How to use the floris.tools.power_rose function in floris

wind_rose.plot_wind_rose()

# =============================================================================
print("Finding power with and without wakes in FLORIS...")
# =============================================================================

# Instantiate the Optimization object
# Note that the optimization is not performed in this example.
yaw_opt = YawOptimizationWindRose(fi, df.wd, df.ws, minimum_ws=minimum_ws)

# Determine baseline power with and without wakes
df_base = yaw_opt.calc_baseline_power()

# Initialize power rose
case_name = "Example " + str(N_row) + " x " + str(N_row) + " Wind Farm"
power_rose = pr.PowerRose()
power_rose.make_power_rose_from_user_data(
    case_name, df, df_base["power_no_wake"], df_base["power_baseline"]
)

# Display AEP analysis
fig, axarr = plt.subplots(2, 1, sharex=True, figsize=(6.4, 6.5))
power_rose.plot_by_direction(axarr)
power_rose.report()

plt.show()

power_dict[i] = {
        "ws": df.ws[i],
        "wd": df.wd[i],
        "power_baseline": np.sum(power_base),
        "turbine_power_baseline": power_base,
        "power_no_wake": np.sum(power_no_wake),
        "turbine_power_no_wake": power_no_wake,
    }

df_base = pd.DataFrame.from_dict(power_dict, "index")
df_base.reset_index(drop=True, inplace=True)

# Initialize power rose
case_name = "Example " + str(N_row) + " x " + str(N_row) + " Wind Farm"
power_rose = pr.PowerRose()
power_rose.make_power_rose_from_user_data(
    case_name, df, df_base["power_no_wake"], df_base["power_baseline"]
)

# Display AEP analysis
fig, axarr = plt.subplots(2, 1, sharex=True, figsize=(6.4, 6.5))
power_rose.plot_by_direction(axarr)
power_rose.report()

plt.show()

maximum_yaw_angle=max_yaw,
        minimum_ws=minimum_ws,
        maximum_ws=maximum_ws,
        include_unc=include_unc,
        unc_options=unc_options,
    )

    # Determine baseline power
    df_base = yaw_opt.calc_baseline_power()

    # Perform optimization
    df_opt = yaw_opt.optimize()

    # Initialize power rose
    case_name = "Example " + str(N_row) + " x " + str(N_row) + " Wind Farm"
    power_rose = pr.PowerRose()
    power_rose.make_power_rose_from_user_data(
        case_name,
        df,
        df_base["power_no_wake"],
        df_base["power_baseline"],
        df_opt["power_opt"],
    )

    # Summarize using the power rose module
    fig, axarr = plt.subplots(3, 1, sharex=True, figsize=(6.4, 6.5))
    power_rose.plot_by_direction(axarr)
    power_rose.report()

plt.show()

df_yaw = pd.DataFrame([list(row) for row in df_opt['yaw_angles']],columns=[str(i) for i in range(1,N_turb+1)])
    df_yaw['ws'] = df.ws
    df_yaw['wd'] = df.wd
    df_turbine_power_no_wake = pd.DataFrame([list(row) for row in df_base['turbine_power_no_wake']],columns=[str(i) for i in range(1,N_turb+1)])
    df_turbine_power_no_wake['ws'] = df.ws
    df_turbine_power_no_wake['wd'] = df.wd
    df_turbine_power_baseline = pd.DataFrame([list(row) for row in df_base['turbine_power_baseline']],columns=[str(i) for i in range(1,N_turb+1)])
    df_turbine_power_baseline['ws'] = df.ws
    df_turbine_power_baseline['wd'] = df.wd
    df_turbine_power_opt = pd.DataFrame([list(row) for row in df_opt['turbine_power_opt']],columns=[str(i) for i in range(1,N_turb+1)])
    df_turbine_power_opt['ws'] = df.ws
    df_turbine_power_opt['wd'] = df.wd

