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Use dimes to generate plots.
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@nathancoliver
nathancoliver committed Jan 24, 2024
1 parent ea9fa86 commit 05b0bb4
Showing 1 changed file with 58 additions and 184 deletions.
242 changes: 58 additions & 184 deletions scripts/python/create_test_plot.py
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Original file line number Diff line number Diff line change
@@ -1,10 +1,12 @@
import sys
from pathlib import Path

import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from koozie import convert
import dimes # type: ignore
from dimes import LineProperties
import pandas as pd # type: ignore
import plotly.graph_objects as go # type: ignore
from plotly.subplots import make_subplots # type: ignore
from koozie import convert # type: ignore

if len(sys.argv) != 4:
sys.exit("Incorrect number of arguments. Must be two: Measured Path, Simulated Path, Output Path")
Expand All @@ -13,19 +15,11 @@
simulated_path = Path(sys.argv[2])
output_path = Path(sys.argv[3])

pd.set_option("display.max_rows", 1500)

DEGREE_SIGN = "\N{DEGREE SIGN}"
GRID_LINE_WIDTH = 1.5
GRID_LINES_COLOR = "rgba(128,128,128,0.3)"
RED_BLUE_DIVERGING_PALLETTE = [
"#003a6d",
"#0072c3",
"#33b1ff",
"#ff8389",
"#da1e28",
"#750e13",
]
# TODO: reverse colors in list below, and revert reverse in variables dictionary
RED_BLUE_DIVERGING_PALLETTE = ["#750e13", "#da1e28", "#ff8389", "#33b1ff", "#0072c3", "#003a6d"]

NUMBER_OF_THERMOCOUPLES = 6

Expand All @@ -47,37 +41,6 @@ def filter_dataframe_range(df, variable_type):
return df[(df[column_time_name] > 0) & (df[column_time_name] <= 1440)].reset_index()


def calculate_energy_consumption():
global variables, df_measured, df_simulated
column_energy_name_measured = variables["Y-Variables"]["Power Input"][
"Column Names"
]["Measured"]
column_energy_name_simulated = variables["Y-Variables"]["Power Input"][
"Column Names"
]["Simulated"]

# set parasitic load to 0
df_measured_no_parasitic_load = df_measured[column_energy_name_measured].where(
df_measured[column_energy_name_measured] > 10, 0
)

print(
f"Measured Energy Consumption: {df_measured[column_energy_name_measured].sum().values[0] / 60:.0f} Wh"
)
# print(f"Measured Energy (no parasitic load) {df_measured_no_parasitic_load[column_energy_name_measured].sum().values[0]/60:.0f} Wh")
print(
f"Simulated Energy Consumption: {df_simulated[column_energy_name_simulated].sum().values[0] / 60:.0f} Wh"
)


def set_inlet_temperature_to_nan_at_no_draw(df, inlet_temperature_column, draw_column):
for index in range(len(df)):
if df.loc[index, draw_column] == 0:
df.loc[index, inlet_temperature_column] = None

return df


def calculate_average_tank_temperature(variable_type):
global df_measured, df_simulated

Expand Down Expand Up @@ -106,9 +69,7 @@ def calculate_average_tank_temperature(variable_type):
}

for index, label in enumerate(TEMPERATURE_DETAILS[variable_type]):
variables["Y-Variables"]["Temperature"]["Column Names"][variable_type].insert(
index, label
)
variables["Y-Variables"]["Temperature"]["Column Names"][variable_type].insert(index, label)

for temperature_column in variables["Y-Variables"]["Temperature"]["Column Names"][
variable_type
Expand All @@ -131,7 +92,7 @@ def add_temperature_details():
],
"Colors": ["black", "orange", "purple", "limegreen"],
"Line Mode": ["lines", "lines+markers", "lines", "lines"],
"Line Visibility": [True, True, "legendonly", "legendonly"],
"Line Visibility": [True, True, False, False],
}

for key in TEMPERATURE_DETAILS.keys():
Expand All @@ -147,23 +108,12 @@ def add_temperature_details():
# convert measured power from kW to W
df_measured["Power_kW"] = convert_values(df_measured["Power_kW"], "kW", "W")
# convert simulated energy consumption (Wh) for every minute to power (W)
# (Wh / min) * (60 min / 1 h)
df_simulated["h_src1In (Wh)"] = convert_values(
df_simulated["h_src1In (Wh)"], "Wh/min", "W"
)

fig = go.Figure()
fig = make_subplots(rows=3, shared_xaxes=True, vertical_spacing=0.05)
df_simulated["h_src1In (Wh)"] = convert_values(df_simulated["h_src1In (Wh)"], "Wh/min", "W")

