１．定期横断測量のDL、データ確認、距離標データの作成
仕事の関係で、堤外地の堤防法尻点の座標点が必要となりました。
定期横断測線に座標点を与えるのは、少々手間なので、その作業をpythonで実施しています。
北海道開発局が定期横断測量データを公開しています。
データはzip形式で、以下の３つのファイルが河川ごとに格納されています。
距離標データ（数値データ）
縦断データ（整理表、数値データ）
横断データ（整理表、数値データ）
定期横断測量のDL
ここでは、以下のデータを利用します。
各種データの確認
距離標データ
距離標データは、杭の座標点が格納されてます。
既設（古い）の距離標の座標点と移設の距離標の座標点が2つ載っており、測量年に併せてデータを加工する必要があります。データを利用するよりも表としての見やすさを優先されているようで、改善が必要な箇所ですね。
距離標データの作成
使用する横断データの計測年に併せて、既設か移設のKP座標のファイルを別途作成します。
サンプルcsv
以下をyuubetsu.csvで保存してください。
KP,LX,LY,RX,RY,sokuten
0,"26,700.580 ","109,456.805 ","26,608.004 ","109,526.703 ",116.000
0.2,"26,717.296 ","109,368.164 ","26,550.595 ","109,358.167 ",169.000
0.4,"26,742.624 ","109,307.594 ","26,557.055 ","109,233.001 ",200.000
0.5,"26,786.367 ","109,190.553 ","26,574.120 ","109,127.200 ",221.500
0.6,"26,853.991 ","109,026.201 ","26,587.064 ","109,001.539 ",228.064
0.8,"26,894.888 ","108,887.798 ","26,582.502 ","108,847.016 ",275.036
1,"26,865.157 ","108,518.789 ","26,525.207 ","108,749.049 ",410.592
1.2,"26,755.704 ","108,384.165 ","26,409.101 ","108,619.067 ",418.703
1.4,"26,617.376 ","108,212.772 ","26,308.052 ","108,478.444 ",407.754
1.6,"26,472.648 ","108,046.080 ","26,179.328 ","108,330.500 ",408.572
1.8,"26,294.429 ","107,885.497 ","26,027.567 ","108,192.131 ",406.497
2,"26,107.890 ","107,748.564 ","25,901.169 ","108,099.636 ",407.413
2.2,"25,945.519 ","107,652.536 ","25,788.879 ","108,031.375 ",409.945
2.4,"25,786.529 ","107,574.742 ","25,640.963 ","107,955.991 ",408.093
2.6,"25,585.687 ","107,498.709 ","25,470.934 ","107,889.390 ",407.185
2.8,"25,423.886 ","107,453.027 ","25,338.836 ","107,852.466 ",408.393
3,"25,273.554 ","107,423.833 ","25,202.898 ","107,825.494 ",407.828
3.2,"25,078.097 ","107,403.076 ","25,045.821 ","107,808.646 ",406.852
3.4,"24,891.559 ","107,397.103 ","24,891.001 ","107,805.154 ",408.051
3.6,"24,712.446 ","107,394.072 ","24,706.799 ","107,801.646 ",407.613
3.8,"24,557.284 ","107,385.711 ","24,512.046 ","107,791.884 ",408.684
4,"24,399.582 ","107,369.810 ","24,325.354 ","107,774.303 ",411.247
4.2,"24,236.140 ","107,347.859 ","24,169.278 ","107,757.436 ",414.999
4.4,"24,002.847 ","107,302.477 ","23,914.929 ","107,701.190 ",408.291
4.6,"23,747.054 ","107,234.104 ","23,698.226 ","107,661.698 ",430.373
4.8,"23,627.863 ","107,197.333 ","23,456.893 ","107,604.406 ",441.519
5,"23,493.592 ","107,148.412 ","23,342.287 ","107,496.470 ",379.523
5.2,"23,320.131 ","107,077.300 ","23,170.845 ","107,434.461 ",387.105
5.4,"23,123.207 ","106,986.404 ","22,973.261 ","107,356.249 ",399.085
5.6,"22,958.679 ","106,909.496 ","22,791.587 ","107,280.481 ",406.878
5.8,"22,797.868 ","106,830.734 ","22,623.395 ","107,204.992 ",412.928
6,"22,637.958 ","106,707.542 ","22,441.505 ","107,118.430 ",455.437
6.2,"22,506.037 ","106,650.711 ","22,233.096 ","107,073.793 ",503.483
