-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathtops_bottoms.py
129 lines (107 loc) · 3.85 KB
/
tops_bottoms.py
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
import numpy as np
import pandas as pd
import seaborn as sns
from datetime import datetime
from sklearn.preprocessing import MinMaxScaler
from sklearn.tree import DecisionTreeRegressor
import random
#commodity_list = []
from update_commodities import update_list
commodity_list=update_list()
annual_rainfall = [29, 21, 37.5, 30.7, 52.6, 150, 299, 251.7, 179.2, 70.5, 39.8, 10.9]
base = {
"Paddy": 1245.5,
"Arhar": 3200,
"Bajra": 1175,
"Barley": 980,
"Copra": 5100,
"Cotton": 3600,
"Sesamum": 4200,
"Gram": 2800,
"Groundnut": 3700,
"Jowar": 1520,
"Maize": 1175,
"Masoor": 2800,
"Moong": 3500,
"Niger": 3500,
"Ragi": 1500,
"Rape": 2500,
"Jute": 1675,
"Safflower": 2500,
"Soyabean": 2200,
"Sugarcane": 2250,
"Sunflower": 3700,
"Urad": 4300,
"Wheat": 1350
}
commodity_dict = {
"arhar": "crop_data/Arhar.csv",
"bajra": "crop_data/Bajra.csv",
"barley": "crop_data/Barley.csv",
"copra": "crop_data/Copra.csv",
"cotton": "crop_data/Cotton.csv",
"sesamum": "crop_data/Sesamum.csv",
"gram": "crop_data/Gram.csv",
"groundnut": "crop_data/Groundnut.csv",
"jowar": "crop_data/Jowar.csv",
"maize": "crop_data/Maize.csv",
"masoor": "crop_data/Masoor.csv",
"moong": "crop_data/Moong.csv",
"niger": "crop_data/Niger.csv",
"paddy": "crop_data/Paddy.csv",
"ragi": "crop_data/Ragi.csv",
"rape": "crop_data/Rape.csv",
"jute": "crop_data/Jute.csv",
"safflower": "crop_data/Safflower.csv",
"soyabean": "crop_data/Soyabean.csv",
"sugarcane": "crop_data/Sugarcane.csv",
"sunflower": "crop_data/Sunflower.csv",
"urad": "crop_data/Urad.csv",
"wheat": "crop_data/Wheat.csv"
}
def TopFiveCrops():
current_month = datetime.now().month
current_year = datetime.now().year
current_rainfall = annual_rainfall[current_month - 1]
prev_month = current_month - 1
prev_rainfall = annual_rainfall[prev_month - 1]
current_month_prediction = []
prev_month_prediction = []
change = []
for i in commodity_list:
current_predict = i.getPredictedValue([float(current_month), current_year, current_rainfall])
current_month_prediction.append(current_predict)
prev_predict = i.getPredictedValue([float(prev_month), current_year, prev_rainfall])
prev_month_prediction.append(prev_predict)
change.append((((current_predict - prev_predict) * 100 / prev_predict), commodity_list.index(i)))
sorted_change = change
sorted_change.sort(reverse=True)
to_send = []
for j in range(0, 5):
perc, i = sorted_change[j]
name = commodity_list[i].getCropName().split('/')[1]
to_send.append([name, round((current_month_prediction[i] * base[name]) / 100, 2), round(perc, 2)])
return to_send
def BottomFiveCrops():
current_month = datetime.now().month
current_year = datetime.now().year
current_rainfall = annual_rainfall[current_month - 1]
prev_month = current_month - 1
prev_rainfall = annual_rainfall[prev_month - 1]
current_month_prediction = []
prev_month_prediction = []
change = []
for i in commodity_list:
current_predict = i.getPredictedValue([float(current_month), current_year, current_rainfall])
current_month_prediction.append(current_predict)
prev_predict = i.getPredictedValue([float(prev_month), current_year, prev_rainfall])
prev_month_prediction.append(prev_predict)
change.append((((current_predict - prev_predict) * 100 / prev_predict), commodity_list.index(i)))
sorted_change = change
sorted_change.sort()
to_send = []
for j in range(0, 5):
perc, i = sorted_change[j]
name = commodity_list[i].getCropName().split('/')[1]
to_send.append([name, round((current_month_prediction[i] * base[name]) / 100, 2), round(perc, 2)])
return to_send