OutageMap/findWeatherImpact.py at main · UCF-RISES/OutageMap · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
import numpy as np
import pandas as pd
import os
from util.mainHelper import weatherImpact,createLevelsAlt,findWeatherLevel

n=2  # Number of weather scenarios to simulate
network = "P3R"  # Network identifier
directories = [f"./{network}/Rain/nodes/", f"./{network}/Wind/nodes/"]  # Directories containing data
fileNames = [f for f in os.listdir(directories[0]) if os.path.isfile(os.path.join(directories[0], f))]  # List of filenames in the first directory

# Normalization Levels for Weather Data
windSeverityLevels = createLevelsAlt(0,120,10)
rainSeverityLevels = createLevelsAlt(0,6,10)

# Process files for nodes
for name in fileNames:
    alpha = {
        "elevation nodes": [0.4, 0.6],
        "vegetation": [0.8, 0.2]
    }
    rainDf = pd.read_csv(directories[0] + name)  # Load rain data
    windDf = pd.read_csv(directories[1] + name)  # Load wind data

    rainDf.drop(["Unnamed: 0"], axis=1, inplace=True)  # Remove extra index column if present in rain data
    windDf.drop(["Unnamed: 0"], axis=1, inplace=True)  # Remove extra index column if present in wind data

    # Calculate max and min values for rain and wind datasets
    maxValuesRain = np.array(rainDf.max(axis=1))
    maxValuesWind = np.array(windDf.max(axis=1))
    minValuesRain = np.array(rainDf.min(axis=1))
    minValuesWind = np.array(windDf.min(axis=1))

    # Initialize arrays to store scores and vectors in
    weatherVector = np.zeros((2, 767, n))
    score_wind = np.zeros((2, 767))
    score_rain = np.zeros((2, 767))

    # Loop through each node to find the normalized weather value
    for i in range(767):
        score_wind[0,i] = findWeatherLevel(minValuesWind[i],windSeverityLevels)
        score_wind[1,i] = findWeatherLevel(maxValuesWind[i],windSeverityLevels)

        score_rain[0,i] = findWeatherLevel(minValuesRain[i],rainSeverityLevels)
        score_rain[1,i] = findWeatherLevel(maxValuesRain[i],rainSeverityLevels)


    # Create the normalized weather vector
    weatherVector[0,:] = np.linspace(score_wind[0,:], score_wind[1,:], num=n).T
    weatherVector[1,:] = np.linspace(score_rain[0,:], score_rain[1,:], num=n).T

    # Compute weather impact for both interpolated points
    wi1 = weatherImpact(alpha, weatherVector[:,:,0])
    wi2 = weatherImpact(alpha, weatherVector[:,:,1])

    # Create dictionary to hold final weather impact ranges
    wi = {
        "elevation nodes": [],
        "vegetation": []
    }

    # Combine data for low and high scenarios
    for feature in wi:
        for low, high in zip(wi1[feature], wi2[feature]):
            wi[feature].append(((np.round(low,3), np.round(high,3))))

    # Create DataFrame and save to CSV
    events = pd.DataFrame(wi)
    pd.DataFrame.to_csv(events, f'./{network}/WI/nodes/{name}')

# Process files for edges (similar steps as nodes)
directories = [f"./{network}/Rain/edges/", f"./{network}/Wind/edges/"]
fileNames = [f for f in os.listdir(directories[0]) if os.path.isfile(os.path.join(directories[0], f))]

# Process files for edges
for name in fileNames:
    alpha = {
        "vegetation edges": [0.7, 0.3],
        "length": [0.9, 0.1],
    }

    rainDf = pd.read_csv(directories[0] + name)
    windDf = pd.read_csv(directories[1] + name)

    # Removing both types of unnamed columns that might have been erroneously created
    rainDf.drop(["Unnamed: 0.1", "Unnamed: 0"], axis=1, inplace=True)
    windDf.drop(["Unnamed: 0.1", "Unnamed: 0"], axis=1, inplace=True)

    # Calculate max and min values for rain and wind datasets
    maxValuesRain = rainDf.max(axis=1)
    maxValuesWind = windDf.max(axis=1)
    minValuesRain = rainDf.min(axis=1)
    minValuesWind = windDf.min(axis=1)

    # Initialize arrays to store scores and vectors in
    weatherVector = np.zeros((2, 766, n))
    score_wind = np.zeros((2, 766))
    score_rain = np.zeros((2, 766))

    # Loop through each edge to find the normalized weather value
    for i in range(766):
        score_wind[0,i] = findWeatherLevel(minValuesWind[i],windSeverityLevels)
        score_wind[1,i] = findWeatherLevel(maxValuesWind[i],windSeverityLevels)

        score_rain[0,i] = findWeatherLevel(minValuesRain[i],rainSeverityLevels)
        score_rain[1,i] = findWeatherLevel(maxValuesRain[i],rainSeverityLevels)

    # Create the normalized weather vector
    weatherVector[0,:] = np.linspace(score_wind[0,:], score_wind[1,:], num=n).T
    weatherVector[1,:] = np.linspace(score_rain[0,:], score_rain[1,:], num=n).T

    # Compute weather impact for both interpolated points
    wi1 = weatherImpact(alpha, weatherVector[:,:,0])
    wi2 = weatherImpact(alpha, weatherVector[:,:,1])

    # Create dictionary to hold final weather impact ranges
    wi = {
        "vegetation edges": [],
        "length": []
    }

    # Combine data for low and high scenarios
    for feature in wi:
        for low, high in zip(wi1[feature], wi2[feature]):
            wi[feature].append((np.round(low,3), np.round(high,3)))

    # Create DataFrame and save to CSV
    events = pd.DataFrame(wi)
    pd.DataFrame.to_csv(events, f'./{network}/WI/edges/{name}')