6.4,"22,259.335 ","106,591.929 ","22,094.142 ","106,947.845 ",392.384
6.6,"22,149.378 ","106,324.056 ","21,925.154 ","106,854.571 ",575.954
6.8,"22,009.118 ","106,227.354 ","21,712.441 ","106,720.380 ",575.406
7,"21,796.726 ","106,224.353 ","21,546.586 ","106,602.767 ",453.616
7.2,"21,613.057 ","106,069.973 ","21,320.838 ","106,421.863 ",457.404
7.4,"21,415.835 ","105,984.201 ","21,175.238 ","106,297.228 ",394.807
7.6,"21,404.554 ","105,746.338 ","21,041.919 ","106,175.584 ",561.922
7.8,"21,315.410 ","105,571.696 ","20,887.152 ","106,030.280 ",627.459
8,"21,231.918 ","105,479.250 ","20,742.137 ","105,893.606 ",641.542
8.2,"20,995.135 ","105,343.031 ","20,581.785 ","105,741.753 ",574.315
8.4,"20,830.425 ","105,237.474 ","20,442.428 ","105,610.571 ",538.278
8.6,"20,758.495 ","105,092.449 ","20,344.845 ","105,522.262 ",596.528
8.8,"20,742.887 ","104,917.205 ","20,264.363 ","105,451.552 ",717.295
9,"20,535.580 ","104,820.529 ","20,177.317 ","105,376.800 ",661.657
9.2,"20,390.861 ","104,824.311 ","20,084.403 ","105,298.962 ",564.987
9.4,"20,221.921 ","104,802.800 ","19,961.709 ","105,202.397 ",476.852
9.6,"20,041.100 ","104,717.728 ","19,798.180 ","105,081.061 ",437.059
9.8,"19,847.963 ","104,626.810 ","19,635.338 ","104,965.922 ",400.258
10,"19,718.491 ","104,549.907 ","19,450.980 ","104,842.500 ",395.450
10.2,"19,552.021 ","104,443.731 ","19,330.170 ","104,765.717 ",391.015
10.4,"19,400.488 ","104,348.027 ","19,195.808 ","104,683.644 ",393.106
10.6,"19,216.422 ","104,231.848 ","19,004.591 ","104,576.074 ",404.183
10.8,"19,033.810 ","104,139.015 ","18,846.822 ","104,491.251 ",397.792
11,"18,877.987 ","104,061.177 ","18,680.959 ","104,403.841 ",394.271
11.2,"18,687.581 ","103,966.383 ","18,514.060 ","104,322.114 ",394.795
11.4,"18,513.194 ","103,867.593 ","18,363.019 ","104,256.742 ",416.120
11.6,"18,373.295 ","103,722.518 ","18,191.681 ","104,182.079 ",494.146
11.8,"18,265.417 ","103,591.817 ","18,049.356 ","104,121.992 ",572.510
12,"18,095.572 ","103,480.378 ","17,883.848 ","104,058.444 ",615.619
12.2,"17,915.961 ","103,409.663 ","17,709.257 ","104,004.598 ",629.821
12.4,"17,739.037 ","103,341.059 ","17,532.011 ","103,947.845 ",641.131
12.6,"17,565.904 ","103,277.816 ","17,396.872 ","103,905.310 ",649.862
12.8,"17,339.372 ","103,229.009 ","17,222.260 ","103,856.360 ",638.188
13,"17,162.031 ","103,191.543 ","17,033.838 ","103,816.383 ",637.855
13.2,"16,963.918 ","103,100.202 ","16,846.039 ","103,771.712 ",681.778
13.4,"16,760.075 ","103,025.724 ","16,667.497 ","103,743.593 ",723.814
13.6,"16,550.625 ","103,012.941 ","16,481.406 ","103,717.122 ",707.575
13.8,"16,288.814 ","103,158.900 ","16,285.273 ","103,700.095 ",541.207
14,"16,128.708 ","103,245.769 ","16,079.625 ","103,686.200 ",443.158