# link column names in measured and simulated dataframes to variable name
# link variable name to units and graph color
variables = {
"Y-Variables": {
"Power Input": {
"Column Names": {
"Measured": ["Power_kW"],
"Simulated": ["h_src1In (Wh)"],
},
"Column Names": {"Measured": ["Power_kW"], "Simulated": ["h_src1In (Wh)"]},
"Labels": ["Power Input"],
"Units": "W",
"Colors": ["red"],
Expand All @@ -173,30 +123,29 @@ def add_temperature_details():
"Flow Rate": {
"Column Names": {"Measured": ["flow_out_gpm"], "Simulated": ["draw"]},
"Labels": ["Flow Rate"],
"Units": "Gal/min",
"Units": "gal/min",
"Colors": ["green"],
"Line Mode": ["lines"],
"Line Visibility": [True],
},
"Temperature": {
"Column Names": {
"Measured": [
f"T_Tank_{str(number)}"
for number in range(1, NUMBER_OF_THERMOCOUPLES + 1)
f"T_Tank_{str(number)}" for number in range(1, NUMBER_OF_THERMOCOUPLES + 1)
],
"Simulated": [
f"tcouple{number} (C)"
for number in range(1, NUMBER_OF_THERMOCOUPLES + 1)
for number in reversed(range(1, NUMBER_OF_THERMOCOUPLES + 1))
],
},
"Labels": [
f"Storage Tank Temperature {number}"
for number in range(1, NUMBER_OF_THERMOCOUPLES + 1)
],
"Units": f"{DEGREE_SIGN}F",
"Colors": list(RED_BLUE_DIVERGING_PALLETTE),
"Colors": list(reversed(RED_BLUE_DIVERGING_PALLETTE)),
"Line Mode": ["lines"] * NUMBER_OF_THERMOCOUPLES,
"Line Visibility": ["legendonly"] * NUMBER_OF_THERMOCOUPLES,
"Line Visibility": [False] * NUMBER_OF_THERMOCOUPLES,
},
},
"X-Variables": {
Expand All @@ -217,9 +166,6 @@ def add_temperature_details():
# add average, inlet, and outlet temperature details (ex. visibility, color, etc.) to variables dictionary
add_temperature_details()

# print measured and simulated energy consumption
calculate_energy_consumption()


def retrieve_dataframe(variable_type):
if variable_type == "Measured":
Expand All @@ -228,136 +174,64 @@ def retrieve_dataframe(variable_type):
return df_simulated


def retrieve_line_type(variable_type, value):
def retrieve_line_type(variable_type):
if variable_type == "Measured":
return dict(color=variables["Y-Variables"][variable]["Colors"][value])
return None
elif variable_type == "Simulated":
return dict(
color=variables["Y-Variables"][variable]["Colors"][value], dash="dot"
)
return "dot"


def plot_graphs(variable_type, variable, value, row):
df = retrieve_dataframe(variable_type)
LINE_TYPE = retrieve_line_type(variable_type, value)

if (value in [1, 2]) and (variable_type == "Measured"):
fillcolor = "white"
marker = dict(
size=7,
line=dict(
color=variables["Y-Variables"][variable]["Colors"][value], width=2
),
)
elif (value in [1, 2]) and (variable_type == "Simulated"):
fillcolor = None
marker = dict(
size=7,
color="white",
line=dict(
color=variables["Y-Variables"][variable]["Colors"][value], width=2
),
)
marker_symbol = "circle"
marker_size = 7
marker_fill_color = "white"
marker_line_color = variables["Y-Variables"][variable]["Colors"][value]
elif (value == 1) and (variable_type == "Simulated"):
marker_symbol = "circle"
marker_size = 7
marker_fill_color = variables["Y-Variables"][variable]["Colors"][value]
marker_line_color = variables["Y-Variables"][variable]["Colors"][value]
else:
fillcolor = None
marker = None

