diff --git a/Codes/plot_dp_fom_csv.py b/Codes/plot_dp_fom_csv.py new file mode 100644 index 0000000..065dba9 --- /dev/null +++ b/Codes/plot_dp_fom_csv.py @@ -0,0 +1,74 @@ +#審査書用のプロット用です。 +#下の定数をいじってください。 +#simudata2plotという自作モジュールがあるので読み込めるようにしておいてから実行してください。 +#環境は.tomlを参考 +import simudata2plot +from staticmap import StaticMap +from PIL import ImageDraw + + + + + + + + +#定数############################################################################# +NUMBER_OF_WIND = 8 #風の数 +NUMBER_OF_ANGLE = 8 #風の方位の数 +ANGLE = [0, 45, 90, 135, 180, 225, 270, 315] #ロケットの方位角。シミュレーターを参照 +LON0, LAT0 = 140.012103, 40.249764 #射点の(経度、緯度) +DIR_NAME = "2023-0617-232416" #CSVを探索するディレクトリ +PATH = "C:\\Users\\bestt\\Documents\\NRD_aerodynamics_3Dsimulation\\result\\" #path。デフォルトではほりつかさ仕様なので変更してください。フルパスで、result\\までお願いします.VScodeではダブルスラッシュに変更してください。 +DATE = "2023-0617-232416" #シミュを動かしたときにのresultの下にあるフォルダ名にしてください。 +ZOOMRATE = 17 #地図をどれだけズームするか +MAP_TILE = 'https://cyberjapandata.gsi.go.jp/xyz/std/{z}/{x}/{y}.png' +#MAP_TILE = 'https://cyberjapandata.gsi.go.jp/xyz/seamlessphoto/{z}/{x}/{y}.jpg' #航空写真。ZOOMRATE16以上ならいける + +################################################################################## + + + + + + + + + + + + + + +#z-y平面をシミュレーターのCSVから作成 +x, y = simudata2plot.process_data() + +#z-y平面の原点に緯度経度を与えて、データを緯度経度に変換 +lon1, lat1 = simudata2plot.process_data_to_lonlat(x, y) + +#緯度経度から地図画像上の座標に変換。地図を描画 +map = StaticMap(1500, 1500, url_template=MAP_TILE)# 画像の横幅と縦幅と国土地理院の地理院タイルURLを指定 + +# 地図を描画した Pillow の PIL.Image オブジェクトを取得 +# ズームレベルと経度・緯度のリストを指定(google mapで」しらべればわかる) +pic = map.render(zoom=ZOOMRATE, center=[140.012103,40.249764]) + +#プロット画像の座標を取得 +x_pix, y_pix = simudata2plot.lonlat_to_pixel(lon1, lat1, map) + +# Drawオブジェクト作成 +draw = ImageDraw.Draw(pic) + +#落下分散のプロット +for i in range(NUMBER_OF_WIND): + x_y_pix = list(zip(x_pix[i][:], y_pix[i][:])) + draw.polygon(x_y_pix, outline='black') + +#射点の座標 +x0_pix, y0_pix = simudata2plot.lon_to_pixel(LON0, map), simudata2plot.lat_to_pixel(LAT0, map) + +#射点のプロット +draw.ellipse([(x0_pix-6, y0_pix-6), (x0_pix+6, y0_pix+6) ], fill = "red", outline='red', width = 1) + +# 地図画像を保存 +pic.save(f'dp_from_csv_{DATE}.png') diff --git a/Codes/simudata2plot.py b/Codes/simudata2plot.py new file mode 100644 index 0000000..81c30bf --- /dev/null +++ b/Codes/simudata2plot.py @@ -0,0 +1,142 @@ +#renew_dropplot_fullplow.pyのデータ処理用の関数が収納されています。 +#以下の関数、process_data()のパスをいい感じにいじってください。初期状態ではほりつかさ用になってます。 + + +NUMBER_OF_WIND = 8 #風の数 +NUMBER_OF_ANGLE = 8 #風の方位の数 +ANGLE = [0, 45, 90, 135, 180, 225, 270, 315] #ロケットの方位角。シミュレーターを参照 +LON0, LAT0 = 140.012103, 40.249764 #射点の(経度、緯度) +DIR_NAME = "2023-0617-232416" #CSVを探索するディレクトリ +PATH = "C:\\Users\\bestt\\Documents\\NRD_aerodynamics_3Dsimulation\\result\\" #path。VScodeではダブルスラッシュに変更してください。 + + +import pyproj +import numpy as np +import pandas as pd +from math import log, tan, pi, cos +import math + + +#シミュレータのCSVからz、y平面のデータを作成する。zが横、yは縦。慣習上、x, yに対応させる。 +def process_data():#シミュレータ内部のresultフォルダの内、処理してほしいフォルダの名前を入れる。例、2023-0604-084950 半角 + y, z = [],[] + + #wind2の時の8方位との落下地点を得る。y,z平面上のデータ + for j in range(NUMBER_OF_WIND): + + y_temp, z_temp = [], [] + + for i in range(NUMBER_OF_ANGLE): + + filename = PATH + DIR_NAME +"\\history_" + str(ANGLE[i]) + "deg_wind" + str(j) + ".csv" + + #ndarryに変更 + data_1 = pd.read_csv(filename).values + + #落下地点 + y_temp.append(data_1[-1][2]) #縦 yとしたい + z_temp.append(data_1[-1][3]) #横 xとしたい + + y_temp.append(y_temp[0]) + z_temp.append(z_temp[0]) + + y.append(y_temp) + z.append(z_temp) + + return z, y #横、縦で返す + + +#x-y平面で原点からの方位角と距離を計算する。この時、関数内部でy軸正の向きし縄稚シミュレーターでいう90degを北と定義している。更にpyprojのために時計周りを正としている。 +#x-y座標を回転させたものを代入した時の場合を考えていない。 +def calculate_azimuth_dist(x, y): + azimuth = [] + dist = [] + v_north = [0,1] + for i in range(8): + x_y= np.array(list(zip(x[i], y[i]))) + azimuth_temp = [] + x_y_dist = [] + for j in range(9): + x_y_dist.append(math.hypot(x_y[j, 0], x_y[j][1])) + if x_y[j, 0] < 0: + azimuth_temp.append(360 - (np.rad2deg(math.acos(np.dot(v_north, x_y[j]) / x_y_dist[j]))))#acosは[-pi/2, pi/2] + else: + azimuth_temp.append(np.rad2deg(math.acos(np.dot(v_north, x_y[j]) / x_y_dist[j])))#acosは[-pi/2, pi/2] + + azimuth.append(azimuth_temp) + dist.append(x_y_dist) + + azimuth = np.array(azimuth) + return azimuth, dist + + +#平面上のデータの原点の緯度経度を指定することにより、各点の緯度経度を得る +#平面の座標X, yと原点の緯度経度を渡す +def process_data_to_lonlat(x, y): + + #並列計算のためにndarryに変更 + x, y = np.array(x), np.array(y) #風の数 x (方角の数+1) +1なのはプロットするときの都合上で、初めの値と終わりの値が同じ。 + lat0, lon0 = np.full(NUMBER_OF_ANGLE + 1, LAT0), np.full(NUMBER_OF_ANGLE + 1, LON0) + + #距離と方位角 + azimuth , dist = calculate_azimuth_dist(x, y) + + lon1, lat1 = [], [] + + for i in range( NUMBER_OF_WIND ): + print(f"出発点:{LAT0}N, {LON0}E") + print(f"進む方向:{azimuth[i]}度、進む距離:{dist[i]}m") + #計算 + grs80 = pyproj.Geod(ellps = "GRS80") + lon1_temp, lat1_temp, back_azimuth = grs80.fwd(lon0, lat0, np.array(azimuth[i]), np.array(dist[i]))#ndarrayを渡す + lon1.append(lon1_temp) + lat1.append(lat1_temp) + + return np.array(lon1), np.array(lat1) + + +#経度座標から画像上の座標に変換。経度と地図を渡す +def lon_to_pixel(lon, map): + from math import log, tan, pi, cos + # 経度→タイル番号 + if not (-180 <= lon <= 180): + lon = (lon + 180) % 360 - 180 + x = ((lon + 180.) / 360) * pow(2, map.zoom) + # タイル番号→キャンバスの座標 + pixel = (x - map.x_center) * map.tile_size + map.width / 2 + return pixel + + +#緯度座標から画像上の座標に変換。緯度と地図を渡す +def lat_to_pixel(lat, map): + # 緯度→タイル番号 + if not (-90 <= lat <= 90): + lat = (lat + 90) % 180 - 90 + y = (1 - log(tan(lat * pi / 180) + 1 / cos(lat * pi / 180)) / pi) / 2 * pow(2, map.zoom) + # タイル番号→キャンバスの座標 + pixel = (y - map.y_center) * map.tile_size + map.height / 2 + return int(pixel) + + +#緯度経度から画像上の座標に変換。経度と緯度と地図を渡す +# #mapにはstaticmapを用いて8oox800で作成してください。 +#また、image=map.render(zoom=14, center=[140.012103,40.249764])で縮尺と中心座標を決定してください。 +def lonlat_to_pixel(lon1, lat1, map): + + x_pix, y_pix = [], [] + + for j in range(lon1.shape[0]): + + x_pix_temp, y_pix_temp= [], [] + + for i in range(lon1.shape[1]): + + x_pix_temp.append(lon_to_pixel(lon1[j][i], map)) + y_pix_temp.append(lat_to_pixel(lat1[j][i], map)) + + x_pix.append(x_pix_temp) + + y_pix.append(y_pix_temp) + + return x_pix, y_pix + diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..fbeefa0 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,789 @@ +# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. + +[[package]] +name = "certifi" +version = "2023.5.7" +description = "Python package for providing Mozilla's CA Bundle." +category = "main" +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2023.5.7-py3-none-any.whl", hash = "sha256:c6c2e98f5c7869efca1f8916fed228dd91539f9f1b444c314c06eef02980c716"}, + {file = "certifi-2023.5.7.tar.gz", hash = "sha256:0f0d56dc5a6ad56fd4ba36484d6cc34451e1c6548c61daad8c320169f91eddc7"}, +] + +[[package]] +name = "chardet" +version = "5.1.0" +description = "Universal encoding detector for Python 3" +category = "main" +optional = false +python-versions = ">=3.7" +files = [ + {file = "chardet-5.1.0-py3-none-any.whl", hash = "sha256:362777fb014af596ad31334fde1e8c327dfdb076e1960d1694662d46a6917ab9"}, + {file = "chardet-5.1.0.tar.gz", hash = "sha256:0d62712b956bc154f85fb0a266e2a3c5913c2967e00348701b32411d6def31e5"}, +] + +[[package]] +name = "charset-normalizer" +version = "3.1.0" +description = "The Real First Universal Charset Detector. 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