14.2,"15,902.368 ","103,249.559 ","15,867.918 ","103,672.030 ",423.873
14.4,"15,731.328 ","103,196.838 ","15,638.184 ","103,656.173 ",469.084
14.6,"15,498.684 ","103,059.806 ","15,432.692 ","103,642.531 ",586.450
15,"15,214.686 ","103,041.076 ","15,188.890 ","103,625.049 ",584.542
15.2,"15,071.395 ","103,070.200 ","14,979.523 ","103,621.896 ",559.293
15.4,"14,763.596 ","103,194.506 ","14,705.963 ","103,592.970 ",403.010
15.6,"14,483.551 ","103,173.920 ","14,508.344 ","103,578.675 ",405.514
15.8,"14,253.487 ","103,156.999 ","14,333.523 ","103,566.683 ",417.429
16,"14,121.997 ","103,126.070 ","14,158.450 ","103,567.868 ",443.699
16.2,"13,902.504 ","103,124.478 ","14,016.304 ","103,594.630 ",483.729
16.4,"13,721.649 ","103,147.075 ","13,839.226 ","103,630.651 ",497.965
16.6,"13,440.581 ","103,189.910 ","13,679.346 ","103,677.932 ",526.299
17,"13,281.440 ","103,372.115 ","13,534.654 ","103,731.312 ",439.477
17.2,"13,080.452 ","103,560.488 ","13,379.338 ","103,855.559 ",420.000
17.4,"12,969.605 ","103,626.601 ","13,286.952 ","103,978.556 ",468.400
17.6,"12,817.147 ","103,678.650 ","12,853.116 ","104,236.611 ",552.619
18,"12,625.985 ","103,689.956 ","12,579.640 ","104,175.683 ",487.933
18.2,"12,428.074 ","103,669.603 ","12,385.042 ","104,163.687 ",495.954
18.4,"12,243.636 ","103,651.909 ","12,227.727 ","103,939.608 ",288.139
18.6,"12,095.462 ","103,619.012 ","12,063.241 ","103,920.210 ",298.917
18.8,"11,923.217 ","103,580.169 ","11,880.158 ","103,881.487 ",304.379
19,"11,799.292 ","103,516.556 ","11,651.349 ","103,753.646 ",279.462
19.2,"11,706.492 ","103,397.329 ","11,434.578 ","103,595.624 ",336.538
19.4,"11,601.620 ","103,241.414 ","11,328.268 ","103,455.749 ",347.363
19.6,"11,360.357 ","103,068.631 ","11,155.286 ","103,357.336 ",354.125
19.8,"11,137.248 ","102,974.480 ","11,003.251 ","103,302.192 ",355.049
20,"10,991.852 ","102,912.539 ","10,954.860 ","103,261.016 ",351.435
20.2,"10,803.846 ","102,832.573 ","10,691.133 ","103,214.451 ",398.165
20.4,"10,639.843 ","102,765.367 ","10,565.593 ","103,153.905 ",394.569
20.6,"10,500.617 ","102,704.629 ","10,382.594 ","103,107.755 ",420.048
20.8,"10,294.794 ","102,661.468 ","10,251.939 ","103,069.315 ",410.092
21,"10,051.581 ","102,644.188 ","10,128.298 ","103,119.968 ",481.925
21.2,"9,901.248 ","102,650.157 ","9,923.507 ","103,200.849 ",551.142
21.4,"9,657.805 ","102,683.107 ","9,765.980 ","103,204.002 ",532.009
21.7,"9,447.952 ","102,713.037 ","9,494.084 ","103,247.760 ",536.709
22,"9,220.075 ","102,763.156 ","9,263.475 ","103,105.398 ",344.983
22.2,"8,982.377 ","102,820.375 ","9,065.935 ","103,151.833 ",341.828
22.4,"8,791.737 ","102,880.988 ","8,913.913 ","103,114.200 ",263.277
22.6,"8,662.858 ","102,921.847 ","8,735.274 ","103,160.428 ",249.329
22.8,"8,467.144 ","102,984.240 ","8,551.817 ","103,267.801 ",295.933
