I was given a script that has to use as input data four columns of my dataframe to calculate a new volume flow rate and energy use. I imported the Dataframe, but when I run the script, no new column is added to the df.
I changed the variables in the functions so the name of my df headers are the same in the script (therefore they can be used as input data). However still no column is added and I do not know what I am doing wrong.
The original script and the modified one are below:
import pandas as pd
import math
import pickle
import pytz
Ventil_Spec = [[0, 17.39130435, 34.7826087, 45.65217391, 65.2173913, 78.26086957, 95.65217391, 121.7391304, 139.1304348,
165.2173913, 186.9565217],
[0, 17.39130435, 34.7826087, 60.86956522, 86.95652174, 106.5217391, 139.1304348, 171.7391304, 200,
230.4347826, 265.2173913],
[0, 23.91304348, 43.47826087, 65.2173913, 100, 117.3913043, 147.826087, 178.2608696, 210.8695652,
236.9565217, 269.5652174]] # Air flow through the FVU in m3/h
def bilinear_interpolation(x, y, points=None):
''' based on https://stackoverflow.com/a/8662355
x is the Fan power, y the Heat recovery opening, both in the case of a closed secondary air damper
This function only interpolates the volume flow rate from the given Ventil_Spec list, using the
Fan power and Heat recovery opening
Interpolate (x,y) from values associated with four points.
The four points are a list of four triplets: (x, y, value).
The four points can be in any order. They should form a rectangle.
>>> bilinear_interpolation(12, 5.5,
... [(10, 4, 100),
... (20, 4, 200),
... (10, 6, 150),
... (20, 6, 300)])
165.0
:param x: float Fan Power in percent
:param y: float Heat recovery opening position in percent
:param points: list List of corner points for bilinear interpolation
:return:
'''
if math.isnan(x):
return x
if math.isnan(y):
y = 0
# See formula at: http://en.wikipedia.org/wiki/Bilinear_interpolation
def points_fvu_volume_flow(x_fan):
# returns the corner points for the bilinear interpolation for the EBC FVUs, depending on the fan power,
# in the case of a closed secondary air damper
if x_fan > 100:
x_fan = 100
x1_fan = int(x_fan / 10)
x2_fan = math.ceil(x_fan / 10)
z1 = Ventil_Spec[1][x1_fan]
z2 = Ventil_Spec[1][x2_fan]
z3 = Ventil_Spec[2][x1_fan]
z4 = Ventil_Spec[2][x2_fan]
return [(x1_fan * 10, 0, z1), (x2_fan * 10, 0, z2), (x1_fan * 10, 100, z3), (x2_fan * 10, 100, z4)]
if points is None:
points = points_fvu_volume_flow(x)
points = sorted(points) # order points by x, then by y
(x1, y1, q11), (_x1, y2, q12), (x2, _y1, q21), (_x2, _y2, q22) = points
# if x1 != _x1 or x2 != _x2 or y1 != _y1 or y2 != _y2:
# raise ValueError('points do not form a rectangle')
# if not x1 <= x <= x2 or not y1 <= y <= y2:
# raise ValueError('(x, y) not within the rectangle')
if x1 == x2 and _x1 == _x2 and x1 == _x1:
y_top = max(q11, q12, q21, q22)
y_bot = min(q11, q12, q21, q22)
return (y_top - y_bot) / 100 * y + y_bot
if y1 == y2 and _y1 == _y2 and y1 == _y1:
x_top = max(q11, q12, q21, q22)
x_bot = min(q11, q12, q21, q22)
return (x_top - x_bot) / 100 * x + x_bot
return (q11 * (x2 - x) * (y2 - y) +
q21 * (x - x1) * (y2 - y) +
q12 * (x2 - x) * (y - y1) +
q22 * (x - x1) * (y - y1)
) / ((x2 - x1) * (y2 - y1) + 0.0)
def calc_energy_use(df):
'''
:param df: pandas DataFrame Dataframe mit zumindest den folgenden Spalten:
Fan_Power: float Zuluftventilatorleistung in %
Heat_Recovery: float Wärmerückgewinnungsklappenstellung in %
#####SEC_Air_Damper: float Umluftklappenstellung in % [nur fuer Frischluftanteil benötigt]
Inlet_Temperature: float Temperatur der Zuluft hinterm Heizregister
Mixed_Temperature: float Temperatur der Zuluft vor dem Heizregister
:return: pandas DataFrame Eingangs-DataFrame mit zwei zusätzlichen Spalten:
Volume_Flow: float Luftvolumenstrom Lufteinlass in m³/h
Energy_Flow: float Kühl-/Heizleistung des Kühl-/Heizregisters in kW
'''
try:
df['Volume_Flow'] = df.apply(lambda row: bilinear_interpolation(row['Fan_Power'], row['Heat_Recovery']), axis=1)
#df['Fresh_Air_Flow'] = df.apply(lambda row: V_i_cond(row['SEC_Air_Damper'],
# row['Heat_Recovery'],
# row['Fan_Power']), axis=1)
# Volume_Flow [m³/h] * rho_L [kg/m³] * h/s * (T_out-T_in) [K] * cp_L [KJ/kg*K]
df['Energy_Flow'] = df.apply(lambda row: row['Volume_Flow'] * 1.2041/3600 *
(row['Inlet_Temperature'] - row['Mixed_Temperature']) * 1.01, axis=1)
return df
except:
df['Volume_Flow'] = NaN
df['Energy_Flow'] = NaN
return df
modified script
from numpy.core.numeric import NaN
import pandas as pd
import math
import pickle
import pytz
df = pd.read_csv('(.....)/Tnew.csv')
Ventil_Spec = [[0, 17.39130435, 34.7826087, 45.65217391, 65.2173913, 78.26086957, 95.65217391, 121.7391304, 139.1304348,
165.2173913, 186.9565217],
[0, 17.39130435, 34.7826087, 60.86956522, 86.95652174, 106.5217391, 139.1304348, 171.7391304, 200,
230.4347826, 265.2173913],
[0, 23.91304348, 43.47826087, 65.2173913, 100, 117.3913043, 147.826087, 178.2608696, 210.8695652,
236.9565217, 269.5652174]] # Air flow through the FVU in m3/h
#def bilinear_interpolation(x, y, points=None):
def bilinear_interpolation(Fan_Power, Heat_Recovery, points = None):
''' based on https://stackoverflow.com/a/8662355
x is the Fan power, y the Heat recovery opening, both in the case of a closed secondary air damper
This function only interpolates the volume flow rate from the given Ventil_Spec list, using the
Fan power and Heat recovery opening
Interpolate (x,y) from values associated with four points.
