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2 | 2 | import numpy as np
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3 | 3 |
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4 | 4 | # raw_df = pd.read_csv(cv_file, names=header_list, error_bad_lines=False, dtype=str)
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5 |
| -raw_df1 = pd.read_csv("data/Sample_Game_2_RawTrackingData_Away_Team.csv") |
| 5 | +raw_df1 = pd.read_csv("data/source/Sample_Game_2_RawTrackingData_Away_Team.csv") |
6 | 6 | # sample every 5th row
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7 | 7 | raw_df1 = raw_df1.iloc[::7, :]
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8 | 8 |
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9 |
| -raw_df2 = pd.read_csv("data/Sample_Game_2_RawTrackingData_Home_Team.csv") |
| 9 | +raw_df2 = pd.read_csv("data/source/Sample_Game_2_RawTrackingData_Home_Team.csv") |
10 | 10 | raw_df2 = raw_df2.iloc[::7, :]
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11 | 11 |
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12 | 12 | column = 1
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13 | 13 | df = pd.DataFrame(columns=["half", "frame", "time", "x", "y"])
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14 | 14 | # x range needs adjusted depending on how many columns there are. Should really calculate this not eyeball items
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15 |
| -# and do it manually. But hey it's just for a demo not ongoing |
| 15 | +# and do it manually. |
16 | 16 | for x in range(0, 13):
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17 | 17 | column = column + 2
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18 | 18 | df_temp = raw_df1.iloc[:, [0, 1, 2, column, column + 1]].copy()
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35 | 35 | df_temp2["jersey_number"] = raw_df2.columns[column]
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36 | 36 | df2 = pd.concat([df2, df_temp2]).reset_index(drop=True)
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37 | 37 | df2["team"] = "Home"
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38 |
| -df2.loc[df2["x"].isna(), "x"] = 0.5 |
39 |
| -df2.loc[df2["y"].isna(), "y"] = 0.5 |
| 38 | +df2.loc[df2["x"].isna(), "x"] = None |
| 39 | +df2.loc[df2["y"].isna(), "y"] = None |
40 | 40 | df2 = df2[df2["x"].notna()]
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41 | 41 | df2.drop(df2.loc[df2["half"] == "Period"].index, inplace=True)
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42 | 42 |
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