    # Summarize using the power rose module
    case_name = 'Example '+str(N_row)+' x '+str(N_row)+ ' Wind Farm'
    power_rose = pr.PowerRose(case_name, df_power, df_turbine_power_no_wake, df_turbine_power_baseline,df_yaw, df_turbine_power_opt)


    fig, axarr = plt.subplots(3, 1, sharex=True, figsize=(6.4, 6.5))
    power_rose.plot_by_direction(axarr)
    power_rose.report()

    plt.show()

df.ws,
    minimum_yaw_angle=min_yaw,
    maximum_yaw_angle=max_yaw,
    minimum_ws=minimum_ws,
    maximum_ws=maximum_ws,
)

# Determine baseline power with and without wakes
df_base = yaw_opt.calc_baseline_power()

# Perform optimization
df_opt = yaw_opt.optimize()

# Initialize power rose
case_name = "Example " + str(N_row) + " x " + str(N_row) + " Wind Farm"
power_rose = pr.PowerRose()
power_rose.make_power_rose_from_user_data(
    case_name,
    df,
    df_base["power_no_wake"],
    df_base["power_baseline"],
    df_opt["power_opt"],
)

# Summarize using the power rose module
fig, axarr = plt.subplots(3, 1, sharex=True, figsize=(6.4, 6.5))
power_rose.plot_by_direction(axarr)
power_rose.report()

plt.show()

df_base["power_no_wake"],
    df_base["power_baseline"],
    df_opt["power_opt"],
)

# Summarize using the power rose module
fig, axarr = plt.subplots(3, 1, sharex=True, figsize=(6.4, 6.5))
power_rose.plot_by_direction(axarr)
power_rose.report()

plt.show()

# Summarize AEP gains with uncertainty
# Initialize power rose
case_name = "Example 2 Turbine Wind Farm With Uncertainty and Robust Yaw Angles"
power_rose = pr.PowerRose()
power_rose.make_power_rose_from_user_data(
    case_name,
    df,
    df_base_unc["power_no_wake"],
    df_base_unc["power_baseline"],
    df_opt_unc["power_opt"],
)

# Summarize using the power rose module
fig, axarr = plt.subplots(3, 1, sharex=True, figsize=(6.4, 6.5))
power_rose.plot_by_direction(axarr)
power_rose.report()

plt.show()

df_yaw = pd.DataFrame([list(row) for row in df_opt_unc['yaw_angles']],columns=[str(i) for i in range(1,N_turb+1)])
df_yaw['ws'] = df.ws
df_yaw['wd'] = df.wd
df_turbine_power_no_wake = pd.DataFrame([list(row) for row in df_base_unc['turbine_power_no_wake']],columns=[str(i) for i in range(1,N_turb+1)])
df_turbine_power_no_wake['ws'] = df.ws
df_turbine_power_no_wake['wd'] = df.wd
df_turbine_power_baseline = pd.DataFrame([list(row) for row in df_base_unc['turbine_power_baseline']],columns=[str(i) for i in range(1,N_turb+1)])
df_turbine_power_baseline['ws'] = df.ws
df_turbine_power_baseline['wd'] = df.wd
df_turbine_power_opt = pd.DataFrame([list(row) for row in df_opt_unc['turbine_power_opt']],columns=[str(i) for i in range(1,N_turb+1)])
df_turbine_power_opt['ws'] = df.ws
df_turbine_power_opt['wd'] = df.wd

# Summarize using the power rose module
case_name = 'Example 2 Turbine Wind Farm With Uncertainty and Robust Yaw Angles'
power_rose = pr.PowerRose(case_name, df_power, df_turbine_power_no_wake, df_turbine_power_baseline,df_yaw, df_turbine_power_opt)


fig, axarr = plt.subplots(3, 1, sharex=True, figsize=(6.4, 6.5))
power_rose.plot_by_direction(axarr)
power_rose.report()

plt.show()

df_yaw = pd.DataFrame([list(row) for row in df_opt['yaw_angles']],columns=[str(i) for i in range(1,N_turb+1)])
df_yaw['ws'] = df.ws
df_yaw['wd'] = df.wd
df_turbine_power_no_wake = pd.DataFrame([list(row) for row in df_base['turbine_power_no_wake']],columns=[str(i) for i in range(1,N_turb+1)])
df_turbine_power_no_wake['ws'] = df.ws
df_turbine_power_no_wake['wd'] = df.wd
df_turbine_power_baseline = pd.DataFrame([list(row) for row in df_base['turbine_power_baseline']],columns=[str(i) for i in range(1,N_turb+1)])
df_turbine_power_baseline['ws'] = df.ws
df_turbine_power_baseline['wd'] = df.wd
df_turbine_power_opt = pd.DataFrame([list(row) for row in df_opt['turbine_power_opt']],columns=[str(i) for i in range(1,N_turb+1)])
df_turbine_power_opt['ws'] = df.ws
df_turbine_power_opt['wd'] = df.wd