23,"8,257.983 ","103,051.101 ","8,505.357 ","103,416.554 ",441.305
23.2,"8,172.252 ","103,076.370 ","8,171.204 ","103,496.890 ",420.521
23.4,"8,048.327 ","103,072.080 ","7,748.145 ","103,586.007 ",595.172
23.7,"7,870.657 ","102,981.604 ","7,619.957 ","103,342.093 ",439.093
24,"7,698.027 ","102,880.022 ","7,494.336 ","103,247.692 ",420.323
24.2,"7,548.238 ","102,786.894 ","7,223.354 ","103,044.228 ",414.452
24.4,"7,303.463 ","102,621.495 ","7,131.803 ","102,975.363 ",393.306
24.6,"7,117.375 ","102,542.931 ","6,980.587 ","102,859.716 ",345.056
横断データ
WZAAA4001.CSVの中身をみると、数値データが羅列されています。
1行目は、以下に示す基本的な内容が記載されてます。
なお、堤防法尻は、堤内地側の法尻を示しています。
詳細は、河川定期縦横断データ作成ガイドライン（H20) を参考にしてください。
| 距離標 | 流心間距離 | 左岸距離杭高 | 右岸距離杭高 | 左岸水際杭高 | 右岸水際杭高 | 測点数 | 左岸堤防法尻高 | 左岸堤内地平均 | 右岸堤防法尻高 | 右岸堤内地平均 | 河川 構造フラグ | 測量年月日 | 河川番号 | 水系名 | 河川名称 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.2 | 170 | 7.249 | 6.732 | 3.608 | 3.94 | 110 | 3.54 | 2.55 | 3.69 | 3.48 | 0 | 20190122 | 8.1E+09 | 湧別川水系 | 湧別川 |
測線データは2行目以下になり、以下の順に並んでいます。
標高は平面直角座標系（12系）での値を示しています。
| flag | 左岸杭からの距離 | 標高 |
|---|---|---|
| 4 | -28.5 | 2.37 |
| 4 | -21.53 | 2.4 |
| 28 | -14.99 | 2.55 |
flagの意味
| 記号 | 座標点の特性 |
|---|---|
| 1 | 距離杭高（左岸） |
| 2 | 距離杭下 |
| 3 | 堤防法肩位置（始点，終点） |
| 4 | 道路（始点，終点） |
| 5 | 水路（始点，終点） |
| 6 | 河川管理境界 |
| 7 | 官民界 |
| 8 | コンクリート張（始点，終点） |
| 9 | 石張（始点， 終点） |
| 10 | コンクリートブロック張（始点，終点） |
| 11 | じゃかご張（始点，終点） |
| 12 | 水際杭高（左岸，右岸） |
| 13 | 低水路肩（右岸，左岸） |
| 14 | アスファルト（始点，終点） |
| 15 | 水制（始点，終点） |
| 16 | 水面（右岸，左岸） |
| 17 | 根固ブロック（始点，終点） |
| 18 | 距離杭高（右岸） |
| 19 | 鉄柵（地盤高，鉄柵の上端） |
| 20 | 生垣（地盤高，生垣の上端） |
| 21 | ブロック塀（始点，終点） |
| 22 | ガードレール（地盤高，生垣の上端） |
| 23 | 家屋（地盤高） |
| 24 | 左岸本堤防法尻（表・裏2点） |
| 25 | 右岸本堤防法尻（表・裏2点） |
| 26 | その他の堤防法尻 |
| 27 | ガンバン（始点，終点） |
| 28 | 堤内地盤（始点，終点） |
この表は、Table Generatorを用いて作成されてます。
２．座標変換からgeopkageの作成
横断側線上の各点に座標を与える処理をします。コードは以下のサンプルコードを参照してください。
左岸杭はleft_point、同様に左岸の堤外法尻はleft_toe_pointとしています。
| kp | flag | distance | elevation | x | y | type |
|---|---|---|---|---|---|---|
| 2.2 | 4 | -28.5 | 2.37 | 25956.41 | 107626.2 | |
| 2.2 | 1 | 0 | 7.249 | 25945.52 | 107652.5 | left_point |
| 2.2 | 2 | 0 | 7.06 | 25945.52 | 107652.5 | |
| 2.2 | 24 | 14.02 | 3.66 | 25940.16 | 107665.5 | left_toe_point |
座標変換
#zahyou_henkan.pyとして保存
import math
import pandas as pd
import numpy as np
import os
import glob
def process_files(coordinate_file_path, input_pattern, output_dir):
"""
ファイルを処理する主要な関数
Args:
- coordinate_file_path: 座標ファイル（yuubetsu.csv）のパス
- input_pattern: 入力ファイルのパターン（出力フォルダ内のW*.CSV）
- output_dir: 出力フォルダのパス
"""
try:
# 1. yuubetsu.csvを読み込む
df_coordinates = pd.read_csv(coordinate_file_path, encoding='shift_jis', thousands=',')
# 座標列の型変換
coordinate_columns = ['KP', 'LX', 'LY', 'RX', 'RY', 'sokuten']
for col in coordinate_columns:
df_coordinates[col] = pd.to_numeric(df_coordinates[col], errors='coerce')
print("座標データの最初の行:")
print(df_coordinates.head(1))
except Exception as e:
print(f"座標ファイル読み込みエラー: {str(e)}")
return
# 2. outputフォルダのcsvファイルを読み込む（W始まりのファイル）
input_files = glob.glob(input_pattern)
for file in input_files:
try:
# 出力ファイルのキロポストを読み取る
result_df = pd.read_csv(file, encoding='shift_jis', skiprows=1, header=None)
result_df = result_df.iloc[:, :3]
result_df.columns = ['flag', 'distance', 'elevation']
# ファイルのキロポストを取得
kilopost = pd.read_csv(file, encoding='shift_jis', nrows=1, header=None).iloc[0, 0]
print(f"\n処理中のファイル: {file}")
print(f"ファイルのキロポスト: {kilopost}")
# 3. yuubetsu.csvのKPと完全一致するかを確認
matching_coords = df_coordinates[df_coordinates['KP'] == float(kilopost)]
if matching_coords.empty:
print(f"警告: キロポスト {kilopost} に一致する座標が見つかりません。スキップします。")
continue
# 4. マッチングした座標を取得
matching_row = matching_coords.iloc[0]
# flag=1の箇所にLX, LYを転記
left_point_mask = result_df['flag'] == 1
if left_point_mask.any():
left_point_index = left_point_mask[left_point_mask].index[0]
result_df.loc[left_point_index, 'x'] = matching_row['LX']
result_df.loc[left_point_index, 'y'] = matching_row['LY']
result_df.loc[left_point_index, 'type'] = 'left_point'
# flag=18の箇所にRX, RYを転記
right_point_mask = result_df['flag'] == 18
if right_point_mask.any():
right_point_index = right_point_mask[right_point_mask].index[0]
total_line_length = result_df.loc[right_point_index, 'distance']
else:
# flag=18がない場合、RX, RYを新しい行として追加
new_row = pd.DataFrame({
'flag': [18],
'distance': [matching_row['sokuten']],
'elevation': [0], # デフォルト値
'x': [matching_row['RX']],
'y': [matching_row['RY']],
'type': ['right_point_added']
})
result_df = pd.concat([result_df, new_row], ignore_index=True)
total_line_length = matching_row['sokuten']
print(f"注意: キロポスト {kilopost} にflag=18の点がないため、sokuten値を使用して追加しました。")
# 左右の杭座標
left_bank_x, left_bank_y = matching_row['LX'], matching_row['LY']
right_bank_x, right_bank_y = matching_row['RX'], matching_row['RY']
# 線分の方向ベクトルを計算
line_dx = right_bank_x - left_bank_x
line_dy = right_bank_y - left_bank_y
# まだx,yが設定されていない行の座標を計算
mask_without_coords = result_df['x'].isna()
result_df.loc[mask_without_coords, 'x'] = (
left_bank_x +
(line_dx / total_line_length) * result_df.loc[mask_without_coords, 'distance']
)
result_df.loc[mask_without_coords, 'y'] = (
left_bank_y +
(line_dy / total_line_length) * result_df.loc[mask_without_coords, 'distance']
)
# KPの列を追加
result_df.insert(0, 'kp', kilopost)
# 6. 法尻高情報の追加, 法尻高の抽出
result_df['left_toe_point'] = None
result_df['right_toe_point'] = None
# 法尻高の抽出
left_toe_flags = result_df[result_df['flag'] == 24]
if len(left_toe_flags) >= 2:
left_toe_point = left_toe_flags.iloc[1]['elevation']
result_df.loc[left_toe_flags.index[1], 'type'] = 'left_toe_point'