The four points are a list of four triplets: (x, y, value).
The four points can be in any order. They should form a rectangle.
>>> bilinear_interpolation(12, 5.5,
... [(10, 4, 100),
... (20, 4, 200),
... (10, 6, 150),
... (20, 6, 300)])
165.0
:param x: float Fan Power in percent
:param y: float Heat recovery opening position in percent
:param points: list List of corner points for bilinear interpolation
:return:
'''
if math.isnan(Fan_Power):
return Fan_Power
if math.isnan(Heat_Recovery):
Heat_Recovery = 0
# See formula at: http://en.wikipedia.org/wiki/Bilinear_interpolation
# def points_fvu_volume_flow(x_fan):
def points_fvu_volume_flow(Fan_Power):
# returns the corner points for the bilinear interpolation for the EBC FVUs, depending on the fan power,
# in the case of a closed secondary air damper
if Fan_Power > 100:
Fan_Power = 100
x1_fan = int(Fan_Power / 10)
x2_fan = math.ceil(Fan_Power / 10)
z1 = Ventil_Spec[1][x1_fan]
z2 = Ventil_Spec[1][x2_fan]
z3 = Ventil_Spec[2][x1_fan]
z4 = Ventil_Spec[2][x2_fan]
return [(x1_fan * 10, 0, z1), (x2_fan * 10, 0, z2), (x1_fan * 10, 100, z3), (x2_fan * 10, 100, z4)]
if points is None:
points = points_fvu_volume_flow(Fan_Power)
points = sorted(points) # order points by x, then by y
(x1, y1, q11), (_x1, y2, q12), (x2, _y1, q21), (_x2, _y2, q22) = points
# if x1 != _x1 or x2 != _x2 or y1 != _y1 or y2 != _y2:
# raise ValueError('points do not form a rectangle')
# if not x1 <= x <= x2 or not y1 <= y <= y2:
# raise ValueError('(x, y) not within the rectangle')
if x1 == x2 and _x1 == _x2 and x1 == _x1:
y_top = max(q11, q12, q21, q22)
y_bot = min(q11, q12, q21, q22)
return (y_top - y_bot) / 100 * Heat_Recovery + y_bot
if y1 == y2 and _y1 == _y2 and y1 == _y1:
x_top = max(q11, q12, q21, q22)
x_bot = min(q11, q12, q21, q22)
return (x_top - x_bot) / 100 * Fan_Power + x_bot
return (q11 * (x2 - Fan_Power) * (y2 - Heat_Recovery) +
q21 * (Fan_Power - x1) * (y2 - Heat_Recovery) +
q12 * (x2 - Fan_Power) * (Heat_Recovery - y1) +
q22 * (Fan_Power - x1) * (Heat_Recovery - y1)
) / ((x2 - x1) * (y2 - y1) + 0.0)
def calc_energy_use(df):
'''
:param df: pandas DataFrame Dataframe mit zumindest den folgenden Spalten:
Fan_Power: float Zuluftventilatorleistung in %
Heat_Recovery: float Wärmerückgewinnungsklappenstellung in %
#####SEC_Air_Damper: float Umluftklappenstellung in % [nur fuer Frischluftanteil benötigt]
Inlet_Temperature: float Temperatur der Zuluft hinterm Heizregister
Mixed_Temperature: float Temperatur der Zuluft vor dem Heizregister
:return: pandas DataFrame Eingangs-DataFrame mit zwei zusätzlichen Spalten:
Volume_Flow: float Luftvolumenstrom Lufteinlass in m³/h
Energy_Flow: float Kühl-/Heizleistung des Kühl-/Heizregisters in kW
'''
try:
df['Volume_Flow'] = df.apply(lambda row: bilinear_interpolation(row['Fan_Power'], row['Heat_Recovery']), axis=1)
#df['Fresh_Air_Flow'] = df.apply(lambda row: V_i_cond(row['SEC_Air_Damper'],
# row['Heat_Recovery'],
# row['Fan_Power']), axis=1)
# Volume_Flow [m³/h] * rho_L [kg/m³] * h/s * (T_out-T_in) [K] * cp_L [KJ/kg*K]
df['Energy_Flow'] = df.apply(lambda row: row['Volume_Flow'] * 1.2041/3600 *
(row['Inlet_Temperature'] - row['Mixed_Temperature']) * 1.01, axis=1)
return df
except:
df['Volume_Flow'] = NaN
df['Energy_Flow'] = NaN
return df