fig.add_trace(
go.Scatter(
x=df[variables["X-Variables"]["Time"]["Column Names"][variable_type]],
y=df[
variables["Y-Variables"][variable]["Column Names"][variable_type][value]
],
marker_symbol = None
marker_size = None
marker_fill_color = None
marker_line_color = None

plot.add_time_series(
dimes.TimeSeriesData(
df[variables["Y-Variables"][variable]["Column Names"][variable_type][value]],
name=f"{variables['Y-Variables'][variable]['Labels'][value]} - {variable_type}",
mode="lines+markers"
if (value == 2) and (variable_type == "Measured")
else variables["Y-Variables"][variable]["Line Mode"][value],
marker=marker,
fillcolor=fillcolor,
line=LINE_TYPE,
visible=variables["Y-Variables"][variable]["Line Visibility"][value],
dimension=variable,
native_units=variables["Y-Variables"][variable]["Units"],
line_properties=LineProperties(
color=variables["Y-Variables"][variable]["Colors"][value],
line_type=retrieve_line_type(variable_type),
marker_symbol=marker_symbol,
marker_size=marker_size,
marker_line_color=marker_line_color,
marker_fill_color=marker_fill_color,
is_visible=variables["Y-Variables"][variable]["Line Visibility"][value],
),
),
row=row,
col=1,
subplot_number=row,
axis_name=variable,
)


def retrieve_y_axis_range(y_max, y_min, row, variable_type):
df = retrieve_dataframe(variable_type)
y_max[row].append(
max(
df[variables["Y-Variables"][variable]["Column Names"][variable_type][value]]
)
)
y_min[row].append(
min(
df[variables["Y-Variables"][variable]["Column Names"][variable_type][value]]
)
)
return y_max, y_min

# remove rows from dataframes outside of inclusive range [1,1440]
df_measured = filter_dataframe_range(df_measured, "Measured")
df_simulated = filter_dataframe_range(df_simulated, "Simulated")

y_max = [[], [], []]
y_min = [[], [], []]
plot = dimes.TimeSeriesPlot(
df_measured[variables["X-Variables"]["Time"]["Column Names"]["Measured"]]
)

for row, variable in enumerate(variables["Y-Variables"].keys()):
for variable_type in variables["Y-Variables"][variable]["Column Names"].keys():
for value in range(
len(variables["Y-Variables"][variable]["Column Names"][variable_type])
):
for value in range(len(variables["Y-Variables"][variable]["Column Names"][variable_type])):
plot_graphs(variable_type, variable, value, row + 1)
y_max, y_min = retrieve_y_axis_range(y_max, y_min, row, variable_type)

BLANK_SPACE = 0.1
y_range = [[], [], []]

for row in range(3):
y_max_row = max(y_max[row])
y_min_row = min(y_min[row])
y_range_row = y_max_row - y_min_row
y_range[row].append(y_min_row - y_range_row * BLANK_SPACE)
y_range[row].append(y_max_row + y_range_row * BLANK_SPACE)

fig.update_layout(
xaxis_rangeslider_visible=False,
xaxis2_rangeslider_visible=False,
xaxis3_rangeslider_visible=False,
yaxis_title="Power Input<br>(W)",
yaxis2_title="Flow Rate<br>(gpm)<br>",
yaxis3_title=f"Temperature<br>({DEGREE_SIGN}F)",
yaxis=dict(range=y_range[0]),
yaxis2=dict(range=y_range[1]),
yaxis3=dict(range=y_range[2]),
xaxis3_title="Time (min)",
title="HPWH Data",
title_x=0.5,
plot_bgcolor="white",
font_color="black",
margin=dict(l=0, r=0, t=30, b=40),
)
fig.update_xaxes(
linecolor="black",
linewidth=GRID_LINE_WIDTH,
mirror=True,
ticks="outside",
tickson="boundaries",
tickwidth=GRID_LINE_WIDTH,
tickcolor="black",
minallowed=1,
maxallowed=1440,
rangeslider=dict(bgcolor="grey", thickness=0.05),
)
fig.update_yaxes(
showline=True,
linecolor="black",
linewidth=GRID_LINE_WIDTH,
mirror=True,
showgrid=True,
gridcolor=GRID_LINES_COLOR,
gridwidth=GRID_LINE_WIDTH,
fixedrange=False,
zeroline=True,
zerolinecolor=GRID_LINES_COLOR,
)

fig.write_html(output_path)
plot.write_html_plot(output_path)

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