-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathbirkie_data_plotting.py
263 lines (240 loc) · 11.7 KB
/
birkie_data_plotting.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
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
#random scritps for playing with birkie results data
import math
import argparse
import matplotlib.pyplot as plt
from scipy import stats
import numpy as np
import sys
import datetime
from bisect import bisect
import pandas as pd
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--tech", type=str, default='skate')
parser.add_argument("--length", type=str, default='birkie')
parser.add_argument("--wave", type=int, default=1)
parser.add_argument("--year", type=str, default='2024')
parser.add_argument("--plot", type=str, default='byWave')
parser.add_argument("--name", type=str, default='')
args = parser.parse_args()
allResults = readIn(args.length, args.tech)
#print(allResults.keys())
#resultsByYear(args.tech, args.length, allResults)
#allResults = readIn(args.length, 'skate')
if args.plot == 'byYear':
resultsByYear(args.tech, args.length, allResults)
elif args.plot == 'byWave':
resultsByWave(args.tech, args.length, allResults, int(args.year))
elif args.plot == 'wavePlacement':
getWavePlacement(allResults, int(args.year), args.tech,args.length,args.wave, args.name)
elif args.plot == 'wave_gaps':
wave_gaps(args.tech, args.length, allResults, int(args.year))
def resultsByWave(tech, length, allResults, plot_year):
waveTimes = {}
threeGap = []
cutoffs = {'skate':{2023:[180,193,207,221,238,260,289,339],2020: [161,174,187,205,224,262], 2019:[191,210,227,244,268,304], 2018:[177,194,210,226,248,281], 2016:[181,199,215,231,254,288]}, 'classic':{2020:[227, 262, 302, 350], 2019:[257, 294, 328, 372], 2018:[251, 287, 326, 374]}} #wave placement cutoff times, to the nearest minute
for year in [plot_year]:
waves = {}
allResults[year]['times'] = allResults[year][' Finish Time'].dt.hour*3600 + allResults[year][' Finish Time'].dt.minute*60 + allResults[year][' Finish Time'].dt.second
for index, row in allResults[year].iterrows(): #iterate over all skiers
bib = int(row[' Bib Number'])
wave = math.floor(bib / 1000)
seconds = row['times']
if not np.isnan(seconds):
if wave in waves:
waves[wave].append(seconds)
else:
waves[wave] = [seconds]
waveGaps = []
prevWaveAvg = 0
order_waves = {wave:waves[wave] for wave in sorted(list(waves.keys()))}
waves = order_waves
for wave in waves:
if len(waves[wave]) > 10:
#print(wave)
waveAvg = sum(waves[wave]) / float(len(waves[wave]))
waveGap = math.floor(waveAvg - prevWaveAvg)
if prevWaveAvg != 0:
waveGaps.append(waveGap)
prevWaveAvg = waveAvg
waveHist = stats.kde.gaussian_kde(waves[wave])
if tech == 'skate':
maxT = 28000
minT = 5000
if year == 2024:
maxT = 4*3600
minT = 3600
if tech == 'classic':
maxT = 32000
minT = 7000
if year == 2024:
maxT = 4*3600
minT = 3600
x = np.linspace(minT, maxT, 200)
plt.plot(x, waveHist(x), label = "Wave" + str(wave), linewidth = 1.5)
if year in cutoffs[tech]:
for i in range(len(cutoffs[tech][year])):
plt.axvline(cutoffs[tech][year][i]*60, linestyle = 'dashed')
print(year, waveGaps)
#if year != 2008 and year != 2016:
# threeGap.append(waveGaps[0])
plt.legend(prop = {'size':10})
plt.xlim([minT -200,maxT + 200])
plt.ylim([0,.0012])
plt.ylabel("Frequency")
plt.xlabel("Finishing times")
#times = ["2:00", "2:30", "3:00", "3:30", "4:00", "4:30", "5:00", "5:30", "6:00", "6:30", "7:00", "7:30","8:00"]
times = ["1:00","1:30", "2:00", "2:30", "3:00", "3:30", "4:00"]
#xticksValues = [7200, 9000, 10800, 12600, 14400, 16200, 18000, 19800, 21600, 23400, 25200, 26000, 27800]
xticksValues = [3600, 5400, 7200, 9000, 10800, 12600, 14400]
plt.xticks(xticksValues, times)
plt.title(length + " " + tech + " Finish Times by wave for " + str(year))
if year == plot_year:
plt.grid(True)
plt.savefig('graphs/' + length + "_" + tech + "FinishTimesbyWave_" + str(year) + '.png')
plt.show()
else:
plt.clf()
#print(sum(threeGap) / float(len(threeGap)))
def wave_gaps(tech, length, allResults, plot_year):
#calculate the percent back between the waves over the years
years = sorted(list(allResults.keys()))
waveGaps = {}
for year in years:
#print(year)
waves = {}
allResults[year]['times'] = allResults[year][' Finish Time'].dt.hour*3600 + allResults[year][' Finish Time'].dt.minute*60 + allResults[year][' Finish Time'].dt.second
for index, row in allResults[year].iterrows(): #iterate over all skiers
bib = int(row[' Bib Number'])
wave = math.floor(bib / 1000)
seconds = row['times']
if not np.isnan(seconds):
if wave in waves:
waves[wave].append(seconds)
else:
waves[wave] = [seconds]
prevWaveAvg = 0
order_waves = {wave:waves[wave] for wave in sorted(list(waves.keys()))}
waves = order_waves
for wave in waves:
if wave not in [35, 70] and wave < 90:
if len(waves[wave]) > 10:
waveAvg = sum(waves[wave]) / float(len(waves[wave]))
if prevWaveAvg != 0:
waveGap = math.floor(waveAvg - prevWaveAvg)/prevWaveAvg*100
wavePair = str(prevWave)+'-'+str(wave)
if wavePair not in waveGaps:
waveGaps[wavePair] = [[],[]]
waveGaps[wavePair][0].append(year)
waveGaps[wavePair][1].append(waveGap)
prevWaveAvg = waveAvg
prevWave = wave
#print(waveGaps['1-2'][1])
for wavePair in waveGaps:
plt.plot(waveGaps[wavePair][0], waveGaps[wavePair][1],label=wavePair)
plt.title('Wave gaps by year '+' '.join([length, tech]))
plt.ylabel('Percent difference between mean wave times')
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.tight_layout()
plt.savefig('./graphs/wave_gaps_by_year_'+'_'.join([length, tech]))
plt.show()
def resultsByYear(tech, length, allResults): #graphs histogram of results by year
for year in allResults:
allResults[year]['times'] = allResults[year][' Finish Time'].dt.hour*3600 + allResults[year][' Finish Time'].dt.minute*60 + allResults[year][' Finish Time'].dt.second
if tech == 'skate':
maxT = 28000
minT = 5000
if tech == 'classic':
maxT = 32000
minT = 7000
plotTimes = {}
for year in allResults:
results_array = allResults[year]['times'].dropna()
plotTimes[year] = stats.kde.gaussian_kde(results_array)
print(maxT)
x = np.linspace(minT, maxT, 200)
plt.gca().set_prop_cycle(plt.cycler('color', plt.cm.jet(np.linspace(0, 1, len(allResults.keys())))))
for year in plotTimes:
plt.plot(x,plotTimes[year](x), label = str(year)+ ' results', linewidth = 1.5 )
plt.legend(prop = {'size':10})
plt.xlim([minT -200,maxT + 200])
times = ["2:00", "2:30", "3:00", "3:30", "4:00", "4:30", "5:00", "5:30", "6:00", "6:30", "7:00"]
xticksValues = [7200, 9000, 10800, 12600, 14400, 16200, 18000, 19800, 21600, 23400, 25200]
plt.xticks(xticksValues, times)
plt.ylim([0,.00015])
plt.ylabel("Frequency")
plt.xlabel("Finishing times")
plt.grid(True)
plt.title(length + " " + tech + " Finish Times by year")
plt.savefig('graphs/'+length + "_" + tech + "FinishTimesbyYear_" + str(year) + '.png')
plt.show()
def getWavePlacement(allResults, year, tech, length, target_wave, skier):
#get the placement of an indididual skier in a given wave. The skier does not have to have been in the specified wave, but does have to have skied that race in that year and in that technique
if tech == 'classic' and target_wave <10:
target_wave+=10
waveTimes = []
allResults[year]['times'] = allResults[year][' Finish Time'].dt.hour*3600 + allResults[year][' Finish Time'].dt.minute*60 + allResults[year][' Finish Time'].dt.second
for index, row in allResults[year].iterrows(): #iterate over all skiers
bib = int(row[' Bib Number'])
wave = math.floor(bib / 1000)
#print(row['Time'], skier.lower().strip(), row['Name'].lower().strip())
if row['Name'].lower().strip() == skier.lower().strip():
targetTime = row['times']
elif target_wave == wave:
waveTime = row['times']
waveTimes.append(waveTime)
waveTimes = sorted(waveTimes)
place = bisect(waveTimes, targetTime)
print('place in wave '+str(target_wave)+' for '+str(skier)+':')
print(place+1)
print('out of: ')
print(len(waveTimes)) #if the wave is the wave the skier is in, this will be off by one
def parseTime(time):
#deprecated in favor of datetime
time = racer[4]
time = time.split(':')
hours = float(time[0])
minutes = float(time[1])
seconds = float(time[2][0:2])
seconds += 3600 * hours + 60 * minutes
return seconds
def readIn(distance, technique, path='yearly_data/', start_year = 2009, end_year=2024):
years = list(range(start_year, end_year+1))
years.remove(2017)
years.remove(2021)
allResults = {} #data will be a dictonary with entries for each year. within each year there will be a list of lists, with each lowest level list containing all the elements scraped from the results website
for year in years:
#print(year)
yearResults = []
event = distance + " " + technique + " " + str(year) + ".csv"
year_results = pd.read_csv(path+str(year)+'/'+event)
if 'Time' in year_results.columns:
year_results[' Finish Time'] = year_results['Time']
year_results[' Bib Number'] = year_results['Bib']
year_results['Name'] = year_results['Name'].str.split(',', expand=True)[1] + ' ' + year_results['Name'].str.split(',', expand=True)[0]
try:
year_results[' Finish Time'] = pd.to_datetime(year_results[' Finish Time'].str.strip(), format='%H:%M:%S.%f')
except ValueError:
year_results[' Finish Time'] = pd.to_datetime(year_results[' Finish Time'].str.strip().str.split(".").str[0], format='%H:%M:%S', errors='coerce')
allResults[year] = year_results
return allResults
def readIn_old(distance, technique, path='yearly_data/', start_year = 2010, end_year=2022):
years = list(range(start_year, end_year+1))
years.remove(2017)
years.remove(2021)
allResults = {} #data will be a dictonary with entries for each year. within each year there will be a list of lists, with each lowest level list containing all the elements scraped from the results website
for year in years:
yearResults = []
event = distance + " " + technique + " " + str(year) + ".csv"
try:
dataIn = open(path+str(year)+'/'+event, 'r')
for line in dataIn:
l = line.split(',')
if l[0] != "Name":
yearResults.append(l)
allResults[year] = yearResults
except IOError:
pass
return allResults
if __name__ == '__main__':
main()