Initial commit.

feutron_climateAnalysis.py

+import pandas as pd
+import plotly.express as px
+from IPython.display import display
+
+
+'''
+Script applies to Feutron climate chamber
+
+Depending on what kind of sensors are selected for data registration the number of columbs and their respective namming will varry.
+Check available data columns.
+
+File header looks as follows:
+
+"Anlage";;"0";"0";"0";"0"
+"Messstelle";;"1";"2";"3";"4"
+"Kommentar";;"Temperatur";"R.H.";"Solltemperatur";"Sollfeuchte"
+"Sensor";;"MV01";"MV11";"SV01";"SV11"
+"Einheit";;"°C";"%";"°C";"%"
+'''
+
+filename = 'ANJO_RH80-T30_Week22.data'
+
+
+def columns_available(filename):
+    '''Displays column names and some rows of data.'''
+    df = pd.read_csv(filename, skiprows=[0,1,3,4], sep=';', nrows=5)
+
+    display(df)
+ 
+
+def plot_data(column_names):
+    '''Plots data of selected columns.'''
+    df = pd.read_csv(filename, skiprows=[0,1,3,4], sep=';')
+
+    datetime_column  = df[df.columns[0]] + df[df.columns[1]]
+    df['datetime'] = pd.to_datetime(datetime_column, format='%d.%m.%Y %H:%M:%S' )
+    
+    # For simplicity and convenience Plotly Express is used for plotting the data.
+    fig = px.line(df, x='datetime', y=column_names)
+    fig.show()
+    # The output is opened in a Webrowser and can be easily explored.
+
+
+columns_available(filename)
+
+# -->  Define here the columns to be plotted <--
+columns_to_plot = ['R.H.', 'Temperatur']
+
+plot_data(columns_to_plot)
+

requirements.txt

+numpy==1.20.2
+pandas==1.2.3
+python-dateutil==2.8.1
+pytz==2021.1
+six==1.15.0