result_df.loc[left_toe_flags.index[1], 'left_toe_point'] = left_toe_point
right_toe_flags = result_df[result_df['flag'] == 25]
if len(right_toe_flags) >= 1:
right_toe_point = right_toe_flags.iloc[0]['elevation']
result_df.loc[right_toe_flags.index[0], 'type'] = 'right_toe_point'
result_df.loc[right_toe_flags.index[0], 'right_toe_point'] = right_toe_point
# 丸め処理
columns_to_round = ['x', 'y', 'distance', 'elevation']
result_df[columns_to_round] = result_df[columns_to_round].round(6)
# 結果の保存
base_name = os.path.basename(file)
output_filename = os.path.join(output_dir,
os.path.splitext(base_name)[0] + '_points.csv')
result_df.to_csv(output_filename, index=False)
print(f"処理完了: {base_name} → {os.path.basename(output_filename)}")
except Exception as e:
print(f"エラー: {os.path.basename(file)} の処理中にエラーが発生しました: {str(e)}")
def main():
# スクリプトがあるディレクトリを取得
current_dir = os.path.dirname(os.path.abspath(__file__))
# 設定パラメータ
input_dir = os.path.join(current_dir, 'oudanseika_03')
coordinate_file_path = os.path.join(current_dir, 'yuubetsu.csv')
input_pattern = os.path.join(input_dir, 'W*.CSV')
output_dir = os.path.join(current_dir, 'output')
# 出力ディレクトリ作成
os.makedirs(output_dir, exist_ok=True)
print(f"出力ディレクトリ: {output_dir}")
# ファイル処理の実行
process_files(coordinate_file_path, input_pattern, output_dir)
print("\n注意: このデータの座標系は JGD2000 / 平面直角座標系 12系 (EPSG:2451) です")
if __name__ == "__main__":
main()
geopkgeへの変換
#geopkge_hankan.pyで保存
import os
import pandas as pd
import geopandas as gpd
from shapely.geometry import Point
def csv_to_geopackage(csv_path, output_path, epsg_code):
# CSVファイルを読み込む
df = pd.read_csv(csv_path)
# 必要な列だけを選択し、データ型を最適化する
df = df[['x', 'y', 'type', 'left_toe_point', 'right_toe_point']].copy()
# Toe heightが設定されているかどうかを確認する列を追加
df['marker'] = df.apply(lambda row: 'toe_point' if row.get('left_toe_point', 0) > 0 or row.get('right_toe_point', 0) > 0 else '', axis=1)
# GeoDataFrameを作成するためにポイントを作成
geometry = [Point(xy) for xy in zip(df['y'], df['x'])]
gdf = gpd.GeoDataFrame(df, geometry=geometry)
# CRSを設定（引数で指定されたEPSGコードを使用）
gdf.set_crs(epsg=epsg_code, inplace=True)
# GeoPackageに保存
gdf.to_file(output_path, driver='GPKG')
print(f"Converted {csv_path} to {output_path}")
def main():
# ファイルの箇所を指定
input_dir = r'E:\python_zahyou\output'
output_dir = os.path.join(input_dir, 'geopackage_output')
os.makedirs(output_dir, exist_ok=True)
# EPSGコードを指定
epsg_code = 2454 # JGD2000の場合
# ファイル数をカウント
csv_files = [f for f in os.listdir(input_dir) if f.endswith('.csv')]
total_files = len(csv_files)
for i, filename in enumerate(csv_files, start=1):
input_path = os.path.join(input_dir, filename)
output_path = os.path.join(output_dir, f'{os.path.splitext(filename)[0]}.gpkg')
try:
csv_to_geopackage(input_path, output_path, epsg_code)
print(f"Progress: {i}/{total_files} files processed.")
