+
+
+cur.execute("SELECT JSON_EXTRACT(body, '$.content.days') "
+            "FROM extractions "
+            "WHERE type='calendar' AND property_id = 200;")
+
+output = cur.fetchall()
+db.close()
+
+#createScrapedate Liste
+ytickVals = list(range(0, 30, 5))
+scrapeDates = []
+#print(dateoutput)
+for row in dateoutput:
+    date = datetime.strptime(json.loads(row[0])[0], '%a, %d %b %Y %H:%M:%S %Z').date()
+    str = date.strftime('%d/%m/%Y')
+    scrapeDates.append(str)
+
+#minimales und maximales Datum ermitteln
+fullDateList = []
+for row in output:
+    tempJson = json.loads(row[0]).keys()
+    for key in tempJson:
+        #print(key)
+        fullDateList.append(datetime.strptime(key, '%Y-%m-%d').date())
+
+end_dt = max(fullDateList)
+start_dt = min(fullDateList)
+delta = timedelta(days=1)
+HeaderDates = []
+
+while start_dt <= end_dt:
+    HeaderDates.append(start_dt)
+    start_dt += delta
+
+#Create data-Matrix
+data = []
+for row in output:
+    tempList = [-1] * len(HeaderDates)
+    tempJson = json.loads(row[0])
+    for key in tempJson:
+        date = datetime.strptime(key, '%Y-%m-%d').date()
+        content = tempJson[key]
+        index = [i for i, x in enumerate(HeaderDates) if x == date]
+        tempList[index[0]] = content
+    data.append(tempList)
+
+#Transform to Dataframe for Plotly
+df = pd.DataFrame(data, columns=HeaderDates)
+
+#Generate Plotly Diagramm
+colScale = [[0, 'rgb(0, 0, 0)'], [0.33, 'rgb(204, 16, 16)'], [0.66, 'rgb(10, 102, 15)'], [1, 'rgb(17, 184, 26)']]
+fig = px.imshow(df, color_continuous_scale= colScale)
+lines = list(range(0,30,1))
+for i in lines:
+    #fig.add_hline(y=i+0.5, line_color="white")
+    fig.add_hline(y=i+0.5)
+
+fig.update_layout(yaxis = dict(tickfont = dict(size=50))),
+fig.update_layout(xaxis = dict(tickfont = dict(size=50)))
+fig.update_layout(xaxis_title="Verfügbarkeitsdaten Mietobjekt", yaxis_title="Scrapingvorgang")
+fig.update_xaxes(title_font_size=100, title_font_weight="bold")
+fig.update_yaxes(title_font_size=100, title_font_weight="bold")
+fig.update_layout(yaxis = dict(tickmode = 'array',tickvals = ytickVals, ticktext = scrapeDates))
+fig.update_xaxes(title_standoff = 80)
+fig.update_yaxes(title_standoff = 80)
+fig.update_layout(xaxis={'side': 'top'})
+fig.show()
+