# Summarize using the power rose module
case_name = 'Example '+str(N_row)+' x '+str(N_row)+ ' Wind Farm'
power_rose = pr.PowerRose(case_name, df_power, df_turbine_power_no_wake, df_turbine_power_baseline,df_yaw, df_turbine_power_opt)


fig, axarr = plt.subplots(3, 1, sharex=True, figsize=(6.4, 6.5))
power_rose.plot_by_direction(axarr)
power_rose.report()

plt.show()

df_yaw = pd.DataFrame([list(row) for row in df_opt['yaw_angles']],columns=[str(i) for i in range(1,N_turb+1)])
df_yaw['ws'] = df.ws
df_yaw['wd'] = df.wd
df_turbine_power_no_wake = pd.DataFrame([list(row) for row in df_base['turbine_power_no_wake']],columns=[str(i) for i in range(1,N_turb+1)])
df_turbine_power_no_wake['ws'] = df.ws
df_turbine_power_no_wake['wd'] = df.wd
df_turbine_power_baseline = pd.DataFrame([list(row) for row in df_base['turbine_power_baseline']],columns=[str(i) for i in range(1,N_turb+1)])
df_turbine_power_baseline['ws'] = df.ws
df_turbine_power_baseline['wd'] = df.wd
df_turbine_power_opt = pd.DataFrame([list(row) for row in df_opt['turbine_power_opt']],columns=[str(i) for i in range(1,N_turb+1)])
df_turbine_power_opt['ws'] = df.ws
df_turbine_power_opt['wd'] = df.wd

# Summarize using the power rose module
case_name = 'Example 2 Turbine Wind Farm Without Uncertainty'
power_rose = pr.PowerRose(case_name, df_power, df_turbine_power_no_wake, df_turbine_power_baseline,df_yaw, df_turbine_power_opt)


fig, axarr = plt.subplots(3, 1, sharex=True, figsize=(6.4, 6.5))
power_rose.plot_by_direction(axarr)
power_rose.report()

# Summarize AEP gains with uncertainty
# combine wind farm-level power into one dataframe
df_power = pd.DataFrame({'ws':df.ws,'wd':df.wd, \
    'freq_val':df.freq_val,'power_no_wake':df_base_unc.power_no_wake, \
    'power_baseline':df_base_unc.power_baseline,'power_opt':df_opt_unc.power_opt})

# initialize power rose
df_yaw = pd.DataFrame([list(row) for row in df_opt_unc['yaw_angles']],columns=[str(i) for i in range(1,N_turb+1)])
df_yaw['ws'] = df.ws
df_yaw['wd'] = df.wd

minimum_ws=minimum_ws,
    maximum_ws=maximum_ws,
    include_unc=True,
    unc_options=unc_options,
)

# Determine baseline power with and without wakes
df_base_unc = yaw_opt.calc_baseline_power()

# Perform optimization
df_opt_unc = yaw_opt.optimize()

# Summarize AEP gains without uncertainty
# Initialize power rose
case_name = "Example 2 Turbine Wind Farm Without Uncertainty"
power_rose = pr.PowerRose()
power_rose.make_power_rose_from_user_data(
    case_name,
    df,
    df_base["power_no_wake"],
    df_base["power_baseline"],
    df_opt["power_opt"],
)

# Summarize using the power rose module
fig, axarr = plt.subplots(3, 1, sharex=True, figsize=(6.4, 6.5))
power_rose.plot_by_direction(axarr)
power_rose.report()

plt.show()

# Summarize AEP gains with uncertainty