created flow_pattern function with constant, linearly increasing and sawtooth flow patterns (incl outflux delay)

functions/flow_patterns.py

@@ -0,0 +1,67 @@
+import numpy as np
+import pandas as pd
+import plotly.express as px
+from plotly.subplots import make_subplots
+import plotly.graph_objects as go
+
+
+def return_flux_profiles(number_of_steps = 1,influx_identifier = 0, outflux_identifier = 0,influx_offset=0,outflux_offset=0, outflux_delay = 0):
+    ''' Identifier patterns:
+    0                   ... constant
+    'lin_SSSS'          ... linear increase with slope int(SSSS)
+    'st_SSSS_PPPP'      ... sawtooth pattern with slope int(SSSS) and period int(PPPP) steps
+    '''
+    
+    # case identifiers for if statment
+    i = influx_identifier
+    o = outflux_identifier
+    
+    n = number_of_steps
+    #starting value for the influx and outflux
+    i_o = influx_offset
+    o_o = outflux_offset
+    # number of steps, the outflux is held at 0 at the beginning
+    o_d = outflux_delay
+
+
+# get base profile for the influx (offset will get applied later)
+    if i == 0:
+        influx_profile = np.zeros(n)
+    elif 'lin' in influx_identifier:
+        k = int(influx_identifier[-4:])
+        influx_profile = np.linspace(0,k*(n-1),n)
+    elif 'st' in influx_identifier:
+        k = int(influx_identifier[3:7])
+        p = int(influx_identifier[-4:])
+        influx_profile = np.tile(np.linspace(0,k*(p-1),p),int(np.ceil(n/p)))
+
+
+# apply influx offset
+    influx_profile = influx_offset + influx_profile
+
+    if o == 0:
+        outflux_profile = np.zeros(n)
+    elif 'lin' in outflux_identifier:
+        k = int(outflux_identifier[-4:])
+        outflux_profile = np.linspace(0,k*(n-1),n)
+    elif 'st' in outflux_identifier:
+        k = int(outflux_identifier[3:7])
+        p = int(outflux_identifier[-4:])
+        outflux_profile = np.tile(np.linspace(0,k*(p-1),p),int(np.ceil(n/p)))
+
+#apply outflux offset and delay (delay means, that the first o_d steps, the outflux will be 0)
+    outflux_profile = np.concatenate((np.zeros(o_d),outflux_profile[:-o_d]+o_o))
+    
+    return influx_profile,outflux_profile
+
+def make_flux_df(influx_profile,outflux_profile, time = 0):
+    if time == 0:
+        time = np.arange(0,len(influx_profile))
+    flux_df = pd.DataFrame(np.transpose([time, influx_profile, outflux_profile]), \
+        columns=['time', 'influx', 'outflux'])
+    return flux_df
+
+
+if __name__ == "__main__":
+    influx_profile,outflux_profile = return_flux_profiles(100,influx_identifier='st_0010_0010',influx_offset=10)
+    print(influx_profile)