except Exception as e:
print(f"Error processing {filename}: {e}")
if __name__ == '__main__':
main()
任意のflag（堤外地の法尻抽出）データを抽出する
法尻点のみ抽出したpkgeとcsvファイルの作成をします。
法尻点の抽出
#pont.pyとして保存
import os
import pandas as pd
import geopandas as gpd
from shapely.geometry import Point
def csv_to_geopackage(csv_path, output_path, epsg_code):
# CSVファイルを読み込む
df = pd.read_csv(csv_path)
# 必要な列だけを選択し、データ型を最適化する
df = df[['x', 'y', 'type', 'left_toe_point', 'right_toe_point']].copy()
# Toe heightが設定されているかどうかを確認する列を追加
df['marker'] = df.apply(lambda row: 'toe_point' if row.get('left_toe_point', 0) > 0 or row.get('right_toe_point', 0) > 0 else '', axis=1)
# GeoDataFrameを作成するためにポイントを作成
geometry = [Point(xy) for xy in zip(df['y'], df['x'])]
gdf = gpd.GeoDataFrame(df, geometry=geometry)
# CRSを設定（引数で指定されたEPSGコードを使用）
gdf.set_crs(epsg=epsg_code, inplace=True)
# GeoPackageに保存
gdf.to_file(output_path, driver='GPKG')
print(f"Converted {csv_path} to {output_path}")
def main():
# ファイルの箇所を指定
input_dir = r'E:\python_zahyou\output'
output_dir = os.path.join(input_dir, 'geopackage_output')
os.makedirs(output_dir, exist_ok=True)
# EPSGコードを指定
epsg_code = 2454 # JGD2000の場合
# ファイル数をカウント
csv_files = [f for f in os.listdir(input_dir) if f.endswith('.csv')]
total_files = len(csv_files)
for i, filename in enumerate(csv_files, start=1):
input_path = os.path.join(input_dir, filename)
output_path = os.path.join(output_dir, f'{os.path.splitext(filename)[0]}.gpkg')
try:
csv_to_geopackage(input_path, output_path, epsg_code)
print(f"Progress: {i}/{total_files} files processed.")
except Exception as e:
print(f"Error processing {filename}: {e}")
if __name__ == '__main__':
main()
一括処理(おまけ)
'''
上記のすべてのファイルを一括で実行するコードです。
各ファイル中のmain()で設定されているdirectory等も
ここで修正すると便利だと思います。
'''
import os
import sys
import subprocess
def run_script(script_path):
"""
スクリプトを実行し、エラーがあれば表示する関数
"""
try:
print(f"\n----- {os.path.basename(script_path)} の実行を開始 -----")
result = subprocess.run([sys.executable, script_path],
capture_output=True,
text=True,
check=True)
print(result.stdout)
print(f"----- {os.path.basename(script_path)} の実行が完了 -----\n")
except subprocess.CalledProcessError as e:
print(f"エラー: {script_path} の実行中にエラーが発生しました")
print("標準出力:", e.stdout)
print("標準エラー:", e.stderr)
def main():
# スクリプトの相対パスまたは絶対パスを指定
scripts = [
'zahyou_henkan.py', # 座標変換スクリプト
'geopkge_henkan.py', # GeoPackage変換スクリプト
'point.py' # ポイント処理スクリプト
]
# カレントディレクトリを設定（このmain.pyがある場所）
script_dir = os.path.dirname(os.path.abspath(__file__))
# 各スクリプトを順番に実行
for script in scripts:
script_path = os.path.join(script_dir, script)
if os.path.exists(script_path):
run_script(script_path)
else:
print(f"警告: スクリプト {script} が見つかりません")
if __name__ == "__main__":
main()
３．QGISへ図化する
背景画像の表示
国土地理院QGISマニュアルを参考に、背景に写真を追記します。
XYZ Tiles－新規接続を選び、接続の詳細にURL、最小・最大ズームを入れてください。
測点を表示
geopacage_outputのファイルをQGIS上にドラック＆ドロップすると側線のポイントデータが表示されます。
任意のflag点（法尻のpointデータ）を表示
任意のflagのデータについても同様に表示できます。
